This book includes papers presented at the Second International Conference on Electronic Engineering and Renewable Energ
1,964 84 50MB
English Pages XIX, 896 [858] Year 2021
Table of contents :
Front Matter ....Pages ixix
Front Matter ....Pages 11
Autonomous Vehicle Platooning and Motion Control (Nacer K. M’Sirdi)....Pages 319
Improving Human Health: Challenges and Methodology for Controlling Thermal Doses During Cancer Therapeutic Treatment (Ahmed Lakhssassi, Idir Mellal, Mhamed Nour, Youcef Fouzar, Mohammed Bougataya, Emmanuel Kengne)....Pages 2137
Active and Reactive Power Regulation in Nano GridConnected Hybrid PV Systems (Giuseppe Marco Tina)....Pages 3954
An Overview on the Application of Machine Learning and Deep Learning for Photovoltaic Output Power Forecasting (Adel Mellit)....Pages 5568
Front Matter ....Pages 6969
Efficient Memory Parity Check Matrix Optimization for Low Latency Quasi Cyclic LDPC Decoder (Mhammed Benhayoun, Mouhcine Razi, Anas Mansouri, Ali Ahaitouf)....Pages 7179
Monitoring Energy Consumption Based on Predictive Maintenance Techniques (Bouchra Abouelanouar, Ali Elkihel, Fatima Khathyri, Hassan Gziri)....Pages 8187
An Antenna Selection Algorithm for Massive MIMO Systems (Yassine Garrouani, Fatiha Mrabti, Aicha Alami Hassani)....Pages 8995
Compact Structure Design of Band Pass Filter Using Rectangular Resonator and Integrated Capacitor for Wireless Communications Systems (A. Belmajdoub, M. Jorio, S. Bennani, A. Lakhssassi)....Pages 97103
Embedded Implementation of HDR Image Algorithm (Mohamed Sejai, Anass Mansouri, Saad Bennani Dosse, Yassine Ruichek)....Pages 105113
Density, Speed and Direction Aware GPSR Protocol for VANETs (Amina Bengag, Asmae Bengag, Mohamed Elboukhari)....Pages 115122
IoTScalC: A Based Cloud Computing Collaboration Solution for Scalability Issue in IoT Networks (Mohamed Nabil Bahiri, Abdellah Zyane, Abdelilah Ghammaz)....Pages 123133
Monitoring of Industrial Equipment Using Thermography Technique in Passive and Active Form (Fatima Khathyri, Bouchra Abouelanouar, Ali Elkihel, Abd al Motalib Berrehili)....Pages 135140
Enhancing Performance of a 60 GHz Patch Antenna Using Multilayer 2D Metasurfaces (Feriel Guidoum, Mohamed Lamine Tounsi, Noureddine Ababou, Mustapha C. E. Yagoub)....Pages 141149
Enhancing the Performance of Grayscale Image Classification by 2D Charlier Moments Neural Networks (Zouhir Lakhili, Abdelmajid El Alami, Hassan Qjidaa)....Pages 151159
Encrypted Data Sharing Using Proxy ReEncryption in Smart Grid (Anass Sbai, Cyril Drocourt, Gilles Dequen)....Pages 161167
Effective and Robust Detection of Jamming Attacks for WBANBased Healthcare Monitoring Systems (Asmae Bengag, Amina Bengag, Omar Moussaoui)....Pages 169174
Design of Compact Bandpass Filter Based on SRR and CSRR for 5G Applications (Mohamed Amzi, Saad Dosse Bennani, Jamal Zbitou, Abdelhafid Belmajdoub)....Pages 175181
Guidelines for Scalable and Reliable Photovoltaic Wireless Monitoring System: A Case of Study (Kamal Azghiou, Manal El Mouhib, Youssef Bikrat, Ahmad Benlghazi, Abdelhamid Benali)....Pages 183191
Front Matter ....Pages 193193
Electromagnetic MultiFrequencies Filtering by a Defective Asymmetric Photonic Serial Loops Structure (M. ElAouni, Y. BenAli, I. El Kadmiri, Z. Tahri, D. Bria)....Pages 195202
Effect of the Hydrostatic Pressure on the Electronic States Induced by a GeoMaterial Defect Layer in a Multiquantum Wells Structure (Fatima Zahra Elamri, Farid Falyouni, Driss Bria)....Pages 203210
Simulation and Optimization of Cds/ZnSnN2 Structure for Solar Cell Applications with SCAPS1D Software (A. Laidouci, A. Aissat, J. P. Vilcot)....Pages 211222
Numerical Characteristics of Silicon Nitride SiH4/NH3/H2 Plasma Discharge for Thin Film Solar Cell Deposition (Meryem Grari, CifAllah Zoheir)....Pages 223230
A Numerical Study of InGaAs/GaAsP Multiple Quantum Well Solar Cells Using Radial Basis Functions (M. A. Kinani, A. Amine, Y. Mir, M. Zazoui)....Pages 231238
Plasmonic Demultiplexer Based on Induced Transparency Resonances: Analytical and Numerical Study (Madiha Amrani, Soufyane Khattou, Adnane Noual, El Houssaine El Boudouti, Bahram DjafariRouhani)....Pages 239247
Experimental and Theoretical Study of Group Delay Times and Density of States in OneDimensional Photonic Circuit (Soufyane Khattou, Madiha Amrani, Abdelkader Mouadili, El Houssaine El Boudouti, Abdelkrim Talbi, Abdellatif Akjouj et al.)....Pages 249256
Optical Properties of OneDimensional Aperiodic Dielectric Structures Based on ThueMorse Sequence (Hassan Aynaou, Noama Ouchani, El Houssaine El Boudouti)....Pages 257265
Numerical Simulation of Direct Carbon Fuel Cell Using MultipleRelaxationTime Lattice Boltzmann Method (I. Filahi, M. Hasnaoui, A. Amahmid, A. El Mansouri, M. Alouah, Y. Dahani)....Pages 267274
Optical Properties and First Principles Study of CH3NH3PbBr3 Perovskite Structures for Solar Cell Application (Asma O. Al Ghaithi, S. Assa Aravindh, Mohamed N. Hedhili, Tien Khee Ng, Boon S. Ooi, Adel Najar)....Pages 275282
Front Matter ....Pages 283283
Numerical Study of the Effect of Applied Voltage on Simultaneous Modes of Electron Heating in RF Capacitive Discharges (Abdelhak Missaoui, Morad Elkaouini, Hassan Chatei)....Pages 285291
Comparison of State of Charge Estimation Algorithms for Lithium Battery (Mouncef Elmarghichi, Mostafa Bouzi, Naoufal Ettalabi, Mounir Derri)....Pages 293300
GATE Simulation of 6 MV Photon Beam Produced by Elekta Medical Linear Accelerator (DeaeEddine Krim, Abdeslem Rrhioua, Mustapha Zerfaoui, Dikra Bakari, Nacira Hanouf)....Pages 301307
Application of HPSGWO to the Optimal Sizing of Analog Active Filter (Abdelaziz Lberni, Malika Alami Marktani, Abdelaziz Ahaitouf, Ali Ahaitouf)....Pages 309315
Study of Graded Ultrathin CIGS/Si Structure for Solar Cell Applications (M. Boubakeur, A. Aissat, J. P. Vilcot)....Pages 317324
Investigation of Temperature, Well Width and Composition Effects on the Intersubband Absorption of InGaAs/GaAs Quantum Wells (L. Chenini, A. Aissat, S. Ammi, J. P. Vilcot)....Pages 325332
Theoretical Modeling and Optimization of GaAsPN/GaAs Tandem DualJunction Solar Cells (A. Bahi azzououm, A. Aissat, J. P. Vilcot)....Pages 333338
Design of a DC and Low Frequency CMOS Active Voltage Attenuator and Level Shifter with Minimal Thermal Sensitivity (Abdelkhalak Harrak, Salah Eddine Naimi)....Pages 339345
Impact of InGaAs Thickness and Indium Content on the Performance of (InP/InGaAs/InAlAs) MOSFET Structure (S. Ammi, L. Chenini, A. Aissat)....Pages 347352
A Comparative Study Between a Unipolar and a Bipolar PWM Used in Inverters for Photovoltaic Systems (J. Blaacha, R. Aboutni, A. Aziz)....Pages 353360
Medical Cyclotron \(^{18}F\) Radionuclides Production Simulation in a Liquid Target with 16.5 MeV Proton Beam (Camelea Miry, Mustapha Zerfaoui, Abdeslem Rrhioua, Abdelkader El Hamli, Karim Bahhous, Mohammed Hamal et al.)....Pages 361366
Investigation of TG43 Dosimetric Parameters for \(^{192}Ir\) HDR Brachytherapy Source Using FLUKA (Nacira Hanouf, Deaeeddine Krim, Mustapha Zerfaoui, Dikra Bakari, Abdeslem Rrhioua)....Pages 367374
Design of an ISFET Readout Circuit with Minimum Temperature Drift and Good Linearity (Abdelkhalak Harrak, Salah Eddine Naimi)....Pages 375386
Simulation and Performance Study of Silicon Nanowire (SiNW) FieldEffect Transistor (FET) pH Microsensor (N. Ayadi, B. Hajji, H. Madani, A. Lale, J. Launay, P. TempleBoyer)....Pages 387398
Front Matter ....Pages 399399
Modeling Traction Propulsion System and Electromagnetic Disturbances of the Feeding Cables of Machine (Moine El Hajji, Hassane Mahmoudi, Labbadi Moussa)....Pages 401410
Traction Inverter Fault Detection Method Based on Welch and KNearest Neighbor Algorithm (Sara Zerdani, Mohamed Larbi El Hafyani, Smail Zouggar)....Pages 411419
Voltage Regulation of HV Grid Connected to a 40MVA Photovoltaic Power Plant (Mohamed Dib, Ali Nejmi, Mohamed Ramzi)....Pages 421427
Fuzzy Control Techniques Applied for Stabilization of a Quadrotor (Iliass Ouachani, Katell Gadonna, Bilal Belaidi, Herve Billard)....Pages 429440
Mechanical Modeling, Control and Simulation of a Quadrotor UAV (Hamid Hassani, Anass Mansouri, Ali Ahaitouf)....Pages 441449
Optimal Robust ModelFree Control for Altitude of a MiniDrone Using PSO Algorithm (Hossam Eddine Glida, Latifa Abdou, Abdelghani Chelihi, Chouki Sentouh, Gabriele Perozzi)....Pages 451459
Experimental Assessment of Perturb & Observe, Incremental Conductance and Hill Climbing MPPTs for Photovoltaic Systems (N. Rouibah, L. Barazane, A. Rabhi, B. Hajji, R. Bouhedir, A. Hamied et al.)....Pages 461467
Circulating Current Control for Parallel ThreeLevel TType Inverters (Abdelmalik Zorig, Said Barkat, Mohamed Belkheiri, Abdelhamid Rabhi)....Pages 469479
An Improved Sinusoidal (PWM) and Vector (SVPWM) Current Control for a ThreePhase Photovoltaic Inverter Connected to a Nonlinear Load (Abdelhak Lamreoua, Anas Benslimane, Jamal Bouchnaif, Mostafa El Ouariachi)....Pages 481494
Processor in the Loop Implementation of State of Charge Estimation Strategies for Electric Vehicle Applications (Hicham Ben Sassi, Yahia Mazzi, Fatima Errahimi, Najia EsSbai)....Pages 495501
Adaptive Intelligent Control of the ABS Nonlinear Systems Using RBF Neural Network Based on KMeans Clustering (Hamou Ait Abbas, Abdelhamid Rabhi, Mohammed Belkheiri)....Pages 503512
The Best Place of STATCOM in IEEE 14 Bus System to Improve Voltage Profile Using Neplan Software (Ismail Moufid, Hassane El Markhi, Hassan El Moussaoui, Lamhamdi Tijani)....Pages 513521
Optimization of Electromagnetic Interference Conducted in a Devolver Chopper (Zakaria M’barki, Kaoutar Senhaji Rhazi)....Pages 523529
Design and Implementation of a Photovoltaic Emulator Using an Insulated Full Bridge Converter Based Switch Mode Power Supply (Mohammed Chaker, Driss Yousfi, Bekkay Hajji, Mustapha Kourchi, Mohamed Ajaamoum, Ahmed Belarabi et al.)....Pages 531541
Breakdown Voltage Measurement in Insulating Oil of Transformer According to IEC Standards (Mohamed Seghir, Tahar Seghier, Boubakeur Zegnini, Abdelhamid Rabhi)....Pages 543551
Front Matter ....Pages 553553
Energy Management Strategy for Hybrid Electric Vehicle Using Fuzzy Logic (Bilal Belaidi, Iliass Ouachani, Katell Gadonna, David Van Rechem, Hervé Billard)....Pages 555564
Simulation of a MicroGrid for Electric Vehicles Charging Station (R. Bouhedir, A. Mellit, N. Rouibah)....Pages 565571
Design of Fractional Order Sliding Mode Controller for Lateral Dynamics of Electric Vehicles (Imane Abzi, Mohammed Nabil Kabbaj, Mohammed Benbrahim)....Pages 573581
A Decentralized Multilayer Sliding Mode Control Architecture for Vehicle’s Global Chassis Control, and Comparison with a Centralized Architecture (Ali Hamdan, Abbas Chokor, Reine Talj, Moustapha Doumiati)....Pages 583591
Energy Management Strategy Based on a Combination of Frequency Separation and Fuzzy Logic for Fuel Cell Hybrid Electric Vehicles (M. Essoufi, B. Hajji, A. Rabhi)....Pages 593606
Front Matter ....Pages 607607
Physicochemical Characterization of Household and Similar Waste, for Efficient and IncomeGenerating Waste Management in Morocco, City of Mohammadia (Akram Farhat, Kaoutar Lagliti, Mohammed Fekhaoui, Hassan Zahboune)....Pages 609616
Experimental Analysis on Internal Flow Field of Enhanced Heat Transfer Structure for Clean Gas Bus Engine Compartment (Jiajie Ou, Lifu Li)....Pages 617628
Trade Openness and CO2 Emissions in Morocco: An ARDL Bounds Testing Approach (A. Jabri, A. Jaddar)....Pages 629636
Sizing of a Methanation Unit with Discontinuous Digesters to Optimize the Electrical Efficiency of a Biogas Plant, City of Oujda (Akram Farhat, Hassan Zahboune, Kaoutar Lagliti, Mohammed Fekhaoui)....Pages 637645
Heat Loss in Industry: Boiler Performance Analysis (A. Meksoub, A. Elkihel, H. Gziri, A. Berrehili)....Pages 647657
Numerical Simulation of the Flood Risk of the Deviation Hydraulic Structure at Saidia (NorthEast Morocco) (Farid Boushaba, Abdellatif Grari, Mimoun Chourak, Youssef Regad, Bachir Elkihel)....Pages 659665
Numerical Simulation of the Sediment Transport of the Hydraulic Diversion Structure in Saidia (NorthEast of Morocco) (Farid Boushaba, Abdellatif Grari, Mimoun Chourak, Youssef Regad, Bachir Elkihel)....Pages 667673
Industrial Energy Audit Methodology for Improving Energy Efficiency  A Case Study (Ali Elkihel, Bouchra Abouelanouar, Hassan Gziri)....Pages 675681
Prediction of ShortTerm and LongTerm Hourly Global Horizontal Solar Irradiation Using Artificial Neural Networks Techniques in Fez City, Morocco (Zineb Bounoua, Abdellah Mechaqrane)....Pages 683690
Trays Effect on the Dynamic and Thermal Behavior of an Indirect Solar Dryer Using CFD Method (Dounia Chaatouf, Mourad Salhi, Benyounes Raillani, Nadia Dihmani, Samir Amraqui, Mohammed Amine Moussaoui et al.)....Pages 691697
The Application of Artificial Neural Network to Predict Cleanliness Drop in CSP Power Plants Using Meteorological Measurements (Hicham El Gallassi, Ahmed Alami Merrouni, Mimoun Chourak, Abdellatif Ghennioui)....Pages 699707
Comparative Study of Different Conical Receiver’s Materials of a Parabolic Solar Concentrator (Raja Idlimam, Mohamed Asbik, Abdellah Bah)....Pages 709717
ThreeDimensional Analysis of the Effect of Transverse Spacing Between Perforations of a Deflector in a Heat Exchanger (JamalEddine Salhi, Najim Salhi)....Pages 719728
Analysis of a BuildingMounted WindSolar Hybrid Power System in Urban Residential Areas: The Case Study of Istanbul (B. Oral, S. Sağlam, A. Mellit)....Pages 729737
Analysis of the Energy Produced and Energy Quality of Nanofluid Impact on PhotovoltaicThermal Systems (Stefano Aneli, Antonio Gagliano, Giuseppe M. Tina, Bekkay Hajji)....Pages 739745
Heat Transfer and Entropy Generation for Natural Convection in a Cavity with Inner Obstacles (Jamal Baliti, Mohamed Hssikou, Youssef Elguennouni, Ahmed Moussaoui, Mohammed Alaoui)....Pages 747752
Behavior Study of a New Inverter Topology for Photovoltaic Applications (Y. Amari, S. Labdai, M. Hasni, A. Rabhi, B. Hajji, A. Mellit)....Pages 753760
Application of the Random Walk Particle Tracking for ConvectionDiffusion Problem Within Strait of Gibraltar (Hind Talbi, Mohammed Jeyar, Elmiloud Chaabelasri, Najim Salhi)....Pages 761766
The Impact of the Tilt Angle on the Sizing of Autonomous Photovoltaic Systems Using Electric System Cascade Analysis (Mohammed Chennaif, Mohamed Larbi Elhafyani, Hassan Zahboune, Smail Zouggar)....Pages 767776
Technical and Economic Analysis of Solar Hydrogen Production in Morocco (Samir Touili, Ahmed Alami Merrouni, Youssef El Hassouani, Abdelillah Amrani, Samir Rachidi)....Pages 777783
Production of Hydrogen by Excess Energy Resulting from a Photovoltaic System Supplying a Load of Nominal Power (Abdelhafid Messaoudi, Sanae Dahbi, Abdelhak Aziz, Kamal Kassmi)....Pages 785795
Performances MPPT Enhancement in PMSG Wind Turbine System Using Fuzzy Logic Control (Mhamed Fannakh, Mohamed Larbi Elhafyani, Smail Zouggar, Hassan Zahboune)....Pages 797807
Prediction of Particle Deposition Efficiency in a 90° Turbulent Bend Pipe Flow—A Numerical Study (Fatima Zahrae Erraghroughi, Kawtar Feddi, Anas El Maakoul, Abdellah Bah, Abdellatif Ben Abdellah)....Pages 809817
Maximum Power Extraction from a Wind Turbine Energy Source Based on Fuzzy and Conventional Techniques for Integration in Microgrid (Salaheddine Zouirech, Mohammed Zerouali, Abdelghani El Ougli, Belkassem Tidhaf)....Pages 819829
Management Strategy of Power Exchange in a Building Between Grid, Photovoltaic and Batteries (Mohammed Dhriyyef, Abdelmalek El Mehdi, Mohammed Elhitmy, Mohammed Elhafyani)....Pages 831841
Modeling, Simulation and Real Time Implementation of MPPT Based Field Oriented Control Applied to DFIG Wind Turbine (Nabil Dahri, Mohammed Ouassaid, Driss Yousfi)....Pages 843854
Energy Management Strategy for an Optimum Control of a Standalone PhotovoltaicBatteries Water Pumping System for Agriculture Applications (Mohammed Benzaouia, Bekkay Hajji, Abdelhamid Rabhi, Adel Mellit, Anas Benslimane, Anne Migan Dubois)....Pages 855868
Mass Flow Rates Effect on the Performance of PV/T Bifluid Hybrid Collector (Single and Simultaneous Modes) (Oussama El Manssouri, Chaimae El Fouas, Bekkay Hajji, Abdelhamid Rabhi, Giuseppe Marco Tina, Antonio Gagliano)....Pages 869878
Study and Modeling of Energy Performance of PV/T Solar Plant for Hydrogen Production (C. El Fouas, O. El Manssouri, B. Hajji, G. M. Tina, A. Gagliano)....Pages 879891
Back Matter ....Pages 893896
Lecture Notes in Electrical Engineering 681
Bekkay Hajji · Adel Mellit · Giuseppe Marco Tina · Abdelhamid Rabhi · Jerome Launay · Salah Eddine Naimi Editors
Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems ICEERE 2020, 13–15 April 2020, Saidia, Morocco
Lecture Notes in Electrical Engineering Volume 681
Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, Humanoids and Intelligent Systems Laboratory, Karlsruhe Institute for Technology, Karlsruhe, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPMCSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, Munich, Germany Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Sebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering & Advanced Technology, Massey University, Palmerston North, ManawatuWanganui, New Zealand CunZheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Optoelectronics, Chinese Academy of Sciences, Beijing, China Junjie James Zhang, Charlotte, NC, USA
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Bekkay Hajji Adel Mellit Giuseppe Marco Tina Abdelhamid Rabhi Jerome Launay Salah Eddine Naimi •
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Editors
Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems ICEERE 2020, 13–15 April 2020, Saidia, Morocco
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Editors Bekkay Hajji National School of Applied Sciences Mohamed Premier University Oujda, Morocco
Adel Mellit Faculty of Sciences and Technology Jijel University Jijel, Algeria
Giuseppe Marco Tina DIEEI University of Catania CATANIA, Catania, Italy
Abdelhamid Rabhi EEA Department of the Faculty of Sciences University of Picardie Jules Verne Amiens, France
Jerome Launay Laboratory for Analysis and Architecture of Systems Toulouse, France
Salah Eddine Naimi National School of Applied Sciences Oujda Mohammed Premier University Oujda, Morocco
ISSN 18761100 ISSN 18761119 (electronic) Lecture Notes in Electrical Engineering ISBN 9789811562587 ISBN 9789811562594 (eBook) https://doi.org/10.1007/9789811562594 © Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #2101/04 Gateway East, Singapore 189721, Singapore
Brief Synopsis About ICEERE’20 Book
The ICEERE’20 book provides the latest advance in electronic engineering and renewable energy systems; it focuses mainly on the application of artiﬁcial intelligence techniques, emerging technology and Internet of Things in electrical and renewable energy systems including hybrid systems, microgrids, networking, smart health applications, smart grid, electric vehicle, etc. The advance of renewable energy applications would not have been possible without the advance of electronic and information technologies. With the successful experience of the ﬁrst edition (in Saidia, Morocoo, April 15–17, 2018), we truly believe that the second edition of ICEERE’20 will achieve greater success and provide a better platform for all the participants (scientists and engineers from all over the world) to have fruitful discussions and discuss the latest issues and progress in the area of electronic engineering and renewable energy. We expect that the published papers in the conference will be a trigger for further related research and technology improvements in this importance. ICEERE’20 will also include presentations of contributed papers and stateofthe art lectures by invited keynote speakers. The book has a special focus on electric vehicles and the control of connected vehicles systems. Special interest will also be given to the energy challenges for developing the EuroMediterranean regions through new renewable energy technologies in the agricultural and rural areas. We would like to thank the program chairs, organization staff and the members of the program committees for their hard work. Special thanks go to Springer Publisher. We hope that ICEERE 2020 will be successful and enjoyable to all the participants. We look forward to seeing all of you in two years at the ICEERE 2022.
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Organization
Honorary Committee Mohamed Ali Habouha Yassine Zarloul Mohamed Naciri Mohamed Ibrahimi Farid Elhebil Hassan Ettahiri Mohamed Addou Moulay Brahim Sedra
Governor of Berkane Province, Morocco President of Mohammed First University, Morocco President of the Council of the Berkane Province, Morocco President of Berkane Urban Commune, Morocco Director of National School of Applied Sciences, Oujda, Morocco Director of Colaimo, Oujda, Morocco. Dean of the Faculty of Sciences and Techniques, Tanger, Morocco Dean of the Faculty of Sciences and Techniques, Errachidia, Morocco
General Chairs Bekkay Hajji Abdelhamid Rabhi
ENSAOujda, Mohammed First University, Morccco University of Picardy Jules Verne, France
General Cochairs Adel Mellit Giuseppe Marco Tina Jerome Launay
University of Jijel, Algeria University of Catania, Italy LAASCNRS, Toulouse, France
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Organization
Technical Program Committee A. Massi Pavan S. Mekhilef A. Elahi E. Quaranta A. Mellit M. Jouid W. Dimassi H. El Fadili S. Safak M. Benghanem R. Benabderrahmane Zaghouani A. Gagliano Y. Al Younes J. Launay M. Ben Ammar F. Farmakis P. TempleBoyer N. Msirdi F. Tadeo A. Migandubois A. Lakhssassi K. Khodja A. Rabhi A. Boualit G. M. Tina I. Abdi Hadi Geetanjali Deokar A. Kheldoun B. Bousouﬁ C. EL Mmasides M. El Yaakoubi Emanuele Ogliari D. Benhaddou M. Belkheiri C. JeanYves D. Rekioua D. M. Grasso M. A. Moutaouekkil Naamane Abdelaziz Ahmed Elakkary
University of Trieste, Italy University of Malaya, Malaysia Southern Connecticut State University, USA European Commission, Joint Research Centre, Italy University of Jijel, Algeria United Arab Emirates CRTEN, Tunis ENSAFès, Morocco University of Marmara, Turkey University of Madinah, KSA CRTEN, Tunis Université de Catane, Italy United Arab Emirates LAASCNRS, France ENIS, Tunisia University of Thrace, Greece LAASCNRS, France LSISUMR France Universidad de Valladolid, Spain SUPELEC, France University of Quebec, Canada University of Sciences and Technology, Algeria University of Picardie, France URAER, Algeria University of Catania, Italy Université, Djibouti KAUST, Kingdom of Saudi Arabia University Boumerdes, Algeria USMBA, Fes, Morocco Democritus University of Thrace, Greece TFSCInstrument, France Politecnico di Milano, Italy University of Houston, USA Université Amar Telidji de Laghouat, Algeria University of Laval, Canada Univ. of Bejaia, Algeria Univ. of Catania, Italy ENSAOujda, Morocco Universitéd’Aix Marseille, France ESTSalé, Morocco Francesco Nocera, University of Catania, Italy
Organization
M. Kodad M. Nasiruddin Mahyuddin D. Ishak Chettibi Nedjwa Boualit Hamid A. Kaaouachi Belaid Sabrina Laili Djaafer Y. Reggad M. Hajji H. El Boustani O. El Mrabet M. Saber Boukenoui Rachid M. G. Belkasmi Gilles Dequen A. El Moussati Y. G. Dessouky B. Oral L. Bouselham Soﬁane Haddad Reine Talj A. Alami Hassani M. Belkheiri A. Aissat A. Messaoudi H. Aitabbas Asmaa Zugari F. ABDI D. Bria A. El Ougli Zyane Abdellah H. Zahboune Elwarraki Elmostafa Chouki Sentouh Aumeur El Amrani Amraqui Samir S. Zougar Benslimane Anas Guerbaoui Mohammed El Houssaine El Boudouti Abdelali EdDahhak A. Mbarki MohammedAmine Koulali
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ESTOujda, Morocco Universiti Sains, Malaysia Universiti Sains, Malaysia Jijel University, Morocco CDER, Algeria ESTOujda, Morocco CDER, Algeria Jijel University, Algeria Med First University, Morocco ENSAOujda, Morocco ENSA Saﬁ, Univ. Cadi Ayyad, Morocco FSTanger, Univ. Abdelmalek Essaadi, Morocco ENSAOujda, Morocco Blida University, Algeria ENSAOujda, Morocco University of Picardie Jules Verne, France ENSAOujda, Morocco AASTMT, Egypt Marmara University, Turkey ENSAOujda, Morocco Jijel University, Algeria Université de technologie de Compiègne, France USMBA, Fes, Morocco Université Amar Telidji de Laghouat, Algeria University of Blida, Algeria ESTOujda, Morocco UATL, Algeria University Abdelmalek Essaadi, Morocco FSTFes, Morocco Med First University, Morocco ENSA Oujda, University Med First, Morocco ENSA Saﬁ, University Med First, Morocco ESTOujda, Morocco University Cadi Ayyad, Morocco HautsdeFrance Polytechnic University, Valenciennes, France FST Errachidia, Université My Ismail, Morocco EST Oujda,University Med First, Morocco ESTOujda, Morocco ENSA Oujda, University Med First, Morocco EST University Moulay Ismail, Morocco FSO Oujda, University Med First, Morocco Moulay Ismail University, Morocco ENSAOujda, Morocco ENSAOujda, Morocco
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Ait Madi Abdessalam Kassmi Kamal Falyouni Farid A. Mazari E. Llobet Y. Khliﬁ M. Koudad R. El ghouri D. Lara S. Naimi A. Azizi M. El Arbi El hafyani M. EL Ouariachi A. Galadi A. Mansouri S. D. Bennani D. Yousﬁ A. Ahaitouf A. Alami Merrouni Hanae Azzaoui H. Qjidaa A. El Mehdi Michele Calì T. Sgheir R. El Bouayadi Iliass Ouachani Bilal Belaidi A. Soukkou C. El Fouas Wael M. Elshemey ElKaber Hachem Hadjaissa Boubakeur
Organization
IbnTofail University, Morocco EST Oujda, University Med First, Morocco Med First University, Morocco Med First University, Morocco University Rovirai Virgili, Espagne ENSA, Oujda, Morocco UMPOujda, Morocco ENSAKenitra, Morocco TecnologicoNacional de México ENSAOujda, Morocco ESTOujda, Morocco ENSAOujda, Morocco ESTOujda, Morocco ENSASaﬁ, Morocco ENSAFes, Morocco ENSAFES, Morocco ENSAOujda, Morocco Université Sidi Mohammed Ben Abdellah – Fès, Morocco FSOUMP, Morocco ENSAOujda, Morocco. Sidi Mohamed Ben Abdellah University, Fes, Morocco ENSAOujda, Morocco University of Catania, Italy University of Laghouat ENSAK, Morocco Polymont Engineering, France Polymont Engineering, France Jijel University, Algeria ENSAOujda, Morocco Cairo University, Egypt University Moulay Ismail, Morocco University of Laghouat, Algeria
Contents
Invited Speaker Autonomous Vehicle Platooning and Motion Control . . . . . . . . . . . . . . . Nacer K. M’Sirdi Improving Human Health: Challenges and Methodology for Controlling Thermal Doses During Cancer Therapeutic Treatment . . . . Ahmed Lakhssassi, Idir Mellal, Mhamed Nour, Youcef Fouzar, Mohammed Bougataya, and Emmanuel Kengne
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Active and Reactive Power Regulation in Nano GridConnected Hybrid PV Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Giuseppe Marco Tina
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An Overview on the Application of Machine Learning and Deep Learning for Photovoltaic Output Power Forecasting . . . . . . Adel Mellit
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Communication, Signal Processing and Information Technology Efﬁcient Memory Parity Check Matrix Optimization for Low Latency Quasi Cyclic LDPC Decoder . . . . . . . . . . . . . . . . . . . . Mhammed Benhayoun, Mouhcine Razi, Anas Mansouri, and Ali Ahaitouf
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Monitoring Energy Consumption Based on Predictive Maintenance Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bouchra Abouelanouar, Ali Elkihel, Fatima Khathyri, and Hassan Gziri
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An Antenna Selection Algorithm for Massive MIMO Systems . . . . . . . . Yassine Garrouani, Fatiha Mrabti, and Aicha Alami Hassani Compact Structure Design of Band Pass Filter Using Rectangular Resonator and Integrated Capacitor for Wireless Communications Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Belmajdoub, M. Jorio, S. Bennani, and A. Lakhssassi
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Embedded Implementation of HDR Image Algorithm . . . . . . . . . . . . . . 105 Mohamed Sejai, Anass Mansouri, Saad Bennani Dosse, and Yassine Ruichek Density, Speed and Direction Aware GPSR Protocol for VANETs . . . . 115 Amina Bengag, Asmae Bengag, and Mohamed Elboukhari IoTScalC: A Based Cloud Computing Collaboration Solution for Scalability Issue in IoT Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Mohamed Nabil Bahiri, Abdellah Zyane, and Abdelilah Ghammaz Monitoring of Industrial Equipment Using Thermography Technique in Passive and Active Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Fatima Khathyri, Bouchra Abouelanouar, Ali Elkihel, and Abd al Motalib Berrehili Enhancing Performance of a 60 GHz Patch Antenna Using Multilayer 2D Metasurfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Feriel Guidoum, Mohamed Lamine Tounsi, Noureddine Ababou, and Mustapha C. E. Yagoub Enhancing the Performance of Grayscale Image Classiﬁcation by 2D Charlier Moments Neural Networks . . . . . . . . . . . . . . . . . . . . . . 151 Zouhir Lakhili, Abdelmajid El Alami, and Hassan Qjidaa Encrypted Data Sharing Using Proxy ReEncryption in Smart Grid . . . 161 Anass Sbai, Cyril Drocourt, and Gilles Dequen Effective and Robust Detection of Jamming Attacks for WBANBased Healthcare Monitoring Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Asmae Bengag, Amina Bengag, and Omar Moussaoui Design of Compact Bandpass Filter Based on SRR and CSRR for 5G Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Mohamed Amzi, Saad Dosse Bennani, Jamal Zbitou, and Abdelhaﬁd Belmajdoub Guidelines for Scalable and Reliable Photovoltaic Wireless Monitoring System: A Case of Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Kamal Azghiou, Manal El Mouhib, Youssef Bikrat, Ahmad Benlghazi, and Abdelhamid Benali Materials and Devices Applications Electromagnetic MultiFrequencies Filtering by a Defective Asymmetric Photonic Serial Loops Structure . . . . . . . . . . . . . . . . . . . . . 195 M. ElAouni, Y. BenAli, I. El Kadmiri, Z. Tahri, and D. Bria
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Effect of the Hydrostatic Pressure on the Electronic States Induced by a GeoMaterial Defect Layer in a Multiquantum Wells Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Fatima Zahra Elamri, Farid Falyouni, and Driss Bria Simulation and Optimization of Cds/ZnSnN2 Structure for Solar Cell Applications with SCAPS1D Software . . . . . . . . . . . . . . 211 A. Laidouci, A. Aissat, and J. P. Vilcot Numerical Characteristics of Silicon Nitride SiH4/NH3/H2 Plasma Discharge for Thin Film Solar Cell Deposition . . . . . . . . . . . . . . . . . . . . 223 Meryem Grari and CifAllah Zoheir A Numerical Study of InGaAs/GaAsP Multiple Quantum Well Solar Cells Using Radial Basis Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 M. A. Kinani, A. Amine, Y. Mir, and M. Zazoui Plasmonic Demultiplexer Based on Induced Transparency Resonances: Analytical and Numerical Study . . . . . . . . . . . . . . . . . . . . . 239 Madiha Amrani, Soufyane Khattou, Adnane Noual, El Houssaine El Boudouti, and Bahram DjafariRouhani Experimental and Theoretical Study of Group Delay Times and Density of States in OneDimensional Photonic Circuit . . . . . . . . . . 249 Soufyane Khattou, Madiha Amrani, Abdelkader Mouadili, El Houssaine El Boudouti, Abdelkrim Talbi, Abdellatif Akjouj, and Bahram DjafariRouhani Optical Properties of OneDimensional Aperiodic Dielectric Structures Based on ThueMorse Sequence . . . . . . . . . . . . . . . . . . . . . . 257 Hassan Aynaou, Noama Ouchani, and El Houssaine El Boudouti Numerical Simulation of Direct Carbon Fuel Cell Using MultipleRelaxationTime Lattice Boltzmann Method . . . . . . . . . . . . . . 267 I. Filahi, M. Hasnaoui, A. Amahmid, A. El Mansouri, M. Alouah, and Y. Dahani Optical Properties and First Principles Study of CH3NH3PbBr3 Perovskite Structures for Solar Cell Application . . . . . . . . . . . . . . . . . . 275 Asma O. Al Ghaithi, S. Assa Aravindh, Mohamed N. Hedhili, Tien Khee Ng, Boon S. Ooi, and Adel Najar Electronics Numerical Study of the Effect of Applied Voltage on Simultaneous Modes of Electron Heating in RF Capacitive Discharges . . . . . . . . . . . . 285 Abdelhak Missaoui, Morad Elkaouini, and Hassan Chatei
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Comparison of State of Charge Estimation Algorithms for Lithium Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 Mouncef Elmarghichi, Mostafa Bouzi, Naoufal Ettalabi, and Mounir Derri GATE Simulation of 6 MV Photon Beam Produced by Elekta Medical Linear Accelerator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 DeaeEddine Krim, Abdeslem Rrhioua, Mustapha Zerfaoui, Dikra Bakari, and Nacira Hanouf Application of HPSGWO to the Optimal Sizing of Analog Active Filter Abdelaziz Lberni, Malika Alami Marktani, Abdelaziz Ahaitouf, and Ali Ahaitouf
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Study of Graded Ultrathin CIGS/Si Structure for Solar Cell Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 M. Boubakeur, A. Aissat, and J. P. Vilcot Investigation of Temperature, Well Width and Composition Effects on the Intersubband Absorption of InGaAs/GaAs Quantum Wells . . . . 325 L. Chenini, A. Aissat, S. Ammi, and J. P. Vilcot Theoretical Modeling and Optimization of GaAsPN/GaAs Tandem DualJunction Solar Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 A. Bahi azzououm, A. Aissat, and J. P. Vilcot Design of a DC and Low Frequency CMOS Active Voltage Attenuator and Level Shifter with Minimal Thermal Sensitivity . . . . . . . . . . . . . . . 339 Abdelkhalak Harrak and Salah Eddine Naimi Impact of InGaAs Thickness and Indium Content on the Performance of (InP/InGaAs/InAlAs) MOSFET Structure . . . . . . . . . . . . . . . . . . . . . 347 S. Ammi, L. Chenini, and A. Aissat A Comparative Study Between a Unipolar and a Bipolar PWM Used in Inverters for Photovoltaic Systems . . . . . . . . . . . . . . . . . . . . . . 353 J. Blaacha, R. Aboutni, and A. Aziz Medical Cyclotron 18 F Radionuclides Production Simulation in a Liquid Target with 16:5 MeV Proton Beam . . . . . . . . . . . . . . . . . . 361 Camelea Miry, Mustapha Zerfaoui, Abdeslem Rrhioua, Abdelkader El Hamli, Karim Bahhous, Mohammed Hamal, and Abdelilah Moussa Investigation of TG43 Dosimetric Parameters for 192 Ir HDR Brachytherapy Source Using FLUKA . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Nacira Hanouf, Deaeeddine Krim, Mustapha Zerfaoui, Dikra Bakari, and Abdeslem Rrhioua
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Design of an ISFET Readout Circuit with Minimum Temperature Drift and Good Linearity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Abdelkhalak Harrak and Salah Eddine Naimi Simulation and Performance Study of Silicon Nanowire (SiNW) FieldEffect Transistor (FET) pH Microsensor . . . . . . . . . . . . . . . . . . . . 387 N. Ayadi, B. Hajji, H. Madani, A. Lale, J. Launay, and P. TempleBoyer Power Electronics and Control Systems Modeling Traction Propulsion System and Electromagnetic Disturbances of the Feeding Cables of Machine . . . . . . . . . . . . . . . . . . . 401 Moine El Hajji, Hassane Mahmoudi, and Labbadi Moussa Traction Inverter Fault Detection Method Based on Welch and KNearest Neighbor Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 Sara Zerdani, Mohamed Larbi El Hafyani, and Smail Zouggar Voltage Regulation of HV Grid Connected to a 40MVA Photovoltaic Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 Mohamed Dib, Ali Nejmi, and Mohamed Ramzi Fuzzy Control Techniques Applied for Stabilization of a Quadrotor . . . 429 Iliass Ouachani, Katell Gadonna, Bilal Belaidi, and Herve Billard Mechanical Modeling, Control and Simulation of a Quadrotor UAV . . . 441 Hamid Hassani, Anass Mansouri, and Ali Ahaitouf Optimal Robust ModelFree Control for Altitude of a MiniDrone Using PSO Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 Hossam Eddine Glida, Latifa Abdou, Abdelghani Chelihi, Chouki Sentouh, and Gabriele Perozzi Experimental Assessment of Perturb & Observe, Incremental Conductance and Hill Climbing MPPTs for Photovoltaic Systems . . . . . 461 N. Rouibah, L. Barazane, A. Rabhi, B. Hajji, R. Bouhedir, A. Hamied, and A. Mellit Circulating Current Control for Parallel ThreeLevel TType Inverters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Abdelmalik Zorig, Said Barkat, Mohamed Belkheiri, and Abdelhamid Rabhi An Improved Sinusoidal (PWM) and Vector (SVPWM) Current Control for a ThreePhase Photovoltaic Inverter Connected to a Nonlinear Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481 Abdelhak Lamreoua, Anas Benslimane, Jamal Bouchnaif, and Mostafa El Ouariachi
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Processor in the Loop Implementation of State of Charge Estimation Strategies for Electric Vehicle Applications . . . . . . . . . . . . . . . . . . . . . . 495 Hicham Ben Sassi, Yahia Mazzi, Fatima Errahimi, and Najia EsSbai Adaptive Intelligent Control of the ABS Nonlinear Systems Using RBF Neural Network Based on KMeans Clustering . . . . . . . . . . . . . . . . . . . 503 Hamou Ait Abbas, Abdelhamid Rabhi, and Mohammed Belkheiri The Best Place of STATCOM in IEEE 14 Bus System to Improve Voltage Proﬁle Using Neplan Software . . . . . . . . . . . . . . . . . . . . . . . . . . 513 Ismail Mouﬁd, Hassane El Markhi, Hassan El Moussaoui, and Lamhamdi Tijani Optimization of Electromagnetic Interference Conducted in a Devolver Chopper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 Zakaria M’barki and Kaoutar Senhaji Rhazi Design and Implementation of a Photovoltaic Emulator Using an Insulated Full Bridge Converter Based Switch Mode Power Supply . . . 531 Mohammed Chaker, Driss Yousﬁ, Bekkay Hajji, Mustapha Kourchi, Mohamed Ajaamoum, Ahmed Belarabi, Nasrudin Abd Rahim, and Jeyrage Selvaraj Breakdown Voltage Measurement in Insulating Oil of Transformer According to IEC Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Mohamed Seghir, Tahar Seghier, Boubakeur Zegnini, and Abdelhamid Rabhi Electric Vehicle Energy Management Strategy for Hybrid Electric Vehicle Using Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 Bilal Belaidi, Iliass Ouachani, Katell Gadonna, David Van Rechem, and Hervé Billard Simulation of a MicroGrid for Electric Vehicles Charging Station . . . . 565 R. Bouhedir, A. Mellit, and N. Rouibah Design of Fractional Order Sliding Mode Controller for Lateral Dynamics of Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Imane Abzi, Mohammed Nabil Kabbaj, and Mohammed Benbrahim A Decentralized Multilayer Sliding Mode Control Architecture for Vehicle’s Global Chassis Control, and Comparison with a Centralized Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 Ali Hamdan, Abbas Chokor, Reine Talj, and Moustapha Doumiati Energy Management Strategy Based on a Combination of Frequency Separation and Fuzzy Logic for Fuel Cell Hybrid Electric Vehicles . . . . 593 M. Essouﬁ, B. Hajji, and A. Rabhi
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Renewable Energy Physicochemical Characterization of Household and Similar Waste, for Efﬁcient and IncomeGenerating Waste Management in Morocco, City of Mohammadia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 Akram Farhat, Kaoutar Lagliti, Mohammed Fekhaoui, and Hassan Zahboune Experimental Analysis on Internal Flow Field of Enhanced Heat Transfer Structure for Clean Gas Bus Engine Compartment . . . . . . . . . 617 Jiajie Ou and Lifu Li Trade Openness and CO2 Emissions in Morocco: An ARDL Bounds Testing Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629 A. Jabri and A. Jaddar Sizing of a Methanation Unit with Discontinuous Digesters to Optimize the Electrical Efﬁciency of a Biogas Plant, City of Oujda . . . . 637 Akram Farhat, Hassan Zahboune, Kaoutar Lagliti, and Mohammed Fekhaoui Heat Loss in Industry: Boiler Performance Analysis . . . . . . . . . . . . . . . 647 A. Meksoub, A. Elkihel, H. Gziri, and A. Berrehili Numerical Simulation of the Flood Risk of the Deviation Hydraulic Structure at Saidia (NorthEast Morocco) . . . . . . . . . . . . . . . . . . . . . . . 659 Farid Boushaba, Abdellatif Grari, Mimoun Chourak, Youssef Regad, and Bachir Elkihel Numerical Simulation of the Sediment Transport of the Hydraulic Diversion Structure in Saidia (NorthEast of Morocco) . . . . . . . . . . . . . 667 Farid Boushaba, Abdellatif Grari, Mimoun Chourak, Youssef Regad, and Bachir Elkihel Industrial Energy Audit Methodology for Improving Energy Efﬁciency  A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675 Ali Elkihel, Bouchra Abouelanouar, and Hassan Gziri Prediction of ShortTerm and LongTerm Hourly Global Horizontal Solar Irradiation Using Artiﬁcial Neural Networks Techniques in Fez City, Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683 Zineb Bounoua and Abdellah Mechaqrane Trays Effect on the Dynamic and Thermal Behavior of an Indirect Solar Dryer Using CFD Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691 Dounia Chaatouf, Mourad Salhi, Benyounes Raillani, Nadia Dihmani, Samir Amraqui, Mohammed Amine Moussaoui, and Ahmed Mezrhab
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The Application of Artiﬁcial Neural Network to Predict Cleanliness Drop in CSP Power Plants Using Meteorological Measurements . . . . . . 699 Hicham El Gallassi, Ahmed Alami Merrouni, Mimoun Chourak, and Abdellatif Ghennioui Comparative Study of Different Conical Receiver’s Materials of a Parabolic Solar Concentrator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 709 Raja Idlimam, Mohamed Asbik, and Abdellah Bah ThreeDimensional Analysis of the Effect of Transverse Spacing Between Perforations of a Deﬂector in a Heat Exchanger . . . . . . . . . . . 719 JamalEddine Salhi and Najim Salhi Analysis of a BuildingMounted WindSolar Hybrid Power System in Urban Residential Areas: The Case Study of Istanbul . . . . . . . . . . . . 729 B. Oral, S. Sağlam, and A. Mellit Analysis of the Energy Produced and Energy Quality of Nanoﬂuid Impact on PhotovoltaicThermal Systems . . . . . . . . . . . . . . . . . . . . . . . . 739 Stefano Aneli, Antonio Gagliano, Giuseppe M. Tina, and Bekkay Hajji Heat Transfer and Entropy Generation for Natural Convection in a Cavity with Inner Obstacles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747 Jamal Baliti, Mohamed Hssikou, Youssef Elguennouni, Ahmed Moussaoui, and Mohammed Alaoui Behavior Study of a New Inverter Topology for Photovoltaic Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753 Y. Amari, S. Labdai, M. Hasni, A. Rabhi, B. Hajji, and A. Mellit Application of the Random Walk Particle Tracking for ConvectionDiffusion Problem Within Strait of Gibraltar . . . . . . . . . 761 Hind Talbi, Mohammed Jeyar, Elmiloud Chaabelasri, and Najim Salhi The Impact of the Tilt Angle on the Sizing of Autonomous Photovoltaic Systems Using Electric System Cascade Analysis . . . . . . . . 767 Mohammed Chennaif, Mohamed Larbi Elhafyani, Hassan Zahboune, and Smail Zouggar Technical and Economic Analysis of Solar Hydrogen Production in Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 777 Samir Touili, Ahmed Alami Merrouni, Youssef El Hassouani, Abdelillah Amrani, and Samir Rachidi Production of Hydrogen by Excess Energy Resulting from a Photovoltaic System Supplying a Load of Nominal Power . . . . . . . . . 785 Abdelhaﬁd Messaoudi, Sanae Dahbi, Abdelhak Aziz, and Kamal Kassmi
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Performances MPPT Enhancement in PMSG Wind Turbine System Using Fuzzy Logic Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 797 Mhamed Fannakh, Mohamed Larbi Elhafyani, Smail Zouggar, and Hassan Zahboune Prediction of Particle Deposition Efﬁciency in a 90° Turbulent Bend Pipe Flow—A Numerical Study . . . . . . . . . . . . . . . . . . . . . . . . . . 809 Fatima Zahrae Erraghroughi, Kawtar Feddi, Anas El Maakoul, Abdellah Bah, and Abdellatif Ben Abdellah Maximum Power Extraction from a Wind Turbine Energy Source Based on Fuzzy and Conventional Techniques for Integration in Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819 Salaheddine Zouirech, Mohammed Zerouali, Abdelghani El Ougli, and Belkassem Tidhaf Management Strategy of Power Exchange in a Building Between Grid, Photovoltaic and Batteries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 831 Mohammed Dhriyyef, Abdelmalek El Mehdi, Mohammed Elhitmy, and Mohammed Elhafyani Modeling, Simulation and Real Time Implementation of MPPT Based Field Oriented Control Applied to DFIG Wind Turbine . . . . . . . 843 Nabil Dahri, Mohammed Ouassaid, and Driss Yousﬁ Energy Management Strategy for an Optimum Control of a Standalone PhotovoltaicBatteries Water Pumping System for Agriculture Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855 Mohammed Benzaouia, Bekkay Hajji, Abdelhamid Rabhi, Adel Mellit, Anas Benslimane, and Anne Migan Dubois Mass Flow Rates Effect on the Performance of PV/T Biﬂuid Hybrid Collector (Single and Simultaneous Modes) . . . . . . . . . . . . . . . . . . . . . . 869 Oussama El Manssouri, Chaimae El Fouas, Bekkay Hajji, Abdelhamid Rabhi, Giuseppe Marco Tina, and Antonio Gagliano Study and Modeling of Energy Performance of PV/T Solar Plant for Hydrogen Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 879 C. El Fouas, O. El Manssouri, B. Hajji, G. M. Tina, and A. Gagliano Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893
Invited Speaker
Autonomous Vehicle Platooning and Motion Control Overview on Models and Control Approaches? Features and Characters Nacer K. M’Sirdi Abstract This note presents an overview of the modeling and control strategies on vehicle platooning of road vehicles and focuses specifically on the modeling and control strategies. In general, independent (simplified) vehicle models are related and coupled only through the control laws. The control problem is then studied and several strategies are considered (local, global and mixed) in literature. The modeling approach that we prefer is the one of robotics considering the geometric, the kinematic and the dynamic models. Several models exist in literature [1–4]. The use of nonlinear robust approaches gives a better controllability of the fleet and more robust behavior against uncertainties and modeling errors. Keywords Vehicle fleet dynamics · Modeling and control · Vehicle platooning · Global behavior · Control strategies · Automatic cruise control
1 Introduction 1.1 Context and Motivations More and more projects deal with vehicle platooning (or a collection of coordinated vehicles traveling together). Platooning increases the capacity of the infrastructures and improving the safety and comfort. Among the advantages of platooning, we can note fuel or energy economy, traffic efficiency, safety, and driving comfort. Automating vehicles at low speeds can lead to better use of available space. A first strategy was to control the intervehicular distances [4]. To deal with these problems, several solutions have been proposed, in the urban environment and on the highway, based on the change of infrastructure, alternative transportation [5], and the convoy of autonomous vehicles. Research suggests controlling the road vehicle’s position and velocity. This can be used for autonomous platooning. N. K. M’Sirdi (B) Aix Marseille Université, Université de Toulon, CNRS, LIS, UMR CNRS, 7020 Marseille, France email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_1
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The vehicle fleet is a very efficient means of transportation which increases traffic capacity [5, 6]. Other benefits, such as reducing fuel consumption and minimizing labor, may exist in piggybacking or truckmounted cars. The convoy consists of a leading vehicle and trailing trucks. The leader can be autonomous or driven by a driver, other vehicles follow him respecting a safety distance to avoid collisions.
1.2 Note Organization For fleets, the models generally used ignore the details of vehicle specific dynamics and focus on the representation of relative movements of vehicles. This note is composed of three main parts: – State of the art on platooning, where some research projects on convoys are cited – Modeling of the convoy with the required features, – Control approaches analysis, the proposition of a global fleet model. A quick state of the art citing some research projects on convoys is given. Then we give the simplified models most often used for the convoys and present the local and global control strategies, most often used. Then we propose models to represent a fleet of vehicles. These are the geometric, kinematic and dynamic models that will be used to describe the behavior of the fleets. The conclusion will give some of the many perspectives and point out the open problems.
2 State of the Art in Vehicles Platooning Mobility and transport are of main importance in the world. The EU tries to build on the political regulation for truck platooning in the European roads in the near future. The Electric Mobility Europe Call (EMEurope Call 2016)1 funded 13 projects which address 5 key areas of electric mobility (see https://www.acea.be/uploads/ publications/Platooning_roadmap.pdf) (Fig. 1). Several research projects dealt with control of fleets of vehicles. Volvo has also demonstrated platooning on normal roads with three cars driven autonomously behind a lead truck driven at speeds up to 90 km/h with a gap of no more than 6m. This system is called SARTRE (Safer Road Trains for the Environment). The SARTRE project was successfully completed by the European Union in 2012, its goal was to circulate at high speeds, a convoy of autonomous vehicles, without modifying the infrastructure. The installation of sensors on the roads requires a lot of investment and is expensive for that a solution to this problem was SARTRE [5]. 1 https://www.electricmobilityeurope.eu/projects/.
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Fig. 1 EU road map for trucks platooning
Fig. 2 SARTRE project and Chauffeur project
The fleet vehicles would follow a leader. The leader is driven by a human driver on the highway, with a 4.5 m safety distance, which runs at a speed of 90 km/hr. The control law applied on each vehicle of the convoy is based on the decentralized global approach, as the leader information and the neighbors data are used to build the control [7] of each vehicle. Follower vehicles would follow the leader’s course (not the curvature of the highway) to stop in case a hazard occurs on the lead vehicle (Fig. 2). Another important project is the one called Chauffeur, which has been tested the conveying of trucks. https://trimis.ec.europa.eu/project/promotechauffeurii. The leading vehicle was controlled manually by a driver and the other vehicles (trucks) would automatically follow the truck ahead. The safety distance has been set at 10 m minimum to avoid collisions between trucks. The control law is calculated from information from the leader and neighboring vehicles. The PATH project has been developed in 1986, by the California Department of Transportation (Caltrans) and the Institute of Transportation Studies (ITS), to solve
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Fig. 3 Path project and Volvo project
Fig. 4 Vehicles convoy and LBM MassSpringDamper [6]
congestion problems in transportation, enhance system safety, save air quality and energy consumption. The research on transportation systems, of University of the California at Berkeley includes Automated Highway Systems (AHS) [8, 9]. The Danish project called EDISON investigated how large can be a fleet of electric vehicles (EVs) [10] which can be integrated in a way that supports the electric grid, for reductions in CO2 emissions. The FleetNet project [11] develop a wireless network for inter vehicle communications to distribute locally relevant data. Vehicle platooning is practical only on the righthand lane of motorways (Fig. 3).
2.1 1D Longitudinal Models Used in Literature To control a convoy of autonomous vehicles, several modeling approaches have been used in literature for fleets of vehicles. In general, a set of simplified and individual vehicle 1D models are considered. The only interest of 1D models is the study of the IVSD (InterVehicle Safety Distance) The fleet is assumed to move on a straight line. This type of model and its controls neglect the lateral movement and curvatures of the trajectory. Note that there is no relation between the vehicles equations except by the relative distance variables ei , which is used in the control. Longitudinal Double Integrator. The double integrator linear model is the most used for the longitudinal control of the convoy [12]. Many parameters are neglected. xi is the vehicle i longitudinal position and u i the force or torque applied to the corresponding vehicle. In [12], the motion model of a vehicle i is represented by:
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x¨i = u i
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(1)
The vehicles are considered independent and linked only by the control. Let dr represents the desired relative InterVehicle Distance. The differences between the positions of the vehicles are noted ei = ei,i−1 : ei, j = x j − xi − (i − j)dr
(2)
Longitudinal Unidirectional Model: LUM with Simple Mass. To the previous model we add a mass and a damper to take into account some dynamic. m is the mass of this vehicle and b represents a damping coefficient. The fleet model is considered as a set of independent model equations [6]. The desired IVD dr is also considered in the control trough the error or relative distance with the preceding vehicle ei = ei,i−1 . m x¨i + b x˙ = u i
(3)
Figure 4 represents the convoy longitudinal motion in a unidirectional space. Longitudinal Bidirectional Model: LBM with MassSpringDamper. The longitudinal motion of the vehicles is modeled by a secondorder system and the distances to the neighbors (preceding ei,i−1 and following ei,i+1 ) are fed back through the control [6]. In this scheme, the individual vehicle models are coupled by interaction of reactive forces. Figure 4 shows a bidirectional longitudinal model mass, spring and damper. LBM1VID with Constant Inter Vehicules Distance (IVD). The model dynamic equations, for a fleet of n vehicles, may be written as follows: ⎧ m x¨1 = −k(x1 − x2 − d) − c(x˙1 − x˙2 ) + u ⎪ ⎪ ⎨ m x¨i = k(xi−1 − 2xi + xi+1 ) + c(x˙i−1 − 2 x˙i + x˙i+1 ) .... .... ⎪ ⎪ ⎩ −k(xn−1 − xn − d) − c(x˙n−1 − x˙n ) m x¨n =
(4)
where k is the coefficient of stiffness, c the damping coefficient and d the length of the spring (intervehicle distance). A constant distance is defined for the intervehicle distance. The leading vehicle is driven by force input control u. It receives the forces of constraints transmitted by the links springs  dampers, on the chain of the vehicles of the convoy. The stability study was processed in [13]. To ensure the stability of the fleet according to the authors [6, 13], the ratio c2 /km must be greater than constants that increase proportionally to the vehicle index i in the convoy. LBM2 with IVD and Anticollision Margin h ACM. Another model defined as follows is proposed to reduce the stability constraint [14]. The ACM h is added as a time distance margin to avoid collisions between vehicles. h depends on the speed
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of the vehicles in the fleet. ⎧ m x¨1 = −k(x1 − x2 − d) − c(x˙1 − x˙2 ) − kh x˙1 + u ⎪ ⎪ ⎪ ⎪ k(xi−1 − 2xi + xi+1 ) − kh x˙i ⎨ m x¨i = +c(x˙i−1 − 2 x˙i + x˙i+1 ) ⎪ ⎪ . . . ... ⎪ ⎪ ⎩ m x¨n = −k(xn−1 − xn − d) − c(x˙n−1 − x˙n ) − kh x˙n
(5)
The fleet stability condition decreases compared to the previous model case [14]. Enhanced LBM (ELBM). Another model has been proposed, to improve the previous model, proposed in [15]; It consists of choosing, for each vehicle, a distance command di−1,i between the vehicles of the convoy, according to the state of the fleet and stability constraints. di, j is the desired Euclidean distance between the vehicles of the convoy. ⎧ m x¨1 = −k(x1 − x2 − d1,2 ) − c(x˙1 − x˙2 ) ⎪ ⎪ ⎨ m x¨i = k(xi−1 − xi − di−1,i ) − k(xi − xi+1 − di,i+1 ) ⎪ ⎪ ⎩ ...
m x¨n =
+c(x˙i−1 − 2 x˙i + x˙i+1 ) ... −k(xn−1 − xn − dn−1,n ) − c(x˙n−1 − x˙n )
(6)
The study of stability in [15], shows that the bidirectional control architecture gives good theoretical results when the number of vehicles of the convoy is limited. In general, these models are used for convoys that move at low speeds, do not take into account the risk of failures, or the effects of errors and noise measurement [6].
2.2 2D Convoy Models Used in Literature Use of the Kinematic Models. Several applications use only a kinematic model, in a 2D space with limited speeds in the urban frame. The dynamics are neglected like in the following unicycle model. This is not advisable for trucks convoy on the highway. Using a bidirectional control can alleviate the problem [16]. The model equations are presented in Eq. (7) and Fig. 5. See also https://ch.mathworks.com/matlabcentral/ fileexchange/67034simpleanimationfornvehicles?focused=9173618&tab=func tion ⎧ ˙ L ⎨ X i = vxvi .cos(ψi ) − 2 .ψ˙i . sin ψ L ˙ ˙ Yi = vxvi . sin(ψi ) + 2 .ψi . cos ψ (7) ⎩ ˙ v .δ ψi = xviL i i UniCycle Model (UCM). A unicycle uses a kinematic representation with a minimal configuration variables number. vi is the linear velocity of the vehicle i and θi is the orientation angle of the vehicle wheel i [17, 18].
Autonomous Vehicle Platooning and Motion Control ...
⎧ ⎨ x˙i = vi .cos(θi ) y˙i = vi .sin(θi ) ⎩˙ θi = wi K γ γi
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(8)
[17] study only longitudinal motion to avoid the nonlinear kinematics. The vehicle moves in a straight line with the linear velocity vi . A study of chain transformation for a convoy is proposed, to determine the InterVehicle Distances (dv ) and the relative caps (γ ) between the neighboring vehicles. The control applied to the convoy vehicles is based on a tangential linearization; The overall stability has been proven only for the linearized model. BiCycle Model. The BCM, also known as the Ackermann model, is a Longitudinal and lateral model describing the vehicle motions [19, 20]. Lateral and longitudinal control laws of the convoy based on this model are proposed [21, 22]. Let vi denote the linear velocity of the vehicle i and θi its wheel orientation angle and δi the vehicle’s steering angle i. The simplified dynamic model [23–26] is written in a three DoF system: ⎧ ⎨ m v˙i = f xi cos δi + f yi sin δi 1 1 δ˙i = − mv f xi sin δi + mv f yi cos δi − wi i i ⎩ 1 w˙ i = I f θ
(9)
The bicycle Kinematics model is: ⎧ ⎨ x˙i = vi cos(ψi + δi ) y˙i = vi sin(ψi + δi ) ⎩ ˙ ψi = wi
(10)
Robotics Models. The longitudinal and lateral positions and orientation (X, Y, θ ) of each vehicle i (with mass m i and inertia Ii ) of the convoy are represented in a Cartesian frame R0 . G is the gravity center of the vehicle, (vxi , v yi ) are its longitudinal and lateral velocities. Robotic Models are composed of Dynamic equations, Kinematic transformation, and a geometric representation. This is why they are more precise and advisable. Dynamic Equations. The Lagrange method leads to a set of dynamic Eq. (11) to describe the motion of one vehicle (see the left scheme of Fig. 5), in vehicle reference frame Rv = (G, xvi , yvi ) [27–29]. The input force is Fxr = Fmoti and Fr esi gathers the resistance forces from the slope gravity and aerodynamics. The longitudinal and lateral wheel forces are noted Fx f , Fxr , Fy f , Fyr . The rolling resistance is dvi and the road slope is ζ . δi is the steering front wheel angle and ψ: the yaw angle. Fr esi = mg sin ζ +
ρ ACd 2 v˙ xv sgn(v˙ xv ) − dvi I
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Fig. 5 Bicycle Model and trajectory Γ [30]
⎧ ⎨ m i .v˙ xvi = Fx f . cos δi − Fy f . sin δi + Fxr − Fr es m i .v˙ y = Fx f . sin δi + Fy f . cos δi + Fyr ⎩ ¨ vi Ii .ψi = Fx f .l f sin δi − Fy f .l f + Fy f .l f . cos δi − Fyr .lr
(11)
l f and lr are distances to G from the front wheel and from the rear wheel (respectively). The actuation dynamics when considered can be written as follows, where Jx , r x , Tx are the wheels inertia, rays, and torques. J f .ω˙ f i = (T f i − r f i .Fx f ) (12) Jr .ω˙ ri = (Tri − rri .Fxr )
Geometric Convoy Model. Now we need to localize the vehicle with regard to the reference trajectory to be followed. The road reference path is noted Γ . Figure 5 shows the geometric scheme of the vehicles with regard to the path Γ in the absolute frame R0 . The vehicle is modeled with respect to the reference path Γ [30, 31]. The geometric model defines the relations between vehicle variables (in the vehicle frame Rv ) to Cartesian ones and to the reference trajectory (see the right figure of Fig. 5. Let us recall that ψi is the orientation in absolute reference R0 and ldi denotes the desired IVSD distance between 2 vehicles. Let si be the curvilinear abscissa of the vehicle i. This abscissa is at a distance di from the reference (desired) trajectory Γ (at point M). c(s): the curvature of the trajectory Γ at the point M θΓ (s): Orientation of the tangent at M, in the absolute frame R0 θ pi = ψi − θΓ (s) is the angular deviation of the vehicle i relative to Γ . esi = si−1 − si − ldi is the curvilinear spacing error, or difference between 2 vehicles Kinematics. The Kinematic equations are needed to get the Cartesian velocities to follow the motion of the vehicle i and determine its orientation. The kinematic model is written as follows [32] (see Figs. 6).
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Fig. 6 The geometric model for the description of the convoy motion
⎧ cos θ pi ⎪ ⎨ s˙i = 1−d.c(s) vxvi d˙i = sin θ pi vxvi ⎪ ⎩ θ˙ = ( tan δi − c(s) cos θ pi )v pi xvi L 1−dc(s)
(13)
The mathematical singularity (dc (s) = 1) will never appear because the point Ov is not at the center of the curve of the desired trajectory. Oi is the center of the rear axle of the ith vehicle. c(si ) is the curvature of the Γ path in si . Some authors, to be complete, add to this model a representation of tractor wheel slip (or drift angles). [4, 30, 31, 33, 34]. In [35] a more complete dynamic model is used.
2.3 Control Strategies for Convoy Vehicles The control architectures can be classified into several categories: Kinematic/ Dynamic, Local/Global, Uni or Bidirectional. This is related to the information used to control each vehicle. They are local or global depending on sensors information they use for control (Global: from all the vehicles, or Local only neighbors). The two approaches can be unidirectional (preceding neighbors) or bidirectional (neighbors in front and behind). Global Fleet Control Strategy. For the global or centralized architecture (GUC, GBC), the control law applied to each vehicle of the fleet is based on the data (positions, velocities, ...) of all the vehicles of the convoy [7]. Sometimes it can be partial with data limited to the leader and some of the neighboring vehicles if the convoy chain is too long. For example in partial GBC, one uses the states of the leader and the 4 (front and rear plus left and right if any) or only 2 neighboring vehicles. This approach has been used in [34], for a convoy moving in a straight line. This makes global approaches more expensive [5]. Local or Decentralized Control Architecture. In general, the most of vehicle pilots use only the information of the previous vehicle and possibly (only partially) that of
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the following one (LUC, LBC). This decentralized control (LUC) approach requires fewer sensors and very little information exchange between vehicles of the convoy, than the centralized approach. It also requires less computations and information. The control is based on the data restricted to neighbours in the convoy, to minimize the numbers of the sensors used [4, 36, 37]. Local Bidirectional Control Architecture (LBC). With bidirectional architecture, we are interested in information about the two neighbors, the previous vehicle and the follower one. Each vehicle is controlled targeting the previous one with regard to its followers and leaders. In this case, the disadvantages are the availability of information on the vehicles of the chain, the need for sensors, the communication of the data, and the observability. Local Unidirectional Control (LUC). Each vehicle is controlled targeting the previous one regardless to its follower. The driven vehicle in LUC is slaved to follow its predecessor [16]. Tracking errors, introduced by sensors, actuators, and delays accumulate from the leader vehicle to the last one, in the convoy chain and affect the stability of the convoy motion. This causes oscillations due to accumulated errors [5]. This also causes unacceptable disturbances if the string is long [6]. These problems are only partially avoided in the global strategy. Vehicles Interdistance and Stability. InterVehicle Distances (IVD): For safety requirements, the desired convoy IVD used for the longitudinal control law, is such that the error (the distance between two successive vehicles) of the convoy is defined as follows (Eq. (15)): (14) ei = xi−1 − xi − dmin − h x˙i A PID control law is used for vehicles driving: xi = u i u i = K v e˙i + K p ei
(15)
The control gains are chosen to get an achievable acceleration margin for each vehicle of the convoy and ensure the IVD requirement to avoid collisions. The stability of LUC, for a constant intervehicle safety distance and collision margin, has been studied in [4, 6, 22]. The evolution of the minimum interdistance is studied in [4] as a function of the braking capacities and the speed of the fleet. The author used a suboptimal unidirectional longitudinal control to drive a convoy of vehicles in speed and position. This study uses also a dynamic model integrating the actuation and the chain of transmission of the vehicle to take into account the mechanical transmission effects. In Fig. 7, we can see that the IVD increases when the velocity increases and the brake capacity decreases. In [22] a PID control, with specific gains for each vehicle of the convoy, is proposed to ensure stability. The PID gains increase with the vehicle index (see the Eq. (16), [16]. The gains of the i th vehicle are determined with regard to the following conditions:
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Fig. 7 Intervehicle distances in function of the brake velocities [4]
K Ii ≥ K Ii−1 K K Pi = K I Ii K Pi−1 + K Di =
i−1 K Ii
K Ii−1
K Di−1
m K Ii−1 K Di−1 m + K D K Pi−1 i−1
(16) −b
The control, the IVSD and the model must be well adapted to the driving situations. Conclusion on Vehicles Models. We have presented so far the different models, IVSD and control strategies. Vehicle modeling is very often simplified and without coupling between vehicles. This does not take into account the vehicle dynamics and does not completely reflect the reality on physical level [32]. The couplings are introduced at trough control. The half vehicle model, known as the bicycle, is most often used and gives acceptable representation for the dynamics of the convoy and [4, 38]. For a good motion control of the vehicle chain, the robotics models are the most appropriate and more complete for the description of motions, while taking into account their dynamics, kinematic and geometric relations. In the case of automatic driving in a highly uncertain environment and with high speeds, the model must be able to precisely describe the dynamics that must be well controlled in the vehicle. Note that up to now only the individual model of one vehicle of the fleet have been considered. In the next sections, we will propose a new more complete model by adding (to robotics equations) the relations describing the links and the couplings of the vehicles within the fleet and with their environment.
3 A New Approach for Platooning In what follows, we will start with a new modeling approach that seems more appropriate for driving fleets of vehicles, robots or mobile devices. Then we will discuss the problems of observation and control. We use the robotics approach to describe of the vehicle dynamics in the group. This proposal facilitates the application of nonlinear controls (longitudinal and lateral) for
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tracking a trajectory expressed in the absolute reference frame, with a safety IVD distance between vehicles, to avoid collisions. Several nonlinear control laws will be applied using different control strategies.
3.1 Observation of Natural Processes We often draw inspiration from a school of fishes (see Fig. 8), a flow of migratory birds or group of animals moving in packs or in hordes or in colonies. But let’s be more observant and more careful. For example, migrating ducks fly in formation respecting distances between them, as shown in the Fig. 8. Is there any geometry and kinematics for each duck motion? Probably they look for ascendant hot air flows and minimum aerodynamics resistance. In the movement of each duck, there is geometry and kinematics favoring some of the positions and speed, to reduce the efforts. For a fish school, the water perturbations produced by a fish are transmitted to the neighbors as stressing or attracting forces. Moving in water creates turbulence which leads to buoyancy and currents. Do they take advantages of neighbors’ vortexes (created turbulence)? Is that a kind of Kinematics and dynamics due to environmental reactions and transmission of couplings? (Fig. 9). For a fish scool, the constraints (on positions, velocities and forces) are exerted on the 3 axes x, y and z. Like for birds a 3D model is necessary. There are Probably
Fig. 8 Flock of ducks in migration and a school of Fishes
Fig. 9 A group of sheep in motion and horses group
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Fig. 10 Pack of dogs and a horde of wolves
some attractive forces in the distributed environment, suggesting specific motions and shapes. This may be quite different for other kinds of animals see Fig. 10 or 8. For a group of sheep, as another example, the spring  mass stress is on the two axes Ox and Oy (2D). Each sheep can be jostled in front of and behind then to the right and to the left. If we send the sheep toward a wall, the following, until the last will undergo a braking (damping effect). On the other hand if one pulls a sheep, the others will follow it. This is the spring effect or ‘Panurge’ effect (see Rabelais Quart Book). For a flock of sheeps, is there not a fearful and passive or dissipative character? Why does the flock of horses seem more fearful and less passive (probably more active) than sheeps? Their Latency is also different. The inter distance seems greater. What is a group character and how can we describe it? How the individual motion reactivity and character propagates in the pack? Does it change as people get closer and hug. Let us do the same exercise with a horde of wolves and a pack of hunting dogs (pack of hounds). For a horde of wolves, isn’t there a fearful and active or aggressive character? Does this aggressive feature change with interdistance and number of animals. Latency, fearfulness and passivity/reactivity are quite different characters. Is the behaviour (model) constant or time varying during the harnessing or hunting? Does it depend on the side the animal is on in the hunting game? In summary as the number of elements in the group increases the characteristic feature is accentuated.
3.2 A New Modelling for Platooning Let us try a new modelling approach for vehicle platooning which integrates the group character and is more complete. This approach is really inspired by animals behaviour in a group, which is quite different when the animal is alone or in a group. The vehicle fleet Models (geometric, kinematic and dynamic) to be used is related to the control approach to be applied.
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Fig. 11 Fleets of 5 vehicles
Let us consider for example the two fleets of 5 vehicles represented in Fig. 11, to illustrate our modeling approach. Their geometries are different, so we first have to define their geometric models with regard to the trajectory to be followed. First the geometric figure defined by the five gravity centers of the 5 vehicles has to be written. This geometry can be centered (for example) at the gravity center or the red vehicle (with regard to desired trajectories) and then each vehicle is localized with regard to this point. Then follows the geometric models for each vehicles can be drawn regard to the trajectory. Consequently the kinematics will be developed and then we must write the 5 dynamic equations for each vehicle. We can proceed as has been done previously. The Lagrange method can be used to get the dynamic model equations [35] for each vehicle knowing it is related to its neighbours. Mi (qi ).q¨i + Hi (q˙i , qi ) = u i − τri+1 − τri−1 ... = Ui
(17)
q¨i = Mi (qi )−1 (−Hi (q˙i , qi ) + Ui )
(18)
with the following variables in the vehicle frame R0 , qi = [xvi , yvi , ψi ]T the position vector, the control inputs are u i and the forces/torques τr j , represents the traction constraints from the nearby vehicles. The outputs are the longitudinal and lateral position y = [x, y]T . ⎧ i f or i = 1...5 ⎨ q˙i = dq dt (19) q¨i = f (qi , q˙i ) + g(qi ).Ui ⎩ y = h(q) The constraints torques τr j are linked by pairs through the desired Reference Models for the Inter Vehicles Dynamic Relations (RMIVDR). Please note that for the first fleet (of Fig. 11) we need Four reference models (2 longitudinal and 2 lateral) and for the second one 5 RMIVDR are needed. In addition, note that the RMIVDR (massspring) are here for simplicity, linear and bidirectional.
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3.3 The Platooning Control By the proposed modeling approach, we then can conclude that the vehicle fleet is then equivalent to a unique and complex robot with N+M Degrees Of Freedom (DDL), where N is the number of vehicles and M the number of the required RMIVDR. So the consequence will the be that every robot control approach will apply to the platooning. The same comment holds for the observers. The problem remain easier that legged robots control and can be structured depending on the chosen control approach.
4 Conclusion An overview of convoy modeling for control of fleet of vehicles has shown that, in general, the models used are too simplified. Our preferred modeling approach is that of robotics considering the geometric, the kinematic and the dynamic models. We have revisited it to propose an approach, well inspired from the nature, which tries to catch the character feature of the flock. This model is more complete to describe a motion and allows to better describe the behavior of the vehicles and especially to better control their movements and the trajectories tracking. The proposed approach is more appropriate for bilateral vehicles couplings and for a large number of vehicles. The control problem is then simplified and several strategies, well known for complex robots control, are applicable. A good perspective will be to study reflex actions and interaction between vehicles and reactions to vehicles  environment (obstacles). A good question may be asked is how builtin reflexes (like the Panurge effect, for sheep, fish frictions and LeaderFollower role changes for birds) can be induced. Our next study will be to know more about the controllability and the observability in the presence of obstacles and/or how predict the Maneuverability. In the case of obstacles, one should be more interested in the diagnosis and recognition of driving situations. It will therefore be necessary to approach socalled intelligent commands with learning and adaptations. Acknowledgment Many thanks to the ICEERE committee for the invitation to give this invited conference, namely Hajji Bekkai and Abdelhamid Rahi. I would like to thank also the colleagues and friends who interested by this point of vues gave suggestions.
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Improving Human Health: Challenges and Methodology for Controlling Thermal Doses During Cancer Therapeutic Treatment Ahmed Lakhssassi, Idir Mellal, Mhamed Nour, Youcef Fouzar, Mohammed Bougataya, and Emmanuel Kengne Abstract Controlled thermal ablation in order to maximize the therapy and minimize the side effects poses a challenge during the heating of the biological tissue. Traditionally, these processes are modelled by the bio heat equation introduced by Pennes, who used the Fourier’s theory of heat conduction. During my talk I will present our automated thermal dose control and prediction system for cancer tumors therapy by using Implantable Biochip solution. The proposed system is able to control thermal ablation doses deposition during a laser surgery/cancer treatment. A system would help physicians to predict thermal diffusion to organize the treatment as well as maximize therapeutic effects while minimizing side effects. An innovative approach is proposed to improve the quality of thermal treatments in oncology. A biochip platform will be investigated, designed, and prototyped on an FPGA board. The destruction of tumors using a heating source has been widely used as an efficient approach for cancer treatment, where the oncologists use a heating source to destroy the targeted tumoral tissue. A case study of the Laser Interstitial Thermal Therapy (LITT) will demonstrate his feasibility as Cancer therapeutic treatment. Furthermore, our Dosimetry Framework of the Bioheat Transfer for Laser/Cancer Treatment will be introduced. This would provide a precise idea of the predicted reaction depending on selected doses, tissue geometry, and the laser source prior to the treatment; so new treatment strategies can be proposed and evaluated. Keywords Realtime monitoring · Thermal ablation · BIOCHIP · Cancer tumor · FPGA · FDM · Laser Interstitial Thermal Therapy · Thermal damage · Brain cancer · Bio heat transfer simulation · Thermal sensor · Minimally invasive surgery · Robotic surgical assistants · Robotic arm · Raspberry Pi B+
A. Lakhssassi (B) · I. Mellal · M. Nour · Y. Fouzar · M. Bougataya · E. Kengne LIMA – Laboratoire d’Ingénierie des Microsystèmes Avancés, Computer Science and Engineering Department, Université du Québec en Outaouais, Gatineau, Canada email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_2
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1 Introduction During the laser thermal ablation process, it is challenging to control the side effects and optimize the planning of the dosimetry process for all patients. Due to the restriction of the number of probes that a patient can tolerate and the inaccurate information provided by the invasive temperature measurements, which provide information only at discrete points, a mathematical model simulation is more effective to help physicians in planning their thermal treatment doses. Prior to the treatment, it will provide a precise idea of the predicted reaction depending on selected doses; so new treatment strategies can be proposed and evaluated. Two primary objectives of any thermal therapy must be considered. The first one is how to ensure the total removal of the tumor. To avoid the regeneration of the tumor, it is critical to provide that the entire tumoral tissue was destroyed during the treatment. A second point, as important as the first, is how to save the healthy surrounding tissue. As a result of the temperature diffusion in the tissue, a margin of the surrounding tissue is destroyed during the treatment [1]. The consequences of this collateral damage can be of significant impact in some cases, especially near sensitive organs or vital arteries. To achieve a safer and efficient treatment, the dosimetry has been developed as a new science to control the injected power during the treatment to avoid significant collateral damage by defining the optimal dose [2, 3]. Consequently, thermal therapy has been considered as an efficient treatment for many diseases, especially in cancerous tumors [4, 5]. This therapy was born a long time ago, and it has been improved and entertained for decades. Nowadays, different techniques involving thermal therapy principles are available. We can categorize these techniques into diverse groups following the nature of the injected energy and the modality of administration. It contains, among others, the sources using electromagnetic (EM), UltraSound (US), and Radio Frequency (RF). Thermal therapy, known commonly as thermotherapy, uses heating sources as well as cooling sources [1, 2, 5]. Realtime monitoring of a thermal therapy used for cancer treatment can improve the efficiency of the treatment and reduce damage to healthy tissue surrounding the targeted tumor. For this reason, different systems based mostly on image processing and sensors network, are used nowadays. Although a significant development was accomplished in the technological devices and numerical methods, thermal therapy still suffers from a lack of precision and high accuracy. Significant collateral damage will, indeed, occur during the thermal treatment procedure. This collateral damage could be fatal for the patient. For this reason, a new technique has been developed to control the injected power to minimize the risk of overheating the healthy surrounding tissue. This technique is known as dosimetry, which is associated with an imaging system as a Magnetic Resonance Image (MRI) for monitoring the delivered power. This novel approach, MRIguided thermal therapy, has been widely deployed in the last decades [5, 6]. Despite the developments in technological devices, modeling, and simulation tools, the lack of precision and accuracy of thermal therapy could result in significant damage to healthy tissue surrounding the targeted tissue. Hyperthermia is particularly challenging due to the lack of visual cues such as thermal ablation
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or coagulation. In response to the imprecision of thermal therapy, researchers have worked to improve technological devices, numerical modeling, and simulation tools. A technique known as dosimetry, which employs imaging systems, was developed to monitor and control the delivered power [7]. Many researchers investigated the limits of the MRI guided technique and simulation models. Kabil et al. [8] reviewed the past and present challenges of numerical modeling and simulation for medical device safety in MRI. They showed the difficulties of designers to reproduce and simulate complex shapes. Other research works are investigating new methods for realtime monitoring and direct feedback, especially with hyperthermia modality [9]. For this reason, researchers continue to explore methods to improve the clinical results of thermal therapy by controlling the heating source parameters. In contrast, others seek alternative methods for obtaining realtime thermal therapy monitoring and tumor feedback. One such approach requires the use of a sensor network for realtime monitoring of thermal therapy. Schena et al. overviewed the fiber optic sensors for temperature monitoring during thermal treatments. They described the advantages, flexibility, and ease of using fiber optic sensors for monitoring temperature during thermal treatments. Tosi et al. used the optical measurement techniques based on different optical sensors to evaluate the temperature distribution and the pressure during an RF ablation process. They demonstrated the promise of these techniques for minimizing the damage of healthy tissue during thermal therapy. Other research works have been done using sensor networks for realtime monitoring of thermal therapy. However, other approaches using biosensors, biomarkers, and microfluidic devices offer enormous potential for treatment and early detection of tumorous cells. These devices also enable the tracking of disease progression and recurrence. In this paper, we propose an innovative approach to thermal therapy monitoring in cancer treatment based on biomicrochip technology. The proposed system can work in realtime to take localized measurements of a tissue’s parameters and predict the evolution of its temperature to monitor the thermal treatment process safely. These local measurements improve the accuracy of treatment administration and reduce the potential damage to healthy tissues. To accomplish this, a smart module consisting of bioprobes and temperature sensor characterizes the tumor at particular points along its perimeter to determine the tissue’s temperature and other parameters. Then the data collected by the miniature biochip will be transmitted by a Radio Frequency IDentification (RFID) module to the user to adjust the next injected dose. Hence, an implementation of the Laser interstitial thermal therapy is presented, which include the thermal damage calculation, the thermal control at the edges, robot arm positioning and the laser ablation. The laser ablation demo shows the laser ablation sphere deformation formed during the ablation. The form of the deformation caused by the laser ablation is assumed to be as a sphere. The volume of the sphere will be defined during the simulation with respect to the temperature limit at the border between healthy and tumor tissues. Each sphere represents the volume deformation of the tissue caused by a laser ablation of its sphere volume. Any sphere ablation will be represented with a sphere volume, sphere radium, laser power distribution through a time limit.
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Since the tumor tissue is surrounded at the edge with thermal sensors, so they will not exceed a temperature limit, each sphere ablation assigned to the structure should verify this side effect constraint. To realize this biochip, we should imperatively start by developing a prototype and verify the feasibility and the functionalities of the designed system. Therefore, the hardware solution of the Bio Heat Transfer (BHT) equation was successfully implemented and verified based on the Virtex 6 FPGA board. With the boom of the semiconductor industry during the last two decades, biochip technology, along with its developed implants has been found useful in many medical applications, including monitoring and diagnosing diseases, detecting undesirable agents, and delivering therapeutic drugs. Nowadays, the biochips implants proved their efficiency in many treatments. In 2017, Mirela et al. developed a personal electromicrofluidic platform allowing users to develop and program their bioapplications. The platform is controlled by automated software with a simple graphical interface. This study proposes a new approach based on bioimplants for Realtime monitoring of the thermal therapy applied in oncology for the removal of tumors. We performed numerical modeling and simulation using MATLAB and COMSOL Multiphysics. To further verify the feasibility and functionalities of the proposed biomicrochip system, we implemented a hardware prototype using a fieldprogrammable gate array (FGPA). The test results extracted from Xilinx Virtex6 Board implementation are reported in this paper. However, the collateral damage cannot be avoided because of the diffusion of the heat inside the healthy tissue. Hence, it can be minimized with optimal planning, which is more realistic and more appropriate. The remaining of the paper is organized as follows. Section 2 presents the theoretical model and the fundamentals of monitoring based on biochip technology. Section 2.6 shows the proposed hardware architecture implementation and measured results. Section 3 completes the paper with the conclusion.
2 Materials and Methods 2.1 The Bio Heat Model: Pennes’ Equation A theoretical study is conducted to The BHT equation governing temperature distribution in biological tissue was proposed by Pennes in 1948 [10]. This equation has been modified and improved by many researchers [11]. Despite the limits of the Pennes model, it is still the most used [12]. For x and t > 0, Pennes’ equation is presented as follows: ρc
∂T = kT − ρb cb ω(T − Ta ) + Q m + Q r ∂t
(1)
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where ρ, c, and k are, respectively, the density (kg/m3 ), the specific heat (J/kg K), and the thermal conductivity (W/m k) of the tissue. T is the local tissue temperature T(x, t) in (K) with 0 ≤ x ≤ L. x = 0 corresponds to the skin surface, and x = L is the inner boundary. Ta is the arterial blood temperature (K). ρb and cb are the density and specific heat of the blood, respectively. The perfusion rate is represented by ω (ml/ml/s). Qm is the metabolic heat production per volume (W/m3 ). Qr stands for the deposited energy per volume. In this study, we utilized an RF probe as an external heating source that produces pulses with a duration of 2.5 s with an amplitude of 2 W. The lefthand side of Eq. (1) refers to the stored energy. The first term on the righthand side stands for the energy diffusion within the tissue; the second term describes the thermal energy exchange between the blood and the surrounding tissue, due to blood convection. COMSOL Multiphysics uses this equation to assess and quantify the damaged tissue. Due to the difference in biological activities between the tumor and the healthy surrounding tissue, the temperature of the tumor is always higher than that of the healthy tissue. For this reason, thermography provides an efficient method for tumor detection.
2.2 Finite Difference Method Discretization To realize a hardware implementation of Eq. (1), we used the Finite Difference Method (FDM) to discretize it. The FDM was preferred over the other methods, like the Finite Element Method (FEM) or Finite Volume Method (FVM), because of its simplicity, shorttime development, and efficiency.
2.3 Hardware Implementation of the FDM To verify the functionality and the feasibility of such an approach, we proposed an FPGA hardware implementation of the 1D FDM approximation of the BHT equation. In fact, because of the high parallelism of the FDM and the enormous number of computing operations, the hardware implementation of the FDM is a tedious and complicated task to achieve on a single chip. The management of memories and the updating of each point in the grid is a considerable challenge that reduces the speed of execution and increases the used resources. Consequently, the hardware implementation of the FDM was a tough task for many years. The first hardware implementation was realized by Marek and al. in 1992. However, the lack of powerful computers and large memory limited the performance of his architecture. In 2002, Schneider and al. and Placidi et al. proved the feasibility and efficiency of the hardware implementation of the FDM. Since this date, many other works treating the hardware implementation of the FDM have been published.
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To implement the hardware of the 1D FDM Pennes’ equation, we proposed an architecture based on simple components such as adders, multipliers, and register.
2.4 New Approach Based on Biochip Technology for RealTime Monitoring of Thermal Therapy The challenge of thermal therapy for cancer treatment is to use enough power to destroy the cancerous tissue while minimizing collateral damage to the surrounding tissue. As the amount of power injected increases, so too does the margin of healthy tissue that may become damaged. In this approach, the intended biochip is supposed to monitor in realtime a thermal therapy process to kill the tumorous tissue and save the healthy tissue surrounding the target. Because the diffusion of the heat in the tissue continues beyond the tumor boundary, dosimetry has been widely explored as a means of controlling the dose of the delivered power. Figure 1 shows an example of a real brain tumor with temperature isocontour distribution on the tissue using one central heat source. To destroy the entire tumor, the clinician should inject more power while minimizing damage to the surrounding tissue. An imaging system is used to position the biochips with precision. The margin of the healthy tissue damaged during thermal therapy depends on the injected power. For this reason, we propose this realtime system to monitor the temperature distribution and control the injected power. Our approach is original and entirely different from what is used and available in the literature. It is based on feedback collected by biochips placed in the targeted tissue. The primary purpose of our system is to save the healthy tissue neighboring the tumor while destroying the malignant cells. Conventional approaches suggest that the shape of tumors is
Fig. 1. Brain tumor with nonstandard geometry and nonhomogeneous characteristics. To ensure the total destruction of the tumor, the therapist must inject more power. However, the diffusion of heat in healthy tissue creates collateral damage. The RFID biochips placed around the tumor follow the temperature distribution in realtime and locally measure the tumor parameters
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Fig. 2. Different parts of the biochip implant. It includes four components: bioprobes, temperature sensor, RFID module, and a smart unit. The temperature sensor and the bioprobes characterize the tissue locally in realtime. These measurements will be used for better temperature diffusion calculation
standard, such as a cylinder or cube. The physiological properties of a tumor are mostly taken as a spacedependent simple function or as a constant. We propose a biochipbased implant system for maximizing the efficacy of cancer treatment while minimizing harm to healthy tissue. Biochip implants will be inserted all over the tumor, providing realtime data for the entire tissue mass. The positioning of the biochip depends on the complexity of the shape of the tumor. The positions are selected in a way that they cover the entire tumor, especially the corners and the angles. The goal of doing this is to prevent the overheating of the adjacent tissue. The implants will characterize the tissue and monitor its temperature. They will then process the data and inform the user of the next dose of power to inject. Figure 2 shows the components of the biochip implant: Integrated thermal probes for local tissue characterization, a temperature sensor for local temperature measurements, and an RFID module for communicating with the user. Figure 3 gives a description of the implementation of the automatic ablation process. – COMSOL module: For a specific point, this procedure will check if the temperature at the edges exceed the temperature limit, and return the information to MATLAB. It will also return the power distribution that has to be executed by the Led to have a safe laser ablation. – Raspberry Module: This module will simulate the laser ablation using the power distribution. – RobotStudio module: This module will move the 6axis robotic arm to the point for the laser ablation. – MATLAB Portable: all these applications can be run from MATLAB Portable App on any Phone.
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Conductivity, heat capacity
Power, Time, Ablation Point, Geometry of the tissue
Comsol MATLAB
regulator Thermal Switches
Matlab Portable
Raspeberry Pi
RobotStudio
Source laser
Laser distribution
Ablation Point
Laser Beam
Robotic Arm
Fig. 3. General description of the implementation of the automatic ablation process
2.5 Presentation of the New Thermal Monitoring and Dosimetry System In this paper, we propose a new approach to cancer thermotherapy using a biochip system that performs realtime monitoring of thermal diffusion. The biochip implant is the central element of the proposed system. It predicts the tissue’s temperature, defines the tissue’s characteristics, and communicates this data to the user. Furthermore, parallel modeling and simulation of the process should be done with the real data of the tissue collected locally by the biochip probes and sensors. To supervise the process, the user follows the realtime evolution of the temperature in the biological tissue. This data enables the user to modify the injected power and adjust the heating source’s settings. The biochip system predicts the future temperature of specific points of the tissue by solving the BHT equation locally. The system measures the local temperature at these points, characterizes the targeted tissue, and sends the data to the user. Figure 4 describes the components of the realtime monitoring system: the biochip, the heat source, and the control console. We numerically simulated the biochip system process based on actual tissue parameters measured by the integrated bioprobes. By using the measurements from the local tissue, we can ensure higher precision and higher computational accuracy. Indeed, increased precision improves therapeutic outcomes due to less damaged tissue. Figure 4 explains the treatment process using the biochip’s realtime monitoring system. We developed a 3D model using COMSOL Multiphysics to simulate an RF ablation of a brain tumor surrounded by healthy tissue. The tumoral tissue represents the target to be ablated and removed. The surrounded healthy tissue should be spared
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Fig. 4. The components of the realtime monitoring system: The biochip, the heat source, and the control console. The system measures the local temperature, characterizes the tissue, and sends the data to the user. The user reviews the data and adjusts the injected data to destroy the cancerous cells and preserve the surrounding healthy tissue
and preserved. To do so, we should entirely control the dose of the injected power during the treatment. As an example, the tumor was modeled as a sphere with a 15 mm radius, and the healthy tissue was modeled as a cylinder of 50 mm radius and 150 mm height. We used a probe connected to a cathode of 10 mm length and 0.9 mm radius with a cylindrical shape to overheat the tumoral tissue. The heat spread all over the neighboring tissue forming a spherical shape and causing a necrotic tissue. The physiological parameters are reported in Table 1. The diffusion of the heat in the tissue can increase the temperature at the surrounding healthy tissue. To achieve a precise and accurate treatment and to reduce the margin of collateral damage, the biochip implant will perform a local and realtime measurement of the temperature using a Micro Electro Mechanical System Table 1. Tissue physical parameters Name
Unit
Brain
Blood
Tumor
Cathode
Heat capacity at constant pressure, cb
J/(kg·K)
3630
4180
3540
840
Density, ρ
kg/m3
1050
1000
1079
6480
Thermal conductivity, k
W/(m·K)
0.527
–
0.55
18
Electrical conductivity, σ
S/m
0.258
–
0.43
1 × 108
perfusion rate, ω
l/s
–
0.0064
–
–
Arterial blood temperature Ta
K
–
310.15
–
–
Initial temperature T0
K
310.15
–
312.15
310.15
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(MEMS) sensor. Furthermore, the biochip will measure the local parameters of the tissue (thermal conductivity and density). Using these accurate measured data, the biochip will be able to predict the variation of the temperature and define the optimal dose to be injected and communicate with the user. Figure 5 shows diagram of the treatment process using the biochip system. Figure 6 shows a 3D model of an RF tumor ablation to show how the heat diffusion can produce significant collateral damage on the surrounding healthy tissue. The heating source overheats the tissue and causes its destruction. In contrast to the imaging systems (MRI) used nowadays where an estimation of the temperature is performed by computers, the biochip implants placed around the Fig. 5. Diagram of the treatment process using the biochip system. Step 1: The biochip measures the initial temperature of the targeted tissue and characterizes it. Step 2: The memory and registers with the measured temperatures are initialized. Step 3: The user begins the power injection to destroy malignant cells. Step 4: The power is injected. Step 5: The biochip calculates the expected temperature. Step 6: The biochip communicates the predicted value to the user. Step 8: The user sets the value of the next dose if required
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Fig. 6. COMSOL 3D model for an RF ablation of a spherical brain tumor. The healthy tissue was modeled as a cylinder with a 50 mm radius and 150 mm. The tumor was designed as a sphere with a 15 mm radius, where we inserted the heating source to overheat to the targeted tissue. With proper adjustment of the injected power, overheating will destroy only the tumoral tissue and save the healthy tissue surrounding the tumor
tumor will measure locally and in realtime the temperature of the tissue. Moreover, they will define the local parameters of the tissue, like the thermal conductivity and the density, to perform a more precise calculation of the optimal dose to be injected.
2.6 Numerical Simulation Results To verify the accuracy of the FDM approximation and the developed architecture, we simulated the BHT model with COMSOL Multiphysics. Figure 7 shows the temperature of the heated tissue after three successive pulses. The different values of the parameters used for the COMSOL and MATLAB simulations are inspired in the literature and previous works. Table 1 summarizes the different physiological properties of the brain tissue, blood, tumor, and cathode. MATLAB script was developed to simulate the 1DFDM approximation of the BHT equation. For the domain with L = 0.03 and the final time tf = 25 s, we use the following parameters to define the space and time mesh: tf = N*t, L = M*x where N = 1000 and M = 30. We injected three successive pulses at x = 0; then, we simulated the temperature at x = 0.0001 m. The final time is tf = 25 s. Figure 8 Shows LITT Laser Interstitial Thermal Therapy Framework prediction results including volume damage prediction genrated during thermal therapy with laser for the wavelength equal to 1064 nm. We tested four different scenarios that were centered on varying the amplitude and the duration of the pulse, as shown in Fig. 9. We used a combination of two different amplitudes, 20 KW/m2 and 30 KW/m2 , and two different pulse durations, 2.5 s and 1.7 s. As a result, the temperature behavior changes as a function of the amplitude and the duration of the pulse. We measured the highest temperature in 6c, where amplitude and pulse duration are the highest. The lowest temperature was noticed in the case 6b, where the amplitude and the
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Fig. 7. COMSOL modeling of the temperature profile after injection of three pulses. The RF probe is positioned within the tumor, and a succession of pulses of a duration of 2.5 s with an amplitude of 2 W are injected
pulse duration are the lowest. We can clearly understand how the amplitude and the pulse duration infect the thermal behavior of the tissue.
2.7 Architecture of a Processing Element The hardware implementation of the model was realized on an FPGA Virtex 6 platform. The numerical simulations were performed with MATLAB and Simulink.
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Fig. 8. LITT Laser Interstitial Thermal Therapy Framework prediction results including volume damage prediction generated during thermal therapy with laser for the wavelength equal to 1064 nm
The hardware’s architecture is composed of many processing elements (PE). Each PE computes the values of a specific node “n.” The PE consists of simple elements: Adders, subtractors, registers, and multipliers. The entire architecture of the system is composed of “n” PEs. Each PE computes the new value of the associated node, then updates itsvalue. Each node of the grid updates its value simultaneously. Figure 10 depicts the architecture of one PE. The results of the simulation are reported in Fig. 11, which shows the temperature response after injection of three pulses. As expected, the values the prototype returned correspond to the temperatures predicted by the FDM implementing the BHT equation. The results reported by the COMSOL simulations, in Fig. 11, and the results predicted of the biochip, in Fig. 9, have similar thermal behavior. After three successive pulses, the highest temperature reached is around 325 K in both cases. Even though we can see a few differences between the two figures due to the different data type used in both cases and different algorithms, where COMSOL uses the Finite Element Method (FEM) and the FPGA implementation is based on the FDM method, we have proven that the prediction made by the FPGA prototype of the biochip is approximately the same as the simulated results made by COMSOL. Figure 12 shows a fraction of necrotic tissue visualized by COMSOL Multiphysics. Cell destruction approaches 100% as the cells approach the heat source.
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Fig. 9. Numerical simulation results of the 1D BHT approximated by FDMFTCS. We inject three successive pulses. The profile of the temperature corresponds to the expected thermal behavior. In (a) and (b), we injected three successive pulses with different pulse durations, 2.5 s in (a), and 1.7 s in (b), and we kept the amplitude of the pulses constant at 2 kW/m2 . We reproduced the same simulation by fixing the amplitude at 30 KW/m2 and varying the pulse duration as reported in (c) and (d) to 2.5 s and 1.7 s, respectively. The highest temperature was reached in (d) with the highest amplitude (30 KW/m2 ) and the larger pulse duration (2.5 s). The lowest temperature was registered in case (b) where the amplitude was fixed to 20 KW/m2 , and the pulse duration was 1.7 s
Fig. 10. PE’s structural diagram of one PE. The value of T (i, n). Each node of the grid is updated simultaneously. The architecture is composed only of simple components: Adders, multipliers, and registers. The value of each node ‘i’ is sent to the direct neighbors (‘i + 1’ and ‘i − 1’)
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Fig. 11. Simulation results: the temperature response after the injection of three pulses. The rise in temperature is due to the injection of power. During the relaxation time, the temperature decreases gradually until the next pulse is injected
Fig. 12. Tissue destruction rate. The points of the tumor surrounding the heat source reach destruction faster than the points on the border with the healthy tissue. The healthy tissue not directly adjacent to the tumor exhibit zero necrotic tissue. This tissue is spared and saved entirely
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Nearest the probe, complete cell death occurs within 10 s, destroying the tumor. The rate of cell death is slower near the border of the tumor, which preserves the surrounding healthy tissue.
3 Conclusion In this paper, a biochip system for realtime monitoring of tumor ablation thermal treatment was proposed and prototyped on FPGA Virtex 6 platform. The developed system is based on the use of a biochip implant to monitor and control the thermal treatment by measuring the temperature locally with a temperature sensor, characterize the tissue locally, and define the optimal required dose. This biochipbased system includes three main blocks for measurements, prediction, and communication. Moreover, a smart module, which is the central element of the system, predicts the expected temperature variation in the targeted tissue to limit its effects to nearby healthy cells. Finally, an RFID module is used to communicate the data with the user. Before implementing the hardware architecture on an FPGA platform, we performed a numerical simulation with MATLAB and COMSOL Multiphysics. The results of the simulations with COMSOL and MATLAB show the similarity of the thermal behavior of the tissue. An FDM approximation of the Pennes’ equation solution has been implemented, and the results of all simulations were reported. The FPGA prototype proved the feasibility and efficiency of the proposed model. Realtime prediction of the temperature was performed and tested with the mentioned platform. The novelty of this system lies in the fact that it can accurately predict the variation of temperature in the targeted tissue and characterizes in situ the tissue locally and in realtime. With this prediction, the radiotherapists can adjust the injected power to prevent possible significant collateral damage by manipulating the signal amplitude and exposure time. Using our proposed approach, we can reduce the potential damage to surrounding healthy tissue during the thermal treatment. Without an MRI room, we believe that the proposed method will be adopted by oncologists and radiotherapists for safety and low cost. With the use of the proposed bioimplant, we enhanced the precision and efficiency of the treatment and improved the quality of the treatment with the highest accuracy. Furthermore, a framework for automatic laser ablation was implemented, which include all steps from the calculation of the temperature distribution and tissue damage, the control of the temperature at the edges, then the safe automatic ablation process. Next step will be the implementation of the whole framework on a bizarre geometry structure which will be more realistic and extracted from the geometry of MRI stacks. Further research can be done to design the final prototype of the biochip system and integrate its different parts. Experimental validations of the developed system can be done using ghost tissue.
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References 1. Singh S, Melnik R (2020) Thermal ablation of biological tissues in disease treatment: a review of computational models and future directions. Electromagn Biol Med 39:1–40 2. Andreozzi A, Brunese L, Iasiello M, Tucci C, Vanoli GP (2019) Modeling heat transfer in tumors: a review of thermal therapies. Ann Biomed Eng 47(3):676–693 3. Gas P, Wyszkowska J (2019) Influence of multitine electrode configuration in realistic hepatic RF ablative heating. Arch Electr Eng 68(3):521–533 4. Huang HW, Liauh CT (2012) Review: Therapeutical applications of heat in cancer therapy. J Med Biol Eng 32(1):1–10 5. Mellal I, Oukaira A, Kengene E, Lakhssassi A (2017) Thermal therapy modalities for cancer treatment: a review and future perspectives. Int J Appl Sci Res Rev 4(2):14 6. Dewhirst MW, Viglianti BL, LoraMichiels M, Hanson M, Hoopes PJ (2003) Basic principles of thermal dosimetry and thermal thresholds for tissue damage from hyperthermia. Int J Hyperth Off J Eur Soc Hyperth Oncol North Am Hyperth Group 19(3):267–294 7. Habash RW, Bansal R, Krewski D, Alhafid HT (2006) Thermal therapy, part 2: hyperthermia techniques. Critical Rev Biomed Eng 34(6):491–542 8. Kabil J, Belguerras L, Trattnig S, Pasquier C, Felblinger J, Missoffe A (2016) A review of numerical simulation and analytical modeling for medical devices safety in MRI. IMIA Yearbook:152–158 9. Rhoon GCV, Paulides MM, Holthe JMLV, Franckena M (2016) Hyperthermia by electromagnetic fields to enhanced clinical results in oncology. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 16–20 August 2016, pp 359–362. https://doi.org/10.1109/embc.2016.7590714 10. Pennes HH (1948) Analysis of tissue and arterial blood temperatures in the resting human forearm. J Appl Physiol 1(2):93–122 11. Lakhssassi A, Kengne E, Semmaoui H (2010) Modifed pennes’ equation modelling bioheat transfer in living tissues: analytical and numerical analysis. Natural Sci 2(12):1375 12. Wissler EH (1998) Pennes’ 1948 paper revisited. J Appl Physiol 85(1):35–41
Active and Reactive Power Regulation in Nano GridConnected Hybrid PV Systems Giuseppe Marco Tina
Abstract The electrical systems are moving very rapidly to small/medium size distributed generation and storage system with the main goal to improve the level of energy selfsufficiency, with a drastic change of the user model from passive to active (prosumers). The prosumers will group themselves at the level of nano and micro smart grids. In this context models of hybrid systems with photovoltaic (PV) system, battery energy storage system (BEES) and/or diesel generator are needed. Since power quality represents an important issue for smarts grid that can work also in offgrid configuration, the aim of the control logic is the achievement of voltage and frequency regulation. For this purpose, different reference standards are considered. In the proposed case study, two different operation modes are investigated: gridconnected mode and standalone mode, depending on the different role of diesel generator and external power grid. Two different control logics for the system PV+BEES are adopted for the two operation modes under study. Accordingly, two different simulations with a 24h horizon are run and the results are discussed, in terms of active and reactive power of the hybrid system components, voltage and frequency profile at the connection bus. Also a transient analysis on the diesel generator connection and disconnection is conducted. Keywords Smart grid operation · PV system · Diesel generator · Energy storage · Voltage control · Frequency control
1 Introduction During last years, the increasing of energy consumption, concurrently with the necessity to reduce global warming, promotes the development of a high number of distributed generators (DGs), especially from renewable energy sources, mainly from solar and wind energy, but at the level of small size generating system in urban and suburban area the photovoltaic (PV) systems is surely the most suitable. However, G. M. Tina (B) Electrical, Electronic and Computer Engineering, University of Catania, 95125 Catania, Italy email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_3
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the intermittency of PVs has long been viewed as a drawback to widespread deployment as a substitute for 24/7 fossil fuel generation. Rooftop solar PV in particular can feature capacity factors as low as 20%. If such small systems are coupled with energy storage systems, the value of solar energy is magnified. In essence, it can be stored and then discharged during time periods most advantageous to asset owner. These same storage systems can also offer resiliency benefits when the larger grid goes down. The present trend in distributed generation is the grouping of a certain number of active users (named prosumers) in nano smart grids. These smaller, modular, and flexible distribution networks are the antithesis of the bigger is better, economies of scale thinking that has guided energy resource planning over much of the past century. Nanogrids take the notion of a bottomup energy paradigm to extreme heights. In some cases, nanogrids help articulate a business case that is even more radical than a microgrid; in other cases; nanogrids can peacefully coexist with the status quo. The architecture of nanogrids can be very different they can be equipped with different types of generating and storage units [1]. The DGs can also participate to the ancillary service for providing voltage and frequency regulation services to the main grid. In PV+BEES system, a regulation in voltage and frequency can be obtained by the control of the inverter that interfaces the grid [2–9]. Diesel generators, that can e used for long term storage or during the winter season in a case of a long period of insufficient solar energy availability, can surely play a fundamental role in isolated system, usually placed in remote or geographically isolated locations, mainly on islands or settlements in developing countries, in areas of high ecological interest which must be protected and where there is abundance of renewable energy resources [10]. In fact, in isolated systems, only diesel generator and hydro systems are able to efficiently ensure a secure and reliable supply [11, 12]. In order to reduce the power produced by these nonrenewable energy generators, for both environmental and economical issues, several renewable energy plants, such as PV systems and wind turbines, are installed in isolated grid all over the world [13–16]. In [17], a typical structure of isolated grid with PV, wind turbines and storage, is shown and a hierarchical twolayered frequency control is proposed, with detailed explanations about the control diagrams for primary and secondary control. Other control strategies for isolated microgrid operation, based on controllable loads and multiagent systems, are proposed in [18, 19], where a variable customer participation degree for frequency regulation and power balancing is adopted. In addition, also economic analyses for cost reduction in grid operation need to be conducted for microgrid with presence of hybrid systems [20]. In this particular scenario, an accurate modelling of the components and a correct implementation of their control becomes crucial. For this purpose, in this paper, an hybrid system is considered. In literature, different model of similar hybrid systems are implemented, using different simulation tools, and their performance are studied [21–23]. The MATLAB/Simulink model of the hybrid system used in this paper starts from the model of each component (Fig. 1): PV system; BESS;
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Fig. 1 System architecture of a nano smart grid with local generation and short and long storage systems
Diesel generator (Genset); Electrical load. A description of the control of the Genset is given, aimed at achieving voltage and frequency regulation, taking into account the requirements of the grid code, in terms of voltage amplitude and frequency limits [24, 25]. Two different operation modes are considered in this paper for the hybrid system [18]: Gridconnected mode: the system is connected to the external AC grid and the Genset is always disconnected from the bus (IG closed, ID open); Standalone mode: the Genset is disconnected when the system PV+BEES can fully supply the load (IG open). In standalone mode, the Genset control system provides voltage and frequency control until the PV system is able to supply the entire load. When this condition occurs, the Genset is disconnected, and the PV inverter provides the control of voltage and frequency. In Sect. 2, the model of each component is described, including a detailed list of the parameters used for the diesel generator model. In Sect. 3, the results of the 24h horizon simulations are exposed and discussed, in terms of power output of each component. Also the effectiveness of the control strategies, in complying with voltage amplitude and frequency constrains is demonstrated focusing on the role of BESS and diesel generator and on their interaction.
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2 Model and Control of the Hybrid System Components 2.1 Photovoltaic System and Energy Storage System Model In this paper a 2.88 kWp PV plant, composed of 12 × 240 Wp modules, is considered, with battery storage and a DCAC converter interfacing the LV grid. The electronic converter is composed of a DCDC boost converter and a DCAC inverter. Accurate models of the PV system and the singlephase inverter are exposed in [26, 27], in which a onediode model of a PV cell is considered. Typical daily global irradiance and temperature for the site of Catania are used as inputs of the model (Fig. 2). Since electrochemical systems are the most widely used type of storage, a BEES with a rated capacity of 80 Ah (2 × 40 Ah) and a rated voltage of 48 V is chosen. The model of the BESS is built starting from a two timeconstants model of a single Liion cell [33, 34]. Since this model is not able to give very accurate values of the output for low and high level of the state of charge (SOC), a depth of discharge (DOD) of 70% and a maximum level of SOC of 95% are used as limits in the Battery Management System (BMS). In order to simulate the possible occurrence of overvoltage problems due to the high PV active power injection, the external low voltage (LV) network is modeled as an ideal voltage generator and an equivalent impedance, composed of a resistance R = 1.3 and an inductance L = 0.0025 H. PV Inverter Control in GridConnected Mode In gridconnected mode, the PV inverter is controlled by the Voltage Oriented Control (VOC) method, that allows to decouple the control of active and reactive power [32, 34] (see Fig. 3). The active power is controlled by a maximum power point tracking (MPPT), obtained by a Perturb&Observe (P&O) algorithm, varying the cycle of the boost converter. Concerning the inverter reactive power, five different strategies are considered for voltage regulation [29, 36, 37], each denoted by a different value of the index N:
Fig. 2 Irradiance (a) and ambient temperature (b) daily profiles
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Fig. 3 Voltage Oriented Control scheme
N = 0: Q = 0 (i.e. no regulation); N = 1: fixed ucos(ϕ) = 0.9; N = 2: cos(ϕ) = f(P); N = 3: Q(U); N = 4: cos(ϕ) = f(P,U). Since these voltage regulation strategies require the use of PV inverter reactive power, the inverter capability curve constrains are considered in this study [24]. The role of the BEES in gridconnected mode is limited to performing a peakshaving of the PV power output, obtained by a scheduling of the active power. PV Inverter Control in StandAlone Mode In standalone mode, the DCAC inverter provides the voltage control (see Fig. 4) following the voltage droop equation: Ureg = U0 − n · Qm
(1)
Where: U0 is the rated voltage; Ureg is the measured voltage; n is the voltage regulation coefficient (0.0025 V/VAr); Qm is the inverter reactive power [40]. If the value Ureg exceeds the range of ±0.05 of U0 , new value dU is added to (1), in order to restore the voltage stability, obtaining: Ureg = U0 − n · Qm + dU
(2)
Also frequency control is required in standalone mode (the allowed range for frequency is 50 Hz ± 1 Hz). For this purpose, a similar droop control is implemented
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Fig. 4 Inverter control in standalone mode (voltage droop control)
on the DCDC converter control: ωreg = ω0 − m · Pm
(3)
ωreg = 2 · π · freg
(4)
ω 0 = 2 · π · f0
(5)
with:
where: f0 is the rated frequency; freg is the measured frequency; m is the frequency regulation coefficient (0.00105 rad/Ws); Pm is the inverter active power. If freg is inside the range of stability, the power converter adopts the MPPT technique for active power control. If freg is outside the range of stability, the inverter active power comes from (2). In standalone mode, the BESS absorbs active power when the PV output is greater than the load, in order to avoid energy wasting. For this purpose, the control scheme proposed in [39] is adopted and implemented in a Matlab/Simulink block. When the BESS is fully recharged, PV active power is no longer controlled by the MPPT algorithm, but it is curtailed, following the (3), in order to guarantee the power balance at the LV bus.
2.2 Genset Model and Control The model for the dynamic simulation of the Genset is described in [33], and it consists of three main blocks (see Fig. 5): Synchronous generator;
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Fig. 5 Genset model: block diagram in MATLAB/Simulink
Excitation system; Diesel engine governor. The parameters of the synchronous generator are listed in Table 1. Table 1 Genset model: synchronous machine parameters
Quantity
Symbol
Value
Nominal power
A
600 kVA
Linetoline voltage
Un (rms)
400 V
Frequency
fn
50 Hz
Reactances
Xd
2.24 pu
Xd
0.17 pu
Xd
0.12 pu
Xq
1.02 pu
Xl
0.08 pu
Stator resistance
Rs
0.101 pu
Time costants
Td
0.028 s
Td
0.007 s
Tq
0.007 s
Friction factor
F
0.02005 pu
Pole pairs
P
2
46 Table 2 Genset model: excitation system parameters
G. M. Tina Quantity
Symbol
Value
Lowpass filter time constant
Tr
50 × 10e−4 s
Voltage regulator gain
Ka
60
Voltage regulator time constant
Ta
0.001 s
Voltage regulator output limits
URmin
−0.7 pu
URmax
2 pu
Damping filter gain
Kf
0.05
Damping filter time constant
Tf
1.5 s
Exciter gain
Ke
0.05
Damping filter time constant
Tf
1.5 s
Exciter gain
Ke
0.05
Exciter time constant
Te
0.46 s
Field voltage values
Efd1
3.1 pu
Exciter saturation function values
Efd2
2.3 pu
SeEfd1
0.33 pu
SeEfd2P
0.10 pu
Fig. 6 Genset model: Simulink diagram block of diesel engine governor
The diesel engine governor and the excitation system provide the mechanical powers, Pm , and the excitation voltage, Uf , used as input for the synchronous machine, respectively. In the excitation system Simulink block, taken from the library Simulink/Sym PowerSystem, Uf is controlled in order to keep the measured stator voltage, Ut , close to a reference value, Uref . The parameters of the excitation system are listed in Table 2. The task of the diesel engine governor is the control of Pm , in order to keep the measured rotor speed close to a reference value (see Fig. 6). The transfer functions of controller, Hc, and actuator, Ha, are respectively: 1 + T3 · s 1 + T1 · s + T2 · s 2
(6)
1 + T4 · s s · (1 + T5 · s + T6 · s)
(7)
Hc = K Ha = K
The parameters used in (6) and (7) are listed in Table 3.
Active and Reactive Power Regulation in Nano Grid … Table 3 Genset model: governor parameters
Quantity
47 Symbol
Value
Regulator gain
K
40
Regulator time constant
T1
0.003 s
T2
0.002 s
Actuator time costants
Delay time
T3
0.02 s
T4
0.08 s
T5
0.003 s
T6
0.02 s
Td
10e−3 s
2.3 Electrical Load A typical domestic load, with constant power factor PF = 0.9, is considered in this paper. The local load can be modeled in such a way to obtain a correct transient response, on this regard a suitable model has to be used; specifically, the IEEL (IEEE model) is applied. The algebraic representation of the load is reported in (8), (9). P = Pload · a1 · un1 + a2 · un2 + a3 · un3 · (1 + a7 · f)
(8)
Q = Qload · a4 · un4 + a5 · un5 + a6 · un6 · (1 + a6 · f)
(9)
The load is modeled using variable impedance, composed of a variable resistance, Rvar , and a variable reactance, Xvar . Starting from the active and reactive power absorption, Pload and Qload , the load impedance is defined as follow: Rvar = U 2 ·
Pload 2 2 Pload + Q load
(10)
X var = U 2 ·
Q load 2 2 Pload + Q load
(11)
where U is the voltage at the bus.
3 Simulations and Results In order to evaluate the effectiveness of the proposed control logics, 24 hsimulations are run for both gridconnected and standalone operation modes and the obtained results are exposed.
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3.1 GridConnected Mode In gridconnected operation mode, the external power grid is always connected to the LV bus. The Genset is not connected to the bus during the simulation. In this configuration, the Genset can be used for load absorption leveling, in case of limitations in the maximum active power that the user can absorb, for technical or economical issues. In the proposed study, this eventuality is not considered. The active power profile of each component of the system is depicted in Fig. 7. The external grid acts as a slack node, providing the active power balance. All the reactive control strategies previously exposed are simulated, and the voltage profiles at LV bus are shown in Fig. 8. The fixed cos(ϕ) strategy requires an high amount of reactive power, even if no overvoltage problems occur at the LV bus. For this reason, the other control strategies are preferred for voltage regulation, since they limit the reactive power flow in the line, reducing active power losses [30]. The balancing of reactive power at the bus is guaranteed by the external grid, which represents the slack node of the system.
Fig. 7 Active power profiles in gridconnected mode
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Fig. 8 Voltage profile in gridconnected mode
3.2 StandAlone Mode In standalone mode the Genset is disconnected from the bus when the system PV+BEES can fully supply the load. In the case study, this condition occurs between 07:00 and 08:00, as shown in Fig. 9. Starting from this point, if the PV output exceeds the load demand, the BEES absorbs active power. When the PV output is not more able to supply the load, the BEES goes in discharge mode, supplying the load. Five different stages can be identified: Inverter active power output is equal to zero and the Genset fully supply the load (00:00–05:00 ca.). PV system starts to produce active, hence the Genset active power decreases (05:00–07:30 ca.). The power of PV inverter is greater than the power of load. In this configuration, the logic of control changes from gridconnected to standalone. (IG open) (07:30 ca.). The power of inverter supports the load (07:30–19:00 ca). In the first part, the system recharges the BEES until SOC = 0.95. In the second part of this phase, the BEES is discharged until SOC = 0.30. The power of PV inverter is less than the power of the load. In this point, the Genset must be reconnected to the electrical bus (19:00 ca.).
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Fig. 9 Active power profiles in standalone mode
Fig. 10 Focus on PV active power output and BEES charge/discharge profile
The Genset fully supplies the load demand (19:00–24:00 ca.). In Fig. 10, the profiles of active power of PV system and BEES are reported in details. As visible, the control system recharges the battery in order to balance the
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Fig. 11 Frequency profile in standalone mode
power at the bus. When the battery is fully recharged, a PV power curtailment is required. Until its disconnection from the bus, the Genset supplies the load in reactive power, while, after the disconnection of the Genset, the reactive power required by the load is produced by PV inverter. When the Genset is reconnected to the bus, it produces the reactive power needed by the load. Frequency and voltage profiles in standalone mode are shown in Figs. 11 and 12. The transients are related to the Genset breakers opening and closing operation. In particular, when the Genset is disconnected from the bus, the occurrence of a voltage fall is visible from Fig. 12. This is due to the different impact of the voltage control strategies of PV system and Genset. As noticeable, the Genset is abler than the PV inverter to fix the voltage bus at the desired value, Nevertheless, the voltage droop control implemented on the PV inverter is able to guarantee the compliance of the voltage regulation issues. As expected, the voltage become closer to the rated voltage when the Genset is reconnected to the bus. From Fig. 11, it is evident how the frequency is kept inside the allowed range, even during the Genset connection and disconnection transients.
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Fig. 12 Voltage profile at LV bus in standalone mode
4 Conclusions A case study of a hybrid system with PV, BEES and diesel generator has been presented. Different control solutions have been implemented in MATLAB/Simulink. Two different operation modes have been studied: gridconnected mode and standalone mode. The strategies for the control of both PV inverter and diesel engine governor have been described, with focus on voltage and frequency regulation. In gridconnected mode, four different voltage control strategy have been exposed and simulated, based on the reactive power control of the photovoltaic inverter. Hence, the paper has shown how the reactive power production of the PV inverter can regulate the voltage at the considered bus, when the different reactive power control strategies are adopted. In standalone mode, the focus was on the presence of a diesel generator, able to supply the load when the PV+BEES output is insufficient. In this operation mode, the frequency and voltage control of the diesel engine governor have been studied. Different simulations have been run and the results have been discussed. The effectiveness of the different control logics has been demonstrated by the analysis of active and reactive power profile, frequency profile and voltage profile. Also the transients of connection and disconnection have been analyzed, focusing on the effectiveness of the implemented voltage control strategies. Due the large spread of static generators in the modern power system further studies should be conducted for the modelization of innovative services related with frequency controls (e.g. synthetic inertia and fast reserve) in nano smart grids.
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Acknowledgment Many thanks to the ICEERE 2020 committee for this invited talk, namely Hajji Bekkai and Abdelhamid Rahi. I would like to thank Dario Garozzo from University of Catania for his great help in developing the model implementation and simulations.
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An Overview on the Application of Machine Learning and Deep Learning for Photovoltaic Output Power Forecasting Adel Mellit
Abstract By the end of 2019 the global cumulative installed photovoltaic (PV) capacity is more than 600 GWp corresponding to several millions of photovoltaic (PV) systems installed worldwide. Thus, the operation and maintenance activities of such plants are today important for a great number of professionals working in this solar sector. Forecasting of PV output power play very important role in power planning and dispatching, optimal management, grid quality and stability. Designing of an accurate PV output power forecasting models stay quite challenging issue and a crucial task, as the PV output power is extremely uncertain due mainly to solar irradiance variation. Broadly forecasting methods can be classified mainly into four groups: Physical model (e.g., numerical weather prediction models), statistical methods (e.g., AR, ARMA, ARIMA, etc.), methodbased artificial intelligence techniques, including machine learning (ML) and deep learning (DL), and the group named hybrid methods (e.g., Combining two methods). Different timescales forecasting are important for PV plants, for example intrahour forecasts (up to 1 h) are useful for grid quality and stability. Intraday forecasts (up to 6 h) are essential and could be used for optimal integration. Forecasts up to oneday mainly used for unit commitment planning and dispatching power. Up to oneweek forecasts could be used for trading, management and maintenance. The main aim of this talk is to give an overview on the available forecasting methods, special attention will be paid to methods recently developed, including ML and DL. Pros and cons of reviewed methods in terms of accuracy and complexity will be discussed in this presentation. Keywords Photovoltaic plants · Output power · Forecasting · Artificial intelligence · Machine learning and deep learning
A. Mellit (B) Renewable Energy Laboratory, University of Jijel, Jijel, Algeria email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_4
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1 Introduction Over the last decade, a rapid growth of the photovoltaic (PV) market has been observed worldwide, and according to the International Energy Agency (IEA) the global PV capacity exceeds 600 GWp [1]. As the produced PV power depends on the weather conditions that are by nature highly uncertain [2], the penetration of such systems in the actual power system benefit represents a challenge [3]. The power produced by the PV plants depends basically on a number of meteorological variables such as solar irradiance, air temperature, cloud variation and wind speed. PV output power forecasting is a challenge in particular in the case of multistep applications, large databases, noisy measurements, and multiple input–output observations. On the other hand, reliable forecasts allow avoiding penalties to plant managers caused by deviations between the scheduled and the produced power [4]. The forecast accuracy is generally improved by a preprocessing and a post processing of historical and forecasted PV output power [5]. In the literature, numerous PV power forecasting methods have been developed and, with reference to the forecast horizon [6], these can be divided into four types. Very shortterm forecasting with a time horizon ranging from a few seconds to some minutes; shortterm forecasting up to 48–72 h ahead; mediumterm forecasting from a few days to one week ahead; longterm forecasting from a few months to a year or more. Each forecasting horizon has its specific application so that, for example, very shortterm forecasters are used for the control and management of PV systems, in the electricity market, for the control of microgrid. Shortterm horizons are adopted for the control of power system operations, economic dispatch, unit commitment, etc. Medium and longterms horizon are usually used for the maintenance and the planning of PV plants. In this talk we are very motivated by Machine learningbased forecasting methods (ML) [7], including artificial neural networks (ANNs), k nearest neighbor (kNN), extreme learning machine (ELM), support vector machine (SVM), etc. These methods that do not need any information regarding the PV systems. They are used when measurements from the PV arrays are available, and basically for shortterm applications [8]. Recently Deep learning (DL) is also investigated, in this topic, in order to improve some shortcoming of methodbased ML algorithms, e.g., Overfitting problems in multilayer perceptron (MLP). This talk offers a short review of the most relevant techniques for PV power forecasting based on ML and DL. More details can be found in our work recently published in [9]. The talk is organized as follows: • • • •
Presentation of the problem in a general way Brief introduction to ML and DL; Recent applications of ML and DL learning in PV power forecasting; Concluding remarks and future directions.
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2 Problem Formulation Figure 1 shows a basic structure of a PV plant. It consists mainly of PV arrays, converters, inverters, filters, transformers and protection devices. As example, Fig. 2 depicts the evolution of DC output power of a real PV plant (capacity = 0.5 MW). PV power forecasting has always been an important part in the performance analysis, dispatching power, optimal schedule, planning and operation of the PV plants. The problem can be formulated by the following relationship: (Future values of power) = F(historical powers, forecasted meteorological parameters) where F is a function which should be able to forecast the future values of power based on some input parameters, that could be historical powers or forecasted parameters such as solar irradiance, air temperatures, wind speed, relative humidity, etc. So, mathematically there are three approaches or methods: The first one use only historical powers which can be written as follows: = f(Pt−n , Pt−n−1 , . . . .Pt−1 ,)
(1)
where pt is the actual power, pt–n is the previous power, pt+k is the forecasted power at step k, and f is a functional dependency between past and future t between varies from 1 to n, n is the length of the measurements. The second approach rely upon meteorological parameters. These parameters can be forecasted from satellite images, numerical weather prediction models, or statistical models, it can be written as: = f(Gt+k , Tt+k , WSt+k , . . . .)
(2)
where Gt+k , Tt+k , WSt+k are the forecasted solar irradiance, air temperature and wind speed respectively. The last one combines both historical and forecasted meteorological data, the formula can be given as: External parameters: Solar irradiation, air temperature, wind speed, relative humidity, etc
Forecasted power?
Produced DC power
Inverters DC/AC with MPPT Ground protection
Ground protection
PV plant
DCBox Combiner ACCircuit Breacker
ACCircuit Breaker
Produced AC power
AC filter
ACCircuit Breaker AC surge protection Lighting protection
Transform
Grid
Fig. 1 Basic structure of a gridconnected PV plant with protection devices
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A. Mellit 450
Sunny days
Cloudy days
400
Power (kW)
350 300
Forecast ?
250 200 150 100 50 0 0
50
100
150
200
250
Time (hour) Fig. 2 Measured DC output power of a 0.5 MW gridconnected PV plant (for 8 days)
= f(Pt−n , Pt−n−1 , . . . .Pt−1 , Gt+k , Tt+k , WSt+k , . . . .)
(3)
3 Machine Learning and Deep Learning 3.1 Machine Learning As reported in [10], ML refers to techniques able to give computers the ability to learn automatically from experience (i.e., dataset) without being explicitly programmed by human beings. ML algorithms can be classified into four major algorithms [11] (See Fig. 3): supervised learning, unsupervised learning, reinforcement learning and semisupervised learning. In the first case, an algorithm tries to create some relationships and dependencies between input and output features (Classification and regression problems). In the case of unsupervised learning, there is no output and the algorithm searches for rules and patterns in the available dataset in order to better describe the data (clustering problem, anomaly detection, etc.). The reinforcement type is mainly used to bring high dimensional into lower dimensional data for visualization or analysis purposes (mainly used for clustering and association problems). The last one combines both kind of learning, most data are not labelled (used mainly in control and classification problems). The main ML algorithms used in PV power forecasting are: Support Vector Machine (SVM), kNearest Neighbors (kNN), Linear Regression (LR), Neural Networks (NNs), Fuzzylogic, Random forest (RF) and Extreme Machine learning
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Classification Supervised learning
Regression Unsupervised learning Anomaly detection Machine learning
Clustering Semisupervised learning Dimensionality reduction
Reinforcement learning Control
Fig. 3 Machine learning classes
pt
Fig. 4 RNN for PV power forecasting at one step ahead
pt1
pt+1
1
Z
Zn
ptn
(ELM). As example Fig. 4 shows a Recurrent Neural Network (RNN) for PV output forecasting for one step ahead (t + 1).
3.2 Deep Learning Deep learning is a relatively new advancement in NN programming and represents a way to train deep neural networks (DNNs), as traditional NNbased methods might be affected by problems such as overfitting, diminishing gradients, etc. [12]. In the last few years, DL has led to very good performance on a variety of problems, such as speech recognition, visual recognition, natural language processing, pattern
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recognition, automatic translations, selfdriving cars, medical diagnosis, financial prediction, automatic trading, etc. On the contrary, the application of DL in PVs is still limited. DNNs are able to automatically learn arbitrary complex mappings from inputs to outputs and support multiple inputs and outputs. The main DL method used in PV power forecasting is the long shortterm memory (LSTM). LSTM is a kind of RNN with a memory cell, an input gate, an output gate, and a forget gate in addition to the hidden state always present in traditional RNNs (See Fig. 5). The main drawback of RNNs is that they practically fail to handle longterm dependencies. As the gap between the output and the input data point increases, RNNs fail in connecting the information between the two. In the last decades, researchers have proposed a number of new recurrent units (RU) to solve this problem, and the most effective solution are LSTM [13] and gated recurrent units (GRU) [14]. Where Xcs is a matrix of number of features, Hcs is a matrix of a number of hidden units, xt , input, ht , is the hidden sate, ct is the cell state, o is the output gate and f is the forget gate. Number of hidden units HCS
htInitial state
LSTM Bloc
ct
LSTM Bloc
LSTM Bloc
ct
xCS Number of time steps
Update
Forget
Output
ct1
f
g
f
ct
o
ht1 ht Xt
Fig. 5 LSTM network structure
Final state
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4 Application of Machine Learning and Deep Learning in PV Power Forecasting 4.1 Application of Machine Learning Table 1 reports most applications of machine learning in PV power forecasting. For example, in [15] the authors developed a method using an ANN for a one dayahead PV power forecasting. The aerosol index has been used as an input as the solar irradiance is a parameter that is not always measured and/or available. The Mean Absolute Error (MAE) was 7.65%, and the authors concluded that in the future data from remote sensors could represent a valuable input in the field of PV power forecasting different methods have been investigated in [16] including a greybox model, NNs, kNN, RF, SVM, and ensemble of methods (ENS). The application of these techniques gave similar performances showing a MAE close to 5%. However, ENS was the best forecaster considering variable weather condition. The authors concluded that the investigated methods proved the feasibility to produce good results even without using the temperature as an input parameter. In [17] the authors used a kNNbased method for forecasting the power produced by smallscale PV plants installed in three different regions: SanDiego, Braedstrup and Catania. They concluded that simple techniques such as kNNs can produce relatively accurate forecasts (the nMAE was in fact 0.96%). In [18] the authors used a SVRbased method applied to a smallscale PV plant located in Melaka, Malaysia. The use of different input has been investigated including the tilted and horizontal global irradiance, and the module temperature. The results showed that the model performs well in the tropical climate with a RMSE that was in the range (4.29–6.85%). A large dataset both from NPW and measurements from the field has been used to train different multimodel ensembles (MME) including SVM, ANN, and statistic models [19]. The investigated PV plant is located in Bolzano (Italy), and its capacity is 662 kWp. This work showed that the same algorithms differ in performance when using as input NWP data with comparable accuracy. A hierarchicalbased approach with different time horizons (15 min, 1 h, and 24 h) was used in [20]. In this case, many different parameters have been used as an input including the plant output power, a number of environmental variables coming from NWP, and the geometry of the system. The conclusion was that this method performs better than others based on ANNs and SVR. In [21] the authors developed an advanced FL method for forecasting the output power of two PV plants installed in Milano and Catania, Italy. The model was used to forecast the output power with a time horizon in the range (1–72 h). The MAE was 0.56 kW for the PV plant installed in Catania and 0.64 kW for the one in Milano. This work showed that all investigated models including generalized adaptive, physical inspired, semistatistical methods perform better in summer than in winter, while have similar performance in summer and autumn. The T–S fuzzybased approach proposed in [22] uses as an input a number of meteorological parameters. The model
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Table 1 Papers on the application of ML in PV power forecasting for the last five years (2015–2019) Ref. & Method year
Time Used horizon and parameters resolution
Point or regional forecast
[15] 2015
ANN
1 day ahead Historical powers, temperatures, aerosol indexes, wind speeds, and humidity
Regional Minqin, forecast Gansu 10 MWp
[16] 2015
ENS
1 day ahead Forecast of Regional Italy 1h solar irradiance forecast 114 MWp
[17] 2015
kNN
1 day ahead Onsite 3 points measurements: solar irradiance, temperature, wind speed, and relative humidity
SanDiego, nMAE: 7.4, Braedstrup 6.38 and 7.74 and Catania, Italy 49.2 kWp, 5.21 kWp, and 15 kWp
[18] 2016
SVR
12 h ahead min
Melaka, Malaysia 6 kWp
RMSE: 4.29%–6.85%
[19] 2016
MME
1 day ahead NWP models
1 point
Bolzano, Italy, 662 kWp
RMSE = 10.5%
[20] 2016
MLH
15 min, 1 h and 24 h ahead
Historical power and NWP
1.point
Florida, U.S. MAE = 6 MWp 128.77 kWh
[21] 2017
FL
72 h ahead 1h
Forecasted 2 points solar irradiance and estimated solar cells
Catania, Italy 5.21 kWp
[22] 2017
FL
1 h ahead
Historical powers, air temperature, humidity, and insolation
1 point
Queensland, MAE = Australia. 9.77% 433 kWp
[23] 2017
ANN
1 day ahead Weather data 1h and historical measurements
1 point
Milano, Italy MAE < 15% 264 kWp
On site 1 point measurements: solar irradiance and module temperature
Region and PV nominal power
Accuracy
MAPE = 7.65%
nMAE: 1.27–4.04
MAE: 0.56 and 0.64 kW
(continued)
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Table 1 (continued) Ref. & Method year
Time Used horizon and parameters resolution
Point or regional forecast
[24] 2017
ANN
Up to 48 h 1h
Satellite data and NWP models
Regional Italy, 68.2 MW
[25] 2018
SVM and ANN
1 h ahead 
Onsite 1 point measurements: temperature, relative humidity, and aerosol
Beijing, China 1.2 kWp
MRE = 11.61%
[26] 2018
ELMANN 24 h ahead 
Onsite 1 point measurements: solar irradiance and air temperature
Amman, Jordan 264 kWp
MAE = 1.08%
[27] 2019
ESN
1 h ahead
Historical output powers
China 
MAPE = − 0.00195%
[28] 2019
ELM
Few hours interval
Historical data 1 point powers and NWP meteorological data: solar irradiance, air temperature, wind speed, and relative humidity
China 250 kWp
MAE = 2.13%
1 point
Region and PV nominal power
Accuracy
RMSE: 5%–7% for 1–4 h RMSE: 7%–7.5% for 1–2 days
was compared with other methods such as SVM, MLPANN, RNN, and other empirical models. The results showed that the proposed model outperforms all others with a quite low MAE = 9.77% in summer, but a high MAE = 30% in spring. In [23] a MLPbased forecaster was trained using weather forecasts and historical data. The model performed better during sunny than partially cloudy days. The normalized MAE was lower than 15% for all the investigated cases. A new upscaling method was developed for estimating the power produced by a PV plant installed in Italy [24]. The method uses data from satellite and NWP to estimate the solar generation on a regional scale. The method was applied to the power generation of 1985 smallscale PV plants installed in the South Tyrol Region, Italy (the total covered area was 800 km2 ). The RMSE was in the range (5–7%) for a time horizon of 4 h, and in the range (7–7.5%) for the 1day estimation. SVM and a MLP have been used to for the ultrashortterm forecasting of a smallscale PV plant installed
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in Beijing, China [25]. According to the authors, the designed model is particularly efficient and especially designed for particular environmental conditions with fog and haze. The input of these forecasters comprises the onsite measurements of air temperature, relative humidity, and aerosol indexes. In [26] an ELM algorithm has been developed in order to train a MLP network that forecasts 24h ahead the power produced by a PV plant installed at Amman, Jordan. The ELM outperforms the classical back propagation (BP) algorithm in terms of accuracy. The technique showed the smallest MAE = 1.08% in June, while the biggest MAE = 18.83% and corresponded to February and March. A multiple reservoirs echo state network (MRESN) based model has been proposed for in [27]. The quasi Newton algorithm has been used to optimize the reservoir parameters. The results showed a MAPE very close to zero (0.00195%) and, with reference to onehour forecast horizon, the model performed better than other techniques such as SVM, backpropagation neural networks (BPNNs), support vector regression (SVRANN), and wavelet transform (WT). A multimodel ELMbased forecaster was proposed in [28] for the forecasting of the power produced by 250 kWp PV plant installed in Beijing, China. With reference to the accuracy of the multimodel, the MAE was 2.13% in spring and 1.7% in summer, while for an annual single model the MAE was 2.43% in spring and 1.81% in summer. The designed multimodel takes into account the fluctuation of the power output in order to improve the accuracy.
4.2 Application of Deep Learning Table 2 reports a summary of different deep learningbased techniques published during the period 2017–2019 for forecasting the power produced by PV plants. For example, in [29] five LSTMbased neural networks have been designed to forecast the hourly PV output power. The proposed model does not use any meteorological data and is based on historical powers, offered a reduction in the forecasting error compared with other methods. A sixlayer feedforward deep neural network (FFDNN) for one dayahead PV power forecasting of a grid connected photovoltaic system installed in Seoul, Korea has been presented in [30]. The method, that does not require the use of any onsite sensors, has shown better performance than other models using local measurements. Nevertheless, the achieved errors during summer and cloudy weather were not satisfactory. In [31], the authors proposed a comparative study between different deep neural networksbased oneday ahead forecasters. The study includes CNNs, LSTM, and a hybrid model that combine CNNs and LSTMs. It has been shown that the accuracy of the three models mainly depends on the size of the available database. Generally, the experimental results show that the deep learning network has a good effect on the prediction of photovoltaic power generation and the stability and robustness of the model are high. A recurrent LSTMbased method has been designed for the hourly shortterm forecasting of the power produced by a PV plant installed in Gumi, South of Korea [32]. The model accepts as an input the solar irradiance, the ambient
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Table 2 Recent applications of DL in PV power forecasting for the last three years (2017–2019) Ref & Year
Method
Time horizon
Used parameters
Point or regional forecast
Region and PV nominal power
Accuracy
[29] 2017
Deep LSTM network
1 h ahead Historical powers
1 point
Aswan, Egypt
RMSE = 82.15
[30] 2018
DNN
24 h ahead
Weather forecast 1 point
Seoul, Korea 2.448 kWp
MAE = 2.9%
[31] 2019
CNN, LSTM and CNN+LSTM
1 day ahead
Onsite 1 point measurements: active power, current, wind speed, irradiance, humidity, and air temperature
Trina, China, 23.4 kWp
RMSE = 0.343%, MAE = 0.126%, MAPE = 0.022%
[32] 2019
RNNLSTM DNN
1 h ahead Onsite measurement and cloudiness data
Gumi, South of Korea 40 kWp
MAE = 0.23%
1 point
temperature, and the cloudiness index. The results showed the best performance compared with other approaches based on DNN, ANN, auto regressive integrated moving average (ARIMA), and seasonalARIMA. LSTMs perform particularly well, especially in the case of instable power output.
5 Concluding Remarks In the present talk, a brief review on the application of Machine learning and recently Deep learning methods to PV output power forecasting, is presented. The key conclusions and future directions that can be highlighted are [9]: • While the development of forecasters based on ML in general has been investigated rather intensively, the application of DL for PV power prediction has been rather limited so far. • Most researchers have focused on forecasting at single locations, while little work has been done on regional models; no accurate general regional model has been proposed to date. • The most investigated time horizon is in the shortterm regime (up to few days)— which is also the most requested and used. MLbased forecasters are well suited
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for this case, particularly when combined with appropriate algorithms—such as ANNoptimized Genetic Algorithm or Particle Swarm Optimization. Most ML and DLbased models perform well for sunny days, while for cloudy days the forecasting accuracy decreases significantly. In addition, the accuracy of ML and DLmodels decreases for longer time horizons, especially beyond 72 h. ML methods based on historical power output, and the use of meteorological parameters (such as air temperature, solar irradiance, relative humidity, wind speed, cloud cover), combined with an optimal learning algorithm and weather classification can improve forecasting accuracy. Onestep ahead forecasting performs best, and has been extensively investigated. Conversely, multistepahead predictions remain a challenging task. The improvement of model accuracy for cloudy days is still only marginally investigated. Forecasting approaches able to estimate and classify cloud cover and to use these parameters for DL models is expected to lead to sizeable accuracy improvement
Generally, in order to increase the accuracy of the forecasters based on AI techniques, the following points should be considered: • large datasets with goodquality data are preferable; • pretreatment and analysis of the database to identify outliers and missing data is required; • exogenous inputs should be taken into account, such as cover cloud variation; • combination with other physical models. We believe that this talk can help readers (academic researches), to get ideas on the application of machine learning and deep learning for output PV power forecasting, as well as the future direction in this topic. Acknowledgements The author would like to thank Dr. B. Hajji and Dr. H. Rabhi for the invitation, as well as the organization committee. The author would like also to thank Dr. A. Massi Pavan, Dr. V. Lughi from Trieste Univ., Trieste, Italy, Dr E. Ogliari and Prof. S. Leva from Polytechnic of Milan, Italy for their valuable comments reported in [9].
References 1. IEA (2020) Sunspot of global markets. (Accessed April 2020) 2. Sperati S, Alessandrini S, Pinson P, Kariniotakis G (2015) The weather intelligence for renewable energies benchmarking exercise on shortterm forecasting of wind and solar power generation. Energies 8:9594–9619 3. Pelland S, Remund J, Kleissl J, Oozeki T, De Brabandere K (2013) Photovoltaic and solar forecasting: state of the art; IEA PVPS Task 14, Subtask 3.1. Report IeaPVPS T14–01: 2013. International Energy Agency, Paris, France 4. Antonanzas J, Osorio N, Escobar R, Urraca R, MartinezdePison FJ, AntonanzasTorres F (2016) Review of photovoltaic power forecasting. Sol Energy 136:78–111
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5. Raza MQ, Nadarajah M, Ekanayake C (2016) On recent advances in PV output power forecast. Sol Energy 136:125–144 6. Wan C, Zhao J, Song Y, Xu Z, Lin J, Hu Z (2015) Photovoltaic and solar power forecasting for smart grid energy management. IEEE CSEE J Power Energy Syst 1:38–46 7. Russell SJ, Norvig P: Artificial intelligence: a modern approach, 3rd ed.; PrenticeHall, Inc.: Upper Saddle River, NJ, USA, 2009 8. Mellit A, Kalogirou SA (2008) Artificial intelligence techniques for photovoltaic applications: a review. Prog Energy Combust Sci 34:574–632 9. Mellit A, Massi Pavan A, Ogliari E, Leva S, Lughi V (2020) Advanced methods for photovoltaic output power forecasting: a review. Appl Sci 10(2):487 10. Arthur S (1959) Some studies in machine learning using the game of checkers. IBM J 3:211–229 11. Alpaydin E (2016) Machine Learning: The New AI. MIT Press, Cambridge 12. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, New York 13. Hochreiter S, Schmidhuber J (1997) Long shortterm memory. Neural Comput 9:1735–1780 14. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoderdecoder for statistical machine translation. arXiv:1406.1078 15. Liu J, Fang W, Zhang X, Yang C (2015) An improved photovoltaic power forecasting model with the assistance of aerosol index data. IEEE Trans Sustain Energy 6:434–442 16. Gigoni L, Betti A, Crisostomi E, Franco A, Tucci M, Bizzarri F, Mucci D (2015) Dayahead hourly forecasting of power generation from photovoltaic plants. IEEE Trans Sustain Energy 9:831–842 17. Zhang Y, Beaudin M, Taheri R, Zareipour H, Wood D (2015) Dayahead power output forecasting for smallscale solar photovoltaic electricity generators. IEEE Trans Smart Grid 6:2253–2262 18. Baharin KA, Abdul Rahman H, Hassan MY, Gan CK (2016) Shortterm forecasting of solar photovoltaic output power for tropical climate using groundbased measurement data. J Renew Sustain Energy 8:053701 19. Pierro M, Bucci F, De Felice M, Maggioni E, Moser D, Perotto A, Cornaro C (2016) Multimodel ensemble for day ahead prediction of photovoltaic power generation. Sol Energy 134:132–146 20. Li Z, Rahman SM, Vega R, Dong B (2016) A hierarchical approach using machine learning methods in solar photovoltaic energy production forecasting. Energies 9:55 21. Paulescu M, Brabec M, Boata R, Badescu V (2017) Structured, physically inspired (gray box) models versus black box modeling for forecasting the output power of photovoltaic plants. Energy 121:792–802 22. Liu F, Li R, Li Y, Yan R, Saha T (2017) TakagiSugeno fuzzy modelbased approach considering multiple weather factors for the photovoltaic power shortterm forecasting. IET Renew Power Gener 11:1281–1287 23. Leva S, Dolara A, Grimaccia F, Mussetta M, Ogliari E (2017) Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power. Math Comput Simul 131:88–100 24. Pierro M, De Felice M, Maggioni E, Moser D, Perotto A, Spada F, Cornaro C (2017) Datadriven upscaling methods for regional photovoltaic power estimation and forecast using satellite and numerical weather prediction data. Sol Energy 158:1026–1038 25. Liu W, Liu C, Lin Y, Ma L, Xiong F, Li J (2018) Ultrashortterm forecast of photovoltaic output power under fog and haze weather. Energies 11:528 26. AlDahidi S, Ayadi O, Adeeb J, Alrbai M, Qawasmeh B (2019) Extreme learning machines for solar photovoltaic power predictions. Energies 11:2725 27. Yao X, Wang Z, Zhang H (2019) A novel photovoltaic power forecasting model based on echo state network. Neurocomputing 325:182–189 28. Han Y, Wang N, Ma M, Zhou H, Dai S, Zhu H (2019) A PV power interval forecasting based on seasonal model and nonparametric estimation algorithm. Sol Energy 184:515–526
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Communication, Signal Processing and Information Technology
Efficient Memory Parity Check Matrix Optimization for Low Latency Quasi Cyclic LDPC Decoder Mhammed Benhayoun, Mouhcine Razi, Anas Mansouri, and Ali Ahaitouf
Abstract Implementation of Low Density Parity Check (LDPC) decoders using conventional algorithms such as LLR BP or MinSum requires large amount of memory resources for storing the parity check matrix. This paper presents a soft implementation of irregular LDPC decoding for Wimax application, which achieve better BER performance and faster convergence with less memory requirement. The proposed construction reduce the memory required for loading the LDPC paritycheck matrix by up to 98%, and subsequently reduce the decoding latency to 0.7 ms by iteration. Keywords Quasi cyclic LDPC codes · LLR BP · Min_sum · Parity check matrix · WiMAX · BER · Latency
1 Introduction Low Density Parity Check (LDPC) codes have attracted much significant interest in channel coding because of their excellent errorcorrecting performance. LDPC codes were initially introduced by Gallager [1] in 1962 and rediscovered by David Mckay [2] in 1996. LDPC codes have been adopted by many standards such as Digital Video Broadcasting–SatelliteSecond Generation DVBS2 [3], and in IEEE WiMax 802.16e [4]. M. Benhayoun (B) · M. Razi · A. Mansouri · A. Ahaitouf Laboratory of Intelligent Systems, Georesources and Renewable Energies, USMBA, Fez, Morocco email: [email protected] M. Razi email: [email protected] A. Mansouri email: [email protected] A. Ahaitouf email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_5
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Their excellent threshold performance is achieved at large code lengths (64800 in DVBS2 [3], between 576 to2304 in WiMax [4]), which represents a significant challenge for implementation, particularly for the storage of the generator matrix G and parity check matrix H, which are using respectively in LDPC encoding and decoding process. Several recent works [5, 6, 7], have proposed a materiel design for memory loading H matrix, aiming to increase the memory required for presenting the paritycheck matrix. In this work, we propose a software implementation of LDPC decoder, with a structured paritycheck matrix memory loading aiming to reduce memory access and subsequently the decoding latency.
2 Theoretical Background In this section, we first summarize the fundamentals of construction methods of the parity check matrix and the iterative decoding BP algorithm.
2.1 Parity Check Matrix H A binary LDPC codes are an iterative linear block codes. Encoding K bits message, u, to N bits code words, c, is performed using a K * N generator matrix, G, according to the following formula: c = uoG
(1)
Where o denotes the modulo 2 matrix multiplication. A binary LDPC decoding use a low density parity check matrix H defined as the dual G matrix, it’s associated to the G matrix as follows: G o Ht = 0
(2)
If the parity check matrix contains the same number of ones per column (noted dv), and the same number of ones per row (noted dc), the code is called a regular LDPC code. Otherwise, the code is called irregular code. In this paper we are interested to the irregular code for their good errorcorrecting performance [6]. It’s easier to calculate the G matrix from the H matrix using the Eq. (2), but the matrix associated to low density matrix is dense. The encoding complexity is about O(N2 ) [8]. To reduce this complexity, the quasicyclic (QC) LDPC codes based on circulates permutation of z * z identity matrices are used in the Wimax 802.16e standard [4], in this case, the encoding processing can be done with a simple shift registers reducing the complexity encoding to O(N) [9, 10].
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(a): Hbmmatrix
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(b)Wimax 1152*2304 matrix
Fig. 1 Example of Hbm expended to large matrix H
The QC LDPC codes present also a good error performance [11, 12] and efficiently decoded with partly parallel decoder architectures [13]. The Wimax 802.16e standard defines the expansion matrix Hbm, which is expanded to the large parity check matrix H. The Hbm matrix size are nb = N/z column and mb = M/z row, the matrix elements are the right circular shift coefficients, or − 1 value in the case of all zeroes z * z matrix. The Fig. 1a, showed the Hbm (12, 24) matrix related to the Wimax parity check matrixH (1152,2304) showed in Fig. 1b, in this case z = 96 and each Hbm shift coefficients are replaced by the right circular permutation of 96 * 96 identity matrices; the “−1” elements are replaced by 96 * 96 allzeroes matrices.
2.2 LLR BP LDPC Decoding Algorithms The H matrix can be represented with a Tanner graph, consisting N variable nodes (VNs) and M check nodes (CNs). The nth VN is connected to the mth CN if Hmn = 1. Initialization The initial LLR information of every variable node VNs is: p(yn /vn = 0) Cvn = log p(yn /vn = 1)
(3)
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Where yn denotes the channel information of the nth variable node, vn denotes the nth code bit. Horizontal Step After initialization, the C2V messages, Lmn , which propagates from the control node cm to the variable node vn is generated according to L mn = 2 tanh
−1
Z n m tanh n ∈N (m)\n 2
(4)
Where N(m)\n denotes the neighboring variable nodes which are connected to check node cm , excluding variable node vn . Vertical Step The V2C message, Znm , which propagates from the variable node vn to the control node cm is calculated as: L mn (5) Z nm = Cvn + m ∈N (n)\m
Where N(n)\m denotes the neighboring check nodes which are connected to variable node vn , except for check node cm . The new LLR value Zn which is calculated as: L mn (6) Z n = Cvn + m ∈N (n)
Where N(n) denotes all the neighboring check nodes which are connected to variable node vn . Hard Decision After generating a new LLR value, a hard decision of the variable node vn is made based on the Zn value: if Zn ≥ 0 un = 0 else un = 1. Messages C2V and V2C are exchanged until a valid code word is found (Syndrome S = H o ut = 0) or until the maximum number of iterations is reached. Various algorithms are available for C2V messages updates simplification. The widely used algorithms are the minsum algorithm (MSA) [14]. The update Eq. (4) became: sign(Z n m ) minn ∈N (m)\n (Z n m ) L mn = (7) n ∈N (m)\n
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3 Proposed Memory Loading Parity Check Matrix 3.1 Proposed Method In this paper we propose to represent the parity check matrix by loading only some parameters of the expansion matrix using data structures: Cm, Vn, Tcol and Trow. Cm structure contains the dv value by row and the Trow start index for each Hbm row. Trow structure is generated by scanning the Hbm matrix in a row major order and by sequentially mapping into Trow the positive permutation coefficient with the related index column. Vn structure contains the dc value by column and the Tcol start index for each Hbm column. Tcol structure is generated by scanning the Hbm matrix in column major order and sequentially mapping into Tcol the positive permutation coefficient with the related index row. Data structures Cm, Vn, Trow and Tcol presented in Fig. 2, representing the Hbm Wimax matrix shown in figure. In horizontal decoding processing described in Sect. 2.3, we need to read/write in a row major order the V2C and C2V messages corresponding to a nonull element in the large matrix H. The proposed Algorithm 1a described the large matrix H scanning
(a) Cm and Trow structures used in horizontal processing
(b) Vn and T col structures used in vertical processing Fig. 2 Data structures representing the example shown in Fig. 1
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method from the Cm and Trow structures; the step 8 shows the equation allowing the Hbm to H expansion in a row major order. The Algorithm 1b performs the opposite operation, in the vertical processing by using the Vn and Tcol structure; the step 8 shows the equation allowing this the Hbm to H expansion in a column major order. Algorithm 1 : (a) Horizontal processing 1: for each row i of Hbmdo 2:dv=Cm[i].dv 3: pos = Cm[i].index 4: for each j of [pos, pos + dv 1] do 5: col = Trow[j].col 6: per = Trow[j].per 7: for each column mof [0, z1] do 8: n = (col x z) + ( per + m) % z 9: read Znm 10:calculate and writeLmn
(b) Vertical processing 1: for each column j of Hbmdo 2: dc = Vn[i].dc 3: pos = Vn[i].index 4: for each j of [pos, pos + dc 1] do 5: row = Tcol[i].row 6: per = Tcol[i].per 7: for each column n of [0, z1] do 8: m=(rowxz)+((zper)+n)%z 9: read Lmn 10:calculate and write Znm
3.2 Discussion and Optimization The memory required to store the four structures Cm, Vn, Tcol and Trow is calculate as: Memor y H bm = (2n1 + 2nb + 2mb) ∗ si zeo f (varaibe) Where n1 denotes the number of positive elements in the mb *nb Hbm matrix. We can realize that n1 = N1/z, where N1 indicates the number of ones in the large matrix H. Consequently the memory requirement of the proposed method is reduced by z factor than the large matrix loading method [7]. However, in the horizontal and vertical processing we use the expansion equations showed in step 8 of the Algorithms 1a and 1b, these equation adds decoding cycles. To reduce equations complexity, we propose to store col’ as the result of the multiplication (col x z) in the structure Trow instead the column value; likewise the row’ as result of the multiplication (row x z) and per’ as the result of (z – per) will be stored in the structure Tcol instead the row value and the permutation coefficient respectively. Moreover we use a z value power of 2, in this case a modulo operator (%z) can be replaced by a logically (and) operator (&z’) with z’ = z − 1, the two equations showed in step 8 of the Algorithms 1a and 1b became as:
Efficient Memory Parity Check Matrix Optimization … Horizontal processing
Vertical processing
n = col’ + (per + m)&z’
m = row’ + (per’ + n)&z’
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(a) Memory required for load matrix H (b) Memory required for loading all variables Fig. 3 Memory load requirement
3.3 Simulation and Results The decoding performance of the MSA algorithm using the proposed method is obtained through a large number of simulations using C language implementation and a code profiler on a core2duo machine with a processor clock speed at 2000 MHz and 32 Ko for levelone (L1) cache memory. The computation complexity of these algorithms is evaluated according to the number of read/write memory access, the number of L1 read/write memory missing and the number of instruction required decoding processing. All simulations are performed over the additive white Gaussian noise (AWGN) channel, and binary phaseshift keying (BPSK) modulation. The codes used in the simulation are the irregular LDPC codes constructed based on the IEEE 802.16e standard [24], codes (576, 288), (1152, 576) and (1152,2304). The simulation profiling, presented in the Fig. 3a shows that the required memory for loading the H matrix using the proposed method is reduced by up to 98% (448 Octet) than the large matrix loading method (43008Koctet ≈ 43Koctets), and is reduced by up to 35% taking in consideration all decoding variables, Fig. 3b: matrix H, V2C and C2V messages, Zn variable and Cvn variables described in Sect. 2.1 The simulation profiling, shows (see Fig. 4a) that the number of read/write data not found in L1 and lastlevel cache memory has decreased using the proposed method (80% in L1 and 70% in lastlevel). Moreover, the Fig. 4b showed that the number of memory read/write access has decreased using the proposed method and subsequently the decoding estimation cycle (55% for access memory and 48% for decoding cycle estimation), so the decoding latency using the proposed method (≈0.7 ms) is faster than the large matrix H loading method (≈1.4 ms)
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(a) L1 and LL data missing
(b) Estimation decoding cycle
Fig. 4 Results of code profiling
4 Conclusion In this paper, the proposed parity check matrix loading reduce the memory requirement by up to 98% allowing a matrix hard implementation in small area and reducing the number of decoding cycle decreasing subsequently the latency to 0.7 ms by iteration. However, as perspective we proposed to use this model for parallel embedded implementation.
References 1. Gallager RG (1962) Low density parity check codes. IRE Trans Inf Theor IT8:21–28 2. MacKay DJ (1999) Good errorcorrecting codes based on very sparse matrices. IEEE Trans Inf Theor 45(2):399–431 3. Digital Video Broadcasting (DVB) (2009) Second generation framing structure, channel coding and modulation systems for Broadcasting, Interactive Services, News Gathering and other broadband satellite applications (DVBS2), ETSI EN 302 307, V1.2.1, April 2009 4. IEEE (2006) IEEE Std. 802.16e2005 and IEEE Std.802.162004/Cor12005 5. Shih XY, Chou HR (2018) Flexible design and implementation of QCBased LDPC decoder architecture for online userdefined matrix downloading and efficient decoding. Integration. https://doi.org/10.1016/j.vlsi.2018.07.008 6. Ngangom L, Manikandan V (2013) Efficient memory optimization and high throughput decoding architecture based on LDPC codes. In: 2013 international conference on information communication and embedded systems (ICICES) 7. Benhayoun M, Razi M, Mansouri A, Ahaitouf A (2019) New memory load optimization approach for software implementation of irregular LDPC encoder/decoder. In: 2019 international conference on wireless technologies, embedded and intelligent systems (WITS). https:// doi.org/10.1109/wits.2019.8723841 8. Malema GA (2007) Lowdensityparitycheck codes: Construction and implementation. Ph.D. thesis, The University of Adelaide, Australia, 18 November 2007 9. Jin H, Khandekar A, McEliece R (2000) Irregular repeataccumulate codes. In: Second international conference on turbo codes, September 2000 10. Li Z, Chen L, Zeng L, Lin S, Fong W (2005) Efficient encoding of lowdensity paritycheck codes. In: Proceedings Global Telecommunications Conference (Globecom), vol 3, pp 1205– 1210, December 2005
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11. Chen Y, Parhi KK (2004) Overlapped message passing for quasicyclic low density parity check codes. IEEE Trans Circuits Syst I, Reg Papers 51(6):1106–1113 12. Dai Y, Yan Z (2005) Optimal overlapped message passing decoding for quasicyclic lowdensity paritycheck codes. In: Proceedings Global Telecommunications Conference (Globecom), vol 4, pp 2395–2399, December 2005 13. Shimizu K, Ishikawa T, Togawa N, Ikenaga T, Goto S (2005) Partiallyparallel LDPC decoder based on highefficiency messagepassing algorithm. In: Proceedings International Conference Computer Design (ICCD), pp 503–510, October 2005 14. Ji W, Hamaminato M, Nakyama H, Goto S (2010) Selfadjustable offset minsum algorithm for ISDBS2 LDPC decoder. IEICE Electron. Expr. 7(17):1283–1289
Monitoring Energy Consumption Based on Predictive Maintenance Techniques Bouchra Abouelanouar, Ali Elkihel, Fatima Khathyri, and Hassan Gziri
Abstract This study covers the new benefits of using predictive maintenance in industries. Reducing unplanned downtime, increasing productivity, and feeling safe and reliable are the most common benefits of predictive maintenance. Recently, it has been used as a tool for energy efficiency and reducing energy consumption. The objective of this article is to demonstrate that predictive maintenance techniques, including vibration analysis and infrared thermography, can monitor energy losses due to machine faults such as misalignment and unbalance. Vibration and thermal measurements were outlined and compared. The comparison was validated and investigated through laboratory test rig. Different modes of misalignment and unbalance were investigated. It was found that the measured temperature also indicate the presence of faults and can be used as energy monitoring tool at least as good as vibrations analysis technique. The methodology developed in this paper, which is based on the combination of the two techniques, aims to prove the use of infrared thermography in an energy efficiency program. Keywords Predictive maintenance · Energy consumption · Vibration analysis · Infrared thermography misalignment · Unbalance
1 Introduction Predictive maintenance is a technique to predict the future failures of a machine component; its main benefit is to reduce unplanned outages by optimizing the maintenance schedule that based on sensor data: vibration level, noise, temperature or lubrication. Many companies used predictive maintenance techniques to establish, B. Abouelanouar (B) · F. Khathyri Laboratory of Industrial Engineering and Seismic Engineering, National School of Applied Sciences, University of Mohamed First, Oujda, Morocco email: [email protected] A. Elkihel · H. Gziri Laboratory of Engineering, Industrial Management and Innovation, Faculty of Sciences and Techniques Settat, University Hassan First, Casablanca, Morocco © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_6
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firstly, a database based on history of selected variables (symptoms) such as vibration, temperature and their relationship with the remaining life of the component. Recently, predictive maintenance becomes an energy efficiency opportunity which leads to reduce energy consumption and environmental impacts (CO2 emissions, wastes…) [1]. The industrial sector is one of the largest segments of energy demand on an international scale. That’s why companies become increasingly aware of the necessity to adopt newer technologies that allow energy saving and machine efficiencies. Condition monitoring and all maintenance operations are fundamental in granting machine reliability and consequently the energy efficiency, and therefore, cost savings. Equipment failures such as shaft misalignment, rotor unbalance and bearing defects not only affect plant availability but also energy consumption. It is proven that most of these failures are energy wasters. Vibration analysis is the most widely used predictive technique that can be applied also to monitoring energy consumption in rotating machine equipments [2]. A. Elkhatib [3] experimentally investigated the loss of energy using vibration analysis for different machine faults, and the results show the strong correlation between vibration levels measured and the power consumption. M. B. de Carvalho et al. [4] proposed the inclusion of vibration analysis to achieve lower power consumption and higher productivity, the proposed methodology can reduce electricity consumption by 23%. E. Estupinan et al. [5] analyzed energy losses generated by misalignment shaft by using vibration measurements for laboratory test rig and industrial case studies. The results showed that misalignment impacts the vibratory behavior as well as energy consumption. Without doubt, vibration analysis is generally able to detect failures in their early stage and perform effectively energy monitoring by accurately showing the percentage of energy waste. Therefore, several works has focused on the use of infrared thermography as condition monitoring for energy efficiency. For example, A. Gaberson used thermal testing to evaluate energy consumption generated by misalignment of rotating machinery. He concluded that predictive maintenance practices based on temperature measurements can identify energy losses, estimated by 2%, due to misalignment. In Ref. [6], it was clearly demonstrated that infrared thermography make energy wasted visible and shows that proper shaft alignment leads to energy savings, even if may not seem very significant. Recently, research works have proposed the combination vibration analysis with infrared data analysis in order to reach a more reliable conclusion about the condition of the rotating machinery [7]. The interesting conclusions of this study revealed that the infrared technique was able to provide very useful information complementing vibration analysis measurements for the monitoring of energy losses due to shaft misalignment and/or unbalance The paper is structured as follows: Sect. 2 presents the experimental work and the results obtained Sect. 3 compares and discusses the result of the combination of techniques in the different case studies; finally, in Sect. 4, the conclusions of the work are summarized.
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2 Experimental Work Shaft misalignment and rotor unbalance are the two main sources of vibration which may destroy critical components (bearing, gears, coupling), and increase the energy consumption in rotating machines. By definition, misalignment occurs when the centerline of two shafts is not collinear, while unbalance is an uneven distribution of mass around the center of rotation [8]. It is from this perspective that we have designed and realized a test bench to set up the conditions for unbalance, misalignment and the combination of both at the same rotor–shaft system as shown in Fig. 1(A). The shaft is supported by two identical ball bearings and connected to the asynchronous motor and an over hung circular disc with holes evenly distributed. In this research, both of parallel and angular shaft misalignment was artificial generated by inserting shims of different thicknesses underneath the motor (Fig. 1(B)). Furthermore, to study the unbalanced rotor condition, a screw with nut was mounted on different angles and locations of one rotor (see Fig. 1(C)). Vibration data is measured by RMS (Root Mean Square), using vibration collector analyzer Vibrotest 60 with an accelerometer positioned at radial and axial directions, in different measurement positions: motor and bearings. For temperature measurements, this study focuses on the passive approach of infrared thermography, using a thermal camera Flir T440, to evaluate the effectiveness of this technique to detect unbalance and misalignment defects under various conditions. Thus, electrical measurements are taken, using an electrical network analyzer Qualistar C.A 8336, in ‘healthy’ and defective cases to illustrate the effect of unbalance, misalignment and the combination of both on energy consumption. In order to achieve this, the power consumption of the rotor is measured in the cases of unloaded and loaded rotor for different conditions.
Fig. 1 (A) Experimental set up, P1, P2 and P3: Measurement positions in Motor, Bearing 1 and Bearing 2, respectively, (B) Unbalanced and (C) misalignment, condition simulation
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3 Results and Discussions Before inducing any faults, a complete condition monitoring of the test rig was carried out using vibration analysis and thermal imaging data on each components: motor and bearings for the set rotation speed. Thus, the electrical measurements have been recorded in order to have a reference for the comparison with the faulty cases. Figure 1 shows the RMS values and the thermal image, in the healthy case (without any fault) for the three measurement positions (Fig. 2). From these first measurements, it is clear that the vibration level indicated by the RMS values in the healthy case (no fault) remain admissible according to the ISO 10816 standard. In return, the highest RMS value is recorded at the first bearing (next to the coupling); this is due to a defect of the outer ring (which has been identified using spectral analysis). For thermal measurement, the thermogram of the test bench shows that the temperatures recorded are between 12.2 °C and 28 °C and that the highest temperature is recorded this time at the motor. Table 1 presents electrical characteristics of the motor in both cases unloaded (motor only) and loaded (presence of the all components). These values are then used to evaluate the energy consumption in the defective case (presence of defects of misalignment, unbalance and the combination of both). Then, a series of experiments was carried out in order to evaluate the energy consumption under different degree of unbalance and misalignment in order to find a correlation between the energy consumption, level of vibration (RMS values (mm/s))
RMS (mm/s)
A
1
B
0.5
0 P1
P2
P3
Measuring position Radial RMS values
Axial RMS values
Fig. 2 Vibration measurement and thermal imaging in the healthy case, (A) Radial and axial RMS values in different measurement positions, (B) Thermal image of test rig components
Table 1 Electrical characteristics of the motor
Electrical characteristics
Unloaded motor
Loaded motor
Tension (V)
425.4
425.4
Current (A)
1.5
1.01
Power (W)
300
582
Power factor (cosϕ)
0.29
0.78
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Table 2 Vibration, thermal and electrical measurements under unbalance conditions Unbalance mass (g)
P1: motor
P2: bearing 1
P3: bearing 1
RMS
T°
RMS
T°
RMS
T°
Power(W)
0
0.553
46.7
0.664
41.8
0.596
36.8
250.721
10
0.752
58.3
0.877
43.2
0.722
38.1
268.824
20
0.817
63.4
1.041
48.4
0.817
39.4
289.142
40
0.968
65.7
1.523
51.7
0.925
40.1
290.315
Table 3 Vibration, thermal and electrical measurements under misalignment conditions Misalignment (mm)
P1: motor
P2: bearing 1
P3: bearing 1
Power(W)
RMS
T°
RMS
T°
RMS
T°
0
0.553
46.7
0.664
41.8
0.596
36.8
250.721
Angular
1.861
51.9
1.905
45.3
1.714
39.7
321.653
Parallel
1.956
54.3
2.006
49.6
1.854
40.5
402.304
and the temperature (T °C). Tables 2 and 3 summarize the results obtained from vibration, thermal and electrical measurements. From the measurement taken, it can be noted that the change in fault conditions changes the amplitude of the vibrations, the temperature and the electrical power with a similar trend. Indeed, when the value of the imbalance increases, the level of vibration and the temperature increases. In the case of misalignment, it can be seen that the vibration, temperature and power values increased more when there is a parallel misalignment. Thus, RMS and temperature values have shown their potential to detect faults and monitor energy. Now, we combined the unbalance and misalignment faults in the same experiment. The thermal imaging and temperature distribution of the test rig components are summarized in Fig. 3. From Fig. 3, we notice an increase in temperature especially at the motor (a difference of 13 °C was observed between the healthy and defective state). Also, the electrical measurements obtained in this case showed an increase in the active power (425 W compared to the initial value 250 W). The vibration measurements were done on the motor, bearing 1 and bearing 2. The radial levels are high for the faulty case (P1: 2.653 mm/s, P1: 2.453 mm/s, P1: 2.001 mm/s) in comparison to the healthy case (P1: 0.553 mm/s, P1: 0.664 mm/s, P1: 0.596 mm/s).
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L2
Temerature (°C)
35 30 25 L2:Faulty
20 15
L1:Healthy
L2
10 5 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 151
0
Pixels
Fig. 3 Temperature distribution in the test rig for both faulty and healthy cases
4 Conclusion From this experimental study, it can be concluded that the reliability of rotating machines is a major responsible for the increase of energy consumption and that predictive maintenance techniques present an effective solution for the evaluation of energy consumption. All power measurements into the motor showed an increase in values between the healthy and the defective case. So, it can be confirmed that misalignment and unbalance that vibration and thermal monitoring detect are severe energy wasters. Moreover, the RMS values of vibration level showed its potential as a prediction tool than in different defects studied, and this sensitivity is proven even in cases where the defects are in their early stage. Furthermore, infrared thermography technique is convenient since it a noncontact and realtime temperature monitoring and can be applied successfully where vibration monitoring may difficult.
References 1. Darabnia B, Demichela M (2013) Maintenance an opportunity for energy saving. Chem Eng Trans 32:259–264 2. Abouelanouar B, Elamrani M, Elkihel B, Delaunois F (2018) Application of wavelet analysis and its interpretation in rotating machines monitoring and fault diagnosis. A review. Int J Eng Technol 7(4):3465–3471 (2018) 3. Elkhatib A (2007) Energy consumption and machinery vibrations. In: 14th international conference on sound & vibrations, Australia, pp 1–6 4. Carvalho H, Gomes O (2015) Method for increasing energy efficiency in flexible manufacturing systems: a case study. In 22nd CIRP conference on life cycle engineering. Elsevier, pp 40–44 5. Estupinan E (2008) Energy losses caused by misalignment in rotating machinery: a theoretical, experimental and industrial approach. COMADEM Int. UK
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6. Gaberson H (1996) Rotating machinery energy loss due to misalignment. In: IECEC 96. Proceedings of the 31st intersociety energy conversion engineering conference, vol 3, pp 1809–1812 7. Abouelanouar B, Elamrani M, Elkihel B, Delaunois F, Manssouri I (2017) A comparative experimental study of different methods in detection and monitoring bearing defects. Int J Adv Sci Tech Res 7(1):409–423 8. Nejadpak A, Yang C (2017) Misalignment and unbalance faults detection and identification using KNN analysis. In: 26th Canadian congress of applied mechanics, Canada, pp 1–4
An Antenna Selection Algorithm for Massive MIMO Systems Yassine Garrouani, Fatiha Mrabti, and Aicha Alami Hassani
Abstract Recently, antenna selection has attracted researchers’ attention world widely. It is a promising solution for the design of optimal multiantenna systems. Many selection algorithms were proposed as solutions for the heavier selection scheme based on exhaustive search. In this paper, we propose an antenna selection algorithm that aims to make a tradeoff between performance and complexity, it encompasses two phases: training as well as decision making and as an evaluation metric, it uses spectral efficiency. For new channel state realizations, the algorithm evaluates only the antenna combinations that mostly occurred during the training phase and its decisions are based on thresholds gotten from the aforementioned metric. Keywords Antenna selection · Massive MIMO · Spectral efficiency
1 Introduction In multi antenna systems (single user MIMO, MuMIMO and massive MIMO), many antennas are deployed at either the transmitter or the receiver or at the both of them, providing multiple links over which data could be sent and received. While designing such systems, there are many critical factors that must be taken into consideration. As it is known, it is quite hard to acquire full and accurate channel state information (CSI) especially in an ever changing environment that might suffer from fastfading, pilot contamination, intentional jamming…etc. In addition, antennas in the array might not be performing the same way [1]. On the other hand, equipping every antenna with Y. Garrouani · F. Mrabti (B) · A. Alami Hassani (B) Sidi Mohamed Ben Abdellah University, Fez, Morocco email: [email protected] A. Alami Hassani email: [email protected] Y. Garrouani email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_7
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its own radio frequency (RF) module still presents some concerns in terms of hardware cost and energy consumption. Therefore, compromising for an optimal system would be of great benefit [2], in other words, a part of the system is temporarily put aside in order to reduce cost expenditures as well as energy consumption. But, at the same time, a good system capacity is still achieved [3] and the unused antennas are considered as degrees of freedom. In such system, the number of RF modules is less than the number of antennas in the array, this implies that only a subset of antennas is selected out of the set of available antennas and is activated for communication. The selection is based on some metrics such as signaltonoise ratio (SNR) at the receiver or biterrorrate (BER)…etc. Diverse works have proposed some optimization techniques as [4–6]. In this paper, we propose our proper antenna selection algorithm called OTSA for OccurrenceThreshold Selection Algorithm. In Sect. 2, the adopted system model is presented as well as the assumptions we made. Section 3 describes in details our proposed antenna selection algorithm, we present the simulations we did in Sect. 4 and finally, the last section wraps up the paper.
2 System Model We consider a massive MIMO system operating in time division duplex (TDD) where the base station has Nt transmitting antennas, Ns radio frequency (RF) chains where Ns < Nt and serving K user equipments (UEs) simultaneously. The wireless communication channel between the base station and UEs is assumed to be a Rayleigh fading one. i.e. the channel is characterized by a matrix H described as follows: ⎛
⎞ h 11 · · · h 1Nt ⎜ ⎟ H = ⎝ ... . . . ... ⎠ h K 1 · · · h K Nt
(1)
Where h i j is the complex fading coefficient between the jth transmitting antenna and the ith user equipment, its magnitude follows the Rayleigh distribution and its phase is uniformly distributed over [−π, π ]. Knowledge of the channel is acquired through pilot signals sent by UEs during the uplink phase. Since there are only Ns radio frequency chains, NNst rounds are needed for a complete acquisition of one channel realization. For the sake of simplicity, we assume that during the NNst rounds, the wireless channel stays almost unchanged. Furthermore, we assume the acquired channel state information to be perfect for an optimal antenna selection. The following figure illustrates well the massive MIMO system described above (Fig. 1): In the following section, we describe in details our antenna selection algorithm which encompasses two main phases.
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Fig. 1 Massive MIMO system with antenna selection
3 Proposed Model 3.1 Training Phase During this phase, the base station collects a sufficient number of channel state realizations (2.103 CSI samples in our case) from which the algorithm will derive its future decisions. After collection, each CSI sample is examined to derive its best antenna subset in term of the achieved spectral efficiency (SE) and only the subset that offers the maximum SE is kept. In this phase, performance is scarified in order to form a benchmark based on which the best antenna subset will be derived from upcoming channel realizations. At the end of this phase, we get the best antenna combinations as well as their corresponding achieved SE values. In addition, from the training dataset, we extract the n most occurring antenna combinations against which new channel realizations will be evaluated.
3.2 Decision Making Phase As depicted in the figure below, the algorithm looks like a decision tree with a yes or no question. From the information acquired during the training phase and more specifically the vector of achieved SEs, the maximum SE value will serve as a threshold for the evaluation of new CSI inputs. The purpose of the training phase is firstly the foundation of a benchmark and secondly extracting the n antenna combinations that have mostly occurred in this phase. With such information, the number
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Fig. 2 Our proposed Antenna Selection algorithm
Nt which Ns is a very large number in the case of massive MIMO systems to only n which is in the order of 5, 10, 20. The following section provides the detailed steps followed to get the best antenna combination for new channel realization (Fig. 2).
of antenna combinations to evaluate will dramatically be reduced from
Step 1: For a new given channel matrix H, let H min 1 , H min 2 , . . . . . . . . . , H min n be the n antenna combinations deduced from the training dataset where n max S E T r ai ni ng− phase then stop and declare H min K as being the best antenna subset for that CSI input. In addition, we update the S E T r ai ni ng− phase vector by deleting its minimum value and inserting the current SE value. i.e. S E m at its end, otherwise, store S E m and move to the next antenna combination: H min m+1 . If required, repeat the same process until an optimal antenna subset is found. In the worst case, we will have to evaluate all the n antenna combinations and then choose the one with the maximum SE value.
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4 Simulations We evaluated our proposed algorithm against the best but the heaviest selection scheme based on exhaustive search (ES) as well as random selection (RS). As a simulation environment, MATLAB and Octave were used interchangeably. As training data, we used m = 104 CSI samples and for evaluation, we used 400 CSI samples. The massive MIMO system we simulated has the following parameters: Nt = 16 antennas, Ns = 10 radio frequency chains and K = 8 users, we used spectral efficiency as a metric. As explained above, for new CSI inputs, only n antenna combinations will be evaluated. To prove the performance and accuracy of our algorithm, we ran it for three different values of n which are: n = 5,n = 10 and n = 20. We 16 Nt = can see clearly that n is too small compared to = 8008. In Fig. 3, Ns 10 the cumulative distribution function (CDF) plot compares our algorithm against the two aforementioned selection schemes. For random selection, as columns are being selected randomly, there is a chance Nt 1 = 8008 to get the best antenna combination. As depicted in the figure, of 1/ Ns RS performance fluctuates between acceptable SE values and low ones making its decisions unpredictable and its performance unsteady. As far as our algorithm is concerned and given the n most occurring antenna combinations, there are 1/n chances for OTSA to approach ESperformance and as depicted on the figure, approaching ES performance is always guaranteed regardless the value of n and with less computational complexity. Furthermore, as our algorithm comprises a training as well as a decision making phase, it would be fair to compare it with machine learning (ML) based antenna selection schemes. Authors of [7, 8] have proposed two MLbased antenna selection schemes for MIMO systems using
Fig. 3 CDF plot of system capacity for various values of n
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the kNN and the SVM algorithms, the training phase almost consists of the same procedures as in our model i.e. evaluating all possible combinations and preserving the best one with respect to a key performance indicator (KPI), the only difference that exists is the labeling of training data. After this latter operation is completed, new inputs can be evaluated using the built model. For kNN, it computes CSI Nt Euclidean distances and bases its decisions only on the k smallest ones. Ns Nt Computing is relatively acceptable in the case of MIMO systems as the Ns number of antenna does not exceed eight, but it becomes prohibitive in the case of massive ones. Moreover, the choice of k is crucial as different values of it can lead Nt to different outcomes. Regarding SVM, binary classification was used i.e. Ns binary classifications are required before finding out the class of the best antenna combination, it can be clearly stated that such model cannot be scaled to serve antenna selection in massive MIMO systems.
5 Conclusion Antenna selection is a promising solution for the design of less complicated but optimal multiantenna systems. There are many practical scenarios where it could be of good use, the trigger of such solution might be the decrease of the traffic load whether due to users’ departure or users moving to an idle state. As in the implementation described in Sect. 2, a good system capacity is achieved only by mean of a subsystem. In this paper, the problem of antenna selection was formulated and treated through our proposed algorithm. Our aim was the suggestion of a scheme that makes a tradeoff between performance and complexity i.e. an algorithm that achieves a good performance compared to that of the exhaustive search selection schemes but with low implementation complexity.
References 1. Gao X, Edfors O, Tufvesson F, Larsson EG (2015) Massive MIMO in real propagation environments: do all antennas contribute equally? IEEE Trans Commun 1–12. (early access articles) 2. Arash M, Yazdian E, sadegh Fazel M, Brante G, Imran M (2017) Employing antenna selection to improve energyefficiency in massive MIMO systems. arXiv:1701.00767 [cs.IT] 3. Ouyang C, Yang H (2018) Massive MIMO antenna selection: asymptotic upper capacity bound and partial CSI. arXiv:1812.06595 [eess.SP] 4. Jounga J, Sun S (2016) Twostep transmit antenna selection algorithms for massive MIMO. In: IEEE international conference on communications
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5. Gorokhov A, Gore D, Paulraj A (2003) Receive antenna selection for MIMO flatfading channels: theory and algorithms. IEEE Trans Inf Theor 6. GharaviAlkhansari M, Gershman AB (2004) Fast antenna subset selection in MIMO systems. IEEE Trans Sig Proces 7. Joung J (2016) Machine learningbased antenna selection in wireless communications. IEEE Commun Lett 8. Yao R, Zhang Y, Qi N, Tsiftsis TA (2018) Machine learningbased antenna selection in untrusted relay networks. arXiv:1812.10318v1 [eess.SP]
Compact Structure Design of Band Pass Filter Using Rectangular Resonator and Integrated Capacitor for Wireless Communications Systems A. Belmajdoub, M. Jorio, S. Bennani, and A. Lakhssassi
Abstract The main objective of this work is the study, design and simulation of a narrowband compact band pass filter using the technique of a rectangularshaped linear resonator closed on a capacitive load and based on magnetic coupling. This filter is intended for wireless communication systems (WiFi, Bluetooth, RFID, and ISM). It is attractive considering the substrate and tracks used and also in terms of size (7.485 × 8.18) mm2 . It also has good selectivity (300 MHz bandwidth) and low insertion loss (−0.01 dB). Keywords Band pass filter · Linear resonators · Narrow band
1 Introduction The demand for compact and reconfigurable wireless communication systems is growing. They are more and more complex, and at the same time, they must be more and more economical with a reduced size. These constraints (cost, size, autonomy…) require designers to propose new technological solutions for RF circuits [1, 2]. The RF filter, especially band pass, remains a device that occupies a large area in communications systems (WiFi, Bluetooth, RFID, and ISM) and others. They must be easily integrated, and reducing their size is an important research topic in this context [1, 2]. Several research works have proposed techniques to reduce filter size while keeping good electrical performance. We can mention: filters with coupled lines [3– 6], with linear resonators [7–9], with defected microstrip structure (DMS) [10–12] and with defected ground structure (DGS) [3, 13, 14].
A. Belmajdoub (B) · M. Jorio · S. Bennani SIGER Laboratory, USMBA, FST, Fez, Morocco email: [email protected] A. Lakhssassi Laboratory of Advanced Microsystems Engineering, University of Quebec, Outaouais, Canada © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_8
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In this paper, we are interested in the linear resonators technique, which offers the possibility of having compact structures that can be integrated in embedded systems. As part of this work, we will present the design methodology and steps to follow to obtain the filtering structure corresponding to the specifications below. • • • •
2.4 GHz center frequency 300 MHz bandwidth RT6010 substrate type Copper metallization type
2 Design of Band Pass Filter Based on Linear Resonator The design of linear resonators filters is based on two steps: the first one is the design of the resonator at 2.4 GHz with a good unloaded quality factor, and the second is to associate two resonators of the same type [1, 2].
2.1 Design of a 2.4 GHz Resonator First, we determine the implementation technology to obtain the desired resonance frequency. Our choice is microstrip technology. It allows us to have a filter with a shape of a printed circuit board. Figure 1 shows the geometric of this technology. The dimensions of the metal track are determined using the CST Microwave software (Table 1). Fig. 1 Structure of the line resonator
Table 1 Dimensions of the metal track
Parameters
Values (mm)
Wr
1.17
Lr
19.57
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Parameters
Values (mm)
L1
7.285
L2
3
g
1
Fig. 2 Metal track: (a) simple shape and (b) rectangular shape in open loop
Fig. 3 Simulated S11 and S21 parameters of proposed rectangular openloop resonator
Once the geometrical dimensions of the metal track are determined (Table 2), it is folded on itself in order to have a rectangular openloop resonator (Fig. 2). The following figure shows the simulation results performed using the CST Microwave software. From the obtained simulation results, we can see that the designed resonator operates far from the desired frequency (2.4 GHz) fixed by the specifications. This frequency shift is explained by the electromagnetic coupling induced by the meander obtained during folding. To lower the resonance frequency from 9 GHz (Fig. 3) to 2.4 GHz, we have inserted a capacitive test load at the resonator gap. The principle used is to vary the value of the capacitance to shift the resonance frequency to 2.4 GHz with a high quality factor. Figure 4 shows the configuration of the weakly coupled resonator at the input and the output. To calculate the unloaded quality factor, we use the following relation [1, 2]: Q0 =
f0 Q Or Q = 1 − S21 ( f 0 ) f
(1)
With S21 is transmission coefficient module (in linear scale) of the resonator at the resonance frequency (f0 ). This parameter must not exceed −20 dB to make unload
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Fig. 4 Configuration of the resonator with weak coupling to the input and the output
Table 3 Simulation results of a closed rectangular resonator on a capacitive load
Capacitor load
f0 (GHz)
Q0
1
2.539
136.147
1.1
2.443
131.376
1.15
2.4
128.55
1.2
2.356
126.59
1.3
2.278
121.8
quality factor Q 0 quasi equal to loaded one Q and f the bandwidth at −3 dB. Table 3 summarizes the results obtained for different values of C. From the analysis of the table, it is clear that the 1.15 pF capacity offers the possibility of having a resonator at 2.4 GHz with a good unloaded quality factor Q 0 equal to 128.55. This value is in the interval values corresponding to microstrip filters [15]. The following table shows the characteristics of the designed resonator. The next step is the codesign of two identical resonators to size the filter and set its bandwidth.
2.2 Codesign of Two Resonators of the Same Type This part is based mainly on the study of the magnetic coupling between the resonators constituting the filter. The principle used is to vary the distance d interresonators in order to fix the bandwidth of the filter as well as its electrical performance. Figure 5 shows the configuration of the proposed filter under CST Microwave software. Figures 6 and 7 present respectively simulation results of S11 and S21 for different values of d (Table 4). From the analysis of this table, we can see that for d value of 0.18 mm, the transmission coefficient decrease, which generates minimum losses −0.01 dB, an adaptation level of −52.73 dB and a bandwidth of 300 MHz, which corresponds to the specifications (Fig. 8). In order to validate our choice (magnetic coupling), we made a simulation with the same dimensions of the filter and for the same retained value of d by using an electrical coupling. Through the comparison between the two
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Fig. 5 Configuration of the proposed filter
Fig. 6 S11 parameters for different values of d
Fig. 7 S21 parameters for different values of d
couplings (Table 5), it can be seen that the magnetic one is much stronger (good level of adaptation with minimum losses in the bandwidth). The following table summarizes the performance of the filter designed in comparison with other recent research works (Table 6).
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Table 4 Simulation results for the proposed filter d (mm)
f 0 (GHz)
S11 (dB)
S21 (dB)
BW (GHz)
0.1
2.4
−17.4
−0.05
0.338
0.12
2.4
−21.6
−0.055
0.334
0.14
2.4
−21.98
−0.048
0.330
0.16
2.4
−28.65
−0.02
0.314
0.18
2.4
−52.73
−0.01
0.3
0.2
2.4
−32.52
−0.01
0.291
0.3
2.4
−16.22
−0.15
0.257
0.4
2.4
−11.71
−0.59
0.228
Fig. 8 S Parameters results of the proposed filter at d = 0.18 mm
Table 5 Simulation results comparison between magnetic and electrical coupling f 0 (GHz)
S11 (dB)
S21 (dB)
BW (GHz)
Magnetic coupling
2.4
−52.73
−0.01
0.3
Electrical coupling
2.4
−11.7
−0.4
0.256
Table 6 Performance comparison with previous works Ref.
F0 (GHz)
BW (GHz)
S21 (dB)
Size (mm2 )
[3]
2.4
0.344
0.429
43.5 × 34.3
[7]
2.4
0.213
0.1
9 × 7.1
[8]
2.4
0.29
0.23
9.4 × 23.1
[16]
2.4
0.179
1.14
31 × 38
This work
2.4
0.3
0.01
7.485 × 8.18
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3 Conclusion This paper presented a compact structure design of a band pass filter implemented in microstrip technology for use in wireless communication systems. The proposed band pass filter consists of two identical closedloop rectangular resonators with a capacitive load inserted in the gap of the resonator and based on magnetic coupling. The designed filter has a very small size of 7.485 × 8.18 mm, selective (300 MHz) and low insertion losses (−0.01 dB) with a good adaptation level (−52.73 dB).
References 1. Hong JS, Lancaster MJ (2001) Microstrip filters for RF/microwave applications. Wiley series in microwave and optical engineering 2. Pozzar DM (2012) Microwave engineering, 3rd edn. Wiley 3. Belmajdoub A, Boutejdar A, ElAlami A, Bennani S, Jorio M (2019) Design and optimization of a new compact band pass filter using DGS technique and Ushaped resonators for WLAN applications. TELKOMNIKA J 17(3) 4. Lee HM, Tsai CM (2005) Improved coupledmicrostrip filter design using effective evenmode and oddmode characteristic impedances. IEEE Trans Microw Theor Tech 5. Rani P, Gupta S, Prasad RK (2014) Design & optimization of microstrip parallel coupled band pass filter at 20 GHz. Int J Adv Res Comput Eng Technol 6. Mondal P, Roy A, Moyra T, Parui SK (2012) New concept for designing of compact parallel coupled band pass filter. Int J Comput Appl 7. Belmajdoub A, Jorio M, Bennani S, ElAlami A, Boutejdar A (2019) Small integrated band pass filter using two identical closed rectangular resonators on a capacitive load for wireless communications systems. In: The 5th international conference on wireless technologies, embedded and intelligent systems 8. Belmajdoub A, El Alami A, Das S, Madhav CBTP, Bennani SD, Jorio M (2019) Design, optimization and realization of compact band pass filter using two identical square openloop resonators for wireless communications systems. Int J Instrum (JINST) 9. Hong JS, Lancaster M (1999) Aperturecoupled microstrip openloop resonators and their applications to the design of novel microstrip band pass filters. IEEE Trans Microw Theor 10. Zakaria Z, Mutalib MA (2014) Compact structure of bandpass filter integrated with Defected Microstrip Structure (DMS) for wideband applications. In: The 8th european conference on antennas and propagation (EuCAP) 11. Chen L, Li YX, Wei F (2017) A compact quadband band pass filter based on defected microstrip structure. Frequenz J 12. Azizi S, ElHalaoui M (2018) Enhanced bandwidth of band pass filter using a defected microstrip structure for wideband applications. Int J Electr Comput Eng 13. Boutejdar A, Amzi M, Bennani SD (2017) Design and improvement of a compact bandpass filterusing DGS technique for WLAN and WiMAX applications. TELKOMNIKA 15 14. Boutejdar A, AbdelMonem Ibrahim A (2015) DGS resonator form compact filters. Microw RF 15. Cameron RJ, Mansour RR, Kudsia CM (2007) Theory and design of modern microwave filters and systems applications 16. Rahman MU, Park JD (2018) A compact triband band pass filter using two stubloaded dual mode resonators. Prog Electromagn Res 64:201–209
Embedded Implementation of HDR Image Algorithm Mohamed Sejai, Anass Mansouri, Saad Bennani Dosse, and Yassine Ruichek
Abstract In the autonomous vehicle, an image taken by the vehicle can’t be exposed properly due to vehicle movements and road constraints. Fortunately, an efficient and accurate system of multiple exposure fusion technique for creating images of high dynamicrange (HDR image) has come to solve this problem and provide a technical average to recover the lost information and add it via specialized software processing, but HDR has many disadvantage which include high calculation and increase in operational time. In this paper, the algorithm of HDR image is described and implemented in embedded platform to identify the more complex functions and aspect from a profiling analysis of the HDR software implementation. Keywords HDR image · Multiple exposure fusion · Embedded platforms · Profiling code
1 Introduction The term dynamic range is often used to describe the ratio between the brightest and darkest point in a given scene. The contrast of many natural scenes that is visible to a human observer cannot be captured in a single photography due to the limited dynamic range of the sensors found in modern digital cameras. In the field of transport M. Sejai (B) SIGER Laboratory, Faculty of Sciences and Technics Fez, Sidi Mohamed Ben Abdellah University, Fes, Morocco email: [email protected] A. Mansouri · S. Bennani Dosse National School of Applied Sciences Fez, Sidi Mohamed Ben Abdellah University, Fes, Morocco email: [email protected] S. Bennani Dosse email: [email protected] Y. Ruichek BelfortMontbéliard University of Technology, Montbéliard, France email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_9
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Fig. 1 Image quality deteriorates in a high dynamic range environment
a image taken from a moving vehicle can’t be correctly exposed to all the luminous intensity a certain scene this is often due to the external climate, the intensity of the light, the headlights of the oncoming vehicles, shadow of buildings and trees, low lighting [1] as shown in Fig. 1. For this reason, the interest of high dynamic range (HDR) imaging has recently been increasing. A common approach to capturing such HDR scenes is to capture a low dynamic range (LDR) image of multiple differently exposed images [2] and combine them together into a single HDR result Then the HDR image is compressed to the LDR of given display devices. In the last few years there have also been many researches to alleviate the ghost effects in the HDR generation/compression methods. For example, weighted variance of pixel presented in work [3] or the local entropy energy used in work [4] is used for the fusion with less ghost effects. But they fail to remove the ghost when there is a moving object with fewer textures. Khan et al. [5] used a motion detection algorithm to detect the pixels in the moving object and manipulate them accordingly. But this approach requires many computations and requires the assumption that the number of pixels in the background is larger than that of the object. There is also a patch based method [6, 7] where the Poisson blending is performed for removing the boundary discontinuity. This removes the ghost successfully, but some visible seams remain if the difference of brightness among the neighboring patch is large.
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There is a lot of work to improve the processing time of the HDR algorithm. In this work we implement the algorithm of the multiple exposure fusion images in embedded platform based on the proposed algorithm in work [8], and by using full profiling of the software implementation of HDR to analyze and understand its complexity and its major functionalities. This paper is structured as follows; we introduce the combining of the multiple exposure image algorithm to generate the HDR image in Sect. 2. In Sect. 3, we show some experimental results to confirm the validity of our work; we conclude this paper in Sect. 4.
2 Overview of Multiexposure Image Fusion Multiexposure image fusion method fuses multiexposure images generated from a multiple image, the procedure for the HDRI acquisition is informally described by following steps:
2.1 Synthesis of Latent Images The algorithm takes as input a stack of images with different levels of exposure of a dynamic scene and then selects a reference image which is normally the bestexposed image among the image stack. For each image of the stack synthesizes a latent image as shown in Fig. 2. The reference R image is on the left, the source S image is on the right and we want to create a latent L image in the center as presented in work [9], therefore the forms of the objects are in the latent image like the forms of the objects in R except they have the luminance range of S as illustrated in Fig. 3. The L image is first initialized by applying an Intensity Mapping Functions (IMF) represented by τ to the R image, τ represents the way in the pixel values change from the S image to the R image. τ is initialized by using the intensity histograms of the images then is refined at the same time as L update. Thereafter we must find the matches between L and S using the generalize algorithm patch match [7]. Pi is the patch of size P x P centered at pixel i, the exponent R, L and S indicates the images where it belongs and U(i) is called the Nearest Neighbor Field (NNF). NNF indicates the location of the pixels from the L image to the S image whose correspondence has been found. The histogram of an image, the histogram of the second image is necessary and sufficient to determine the intensity mapping function. Each intensity I2 in the second image almost corresponds to intensity I1 in the first image: I1 = τ(I2 ), because they correspond to the same points of the scene: H1 (τ (I2 )) = H2 (I2 )
(1)
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Fig. 2 Global architecture of latent image generation algorithm
Fig. 3 Generation of a latent image from a reference and source image
H1 et H2 the cumulative histograms of the image 1 and 2 respectively. This implies that the intensity mapping function and the first histogram determine the second histogram by using the following equation: τ(x) = H2−1 (H1 (x))
(2)
In order to apply a best optimization and to better synthesize the deleted zones, we use a multiscale approach based on the notion of pyramid images. A Gaussian pyramid can model the image at different resolutions; each level will have a low resolution compared to the previous level.
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2.2 The Weight Map The generated latent images can be merged into a single HDR image with more details, from four images under different input exposure, four latent images are synthesized and merged to obtain an HDR image as illustrate in Fig. 4. The conventional exposure fusion creates the output as a weighted sum of multiple exposure images, where the weights are designed to reflect the quality of the image, this process is guided by a set of quality measurement [10] of the image at each pixel, to generate a scalar weight mapping, this technical is also based on the pyramid decomposition of the images: contrast, saturation and wellexposedness. Contrast is an intrinsic property of an image that quantifies the difference in brightness between the light and dark parts of the images, we apply a Laplacian filter to the grayscale version of each image, then we take the absolute value of the filter response, and saturation is calculated by the standard deviation in channel R, channel G and channel B at each pixel. Images undergo a longer exposure, the resulting colors become unsaturated, saturated colors are desirable because they make the image clearer. Finally the raw intensities in a channel can reveal us as a pixel is exposed; the measure of wellexposedness gives more weight of each intensity by using a curved Gauss. For each pixel, the information of the different measurements is combined in a scalar weight plane with a multiplication. The product is on a linear combination in order to apply all the qualities defined by the measurements at once. The weighted map for each image is calculated by using these measures as: Wi j,k = (Ci j,k )wc × (Si j,k )ws × (E i j,k )w E
(3)
where ij is the pixel position and k means the kth exposure image, C = Contrast, S = Saturation and E = wellExposedness, and wc , ws and w E mean corresponding weighting factors (wc , ws and w E m 0,1), to obtain a coherent result we normalize the weight values of the four latent images. Then, blending is performed at multiple resolutions using pyramidal image decomposition [11].
Fig. 4 Block diagram of the HDR fusion Algorithm
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Fig. 5 Four input LDR images in different exposure times, result of the HDR image
3 Simulation Results The algorithm is implemented in two platforms: firstly in PC on an Intel Core i3 5005U CPU @ 2 GHz, 4.00 GB RAM, under Windows operating system. Secondly in Raspberry Pi 3 platform with CortexA53 (ARMv8) 64bit Soc @ 1.4 GHz 1 GB LPDDR2 SDRAM, under Linux Operating System these two implementations compare the result of generated HDR images. These images represent overexposed and underexposed scenes in various lighting conditions, from very dark to very bright.
3.1 Simulation Results in PC We evaluate the algorithm with various multi exposure image sequences. Figure 5 shows the result of this method, generates high quality LDR image without ghost because of accurate estimation of the weight function. We create the HDRI from the four images.
3.2 Simulation Results in Embedded Platform One of the objectives of this part is to realize and implement the HDR fusion algorithm in the Raspberry pi 3 platform, using C programming language. We create the HDRI from the four images, and the results are illustrated in Fig. 6.
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Fig. 6 Result of HDR image
Inder PC
Fig. 7 The profile results of the program on PC
3.3 Profiling Results Profiling technical consists of analyzing the execution of a program in order to know its behavior at runtime, to determine the computation time of the parts of the code and for knowing the functions which consume the most resources and present a high complexity in computation time. The profiling result of the HDR fusion algorithm was performed on two different platforms first we tested the performance of the program on PC and then we implemented it on the Raspberry embedded card. The application code is profiled (Figs. 7 and 8) using the Gprof tool to determine which function consumes the most resources. The profiling result shows that the GaussDown function consumes the most time when running the application followed by the filterGaussZero function. We note that the program takes a long time to execute because of its iterative structure and the different memory allocations that it performs. The execution time on the Raspberry Pi 3 platform is even more important because of the hardware constraints of the platform.
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Inder the Raspberry card
Fig. 8 Profiling result of the program on the Raspberry
4 Conclusion and Future Work This work can extend to a test and validation of the HDR algorithm to have an HDR image at the output from four images at the input under different exposure times. We have profiling the HDR algorithm based on two different platforms. The profiling results make it possible to identify the parts which require a high computation time. Faced with the increasing complexity of image processing algorithm used we aim to: Parallelize the processing of the functions that consume the most time, implement these functions on an embedded platform to have more speed during calculations and propose a study foe the different possibilities of hardware/software implementation.
References 1. Mlik J (2011) Rcovring high dynamic range radiance map from photographs 2. Rubinstrein R (2013) Fusion of differently exposed images 3. Reinhard E, Ward G, Debevec P, Pattanik S (2005) High dynamic range imaging: acquisition, display, and image based lighting. Morgan Kaufmann 4. Jacobs K, Loscos C, Ward G (2008) Automatic high dynamic range image generation for dynamic scenes. IEEE Comput Graph Appl 28:84–93 5. Khan EA, Akyuz AO, Reinhard E (2006) Robust generation of high dynamic range images. In: Proceedings international conference image processing, pp 2005–2008, October 2006 6. Gallo O, Gelfand N, Chen W, Tico M, Pulli K (2009) Artifactfree high dynamic range imaging. In: Proceeding international conference computational photography, April 2009 7. Barnes Connelly (2010) Eli Shechtman. The generalized patchmatch correspondence algorithm, Dan B Goldman and Adam Finkelstein 8. Li Y, Qiao Y, Ruichek Y (2015) Multiframe based high dynamic range monocular vision system for advanced driver assistance system, October 2015
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9. Hu J, Gallo O, Pulli K, Sun X (2013) HDR deghosting: how to deal with saturation 10. Fakhfakh N (2011) Détection et localisation tridimensionnelle par stéréovision d’objet en mouvement dans des environnements complexe 11. Burt P, Adelson T (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun COM31: 532–540
Density, Speed and Direction Aware GPSR Protocol for VANETs Amina Bengag, Asmae Bengag, and Mohamed Elboukhari
Abstract Vehicular Ad Hoc Networks (VANETs) comprises vehicles equipped with wireless transceivers. These vehicles could exchange information directly via vehicletovehicle communication (V2V) without the need of implementing any preexisting infrastructure. However, Routing in VANET network is not the same as routing in Mobile Ad hoc Network (MANET), due to the specific features of VANET like the high dynamic topology caused by the high speed of vehicles. Hence, many VANET routing schemes have already been proposed, but they are not efficient in terms of Packet Delivery Rate (PDR) and throughput or they have a high routing overhead. In this paper, a new positionbased routing for VANET has been proposed that is efficient in terms of PDR, throughput and has low overhead. Moreover, the proposed protocol named DVAGPSR is based upon the classical GPSR routing by taking into account three new metrics in addition to the position of vehicles. Proper vehicle could be selected as a relaying node based on a weight function that includes the proposed metrics, like the angle direction and the speed variation between the sender and the receiver, the density of the next hop and the current location of the destination vehicle. Simulation studies prove that the proposed protocol maximizes the throughput, increases the PDR and decreases routing overhead. Keywords VANET · Routing protocol · GPSR · DVAGPSR · Angle direction · Speed · Density · Positionbased routing
A. Bengag (B) · A. Bengag · M. Elboukhari MATSI Laboratory, ESTO, University Mohamed 1er Oujda, Oujda, Morocco email: [email protected] A. Bengag email: [email protected] M. Elboukhari email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_10
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1 Introduction VANETs consist of a set of intelligent vehicles equipped with an onboard unit (OBU) to exchange information directly with other vehicles by using vehicletovehicle communication (V2V) or via vehicletoinfrastructure communication (V2I) by using roadside units installed on roads. However, the communication in VANETS that is ensured by routing protocol still suffer from many issues due to the unique features of vehicular networks like the frequently link breakage caused by the high speed of vehicles. Hence, a huge number of researchers have taken a big attention on developing a new enhanced routing protocol that take into account all VAENTs features. According to [1], we can split routing protocols in VANETs into four categories in case of V2V communication. The category of routing protocols based on the position of vehicles is one of the most widely discussed and used in case of VANETs scenarios thanks to their high packet delivery rate and less control overhead [2, 3]. In the literature, many routing protocols based on GPSR have been proposed like MVGPSR, EGPSR and GPSR2P [4–6]. In this paper, we suggest a novel routing protocol based on the position of vehicles by taking into consideration some new metrics that enhance the efficiency of our protocol. These metrics are the density, the variation speed the angle direction and the distance between the target node and all neighbors of the source node. This routing protocol called DensityVelocityAwareGPSR (DVAGPSR) based on the classical GPSR protocol proves its efficiency in terms of PDR, average throughput and routing overhead in the network in the proposed highway scenario. The organization of our paper is as follows. The traditional GPSR routing protocol is presented in Sect. 2; in Sect. 3, we describe the novel strategy that enhance the classical GPSR for VANET and its benefits. Section 4 clarifies the performance evaluation of DVAGPSR that will be compared to the classical GPSR. The conclusion and some future works will be presented in Sect. 5.
2 Overview of the Classical GPSR Routing GPSR [7] is the most wellknown routing protocol based on the position of vehicles. Basically, a source vehicle in GPSR utilizes two techniques for transmitting data packets. The greedy forwarding technique, by transmitting data to the vehicle that has the shortest distance from the destination or the perimeter forwarding technique. In fact, when the source node has no neighbor near to the target node than itself (local maximum problem) the greedy forwarding approach fails. Hence, the perimeter forwarding technique will be applied that is based on the right hand rule. The original GPSR is based only on the location information to select the next relaying hop that could lead to a wrong decision. Additionally, by applying the greedy forwarding technique the number of hops from source to the target node
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will be reduced. However, this procedure ignores the quality of the connection link. Moreover, for each link failure a novel route has to be regenerated consequently the transmission process will be postponed until another relaying node is found. As a result, the routing overhead will be dramatically increased that decreases the PDR and throughput.
3 DVAGPSR Routing Protocol Our proposed routing protocol adopts that all vehicles in VANET are equipped with a GPS device to get the accurate position of vehicles, and with wireless transceivers for exchanging traffic route information. The proposed protocol named DVAGPSR is based upon the classical GPSR routing by taking into account three other parameters in addition to the position of vehicles. The next paragraph describes each metric, the formula to calculate it and its benefits.
3.1 NextHop Selection Procedure The process of selecting the next relaying node is very important and is composed of three steps. The first step applied by the source node, by gathering the mobility parameters: the location and the velocity of its neighbors. The second step is using the gathered parameters to calculate the angle direction (Fig. 1), the speed variation between the sender and the receiver in addition to the number of neighbors of the current node. The calculation process is explained below: 1. To calculate the angle direction ϕ between each next hop candidates and the destination node, we use the following formula (1). ϕ AB = cos −1
((AV elocit y.x ∗ BV elocit y.x) + (AV elocit y.y ∗ BV elocit y.y)) AV elocit y.x 2 + BV elocit y.x 2 ∗ AV elocit y.y 2 + BV elocit y.y 2
Fig. 1 The angle direction ϕ
(1)
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In the formula (1): • AVelocity is the velocity of the next hop candidate • BVelocity is the velocity of the destination. The rational between the concepts of the angle direction is to maintain the connection between vehicles as long as possible by choosing the small value of all calculated ϕid . 2. To calculate the distance between the node that has the transmitted packet and the target node, we use formula (2).
D AB =
(y A − y B )2 + (x A − x B )2
(2)
In the formula (2): • The pair (x A , y A ) designates the neighbor vehicle position called A • The pair (x B , y B ) means the position of the target node. 3. To calculate the speed variation, we use formula (3).
S AB = S A − S B 
(3)
In formula (3): • SA is the neighbor node speed called A • SB signifies the speed of the target node. 4. The third step consists in using the beforehand calculated metrics to formulate a weighted function (4). This function will be used to specify the link weight for every neighbor of the current node that has the transmitted packet. L W F = α ∗ Did + β ∗
1 densit yi
+ θ ∗ Sid + γ ∗ ϕid
(4)
In formula (4): • densityi is the number of neighbors for the next hop candidate i. this metrics will be used to determine the connectivity mode in each path. • α, β, θ and γ are the weighting factors for each metrics. • α + β + θ + γ = 1.
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3.2 The Benefits of DVAGPSR The problem of local maximum mentioned in the previous section, occurs in most of cases by applying the classical GPSR caused by the void area issue or the high speed of vehicles. In fact, in our proposed routing we take into consideration the density parameter in the next hop selection process. By implicating this parameter, the vehicle that has the high density (high number of neighbors) increases the probability of being chosen as a relaying node, hence the void area problem will be reduced. The issue of link breakage caused by the high speed is resolved in DVAGPSR, by using the variation speed calculated previously in the process of selecting the next hop. Therefore, vehicle that has almost the same speed as the destination will be selected as a next hop that enhance the connection lifetime. Moreover, this technique ensure a longest possible duration of communication between vehicles. Hence, the current vehicle will choose the neighbor that has the lowest value of link weight function (LWF) as a nexthop to get suitable results.
4 Simulation Results and Discussion In this section, the performance of DVAGPSR will be evaluated compared to the classical GPSR in terms of PDR, overhead and average throughput by varying the destination number. We have used NS3 [8] as network simulator and SUMO [9] as traffic simulator. To evaluate the performance, we are based on a highway VANET scenario of 300 m * 1500 m with four lanes in two opposite directions that was created and generated by using SUMO where vehicles move following the real traffic rules. The other simulation parameters are presented in Table 1. Packet Delivery Ratio (PDR): Fig. 2 shows the results for GPSR and DVAGPSR protocols in terms of PDR. The PDR for both protocols increases when the number of destination vehicles increases but is very high for the proposed protocols up to 65% while for GPSR does not exceed 57%. Table 1 Simulation parameters
Parameters
Measures
Number of destination nodes
1, 5, 10
Vehicles number
60
Vehicles speed
Max: 30 m/s
Simulation time
200 s
Mac protocol
IEEE 802.11p
Transmission range
145 m
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Fig. 2 The PDR vs number of destination vehicles
Fig. 3 The average throughput vs number of destination vehicles
Average Throughput: Fig. 3 shows the results for GPSR and DVAGPSR protocols in terms of the average throughput. The values of throughput increases for both protocols when the number of destination vehicles increases. However, the throughput is increased up to 13.8 Kbps for DVAGPSR while for the classical GPSR the values do not exceed 11.9 Kbps. Routing Control Overhead: The graph in Fig. 4 presents the impact of varying the destination number on the routing overhead. The values of overhead for DVAGPSR is very low comparing to the classical GPSR and does not exceed 27.5%.
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Fig. 4 The routing overhead vs number of destination vehicles
5 Conclusion In this work, we have suggested a novel routing protocol based on the position of vehicles for VANETs called DVAGPSR. In this routing, the procedure of selecting the next hoprelaying node is based on three new metrics: density, variation speed and angle direction to be more efficient for VANETs scenarios. The simulation results for a highway scenario, demonstrates that DVAGPSR outperforms the classical GPSR in case of PDR, average throughput, and has low values in case of routing overhead. As future works, we aim to evaluate our protocol in more complex scenarios. Besides, we look forward to take into account more impacting parameters to enhance the proposed protocol in order to support urban environment.
References 1. Bengag A, El Boukhari M (2018) Classification and comparison of routing protocols in VANETs. In: 2018 international conference on intelligent systems and computer vision (ISCV 2018), May, vol 2018 2. Amina B (2018) Performance evaluation of VANETs routing protocols using SUMO and NS3. In: 2018 IEEE 5th international congress on information science and technology, pp 525–530 3. Setiabudi A, Pratiwi AA, Perdana D, Sari RF (2016) Performance comparison of GPSR and ZRP routing protocols in VANET environment. In: 2016 IEEE region 10 symposium (TENSYMP), pp 42–47 4. Tu H, Peng L, Li H, Liu F (2014) GSPRMV: a routing protocol based on motion vector for VANET. In: International conference on signal processing proceedings (ICSP) 5. Bouras C, Kapoulas V, Stathopoulos N, Gkamas A (2016) Mechanisms for enhancing the performance of routing protocols in VANETs
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6. Zaimi I, Houssaini, ZS, Boushaba A, Oumsis M (2016) An improved GPSR protocol to enhance the video quality transmission over vehicular ad hoc networks. In: 2016 Proceedings of the International Conference of Wireless Networks and Mobile Communication (WINCOM 2016) Green Communication Network, no Urac 29, pp 146–153 7. Karp, B, Kung, H (2000) GPSR: greedy perimeter stateless routing for wireless networks. In: ACM MobiCom (MobiCom), pp 243–254 8. Manual R et al (2011, January) ns3 tutorial. System, pp 1–46 9. Behrisch M, Bieker L, Erdmann J, Krajzewicz D (2011) SUMO – simulation of urban mobility. In: Iaria, no c, pp 55–60
IoTScalC: A Based Cloud Computing Collaboration Solution for Scalability Issue in IoT Networks Mohamed Nabil Bahiri, Abdellah Zyane, and Abdelilah Ghammaz
Abstract Cloud computing and Internet of Things (IoT) are technologies that provide services to all kind of consumers, allowing any authorized information to be available and providing smart decision automatically. Therefore, ensuring scalability with an acceptable quality of service (QoS) metrics through servicelevel agreements (SLA) is a challenge due the massive grows of the connected devices. In this paper, we will start by presenting our vision of the scalability problem in the IoT networks. Then, we will explain our new proposed collaborative solution based on cloud computing approach for the scalability problem according to ETSI architecture in IoT networks. The objective is to propose a collaboration solution integrating cloud computing, with the purpose of dealing with the scalability issue in IoT networks, by maximizing the number of satisfied requests while keeping the Quality of Service at a good level. Keywords Internet of Things · Machine to machine · Scalability · Autonomic computing · Cloud computing · MAPEK cycle · Middleware · Monitoring
1 Introduction Recently, IoT perspective has become progressively relevant, along with the number of connected objects in every possible field. Particularly, the industrial interest has rapidly increased, such as the smart city application domain. In addition, more research activities are made, leading to an exponential growth of the number of M. N. Bahiri · A. Ghammaz L.E.S.T, FSTG, Cadi Ayyad University, Marrakech, Morocco email: [email protected] A. Ghammaz email: [email protected] M. N. Bahiri · A. Zyane (B) S.A.R.S. Team—ENSA Safi, Cadi Ayyad University, Safi, Morocco email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_11
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IoT devices. In addition, based on ETSI (European Telecommunications Standards Institute) architecture, IoT devices and sensors produce streams of data from every possible location. Consequently, scalability is emerging as one of the key issues for IoT development and exploitation, which is, according to our vision, the ability to automatically maintain acceptable level of systemproduction performances in case of overload caused by applications and/or data [1, 2]. Our objective is to maximize the number of satisfied requests from the sensor layer towards IoT networks, while maintaining the system performances at acceptable levels in terms of QoS (Quality of service) metrics, insuring scalability. In order to do that, based on dynamic adaptation using symptoms detection triggered by the OM2M (Open Source platform for M2M communication) platform, our middleware will make automatic decisions to stabilize the system. The paper is organized as follow; Sect. 2 describes the problematic, explores some IoT application domains and presents related works. The third section details the proposed solution, though Sect. 4 presents and analyzes numerical results. Finally, Sect. 5 concludes the paper.
2 Problematic In the beginning of the Internet of things era, many countries started investing in order to make smart cities. Smart cities are equipped with all modern facilities basically depending on information and communication technology (ICT) [3]. In India for example, the government are expecting to invest around 15 Billion dollars, in order to make smart cities by 2020 [4]. This type of investments is promising. In Barcelona, an investment of 10 Million dollars led to the deployment of three major projects which are: smart parking project, Smart lights and smart gardening, bringing a profit of 145 million dollars every year, over the last 10 years. These days, IoT is every field aim. The demand on connected objects is growing massively. Hence, Cisco is expecting exponential growth, from 7.2 billion in 2012 to 50 Billion of connected objects in 2020. This will make IoT networks highly requested and extremely active motivated by the enormous number of connected objects/users/applications and/or by the massive volume of data, causing overloads [1, 2]. In the literature, since 2010, the scalability problem in IoT networks is mostly mentioned, but not enough analyzed. Furthermore, there is a lack of research tackling the scalability issue in IoT networks [5–7, 10]. First, no research has dealt with the scalability problem in IoT networks while respecting a standard architecture. On the other hand, if we consider the most popular architectural standards ETSI or ITU, we can say that existing solutions focus more on the physical or network layers without paying any particular attention to the most important layers. The middleware and application layers are the most affected by the overload. Based on our vision for the scalability issue, already presented in [1, 2], scalability is classified in terms of consumers Overloads and/or data Overloads. Indeed, IoT
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interconnect an enormous number of users and devices all over the world, relaying on applications and services. The excessive increase of number of consumers causes what we call “consumers overload”. On the other hand, the development of different embedded technologies increased the size of the generated or requested data with various and complex forms by the IoT objects. This results in increasingly complex requests causing what we call «data overload». Current network architectures are unable to deal with this drastic increase in load caused either by the growth in the number of users or by the increase in the size and complexity of the generated data [5–7]. Our previous works [1, 2] are focusing on the use of the middleware to improve scalability, in case of overload caused by a massive number of consumers. Based on several intelligent mechanisms, the middleware takes several decisions such as forwarding traffic to the platform, delaying the traffic, redirecting it to a virtualized platform or dropping it in case of need. Those decisions are taken base on the system state collected by the monitor inside the middleware. In this paper, we concentrate on consumer overload at the application layer of the ETSI architecture [1, 2]. That will affect the processing power of the OM2M platform, caused by the huge number of consumers giving or requesting information to/from different services. Moreover, Cloud computing capabilities are offering all kind of solutions to IoT issues, exclusively scalability. In addition, middleware is software situated between connected objects and platforms [2]. This middleware gives capabilities to developers to improve the IoT systems architecture. In a normal OM2M platform state, when a consumer’s overload occurs at the application layer, the platform starts queueing incoming traffic, increasing the response time (not respecting ITUT G.1010 recommendation for response time). Besides, at a certain level of overload, the platform starts dropping incoming traffic, decreasing the number of satisfied requests, and preventing it from scaling. In order to succeed in dealing with these issues, we aim to make our system scalable in the case of overload. Scaling will be achieved by maximizing the number of satisfied requests (decreasing loss rate), all by respecting the QoS metrics in the OM2M platform (RTT, CPU and RAM consumption). The proposed solution proposes a new way to manage the scalability problem by collaboration between middleware’s already designed according to the ETSI standard [1, 2], called default behavior in this paper. Also, the collaboration decision is based on the system state collected by the monitor inside each middleware. Also, in our solution, when an IoT system already designed according to ETSI standard [1, 2] becomes unable to follow a drastic increase in load, it requests and enters in collaboration with another similar system which has even more performance. To do so, we focuses on the monitoring component and the scalability issue, considering QoS metrics (RTT evolution, RAM consumption and CPU usage), using the middleware monitoring capabilities and cloud computing features [7–9].
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3 Proposed Solution 3.1 Proposed Architecture In this paper, we aim to provide ETSI compliant specification of the Middleware level for IoT networks with dynamic and autonomic scalabilityoriented capabilities, to satisfy a maximum of requests, without deteriorating QoS metrics (RTT, RAM and CPU) of our OM2M platform. To reach our objective, we use autonomic MAPEK cycle of the computing paradigm [11]. Our proposed architecture (see Fig. 1) is composed of two systems. As shown in Fig. 2, both systems are holding the same components which are: Injectors, middleware and platforms. – Injectors: Injectors are simulating realworld sensors, actioners, applications and users. It generates traffic requiring a response time under 4 s and loss rate less than 10%. – The middleware: The middleware is hosted in a physical machine, which composed of two main components: • Autonomic manager will assure the following tasks: (i) Monitor: collects RTT evolution, RAM consumption and CPU usage of the platform, then it sends collected metrics to the Complex Event Processing (CEP) to generate the adequate symptoms. (ii) Analyzer/Plan: generates plans based on analyzed symptoms. (iii) Executer: sends the policy that will be executed by the receiver, and the collaborative component. • Scalability Enhancer (SE): will hold the following components: (i) Traffic receiver: receives requests from the traffic generators. (ii) Collaboration Component (CC): Forwards requests to the OM2M platform, or redirects the requests to the other collaborative system.
Fig. 1 Hight level proposed architecture
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Fig. 2 Proposed system architecture
– OM2M Platform: it represents the ETSIcompliant OM2M open source platform, the destination for forwarded requests. This platform hosts various services satisfying Post or Get requests incoming from the Applications. Injectors generate requests characterized by number of injected requests, its type, its periodicity and the targeted service on the OM2M platform. Those generated requests will go through the middleware. Requests will be received, the Collaboration Component (CC), will apply the Executer policies either by forwarding all the requests automatically to the targeted service in the platform, or by redirecting a portion of the requests to the Other System. Our solution for the middleware layer uses an autonomic computing based on the cloud computing capabilities. Inside the middleware, we implemented an autonomic manager to take adaptive decisions dynamically without any shutdown of the system. These decisions are based on symptoms collected by the monitor hosted into the autonomic manager. Moreover, the proposed solution uses the concept of collaboration based on cloud computing, ensuring scalability, respecting QoS metrics (RTT evolution) and monitoring physical resources consumption (RAM consumption, CPU usage) of the physical machine hosting the middleware.
3.2 Proposed Mechanisms Our new approach objective is to maximize the number of satisfied requests in IoT networks using collaboration concept. In other words, scaling depends on the current
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Fig. 3 Traffic monitor (left) and collaboration components (right) algorithms
load considering QoS metrics (CPU, RAM and RTT). In this section, we will present our proposed mechanisms. In the traffic Monitor (see Fig. 3 left side), after receiving a window of five successive RTT, if all five RTTs are under 4 s, an acceptable symptom is generated; if all five RTTs are over 4 s a critical symptom is generated. The Collaboration Component (CC) algorithm (see Fig. 3. Right side) works as follow, after getting the loss rate (LossRate) and the RTT state (RTTState) of the system, (i) if one of them is at critical symptom of QoS, half of the traffic is forwarded to this systems OM2M platform, the other half will be redirected the collaborative system (The other system), (ii) if both QoS symptoms are at acceptable state, al the traffic will be forwarder to this systems OM2M platform. The traffic will be forwarder the OM2M or redirected the other platform based on the state of each system, so that the collaboration component (CC) will dynamically execute decisions in order to maximize the number of satisfied request while respecting the QoS metrics in terms of RTT (ITUT G.1010 Recommendation for RTT of 4000 ms and 30 s).
4 Numerical Results Pointing at the evaluation of our proposed solution, we compared the number of satisfied requests of the overloaded system presented in Fig. 4. QoS metrics in terms of RTT evolution of the Overloaded system’s OM2M platform is shown Figs. 5 and 6. RAM consumption and CPU usage of the overloaded system is presented in Figs. 7, 8, 9 and 10, without and with our proposed hybrid mechanism (trafficoriented mechanism). In the scenario testbed described in Tables 1 and 2, we consider flows (http requests towards the OM2M platform) coming from different traffic sources simulated by tow injectors. Each injector is characterized by: the type of traffic, the number of http requests (requests number), the request method (e.g. POST, GET), the destination, the period between two successive requests (periodicity in milliseconds) and finally
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Table 1 Scenario testbed of system 1 (overloaded) Injectors
Number of injected request
Sequence in ms
Injector 1
1000
100
Start time in seconds 0
Injector 2
600
100
10
Table 2 Scenario testbed of system 2 (collaborative system) Injectors
Number of injected request
Sequence in ms
Start time in seconds
Injector 2
500
200
10
Fig. 4 Success rate for system 1 and system 2
starting time in seconds. Table 1 presents the flows injected in system 1, the overloaded system, while Table 2 presents the flow injected in system 2, the collaborative system. In Fig. 4, column indicates the number of injected requests. We use different colors to distinguish the number of satisfied and lost requests. The decisions based on the system performances handle any lost or delay of IoT request caused by insufficient resources of the overloaded system’s OM2M platform. Also, redirecting traffic will help to satisfy requests using OM2M resources of the collaborative system. Figure 4 shows that our solution improved the success rate of the overloaded system from 67% (1072 in 1600 requests) to 92% (1478 in 1600 request), without affecting the success rate of the collaborative system. Overall, our IoT system scalability have been improved using our collaboration solution. As shown in Figs. 5 and 6, our solution, almost stabilize instantly (at acceptable QoS symptom) the OM2M platform state of the overloaded system, by using dynamic decision made at the monitoring level, based on symptoms generated from collected events of the system. In Fig. 5, we started the simulation by injecting the first traffic. We take note that without mechanism, after activating the second traffic injector (after 10 s), the platform will fill the queues, causing RTT to exceeds 12,000 ms.
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Fig. 5 RTT evolution for system 1
Fig. 6 RTT evolution for system 2
Furthermore, the platform will drop requests based on its default behavior, increasing lose rate. Both actions will lead to a critical state of the system 1. On the other hand, we use adaptive mechanism which will take decisions to underload the Overloaded system (1). This happens by redirecting portion of the traffic the collaborative system (2), based on QoS symptoms, almost stabilizing instantly the Overloaded system (1). We can take note that by using our collaboration solution, the platform has returned to its acceptable state only after several seconds. We noted that the collaborative system (2) (see Fig. 6) reacted instantly, raising the
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Fig. 7 RAM consumption for system 1
Fig. 8 RAM consumption for system 2
RTT over 4000 ms, and then went back to 3600 ms while using the collaboration policy. We state that our collaboration solution helped the overloaded system to underload almost instantly, without overloading the collaborative system. Overall, our IoT system scalability has been improved using our collaboration solution, without deteriorating the RTT metric in both systems. In Fig. 7, we state that our collaboration solution underloaded the RAM consumption of the overloaded system from 75% to 47%, by only consuming 24% of the RAM of the collaborative system as shown in Fig. 8.
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Fig. 9 CPU usage for system 1
Fig. 10 CPU usage for system 2
In Fig. 9, we state that our collaboration solution underloaded the CPU usage of the overloaded system from 92% to 58%, by only using 22% of the collaborative system (see Fig. 10). Overall, our approach made the OM2M platform scalable, improved RTT average and almost stabilized instantly, without overusing the RAM and CPU resources of both systems OM2M platforms.
5 Conclusion In this paper, we started by debating our vision of scalability in IoT systems. After that, we proposed an enhancement of our system by implementing components, mechanisms and tests aimed at stressing the OM2M platform. For the first time, we are using collaboration between IoT architectures according to ETSI standard, with
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autonomic computing, and a hybrid mechanism composed of both trafficoriented and resourceoriented mechanisms. The present collaboration architecture is proposed to make our IoT system more scalable without any QoS symptom degradation in terms of RTT, RAM and CPU. Simulation results show that our solution helps improving success rate (reduce lose rate), stabilizes the IoT system almost instantly—maintaining QoS metrics in terms of RTT evolution by improving it, and without overusing of resources from the collaborative system. Our new mechanism overmuch other mechanism freshly proposed in the literature for the IoT system. More complex scenarios can be implemented. Our future work and experiments will focus on the use of a bigger number of injectors, with different SLA requirements, and much more complex decision.
References 1. Bahiri MN, Zyane A, Ghammaz A (2018) An enhancement for the autonomic middlewarelevel scalability management within IoT system using cloud computing. In: Lecture notes in electrical engineering book series (LNEE) (ICEERE 2018), vol 51. Springer, pp 80–88 (ISBN 9789811314056) 2. Bahiri MN, Zyane A, Ghammaz A, Chassot C (2017) A new monitoring approach with cloud computing for autonomic middlewarelevel scalability management within IoT systems. In: Advances in intelligent systems and computing book series (AISC) (ITCS2017), vol 640. Springer, pp 281–296 (ISBN 9783319647197) 3. Tryfonas T, Kiountouzis A, Poulymenakou A (2011, October) Embedding security practices in contemporary information systems development approaches. Inf Manag Comput Secur, 183–197. Scalable SQL, Commun ACM, 48–53 (2011) 4. Chatterjee S, Kar AK, Gupta MP (2018) Success of IoT in smart cities of India: an empirical analysis. Gov Inf Q 35(3):349–361 5. Matoba K, Abiru K, Ishihara T (2011) Service oriented network architecture for scalable M2M and sensor network services. In: 15th international conference on intelligence in next generation networks, pp 35–40 6. Zhou J, Leppänen T, Harjula E, Ylianttila M, Ojala T, Yu C, Jin H, Yang LT (2011) Cloudthings: a common architecture for integrating the Internet of Things with cloud computing 7. Geoffrey CF, Kamburugamuve S, Hartman RD (2012) Architecture and measured characteristics of a cloud based Internet of Things. In: 2012 international conference on collaboration technologies and systems (CTS), pp 6–12 8. McCabe L, Aggarwal S (2012, October) La migration vers le Cloud pour les PME. SMB Group, Inc. 9. Ramasahayam R, Deters R (2011) Is the cloud the answer to scalability of ecologies? In: 5th IEEE international conference on digital ecosystems and technologies, pp 317–323 10. Sarkar C, Akshay UNSN, Venkatesha Prasad R, Rahim A (2014) A scalable distributed architecture towards unifying IoT applications. IEEE World Forum on Internet of Things (WFIoT), pp 508–513 11. Horn P (2005) An architectural blueprint for autonomic computing. IBM White Paper Ed 3
Monitoring of Industrial Equipment Using Thermography Technique in Passive and Active Form Fatima Khathyri, Bouchra Abouelanouar, Ali Elkihel, and Abd al Motalib Berrehili
Abstract This study aims to compare the advantages of infrared thermography (IRT) in passive and active use. The IRT is a noncontact, fast and widearea of inspection nondestructive testing (NDT) technique that has been increasingly used in different fields to detect the presence of faults in assets. Indeed, the detection of faults in an early time allows the possibility to reduce the downtimes, thereby offering an important reduction of energy consumptions which ultimately leads to reduced cost. To achieve the object of this work, two experimental studies have been carried out using infrared thermography in passive and active form. The first experience is destined to the control of rotating machine damaged with unbalance using the passive IRT. In the last experiment the active IRT is carried out on a composite plate in order to reveal the presence of internal damage caused by a low velocity impact. The results obtained in this study show that the active thermography offers more details (localization and size of the defect) compared to the passive IRT. Keywords Active thermography · Passive thermography · Control · Defect
1 Introduction Nowadays, the main subject of industry is the reduction of the consumption of energy. In fact, the presence of failures in industrial equipment may cause the increase of energy consumption. For this purpose, it is necessary to insure monitoring to detect damage in structures and machines. Indeed, the non destructive techniques represent one of the most used solutions, has significant impact to improve asset reliability. F. Khathyri (B) · B. Abouelanouar · A. M. Berrehili Laboratory of Industrial Engineering, National School of Applied Sciences, University Mohammed First, BV Mohammed VI, B.P. 524, 60000 Oujda, Morocco email: [email protected] A. Elkihel Laboratoire Ingénierie Management Industriel et Innovation, FST Settat Université Hassan 1er, Settat, Morocco © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_12
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To achieve these objects, several useful NDT are available. They are essential tools to make sure the survival of the asset without changing their properties. It gives the opportunity to identify the initiation of degradation and monitor its growth for timely repair or replacement of defective components. Recently, the IRT is been used in the industries as a predictive maintenance tool. It provides an inspection of large area, noncontact, safe and fast using a thermal camera which gives the opportunity to detect the presence of faults in object according to his thermal emissions. The presences of hotter or colder spots in the thermal image indicate the presence of an anomaly. It should be noted that this method can be apply in any process where the temperature is indicator. Numerous researchers have been successfully utilized this technique for several conditions of monitoring such as buildings [1], electrical power equipment [2], inspection of machineries (bearing [3], misalignment [4], motor [5] and shaft imbalance [6]) and aerospace industries [7, 8]. Furthermore, IRT has also been used in other applications such as corrosion monitoring [9], nuclear industries [10], weld monitoring [11] and medical [12]. In this paper, we are going to study the efficiency of the thermography method in the monitoring of rotating machines and composite material. Firstly, we apply the IRT to reveal the presence of unbalance which is the most difficult faults in rotating machines. Finally, we use the IRT to detect the defect produced in composite material after low velocity impact.
2 Principle IRT is based on the measurement of the thermal emission of the object. Temperature is considered to be one of the most critical factors for verifying the safety and effectiveness of the assets [9]. All objects at a temperature above absolute 0 K emit and absorb the energy in the form of electromagnetic radiation (infrared radiation). This radiation is visually represented by means of thermal camera. This camera is constituted by a captor CCD (charged coupled device) which is able to transform firstly the thermal radiation into electrical charge, and then to a visible image. Thus, the camera records the energy (radiation) composed by: the radiation emitted by the object Wobj , the radiation from the environment and reflected by the surface of the object Wr e f and finally the radiation emitted by the atmosphere Watm (Fig. 1). Therefore, the total energy Wtot recorded by the camera is presented as follows: Wtot = ετatm Wobj + (1 − ε)τatm Wr e f + (1 − τatm )Watm Where, τatm is the atmospheric transmission coefficient and ε is the emissivity of the object. There are two ways to apply this technique, either in passive form, or in active form. In the passive thermography, the camera records the infrared radiation present in the scene. In this case, the measurement device only consists of an infrared camera.
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Fig. 1 Schematic of the total radiation recorded by the camera
Fig. 2 Rotating setup
Motor
Coupling
Bearing 1
Bearing 2 Shaft
In contrast, active thermography allows the inspection of objects which do not emit heat. Indeed, thanks to the contribution of an external heat flow the object emits thermal radiations. In addition to a camera we will need an external excitation source in order to push the object to emit heat.
3 Method and Results 3.1 Passive Thermography Rotating machineries are wildly used in most of the industries. Different failure may occur in the lifetime of the machines. Hence, unbalance represents one of the most common faults in rotating machines [13]. It occurs when the mass centerline of a rotating assembly and the center of rotation do not coincide with each other. In fact, unbalance cause generates excessive force in the rotating part and reduces the life of the machine. To overcome this problem, the application of monitoring methods is important to keep track of machines health at all times. In this part the IRT is used to insure monitoring in a rotating setup damaged with unbalance. In order to create the unbalance condition, masses were attached to the shaft. Data used in this paper come from a series of experiments on rotating setup as is shown in Fig. 2. There are two loading conditions: none mass and 100 g, corresponding to normal operating condition and fault operating condition. For each
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T °C Healthy case
T °C Unbalance
T °C
2.14
11.6
12.7
1.1
4.28
25.5
30.4
4.9
6.42
29.4
33.9
4.5
8.56
31.8
36.3
4.5
10.7
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3.2
12.84
36.7
39.5
2.8
14.89
38.2
41.9
3.7
Tmax= 41.1°C
Teprature °C
Table 1 Temperature of the heated area in the thermograms for each condition
45 40 35 30 25 20 15 10 5 0
With defect Without defect
2.14
4.28
6.42
8.56
10.7
12.84
14.89
Time (min)
(a)
(b)
Fig. 3 a thermogram and b temperature variations versus time in healthy and defective state
condition the setup is run for 15 min. The infrared data are acquired with a FLIR T440 camera; the experimental results (thermal image) are recorded every 2.14 min (Table 1). Through of the analysis of the saved thermograms the temperature variation in each condition is drawn (Fig. 3b). It is observed that after 4.28 min, the temperature increases significantly at the coupling level of the test bench in fault operating condition case. From the results, it is seen that the temperature of the motor increases in the presence of unbalance. The increase in temperature generated by the motor is an indicator of an increase in energy consumption. This is for fact that the added masses in the shaft increase the vibration of the rotor which requires additional power.
3.2 Active Thermography Thermography is applied to verify the internal structure of a composite specimen after a low velocity impact. Indeed, composite materials have excellent mechanical qualities (stiffness, strength…), allowing remarkable properties in relation to their weight. The material used in this part is a hybrid composite of carbon/epoxy type with a thickness of 5 mm. Indeed, the low velocity impact allows the appearance of internal (invisible) defects, which can significantly reduce the performance of
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this material. The anisotropic and heterogeneous structure of this material with the influence of atmosphere and environment, make the monitoring very difficult. For this purpose, we have adopted the following measurement protocol: – Material emissivity = 0.95; – Extinguishing the lights, the experiment is performed in a dark room; – The distance between the specimen and camera = one meter. The Fig. 4 represents the experimental setup carried out for the monitoring of the composite plate. The result obtained is represented in Fig. 5(a) in the form of a thermal image (thermogram). It represents the thermal emissions of the plate recorded after 42 s of external heating. There is an increase in temperature in the damaged area which has been translated in a contrast of color on the surface of the specimen. Indeed, this increase in temperature is an indicator of the presence of an internal anomaly. A pixel line was selected in the damaged area in order to plot it in Excel. Thus, the Fig. 5(b) represents the temperature profile along the designated line which allowed us to know the temperature distribution in each pixel. This figure shows an increase in temperature (48.7 °C) indicating the presence of a defect, which is probably delamination between the plies. The results clearly show the capacity of infrared thermography to detect and determine the size (d = 3 mm) of the internal damage produced after the impact event. Fig. 4 Active thermography experimental setup
Fig. 5 a Thermogram of the test specimen surface recorded after 42 s of heating and b Thermal evolution along the damaged area
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4 Conclusion This work mainly provides a contribution in nondestructive testing to detect presence of damage in rotating machine and in composite material from their thermal image. The major task of this paper is to study the limits of detection of the thermal technique in the active and passive form. In the first experience the IRT is used in passive form to ensure the monitoring of rotating machine. Nevertheless, an unbalance in the machine is created by adding masses at the level of the shaft. In fact, the result obtained from this experience allows the detection of the presence of damage without any perception of its size or its origin. Otherwise, the IRT in active form is applied to control the composite material after impact damage. Indeed, the damage produced after low velocity impact is considered the most critical and difficult to reveal. To overcome this difficulty, a control protocol has been proposed in order to insure the monitoring. We can say that the infrared thermography in active form allowed more information about the defect (location and size) just in a few seconds.
References 1. Rocha J, Santos C, Póvoas Y (2018) Detection of precipitation infiltration in buildings by infrared thermography: a case study. Procedia Struct Integr 11:99–106 2. Jadin M, Taib S (2012) Recent progress in diagnosing the reliability of electrical equipment by using infrared thermography. Infrared Phys Technol 55:236–245 3. Abou Elanouar B, Elamrani M, Elkihel B, Delaunois F, Manssouri I (2017) A comparative experimental study of different methods in detection and monitoring bearing defects. Int J Adv Sci Techn Res 1(7):409–423 4. Mohanty AR, Fatima S (2015) Shaft misalignment detection by thermal imaging of support bearings. Int Fed Autom Control, 554–559 (2015) 5. Singh G, Naikan V (2017) Infrared thermography based diagnosis of interturn fault and cooling system failure in three phase induction motor. Infrared Phys Technol 87:134–138 6. Janssens O, Loccufier M, Van de Walle R, Van Hoecke S (2017) Datadriven imbalance and hard particle detection in rotating machinery using infrared thermal imaging. Infrared Phys Technol 82:28–39 7. Khathyri F, Elkihel B, Delaunois F (2018) Nondestructive testing by ultrasonic and thermal techniques of an impacted composite material. Int J Adv Sci Eng Inf Technol 8:2360–2366 8. Lizaranzu M, Lario A, Chiminelli A, Amenabar I (2015) Nondestructive testing of composite materials by means of active thermographybased tools. Infrared Phys Technol 71:113–120 9. Doshvarpassand S, Wu C, Wang X (2019) An overview of corrosion defect characterization using active infrared thermography. Infrared Phys Technol 96:366–389 10. Itami K, Sugie T, Vayakis G, Walker C (2004) Multiplexing thermography for international thermonuclear experimental reactor divertor targets. Rev Sci Instrum 75:4124–4128 11. Lahiri BB, Bagavathiappan S, Saravanan T, Rajkumar KV, Kumar A, Philip J, Jayakumar T (2011) Defect detection in weld joints by infrared thermography. In: International conference on NDE in steel and allied industries (NDESAI 2011), Jamshedpur, India, pp 191–197 12. Mi B, Song J, Hong W, Zhang W, Wang Y (2019) Evaluation method of infrared thermography on children with idiopathic thrombocytopenic purpura: preliminary. Infrared Phys Technol 102:103027 13. Walker RB, Vayanat R, Perinpanayagam S, Jennions IK (2014) Unbalance localization through machine nonlinearities using an artificial neural network approach. Mech Mach Theory 75:54– 66
Enhancing Performance of a 60 GHz Patch Antenna Using Multilayer 2D Metasurfaces Feriel Guidoum, Mohamed Lamine Tounsi, Noureddine Ababou, and Mustapha C. E. Yagoub
Abstract This paper deals with the design of a 60 GHz microstrip patch antenna using transmitarray structures. The influence on the gain and bandwidth was investigated in terms of size and shape. Simulated results showed that the antenna performance can be significantly improved by using rectangular slots instead of circular slots. Keywords Transmitarray · Patch antenna · Periodic structures · Metasurface
1 Introduction Transmitarray structures are among artificial structured lenses used for beamforming [1]. They are often used to collimate radiation from a source by tuning cells whose phases can be independently controlled. They can be also used to replace traditional phased array elements that exhibit large physical bulk complex feed networks or involve many expensive transceiver modules [2]. The period of the element revolves around half wavelength to avoid the grating lobe [2, 3]. To avoid scan blindness and ensure wideangle scanning, the mutual coupling between transmitarray elements (due to the excitation of surface waves) is often required to be as small as possible [4, 5]. Transmitarray structures are usually fed by a single antenna, which includes horn antennas, openended rectangular waveguide probes [6], patch antennas or substrate integrated waveguide (SIW) slot antennas [7]. They can provide high gain and good aperture efficiency over large bandwidth, avoiding blockage from the feed [8]. They can be also used as amplifiers and phase shifters to increase the spatial power level or to create reconfigurable antennas [9]. 60 GHz antennas are largely involved in F. Guidoum · M. L. Tounsi (B) · N. Ababou Instrumentation Laboratory, Faculty of Electronics and Informatics, USTHB University, P.O. Box 32, ElAlia, BabEzzouar, Algiers, Algeria email: [email protected] M. C. E. Yagoub ELEMENT Laboratory, EECS, University of Ottawa, Ottawa, ON K1N 6N5, Canada © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_13
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millimeter wave wireless communication systems. They are for instance implemented to replace uncompressed highdefinition (HD) video broadcast cables that allow users to display content wirelessly to a remote display with equivalent cable quality, in Gigabit/s file transfer, or for wireless Ethernet networking. In this work, we reconfigured the structures described in [10] and [11] so that they can operate in the 60 GHz range. We started by maximizing their transmitted coefficient (close to 0 dB) and phase variation. Then, we implemented them in the antenna to improve its output parameters. A comparative performance study was made between the antenna alone and the same antenna with two different 2D metasurface structures. It showed that some improvements were obtained by applying the first structure with a gain up to 10 dB and a relative bandwidth up to 6.7%. However, the second structure showed practically no improvement. Otherwise, an enhancement of 2.44 dB was achieved with regard to [10] and [11] where horn antennas were used with 325 and 121 unit cells and operating frequencies of 11.3 GHz and 13.58 GHz, respectively. In our work, only a maximum of 9 unit cells was used.
2 Design of the Metasurface Antenna 2.1 60 GHz Antenna First, we designed the 60 GHz antenna on Rogers RT5880 of relative permittivity εr = 2.2 and thickness h = 0.254 mm (Fig. 1). The dimensions, obtained through the empirical equations in [12], are reported in Table 1. Fig. 1 The 60 GHz antenna
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2.2 Design of the Unit Cells of the Transmitarray Metasurfaces In order to improve the performance of the above antenna, we applied two slot type elements of metasurfaces with no dielectric substrate. The first one is a cross rectangular slot element (Fig. 2), used in [10] to improve the gain of an antenna at 11.3 GHz. The second one is a circular split ring resonator (CSRR, Fig. 3) used in [11] to improve a 13.58 GHz antenna gain with high efficiency and no dependence of the polarization angle. Table 1 The 60 GHz antenna dimensions Parameter
Ws
Ls
W
L
Value
6 mm
9 mm
1.97 mm
1.5 mm
Fig. 2 Unit cell of the first structure, a top view, b side view [10]
Fig. 3 Unit cell of the second structure, a top view, b side view [11]
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200
0.5
150
1.0
100
1.5
50
2.0
0
2.5
50
3.0
100
3.5
150
S12 phase (°)
S12 magnitude (dB)
Fig. 4 Transmission magnitude and phase of the unit cell of the first structure
200 4.0 54 55 56 57 58 59 60 61 62 63 64
Frequency (GHz)
200
0 2 4 6
8 10 12 14 16 18 20
150 100 50 0 50
S12 phase (°)
S12 magnitude (dB)
Fig. 5 Transmission magnitude and phase of the unit cell of the second structure
100 150 50
55
60
65
70
200
Frequency (GHz)
So, we first scaled the dimensions of the two structures to make them functional at 60 GHz and optimized their dimensions to maximize their transmission coefficient, leading to P = 3.1 mm, Ls = 2.5 mm, W = 0.4 mm for the first structure (Fig. 2) and we set P = 2.6 mm, g = 0.34 mm and W = 0.2 mm for the second (Fig. 3). The separation distance between layers is H = λ0 /4 = 1.25 mm for both cases. The phase and magnitude variation of the transmission coefficient versus frequency for each case are represented in Figs. 4 and 5, respectively.
3 Application for Periodic Structures In the next step, we implemented the above unit cells as periodic structures in cross and circular shapes (Figs. 6 and 7). The distance between the antenna and the metasurfaces can be determined using either (1) [13] or (2) [14], for the general case of EBG materials (electromagnetic band gap).
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Fig. 6 Application of the first structure
Fig. 7 Application of the second structure in CST Microwave Studio
D 2 × tan α
(1)
λ0 4
(2)
α = 2α0
(3)
Hp =
Hp =
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where D represents the periodic structure width and α0 the opening angle of the antenna alone (without metasurfaces), in our case α0 = 75.8° obtained by simulation. λ0 is the wavelength in vacuum.
3.1 Cross Periodic Structure The first periodic structure is composed of 3 × 3 cross unit cells (Fig. 6). This array size was found to be the optimized one. The results (simulated in the CST Microwave studio software) are reported in Table 2. It is clear that Eq. (1) exhibits the best efficiency with halfopening angle while the widest bandwidth is achieved with the equation that involves the complete opening angle based on the IEEE 802.15.3c standard. Figures 8 and 9 illustrate the simulated reflection coefficient (S11 ) and radiation diagram, respectively, for all the values of Hp reported in Table 2. For comparison, the responses of the antenna alone (without the implemented structure) are also plotted. As can be noticed, there is some improvements in the overall performance but the significant one in terms of gain is obtained for the half opening angle case (Hp = 5.97 mm) with a value of 10.34 dB. Table 2 Performance results of the 3 × 3 unit cells structure (cross slots) Hp (mm)
Relative bandwidth (%)
Bandwidth (GHz)
60 GHz antenna
4.5
2.63
Gain (dB)
Efficiency (%)
7.90
87.8
1.18 (α = 2α0 )
6.7
3.88
8.31
91.6
5.97 (α = α0 )
4.6
2.69
10.34
84.2
2.5 (λ0 /2)
3.0
1.80
8.20
89.1
0.683
5.00
2.90
8.90
75.70
0
Fig. 8 S11 comparison for the first structure
5 10 S11(dB)
15 20
60 GHz antenna Hp=1.18mm Hp=5.97mm Hp=2.5mm Hp=0.683mm
25 30 35 40 54
56
58 60 62 Frequency (GHz)
64
66
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Gain (dB)
Fig. 9 Gain comparison for the first structure
15 10 5 0 5 10 15 20 25 30 35 40
147
60 GHz antenna Hp=1.18mm Hp=5.97mm Hp=2.5mm Hp=0.683mm
160 120 80 40
0
Theta (°)
40
80 120 160
Table 3 Performance results of the 3 × 3 unit cells structure (csrr) Hp (mm)
Relative bandwidth (%)
Bandwidth (GHz)
Gain (dB)
Efficiency (%)
1 (α = 2α0 )
4.50
2.66
6.17
89.18
5 (α = α0 )
3.40
2.02
7.30
92.26
2.5 (λ0 /2)
4.40
2.60
7.00
91.64
0.683
3.75
2.23
6.26
88.83
Results are obtained for a fixed value of the elevation angle (phi = 0°) and an azimuth angle theta varying from −180° to 180° (E plane).
3.2 Circular Periodic Structure Table 3 gives the performance results for the CSRR structure (Fig. 7). Compared to the first case (cross slot case) and the antenna alone, this structure showed an improvement only in terms of efficiency. Figures 10 and 11 illustrate the comparison on reflection losses S11 and gain respectively for many values of Hp .
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0
Fig. 10 S11 comparison for the second structure
5
S11(dB)
10 15 60 GHz antenna Hp=1mm Hp=5mm Hp=2.5mm Hp=0.683mm
20 25 30
35 55 56 57 58 59 60 61 62 63 64 65 Frequency (GHz)
10
Fig. 11 Gain comparison for the second structure
0 Gain (dB)
10 20 30 40
Hp=0.683mm Hp=1mm Hp=2.5mm Hp=5mm 60 GHz antenna
160 120 80 40 0 40 Theta (°)
80 120 160
4 Conclusion In this work, we implemented two periodic metasurface structures on a 60 GHz rectangular patch antenna, to improve its performance. A comparative study demonstrated that the first structure (the cross rectangular slot) showed better performance since an improvement of more than 2 dB and 4% was observed for gain and bandwidth, respectively, comparing to the single antenna without metasurface structures. As perspectives, it is expected to use 3D metasurfaces to further improve the performance of 60 GHz antennas in order to avoid the difficulties related to the multilayer configuration.
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References 1. Hum SV, PerruisseauCarrier J (2014) Reconfigurable reflectarrays and array lenses for dynamic antenna beam control: a review. IEEE Trans Antennas Propag 62:183–198 2. Mailloux RJ (1993) Phased array antenna handbook. Artech House, Dedham 3. Valavan SE, Tran D, Yarovoy AG, Roederer AG (2014) Planar dualband widescan phased array in XBand. IEEE Trans Antennas Propag 62:5370–5375 4. Pozar DM, Schaubert DH (1984) Scan blindness in infinite phased arrays of printed dipoles. IEEE Trans Antennas Propag 32:602–610 5. Pozar DM, Schaubert DH (1984) Analysis of an infinite array of rectangular microstrip patches with idealized probes feeds. IEEE Trans Antennas Propag 32:1101–1107 6. Qu SW, Feng PY, Yi H, Chen B, Ng KB, Chan CH, Wu GB (2016) Terahertz reflectarray and transmitarray. In: Proceedings of the international symposium on antennas propagation, pp 548–549 7. Jiang M, Chen ZN, Zhang Y, Hong W, Xuan X (2016) Metamaterialbased thin planar lens antenna for spatial beamforming and multibeam massive MIMO. IEEE Trans Antennas Propag 65:464–472 8. Liu G et al (2019) A millimeterwave multibeam transparent transmitarray antenna at KaBand. IEEE Antennas Wirel Propag Lett 18:631–635 9. Tsai FCE, Bialkowski ME (2004) Investigation into the design of a spatial power combiner employing a planar transmitarray of stacked patch antenna. In: International conference on microwaves, radar and wireless communications, pp 509–512 10. Abdelrahman AH, Elsherbeni AZ, Yang F (2014) Transmitarray antenna design using crossslot elements with no dielectric substrate. IEEE Antennas Wirel Propag Lett 13:177–180 11. Liu G, Wang HJ, Jiang JS, Xue F, Yi M (2015) A highefficiency transmitarray antenna using double split ring slot elements. IEEE Antennas Wirel Propag Lett 14:1415–1418 12. Balanis CA (2016) Antenna theory: analysis and design. Wiley, Hoboken 13. Ge Y, Lin C, Liu Y (2018) Broadband folded transmitarray antenna based on an ultrathin transmission polarizer. IEEE Trans Antennas Propag 66:5974–5981 14. Leger L, Serier C, Chantalat R, Thevenot M, Monedière T, Jecko B (2004) 1D dielectric electromagnetic band gap (EBG) resonator antenna design. Annales des Télécomm 59:242–260
Enhancing the Performance of Grayscale Image Classification by 2D Charlier Moments Neural Networks Zouhir Lakhili, Abdelmajid El Alami, and Hassan Qjidaa
Abstract This paper presents a new model for 2D image classification based on 2D discrete Charlier moments and neural networks to enhance the classification accuracy of Grayscale images. Discrete Charlier moments have the ability to extract relevant features from an image even in lower orders, and with high efficiency of the neural networks; we can design the proposed efficient model. Experiments are carried out on Coil20 and ORL datasets to demonstrate the performance of the proposed model. The obtained results show the capability of the proposed model to achieve high classification accuracy on both datasets, and to outperform other recent methods. Keywords Grayscale images · 2D image classification · 2D discrete charlier moments · Neural networks
1 Introduction Image moments have been extensively used for feature extraction in pattern recognition and image analysis tasks [1–6]. They can efficiently extract relevant features of an image with a compact representation. Since, the introduction of moment invariants in the field of image processing community by Hu [1], the geometric moment invariants were presented for image classification applications. However, Hu’s moment invariants suffer from the high information redundancy and noise sensitivity due to the nonorthogonal kernel function of the geometric basis. To overcome the limitations arising from the geometric basis, researchers introduced a set of continuous orthogonal moments such as Zernike and Legendre moments which have orthogonal polynomials as a kernel function, and where the image moments can be represented with a minimum amount of information redundancy [2]. The main disadvantage of the continuous moments is the discretization error which increases as the order of the moments increases. To remedy this drawback, a set of discrete orthogonal moments Z. Lakhili (B) · A. El Alami · H. Qjidaa Sidi Mohamed Ben Abdellah University, Fez, Morocco email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_14
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has been introduced in image analysis such as Tchebichef [3], krawtchouk [4], Charlier [5], and Hahn [6]. Consequently, the discrete orthogonal moments satisfy the orthogonal property on discrete space. Furthermore, low computational complexity and better representation capability can be obtained, which make them more suitable for image classification. In this paper, we propose a new model for 2D image classification which takes features represented by Charlier moments as an input layer in neural networks structure. In fact, neural networks have been shown to be useful models in many areas of science and technology such as identification, control systems and classification. They typically consist of three types of layers which are input layer, hidden layers and output layer [7–10]. Neural networks have the ability to learn and automatically interpret the features by one or more layers, where each layer uses the information from the earlier layer output in order to perform accurate classification. On the other hand, discrete Charlier moments have the ability to capture relevant features even in lower orders. Therefore, the computed feature vectors capture particular information with low dimensionality and high discrimination power. The introduction of discrete Charlier moments descriptor as an input vector in neural networks makes it possible to create models that reduce the computational cost considerably by decreasing the number of layers and parameters to allow obtaining the best classification results. The experimental tests have been performed on Coil20 and ORL datasets to investigate the performance of the proposed model. The experimental results showed that our proposed model outperformed others existing methods. The paper is organized as follows: An overview of discrete Charlier moments is presented in Sect. 2. In Sect. 3, we briefly detail the structure of the proposed model. Section 4 describes the experimental results of the proposed model. The main conclusions are discussed in Sect. 5.
2 2D Charlier Moments In this section, we introduce the mathematical background of Charlier polynomials followed by 2D Charlier moments and reconstruction.
2.1 Charlier Polynomials The n th Charlier polynomials are defined by using hypergeometric function as [5]: Cna1 (x) = 2 F0 (−n, −x; −1/a1 ), a1 > 0; x, n = 0, 1, 2 . . . ∞ The normalized Charlier polynomials are given by:
(1)
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Cˇ na1 (x)
=
Cna1 (x)
wc (x) dn2
153
(2)
e−a1 a x
With wc (x) = x! 1 and dn2 = an!n 1 The discrete Charlier polynomials satisfy the following threeterm recurrence relation: n − 1 ˇ a1 a 1 − x + n − 1 a 1 ˇ a1 a1 ˇ Cn (x) = (3) Cn−1 (x) − Cn−2 (x) a1 n n With Cˇ 0a1 (x) =
wC (x) d02
and Cˇ 1a1 (x) =
a1 −x a1
wc (x) d12
2.2 2D Charlier Moments and Reconstruction The discrete moment of an (m + n)th order of a twodimensional image with intensity function f (x, y) is defined over the domain [0, M − 1] × [0, N − 1] as follows: C Mmn =
M−1 N −1
Cˇ ma1 (x)Cˇ na1 (y) f (x, y)
(4)
x=0 y=0
Due to the orthogonality property of Charlier polynomials, the image f (x, y) can be perfectly reconstructed, when all moments are used, by using the following inverse transform: f (x, y) =
M−1 N −1
Cˇ ma1 (x)Cˇ na1 (y)C Mmn
(5)
m=0 n=0
Therefore, in practical applications, the image can be approximately reconstructed from several order moments. An approximate reconstruction fˆ of f can be written as: fˆ(x, y) =
Nˆ Mˆ
Cˇ ma1 (x)Cˇ na1 (y)C Mmn
(6)
m=0 n=0
Where 0 ≤ Mˆ ≤ M − 1, 0 ≤ Nˆ ≤ N − 1 and C Mmn (0 ≤ m ≤ M − 1, 0 ≤ n ≤ N − 1) Table 1 shows some reconstructions of two original grayscale images selected from Coil20 and ORL datasets with size of 128 × 128 and 112 × 112 in row 1 and 2 respectively, by using Charlier moments. We can observe more resemblance between the original image and reconstructed images in the early orders. The reconstruction
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Table 1 Reconstruction results Original image
Reconstructed images up to orders (14, 14)
(24, 24)
(64, 64)
abilities of Charlier moments indicate their capacity to compact more information from image that is important for classification.
3 The Proposed Model: 2D Charlier Moments Neural Networks In this section, we provide a description of our model which incorporates discrete Charlier moments as input layer. We yield an overview of some functions and parameters which are used to enhance the accuracy, such as ReLU activation function, Batch normalization and dropout. Table 2 depicts the detail of each layer. The constructed neural networks model includes four learned layers: an input layer, two hidden layers, and an output layer. It takes the size of n × n as an input vector where n represents the order of Charlier moments. Two hidden layers with number of neurons 240 and 160 are respectively referred by H1 and H2. The output layer generates the corresponding image categories. The proposed model contains three layer types as follows: Table 2 Details of the constructed model Layer
Purpose
Activation
Input
2D Charlier moments vector
n×n
H1
Fully connected + BN + ReLU + Dropout
240
H2
Fully connected + BN + ReLU + Dropout
160
Output
Softmax
Number of subjects
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• Input layer: The input layer of the proposed model is represented by feature vector of Charlier moments which are computed of each image. Specifically, the descriptor vector is composed of 2D Charlier moments up to order t, where t is experimentally selected. V = [CMnm n × m ∈ [0, 1 . . . , t]]
(7)
• Hidden layer: Two hidden layers noted H1 and H2 contain successively 240 and 160 neurons are used in the constructed model. The output of H1 can be described as: out H 1 = δ H1 b(H1 ) + W (H1 ) out H0
(8)
Where out H0 = V is the input Charlier moments vector, W (H1 ) is the weight matrix, b(H1 ) is the bias vector, and δ H1 is a ReLU activation function. The output of H2 can be defined by Eq. (9): out H 2 = δ H2 b(H2 ) + W (H2 ) out H 1
(9)
• Batch normalization (BN): Batch normalization is applied after each hidden layer in order to further accelerate the training set, as well as reducing the gradients dependencies and avoid the risk of overfitting and divergence [11]. • ReLU activation function: Rectified Linear Unit is expressed mathematically by f (x) = max(0; x), the use of this function accelerates the convergence of the stochastic gradient descent and avoids network saturation [12]. • Dropout: Dropout [13] technique based on removed nodes with a keepprobability is applied after each hidden layer. This is done, in order to overcome the overfitting by regularizing the model and improve the classification accuracy. • Output layer: The output of the model provides the probability distribution of the labels corresponding to utilized classes by using Softmax function. The output function of our model is defined by the following formula Eq. (10): θ (V ) = So f tmax b(H3 ) + W (H3 ) out H2
(10)
4 Experiments In this section, extensive experiments are conducted to assess the performance of the proposed model in 2D image classification. The effectiveness of the proposed model is validated by comparison with several 2D classification methods. Two datasets have been chosen: Coil20 and ORL, the description of these datasets will be presented in this section. All experiments were performed on an office computer equipped with 3.2 GHz Intel Core i5 and 4 GB of RAM.
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Table 3 Classification results on Coil20 for some orders of Charlier moments Order
4
6
14
22
32
38
44
50
Accuracy (%)
97.59
98.70
97.40
96.94
96.66
96.94
96.57
96.29
Table 4 Classification results on ORL for some orders of Charlier moments Order
4
6
14
22
32
38
44
50
Accuracy (%)
89.50
91.50
94.00
94.50
97.00
97.50
99.00
97.00
• Coil20 [14]: The Coil20 dataset consists of 1440 normalized images with the size of 128 × 128 distributed over 20 classes. This corresponds to 72 grayscale images per class, these are images of objects taken from different viewpoint and under various lightings. In the experiments, we have randomly selected 360 (25%) images for training set and 1080 (75%) images for testing set. Table 3 presents the classification accuracy results for some lower orders. It can be observed that the highest classification rate of 98.70% is achieved at the order n = 6. The comparison with other methods is summarized on the left side of Table 5. We can deduce that our result is slightly higher than some best classification accuracy achieved recently. • ORL [15]: The ORL dataset includes a total of 400 grayscale images of size 112 × 92 pixels of 40 persons each providing 10 images per person with different states of variations. All face images are captured on a dark homogenous background. In addition, these images contain some basic facial expressions (open or closed eyes/smiling or not smiling) with slight pose variations. In the classification experiments, we have randomly chosen 200 (50%) face images from training and the remaining for testing. Based on the results of Table 4 it can be seen that the best classification result of 99.00% is obtained at the order n = 44. The comparison to other methods is listed on the right side of Table 5; we can deduce the superiority of our proposed model against other methods.
5 Conclusion This paper presents a new model for 2D grayscale image classification based on 2D Charlier moments and neural networks. The main advantages of Charlier moments and neural networks are used to construct the proposed model. In fact, Charlier moments provide compact representation with high discrimination even in lower orders. The experimental results showed high performance of the proposed model on Coil20 and ORL datasets as compared to other methods. As a future work, it will be interesting to investigate the accuracy of 2D Charlier moments neural networks on large datasets by constructing models that can achieve high accuracy.
Enhancing the Performance of Grayscale Image Classification … Table 5 Comparison of classification accuracy with other methods on Coil20 and ORL datasets on left and right side respectively
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Methods
Accuracy (%)
Methods
Accuracy (%)
ZM [16]
63.70
GHM [18]
46.75
MKIM [17]
98.15
HM [28]
93.00
GHM [18]
55.09
KM [29]
94.50
SKMI [19]
50.20
GZMs + dwpLWLD [30]
98.00
HMI [20]
97.35
RICZM [31]
96.50
FRISSA [21]
98.50
FLWLD [32] 97.50
LP [22]
93.25/96.00
DCV [33]
97.75
NN [23]
82.27
2DPCA [34]
98.30
RSFKM [24]
76.54 ± 1.35
Intrisic faces 97.00 [35]
RGLDA [25]
88.00
LRC [36]
93.50
GCFNCW [26]
80.52
2D2PCA [37]
90.50
DFSC [27]
85.84
DSLSE [38] 95.60
Our
98.70
Our
99.00
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11. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: 32nd international conference on machine learning, Lille, France, pp 448–456 12. Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network. arXiv:1505.00853 13. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958 14. Nene SA, Nayar SK, Murase H (1996) Columbia Object Image Library (COIL20) 15. The Cambridge ORL face database. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatab ase.html 16. Papakostas GA, Koulouriotis DE, Tourassis VD (2012) Feature extraction based on wavelet moments and moment invariants in machine vision systems. In: Humancentric machine vision, p. 31 17. Hmimid A, Sayyouri M, Qjidaa H (2015) Fast computation of separable twodimensional discrete invariant moments for image classification. Pattern Recogn 48(2):509–521 18. Karakasis EG, Papakostas GA, Koulouriotis DE, Tourassi VD (2013) Generalized dual Hahn moment invariants. Pattern Recogn 46(7):1998–2014 19. Papakostas GA, Karakasis EG, Koulouriotis DE (2010) Novel moment invariants for improved classification performance in computer vision applications. Pattern Recogn 43(1):58–68 20. Sayyouri M, Hmimid A, Qjidaa H (2013) Improving the performance of image classification by Hahn moment invariants. JOSA A 30(11):2381–2394 21. Kang LW, Hsu CY, Chen HW (2011) Featurebased sparse representation for image similarity assessment. IEEE Trans Multimedia 13(5):1019–1030 22. Deng W, Liu Y, Hu J, Guo J (2012) The small sample size problem of ICA: a comparative study and analysis. Pattern Recogn 45(12):4438–4450 23. Sossa H, Guevara E (2013) Modified dendrite morphological neural network applied to 3D object recognition on RGBD data. In: 8th international conference (HAIS 2013). Springer, Heidelberg, pp 304–313 24. Xu J, Han J, Xiong K, Nie F (2016) Robust and sparse fuzzy KMeans clustering. In: IJCAI, pp 2224–2230 25. Gao S, Zhou J, Yan Y, Ye QL (2016) Recursively global and local discriminant analysis for semisupervised and unsupervised dimension reduction with image analysis. Neurocomputing 216:672–683 26. Ye J, Jin Z (2014) Dualgraph regularized concept factorization for clustering. Neurocomputing 138:120–130 27. Shang R, Zhang Z, Jiao L, Liu C, Li Y (2016) Selfrepresentation based dualgraph regularized feature selection clustering. Neurocomputing 171:1242–1253 28. Akhmedova F, Liao S (2015) Face recognition using discrete orthogonal Hahn moments. Int J Comput Electr Autom Control Inf Eng 9(6):1550–1556 29. Rani JS, Devaraj D (2012) Face recognition using Krawtchouk moment. Sadhana 37(4):441– 460 30. Singh C, Walia E, Mittal N (2012) Robust twostage face recognition approach using global and local features. Vis Comput 28(11):1085–1098 31. Singh C, Walia E, Mittal N (2011) Rotation invariant complex Zernike moments features and their applications to face and character recognition. IET Comput Vis 5(5):255–266 32. Zhang Z, Wang L, Zhu Q, Chen SK, Chen Y (2015) Poseinvariant face recognition using facial landmarks and Weber local descriptor. Knowl. Based Syst 84:78–88 33. Wen Y (2012) An improved discriminative common vectors and support vector machine based face recognition approach. Expert Syst Appl 39(4):4628–4632 34. Yang J, Zhang D, Frangi AF, Yang JY (2004) Two dimensional PCA: a new approach to appearancebased face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137 35. Wang Y, Wu Y (2010) Face recognition using intrinsic faces. Pattern Recogn 43(10):3580–3590
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Encrypted Data Sharing Using Proxy ReEncryption in Smart Grid Anass Sbai, Cyril Drocourt, and Gilles Dequen
Abstract In a rapidly changing territory, energy networks must be increasingly responsive and flexible. New models of multifluid management and energy production are being created and developed on national and international level. This involves the use, monitoring and supervision of many sensors that reports lot of data. This paper deals with the secure management of large amounts of data within the context of smart grid. We propose a solution based on proxy reencryption designed primarily to allow decryption delegation, which allow a neat management of large amount of data while respecting the GDPR (General Data Protection Regulation) and security standards. Keywords Smart grid · Cloud computing · Security · Proxy reencryption
1 Introduction The fight against global warming led to the emergence of new energy markets and great challenges. It involves the installation of a whole infrastructure and communication networks which requires a great deal of attention at the security level. In 2010, the discovery of the STUXNET virus [1] triggered debates on security in the energy industry. Standardization organisms and agencies were the first to be involved. The European Council entrusted the standardization organisms (CEN, CENELEC and ETSI) with the mandate M490 [2] to adopt security standards for smart grids. Various norms and security methods already existed, the challenge was how to make them combined and harmonized. CEN offers the SmartGrid Architecture Model (SGAM) which gives a threedimensional projection of the entire system in form of layers, areas, and domains. This conceptual representation allows to model the use cases, identify required standards and identify the gaps and standards needs. Thus we can focus on end to end security, from the component layer to the business layer. In the context of VERTPOM project, the goal is to deploy a decision support tool called A. Sbai (B) · C. Drocourt · G. Dequen University Of Picardie Jules Verne, MIS Laboratory, 14 Quai de la Somme, 80080 Amiens, France email: [email protected] URL: https://www.mis.upicardie.fr/ © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_15
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Fig. 1 Architectural model for the Bank of energy. [5]
BANK of ENERGY (BE) that help the transition to positive energy territories. Thus, by maintaining an optimized balance between the produced energy with regard to usage and energy storage means [3]. The energy networks must be more responsive, flexible, and thus foster interactions between market players. As illustrated in Fig. 1, the BE need the consumption data transmitted by smart meters via data concentrators and stored by the NEM (Network Energy Manager). Also production data handled by the EP (Energy Provider) and other data collected by sensors that could be stored and handled by an other NEM. In 2014, a compliance pack for smart meters which provides a regulation for personal data was proposed by the CNIL (Commission Nationale de l’Informatique et des Libertés) [4]. To sum up, they define the consumption data, precisely the load curves, as the property of the consumer and above all as a sensitive data. From the CNIL’s point of view, the provider could have access to theses data only if the consumer gives his consent, which is done in general via the contracts. For commercial prospecting, data processing or sales to subcontractors, the data must be anonymized. In this paper, we propose a novel approach to preserve privacy in the context of smart grids. Instead of anonymizing the data, we opted for an encrypted data storage in the cloud. Only data owners will be able to access the appropriate data and entities to which we have delegated decryption’s right. For this purpose we use the concept of proxy reencryption. In the next section we will present some related works, then we will detail our approach and show its compatibility with the CNIL’s regulation. Finally, we will conclude with a discussion regarding the advantages and limitations of our solution (Fig. 1).
2 Related Works The main inconvenient of todays Cloud Storage Provider (CSP) solutions (e.g iCloud, OVH, GoogleDrive ...), is that they are considered as an alltrusted part. Either the
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data are encrypted under a key known by the cloud or stored in plaintext. Several works aim to enforce confidentiality while allowing efficient data sharing. Starting with the broadcast encryption designed by Fiat [6]. Another similar approach was introduced by Sahai in 2005 [7] which is Attribute Based Encryption. Inspired by [8] system, their idea was to create a new type of IBE that they called fuzzy IBE to combine encryption and access control. But none of these solutions allow for a selective sharing. As an alternative to these solutions, we choose to use the proxy reencryption(PRE). It allows the transformation of ciphers intended to Alice into new ciphers that can be decrypted by Bob. First appeared in 1998, it was designed by the trio BBS [9], where instead of recovering, decrypting then encrypting the data to bob, Alice generate a reencryption key and rely on a semitrusted proxy to convert the ciphers using the key created by Alice. One of the drawbacks of their method is that the system is bidirectional. That is to say, if Alice delegate decryption rights to Bob, the latter would consequently delegate decryption rights to Alice. Y. Dodis [10] formalizes the design of proxy reencryption schemes by categorizing these systems in two types: unidirectional and bidirectional. In [11] proposes a cloud based solution for file sharing called SkyCryptor using PRE. The idea is to use a unique symmetric key for each file to be encrypted with AES and then encrypt the key with the asymmetric public key of the user generated thanks to the PRE algorithm. The solution is dedicated device and now marketed under the name of BeSafe. Each user’s device have it’s own key pair and the reencryption is used to share the files between different devices or users. But above all, the users must install the BeSafe software and use it to encrypt the data. Instead, we proposed in [5] the PREaaS which doesn’t need any heavy client and which is more flexible, modular and transparent.
3 Our Contribution 3.1 PREaaS In Fig. 1, we illustrate an architectural model for the BE, were it interacts with two NEMs and one EP. We can classify all entities into three main actors: – Data producers: Devices generating data (sensors, smartmeters ...). – Data owners: Entities that owns the data produced. – Data consumers: Entities that need to use these data. The idea is that the data produced must be encrypted before storage, in such a way that only the data owner (DO) could decrypt and delegate access rights to data consumers. The DO could retrieve the data directly from the CSP and DO’s authentication would be preferable but not mandatory. Because, even if we give access to every one. Only the holder of the appropriate secret could decrypt it or entities to which the DO has delegated decryption rights. So, authentication will not add any warranty in terms of confidentiality but above all could be used by the CSPs to
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Fig. 2 Main actors in a data sharing scenario
avoid DDosattacks. There are several ways to implement such a mechanism, which we discussed in [5]. To reduce the costs, instead of having each entity implementing a PRE, we proposed the PRE as a service. The PREaaS, has the advantage to manage only encrypted data and handle only public keys and Reencryption keys. Even if CSPs or users are corrupted and collude with the PREaaS, it won’t affect the confidentiality. This is guaranteed by careful choice on the algorithms used by the service. We assume that a shared secret exists between Data Owner (DO) and Data Producer (DP) which encrypt data. This secret will be encrypted by the DO’s public key as any other KEM/DEM mechanism. This already exist and known as hybrid encryption, but the novelty as in SkyCryptor is that the asymmetric encryption scheme used will be a unidirectional proxy reencryption scheme. That way, when the DC wants to access to some data that does not belong to him, the CSP will forward his request to the DO including the DC’s public key. As a response, the data owner creates a reencryption key and transmit it to the CSP. The latter calls the PREaaS to reencrypt the corresponding cipher of the session key and send the encrypted data (Fig. 2). If we take over the CNIL’s obligations, our solution is compatible and allows to have a real consent from an interface and not via contracts with prechecked box. It can be proposed in addition to the anonymization solution, knowing that anonymization is a difficult task specially for dynamic databases.
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Table 1 Computational efficiency of Chow and our algorithm in (ms) Chow Our schceme Function KeyGen ReKeyGen Encrypt ReEncrypt Decrypt
Fp 515 502 1000 967 979
Ecc 68 48 95 89 78
Fp 1653 2450 2313 2372 1649
Ecc 163 235 216 229 137
3.2 Implementation Many solution exist that implement PRE, like BeSafe presented later in the paper, but their main inconvenient is the need to install a software in the client side. We chose to use JavaScript as a core technologie, and thus to be executed in the client side directly by navigator or even mobile devices without any software and also in the server side thanks to NodeJs. For scalability and flexibility purpose, the PREaaS must allow the use of different PRE algorithm. We already implemented one of the most efficient unidirectional PRE which is Chow’s algorithm. But the security of the scheme is still a concern where the security proof of the latter are based on random oracle. It is better to use schemes that are proven CCAsecure in the standard model. But all these schemes uses pairing based solution which is expensive in terms of computation. We proposed the first CCAsecure unidirectional PRE in the standard model without pairings in [12] and present the result of its implementation below. Our scheme is based on the CramerShoup encryption system which is by design CCAsecure in the standard model, while Chow’s algorithm is based on ElGamal encryption scheme and rely on Schnorr signature to reach the CCA security. First we use a generic group with prime order length 3072bit, and the second one using NIST Standard ECC p256 [13] thanks to SJCL [14]. Both correspond to the same security level that is 128bit. For the tests we used a 2,5 GHz intel core i7, with 16 GB RAM. Table 1 shows the time resources consumed by the different function of both algorithms. We can see that our scheme is more expensive than Chow’s which is normal. If we take for example the key generation of our scheme, even before implementing it we can see that it will cost almost 3 times more than Chow’s algorithm since we generate 7 elements for the secret key against 2 for Chow. The most important information that we must take into account, is that encryption and reencryption functions consumes the most compared to keys generation and decryption. Practically, encrypt and key generation functions wont be so called. Generating reencryption keys, depends on the number of delegations needed, but still not constraining in terms of time consuming. On the other side, reencryption function is called for each new user, new delegation and changed session key. Having an independent service that do the reencryption work is lightening (Fig. 3).
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Fig. 3 Tasks distribution between different environments
4 Conclusion In the context of smart grid we proposed the PREaaS to manage large data flows. However, data producers remain limited and constrained in terms of resources. But the implementation of this type of solution based on asymmetric encryption remains possible. Several works aim to optimize FPGA implementation of elliptic curve and now there is even implementation of Weil and Tate pairing. The PREaaS will allow the use of different PRE algorithm in future such as BBS with ephemeral keys, Ateniese scheme... As a part of the VERTPOM’s project, we will work on authentication issues in multi cloud systems which we haven’t treated in this work.
References 1. Matrosov A, Rodionov E, Harley D, Malcho J (2010) Stuxnet under the microscope. ESET LLC (September 2010) 2. Smart Grid Coordination CENCENELECETSI. Group sgcg/m490. B_Smart Grid Report First set of standards Version, 2 (2014) 3. Boronat JP (2017) Véritable énergie du territoire positif et modulaire 4. A compliance package for smart meters. https://www.cnil.fr/en/innovativehomeenergymanagementcompliancepackagesmartmeters 5. Sbai A., Drocourt C, Dequen G (2019) Pre as a service within smart grid cities. In: 16th international conference on security and cryptography 6. Fiat A, Naor M (1993) Broadcast encryption. In: Annual international cryptology conference. Springer, pp 480–491 7. Sahai A, Waters B (2005) Fuzzy identitybased encryption. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 457–473 8. Boneh D, Franklin M (2001) Identitybased encryption from the weil pairing. In: Annual international cryptology conference. Springer, pp 213–229 9. Blaze M, Bleumer G, Strauss M (1998) Divertible protocols and atomic proxy cryptography. In: International conference on the theory and applications of cryptographic techniques. Springer, pp 127–144 10. Ivan AA, Dodis Y (2003) Proxy cryptography revisited. In: NDSS 11. Jivanyan A, Yeghiazaryan R, Darbinyan A, Manukyan A (2015) Secure collaboration in public cloud storages. In: CYTEDRITOS international workshop on groupware. Springer, pp 190– 197 12. Sbai A, Drocourt C, Dequen G (2020) CCA secure unidirectional pre with key pair in the standard model without pairings. In: 6th international conference on information systems security and privacy
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13. Gueron S, Krasnov V (2015) Fast prime field ellipticcurve cryptography with 256bit primes. J Cryptogr Eng 5(2):141–151 14. Stark E, Hamburg M, Boneh D (2013) Stanford javascript crypto library
Effective and Robust Detection of Jamming Attacks for WBANBased Healthcare Monitoring Systems Asmae Bengag, Amina Bengag, and Omar Moussaoui
Abstract In the last few years, WBAN or Wireless Body Area Network has attracted a huge number of researchers to ameliorate the quality of healthcare. Furthermore, due to the sensitive data transmitting in WBAN, we need to enhance the security field that still suffers from various challenges. In this paper, we listed the main constraints and requirements security in WBAN System. Then we present our proposed IDS (Intrusion detection System) for detecting jamming attacks in WBAN based on some network parameters. Finally, to study the severity of jamming attack we applied our proposed IDS by using two types of MAC protocols: ZigBee and TMAC implemented on OMNET++ as simulator tool and Castalia as platform. Keywords WBAN · Security · Jamming · IDS · Severity · ZigBee · TMAC · OMNET++ · Castalia
1 Introduction Medical sensors are the main component in wireless body area network for transmitting and receiving a sensitive data via wireless medium to the PDA (Personal Device Assistant), by using Bluetooth (802.15.1) or ZigBee (802.15.4). The main goal of WBAN technology is to remotely control or supervise the person or the animal wearing these sensors, in order to improve the quality of health and studies [1, 2]. Moreover, the IEEE 802.15.6 standard classifies WBAN applications into two main areas: medical and nonmedical [3]. This later application could be in the sport field for training and monitoring the athlete’s performance or in the military A. Bengag (B) · A. Bengag · O. Moussaoui MATSI Laboratory, ESTO, University Mohammed 1st, Oujda, Morocco email: [email protected] A. Bengag email: [email protected] O. Moussaoui email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_16
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Fig. 1 The WBAN system
domain by monitoring activity of solider by using GPS (Global Positioning System). In the second type of WBAN application by using mini portable sensors, the person can control the state of the health remotely as the diabetes, the activity of heart and muscles activity as shown in Fig. 1. However, there are some serious problems threat the patient’s life due to the wireless communication used, because this mode is open on several types of attacks. In our contribution, we will focus on jamming attacks by proposing a robust IDS based on network parameters: PDR (Packet Delivery Ratio), ECA (Energy Consumption Amount), RSSI (Received Signal Strength Indication), BPR (Bad Packet Received), to efficiently detect this kind of attack. Moreover, to prove the effectiveness of our IDS, we applied it by using two MAC protocols (ZigBee and TMAC), and then identify which one could cause more severe damage in WBAN system. This paper is organized as follows: Sect. 2 describes the specific obstacles and of WBAN systems. Section 3 reviews mainly three types of jamming attacks. In Sect. 4 we explain in detail our proposed IDS. After that, Sect. 5 presents the used scenarios, the simulations and evaluates the results according to two MAC protocols. Finally, we conclude our paper and gives some future works.
2 WBAN Constraints WBAN system has its own unique constraints when compared with other traditional wireless networks that require us to take them in consideration while developing our IDS. Some of the most important constraints are presented and explained above:
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Table 1 Types of jamming attacks Jamming types
Example
Constant jamming
Continuously sends data packets in the wireless network
Reactive jamming
Sends packet when the legitimate node starts to transmit data
Deceptive jamming Transmits packet that seem legitimate in order to saturate the network [7, 8]
Limited Energy. Some securities mechanisms do not take into account the conception of energy, using for instance a complex algorithm. Besides, the security settings, such as storing encryption or decryption keys [4]. Hence, the energy is one of the biggest constraint to wireless network. Storage Space and Memory. The sensors used have very small memory and limited storage space [5]; therefore, for developing a security mechanism, it is necessary to limit the size of the security algorithm code. Wireless Communication Mode. The type of communication used in sensor networks is not 100% reliable; the transmitted packet could be damaged due to anomalies as collision. Moreover, the communication could be blocks or disturbs by an attack like jamming, wormhole and flooding.
3 Review on Jamming Attacks Jamming attacks are a specific type of DoS (Denial of Service) attacks that disturb and block the communication in the network, so for between the nodes. These attacks threat two main layers of OSI (Open System Interconnection) model, physical and data link layer. For the first layer, jamming generates radio interferences and sends signals in the medium [6] that makes collision between the sensors. Besides, the jammer node makes the legitimate nodes consume a lot of energy, which targeted more specifically MAC protocol [5]. There are several types of jamming attacks. In our work, we are focused mainly on three types: reactive jammer, deceptive jammer and constant jammer as shown in Table 1.
4 Proposed IDS for Jamming The IDS is one of the most mechanisms used to identify the existence of jamming attack in the network. Our proposed IDS is based on four main network parameters as Packet Delivery Ratio (PDR), Energy Consumption Amount (ECA), Bad Packet Ratio (BPR) and Received Signal Strength Indication (RSSI). The use of these parameters allows us to detect less false alerts.
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The mechanism of the proposed IDS could be spited in two steps. The first step consists on proposing that WBAN system operates normally, for calculating the threshold of the four parameters of each received sensors. In the second step, the nodes will periodically compare the initial values (PDRth, ECAth, BPRth and RSSIth) with the current input values. Hence, for launching an alert that mentioned the presence of an attack, we are mainly based on three conditions to detect and determine the type of jamming attacks. Each condition consists of four subconditions that should be achieved. Indeed, in any case conditions the PDR is lower and the RSSI is higher to their thresholds. Condition 1: Applied to detect constant jamming, which makes the node in listening mode, thus, cause a higher energy consumption. • • • •
PDR < PDRth BPR < BPRth RSSI > RSSIth ECA > ECAth
Condition 2: Used to detect reactive jamming, which found that the energy still normal • • • •
PDR < PDRth BPR > BPRth RSSI > RSSIth ECA < ECAth Condition 3: Used to detect deceptive jamming
• • • •
PDR < PDRth BPR > BPRth RSSI > RSSIth ECA > ECAth
5 Simulation and Results In this section, we discuss the different scenarios for understanding the mechanism of jamming attack in WBANs system. Moreover, we proved that the used network parameters are very useful for detecting the presence of jamming attacks in WBAN. Then we explain the negative effect of jamming attacks, by specifying their severity according two types of MAC protocols (ZigBee and TMAC). For the simulation, we are used the OMNET++ as simulation tool and Castalia 3.3 as platform, which is the most used for WBAN [9].
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5.1 Proposed Scenarios The first scenario contains a coordinator node and three types of medical nodes (EMG, EEG and ECG), in which we have used the ZigBee MAC protocol, and the time of simulation was about 300 s to get accurate results. In the second scenario, we let the same parameters as the first one, but we replace ZigBee protocol by the TMAC protocol. The two mentioned scenarios was simulated without any attacks to specify the results in a normal network. Then, a jamming attack was applying on these two scenarios in order to identify its severity. Moreover, as mentioned in Sect. 3, the jammer node does not respect the mechanism of MAC protocol, hence, we are used the BypassMAC as mac protocol.
5.2 The Severity of Jamming Attack In this subsection, we will focus on identifying the MAC Protocol that reduces the severity of jamming attack. Indeed, we will based on two main MAC protocols TMAC and ZigBee (802.15.4 MAC). According to the results given in Fig. 2, we can easily observe that in the two cases the jamming attack is detected. However, the TMAC protocol reduce the severity of jamming attack in WBAN system comparing to ZigBee protocol. In fact, in case of ZigBee protocol we have only 670 packets received per node, while the number of packet received per node is 1073 for TMAC protocol.
Fig. 2 Packet received per node under jamming attack using ZigBee and TMAC
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6 Conclusion In this paper, we are presented the main WBAN constraints that lead us to propose an effective and robust mechanism to detect jamming attacks. The proposed IDS is based on four main parameters (PDR, ECA, RSSI and BPR) that allows us to detect less false alerts. Furthermore, we are identify the severity of jamming attack according to MAC protocols: ZigBee and TMAC. More research is needed to develop and enhanced IDS based on other parameters to specify each type of jamming attacks. In future work, we aim to improve our IDS by detecting other types of attacks.
References 1. Saleem S (2009) On the security issues in wireless body area networks. Int J DigitContent Technol Appl 3(3):178–184 2. Pathania S, Bilandi N (2014) Security issues in wireless body area network. Int J Comput Sci Mob Comput 3(4):1171–1178 3. Movassaghi S, Mehran A, Lipman J, Smith D, Jamalipour A (2014) Wireless body area networks: a survey. IEEE Commun Surv Tutor. 16:1658–1686 4. Mostefa B, Abdelkader G (2018) A survey of wireless sensor network security in the context of Internet of Things. In: Proceedings of 2017 4th international conference on information and communication technologies for disaster management, ICTDM 2017, January 2018, pp 1–8 5. Jaitly S, Malhotra H, Bhushan B (2017) Security vulnerabilities and countermeasures against jamming attacks in Wireless Sensor Networks: a survey. In: 2017 international conference on computer, communications and electronics, COMPTELIX 2017, pp 559–564 6. Reyes HI, Kaabouch N (2013) Jamming and lost link detection in wireless networks with fuzzy logic. Int J Sci Eng Res 4(2):1–7 7. Manju VC, Sasi KM (2012) Detection of jamming style DoS attack in Wireless Sensor Network. In: Proceedings of 2012 2nd IEEE international conference on parallel distributed and grid computing, PDGC 2012, pp 563–567 8. DelValleSoto C, Valdivia LJ, RosasCaro JC (2019) Novel detection methods for securing wireless sensor network performance under intrusion jamming. In: CONIELECOMP 2019 2019 international conference on electronics, communications and computing, pp 1–8 9. Castalia (2011) Castalia Manual, March 2011
Design of Compact Bandpass Filter Based on SRR and CSRR for 5G Applications Mohamed Amzi, Saad Dosse Bennani, Jamal Zbitou, and Abdelhafid Belmajdoub
Abstract A new compact millimeterwave bandpass filter combining SRRs and CSRRs is designed to be used in the future communication systems. By etching two CSRRs in the ground plane of the SRR bandpass filter, good performances are obtained. The simulated return loss and insertion loss of the proposed filter operating at 26 GHz are better than −24 dB and −0.4 dB, respectively. The level of the rejection band is about −50 dB with compact size about 3.9 × 4.25 mm2 . Keywords Millimeterwave bandpass filter · Split Ring Resonator (SRR) · Complementary Split Ring Resonator (CSRR)
1 Introduction Unprecedented increases in the volume of wireless data traffic [1], have motivated the research and development of potential next generation wireless system technologies. These efforts have led to the development of 5G system engineering requirements which impose the implementation of relatively low cost and efficient systems. Current technologies use the frequency bands around 2.4 or 5 GHz, but these bands tend to become very saturated. To anticipate this and increase connection speeds, researchers have turned to the millimeterwave band. Filters are very important in different communication systems; they are commonly employed to suppress noise M. Amzi (B) · S. Dosse Bennani · A. Belmajdoub FST, SMBA University, Fez, Morocco email: [email protected] S. Dosse Bennani email: [email protected] A. Belmajdoub email: [email protected] J. Zbitou FST, HASSAN 1 University, Settat, Morocco email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_17
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and undesirable signals. Microwave filters must now meet increasingly imperative constraints in terms of selectivity (bandwidth, rejection), losses, size and production cost. The constraints are all the more difficult to maintain as the frequency increases, taking into account the short wavelengths involved in the various frequency ranges. Many millimeter wave bandpass filters using different topologies have been developed and proposed [2–14, 18]. In [2], waveguide filters offer the best solutions for insertion loss, but the size of such filters is not realistic for mobile applications. CMOS integrated filters are the smallest realizable filters, but the measured insertion loss is important [3]. Another technology called Substrate Integrated Waveguide (SIW) which is widely used for millimeter and sub millimeter applications [4, 5]. However, these filters are either difficult to be integrated with other components, or require high production costs. Therefore, microstrip line looks attractive for designing microwave and millimeterwave (MMW) bandpass filters due to its advantages of lowcost, compactsize, light weight and easy integration with other components. Different MMW microstrip bandpass filters have been recently introduced [6–14, 18]. In this work, a compact millimeterwave bandpass filter with small size, low insertion loss, and high rejection band is proposed. The filter consists of two Split Ring Resonators (SRR) to create the passband response and two Complementary Split Ring Resonators (CSRR) to improve the performances, based on RT/Duroid 5880 substrate. The dielectric constant is 2.2 and the loss tangent is 0.0009, the thickness is 0.127 mm with 35 μm thick copper conductor layer. The simulations are carried out using Ansoft’s HFSS and CSTMS software.
2 Filter Design It has become abundantly clear from the literature review that almost all microstrip bandpass filters employ resonators, due to their remarkable electrical performance. Among the different microstrip resonator structures, we distinguish between SteppedImpedance Resonators (SIR) [6], multimode resonators [7], SRRs and CSRRs [8–10], [16], multiband resonators [18] etc. In this first design, Fig. 3, the bandpass filter consists of a distributed coupling feeding line (port 1), tow SRRs and output feeding line (port 2).
2.1 The SRR and CSRR Analysis In previous research, split ring resonators (SRRs) and complementary split ring resonators (CSRRs) have been successfully applied to the design of microwave filters [8–10], diplexers [5, 11], antennas [15], etc. since, subwavelength measurement at the quasi static resonance, SRR and CSRR have small size with low radiative loss and high Q factors [8, 9]. On the other hand, CSRR as a metamaterial component
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Metallic conductor Substrate (a)
(b)
Fig. 1 Configuration of a the Split Ring Resonator (SRR), b the complementary Split Ring Resonator (CSRR)
provides a negative permittivity in the vicinity of its resonant frequency and produces sharp rejection band [16]. The SRR, Fig. 1(a), is formed by two concentric open rings, these rings can have several shapes (circular, square, triangular, etc.).
2.2 Design and Analysis of the SRR Bandpass Filter In this first design, a millimeterwave bandpass filter based on two identical SRRs is designed to have a fractional bandwidth 12.5% (or FBW = 0.125) at a central frequency f 0 = 26 GHz. f 1 = 24.25 GHz, f 2 = 27.5 GHz are, respectively, the lower frequency and the upper frequency of the bandwidth. A second order (n = 2) Chebyshev lowpass prototype with a passband ripple of 0.1 dB is chosen. The lowpass prototype parameters [17], are g0 = 1.0, g1 = 0.8431, g2 = 0.6220, and g3 = 1.3554. Having obtained the lowpass parameters, the bandpass design parameters can be calculated by [17]: Q e1 =
g0 g1 F BW
& Q e2 =
g2 g3 F BW
F BW & K 12 = √ g1 g2
(1)
where Qe1 and Qe2 are the external quality factors of the resonators at the input and output, respectively, and K 12 is the coupling coefficient between the two SRRs. For this design we have: Q e = Q e1 = Q e2 = 6.7445 & K 12 = 0.1726
(2)
we then carry out fullwave EM simulations to extract the external Qe and coupling coefficient K 12 against the physical dimensions using Ansoft’s HFSS software. Two design curves are obtained and plotted in Fig. 2. The information necessary to calculate the external quality factor is provided by the phase of parameter S11 (Fig. 2(a)), using the Eq. (3).
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Fig. 2 Design curves obtained by HFSS EM simulator for design of a SRR bandpass filter. a External quality factor. b Coupling coefficient
Qe =
f0 f ±90
(3)
To provide enough external couplings to the resonators, the openended feed lines are used [18]. As described in [18], strong magnetic intensity attribute to high conduction current in the openended line. The feed line length is set to be half wavelength (λ/2) of the operation frequency 26 GHz, where the strongest current intensity occurs in the center of the line (at l = λ/4). To obtain the value of the desired external Q e , a gap between the microstrip line and the resonant element equal to 60 μm is required. The second step consists in studying the coupling between the two SRRs. In this case, the excitation lines must be far enough away from the resonators so as not to disturb their operation. The proximity of the two resonators changes their resonant frequency, thus leaving two peaks in the electromagnetic response, Fig. 2(b). One corresponds to the even (or electric) resonance mode of frequency f 0e and the other to the odd (or magnetic) resonance mode of frequency f 0m . The coupling coefficient K 12 between two resonators is expressed from the resonance frequencies f 0e and f 0m as follows: 2 2 f 0e − f 0m (4) K 12 = ¬ 2 2 f 0e + f 0m The nearest value of K 12 of that calculated is obtained when the spacing between two resonators equal to 70 μm. The layout of the SRR bandpass filter design is illustrated in Fig. 3(a), all the determined dimensions are depicted in Table 1. The filter is excited by a 50 microstrip line with 0.39 mm weight. The simulation results for designed bandpass filter are shown in Fig. 3(b). The filter with resonant frequency of 26 GHz has high insertion loss (>−6 dB) and low return loss ( d2 with α = 1.5. • Case of d03 = d02 and d3 = αd2 In this part, we study the case when two defects of lengths d02 = 1.1D and d03 = 0.5D are inserted in the periodic serial loops structure, we take the other parameters constant such d1 = 0.2D, d2 = 0.4D, and N = 7. The Fig. 4 shows the variation of the reduced frequency as a function of the parameter α (α = d3 /d2 ) of the periodic structure of loops for different lengths of d02 and d03 . For α < 1.5, we observe the existence of a defect mode in the first gap, while for the intermediate values of α (1.5 < d3 /d2 < 2.4), we observe the existence of a defect mode in the first and second band gaps, and for the value of α > 2.4, we also observe the existence of a defect mode in the first and third band gaps. We conclude that the number of defect modes remains constant in the case where both defect loops are asymmetric.
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3.3 Electromagnetic Band Structure and the Transmission Coefficient In this section, we discussed in Fig. 5 the evolution of transmission rate as a function of the reduced frequency when d03 = 3d3 . From this Figure, we observe clearly the existence of three very narrow defect modes (high quality factor Q) with very important transmission rates T = 75% (for the first mode around = 8.87), T = 80% (for the second mode around = 9.65), and T = 35% (for the third mode around = 10.9). So we can consider that our structure behaves like a triple frequencies filters with high performances. Now, we study in Fig. 6 the variation of the reduced frequency versus the ratio α = d03 /d3 . The gray areas represent the pass band of the infinite system, while the white areas correspond to the photonic band gaps when exist the defect modes. According this Fig. 6, we can see the defect modes inside gaps, these defect modes Fig. 5 The transmission variation versus the reduced frequency with d1 = 0.2D, d3 = 0.9D d2 = 0.4D, d03 = 3d3 and N = 7
Fig. 6 Variation of the reduced frequency as a function of α = d03 /d3 with d1 = 0.2D, d2 = 0.4D, d3 = 0.9, N = 7
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decrease also in frequencies by increasing the defect length d03 . This variation of the frequencies is due to the interaction between the electromagnetic wave and the Eigen modes of the two defects. This behavior of the defective modes is analogous in the case of the presence of defects (cavities) in a multilayer structure [1] or in the defectives photonic star waveguide structure [11].
4 Conclusion In this paper, we have studied the existence of large photonic band gaps (PBGs) in the band structure and the transmission coefficient of a onedimensional serial loops structure (SLS) with asymmetric loops pasted together with a slender backbone of finite length. The existence of the gaps in the spectrum is attributed to the conjugate effect of the periodicity and the resonant modes of the loops. The single asymmetric defects at the loop level are shown to introduce localized defect modes inside gaps. These defect modes appear as very narrow peaks which show that their quality factor is very higher with strong amplitude in the transmission spectrum. These defect modes are closely related to the various parameters of the structure, in particular the lengths of the two defects. Such systems can find some useful applications in the designing of electromagnetic filters inside large gaps for application in telecommunication field. As perspectives, one can introduce the effect of the attenuation of the electromagnetic wave which makes by the materials constituting the structure.
References 1. BenAli Y, Tahri Z, Bouzidi A, Jeffali F, Bria D, Azizi M, Khettabi A, Nougaoui A (2017) Propagation of electromagnetic waves in a onedimensional photonic crystal containing two defects. J Mater Environ Sci 8:870–876 2. Kumar V, Anis M, Singh KS, Singh G (2011) Large range of omnidirectional reflection in 1D photonic crystal heterostructures. Optik 122:2186–2190 3. Goyal AK, Dutta HS, Pal S (2017) Recent advances and progress in photonic crystalbased gas sensors. J. Phys. D Appl. Phys. 50:203001 4. Ghadban A, Ghoumid K, Bouzidi A, Bria D (2016) Coupled selective electromagnetic waves in 1D photonic crystal with two planar cavities. In: 5th international conference on multimedia computing and systems (ICMCS). IEEE, pp 753–756 5. BenAli Y, Tahri Z, Bria D (2019) Electromagnetic filters based on a single negative photonic comblike structure. Progr Electromagn Res 92:41–56 6. BenAli Y, Ghadban A, Tahri Z, Ghoumid K, Bria D (2020) Accordable filters by defect modes in single and double negative star waveguides grafted dedicated to electromagnetic communications applications. J Electromagn Waves Appl 34(4):539–558 7. Aynaou H, El Boudouti EH, El Hassouani Y, Akjouj A, DjafariRouhani B, Vasseur J, Benomar A, Velasco VR (2005) Propagation and localization of electromagnetic waves in quasiperiodic serial loop structures. Phys. Rev. E 72(5):056601
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8. El Boudouti EH, Fettouhi N, Akjouj A, DjafariRouhani B, Mir A, Vasseur JO, Dobrzynski L, Zemmouri J (2004) Experimental and theoretical evidence for the existence of photonic bandgaps and selective transmissions in serial loop structures. J. Appl. Phys. 95(3):1102–1113 9. Mir A, Akjouj A, Vasseur JO, DjafariRouhani B, Fettouhi N, El Boudouti EH, Dobrzynski L, Zemmouri J (2003) Observation of large photonic band gaps and defect modes in onedimensional networked waveguides. J. Phys.: Condens. Matter 15(10):1593 10. Essadqui A, BenAli J, Bria D, DjafariRouhani B, Nougaoui A (2010) Photonic band structure of 1D periodic composite system with left handed and right handed materials by green function approach. Progr Electromagn Res 23:229–249 11. Benali Y, Tahri Z, Ouariach A, Bria D (2018) Double frequency filtering by photonic comblike. IEEE, pp 1–6 12. Bouzidi A, Bria D, Akjouj A, Pennec Y, DjafariRouhani B (2015) A tiny gassensor system based on 1D photonic crystal. J Phys D Appl Phys 48:495102–495109 13. Bouzidi A, Bria D (2019) Low temperature sensor based on onedimensional photonic crystals. In: International conference on electronic engineering and renewable energy, Springer, Singapore, vol 519, pp 157–163 14. Bouzidi A, Bria D, Falyouni F, Akjouj A, Lévêque G, Azizi M, Berkhli H (2017) A biosensor based on onedimensional photonic crystal for monitoring blood glycemia. J Mater Environ Sci 8:3892–3896
Effect of the Hydrostatic Pressure on the Electronic States Induced by a GeoMaterial Defect Layer in a Multiquantum Wells Structure Fatima Zahra Elamri, Farid Falyouni, and Driss Bria
Abstract In GaAs/Ga1−x Al x As MQW systems, an applied hydrostatic pressure modifies the structure of the electron band, resulting changes in the energy states of the electrons. The application of the hydrostatic pressure modifies the height of the barrier, the effective masses and the thicknesses of the constituent layers. In our study, we apply a hydrostatic pressure on MQWs consisting of altering layers Ga As/Ga0.6 Al0.4 As with a geomaterial defect layer placed in the middle of the structure. So to investigate the effect of the applied pressure, we study the transmission and the variation of the energy levels for three aluminium concentrations used in the defect layer at different applied pressure values. The defect modes are moving toward lower energies when we increase the hydrostatic pressure. It changes also the position and the number of the defect modes appeared inside the gaps. The results show that our structure is sensitive to pressure and temperature variations of approximately 1 kbar, and T = 20 K. Keywords Hydrostatic Pressure · MQWs · Electronic states · Geomaterial · Aluminium concentration · Defect
1 Introduction The change of the energy states in the quantum well with an applied external pressure can be attributed to two major effects: the change in the electrons effective mass and the generated piezoelectric fields inside the well and the barrier materials. And the variation of the carrier effective mass, which shifts the energy states in the well [1]. The applied hydrostatic pressure changes the direct and indirect transitions F. Z. Elamri (B) · F. Falyouni · D. Bria Equipe: Ondes, Acoustique, Photonique et Matériaux, Laboratoire des Matériaux, Ondes, Energie et Environnement, Faculté des Sciences, Université Mohammed Premier, 60000 Oujda, Morocco email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_20
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electronhole. As reported by Dai et al. [2], who have investigated the transitions in Ga As/Ga1−x Al x As multiple QWs as a function of hydrostatic pressure up to 50 kbar. They observe a number of spectral structures associated with both direct and indirect transitions. Morales et al. [3] has calculated and studied the donorrelated density of states and polarizability in a GaAs(Ga, Al)As quantumwell under hydrostatic pressure and applied electric field, and finding that the binding energy increases with increasing hydrostatic pressure for a certain well thickness and temperature. Kasapoglu et al. has studied the combined effects of hydrostatic pressure and temperature on donor impurity binding energy in GaAs/Ga0.7 Al0.3 As double quantum well (DQW) under the external fields [4]. Moreover, Mercy et al. [5] have found that the carrier concentration could be decreased with increasing pressure when the samples are cooled to low temperatures in hydrostatic pressure transport studies of modulated doped QWs. The investigation of the pressure and temperature dependence of the energy gaps in semiconductors has been the subject of many studies [6, 7]. Boucenna et al. [8] were investigated the influence of pressure (0–20 kbar) and temperature (0–300 K) on the electronic band parameters for zincblende Ga1−x Al x As. Also, Làrez et al. [9] have proposed an empirical model to analyze the variation of the direct energy gap Eg with temperature and alloy composition in the system Ga1−x Al x As. Capaz et al. [10] have studied the pressure and composition effects on the gap properties of Ga1−x Al x As theoretically and experimentally. Degheidy et al. [11] calculated the electronic band structure of Ga1−x Al x As alloy under the effects of composition x, temperature T, and hydrostatic. Zhao et al. adopted a variation method to calculate a donor’s binding energy in a QW structure with finite barriers under hydrostatic pressure and found that the donor binding energy increases monotonically with pressure the region from 0 to 40 kbar [12]. In the following, the work is divided into three sections. The first section gives a brief overview about the Ga As/Ga Al As superlattice and the effect of the applied hydrostatic pressure. The second section presents the structure used in this work and the calculation formalism of the main properties of our structure. As a third section, we present the founded results such, the transmission, the band structure and the quality factor. To sum up, a conclusion of the founded results.
2 Structure and Formalism It is known that in GaAs/Ga1−x Al x As MQW systems, an applied hydrostatic pressure modifies the structure of the electron band, resulting in modifications of the energy states of the electrons and holes. This applied pressure modifies also the thickness of the layers in the MQWs, as given by [13]: d(P) = d0 [1 − (S11 + 2S12 P)]
(1)
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where d0 is the initial thickness at zero pressure, the elastic constants are S11 = 1.16 ∗ 10−3 kbar −1 and S12 = −3.7 ∗ 10−4 kbar −1 for the Ga As well, and for the Ga1−x Al x As barrier of concentration x = 0.4, the elastic constants are S11 = (1.16 + 0.03x) ∗ 10−3 kbar −1 and S12 = (−3.7 − 0.02x) ∗ 10−4 kbar −1 . The application of the hydrostatic pressure modifies even the height of the barrier, the effective masses of the wells and the barriers m ∗(w,b) (P, T ). The effective mass of the Wells is given by [13] is: 1 2 m0 = 1 + E p( + ) (2) m ∗w ( p, T ) E g ( p, T ) E g ( p, T ) + Δ0 Here, m 0 is the mass of free electrons, and E p = 7.51 eV is the energy related to the element of the momentum matrix, Δ0 = 0.341eV is the spinorbit fractionation, and E g (P, T) is the pressure and temperature dependent gap energy for QW of Ga As. The expression for E g (P, T ) is: E g (P, T ) = E g (0, T ) + bp + c P 2
(3)
1.519−(5.4 10−04 T 2 ) , b = 0.0126 eV/kbar and c = 3.7710−5 eV/(kbar)2 . with E g (0, T ) = T +204 The effective conduction mass in the barrier is obtained from a linear interpolation between Ga As and Al As compounds , i.e. m ∗b (p,T) = m ∗w ( p, T ) + 0.083xm 0 . x, here is the concentration of Al in the layer. In our study, the MQWs structure consists of N = 10 periods of two semiconductor materials, Ga As, Ga Al As with a thickness respectively d1 = 50 A◦ and d2 = 30 A◦ , under a pressure value P = 0 kbar. The aluminum concentration for the barriers is equal to x = 0.4. The defect here is a Ga Al As layer with a varied aluminum concentration from xde f = 0.2 to xde f = 0.4, a defect thickness d0 = 70 A◦ ; and a position jde f = N2 (the middle of the structure) (Fig. 1).
Fig. 1 A MQWs consisting of N periods, Ga As/Ga Al As of a concentration x = 0.4, and thicknesses respectively equal to d1 and d2 with geomaterial defect inside (a), Energetic profile of the perfect MQWs structure (b).
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Fig. 2 Dispersion curve of an infinite MQWs for different pressure values P = 0 kbar (blue), P = 20 kbar (red), P = 30 kbar (black)
3 Results and Discussions We start with the effet of the applied pressure on the band structure of a MQWs. Figure 2 shows the dispersion curve of an infinite MQWs for different pressure values P = 0 kbar, P = 20 kbar and P = 30 kbar, for a T = 300 K. It is found that for different values of pressure, there is a shift of the spectrum for lower energies. The increase in pressure causes a shif of the permissible and the gaps bands for lower values of energies. This shift increases when the hydrostatic pressure applied increases too. The characteristics of the geomaterial defect modes, such transmission and quality factor, can be significantly affected by the variations of the hydrostatic pressure and the temperature. The defect introduced inside the MQWs, leads to the creation of a defect mode inside the gap bands (localized modes) [14, 15]. For a concentration xde f = 0.2 (Fig. 3a), one mode appears inside the gap. However, two localized modes for xde f = 0.3 − 0.4 (Fig. 3b, c). We find that by increasing the hydrostatic pressure, these localized modes shift to regions of lower energies due to the increase of the effective mass of the defect layer GaAlAs. However, when the temperature increases the effective mass of the defect layer (Ga Al As) decreases too (Fig. 4). The increase of the effective mass due also to the increase of the gap energy, when we increase the applied pressure. To have an over view about the existence and the behavior of the defect modes. Fig. 5, represents the variation of the energy levels as a function of the alumnium concentration used for the defect layer, and under an applied pressure value variad from P = 10 kbar to P = 30 kbar (Fig. 5a, c) with a temperature T = 300 K. One can see a welldefined defect mode localized inside the gap bands for a concentration less than 0.25 for a pressure P = 30 kbar (Fig. 5a), 0.28 for P = 20 kbar (Fig. 5b), and 0.3 for P = 10 kbar (Fig. 5c). But for an aluminuim concentrations higher than those previous concentrations, we have a rise of the number of the defect modes to two modes, with good transmission rates.
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Fig. 3 Electronic transmission spectrum as a function of the energy of incoming electronic wave for different Aluminium concentration (a) xde f = 0.2, (b) xde f = 0.3, (c) xde f = 0.4, under a hydrostatic pressure P = 10 kbar (black), P = 20 kbar (red), P = 30 kbar (blue).
Fig. 4 Variation of the effective mass of Ga Al As as a function of the pressure and the temperature.
Fig. 5 Variation of the energy levels as a function of the alumnium concentration for different pressure values: (a) P = 30 kbar, (b) P = 20 kbar, (c) P = 10 kbar.
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Fig. 6 Electronic transmission spectrum as a function of the energy of incoming electronic wave for different pressure values, with a step of 1 Kbar, (a) for the first mode, (b) for the second mode. Table 1 The quality factor of the two localized modes as a function of the applied pressure with a step of 1 kbar.
P = 30 kbar P = 31 kbar P = 32 kbar Mode 1 39954 Mode 2 2416.43
43768 3781
47138 4142.92
We have also definined the highest sensitivity of the defect modes with the variation of the hydrostatic pressure, for a defect concentration equal to xde f = 0.3. We plot the transmission spectrum as a function of the energy, for an interval of applied pressure. In Fig. 6 the transmittance spectrum for the defective MQWs is presented, we chose a constant temperature of T = 300 K and an applied pressure interval [30 kbar–32 kbar] with a step of 1 kbar. We found then welldefined modes with good transmission rates, so a change of 1 kbar leads to a shift of 0.69 meV for the first mode and a shift of 0.94 meV for the second mode. Additionally, the calculation of the transmittance spectrum allows us to calculate the quality factor Q, which is defined as the ratio between the central energy and the full width at halfmaximum of the transmittance modes. The shift from the defect modes to lower energies is accompanied by an increasing of the Q factor as the hydrostatic pressure increases, from Q 1 = 39954 to Q 1 = 47138 for the first mode, and Q 2 = 2461.43 to Q 2 = 4142.92 for the second mode (Table 1). Unlike the result presented in Fig. 6, the shift of the defect modes in this case move toward the higher energies for P = 30 kbar and varied temperature values 260 K, 280 K and 300 K (Fig. 7). The shift between two successive modes is equal to 0.58 meV for the first mode and 0.78 meV, for the second modes for a temperature variation equal to 20 K. The quality factor in this case increases when the temperature value
Effect of the Hydrostatic Pressure ...
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Fig. 7 Electronic transmission spectrum as a function of the energy of incoming electronic wave for different temperature values, with a step of 20 K, (a) for the first mode, (b) for the second mode. Table 2 The quality factor of the two localized modes as a function of the temperature with a step of 20 K.
T = 300 K T = 280 K T = 260 K Mode 1 35950 Mode 2 3851
35900 3141
35840 2883
increases (from Q 1 = 35840 to Q 1 = 35950 for the first defect mode and from Q 2 = 2883 to Q 2 = 3851.24 for the second defect mode), which is the opposite of the case where the applied pressure increases (Table 2).
4 Conclusion We have studied the dependence of the hydrostatic pressure and temperature on the MQWs transmittance spectrum consisting of alternating layers of GaAs and Ga0.6 Al0.4 As, taking into account the variations caused on the thickness and effective mass of the layers. The effects are mainly due to the variations in the thicknesses and the mass effective of the defect layer Ga Al As by the hydrostatic pressure. As the pressure increases, the effective mass of the defect layer Ga Al As increases causing a shift of the spectrum to lower energies. The number of the defect modes appeared inside the gaps depends on the aluminuim concentration used in the defect layer. On the other hand, the quality factor of these defect modes (localized modes) increases as the applied pressure increases, accompanied by a shift of the spectrum at lower energies. We also found that the defect mode has a shift to higher energies, with a decrease in the quality factor as the temperature increases for a constant pressure value.
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References 1. Carlo AD, Lugli P (1995) Valley mixing in resonant tunnelling diodes with applied hydrostatic pressure. Semicond Sci Technol 10(12):1673–1679 2. Dai N, Huang D, Liu XQ, Mu YM, Lu W, Shen SC (1998) Observation of direct and phononassisted indirect transitions inGaAs/GaAlAs multiquantum wells under hydrostatic pressure. Phys Rev B 57(11):6566–6572 3. Morales AL, Montes A, López SY, Raigoza N, Duque CA (2003) Donorrelated density of states and polarizability in a GaAs(Ga, Al)As quantumwell under hydrostatic pressure and applied electric field. Phys Status Solidi (c), 0(2):652–656 4. Kasapoglu E (2008) The hydrostatic pressure and temperature effects on donor impurities in GaAs/GaAlAs double quantum well under the external fields. Phys Lett A 373(1):140–143 5. Mercy JM, Bousquet C, Robert JL, Raymond A, Gregoris G, Beerens J, Linh NT (1984) Hydrostatic pressure control of the carrier density in GaAs/GaAlAs heterostructures; role of the metastable deep levels. Surf Sci 142(1–3):298–305 6. Elabsy AM, Elkenany EB (2010) Thermal response to electronic structures of bulk semiconductors. Phys B Condens Matter 405(1):266–271 7. Elabsy AM, Degheidy AR, Abdelwahed HG, Elkenany EB (2010) Pressure response to electronic structures of bulk semiconductors at room temperature. Phys B Condens Matter 405(17):3709–3713 8. Boucenna M, Bouarissa N (2005) Effects of hydrostatic pressure and temperature on electronic band parameters in AlGaAs. Czechoslov J Phys 55(1):65–72 9. Lárez C, Rincón C (1997) Alloy composition and temperature dependence of the direct energy gap in AlGaAs. J Phys Chem Solids 58(7):1111–1114 10. Capaz RB, de Araújo GC, Koiller B, von der Weid JP (1993) Pressure and composition effects on the gap properties of AlGaAs. J Appl Phys 74(9):5531–5537 11. Degheidy AR, Elkenany EB (2012) Temperature and hydrostatic pressure dependence of the electronic structure of AlGaAs alloys. Mater Sci Semicond Process 15(5):505–515 12. Zhao G, Liang X, Ban S (2003) Binding energies of donors in quantum wells under hydrostatic pressure. Phys Lett A 319(1–2):191–197 13. SegoviaChaves F, VinckPosada H (2018) Simultaneous effects of the hydrostatic pressure and the angle of incidence on the defect mode of a onedimensional photonic crystal of GaAs/Ga0.7Al0.3As. Optik 164:686–690 14. Elamri FZ, Falyouni F, KerkourEl Miad A, Bria D (2019) Effect of defect layer on the creation of electronic states in GaAs/GaAlAs multiquantum wells. Appl Phys A 125(10):740 15. Elamri FZ, Falyouni F, Tahri Z, Bria D (2018) Localized states in GaAs/GaAlAs multiquantumwells. In: Proceedings of the 1st international conference on electronic engineering and renewable energy, pp 137–145
Simulation and Optimization of Cds/ZnSnN2 Structure for Solar Cell Applications with SCAPS1D Software A. Laidouci, A. Aissat, and J. P. Vilcot
Abstract In this paper, we are interested in simulating and modeling of Cds/ZnSnN2 structure for a solar cell using SCAPS1D. The ZnSnN2 is considered as one of the promising absorber materials for photovoltaic application due to the high optical efficiency and the low cost. In the present work, we have investigated the effects of the thickness of the buffer and the absorber layers, the temperature on electrical parameters (Voc) the opencircuit voltage and (Jsc) the shortcircuit current density, (FF) fill factor and (η) efficiency of the solar cell. The results show a remarkable improved of the efficiency a η = 26.49% under the AM1.5G spectrum, one sun and deformation of 0.51% between the Cds and the ZnSnN2. The achieved results show that the ZnSnN2 is a very promising material for thin film photovoltaics and offers a number of interesting advantages compared to (CIGS) and (CZTS) due to its high efficiency, earthabundant, nontoxic and inexpensive element. Keywords ZnSnN2 · High efficiency · Photovoltaic parameters
1 Introduction ZnSnN2 is an IIIVV2 semiconductor material which composed of only of earthabundant, nontoxic and inexpensive elements [1–3]. The IIIVV2 semiconductors are closely related to the wurtzitestructured IIIN (IIIV) semiconductors so they have similar electronic and optical properties, direct bandgaps and large optical absorption coefficients [4–6]. Inx Ga1x N and ZnSnN2 are both great potentials as photovoltaic absorber layers [7]. The polycrystalline ZnSnN2 films were synthesized on monocrystalline substrates (such as sapphire) by MBE (Molecular Beam Epitaxy) A. Laidouci · A. Aissat (B) Faculty of Technology, University of Blida 1, 09000 Blida, Algeria email: [email protected] A. Aissat · J. P. Vilcot Institute of Electronics, Microelectronics and Nanotechnology (IEMN), UMR CNRS 8520, University of Sciences and Technologies of Lille 1, Avenue Poincare, 60069, 59652 Villeneuve of Ascq, France © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_21
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[8], RF sputter deposition [9], plasmaassisted VLS (VaporLiquidSolid) technique [10]. The optical directbandgap of the ZnSnN2 were determined to be around 1.7 eV [11]. In addition, theoretical studies report that ZnSnN2 has a tunable direct bandgap ranging from 1 to 2 eV depending on the degree of disorder in which the material crystallizes [12]. In this work, we are interested in modeling and simulating of ZnSnN2 solar cell using SCAPS1D in goal to show the effects of the thickness of the buffer and the absorber layers, the temperature on the characteristic’s parameters of the studied solar cell. The Solar Cell Capacitance Simulator structures (Scaps1D) is a onedimensional solar cell device simulator able to solving the basic semiconductor equations, the Poisson and the continuity equations for carriers (electrons and holes) [13].
2 Structure of ZnSnN2 Solar Cells and Simulation The ZnSnN2 solar cell structure consists of a ptype absorber layer ZnSnN2 , the Al is considered as back contact deposited on a glass substrate [14], an ntype buffer Fig. 1 Structure of the ZnSnN2 solar cell
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5x105
4x105
(cm1)
Absorption coefficient of ZnSnN 2 3x105
2x105
1x105
0 0,2
0,4
0,6
0,8
1,0
1,2
( m)
Fig. 2 Absorption coefficient of ZnSnN2
layer made of nCdS and window layer made of nZnO: Al. The cell is illustrated schematically in Fig. 1. The absorption coefficient of the direct bandgap materials used in the simulation is given by [15]: α(λ) =
4πk(λ) λ
(1)
Where k is the extinction coefficient [15], and λ is the wavelength. In Fig. 2, it is clear according to the absorption coefficient curve, we can notice a high absorption coefficient in the UV region due to intrinsic absorption for the energies (E Eg) which made it comparable to IIIV materials [15]. The input parameters used in the simulation are shown in Table 1.
2.1 Effects of ZnSnN2 Absorber Thickness on ZnSnN2 Thin Films Solar Cell The EQE (External Quantum Efficiency) defined as [20]: E QE =
Jph(λ) qF(λ)
Jph : total photogenerated current density. q: electron charge. F(λ): solar flow.
(2)
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Table 1 Physical parameters used in the simulation [10, 16–19]
Input
Materials
Parameters
ZnSnN2 (p)
Cds (n)
ZnO: Al (n)
ε
15
10
9
Eg (eV)
1.5
2.4
3.3
Ea. (eV)
4.1
4.2
4.45
Nc (cm−3 )
1.2 × 1018
2.2 × 1018
2.2 × 1018
Nv (cm−3 )
7.8 × 1019
1.8 × 1019
1.8 × 1019
12.68
100
100
5.26
25
μe
(cm2 V−1 S−1 )
μh (cm2 V−1 S−1 ) Nd
(cm−3 )
2.01 ×
1020
Na
(cm−3 )
1.79 ×
1021
d (nm) a(nm) εxx =
as−ae ae
1×
25 1017
1 × 1018
0
0
10002000
1080
50
0.585
0.582
/
0.515%
/
100
EQE (%)
80
60
Temperature (300K) W Cds= 80 nm wZnSnN2=1.0 wZnSnN2=1.2 wZnSnN2=1.4 wZnSnN2=1.6 wZnSnN2=1.8 wZnSnN2=2.0
40
20
0 300
450
m m m m m m
600
750
900
(nm)
Fig. 3 Effect of ZnSnN2 thickness on the quantum efficiency
Figure 3 shows the spectral response of the device as a function of ZnSnN2 absorber thickness. The simulated results reveal the significant increase of the external quantum efficiency (EQE) with the increase of absorber thickness (ZnSnN2 ) in the range of 400 to 800 nm, this can be explained by the increase of photons collection at longer wavelengths. The absorption of longer wavelengths photons has resulted in the generation of more carriers (electronhole pairs) in the device. In Fig. 4(a and b), at the room temperature, we display the variation of J(V) characteristic of the studied ZnSnN2 solar cell and the emitted power for different thickness wZnSnN2 ( wp ) of our structure, it has been shown according to our results that the shape of the curves increases as the absorber thickness increase. The results
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28 Temperature (300K) W Cds= 80 nm wZnSnN2=1.0 m wZnSnN2=1.2 m wZnSnN2=1.4 m wZnSnN2=1.6 m wZnSnN2=1.8 m wZnSnN2=2.0 m
2
Power density (mW/cm )
26 24 22 20 18 16 14
(b)
12 10 8 6 4 2 0 0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,2
1,4
Voltage (V) 28
2
Current density (mA/cm )
26
(a)
24 22 20 18
Temperature (300K) W Cds= 80 nm
16 14
wZnSnN2=1.0 wZnSnN2=1.2 wZnSnN2=1.4 wZnSnN2=1.6 wZnSnN2=1.8 wZnSnN2=2.0
12 10 8 6 4
m m m m m m
2 0 0,0
0,2
0,4
0,6
0,8
1,0
Voltage (V)
Fig. 4 J(V) characteristics for different wp (wZnSnN2 ), b: P(V) characteristics for different wp (wZnSnN2 ) with wn (wcds ) = 80 nm at 300 K
Table 2 The thickness effects of ZnSnN2 Absorber layer on the photovoltaic parameters at 300 K Wp (μm)
Voc (V)
Jsc (mA/cm2 )
FF (%)
η (%)
1
1.3088
22.00
82.18
23.66
1.2
1.3144
22.63
82.41
24.52
1.4
1.3194
23.12
82.54
25.18
1.6
1.3231
23.50
82.67
25.70
1.8
1.3263
23.80
82.78
26.13
2
1.3292
24.05
82.85
26.49
reveal of varying wZnSnN2 from 1 to 2 μm leads to increasing of Jsc and efficiency due to absorbed photons which made the different wavelengths of illumination to be absorbed and contribute in carrier generation and efficiency will be increased, according to results shown in Table 2 and Fig. 5(a and b), it is shown an improvement
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1,335 Temperature (300K) W Cds = 80 nm
1,325
Voc (Volt)
25
Voc (Volt)) \c4(Jsc (mA/cm 2 ))
24
1,320
23
1,315
22
1,310
21
Jsc (mA/cm 2)
1,330
(a)
20
1,305 1,0
1,2
1,4
1,6
1,8
2,0
W ZnSnN ( m) 2
82,9
27 (b)
82,8
26
FF (%)
82,6
25
82,5 82,4
Temperature (300K) W Cds = 80 nm
82,3
FF (%) Efficiency (%)
24
Efficiency (%)
82,7
23
82,2 82,1
22 1,0
1,2
1,4
1,6
1,8
2,0
W ZnSnN ( m) 2
Fig. 5 a Variation of JSC and VOC as a function of ZnSnN2 thickness, b Variation of efficiency (%) and FF (%) as a function of ZnSnN2 thickness
of the efficiency from 23.66% for 1 μm to 26.49% for 2 μm (same values with maximum optical power due to the AM1.5G spectrum). An improvement of (Jsc ) from 22,00 mA/cm2 for 1 μm to 24.05 mA/cm2 , it is clear be the effect of ( wp ) is remarkable on (Jsc ) and a slight increase of (Voc ). Table 2 represents the variation of electrical parameters with the variation of the absorber thickness ZnSnN2 (wp ), the thickness of the buffer layer Cds (wn ) is set to 80 nm.
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2.2 Effects of Cds Buffer Thickness on ZnSnN2 Thin Films Solar Cell Figure 6 illustrates EQE (External Quantum Efficiency) for many thicknesses of buffer layer (Cds), it is clear that the raise of wn (wCds ), the EQE drop only over range of 0.3–0.51 μm which causes stronger absorption of photons in this interval of wavelengths before reaching the ZnSnN2 absorber layer in second step. Table 3 shows the impact of varying the buffer thickness CdS (wn) on the same electrical performances studied before, while the absorber thickness is fixed at 1 μm at 300 K. 100
80
EQE (%)
Temperature (300K) W ZnSnN = 1
60
m
2
w Cds = 10 nm w Cds = 20 nm w Cds = 30 nm w Cds = 40 nm w Cds = 50 nm w Cds = 60 nm w Cds = 70 nm w Cds = 80 nm
40
20
0 300
450
600
750
900
( nm)
Fig. 6 Effect of Cds thickness on the quantum efficiency
Table 3 The thickness effects of Cds buffer layer on the photovoltaic parameters at 300 K Wn (nm)
Voc (V)
Jsc (mA/cm2 )
FF (%)
η (%)
10
1.3116
22.44
89.40
26.31
20
1.3110
22.41
88.57
26.03
30
1.3105
22.39
87.29
25.61
40
1.3101
22.35
85.91
25.15
50
1.3097
22.29
84.63
24.71
60
1.3094
22.22
83.56
24.31
70
1.3091
22.12
82.76
23.97
80
1.3088
22.00
82.18
23.66
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2.3 Effects of Temperature on ZnSnN2 Thin Films Solar Cell Figure 7 illustrates the EQE (External Quantum Efficiency) measured at various temperature, according to the results, made us conclude that there’s no effect, so the values of EQE is not overly influenced by the temperature rise and our structure can be resistive to high temperatures (Table 4). Figure 8 shown the J(V) characteristics for different operating temperature. At higher temperature, the bandgap energy has been reduced and lead to the recombination of electrons and holes which affect the photovoltaic parameters like (Voc ), In photovoltaic application is the most affected due to the dependence of the reverse saturation current which is a function of temperature and consequently the changing of the other photovoltaic parameters due to derived from the opencircuit voltage (Voc ). The results are shown in Fig. 9(a and b), indeed, figures below confirm these findings. 100
EQE (%)
80
W ZnSnN2 = 1.0
60
W Cds
m
= 80 nm T 1 = 300K T 2 = 320K T 3 = 340K T 4 = 360K T 5 = 380K T 6 = 400K
40
20
0 300
450
600
750
900
(nm) Fig. 7 Effects of operating temperature on the quantum efficiency
Table 4 The effects of operating temperature on the photovoltaic parameters Temperature (K)
Voc (V)
Jsc (mA/cm2 )
FF (%)
η (%)
300
1.3088
22.00
82.18
23.66
320
1.2882
21.99
81.88
23.20
340
1.2671
21.99
81.58
22.73
360
1.2456
21.99
81.27
22.26
380
1.2237
21.98
80.98
21.79
400
1.2015
21.98
80.68
21.30
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24 22
2
Current density (mA/cm )
20 18 T 1 = 300K T 2 = 320K T 3 = 340K T 4 = 360K T 5 = 380K T 6 = 400K
16 14 12 10 8
W ZnSnN2 = 1.0 W Cds
m
= 80 nm
6 4 2 0 0,0
0,2
0,4
0,6
0,8
1,0
1,2
1,4
Voltage (V)
Fig. 8 J(V) characteristics for different operating temperature
Table 5 Our photovoltaic parameters based on ZnSnN2 solar cell compared with other solar cells Thin film solar cell
Voc (V)
Jsc (mA/cm2 )
FF (%)
η (%)
CZTS/Cds/ZnO [22]
0.8202
24.13
61.68
12.21
CIGS/Cds/ZnO [20]
0.6756
25.06
78.52
19.13
ZnSnN2 /Cds/ZnO
1.3292
24.05
82.85
26.49
Table 5 shows our photovoltaic parameters based on ZnSnN2 solar cell compared with other solar cells. Is it clear that ZnSnN2 solar cell offers a number of interesting advantages compared to (CIGS) and (CZTS) due to high efficiency ≈ 30% (ShockleyQuiesser limit [21]), high absorption coefficient ≈ 105 cm1 comparable to IIIV semiconductors [23], earthabundant, nontoxic and inexpensive element.
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A. Laidouci et al. 22,005
1,32 (a)
1,30
22,000
Voc (Volt)
21,990 1,24 21,985
Voc (Volt)) 2 Jsc (mA/cm )
1,22
2
21,995 1,26
Jsc (mA/cm )
1,28
21,980
1,20
21,975
1,18 280
300
320
340
360
380
400
420
operating temperature(K) 24,0
83,0
82,5
23,5
82,0
23,0
81,5
22,5
81,0
22,0 FF (%) Efficiency (%)
80,5
Efficiency (%)
FF (%)
(b)
21,5
21,0
80,0 280
300
320
340
360
380
400
420
operating temperature(K)
Fig. 9 a Variation of JSC and VOC as a function of operating temperature. b Variation of efficiency (%) and FF (%) as a function of operating temperature
3 Conclusion In summary, we have studied the effect of the thickness and the temperature on the characteristics of the solar cell using new material ZnSnN2 (ZnIVN2 ), which is an earthabundant, nontoxic and inexpensive material, the photovoltaic parameters have been calculated under different parameters such as thickness and temperatures using SCAPS1D. The obtained efficiency in the present study is better when we take the effect of the thickness of ZnSnN2 absorber layer and operating temperature into account that gives an enhanced electric efficiency the optimized value of the efficiency of 26.49% was achieved. From this work, we found that ZnSnN2 solar cell offers a number of interesting advantages compared to (CIGS) and (CZTS) due to high efficiency ≈ 30% (ShockleyQuiesser limit), high absorption coefficient ≈ 105 cm1 , earthabundant, nontoxic and inexpensive element.
Simulation and Optimization of Cds/ZnSnN2 ...
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19. Martinez AD, Fioretti AN, Toberer ES, Tamboli AC (2017) Synthesis, structure, and optoelectronic properties of II–IV–V2 materials. J Mater Chem A 5:11418–11435. https://doi.org/10. 1039/C7TA00406K 20. Arbouz H, Aissat A, Vilcot JP (2017) Simulation and optimization of CdSn/Cu2ZnSnS4 structure for solar cell applications. Int J Hydrog Energy 13:8827–8832. https://doi.org/10. 1016/j.ijhydene.2016.06.185.cole 21. Fioretti AN (2017) Development of zinc tin nitride for application as an earth abundant photovoltaic absorber. http://adsabs.harvard.edu/abs/2017PhDT…….111F 22. Mebarkia C, Dib D, Zerfaoui H, BelghitR (2016) Energy efficiency of a photovoltaic cell based thin films CZTS by SCAPS. J Fundam Appl Sci 8:363–371. https://doi.org/10.4314/jfas.v8i 2.13 23. Harchouch N, Aissat A, Laidouci A, Vilcot JP (2018) Temperature effect on InGaN/GaN multiwell quantum solar cells performances. In: Hatti M (éd) artificial intelligence in renewable energetic systems, pp 492–498. Springer, Heidelberg
Numerical Characteristics of Silicon Nitride SiH4 /NH3 /H2 Plasma Discharge for Thin Film Solar Cell Deposition Meryem Grari and CifAllah Zoheir
Abstract The creation of a uniform deposition requires a thorough study and understanding of the different characteristics of plasma discharge. In this work, we are interested in modeling a radiofrequency (RF) plasma discharge using silicon nitride gases SiH4 /NH3 /H2 . The plasma equations are solved using the numerical finite element method until a periodic steady state is obtained. The numerical results show the fundamental characteristics of RF plasma between the two reactor electrodes. These characteristics allow us to describe the physics of plasma discharge so that physicochemical processes can be implemented for more efficient and less costly deposition. Keywords RF plasma · Silicon nitride · Thin film solar cell · Numerical modeling · Numerical finite element method
1 Introduction Silicon nitride is one of the hardest and resistant ceramics. Considerable attention is devoted to the fabrication of thin films and electronic devices such as solar cells, image sensors, thinfilm transistors and many others [1–4]. Thin films based on hydrogenated silicon nitride (SiNx Hy ) can be deposited in radiofrequency (RF) plasma reactors [5, 6]. These discharges present weakly ionized gases, initiated by an external electric or magnetic field. The field applied between the two electrodes produces high energy electrons and neutral species at room temperature. Ions easily transfer energy in elastic collisions with neutral species and are therefore generally near to neutral temperature. Due to the energy difference between electrons and neutral species, the discharge is not at the local thermodynamic equilibrium. In addition, high energy electrons are able to ionize and dissociate neutral species at high speeds even if the gas temperature between sheaths is relatively low. M. Grari (B) · C. Zoheir Department of Physics, LETSER Laboratory, University Mohamed First, Oujda, Morocco email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_22
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The study of chemistry in silicon nitride discharge is important to optimize the properties of the material [7–10]. For economic reasons, a high deposition rate and an efficient use of the gas are desired. In this work, we study a simulation of silicon nitride. The discharge is made in a reactor of PECVD type (plasma enhanced chemical vapor deposition). We have used the onedimensional (1D) finite element method. A layer of silicon nitride is deposited from the silicon diluted in ammonia and hydrogen in a capacitive coupled PECVD reactor. The results have been validated and compared with literature works.
2 Numerical Modeling 2.1 Electric Model Radiofrequency (RF) electromagnetic fields are generated by excitation structures varying between two parallel metallic plates polarized by an RF voltage. These fields transfer their energy to electrons through heating mechanisms that can be collisional or noncollisional. The field form and intensity will depend on the structure used [11, 12]. The plasma is separated at the electrodes by two positive space charge sheaths. The plasma oscillates at the excitation frequency. The radiofrequency power delivered by the generator controls the current and the RF voltage between the electrodes. The other external parameters are: the excitation frequency, the pressure, and the space between electrodes. The electric field is derived from the scalar potential gradient: E = −∇V
(1)
V = Vr f . sin(2π f R F t)
(2)
where f R F and Vr f are, respectively, frequency and amplitude of alternating voltages.
2.2 Plasma Model The following Eqs. (3) to (9) represent a set of equations solved in a low temperature plasma simulation. Electron and ion transport ∂n i + ∇Γ i = Si ∂t
(3)
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∂n e + ∇Γ e = Se ∂t
(4)
Γ e = −n e μe E − ∇(n e D)
(5)
Γ i = −n i μi E − ∇(n i D)
(6)
∂n ε + ∇Γ ε + EΓ e = Sε ∂t
(7)
Γ ε = −n ε με E − ∇(n ε Dε )
(8)
ε0 ∇ E = e(n e − n i )
(9)
Electron and ion flux
Electron energy
Energy flux
Electric field
where n e and n i are the electron and ion densities, μe = m eeveN is the electron mobility and D = m eTveeN is the diffusion constant, veN is the frequency of elastic collision, n ε = n e ε is the electron energy density, E is the electric field, Te is the electronic temperature. Source terms Se = Si = Sε =
x r kr N n n e
(10)
x r kr N n n i
(11)
xr kr Nn n e εr
(12)
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kr = γ ∫ εσr (v) f (ε)dε
(13)
where xr is the molar fraction of species r, Nn is the total density of the neutrals, kr is the kinetic coefficient, σr (v) are the cross sections of elastic and inelastic collisions, f (ε) is the Maxwellian electronic energy distribution function. Boundary Conditions • The limit condition for the Poisson equation is the electric potential value of the electrodes: V = 0 electrical potential at the cathode. Vr f = V0 sin(ωt) electrical potential at the Anode. Here, ω and Vr f are respectively the pulsation and the amplitude of the alternative voltages. • The limit condition of electrons has a proportional flux to their thermal velocity, whereas ions have a zero gradient near the walls: Je = 5
vth n e − γ p Jion 4
Jion = −μi n i ∇V
(14) (15)
where vth is the thermal velocity of electrons and γ p is the secondary electron emission coefficient. • Quantities vth and qe are calculated by: vth k B 4 8k B Te vth = π me qe =
(16)
(17)
2.3 Chemical Reactions The chemical model takes into account elastic and inelastic collisions via electrons for the three species: silicon, nitride and hydrogen. In this section we present the chemical reactions used in our model. In the tables below we present the main reactions taken into account for the calculation of the source terms and which are based on Morgan’s work [13] (Tables 1 and 2).
Numerical Characteristics of Silicon Nitride SiH4 /NH3 /H2 Plasma Discharge … Table 1 Energy of collision reactions NH3 , SiH4 and H2
Table 2 Kinetic coefficient of reactions
Reactions
Energy (eV)
1: e+NH3 => e+NH3 *
0.42
2: e+NH3 => 2e+NH+3
10.2
3: e+SiH4 => e+SiH4 *
0.27
4: e+SiH4 => 2e+SiH+4
12.9
5: e+SiH4 => e+SiH3 + H
4
6: e+SiH4 => e+SiH2 + H2
2.2
7: e+H2 => e+H2 *
15
8: e+H2 => e+H2 *
16.6
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Reaction
Kinetic coefficient m3 /(s.mol)
9: SiH4 + SiH2 => Si2 H6
2.8 107
10: SiH4 + H => SiH3 + H2
1.9 106
11: SiH4 + NH => HSiNH2 + 3.6 106 H2 12: SiH4 + NH2 => SiH3 + NH3
2.4 106
13: SiH4 + NH => SiH3 + NH2
2.4 107
3 Results and Discussion In this work we are interested to study the numerical modeling of the RF plasma discharge in a CCP reactor, driven by a sinusoidal voltage of 13.56 MHz frequency at a temperature of 573 K and a pressure of order of 0.3 Torr. The gas mixture used is the silicon diluted with ammonia and hydrogen. The RF voltage is assumed to be 130 V, applied to the cathode for an interelectrode of 2.7 cm. The onedimensional spatiotemporal variation of various plasma characteristics in four phases of the last RF cycle is shown in Fig. (1) and (2). Figure 3 represents the velocity of the electrons in comparison with the temperature and the ionic velocity in comparison with the field for a discharge time of 7.39 μs. In this section we discuss the results obtained taking into account the analogue works cited in [9, 14–19]. The electron density shown in Fig. 1a is nearly constant for all four phases during the whole period except sheaths, where the density oscillates slightly. The field shown in Fig. 1b is small but nonzero through most of the discharge and includes an ambipolar field which is responsible for the acceleration of the ions up to the anode, and an RF field that drives the current of the electrons. These results are the same to that of Bavefa [9] and Samir [16] with respect to electron, temperature and electric field distributions. Figure 2 shows the variation of the electron temperature between the two electrodes. The maximum occurs around the cathode portion of the cycle for phase 0
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Fig. 1 a Electronic density (cm−3 ), b Electric field (V.cm−1 )
Fig. 2 Electron temperature (eV)
Fig. 3 a Electronic velocity (cm.s−1 ) in comparison with electronic temperature (eV) b Ions velocity (cm.s−1 ) in comparison with electric field (V.cm−1 )
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(average 11 eV); the temperature becomes smaller in the region of the anode (average 4 eV). These results are consistent with Daoxin [18]. By comparing the density and temperature results obtained using ammonia as a precursor gas with the results obtained using argon gas [14], we have concluded that ammonia gas has a higher electron density and a lower electron surface temperature. Consequently, the consideration of ammonia gas instead of argon gas allows a more uniform deposition [19]. Figure 3a shows that the evolution of the electronic velocity follows the evolution of the electronic temperature. The velocity is at maximum in the cathode region so the electrons are strongly accelerated, which explains the high temperature in this region. In the central region the velocity drops due to collisions and strong scattering, which reduces the action of the electric field. Figure 3b shows that the ion velocity follows the evolution of the electric field. This is to be expected since it is consistent with the hypothesis Eq. (15) that ion flux is proportional to the electric field. Examination of the evolution of all these characteristics and the comparison with theory and the literature allows us to conclude the validity of the model used in this study.
4 Conclusion Our work focuses on the numerical modeling of a radiofrequency plasma discharge using silicon diluted with ammonia and hydrogen. Plasma equations are solved using the finite element method, which has the advantage of being more suitable for complex geometries. Solving the plasma equations allowed us to show the evolution of fundamental plasma characteristics such as density, temperature and velocity. A comparison of these results with the literature shows that the model used provides a good description of the evolution of these characteristics. In addition, we have shown that the use of ammonia as a precursor gas instead of argon provides a higher electron density and lower surface temperature. Consequently, the use of ammonia instead of argon results in a more uniform deposition. Finally, this work presents important results in terms of understanding the fundamental structure of radiofrequency plasma discharge, as well as its behavior as physicochemical reactor. This allows us to establish a deposition of thin layers of silicon more efficient and less expensive.
References 1. Bonilla RS, Hoex BP, Hamer, Wilshaw PR (2017) Dielectric surface passivation for silicon solar cells: a review. Phys Status Solidi (a) 214(7):1700293 2. Chen B, Zhang Y, Ouyang Q, Chen F, Zhan X, Gao W (2017) The SiNx films process research by plasmaenhanced chemical vapor deposition in crystalline silicon solar cells. Int J Mod Phys B 31(16–19):1744101
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3. Bonilla RS, Jennison N, ClaytonWarwick D, Collett KA, Rands L, Wilshaw PR (2016) Corona charge in SiO2: kinetics and surface passivation for high efficiency silicon solar cells. Energy Procedia 92:326–335 4. Pan HW, Kuo LC, Huang SY, Wu MY, Juang YH, Lee CW, Chao S (2018) Silicon nitride films fabricated by a plasmaenhanced chemical vapor deposition method for coatings of the laser interferometer gravitational wave detector. Phys Rev D 97(2):022004 5. Kim HJ, Yang W, Joo J (2015) Effect of electrode spacing on the density distributions of electrons, ions, and metastable and radical molecules in SiH4/NH3/N2/He capacitively coupled plasmas. J Appl Phys 118(4):043304 6. Kim BH, Cho CH, Kim TW, Park NM, Sung GY, Park SJ (2005) Photoluminescence of silicon quantum dots in silicon nitride grown by NH 3 and SiH 4. Appl Phys Lett 86(9):091908 7. Novikova T, Kalache B, Bulkin P, Hassouni K, Morscheidt W, Roca i Cabarrocas P (2003) Numerical modeling of capacitively coupled hydrogen plasmas: effects of frequency and pressure. J Appl Phys 93(6):3198–3206 8. Xia H, Xiang D, Yang W, Mou P (2016) Multimodel simulation of 300 mm siliconnitride thinfilm deposition by PECVD and experimental verification. Surf Coat Technol 297:1–10 9. Bavafa M, Ilati H, Rashidian B (2008) Comprehensive simulation of the effects of process conditions on plasma enhanced chemical vapor deposition of silicon nitride. Semicond Sci Technol 23(9):095023 10. Joo J (2011) Numerical modeling of SiH4 discharge for Si thin film deposition for thin film transistor and solar cells. Thin Solid Films 519(20):6892–6895 11. Lieberman MA, Lichtenberg AJ (2005) Principles of plasma discharges and materials processing. Wiley, Hoboken 12. Smirnov BM (2008) Physics of ionized gases. Wiley, Hoboken 13. Morgan database. www.lxcat.net. Accessed 27 Oct 2016 14. Meryem G, CifAllah Z (2019) Numerical modeling of plasma silicon discharge for photovoltaic application. Mater Today Proc 13:882–888 15. Boeuf JP, Pitchford LC (2005) Electrohydrodynamic force and aerodynamic flow acceleration in surface dielectric barrier discharge. J Appl Phys 97(10):103307 16. Samir T, Liu Y, Zhao LL, Zhou YW (2017) Effect of driving frequency on electron heating in capacitively coupled RF argon glow discharges at low pressure. Chin Phys B 26(11):115201 17. Kawamura E, Lieberman MA, Lichtenberg AJ (2018) Symmetry breaking in a high frequency, low pressure, symmetric capacitive coupled plasma (CCP) reactor. In: APS meeting abstracts 18. Daoxin H, Jia C, Linhong J, Yuchun S (2012) Simulation of cold plasma in a chamber under highand lowfrequency voltage conditions for a capacitively coupled plasma. J Semicond 33(10):104004 19. Kim HJ, Lee HJ (2017) Effects of the wall boundary conditions of a showerhead plasma reactor on the uniformity control of RF plasma deposition. J Appl Phys 122(5):053301
A Numerical Study of InGaAs/GaAsP Multiple Quantum Well Solar Cells Using Radial Basis Functions M. A. Kinani, A. Amine, Y. Mir, and M. Zazoui
Abstract In this work, a numerical study using radial basis functions (RBF) is performed on a p+ in+ junction GaAs solar cell. So, we solve the differential equations satisfied by the density of excess photogenerated minority carriers in the front and rear regions of this junction. We observe the effect of back surface recombination velocity on the minority carrier distribution and the internal quantum efficiency (IQE) in the p type and n type regions. Next, we study the effect of insertion into the i region multiple of InGaAs/GaAsP quantum wells (QWs) with ultrathin GaAs spacers inserted between the QW and the barriers. Precisely, we focus attention on the effect of In content and the number of QWs on IQE. Keywords Multiple Quantum Wells (MQWs) · pin solar cell · Radial basis Function Method (RBF)
1 Introduction Significant research on MQW solar cells has been conducted in recent years. The beginning was in 1990, when Barnham et al. [1] suggested inserting stressbalanced MQWs, into the undoped region of a pin bulk material in a solar cell. It extends the absorption edge, and it is possible to convert more photons in the solar spectrum and improve the output current density. We are investigating the use of InGaAs/GaAsP strainbalanced MQWs. Since the InGaAs well and the GaAsP barrier cause an opposite direction of stress on the substrate when grown by epitaxy on GaAs, the stress is totally cancelled [2]. This has been the subject of several research studies, both experimental and theoretical. These have focused on improving performance by the Highaspectratio QW [3], a smart placement of the QWs in the structure [4], M. A. Kinani (B) · A. Amine · Y. Mir · M. Zazoui Laboratory of Condensed Matter and Renewable Energy, FST Mohammedia, University of Hassan II, Casablanca, Morocco email: [email protected] A. Amine Laboratory Instrumentation Measurement and Control, Chouab Doukkali University, Eljadida, Morocco © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_23
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their period number and their composition to keep them strainbalanced [5, 6]. We will try to see the effect of In content and the number of QWs on IQE. But before this, a use of RBF will be introduced at the beginning. We suggest a numerical approach to solve the differential equations that are satisfied by the density of the excess photogenerated minority carriers in the front and rear regions of this junction. Even though the equations have analytical solutions. This method, which is especially used in the area of computational mechanics [7–9] and also has been used for the study of a pn junction [10], would be used for the first time in the field of photovoltaic cells.
2 Governing Equations As shown in Fig. 1, the epitaxial p+ in+ type GaAs solar cell is divided in three main regions (front layer p+ , intrinsic (i) region and back layer n+ region). According to this model the thicknesses of these regions which are x p , xi and xn respectively.
2.1 The Front p+ Region and the Rear n+ Region The steady state continuity equations for the front and rear layers under illumination are expressed by d 2 (n p − n p0 ) n p − n p0 α(λ) (1 − R(λ)) F(λ)e(−α(λ)x) − = − dx2 L 2n Dn
(1)
d 2 ( pn − pn 0 ) pn − pn 0 α(λ) (1 − R(λ)) F(λ)e(−α(λ)x) − = − dx2 L 2p Dp
(2)
where n p − n p0 (resp pn − pn 0 ) is the excess minority carriers in the p+ (resp n+ ) region, L p (resp L n ) is the minority carrier diffusion length, D p (resp Dn ) is the corresponding diffusion coefficient, α(λ) is the material absorption coefficient, F(λ)
0
xp
xp + xi
n+ − GaAs
i − GaAs
M QW s
i − GaAs
p+ − GaAs
Fig. 1 The structure of the solar cell
xp + xi + xn
x
A Numerical Study of InGaAs/GaAsP MQW Solar Cells Using RBF
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the number of photons of wavelength λ incident on the surface per unit area and unit time (depth x = 0) and R(λ) is the reflection coefficient of light at the front side. The boundary conditions which accompany Eqs. (1) and (2) when the solar cell is short circuited are Dn
d(n p − n p0 ) = Sn (n p − n p0 ) dx (n p − n p0 ) = 0 ( pn − pn 0 ) = 0
Dp
d( pn − pn 0 ) = −S p ( pn − pn 0 ) dx
at at
at
x =0
(3)
x = xp
(4)
x = x p + xi
(5)
at
x = x p + xi + xn
(6)
where Sn and S p are the front and the back recombinaison velocities respectively. The corresponding IQE contribution from these regions are −Dn d(n p −n p0 ) dx x=x p [I Q E(λ)]n = (1 − R(λ)) F(λ) D p d( pn − pn0 ) dx x=x p +xi [I Q E(λ)] p = (1 − R(λ)) F(λ)
(7)
(8)
2.2 The i Region The i region of the cell consists of a superlattice (S L) sandwiched between two Ga As layers, with a thickness adapted to obtain a total undoped region (Ga As+S L layers) of 3 µm. SL (N periods) is made alternation of 8.9 nm Ga As0.9 P0.1 barrier, 7 nm I n x Ga1−x As well and two 0.6 nm Ga As interlayers between each barrier and well [11]. The IQE contribution of this region consists of two parts – The barrier region [I Q E(λ)]b = e−α(λ)x p 1 − e−0,5α(λ)(xi −L M QW )
+ 1 − A M QW (λ) 1 − e−0,5α(λ)(xi +L M QW ) – From the SL
[I Q E(λ)] M QW = A M QW e−α(λ)(x p +0,5(xi −L M QW ))
where L M QW is thickness of the SL and A M QW is the absorption in the SL.
(9)
(10)
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3 Radial Basis Function Collocation Method The RBF method is used to solve the Eqs. (1) and (2)
3.1 The Front p+ Region Let {xk }k=1,2,...,N be the points in the interval ]0, x p [ and 0, x p the points on the boundary. This method assumes that the solutions is represented by a linear combination of RBFs at predefined N + 2 nodes (n p − n p0 )(x) =
N +2
αk Ψ (x − xk )
(11)
k=1
where αk are unknown coefficients to be determined, x − xk  being the distance between where the RBF is centered and where it is evaluated as measured, and Ψ is the radial basis function. In this paper we use The normalized Multiquadric (MQ) RBF, i.e., x − xk 2 + c2 (12) Ψ (x − xk ) = xm where xm = min1i< jN xi − x j  and c is called a shape parameter. Substituting Eq. (11) in Eq. (1), Eq. (3) and Eq. (4) gives the linear system of equations ⎛
Γn Ψx1 (x1 ) Γn Ψx1 (x2 ) ⎜ Γn Ψx (x ) Γn Ψx2 (x2 ) ⎜ 2 1 ⎜ . . ⎜ . . ⎜ . . ⎜ ⎜ Ψ (x ) Γ Ψ Γ ⎜ n xN 1 n x N (x 2 ) ⎜ ⎝γn Ψx N +1 (x1 ) γn Ψx N +1 (x2 ) Ψx N +2 (x1 ) Ψx N +2 (x2 )
··· ··· . . . ··· ··· ···
⎞⎛ ⎞ ⎛ ⎞ · · · Γn Ψx1 (x N +1 ) Γn Ψx1 (x N +2 ) α1 f n (λ, x1 ) ⎟ ⎜ f n (λ, x2 ) ⎟ · · · Γn Ψx2 (x N +1 ) Γn Ψx2 (x N +2 ) ⎟ α ⎟⎜ 2 ⎟ ⎜ ⎟ ⎟⎜ ⎜ ⎟ . . . . . ⎟ ⎟⎜ ⎜ ⎟ . . . . . ⎟ ⎟⎜ ⎜ ⎟ ⎜ ⎟ . . . . . = ⎟⎜ ⎟ ⎜ ⎟ ⎟ ⎟ ⎜ · · · Γn Ψx N (x N +1 ) Γn Ψx N (x N +2 ) ⎟ ⎜ α N ⎟ ⎜ f n (λ, x N )⎟ ⎜ ⎟ ⎟⎝ ⎠ ⎝ ⎠ · · · γn Ψx N +1 (x N +1 ) γn Ψx N +1 (x N +2 )⎠ α N +1 0 α N +2 0 · · · Ψx N +2 (x N +1 ) Ψx N +2 (x N +2 )
α(λ) (1 − R(λ)) F(λ)e(−α(λ)x) where f n (λ, x) = − , Dn d d2 1 − Sn Γn ≡ − 2 and γn ≡ Dn dx2 Ln dx
(13)
Ψxi (xk ) = Ψ (xi − xk ),
3.2 The Front n+ Region In a similar way to the preceding subparagraph, let {xk }k=1,2,...,M be the points in the interval ]x p + xi , x p + xi + xn [ and x p + xi , x p + xi + xn the points on the boundary. So
A Numerical Study of InGaAs/GaAsP MQW Solar Cells Using RBF
( pn − pn 0 )(x) =
M+2
235
αk Ψ (x − xk )
(14)
k=1
Substituting Eq. (14) in Eq. (2), Eq. (5) and Eq. (6) gives the linear system of equations ⎛
Γ p Ψx1 (x1 ) Γ p Ψx1 (x2 ) ⎜ Γ p Ψx (x ) Γ p Ψx2 (x2 ) ⎜ 2 1 ⎜ . . ⎜ . . ⎜ . . ⎜ ⎜ ⎜ Γ p Ψx M (x1 ) Γ p Ψx M (x2 ) ⎜ Ψx M+1 (x2 ) ⎝ Ψx M+1 (x1 ) γ p Ψx M+2 (x1 ) γ p Ψx M+2 (x2 )
where
··· ··· . . . ··· ··· ···
⎞⎛ ⎞ ⎛ ⎞ · · · Γ p Ψx1 (x M+1 ) Γ p Ψx1 (x M+2 ) α1 f p (λ, x1 ) ⎟ ⎜ f p (λ, x2 ) ⎟ · · · Γ p Ψx2 (x M+1 ) Γ p Ψx2 (x M+2 ) ⎟ α ⎟⎜ 2 ⎟ ⎜ ⎟ ⎟⎜ ⎜ ⎟ . . . . ⎟ . ⎟⎜ ⎜ ⎟ . . . . ⎟ . ⎟⎜ ⎟=⎜ ⎟ . . . . . ⎟⎜ ⎟ ⎜ ⎟ ⎟⎜ ⎟ ⎜ f p (λ, x )⎟ · · · Γ p Ψx M (x M+1 ) Γ p Ψx M (x M+2 ) ⎟ ⎜ α M M ⎟ ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠ · · · Ψx M+1 (x M+1 ) Ψx M+1 (x M+2 ) ⎠ α M+1 0 α M+2 0 · · · γ p Ψx M+2 (x M+1 ) γ p Ψx M+2 (x M+2 )
d2 1 Γp ≡ − 2, dx2 Lp
α(λ) (1 − R(λ)) F(λ)e(−α(λ)x) Dp xm = min1i< jM xi − x j 
−
(15) d + Sp, γp ≡ Dp f p (λ, x) = dx x − xk 2 and Ψ (x − xk ) = + c2 with xm
4 Results and Discussion The solar cell is illuminated at AM1.5 (1000 W.m−2 ) light intensity and spectrum by using the ASTM G17303 tables [12]. The absorption and the reflection coefficients α(λ) and R(λ) for this type of material are given by Adachi [13]. The values of the parameters used in the calculations were taken from the published literature [14] and are shown in Table 1 Table 1 The parameters used in calculations Parameters
Unit
Surface recombination velocity for electrons (Sn ) cm.s−1 Surface recombination velocity for hole (S p ) cm.s−1 Diffusion length of electrons (L n ) µm Diffusion length of hole (L p ) µm Diffusion constant of electrons (Dn ) cm2 .s−1 Diffusion constant of holes (D p ) cm2 .s−1 Length of pregion (x p ) µm Length of iregion (xi ) µm Length of nregion (xn ) µm
Value 6000 6000 2 3 200 10 0.8 3 2
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All numerical procedures are written in Python language. The numpy.linalg.solve() function is used to solve each of two linear systems (13) and (15), Where – The MQ shape parameter is chosen as 10. – The number of nodes used is 1000 including the two nodes on the boundary. Using (11) and (14), the excess minority carrier concentrations were calculated and the results were plotted in Figs. 2a and 2b for various values of surface recombination velocity. We can see that the magnitude of the excess electron (hole) concentration is much higher near the front (rear) surface and decreases as the junction is reached. Also, its magnitude increases with the decrease of surface recombination velocity. For the calculation of A M QW (λ) we have used Solcore. This is a multiscale, Pythonbased library for modelling solar cells and semiconductor materials developed at Imperial College London [16]. The Eqs. (7) and (8) are then used to calculate IQE in the p and n regions and plotted in Figs. 3a and 3b as a function of wavelength for
(a)
(b)
Fig. 2 Variation of excess minority carrier concentration with distance for (a) different values of Sn (b) different values of S p
(a)
(b)
Fig. 3 Variation of IQE with wavelength of (a) p+ region for various values of Sn (b) n+ region for various values of S p
A Numerical Study of InGaAs/GaAsP MQW Solar Cells Using RBF
(a)
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(b)
Fig. 4 Variation of IQE with wavelength of in iMQWregion (a) for various values of In content (b) for various SL period numbers(x I n = 24%)
different values of recombination velocity. These four curves are identical to those obtained in the analytical expressions of excessive minority carrier concentrations and IQE [15]. We use the solcore.absorption_calculator.transfer_matrix.calculate_rat to Calculate absorbed intensity of the structure for the wavelengths and angles defined. It uses the Transfer matrix method. It can be seen in Fig. 4a that the spectral response beyond the band edge of the host material (GaAs) expands as the molar fraction of the well’s Indium increases. This is because a higher Indium content increases the depth of the well, allowing the absorption of less energetic light and more transitions. Figure 4b shows that increasing the number of wells extends the spectral response over the band edge of the host material (GaAs), and that between N = 90 and N = 100; the IQE saturates before weakening slightly beyond N = 100.
5 Conclusion A numerical study using the RBF was performed on a p+ in+ junction GaAs solar cell. We solved the differential equations satisfied by the density of the excess photogenerated minority carriers in the front and back regions of this junction. It is observed that the minority carrier distributions and the IQE of the front and rear region of the cell depends significantly on the respective surface recombination velocities. We studied the effect of insertion into the i region multiple of InGaAs/GaAsP quantum wells (QWs) with ultrathin GaAs spacers inserted between the QW and the barriers. It has been observed that the spectral response extends beyond the cutoff wavelength of the host material (GaAs), both by deepening the wells and by increasing their number.
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References 1. Barnham KWJ, Duggan G (1990) A new approach to highefficiency multibandgap solar cells. J Appl Phys 67(7):3490–3493. https://doi.org/10.1063/1.345339 2. EkinsDaukes NJ, Barnham KWJ, Connolly JP, Roberts JS, Clark J, Hill CG, Mazzer M (1999) Appl Phys Lett 75:4195. https://doi.org/10.1063/1.125580 3. Fujii H, Wang Y, Watanabe K, Sugiyama M, Nakano Y (2012, June) Highaspectratio structures for efficient light absorption and carrier transport in InGaAs/GaAsP multiple quantum well solar cells. In: 2012 IEEE 38th photovoltaic specialists conference (PVSC) Part 2. IEEE, pp 19. https://doi.org/10.1109/PVSCVol2.2012.6656741 4. AlonsoÁlvarez D, EkinsDaukes N (2016, March) Quantum wells for highefficiency photovoltaics. In: Physics, simulation, and photonic engineering of photovoltaic devices V, vol 9743. International Society for Optics and Photonics, p 974311. https://doi.org/10.1117/12.2217590 5. Sodabanlu H, Ma S, Watanabe K, Sugiyama M, Nakano Y (2012) Impact of strain accumulation on InGaAs/GaAsP multiplequantumwell solar cells: direct correlation between in situ strain measurement and cell performances. Jpn J Appl Phys 51(10S):10ND16. https://doi.org/10. 1143/JJAP.51.10ND16 6. Cabrera CI, Rimada JC, Connolly JP, Hernandez L (2013) Modelling of GaAsP/InGaAs/GaAs strainbalanced multiplequantum well solar cells. J Appl Phys 113(2):024512. https://doi.org/ 10.1063/1.4775404 7. Chen JT, Chen IL, Chen KH, Lee YT, Yeh YT (2004) A meshless method for free vibration analysis of circular and rectangular clamped plates using radial basis function. Eng Anal Bound Elem 28(5):535–545. https://doi.org/10.1016/S09557997(03)001061 8. Chinchapatnam PP, Djidjeli K, Nair PB (2007) Radial basis function meshless method for the steady incompressible NavierStokes equations. Int J Comput Math 84(10):1509–1521. https:// doi.org/10.1080/00207160701308309 9. Li K, Huang QB, Wang JL, Lin LG (2011) An improved localized radial basis function meshless method for computational aeroacoustics. Eng Anal Bound Elem 35(1):47–55. https://doi.org/ 10.1016/j.enganabound.2010.05.015 10. Kosec G, Trobec R (2015) Simulation of semiconductor devices with a local numerical approach. Eng Anal Bound Elem 50:69–75. https://doi.org/10.1016/j.enganabound.2014.07. 013 11. Fujii H, Wang Y, Watanabe K, Sugiyama M, Nakano Y (2012) Suppressed lattice relaxation during InGaAs/GaAsP MQW growth with InGaAs and GaAs ultrathin interlayers. J Cryst Growth 352(1):239–244. https://doi.org/10.1016/j.jcrysgro.2011.11.036 12. ASTM G17303(2012) (2012) Standard tables for reference solar spectral irradiances: direct normal and hemispherical on 37 tilted surface. ASTM International, West Conshohocken, PA. www.astm.org 13. Adachi S (2013) Optical constants of crystalline and amorphous semiconductors: numerical data and graphical information. Springer 14. Aroutiounian V, Petrosyan S, Khachatryan A, Touryan K (2001) Quantum dot solar cells. J Appl Phys 89(4):2268–2271. https://doi.org/10.1063/1.1339210 15. Biswas S, Sinha A (2017) An analytical study of the minority carrier distribution and photocurrent of apin quantum dot solar cell based on the InAs/GaAs system. Indian J Phys 91(10):1197–1203. https://doi.org/10.1007/s126480171026y 16. AlonsoÁlvarez D, Wilson T, Pearce P et al (2018) Solcore: a multiscale, Pythonbased library for modelling solar cells and semiconductor materials. J Comput Electron 17:1099–1123. https://doi.org/10.1007/s1082501811713
Plasmonic Demultiplexer Based on Induced Transparency Resonances: Analytical and Numerical Study Madiha Amrani, Soufyane Khattou, Adnane Noual, El Houssaine El Boudouti, and Bahram DjafariRouhani
Abstract We study both analytically and numerically the possibility to realize a simple plasmonic Yshaped demultiplexer made of an input line and two output lines. Each line consisting of a metalinsulatormetal (MIM) waveguide contains a specific resonator made of two stubs grafted at a given position from the input line. The two stubs on each line induce a plasmonic induced transparency (PIT) resonance in the transmission spectra characterized by a resonance squeezed between two zeros. The idea consists in coinciding at a given wavelength, a resonance on one line with a transmission zero on the other line. We give closedform expressions of the geometrical parameters allowing the selective transfer of a single mode in one line without affecting the other line. The analytical results, obtained by means of the Green’s function method, are confirmed by numerical simulation using finite element method via Comsol Multiphysics software. Keywords Plasmonic structure · Demultiplexer · Bound in continuum (BIC) · PIT resonance
M. Amrani (B) · S. Khattou · A. Noual · E. H. El Boudouti LPMR, Département de Physique, Faculté des Sciences, Université Mohammed I, Oujda, Morocco email: [email protected] S. Khattou email: [email protected] A. Noual email: [email protected] E. H. El Boudouti email: [email protected] B. DjafariRouhani Institut d’Electronique, de Microélectronique et de Nanotechnologie (IEMN), UMR CNRS 8520, Département de Physique, Université de Lille, 59655 Villeneuve d’Ascq, France email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_24
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1 Introduction Electromagnetically induced transparency (EIT) is a wellknown physical effect in atomic systems that arise because of quantum destructive interferences between two excitation pathways to an upper atomic level [1]. Steep dispersion and low absorption take place in a sharp transparency window, which makes it very attractive for a plenty of potential applications in slowing light, enhancing optical nonlinearity and data storage [2–4]. However, it was demonstrated that EIT resonances are not restricted to atomic systems and can be found in various types of classical structures, such as photonic crystal cavities [5], metamaterials [6–8], acoustic waveguides [9], solidliquid multilayers [10] and photonic circuits [11, 12]. In the optical domain, plasmonic structures have emerged as new systems in the photonic domain which enables to confine and manipulate light waves below the classical diffraction limit [13, 14] using the surface plasmon waves which propagate at the interface between dielectric and metal structures. In this context, MetalInsulatorMetal (MIM) structures have been widely used as nanowaveguides for filtering and demultiplexing light using different nanocavities [15–20]. Among different papers dealing with the classical analogue of EIT resonances in plasmonics, the socalled plasmonic induced transparency resonances (PIT), one can cite the work of Huang et al. [17] where two stubs grafted at the same site (with a cross shape) and slightly detuned are used. Each stub induces its own transmission zero and both stubs induce a resonance between the two transmission zeros giving rise to a well defined PIT resonance. Some years ago, some of us have studied both theoretically and experimentally the possibility to realize EIT resonances in a cross shape photonic circuit using coaxial cables [12]. This structure has been exploited to realize a Yshaped photonic demultiplexer based on such EIT resonances [21]. These circuits operate in the radiofrequency domain which renders their usefulness less important. In this paper, we exploit similar ideas to realize both PIT resonances as well as a plasmonic demultiplexer based on these resonances in the telecommunication domain. This study is performed (i) analytically using a onedimensional (1D) Green’s function approach [22], to accurately calculate the lengths of the different waveguides for an efficient demultiplexing and give a physical explanation of the observed phenomena and (ii) numerically, using a 2D finite element method by means of Comsol Multiphysics, to confirm the analytical results and show the capability of this approach for a quantitative prediction of the physical behaviors. The spatial localization of the magnetic field inside the system is performed using the second method. The analytical results are based on the resolution of Maxwell’s equations using the Green’s function method. There are two important physical quantities that enable to treat the 2D system as a 1D structure, namely: the wave vector k and the impedance Z which control the propagating behaviors of the wave within the studied system. The numerical results are based on finite element method using the versatile software ComsolMultiphysics package, namely: the electromagnetic waves interface and a refined triangular mesh. The details of the analytical calculation will be given elsewhere.
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This paper is organized as follows: in Sect. 2, we give an overview about the possibility to realize PIT resonances using two stubs grafted at the same site along a waveguide. In particular, we show how the wavelength and width of the PIT resonance can be tailored by detuning the lengths of the two stubs. In Sect. 3, we demonstrate both analytically and numerically the efficient demultiplexing in a Yshaped structure based on PIT resonances. The main conclusion is summarized in Sect. 4.
2 PIT Resonances in a Cross Structure: An Overview It is well established that the propagation of electromagnetic waves in 2DMIM waveguides can be handled using an analytical 1D model. Both transfer matrix [23] and Green’s function [24, 25] methods have been used to calculate the transmission coefficient through different MIM waveguides in presence of different cavities. In this section we give an overview about the possibility to realize a PIT resonance in a double stub structure [17] using both the Green’s function method and Comsol. The plasmonic structure presented in Fig. 1 is composed of two resonators with different lengths d1 and d2 grafted at the same place on an infinite waveguide. d = 50 nm is the width of the waveguide used in the numerical calculation. The waveguides are filled with air, whereas the surrounding metal is made of silver, it’s permittivity can be expressed by the wellKnown DrudeLorentz model [25]. Each resonator induces its own transmission zero. Between the two zeros, the resonator of length d0 = d1 + d2 induces a complete transmission resonance, this resonance is squeezed between two transmission zeros, its width depends on the detuning δ = d2 − d1 [12]. An example is given in Fig. 2 for two values of δ. In Fig. 2(a), the full line gives the transmission coefficient as function of the wavelength for a cross structure (Fig. 1) where both stubs are identical (d1 = d2 = 274 nm). The vertical arrow at 1577 nm indicates the position of a zero width resonance called bound in continuum (BIC) state [26], with an infinite lifetime. This resonance coincides with the two transmission
Fig. 1 Schematic illustration of the onedimensional plasmonic system with two grafted resonators of lengths d1 and d2 on the same site. The whole structure is inserted between two semiinfinite leads. d is the width of the waveguide used in the numerical calculation. The dashed lines indicate the equivalent one dimensional model used in the analytical calculation
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Fig. 2 a Transmission spectra as a function of the wavelength for d1 = d2 = 274 nm. The analytical results obtained by the Green’s function (continuous line) and numerical results obtained by Comsol (red circles) are plotted around the PIT resonance. The arrow indicates the position of the BIC state. b The Hz field map for the transmission zero mode at λr = 1577.12 nm in (a). c Same as in (a) but for d1 = 214 nm and d2 = 334 nm. d and e Hz field maps for the transmission zero at λ2 = 1877 nm and the PIT resonance at λr = 1577.12 nm (Fig. 2(c)) respectively
zeros induced by both stubs as indicated by the zcomponent of the magnetic field map (Fig. 2(b)). In order to enlarge this resonance, we have to take d1 slightly different from d2 as indicated in Fig. 2(c) where the detuning between the two stubs is taken such that δ = 120 nm. One can notice the existence of a resonance with a quality factor Q = 8.48 squeezed between two transmission zeros induced by both stubs. The Hz field map of the transmission zero at λ2 = 1877 nm (Fig. 2(d)) clearly shows that this mode is confined in the lower stub, similar result is obtained for the second transmission zero around λ1 = 1199.72 nm, whereas the magnetic field associated to the PIT resonance at λr = 1577.12 nm is almost fully transmitted (Fig. 2(e)). It is worth mentioning that the resonance does not reach unity because of the absorption in the metal. The analytical results, obtained by the Green’s function (continuous line), and numerical results, obtained by Comsol (red circles), are plotted around the PIT resonance. Both results are quite similar showing the validity of the 1D analytical model used in this work.
3 Plasmonic Demultiplexer Based on PIT Resonances In Ref. [21], using a demultiplexer based on coaxial cable waveguide with an input and two output lines (Fig. 3), we have given in closed form the necessary conditions
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Fig. 3 Schematic representation of a plasmonic demultiplexer with one input line and two output lines. Along the first output line, two resonators of lengths d1 and d2 are inserted at the same position on the waveguide at a distance d5 from the input line. Along the second output line, two resonators of lengths d3 and d4 are inserted at the same position on the waveguide at a distance d6 from the input line
to obtain a total transmission in one output line without disturbing the other line; these conditions can be obtained as function of the geometrical parameters of the structure for a given value of wavelength. Indeed, it was demonstrated that the six lengths d1 , d2 , d3 , d4 , d5 , and d6 (Fig. 3), should satisfy the following conditions for a given value of d0 and δ in order to reach a good demultiplexing, namely d1 =
δ d0 − 2 2 δ d0 + 2 2
(2)
d0 2
(3)
d0 +δ 2
(4)
d2 = d5 =
d3 = d6 =
d4 =
(1)
Figure 4 gives the analytical (full lines) and numerical (open circles) variations of the transmission spectra T1 , T2 and the reflection spectrum R versus wavelength for different values of δ around δ = 0. We can clearly notice that when the transmission T1 in the first output line (red curve) is maximal (i.e., T1 = 0.9), the transmission T2
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Fig. 4 Variation of the transmission coefficient in output 1 (red curve) and output 2 (blue curve), and the reflected signal in the input of the demultiplexer device (black curve) versus wavelength for different values of δ = d2 − d1 and for d0 = d1 + d2 fixed
in the second output line (blue curve) and the reflection R (black curve) vanish (i.e., T2 = R = 0). Similarly, when the transmission spectrum in the second output line (blue curve) is maximal (T2 = 0.8), the transmission T1 in the first output line (red curve) and the reflection R (black curve) vanish (i.e. T1 = R = 0). Both analytical and numerical results are in good agreement. As mentioned in Sect. 2, for a fixed length d0 = d1 + d2 , the PIT resonance obtained in the first output line appears at the same wavelength whatever the values of δ, its width decreases when δ decreases and vanishes for δ = 0 (Fig. 2(a)). Also, the shape and the width of the PIT resonance slightly change when δ becomes negative (i.e. when permuting the two resonators 1 and 2). However, the position and the width of the resonance in the second output depend strongly on δ. Indeed, as the first PIT resonance exhibits two transmission zeros around λ1 = 1559.65 nm, the position of the second PIT resonance falls above λ1 for δ > 0 at the righthand side
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Fig. 5 Hz field map in both lines for two filtered PIT resonances at λ1 = 1559.65 nm (a) and λ2 = 1887.38 nm (b) in Fig. 4(b)
zero (Figs. 4(a), (b)), crosses the first resonance at δ = 0, and reappears below λ1 for δ < 0 at the lefthand side zero (Figs. 4(c), (d)). Figures 5(a) and (b) give the Hz field maps in both lines for two filtered PIT resonances at λ1 = 1559.65 nm and λ2 = 1887.38 nm respectively (Fig. 4(b)). These modes correspond respectively to a filtered resonance in one line and a stopped resonance in the other line (blue and red curves in Fig. 4(b)). Figure 5(a) clearly shows that the mode λ1 = 1559.65 nm is transferred along the first line, whereas it is stopped along the second line. The transfer of this mode along the second line is due to the excitation of both stubs (of lengths d1 and d2 ) along this line as it illustrated in Fig. 5(a), whereas its stopping along the second line is due to the excitation of the stationary mode of only the stub of length d3 = 260 nm as shown in Fig. 5(a). Figure 5(b) gives the same results as in Fig. 5(a) but for the PIT resonance at λ2 = 1887.38 nm. Here, we obtain a different behavior where the transfer occurs along the second line (Fig. 5(b)) through the excitation of its double stubs of lengths d3 and d4 (Fig. 5(b)), whereas the wave is stopped along the first line (Fig. 5(b)) as a consequence of the excitation of the mode of one of its stubs of length d2 = 310 nm (Fig. 5(b)). These results clearly show how the lengths of the finite guides constituting the demultiplexer should be engineered in order to realize an efficient demultiplexing.
4 Conclusion In this paper, we have shown the possibility to obtain BIC states and PIT resonances in a simple plasmonic structure made of two stubs of lengths d1 and d2 inserted at the same position along a MIM waveguide. The PIT resonance in the transmission spectrum can be tailored by detuning the lengths of the two resonators (i.e., δ = d2 − d1 ). In addition, we have proposed a simple Yshaped plasmonic structure based on PIT resonances. In particular, we have derived closed form expressions of the waveguide
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lengths of the demultiplexer device that enable to achieve a selective transfer of a single propagating mode through one line keeping the other line unaffected. The position and width of the resonances depend on the different lengths of the finite waveguides constituting the demultiplexer which should be chosen appropriately. The details of the analytical calculations as well as the possibility to realize other type of plasmonic demultiplexers, will be given elsewhere.
References 1. Fleischhauer M, Imamoglu A, Marangos JP (2005) Electromagnetically induced transparency: optics in coherent media. Rev Mod Phys 77:633–641 2. Zhang J, Hernandez G, Zhu Y (2008) Slow light with cavity electromagnetically induced transparency. Opt Lett 33:46–48 3. Heinze G, Hubrich C, Halfmann T (2013) Stopped light and image storage by electromagnetically induced transparency up to the regime of one minute. Phys Rev Lett 111:033601–033605 4. Alotaibi Hessa MM, Sanders BC (2016) Enhanced nonlinear susceptibility via doubledouble electromagnetically induced transparency. Phys Rev A 94:053832–0538311 5. Yang X, Yu M, Kwong DL, Wong CW (2009) Alloptical analog to electromagnetically induced transparency in multiple coupled photonic crystal cavities. Phys Rev Lett 102:173902 6. Kurter C, Tassin P, Zhang L, Koschny T, Zhuravel AP, Ustinov AV, Anlage SM, Soukoulis CM (2011) Classical analogue of electromagnetically induced transparency with a metalsuperconductor hybrid metamaterial. Phys Rev Lett 107:043901 7. Jung H, Jo H, Lee W, Kim B, Choi H, Kang MS, Lee H (2019) Terahertz metamaterials: electrical control of electromagnetically induced transparency by terahertz metamaterial funneling. Adv Opt Mater 7:1801205 8. Fan Y, Qiao T, Zhang F, Fu Q, Dong J, Kong B, Li H (2017) An electromagnetic modulator based on electrically controllable metamaterial analogue to electromagnetically induced transparency. Sci Rep 7:40441 9. AlWahsh H, El Boudouti EH, DjafariRouhani B, Akjouj A, Mrabti T, Dobrzynski L (2008) Evidence of Fanolike resonances in monomode magnetic circuits. Phys Rev B 78:075401 10. Quotane I, El Boudouti EH, DjafariRouhani B (2018) Trappedmodeinduced Fano resonance and acoustical transparency in a onedimensional solidfluid phononic crystal. Phys Rev B 97:024304 11. Mouadili A, El Boudouti EH, Soltani A, Talbi A, Akjouj A, DjafariRouhani B (2013) Theoretical and experimental evidence of Fanolike resonances in simple monomode photonic circuits. J Appl Phys 113:164101 12. Mouadili A, El Boudouti EH, Soltani A, Talbi A, DjafariRouhani B, Akjouj B, Haddadi K (2014) Electromagnetically induced absorption in detuned stub waveguides: a simple analytical and experimental model. J Phys Condens Matter 26:505901 13. Wen M, Wang L, Zhai X, Lin Q, Xia S (2017) Dynamically tunable plasmoninduced absorption in resonatorcoupled graphene waveguide. Europhys Lett 116:44004 14. Xia SX, Zhai X, Wang LL, Sun B, Liu JQ, Wen SC (2016) Dynamically tunable plasmonically induced transparency in sinusoidally curved and planar graphene layers. Opt Express 24:17886– 17899 15. Noual A, Amrani M, El Boudouti EH, Pennec Y, DjafariRouhani B (2019) Terahertz multiplasmon induced reflection and transmission and sensor devices in a graphenebased coupled nanoribbons resonators. Opt Commun 440:1–13 16. Noual A, Amrani M, El Boudouti EH, Pennec Y, DjafariRouhani B (2019) Terahertz plasmoninduced transparency and absorption in compact graphenebased coupled nanoribbons. Appl Phys A 125:184
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17. Huang Y, Min C, Veronis G (2011) Subwavelength slowlight waveguides based on a plasmonic analogue of electromagnetically induced transparency. Appl Phys Lett 99:143117 18. Piao X, Sunkyu Y, Park N (2012) Control of Fano asymmetry in plasmon induced transparency and its application to plasmonic waveguide modulator. Opt Express 20:18994 19. Chen J, Wang C, Zhang R, Xiao J (2012) Multiple plasmoninduced transparencies in coupledresonator systems. Opt Lett 37:5133 20. Noual A, Akjouj A, Pennec Y, Gillet JN, DjafariRouhani B (2009) Modeling of twodimensional nanoscale Ybent plasmonic waveguides with cavities for demultiplexing of the telecommunication wavelengths. New J Phys 11:103020 21. Mouadili A, El Boudouti EH, Soltani A, Talbi A, Haddadi K, Akjouj A, DjafariRouhani B (2019) Photonic demultiplexer based on electromagnetically induced transparency resonances. J Phys D Appl Phys 52:075101 22. Vasseur JO, Akjouj A, Dobrzynski L, DjafariRouhani B, El Boudouti EH (2004) Surf Sci Rep 54:1 23. Lin C, Swillam MA, Helmy AS (2012) Analytical model for metalinsulatormetal mesh waveguide architectures. J Opt Soc Am B 29:3157 24. Zhu Q, Wang Z (2013) The Green’s function method for metaldielectricmetal SPP waveguide network. EPL 103:17004 25. Zhu Q, Wang Z (2019) Analytical method for metalinsulatormetal surface plasmon polaritons waveguide networks. Opt Express 27:303 26. Hsu CW, Zhen B, Stone AD, Joannopoulos JD, Solvacic M (2016) Bound states in the continuum. Nat Rev Mater 1:16048
Experimental and Theoretical Study of Group Delay Times and Density of States in OneDimensional Photonic Circuit Soufyane Khattou, Madiha Amrani, Abdelkader Mouadili, El Houssaine El Boudouti, Abdelkrim Talbi, Abdellatif Akjouj, and Bahram DjafariRouhani
Abstract We present a comparative study of density of states (DOS) and group delay times for a onedimensional (1D) coaxial photonic crystal made of N cells attached horizontally along a waveguide. Using the interface response theory of continuous media, we derive exact analytical expressions relating the DOS and reflection and transmission delay times. We demonstrate analytically and experimentally that the reflection and transmission delay times for a symmetric system are not equivalent when we take into account the dissipation in the cables, and the DOS presents a different behavior in comparison with the reflection delay time because of the existence of additional enlarged delta peaks in the latter quantity that cannot be detected without loss.
S. Khattou (B) · M. Amrani · E. H. El Boudouti LPMR, Département de Physique, Faculté des Sciences, Université Mohammed I, Oujda, Morocco email: [email protected] M. Amrani email: [email protected] E. H. El Boudouti email: [email protected] A. Mouadili LPMCER, Département de Physique, Faculté des Sciences et Techniques de Mohammedia, Université Hassan II, Casablanca, Morocco email: [email protected] A. Talbi Univ. Lille, CNRS, Centrale Lille, ISEN, Univ. Valenciennes, UMR 8520 IEMN  LIA LICS/LEMAC, 59000 Lille, France email: [email protected] A. Akjouj · B. DjafariRouhani Institut d’Electronique, de Microélectronique et de Nanotechnologie (IEMN), UMR CNRS 8520, Département de Physique, Université de Lille, 59655 Villeneuve d’Ascq, France email: [email protected] B. DjafariRouhani email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_25
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Keywords Photonic crystals · Transmission · Reflection · Delay times · Density of states · Band structure
1 Introduction The problem of propagation of electromagnetic waves in artificial periodic dielectric materials received a great deal of attention in the last two decades [1, 2]. Of particular interest is the existence of photonic band gaps in the electromagnetic band structures of such materials called photonic crystals. These structures present unusual properties which can be exploited in the control and the guidance of the propagation of light [3]. Besides 2D and 3D photonic crystals, 1D systems like coaxial cables have been shown to be good candidates for highlighting general rules about confined and surface electromagnetic modes in finite size 1D structures [4]. Also, it was shown that coaxial cables present an easily realizable experimental approach to the study of wave interference phenomena such as band gap structures with or without defect modes [5], EIT and Fano resonances [6], superluminal and subluminal effects [7, 8]. In this paper, we present a comparative study of density of states (DOS) and reflection and transmission delay times of a finite 1D coaxial photonic crystal made of N cells attached horizontally along a waveguide (see Fig. 1). Up to now, such a comparison has been only studied theoretically in mesoscopic systems [9, 10] because of the difficulties in the measurement of the reflection coefficient. However, photonic circuits represent an excellent platform to demonstrate all these properties. Also, the theoretical analysis of the DOS has been performed in plasma [11], dielectric [12] and metamaterial layered media [13]. To our knowledge, few works have been performed to compare both analytically and experimentally the calculated DOS and transmission and reflection delay times in photonic crystals [6, 14]. In addition, as complementary to our previous theoretical predictions [12], where we have shown that the reflection and transmission delay times for a symmetric structure are equivalent, and are directly proportional the density of states (DOS), in this work we show both analytically and experimentally that the two latter quantities are not equivalent when we take into consideration the dissipation in the cables. Also, we demonstrate that the DOS presents a different behavior in comparison with the reflection delay time because of the existence of additional enlarged delta peaks in the latter quantity that cannot be detected without loss. Using the interface response theory [15] of continuous media, we derive exact analytical expressions relating the DOS and reflection and transmission delay times for a symmetric photonic crystal made of wires and loops. The structure is inserted between two semiinfinite cables (Fig. 1). The rest of the paper is organized as follows: in Sect. 2, we give the analytical expressions relating the DOS and reflection and transmission delay times for a finite size photonic structure inserted between two semiinfinite wires (Fig. 1). Also, we illustrate both numerically and experimentally the equivalence between the three latter quantities. The conclusions are presented in Sect. 3.
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Fig. 1 Schematic representation of the periodic structure made of 4 cells inserted between two semiinfinite wires. Each cell is composed of a wire of length d1 and a loop formed out by two wires, each of length d2
2 Density of States and Group Delay Times In this section, we discuss analytically and experimentally the relation between the DOS and the group delay times for 1D periodic structure made of N cells inserted between two semiinfinite wires (Fig. 1). Each cell is composed of a wire of length d1 and a loop formed out by two wires, each of length d2 , d = d1 + d2 is the period of the photonic crystal. Using the Green’s function method [15], we derive exact analytical expressions of DOS and reflection and transmission delay times.
2.1 Analytical Calculation Using the Green’s function method [15], the expressions of the transmission and reflection coefficients are given respectively by [4, 5], tN =
A2N
−
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(1a)
(1b)
where A N and B N are given by the following expressions [5]: 1 Y1 Y1 1 − Bb t − A+a t Δ
(2a)
Y1 Y2 1 t (N −1) . B N = Bb t − t (A + a)Δ
(2b)
AN = and
It is worth mentioning that in lossless media, A N and B N are real quantities. The expressions of Y1 , Y2 and Δ are given by:
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Y1 = b2 − a 2 − a A + Bbt, Y2 = a B − Abt and Δ = Y12 − Y22 t 2(N −1)
(3)
where a = − j F CS11 , b = j SF1 , A = −2 j F CS22 , B = 2 j SF2 and Ci = cos(kdi ), Si = √ cos(kdi ) (i = 1, 2), k = ωc ε and F = ωZ · ε = 2.3 and Z = 50 are the permittivity and the impedance of the cables. The parameter t is defined as t = eik B d , where d = d1 + d2 = 2d1 is the period of the photonic crystal and k B is the Bloch wavevector obtained from the dispersion relation of the infinite photonic crystal, namely [5] 9 cos(k B d) = 1 − sin 2 (kd1 ) 4
(4)
From Eq. (1a), one can obtain the transmission delay time τT which is defined as the derivative of the corresponding phase versus the pulsation ω, namely d 2F A N dθT = Ar ctan τT = dω dω A2N − B N2 − F 2 d sgn(B N )ω=ωn δ(ω − ωn ) +π dω n
(5)
By the same way, the reflection delay time τ R can also be derived from Eq. (1b) as d 2F A N dθ R = Ar ctan τR = dω dω A2N − B N2 − F 2 d 2 2 2 sgn(A N − B N + F )ω = ωn δ(ω − ωn ) +π dω n
(6)
where θT and θ R are the phases of the transmission and reflection coefficients respectively. Also, the difference of the DOS for the finite structure and a reference system formed out of the same volumes of the decoupled semiinfinite wires and the finite structure can be obtained from [16], Δn(ω) =
1 d Ar ctan π dω
2F A N 2 A N − B N2 − F 2
(7)
The structure does not give any transmission zero (as B N = 0), then Ar ctan(B N ) = 0 or π and therefore from Eqs. (5) and (7) one can deduce that τT = π Δn(ω)
(8)
However, Eqs. (6) and (7) show that τ R is different from Δn(ω), as the term A2N − B N2 + F 2 can vanish for certain frequencies (see below), therefore,
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τ R = π Δn(ω)
(9)
2.2 Numerical and Experimental Results √ Figure 2(a) gives the band gap structure (i.e., the reduced frequency Ω = ωc d1 ε versus the reduced Bloch wavevector k B d) for an infinite structure (Eq.(4)) made of alternating segments and loops made of standard coaxial cables of length d1 = d2 = 1 m. Figure 2(b) shows the transmission amplitude for a finite structure made of N =
Fig. 2 a Theoretical band gap structure of an infinite structure made of segments and loops as shown in Fig. 1. b Transmission amplitude through the finite size system of Fig. 1. c, d Transmission phase and the corresponding delay time. e Density of states (DOS) through the finite structure of Fig. 1. Red open circles show the experimental results, whereas blue solid lines correspond to the theoretical ones. f Comparison between the DOS (blue curve) and theoretical transmission delay time in the presence of dissipation (green circles)
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Fig. 3 a–e Same results as in Fig. 2(a)–(e), but for the reflection coefficient. Red open circles show the experimental results, whereas blue solid lines correspond to the theoretical ones. f Comparison between the DOS (blue curve) and reflection delay time in the absence of dissipation (red circles)
4 loops. Although the small number of loops, the positions of the gaps (transmission deeps) coincide clearly with those of the infinite system. Figures 2(c) and (d) give the transmission phase and the corresponding delay time. One can see that the phase increases monotonically, and the delay time reflects the density of modes inside the finite structure as described in Fig. 2(e). In Fig. 2(f) we give a comparison between DOS and the transmission delay time in the presence of dissipation. One can notice that the DOS is directly proportional to the transmission delay time in accordance with Eq. (8). The experimental results (circles) are in very good agreement with theory (solid lines). The decreasing in the transmission amplitude is due to the dissipation in the cables which is taken into account in theory by adding a small imaginary part to the dielectric permittivity ε. Figures 3(a)–(e) illustrate the same results as in Figs. 2(a)–(e), but for the reflection coefficient. One can notice that the amplitude of the reflection vanishes N − 1 times
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in each band, giving rise to N − 1 phase drops (Fig. 3(c)) and therefore N − 1 negative delay times (Fig. 3(d)). These results clearly show that the transmission and reflection delay times for a symmetric structure are not equivalent because of the additional negative delta peaks (Eq. (6)) induced by the term A2N − B N2 + F 2 = 0. These delta peaks are enlarged because of the existence of the dissipation in the cables. The experimental results (circles) are in very good agreement with theory (solid lines). Let us mention that negative delta peaks in the reflection spectra have been provided experimentally on microstrips slabs and Bragg reflectors [17] with particular interest in superluminal phenomenon [7, 8]. In the absence of dissipation, these two quantities are equivalent [12] as the negative peaks now become true delta peaks (Fig. 3(f)) and the reflection delay time becomes equivalent to the transmission delay time (Fig. 2(f)) and directly proportional to the DOS.
3 Conclusion In this paper, we have presented a comparative study of DOS and group delay times for a symmetric coaxial photonic crystal made of N cells attached horizontally along a waveguide. We have derived exact expressions relating the DOS and the transmission and reflection delay times. Also, we have illustrated the analytical calculations by numerical and experimental results using standard coaxial cables in radiofrequency domain. In addition, as complementary to our previous theoretical predictions [12], we have shown that the DOS presents a different behavior in comparison with the reflection delay time. This is due to the possibility of existence of additional negative delta peaks in the latter quantity. The theoretical results are obtained within the framework of the interface response theory of continuous media [15], whereas the experiments are provided using standard coaxial cables in radiofrequency domain.
References 1. Yablonovitch E (1987) Inhibited spontaneous emission in solidstate physics and electronics. Phys Rev Lett 58:2059. https://doi.org/10.1103/PhysRevLett.58.2059 2. John S (1987) Strong localization of photons in certain disordered dielectric superlattices. Phys Rev Lett 58:2486. https://doi.org/10.1103/PhysRevLett.58.2486 3. Johnson S, Joannopoulos JD (2002) Photonic Crystals: The Road from Theory to Practice. Kluwer Academic Publishers, Boston 4. El Boudouti EH, El Hassouani Y, DjafariRouhani B, Aynaou H (2007) Two types of modes in finite size onedimensional coaxial photonic crystals: general rules and experimental evidence. Phys Rev E 76:026607. https://doi.org/10.1103/PhysRevE.76.026607 5. El Boudouti EH, Fettouhi N, Akjouj A, DjafariRouhani B, Mir A, Vasseur JO, Dobrzynski L, Zemmouri J (2004) Experimental and theoretical evidence for the existence of photonic bandgaps and selective transmissions in serial loop structures. J Appl Phys 95:1102. https:// doi.org/10.1063/1.1633983
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6. Mouadili A, El Boudouti EH, Soltani A, Talbi A, Akjouj A, DjafariRouhani B (2013) Theoretical and experimental evidence of Fanolike resonances in simple monomode photonic circuits. J Appl Phys 113:164101. https://doi.org/10.1063/1.4802695 7. Haché A, Poirier L (2002) Anomalous dispersion and superluminal group velocity in a coaxial photonic crystal: theory and experiment. Phys Rev E 65:036608. https://doi.org/10.1103/ PhysRevE.65.036608 8. Munday JN, Robertson WM (2002) Negative group velocity pulse tunneling through a coaxial photonic crystal. Appl Phys Lett 81:2127. https://doi.org/10.1063/1.1508172 9. Taniguchi T, Büttiker M (1999) Friedel phases and phases of transmission amplitudes in quantum scattering systems. Phys Rev B 60:13814. https://doi.org/10.1103/PhysRevB.60.13814 10. Lee HW (1999) Generic transmission zeros and inphase resonances in timereversal symmetric single channel transport. Phys Rev Lett 82:2358. https://doi.org/10.1103/PhysRevLett.82.2358 11. Prasad S, Sharma Y, Shukla S, Singh V (2016) Properties of density of modes in one dimensional magnetized plasma photonic crystals. Phys Plasmas 23:032123. https://doi.org/10.1063/ 1.4944505 12. Lahlaouti MLH, Akjouj A, DjafariRouhani B, Dobrzynski L, Hammouchi M, El Boudouti EH, Nougaoui A, Kharbouch B (2001) Theoretical analysis of the density of states and phase times: application to resonant electromagnetic modes in finite superlattices. Phys Rev B 63:035312. https://doi.org/10.1103/PhysRevB.63.035312 13. Wang X, Wang H, Zheng F (2017) Opt Commun 382:371 14. Mouadili A, El Boudouti EH, Soltani A, Talbi A, DjafariRouhani B, Akjouj A, Haddadi K (2014) Electromagnetically induced absorption in detuned stub waveguides: a simple analytical and experimental model. J Phys Condens Matter 26:505901. https://doi.org/10.1088/09538984/26/50/505901 15. Dobrzynski L, El Boudouti EH, Akjouj A, Pennec Y, Al Wahsh H, Leveque G, Djafari Rouhani B (2017) Phononics. Elsevier, Amsterdam 16. DjafariRouhani B, Dobrzynski L (1993) Acoustic resonances of adsorbed wires and channels. J Phys Condens Matter 5:8177. https://doi.org/10.1088/09538984/5/44/010 17. SanchezMerono A, Arias J, SanchezLopez M (2010) Negative group delay of reflected pulses on microstrip slabs and Bragg reflectors. IEEE J Quantum Electron 46:4. https://doi.org/10. 1109/JQE.2009.2036744
Optical Properties of OneDimensional Aperiodic Dielectric Structures Based on ThueMorse Sequence Hassan Aynaou, Noama Ouchani, and El Houssaine El Boudouti
Abstract We investigate from a theoretical point of view the optical properties of the aperiodic photonic crystals. These structures are arranged by stacking together two isotropic layers according to the ThueMorse (TM) substitutional rules. It is demonstrated that the TM dielectric systems exhibit interesting and potentially useful physical properties such as the transmission band gaps, some high localized states and the omnidirectional reflection bands. The transmission spectrum and the spatial distribution of the local density of states in onedimensional T−M structures has been investigated by means of the Green’s function approach. The TM structures could be of practical interest to design alloptical diodes, omnidirectional reflectors and optical filters. Keywords Optical properties · Aperiodic photonic crystal · ThueMorse sequence · Localized modes
1 Introduction The aperiodic photonic structures generated by deterministic rules have recently received extensive attention due to their many interesting properties. They can provide an attractive alternative to photonic crystals for constructing photonic devices [1–3], such as optical filters [1], alloptical diodes [2] and omnidirectional reflectors [3], and so on. H. Aynaou (B) EPSMS, Département de Physique, Faculté des Sciences et Techniques, Université Moulay Ismail, Boutalamine BP 509, 52000 Errachidia, Morocco email: [email protected] N. Ouchani Centre Régional des Métiers de l’Education et de la Formation, 30000 Fès, Morocco N. Ouchani · E. H. El Boudouti LPMR, Département de Physique, Faculté des Sciences, Université Mohammed Premier, 60000 Oujda, Morocco © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_26
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The study of the electromagnetic wave propagation through the aperiodic systems is motivated by the fact that this deterministic structure represent an intermediate organization stage between photonic crystal and random structures. A prominent example of aperiodic sequence is given by the ThueMorse (TM) sequence, from the names of the first mathematicians to study its numerical properties [4]. The ThueMorse multilayered structures have been the subject of intensive theoretical and experimental investigations [3, 5–13]. Liu [5] studied the localization properties of light in ThueMorse sequences at the normal propagation of light waves. Qiu et al. [6] showed that the ThueMorse T i O2 /Si O2 multilayers exhibit the omnidirectional photonic bandgaps (PBGs) in the visible and near infrared range of wavelengths. The optical resonant transmission of TM dielectric multilayers is also reported by Qiu et al. [7]. In addition Dal Negro et al. [3] fabricated Si/Si O2 TM multilayered structures to study the bandgap properties and omnidirectional reflectance at the fundamental optical bandgap. The same group also made siliconrich lightemitting Si N x /Si O2 TM multilayered structures in order to investigate the generation and transmission of light in deterministic aperiodic dielectrics [8]. Lei et al. [9] demonstrated that the PBGs in TM aperiodic systems can be separated into the fractal gaps and the traditional gaps. Moreover, they showed that the origin of these two kind of gaps due to the different interface correlations. Recently, Yue et al. [10] studied the effects of the center wavelength, the relative permittivity and the incident angle on the PBG properties of 1D ThueMorse dielectric multilayers. Different materials such as superconductors [11], graphene [12] and single negative metamaterials [13] have been considered as the constituent materials of the ThueMorse quasiperiodic systems. From the optical point of view, the bandgap properties and the localization properties of light as well as the omnidirectional reflection bands in all previous works have been deduced from the reflection and/or the transmission spectrum. In this paper, we focused our attention to the behavior of the total densities of states (DOS) and the spatial distribution of the local density of states in onedimensional ThueMorse structures to confirm these properties. In addition, the theoretical method adopted in these works for the analysis of the 1D TM systems is the transfer matrix method. In this paper, we use the Green’s function approach which enables to derive the total and local DOS of electromagnetic modes propagating through the structure as well as the transmission and reflection spectra. It is worth noting that some of the authors have clearly proven the interest of the Green’s function approach in studying quasiperiodic structures [14, 15]. For example, in Ref. [14], they compared both theoretically and experimentally results of propagation and localization of electromagnetic waves in Fibonacci structures made of coaxial cables. The accordance between the experimental results and the theoretical model based on the Green’s function approach, has been found in the study of the behavior of the localized surface modes in onedimensional quasiperiodic photonic band gap structures constituted of segments and loops arranged according to a Fibonacci sequence [15]. This paper is organized as follows: in Sect. 2 we present the theoretical model. Section 3 gives the numerical results of the transmittance and the density of the optical mode propagating in some generations of ThueMorse structures. The omnidirec
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μ3 , ε 3
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z
Fig. 1 Schematic representation of the fourth generation of the ThueMorse structure. The layers A (Si) and B (Si O2 ) are characterized by their thicknesses d A and d B , respectively. The input and output isotropic media are air and silicon, respectively. The incident electromagnetic wave is launched with an angle θ with respect to the normal to the superlattice
tional reflection properties and the spatial distribution of the local density of states of localized optical modes along the zaxis of the TM system, has been investigated. Finally, a summary of this work is presented in Sect. 4.
2 Theoretical Model ThueMorse (TM) sequence is one of the well known examples in onedimensional aperiodic structures. The ThueMorse dielectric multilayer can be grown by juxtaposing the two building layers A and B and can be produced by repeating application of the substitution rules A → AB and B → B A. For example, the first few generations Sn of ThueMorse sequence are as follows: S0 = A, S1 = AB, S2 = AB B A, S3 = AB B AB A AB, and so on [16]. All the interfaces of the layers are taken to be parallel to the (X Y ) plane of a cartesian (laboratory) coordinate system and the Z axis is along the normal to the interfaces as schematically illustrated in Fig. 1 for the fourth sequence. The materials constituting the whole layered system are assumed to be homogeneous and non magnetic. Among different techniques used to study the propagation of electromagnetic waves in periodic structures, one can cite the transfer matrix and the Green’s function methods. Both techniques enable to study different scattering properties of the system. However, the Green’s function presents the advantage to deduce easily the spatial distribution of the local DOS in the structures. In this work, we use a simple formulation of the Green’s function called interface response theory of continuous media [17]. This technique is suitable for treating composite systems containing a large number of interfaces [18]. In this theory, the Green’s function of a composite system can be written as [17] g(D D) = G(D D) + G(D M){[G(M M)]−1 g(M M)[G(M M)]−1 − [G(M M)]−1 }G(M D),
(1)
where D and M are respectively, the whole space and the space of the interfaces in the lamellar system. G is a blockdiagonal matrix in which each block G i corresponds to the bulk Green’s function of the subsystem i. All the matrix elements g(D D) of the composite material can be obtained from the knowledge of the matrix
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elements g(M M) in the interface space M. g(M M) is calculated from its inverse g −1 (M M). The latter is formed out by a linear superposition of the surface matrix elements gi−1 (M M) of any independent film bounded by perfectly free interfaces with appropriate boundary conditions. The matrix elements gi−1 (M M) for an isotropic dielectric medium are given in Ref [17]. Within this theory, the reflected and transmitted waves u(D), resulting from a uniform plane wave U (D) incident upon a plane boundary between two different media, are given by [17] u(D) = U (D) + G(D M){[G(M M)]−1 g(M M)[G(M M)]−1 −[G(M M)]−1 }U (M).
(2)
As mentioned above, the Green’s function enables to calculate the density of states, especially, one can determine the variation of the density of states n between the TM structure and a reference system formed out of the same volumes of the bulk semiinfinite substrates s1 and s2 and the finite system. This quantity is given by [19]: n(ω) =
g(M0 M0 ) 1 d Argdet{ }, π dω [gs1 (0, 0)gs2 (L , L)]1/2
(3)
where g(M0 M0 ) is the Green’s function of the whole system at its both extremities M0 = {0, L}, whereas gs1 (0, 0) and gs2 (L , L) are the elements of the Green’s functions at the surfaces 0 and L of the two substrates.
3 Numerical Results and Discussion The ThueMorse photonic structure consists of two isotropic media: Si (layer A) and Si O2 (layer B). The layers are characterized by the refractive indices of these materials, respectively, n A = 3.53 and n B = 1.46. We assume that the physical thicknesses of the layers d A and d B are chosen in such a way that their optical thicknesses are D A = D B = λ0 /4 where λ0 is the central wavelength. The total number of layer of each TM sequence is denoted by N . Both substrates surrounding the aperiodic system are assumed to be homogeneous and non magnetic and characterized by the refractive indices n 0 = 1 and n 3 = 3.53 for air and silicon respectively (Fig. 1). In order to study the main features of optical response for the ThueMorse multilayer structure in the nearinfrared (IR) range, we display in Fig. 2 the transmittance (Fig. 2(a), (b), (c)) and the total density (Fig. 2(a’), (b’), (c’)) of the optical modes as a function of the dimensionless frequency k0 D (where k0 = ω0 /c = 2π/λ0 is the wave number in the free space) for different TM sequences (S3 , S4 and S5 ). In this illustration, we assume that the incident electromagnetic wave is launched normally (θ = 0 ◦ ) to the TM system.
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Analyzing these spectra, we can deduce that the transmission spectra for different generations represent a symmetrical profile around the frequency k0 D = π/2, k0 D = π and k0 D = 3π/2. This peculiarity is similar to that one of the spectra of the Kolakoski and Fibonacci multilayers [20]. It is shown also that the Third TM sequence with eight layers (Fig. 2(a)) has a large transmission band gap around k0 D = π/3, k0 D = 2π/3, k0 D = 4π/3 and k0 D = 5π/3. Each band gap shown for S3 generation splits into two distinct adjacent band gaps, separated by a narrow transmission band for the S4 TM sequence with 16 layers (Fig. 2(b)). The band gaps of S5 generation (Fig. 2(c)) present three adjacent band gaps separated by two distinct narrow transmission regions. One can notice that for these frequencies of localized modes in photonic band gaps, the structure can behave as a highprecision optical filter. The behavior of transmission optical wave shown in the first panel of Fig. 2 is reproduced in the DOS spectra for different TM sequences as depicted in Fig. 2(a’), (b’) and (c’). Let us mention that, to our knowledge, no comparative study of the transmission amplitudes and total density of states between different generations of TM structures has been developed before. Aynaou et al. [21] investigated a theoretical and experimental comparative study of the transmittance and phase time spectra between the ThueMorse, Fibonacci, double period, and RudinShapiro structures. In order to deduce other properties of the localized modes which appear in the band gap of different TM sequences, we have plotted in Fig. 3 the spatial distribution of the
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Fig. 3 Spatial representation of the local density of states of the modes labeled 1 and 2 in Fig. 2(b) at k0 D = 0.74 (a) and k0 D = 1.22 (b), respectively. The input wave is launched normally to the S4 TM structure.
local density of states of the modes labeled 1 and 2 in Fig. 2(b). This quantity reflects the spatial behavior of the square modulus of the electric field inside the structure. The localized mode labeled 1 shows a propagating character in the whole structure (Fig. 3(a)), whereas the mode presented in Fig. 3(b) shows a strong localization in the B blocks at the middle of the system and a decaying behavior on both sides of the interfaces of these blocks. As pointed out in Ref. [14, 15, 21] the local density of states of the localized modes in 1D Fibonacci structure exhibited an important property such as the selfsimilar behavior around the main peak for every three generations. In a forthcoming work, we will investigate the behaviors of different localization properties of ThueMorse 1Dlayered structures. To widen the scope of our findings, we have also investigated the transmittance spectrum of the fourth generation TM structure at different incident angles as shown in Fig. 4. The transmission amplitudes are calculated for both transverse electric (TE) and transversemagnetic (TM) polarization in order to show the omnidirectional reflection bands. Furthermore, The calculated photonic band structures of S4 T−M sequence for both TE and TM modes as function of the incidence angles, is illustrated in Fig. 5. The grey shaded areas in Figs. 4 and 5 show an omnidirectional band gap region. It is shown in these illustrations that the structure displays two omnidirectional photonic band gaps with different widths. The multiple omnidirectional band gap in TM systems are due to the selfsimilarity in the internal TM structures. It is worth mentioning that the structure can behave as an omnidirectional reflector for these forbidden bands of frequencies.
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Fig. 4 The transmittance spectra for both TE (solid curves) and TM modes (dashed curves) for different incident angles. The grey shaded area represent the omnidirectional reflection bands
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Fig. 5 The calculated photonic band structures for both TE and TM modes as function of the incidence angles for the S4 TM structure. The grey shaded area represent the omnidirectional band gap region
4 Conclusion In summary, we have investigated theoretically the properties of optical wave propagating through a onedimensional aperiodic dielectric structure based on ThueMorse sequence. The transmission and the density spectra of the optical modes have the same behaviors for different generations of TM systems. We have shown that the spatial distribution of the local density of states of the localized modes in the band gap has a different propagating character through the whole structure. The TM multilayer structures exhibit a multiple omnidirectional band gap due to the selfsimilarity in the internal structure. These structures could be of practical interest to design omnidirectional reflectors and optical filters.
References 1. Sahel S, Amri R, Bouaziz L, Gamra D, Lejeune M, Benlahsen M, Zellama K, Bouchriha H (2016) Optical filters using Cantor quasiperiodic onedimensional photonic crystal based on Si/SiO2. Superlattice Microstr. 97:429–438. https://doi.org/10.1016/j.spmi.2016.07.007 ISSNs 07496036 2. Biancalana F (2008) Alloptical diode action with quasiperiodic photonic crystals. J Appl Phys 104:2059–2070. https://doi.org/10.1063/1.3010299 3. Dal Negro L, Stolfi M, Yi Y, Michel J, Duan X, Kimerling LC, LeBlanc J, Haavisto J (2004) Photon band gap properties and omnidirectional reflectance in Si/Si O2 ThueMorse quasicrystals. Appl Phys Lett 84:5186–5188. https://doi.org/10.1063/1.1764602 4. Allouche JP, Shallit J (2003) Automatic sequences: theory, applications, generalizations. Cambridge University Press, Cambridge ISBN 9780521823326 5. Liu NH (1997) Propagation of light waves in ThueMorse dielectric multilayers. Phys Rev B 55:3543–3547. https://doi.org/10.1209/epl/i200300608x 6. Qui F, Peng RW, Huang XQ, Hu XF, Wang M, Hu A, Jiang SS, Feng D (2004) Omnidirectional reflection of electromagnetic waves on ThueMorse dielectric multilayers. Europhys Lett 68:658–663. https://doi.org/10.1209/epl/i200410261y 7. Qiu F, Peng RW, Huang XQ, Liu YM, Wang M, Hu A, Jiang SS (2003) Resonant transmission and frequency trifurcation of light waves in ThueMorse dielectric multilayers. Europhys Lett 63:853–859. https://doi.org/10.1209/epl/i200300608x
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8. Dal Negro L, Yi JH, Nguyen V, Yi Y, Michel J, Kimerling LC (2004) Light emission in aperiodic ThueMorse dielectrics. Symp FGroup IV Semicond Nanostruct 832:F1.3.1–F1.3.6. https:// doi.org/10.1557/PROC832F1.3 9. Lei H, Chen J, Nouet G, Feng S, Gong Q, Jiang X (2007) Photonic band gap structures in the ThueMorse lattice. Phys Rev B 75:205109. https://doi.org/10.1103/PhysRevB.75.205109 (10p) 10. Yue C, Tan W, Liu J (2018) Photonic band gap properties of onedimensional ThueMorse alldielectric photonic quasicrystal. Superlattice Microstruct 117:252–259. https://doi.org/10. 1016/j.spmi.2018.03.023 11. Zhang HF, Liu SB, Yang H (2014) Omnidirectional photonic band gap in onedimensional ternary superconductordielectric photonic crystals based on a new ThueMorse aperiodic structure. J Supercond Nov Magn 27:41–52. https://doi.org/10.1007/s1094801322558 12. Saleki Z, Entezar SR, Madani A (2016) Omnidirectional broadband THz filter based on a onedimensional ThueMorse quasiperiodic structure containing graphene nanolayers. J Nanophotonics 10:036010. https://doi.org/10.1117/1.JNP.10.036010 (11p) 13. Liu Y, Deng L, Yi L (2014) Broadband phase retarder based on onedimensional ThueMorse structure containing singlenegative materials. Opt Commun 333:159–166. https://doi.org/10. 1016/j.optcom.2014.07.074 14. El Boudouti EH, El Hassouani Y, Aynaou H, DjafariRouhani B, Akjouj A, Velasco VR (2007) Electromagnetic wave propagation in quasiperiodic photonic circuits. J Phys Condens Matter 19:246217. https://doi.org/10.1088/09538984/19/24/246217 (20p) 15. El Hassouani Y, Aynaou H, El Boudouti EH, DjafariRouhani B, Akjouj A, Velasco VR (2006) Surface electromagnetic waves in Fibonacci superlattices: theoretical and experimental results. Phys Rev B 74:035314. https://doi.org/10.1103/PhysRevB.74.035314 16. Kolar M, Ali MK, Nori F (1991) Generalized ThueMorse chains and their physical properties. Phys Rev B 43:1034–1047. https://doi.org/10.1103/PhysRevB.43.1034 17. Dobrzynski L (1990) Interface response theory of continuous composite systems. Surf Sci Rep 11:139–178. https://doi.org/10.1016/01675729(90)90003V 18. Dobrzynski L, El Boudouti EH, Akjouj A, Pennec Y, AlWahsh H, Lévêque G, DjafariRouhani B (2017) Phononics. Elsevier. https://doi.org/10.1016/C20150069891 19. DjafariRouhani B, Dobrzynski L (1993) Acoustic resonances of adsorbed wires and channels. J Phys Condens Matter 5:139. https://doi.org/10.1088/09538984/5/44/010 20. Fesenko VI (2014) Aperiodic birefringent photonic structures based on Kolakoski sequence. Waves Random Complex Media 24:174–190. https://doi.org/10.1080/17455030.2014.890764 21. Aynaou H, El Boudouti EH, El Hassouani Y, Akjouj A, DjafariRouhani B, Vasseur J, Benomar A, Velasco VR (2005) Propagation and localization of electromagnetic waves in quasiperiodic serial loop structures. Phys Rev E 72:056601. https://doi.org/10.1103/PhysRevE.72.056601
Numerical Simulation of Direct Carbon Fuel Cell Using MultipleRelaxationTime Lattice Boltzmann Method I. Filahi, M. Hasnaoui, A. Amahmid, A. El Mansouri, M. Alouah, and Y. Dahani
Abstract A 2D numerical unit cell mode of Direct Carbon Fuel Cell (DCFC) was developed to simulate the effect of the operating conditions on the performance of the latter considering the electrochemical reaction mechanism and mass transfer. The problem is solved numerically using the Lattice Boltzmann method with MRT scheme to simulate the gas flow inside the electrodes, which are porous media. It was found that the porosities of the two components of the fuel cell, the electrolyte and the anode, have a strong effect on the performance of the fuel cell. The increase of the porosity improves the cell’s performance by reducing the losses due to activation, ohmic and concentration polarization. Keywords Direct carbon fuel cell · LBM · Porous medium · Anode porosity · Electrolyte porosity
Nomenclature Ci
Concentration of species i (mol m−3 )
I. Filahi (B) · M. Hasnaoui · A. Amahmid · A. El Mansouri · M. Alouah · Y. Dahani UCA, Faculty of Sciences Semlalia, Physics Department, LMFE, 2390 Marrakesh, Morocco email: [email protected] M. Hasnaoui email: [email protected] A. Amahmid email: [email protected] A. El Mansouri email: [email protected] M. Alouah email: [email protected] Y. Dahani email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_27
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Open circuit voltage of the DCFC (V) Density distribution functions Faraday’s constant (C mol −1 ) Internal energy distribution functions Current density (A/m2 ) Exchange current density (A/m2 ) Ideal gas constant (J mol −1 K −1 ) Operating temperature (K ) Working voltage (V) Porosity Activation polarization (V) Concentration polarization (V) Ohmic polarization (V) Charge transfer coefficient Effective viscosity
1 Introduction The direct carbon fuel cell (DCFC) is among alternative solutions for the production of electricity without using fossil sources. The DCFC is characterized by its high efficiency compared to other types of fuel cell. It converts the chemical energy of carbon, which is used as a fuel, into electrical energy [1]. This device is composed of two porous electrodes (the anode and the cathode) and an electrolyte. The reactions occurring in both electrodes and the overall reaction are: Cathodic reaction : O2 + 2C O2 + 4e− → 2C O32−
(1)
Anodic reaction : C + 2C O32− → 3C O2 + 4e−
(2)
Overall reaction : C + O2 → C O2
(3)
The DCFC is fueled by all types of hydrocarbons like biomass and coal (biochar) as a fuel. Previously, Vutetakis et al. [2] developed a model of DCFC using an anode of coal or carbon particles dispersed in a molten carbonate at 500°–800 °C. They showed that the increase of the surface area of the working electrode relative to the electrolyte volume has improved the carbon use efficiency. Li et al. [3] investigated the DCFC anode model composed of a mixture of carbon and molten carbonate. They found that the carbon black with much smaller crystallite size is more reactive compared to highly oriented pyrolytic graphite. Recently, Eom et al. [4] modeled electrochemical resistance with coal surface properties in a direct carbon fuel cell based on molten carbonate. They reported that the operating temperature may change
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the sensitivities of the coal surface properties affecting polarization losses. Elleuch et al. [5] presented experimental results of DCFC alimented by almond shell biochar as a fuel. They showed that the almond shell biochar leads to current density higher than that delivered by commercial activated carbon. This performance was explained by the high electrochemical performance of almond shell biochar that depends on physicochemical properties. Two years later, the same authors [6] studied a DCFC with Lithiated N i O as cathode, S DC − Li 2 C O3 − N a2 C O3 composite electrolyte and electrolytical graphite powder as anode. Their results show that the polarization of the DCFC based on oxidecarbonate electrolyte using graphite/carbonate as fuel is dominated by the ohmic and the activation losses. The concentration losses do not have a strong effect on the DCFC polarization curves. The numerous works dealing with the DCFC model testify that this type of fuel cell is of great practical and technological importance. However, the numerical modeling of the DCFC is far from having resulted in the control of the phenomena that take place during its operation because of their complexity and unpredictable local behavior. This being, in this paper we present numerical results of a 2D unit cell model of a direct carbon fuel cell composed of three components (anode, cathode and electrolyte). Based on some assumptions, we simulate the cell performance by examining the effect of the anode and the composite electrolyte porosities.
2 Mathematical Formulation A schematic representation of the 2D model of DCFC is depicted in Fig. 1. The fuel cell is composed of 4 components which are the cathode channel, the cathode, the composite electrolyte, the anode and the anode channel. The latter allows to evacuate Fig. 1 Simplified schematic of a direct carbon fuel cell configuration
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the carbon dioxide resulting from the oxidation of the carbon. The dimensions of the studied fuel cell are specified in Ref. [6] that contains the experimental data used for the numerical validation purpose. To develop the numerical model, the main assumptions made regarding the cell operating conditions are the following: • • • •
The fluids are incompressible; All the flows are laminar and all the processes are isothermal; Material properties are uniform and temperature dependent; The electrolyte is impermeable to gases and electric conduction is uniquely due to carbonate ions transport from the cathode to the anode;
By using these assumptions, the governing equations for the flow field and species concentration within the DCFC are given for both cathode and anode as follows: • Continuity equation div ρ V = 0 (4)
• Momentum equation
∂ ρ V ∂t
V V + ∇. ρ ε
= −∇.(εP) + ∇. μe ∇ V + F (5)
• Species equation
∂Ci + div Ci V = ∇. Di,e f f ∇Ci + Si ∂t
(6)
The source term Si in the species equation was calculated at the reaction zone on the Aact , where δ = −1 interface between the cathode/anode and electrolyte as Si = δ j4F at the cathode for oxygen, −2 for carbon dioxide at the cathode and 3 for carbon dioxide at the anode. Di,e f f = Di ε1.5 is the effective diffusivity given by Sahraoui et al. [7] and j is the current density linked to the activation polarization by the BulterVolmer equation presented as follows [8]: j = j0 ex p(αn Fηact /RT ) − ex p(−(1−α)n Fηact /RT )
(7)
where ηact , α, and n are the activation polarization, the charge transfer coefficient and the number of electrons transferred, respectively. The cell voltage is obtained as: Vcell = E th − (ηact + ηconc + ηohm )
(8)
where ηact , ηconc , and ηohm are the activation polarization, concentration polarization and ohmic polarization [4].
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3 Numerical Method The lattice Boltzmann method is based on the resolution of the lattice Boltzmann Eq. (9), which is written in the presence of an external force F expressing the DarcyBrinkmanForchheimer model, to simulate the gas flow inside the electrodes using the D2Q9 arrangements as follows [9]: eq f k (r + ck t, t + t) − f k (r, t) = Sv f k (r, t) − f k (r, t) + Ft F =−
εν εFε V − √ V V K K
(9) (10)
The equation of species is solved using the D2Q5 arrangements and the corresponding MRTLB equation is expressed as [9]: g(r + ct, t + t) − g(r, t) = Sc g eq (r, t) − g(r, t) + Sit
(11)
The distribution function f k (r, t), is the probability that the fluid particles located in the position r (x, y) at time t move in direction k with the velocity c. t is the time step required for the streaming of fluid particle from one lattice node to its neighbors. Sv and Sc are collision operators. The MRT scheme considers that the collision phase takes place in a macroscopic space formed by the moments of the distribution functions. The mapping between the microscopic space and the moment in the macroscopic space is performed by passageway matrices M and N for f and g, respectively [10]. Further details on the present LBM model are provided in the work by Liu et al. [9].
4 Results and Discussion 4.1 Model Validation The numerical code was validated in terms of polarization curve against experimental data [6]. The comparative results presented in Fig. 2 show a good agreement between the results obtained numerically and those derived experimentally with a maximum deviation of 8%. This maximum difference is observed at low current density. In fact, this difference can be explained by the fact that the activation polarization is governed by the exponential law of Eq. 7, while the experimental data are characterized by a quasilinear behavior at high voltage and low current densities.
272 Fig. 2 Validation of the numerical code against experimental results [6]
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4.2 Effect of the Porosity of the Electrolyte For the results presented in this subsection, the polarization concentration on the anode was neglected. The effect of the electrolyte porosity on cell performance is presented in Fig. 3a. The latter shows that the effect of the porosity increase is important; it leads to an improvement of the performance of the fuel cell. Indeed, when the porosity of the electrolyte becomes important, that makes easier the carbonate ions C O32− to migrate to the anode side and react with carbon fuel, which results in a reduction of the activation losses. Consequently, the drop of the cell voltage induced by the increase of the current density is reduced. The power density variations vs. the current density, exemplified in Fig. 3b, confirm the positive effect of the electrolyte porosity increase on the enhancement the performance of DCFC. From this figure it’s clear that rising the electrolyte porosity plays a significant role in achieving a high performance characterized by an important increase of the maximum power density. The maximums reached are 279.2, 322.84, 498.30 and 767.15 W/m2 for the electrolyte porosities 0.15, 0.20, 0.35 and 0.5, respectively.
4.3 Effect of the Porosity of the Anode In this subsection, the concentration polarization at the anode is considered. Figure 4a shows also a positive impact of the anode porosity increase on the cell performance. In fact, by raising the porosity of the anode, we free up more space making easier mass diffusion, which in consequence reduces the overpotential concentration. The drop of the cell voltage due to the increase of the current density is considerably reduced (beyond 250 A/m2 ). The corresponding effect on the power density delivered by the fuel cell is illustrated in Fig. 4b. The latter shows that the
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Fig. 4 Anode porosity effect on the cell performance. I − Vcell (a) and I − P (b) curves
maximum power density increases importantly by increasing the porosity of the anode. Quantitatively speaking, the power density goes from 372.6 to 580.16 W/m2 when the porosity goes from 0.20 to 0.50. This important relative increase is about 55.7%. This increase is attributed to the increase of mass diffusion accompanying the increase of the anode porosity, leading to a reduction of the concentration losses. In other terms, the porosity increase facilitates the purge of the carbon dioxide produced at the level of the anode, which contributes to the increase of the power produced by the cell.
5 Conclusion The effect of the porosities of the electrolyte and the anode on the direct carbon fuel cell performance is studied numerically. The LatticeBoltzmann method with
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the MRT scheme was used to simulate the gas flow inside the cathode and the anode and the species transfer equations. It is found that the augmentation of the electrolyte and anode porosities enhances significantly the cell performance. Quantitatively, the maximum power density is nearly tripled when the porosity of the electrolyte is increased from 0.15 to 0.5 and it is nearly doubled by incrementing the anode porosity from 0.2 to 0.5. However, the porosity effect on the electric conductivity is known to be negative. This aspect of the problem has not been addressed here due to the limitation of space. Acknowledgements The authors would like to thank ERANETMED (Prog. 337) for the financial support.
References 1. Giddey S, Badwal SPS, Kulkarni A, Munnings C (2012) A comprehensive review of direct carbon fuel cell technology. Prog Energy Combust Sci 38:360–399 2. Vutetakis DG, Skidmore DR, Byker HJ (1987) Electrochemical oxidation of molten carbonatecoal slurries. J Electrochem Soc 134:3027–3035 3. Li H, Liu Q, Li Y (2010) A carbon in molten carbonate anode model for a direct carbon fuel cell. Electrochim Acta 55:1958–1965 4. Eom S, Ahn S, Kang K, Choi G (2017) Modeling electrochemical resistance with coal surface properties in a direct carbon fuel cell based on molten carbonate. J Power Sources 372:54–63 5. Elleuch A, Boussetta A, Yu J, Halouani K, Li Y (2013) Experimental investigation of direct carbon fuel cell fueled by almond shell biochar: Part I. Physicochemical characterization of the biochar fuel and cell performance examination. Int J Hydrogen Energy 38(36):16590–16604 6. Elleuch A, Yu J, Boussetta A, Halouani K, Li Y (2013) Electrochemical oxidation of graphite in an intermediate temperature direct carbon fuel cell based on twophases electrolyte. Int J Hydrogen Energy 38(20):8514–8523 7. Sahraoui M, Kharrat C, Halouani K (2009) Twodimensional modelling of electrochemical and transport phenomena in the porous structures of a PEMFC. Int J Hydrogen Energy 34:3091– 3103 8. Elleuch A, Sahraoui M, Boussetta A, Halouani K, Li Y (2014) 2D numerical modeling and experimental investigation of electrochemical mechanisms coupled with heat and mass transfer in a planar direct carbon fuel cell. J Power Sources 248:44–57 9. Liu Q, He YL, Li Q, Tao WQ (2014) A multiplerelaxationtime lattice Boltzmann model for convective heat transfer in porous media. Int J Heat Mass Transf 73:761–775 10. El Mansouri A, Hasnaoui M, Bennacer A, Amahmid R (2018) Transient modeling of a salt gradient solar pond using a hybrid finitevolume and cascaded latticeBoltzmann method: thermal characteristics and stability analysis. Energy Convers Manage 158(15):416–429
Optical Properties and First Principles Study of CH3 NH3 PbBr3 Perovskite Structures for Solar Cell Application Asma O. Al Ghaithi, S. Assa Aravindh, Mohamed N. Hedhili, Tien Khee Ng, Boon S. Ooi, and Adel Najar
Abstract Solutionprocessed organic–inorganic hybrid perovskites have attracted attention as lightharvesting materials for solar cells and photonic applications. The present study focusses on cubic single crystal; microstructures of CH3 NH3 PbBr3 perovskite fabricated by a onestep solution based selfassembly method. It is seen that, in addition to the nucleation from the precursor solution, the crystallization occurs when the solution was supersaturated, followed by formation of small nucleus of CH3 NH3 PbBr3 that will selfassembled into bigger hollow cubes. A 3D fluorescence microscope investigation of hollow cubes confirmed the formation of hollow plates on the bottom, then the growth starts from the perimeter and propagate to the center of the cube. Furthermore, the growth in the (001) direction follows a layerbylayer growth model to form a complete cube, confirmed by SEM observations. To get more insights into the structural and optical properties, density functional theory (DFT) simulations were conducted. The density of state (DOS) calculations revealed that the valence band maximum (VBM) consists of states contributed by Br and Pb, which agrees with the Xray photoelectron spectroscopy valence band (XPSVB) measurements. Keywords Perovskite · Optical materials · DFT
A. O. Al Ghaithi · A. Najar (B) Department of Physics, College of Science, United Arab Emirates University, 15551, Al Ain, UAE email: [email protected] S. Assa Aravindh Nano and Molecular Systems Research Unit, University of Oulu, P.O. Box 8000, 90014 Oulu, Finland M. N. Hedhili · T. K. Ng · B. S. Ooi King Abdullah University of Science and Technology (KAUST), Thuwal 239556900, Saudi Arabia © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_28
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1 Introduction Organicinorganic perovskites in the form of thin films, microcrystals, nanoparticles and bulk singlecrystals exhibit outstanding optoelectronic properties [1]. They are attractive candidates in many cuttingedge applications such as solar cells, lightemitting diodes (LEDs), lasers, and photodetectors [2–6] and a competitive material to many standard semiconductors [7–12]. The properties of perovskites depend highly on the composition, crystallinity and its morphology. They belong to a large crystallographic family that adopt the same crystal structure as calcium titanate (CaTiO3) [13, 14], and has the general ABX3, threedimensional (3D) structural framework [15], where A and B are cations of different sizes and X is an anion [16]. Different preparation of perovskite nanostructures: thin film for solar cells [17], 2D nanoplates [18], 1D nanowires [19], and quantum dots [20] have been studied at the microscale and nanoscale levels. Also, the trapstate density and carrier diffusion length have been investigated in bulk perovskite single crystal [21]. However, lowdimensional halide perovskites show optical and electrical properties that are different from bulk halide perovskites [22]. Hence the control of the scale and the shape of the synthesized perovskite are necessary for fundamental and applications research. The changes in optical and electrical properties are attributed to the quantum size effects, large surfacetovolume ratio, and anisotropic geometry [23]. Several synthesis methods were used to prepare single crystal CH3 NH3 Pb3 , such as topseed solution growth [24], inverse temperature crystallization [25, 26], and antisolvent vaporassisted crystallization [27]. Recently, researchers were interested in the nucleation and growth mechanisms of perovskite structures prepared by inverse temperature crystallization method, using grazing incidence Xray diffraction or in situ Fourier transform infrared spectroscopy. These techniques can accurately explain the crystallinity of the material and its chemical composition [28]. For example, F. Chen et al. have used filter paper inserted between substrate and precursor solution droplet to separate CH3 NH3 PbBr3 from DMF solution, and followed by the crystallization mechanisms [29]. However, not many studies were conducted focusing on the detailed growth mechanism of cubic CH3 NH3 PbBr3 , evolution of its morphology, and optical properties followed by indepth analysis using first principles methods. In this work, CH3 NH3 PbBr3 microstructures were synthesized using a onestep solution selfassembly method. The morphology and the structure were analyzed using SEM, and Xray diffraction. Scanning electron microscopy (SEM) and 3D fluorescence microscope observations were used to explain the growth mechanism. We also carried out first principles based density functional theory (DFT) simulations to explain the electronic properties of cubic CH3 NH3 PbBr3 microstructures.
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2 Experimental Methods 2.1 Synthesis of Hybrid OrganicInorganic Perovskite CH3 NH3 PbBr3 The hybrid organicinorganic perovskite under this study is CH3 NH3 PbBr3 . The CH3 NH3 PbBr3 microstructures (hollow cubes, plates, cubes, and wires) were synthesized using a onestep solution selfassembly method, which has been reported in Ref. [30–32]. CH3 NH3 Br and PbBr2 were independently dissolved in NNdimethylformamide (DMF) with the same concentration equal to 0.2 M. These two solutions were mixed at room temperature with 1:1 volume ratio to form CH3 NH3 Br—PbBr2 solution with concentration equal to 0.1 M. The diluted solution was dipcasted onto a glass or silicon substrate, which was placed on a Teflon stage in a beaker. Dichlorometane (DCM) of CH2 Cl2 was placed in the beaker and sealed with a porous Parafilm to control the evaporation speed. After 24 h, CH3 NH3 PbBr3 perovskites microstructures were successfully synthesized on the silicon substrate.
2.2 Physical Characterization and Computational Methodology The fabricated structures were then characterized using SEM, and Xray powder diffraction to study its morphology and crystallinity. Scanning electron microscopy (SEM) Jeol operating at 20 keV beam energy was used to analyses the structures. Xray photoelectron spectroscopy (XPS) studies were carried out in a Kratos Axis Supra DLD spectrometer equipped with a monochromatic Al Kα Xray source (hν = 1486.6 eV) operating at 45 W, a multichannel plate and delay line detector under a vacuum of ~10–9 mbar. All spectra were recorded using an aperture slot of 300 μm × 700 μm. Survey spectra were collected using a pass energy of 160 eV and a step size of 1 eV. A pass energy of 20 eV and a step size of 0.1 eV were used for the highresolution spectra. For XPS analysis samples were mounted in floating mode to avoid differential charging. Charge neutralization was required for all samples. Binding energies were referenced to the C 1 s binding energy of adventitious carbon contamination which was taken to be 284.8 eV. We have carried out density functional theory calculations on bulk CH3 NH3 PbBr3 to get further insight into the experimentally observed properties employing the plane wave pseudopotential code, Vienna Abinitio Simulation Package (VASP) [33, 34]. The exchange and correlation are described in the generalized gradient approximation (GGA) [35]. The pseudopotentials were described in the projected augmented wave (PAW) method with PerdewBurkeErnzerhof (PBE) formalism [36]. A kinetic energy cutoff of 650 eV is used to expand the plane waves included in the basis set. Since it is well known that GGA underestimates the bandgap of halide perovskite structures, [37], we have employed
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the Hubbard approximation with U parameter = 8 eV [38] as implemented in the Dudarev approach in VASP. The Brillouin zone is sampled using a Monkhorst Pack grid of 8 × 8 × 8. The energy and force relaxations were performed within tolerances of 1E−06 eV and 1E−03 eV/Å respectively.
3 Results and Discussions The scanning electronic microscopy (SEM) observations of CH3 NH3 PbBr3 structures shows a wide range of shapes; cubes, plates, wires and hallow cubes (Fig. 1(a)) formed on silicon substrate. The range of wires in length is from few microns to more than 100 μm and in width from few hundred nm to 40 μm. Most of wires were found to have rectangular crosssections as shown in Fig. 1(a), (b). A cubes and plates with sharp edges are existing with different sizes. Also, hollow cubes appeared with sharp edge (see Fig. 1(c)). These hollow cubes are in the early crystallization stages due to the formation of agglomerate crystals and it seems that the growth starts from the perimeter and propagate to the center of the cube [39]. To explain the growth mechanism through surface evolution, several plates, cubes, and hollow cubes were observed using 2D and 3D florescence microscope coupled with SEM observations. The schematic representation in Fig. 2(a), present an approach of growth and crystallization mechanisms of CH3 NH3 PbBr3 structures. The growth starts when the crystallization occurs in the supersaturated CH3 NH3 Br•PbBr2•DMF precursor solution, and CH3 NH3 PbBr3 molecules condense into small seeds. These CH3NH3PbBr3 seeds coalesce into bigger particles after short time. Then, CH3 NH3 PbBr3 particles gradually selfassembled into a hollow structure like hollow cage and the growth starts from the perimeter and propagate to the center (see Fig. 1b), giving forms of hollow cubes when the growth is not finished due to the lack in the crystal. These crystals are twisted and their faces peculiarly inclined toward each other. A 3D fluorescence microscope and SEM observations confirm the presence of hollow cubes in Fig. 3b, c with formation of hollow plate in the bottom and then CH3 NH3 PbBr3 crystals accumulate in layered stacked structure, and continued to grow in (001) direction until the final cubic single crystal is formed. Indeed, the growth of CH3 NH3 PbBr3 crystals in (001) direction
Fig. 1 (a) SEM images of CH3 NH3 PbBr3 perovskite structures: (b) plates and (c) hollow cubes
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Fig. 2 (a) Schematic scenario of growth process of CH3 NH3 PbBr3 microstructures. (b), (c) SEM and 3D fluorescence microscope images of perovskite showing hollowed interior. (d) 2D fluorescence microscope images of CH3 NH3 PbBr3 plates. (e) Schematic representation of a layerbylayer (Frank–Van der Merwe) growth model of CH3 NH3 PbBr3 single crystal in (001) direction (b)
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Fig. 3 The optimized structure of CH3 NH3 PbBr3 unit cell. The atoms, respectively are, silver (Carbon), blue (Nitrogen), pink (Hydrogen), green (Pb) and brown (Br). Comparison of (b) calculated total and projected density of states (DOS) of CH3 NH3 PbBr3 using GGA + U, (c) Experimental DOS measured by XPS VB
was done by layerbylayer model also known as the Frank–van der Merwe growth mode till the formation of the complete cube. To explain the growth mechanism of these structures in (100) direction, a schematic scenario is represented in Fig. 2(e). Small plates of CH3 NH3 PbBr3 crystal appeared on the bottom of the substrate on
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(100) facet to play a role of independent seed crystal to form the frame of the cube by selfassembly as described in Fig. 2(e). During this step, new layers come on the top to form cubic plates. Indeed, this model of growth of CH3 NH3 PbBr3 (100) facet is proceed in layerbylayer model. In general, to growth a macroscopic film, it needs a balance of surface energies of the substrate γB , the deposit γA , and the energy of the interface γ∗ formed between the two (Fig. 2(e)), which are controlled by the change in Gibbs free energy needed for the creation of the surface or interface [40, 41]. The layer by layer growth will be characteristic by the balance of energies that will support the increase of the area of the deposit (and the interface) over leaving an exposed substrate surface (γA + γ∗ < γB ). The results of this growth will be a completion of one layer before the nucleation of subsequent layers occurs. This proposed model was confirmed by 3D fluorescence microscope and SEM observations, where the formation of layers is very clear in Fig. 2(c) and support as well Chen’s et al. approach [29]. Since we have obtained the cubic phase for CH3 NH3 PbBr3 perovskites in our experiments, the unit cell of simple cubic structure is considered for the calculations and the optimized crystal structure is shown in Fig. 3(a). The room temperature crystal structure of CH3 NH3 PbBr3 is cubic with Pm3m space group and we have obtained bulk lattice parameter of 5.92 Å after optimization, which agrees with experimentally reported value of 5.94 Å [42]. The total density of states (DOS) as well as the projected density of states calculated for the individual atoms plotted using GGA+U is presented in Fig. 3(b). We can see that main contribution close to the valence band maximum (VBM) comes from the halogen (Br) 4p states. The experimental DOS measured by XPS VB is presented in Fig. 3(c). It can be seen that the features of XPS VB spectrum and theoretical DOS shows good agreement over wide energy range. The VBM also consists of smaller contribution from the Pb 6s and 6p orbitals. The CH3 NH3 PbBr3 microstructures show a band gap of about 2.3 eV.
4 Conclusions In summary, perovskite CH3 NH3 PbBr3 microstructures were synthesized using a onestep solution selfassembly method. The morphology of these microstructures consists of a mixture of plates and cubes. We found that after crystallization of CH3 NH3 PbBr3 , hollow plates are formed on the substrate, and then a layerbylayer growth model was used to the growth CH3 NH3 PbBr3 cubes in (001) direction. The density of states calculated using DFT methods is in good agreement with XPS experimental results. Acknowledgements This work was supported by UAE University, under NSS Center Project No. 21R032 and UPAR project No 31S306. S. Assa Aravindh gratefully acknowledge CSCIT, Finland for computational resources and Academy of Finland (# 311934).
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Electronics
Numerical Study of the Effect of Applied Voltage on Simultaneous Modes of Electron Heating in RF Capacitive Discharges Abdelhak Missaoui, Morad Elkaouini, and Hassan Chatei
Abstract A mathematical model based on the fluid approach is developed to study the capacitively coupled radiofrequency discharges at low pressure. This model allows us to obtain the electron heating profiles under the effects of applied voltage and pressure after 3000 radiofrequency cycles. These informations are very useful to understand the plasma processes used for etching or for the deposition of thin films to manufacture capacitors or micro coils. The results showed an increase whether for the pressure heating or for the ohmic heating when the applied voltage increases from 150 to 220 V. Finally, the results also showed that pressure heating and ohmic heating exist simultaneously and increase rapidly with the increase of the pressure which has similar effect to the applied voltage on the electron heating. Keywords Electron heating · RF voltage · Fluid model · CCPs discharges
1 Introduction In recent years, the fields of micro and nanoelectronics have seen great development in the miniaturization of circuits include active components such as MOSFET, IGBT and PIN diodes, and passive components such as transformers, capacitors, and coils. However, the future of microinverters require the integration of all these passive components in the same electronic card and later, on the same chip [1]. Capacitively coupled radiofrequency plasma discharges at low pressure are mostly used for surface modification of materials by deposition of thin films and etching [2]. These discharges have been widely used in recent decades. However, there are some problems that remain to be understood such as the electron heating mechanism [3]. This A. Missaoui (B) · M. Elkaouini · H. Chatei Laboratory of Physics of Matter and Radiation, Faculty of Sciences, Mohammed I University, Oujda, Morocco email: [email protected] M. Elkaouini Department of Physics, Polydisciplinary Faculty of Nador, Mohammed I University, Nador, Morocco © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_29
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kind of capacitively coupled discharge consists of two parallel electrodes separated by a distance of a few centimeters (cm) and excited by applying a radiofrequency sinusoidal voltage to one of the electrodes, while the other electrode is grounded. The charged particles play an axial role in surface treatment, especially positive ions. It is due to the ion bombardment that the etching can take place, which allows a reproduction of the micrometric motifs. In the case of plasma deposition, ion bombardment can have a profound effect on the microstructure and properties of the deposited film. Electrons also play an important role in the creation of positive ions by converting the electrical power into chemical power by ionization of neutral atoms [4]. The coupling of the energy to the electrons by the rf field depends on several parameters such as the applied voltage, the gas pressure, and the excitation frequency. Besides, the electrons instantaneously respond to the variation of the radiofrequency electric field because of their low mass. Experimentally, it was found that in radiofrequency discharges used for semiconductor manufacturing, the spatial and temporal inhomogeneities of electric fields cause stochastic heating (pressure heating), which is usually a dominant phenomenon for electrons [6]. Recently, several attempts have been made to understand the electron heating mechanism, including experiments [7, 8], PIC simulations (ParticleInCell) [9], and Fluid Modeling [5]. In this paper, the effects of applied voltage on the electron heating are studied using fluid modeling with driftdiffusion approximation in onedimensional (1D). By considering argon as the working gas, the discharge is established between two symmetrical electrodes. It is assumed that the diameter of the electrodes is much larger than the distance between these electrodes. In Sect. 2 the description of the fluid model is provided. The calculation results after 3000 cycles are presented and discussed in the Sect. 3. Finally, we conclude in Sect. 4.
2 Model Description The discharge is described between two large parallelplate electrodes. Therefore, the properties of the discharge change only in the direction perpendicular to the parallel electrodes. Our model consists of the continuity equations, the momentum transfer equation, and the energy equation of electrons. These equations are coupled in a selfconsistent way to the Poisson equation. The continuity equations for electrons and positive ions are ∂ Je,i ∂n e,i + − Ri = 0 (1) ∂t ∂x where n e,i is the number density of electrons and ions, Ri is the source term which contains only the rate of ionization reaction. Je,i is the flux of electrons and ions, which are described by the driftdiffusion approximation and can be written as Je,i = ∓n e,i μe,i E − De,i
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where μe,i and the De,i are the mobility and the diffusion coefficient for electrons and ions respectively. The energy balance of electrons derived from the Boltzmann equation reads as follows ∂qe ∂ 3 ( n e k B Te ) + + e Je .E + Hi Ri = 0 ∂t 2 ∂x
(3)
where Te is the electron temperature, E, Hi and k B are the electric field, the energy loss coefficient with ionization, and the Boltzmann constant respectively. The heat flux of electron energy can be expressed as qe = −κe
5 ∂ Te + k B Te Je ∂x 2
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where κe = 23 k B De n e is the electrons thermal conductivity coefficient. The third term on the lefthand side of Eq. (3) represents the electron heating rate, which is the combination of the pressure heating and the ohmic heating. Pheating = eDe n e
∂n e E + eμe n e E 2 ∂x
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Finally, the calculation of the electron heating in Eq. (5) requires the determination of the local electric field created by the charged particles by using the Poisson equation, which connects the gradient of this electric field by the charge densities as follows e ∂2V = (n e − n i ) 2 ∂x ε0 E =−
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In Eq. (6) ε0 and e are the vacuum permittivity and the elementary charge, respectively. The problem requires the resolution of the coupled system of equations (Eqs. (1), (3) and (6)) with the following boundary and initial conditions [11]. At the powred electrode (x = 0): Ji = n i μi E,
Je = −ks n e − γ Ji ,
Te = 0.5 eV and V = Vr f sin(2π f t).
At the grounded electrod (x = D): Ji = n i μi E,
Je = ks n e − γ Ji ,
Te = 0.5 eV and V = 0 (V ).
Where γ = 0.01 represents the secondary electron emission coefficient and ks is the recombination coefficient. The initial conditions are as follows [10]:
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Table 1 Parameters used in the calculation [10] Symbol Description n n De n n μe n n Di n n μi ks Hi D
Value
Electron diffusivity Electron mobility Ion diffusivity Ion mobility Electron recombination Electron energyloss coefficient Gap between the electrodes
x 2 x 2 , n e = n i = n ε ε + 16 1 − D D
3.86 × 1022 (cm−1 .s−1 ) 9.6 × 1021 (V−1 .cm−1 .s−1 ) 2.07 × 1018 (cm−1 .s−1 ) 4.65 × 1019 (V−1 .cm−1 .s−1 ) 1.19 × 107 (cm.s−1 ) 15.7 (eV) 2.54 (cm)
Te = 1 eV and V = 0 (V ).
where ε is a positive number and n ε is the initial density for the charged particle. The rest of the parameters used in this calculation are listed in Table 1. The calculation aims to determine electron pressure heating and electron ohmic heating as a function of the position between the electrodes for different values of the applied voltage and pressure.
3 Results and Discussion In order to illustrate the effects of the operative parameters, we present the electron pressure heating and ohmic heating profiles as function of various values of applied voltage and pressure. Figure 1 shows the spatial distribution of the electron pressure heating for different applied voltages and pressures. In all case (Fig. 1 (a), (b), (c) and (d)), the electrons pressure heating profiles are peaked near the electrodes but negative or zero in the bulk. Furthermore, as the applied voltage increase, the pressure heating increase in the sheath because electrons diffuse along the gradient of density and move in the opposite direction of the electric field during sheath expansion. While electrons that arrive in the plasma bulk during the sheath collapsing could not lose energy anymore because the electric field in this region is very low. The results also show that the peaks shift slightly toward the bulk region when the applied voltage increases. As one can see in Fig. 1, the pressure also affects the electrons pressure heating, as the gas pressure increases from 0.1 to 1.5 Torr, the electrons pressure heating also increases because the sheath becomes more collisional. However, in the plasma bulk, the shielding affects the absorption of electron energy, that is why there is lower pressure heating despite the pressure increase.
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Fig. 1 Profiles of electron pressure heating after 3000 rf cycles for different applied voltages and pressures (a) at 0.1 Torr, (b) at 0.5 Torr, (c) at 1 Torr and (d) 1.5 Torr.
The ohmic heating as a function of distance between the electrodes, is illustrated in Fig. 2 with various values of applied voltage and pressure, it is found to increase with increasing Vr f . As we can see in Eq. 5, the second term which is the ohmic heating is proportional to the square of the electric field. Besides, the electric field is varied by varying the applied voltage, especially in the sheath region because the conduction current and the power absorption are higher in this region [11]. The positive peaks in the sheaths indicate that the electrons are heated by the strong electric field. Therefore, electron ohmic heating is positive or zero in the plasma bulk due to weak values of the electric field in this region. Also, the results show that the increase of the pressure leads to increasing the ohmic heating in the sheath region. Finally, we demonstrate the simultaneous existence of pressure heating and the ohmic heating modes in capacitively coupled radiofrequency discharge. In addition, the applied rf voltage influences the electron pressure heating much more than the ohmic heating in the plasma sheath. We can also show that the Vr f and the pressure have the same effect on electron heating.
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(a)
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Fig. 2 Profiles of electron ohmic heating after 3000 rf cycles for different applied voltages and pressures (a) at 0.1 Torr, (b) at 0.5 Torr, (c) at 1 Torr and (d) 1.5 Torr.
4 Conclusion Fluid modeling of argon plasma generated by a radiofrequency discharge is simulated to study the effects of the applied voltage and gas pressure on the electron heating mechanism. The calculation results indicate that the applied voltage has a significant effect on the electron heating in the sheath region. It is found that the Vr f can produce a higher pressure heating in the plasma sheath as well as a higher electron ohmic heating. Moreover, the pressure also influences the electron heating mechanism and it has found that the applied voltage and pressure have the same effect on the electron heating. In summary, we find that the electron heating which is the sum of pressure heating and ohmic heating is a process significantly influenced by the applied voltage.
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References 1. Benyoucef D, Yousfi M (2015) Particle modelling of magnetically confined oxygen plasma in low pressure radio frequency discharge. Phys Plasmas 22:013510. https://doi.org/10.1063/1. 4907178 2. Lieberman MA, Lichtenberg AJ (1994) Principles of plasma discharges and materials processing. Wiley, New York 3. Proto A, Gudmundsson JT (2018) The influence of secondary electron emission and electron reflection on a capacitively coupled oxygen discharge. Atoms 6:65. https://doi.org/10.3390/ atoms6040065 4. Belenguer Ph, Boeuf JP (1990) Transition between different regimes of RF glow discharges. Phys Rev A 41:4447–4459. https://doi.org/10.1103/PhysRevA.41.4447 5. Chen G, Raja LL (2004) Fluid modeling of electron heating in lowpressure, highfrequency capacitively coupled plasma discharges. J Appl Phys 96:6073–6081. https://doi.org/10.1063/ 1.1818354 6. Popov OA, Godyak VA (1985) Power dissipated in lowpressure radiofrequency discharge plasmas. J Appl Phys 57:53–58. https://doi.org/10.1063/1.335395 7. Berger B, Brandt S, Franek J, Schüngel E et al (2015) Experimental investigations of electron heating dynamics and ion energy distributions in capacitive discharges driven by customized voltage waveforms. J Appl Phys 118:223302. https://doi.org/10.1063/1.4937403 8. Babu SK, Kelly S, Kechkar S, Swift P et al (2019) Experimental investigation of electron heating modes in capacitively coupled radiofrequency oxygen discharge. Plasma Sources Sci Technol 28:115008. https://doi.org/10.1088/13616595/ab4c59 9. Kawamura EK, Lieberman MA, Lichtenberg AJ (2006) Stochastic heating in single and dual frequency capacitive discharges. Phys Plasmas 13:053506. https://doi.org/10.1063/1.2203949 10. Lymberopoulos DP, Economou DJ (1993) Fluid simulations of glow discharges: effect of metastable atoms in argon. J Appl Phys 73:3668–3679. https://doi.org/10.1063/1.352926 11. Zhao L, Liu Y, Samir T (2017) Effects of gas pressure on plasma characteristics in dual frequency argon capacitive glow discharges at low pressure by a selfconsistent fluid model. Phys Plasmas 26:125201. https://doi.org/10.1088/16741056/26/12/125201
Comparison of State of Charge Estimation Algorithms for Lithium Battery Mouncef Elmarghichi, Mostafa Bouzi, Naoufal Ettalabi, and Mounir Derri
Abstract The state of charge (SOC) is a measurement of the amount of energy available in a battery at a specific point in time expressed as a percentage. The SOC provides the user with information of how much longer the battery can perform before it needs to be recharged. This paper proposes a comparison between common algorithms used to estimate the SOC (state of charge) of a lithium battery cell for electric vehicle application. Results for Extended Kalman Filter (EKF) are shown here. In order to apply this algorithm, a battery model was chosen and parameterized, then the EKF was applied to estimate the battery SOC level. The simulation results were verified using MATLAB software. Keywords State of charge (SOC) · Extended Kalman Filter (EKF) · Battery
1 Introduction To ensure good operation of the lithium battery, a reliable battery management system (BMS) is a must. Which enables not only the supervision of the battery via different indicators (SOC, State of Health (SOH)…), but also the safety and balance between cells. One of the critical functions in a BMS is SOC estimation. The SOC estimation for all cells is an important input for balancing, energy, power calculations, SOH estimation and so one [1, 2]. In this paper, we present a comparative study between four algorithms used to estimate the state of charge for lithium batteries. Also, we expose the simulation results for EKF (Extended Kalman Filter) carried out with MATLAB.
M. Elmarghichi (B) · M. Bouzi · N. Ettalabi FST Hassan I University, Settat, Morocco email: [email protected] M. Derri EHTP, Casablanca, Morocco © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_30
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The paper is organized as follows: Sect. 2 describes what is the state of charge. Section 3 reports the battery modeling system. Section 4 describes the battery soc estimation algorithms discussed here, Sect. 5 reports the comparison results and discussion. Finally, Sect. 6 presents our conclusions.
2 State of Charge (SOC) The state of charge is defined as the ratio of residual capacity to total capacity. The coulomb counting is the easiest way to estimate the SOC: 1 S OC(t) = S OC(t0) − Q
t
∫ η.i(t).dt
(1)
t0
Q is the rated capacity of the battery, SOC(t0) is the SOC level at the initial time t0, η is the coulombic efficiency, i(t) is the current which is positive at discharge and negative at charge. The equation above works by integrating the current over time to derive the total sum of energy entering or leaving the battery, thus, enabling the track of the SOC in a battery. The problem is that this equation is not selfcorrective, and subject to drift due to current sensor fluctuation. In addition, this method needs precise initial state. An alternative to estimate SOC is to use modelbased approaches. These methods implement algorithms that use sensed measurements to infer hidden state, these algorithms assume a known mathematical model for the cell.
3 Battery Modeling System Battery model is classified into five categories namely: empirical model (EM), electrochemical model (ECM), electrical equivalent circuit model (EECM), electrochemical impedance model (ECIM), and datadriven mode (DDM) [2]. We discuss here only the EECM model which is suitable for online estimation because of its simplicity, low computational requirements, and high compatibility for embedded system applications. The Rint and Thevenin model are the most used [2]. Fig. 1 Rint model [2]
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Fig. 2 nRC model with one RC (right) and two RC (left) branches [2]
3.1 Rint Model The Rint model is the easiest one, it contains an OCV voltage source in series with one resistor to model instantaneous response for a given current input, but this model lacks accuracy due to the fact that it neglects hysteresis effect and diffusion phenomena (Figs. 1 and 2).
3.2 nRC Model (Thevenin Model) Thevenin model is an enhanced model that takes into account not only the polarization effect but the hysteresis and diffusion phenomena as well. The accuracy of the model depends on the number of RC parallel branches [2]. n number of parallel RC branches can be added to the original model (Rint model) to analyse the more transient response. According to the literature, a Thevenin model with three RC branches accurately describe the behavior of a lithium battery cell [2].
4 Algorithms Several algorithms are used to estimate the SOC level, we discuss here four algorithms.
4.1 Linear Kalman Filter Kalman filter is a set of mathematical equations used recursively with measurement data to estimate the hidden state of a given system [2]. The Linear Kalman filter does not perform well especially when used to estimate the SOC of a battery, that is normal due to the fact that the battery model is not linear.
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4.2 Extended Kalman Filter (EKF) Extended Kalman Filter is an enhanced version of the linear Kalman filter [2], EKF uses first order Taylor series expansion to linearize the system equations. The computation of the Jacobian matrix is required during estimation through the EKF algorithm which conversely effects the accuracy of the estimated SOC. A limitation of the EKF algorithm is that only firstorder accuracy can be achieved by using firstorder Taylor expansion in linearization. We used real data provided by CALCE Battery Research Group to test the performance of the algorithm. The table below resumes the parameter of the battery cell [2, 4, 5] (Table 1). In the first step an OCV (Open circuit voltage)/SOC lookup table is established using low current OCV test [2, 6, 7]. As the SOC charge and discharge curves are different due to hysteresis effect, a combined curve must be drawn. Figure 3 shows separately the three curves for charging, discharging, and the mixed curve. We used for our case the Thevenin model with one RC parallel branch (Fig. 2) as a model. The Dynamic stress test (DST) is used to identify the model parameters while later to validate the performance of the SOC estimation, Fig. 4 illustrate the current profile, we can see that the cell is highly stressed with a current that varies between +2A (charge) and −4A (discharge). Figure 5 shows that the estimated and true terminal voltage are very close from each other despite the aggressive variation of the current in DST test. The table below summarizes the different errors (Table 2). Table 1 Battery parameters
INR 1865020R battery Battery (Parameters)
Specifications (Value)
Capacity rating
2000 mAh
Cell chemistry
LNMC/Graphite
Dimensions (mm)
18.33 ± 0.07 mm
Fig. 3 OCV SOC for charge, discharge, and combined charge/discharge
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Fig. 4 Dynamic stress test (DST) Current Profile
Fig. 5 True and estimated terminal voltage
Table 2 RMS, Mean and Max error
RMS error (mv)
Mean error (mv)
Max error (mv)
1.6533
2.7335
−147.679
The model and OCV/SOC lookup table are essential ingredient for the extended Kalman filter. Now we can apply the extended Kalman filter. The results show that despite modeling error, the algorithm was able to follow the variation of SOC in a DST Profile Test with great accuracy, actually the RMS SOC estimation error was below 2% which is satisfactory. Figure 6 shows the SOC estimation compared with the true value.
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Fig. 6 Estimate SOC of EKF
4.3 Sigma Point Kalman Filter (SPKF) Sigmapoint approach uses statistical linearization, by selecting deterministic sampling points called sigma points to find mean, covariance. In [3], an SOC estimation approach was proposed and investigated. Consideration was given to the Thevenin model with included hysteresis and the improved adaptive unscented Kalman filter (AUKF) technique. To verify the performance of the proposed algorithm, the experiments were conducted under Hybrid Pulse Power Characterization Test (HPPC) condition. An initial SOC error of 20% was applied to the 1865022FM Liion battery to analyze the robustness of the method. Compared with the other methods, the proposed method demonstrated a higher SOC estimation accuracy and better robustness.
4.4 Neural Network (NN) With a selfadaptability and selflearning, neural network can be used to estimate the SOC of a battery cell. NN is a mathematical tool that uses trained data without a need to know the initial SOC state. Formed by three layers an input output layer and one or more hidden layers. The NN has great capability but requires large memory. The input layer has essentially three input current, voltage, and one for temperature, the output is the SOC. In [4] Wei. He developed a State of Charge Estimation for LiIon Batteries Using Neural Network Modeling and Unscented Kalman Filterbased Error Cancellation. To estimate battery SOC, the inputs of the neural network were the voltage, current, and temperature, and the output was the SOC, the results showed a good SOC estimation with error less than 2.5%.
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5 Comparison Results Lithium battery is a highly nonlinear system; therefore, Standard Kalman Filtering (KF) can’t be used as an algorithm to accurately estimate the state of charge due to the fact that KF can only be applied to a linear model. Extended Kalman Filter (EKF) on the other side can handle the nonlinearity of the cell, but with a high computational cost. EKF proposed in this paper gave us an estimated error below 2% which is quite good, but high error can occur with highly nonlinear cases. Sigma Point Kalman Filter is an attractive alternative. SPKF has an identical calculation complexity as EKF but without considering Jacobian matrices. Based on statistical linearization, SPKF can deal with high nonlinear system. Neural Network (NN) are heavily used in literature to estimate the SOC level. NN performs well even if the system is highly nonlinear: the case for the battery. The table below compares the presented SOC estimation algorithms (Table 3). Table 3 Comparison between different algorithms Algorithm Advantages
Disadvantages
KF
– Accurately estimates states affected by – Don’t perform on nonlinear system external disturbances – Divergence if the model is inaccurate
EKF
– Predicts a nonlinear dynamic state with good precision
– Limited robustness – Error could occur if the system is highly nonlinear
SPKF
– No Jacobian matrices calculation
Complicated
NN
– Performs well in modeling a nonlinear – Has a complex computation dynamic system – Needs costly processing unit
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6 Conclusion In this work, we presented a comparative study between four algorithms used to estimate the state of charge of lithium batteries for electric vehicle application. Results of Extended Kalman filter on a one RC Thevenin Model were exposed and discussed, the results showed that EKF was able to achieve good results, but with high computation cost. Investigation of other algorithms is required in order to choose the best candidate to estimate the State of Charge with high accuracy and low calculation speed. Neural Network (NN) and Fuzzy Logic [5] are powerful algorithms, maybe through some hybrid combination between these algorithms and Kalman filtering (SPKF, EKF) we can achieve better results.
References 1. Hannan MA, Lipu MSH, Hussain A, Mohamed A (2017) A review of lithiumion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations. Renew Sustain Energy Rev 78:834–854. https://doi.org/10.1016/j.rser.2017. 05.001 2. Shrivastava P, Soon TK, Bin Idris MYI, Mekhilef S (2019) Overview of modelbased online stateofcharge estimation using Kalman filter family for lithiumion batteries. Renew Sustain Energy Rev 113:109233. https://doi.org/10.1016/j.rser.2019.06.040 3. Chen Z, Yang L, Zhao X et al (2019) Online state of charge estimation of Liion battery based on an improved unscented Kalman filter approach. Appl Math Model 70:532–544. https://doi. org/10.1016/j.apm.2019.01.031 4. He W, Williard N, Chen C, Pecht M (2014) State of charge estimation for Liion batteries using neural network modeling and unscented Kalman filterbased error cancellation. Int J Electr Power Energy Syst 62:783–791. https://doi.org/10.1016/j.ijepes.2014.04.059 5. Salkind AJ, Fennie C, Singh P et al (1999) Determination of stateofcharge and stateofhealth of batteries by fuzzy logic methodology. J Power Sources 80:293–300. https://doi.org/10.1016/ S03787753(99)000798 6. Zheng F, Xing Y, Jiang J et al (2016) Influence of different open circuit voltage tests on state of charge online estimation for lithiumion batteries. Appl Energy 183:513–525. https://doi.org/ 10.1016/j.apenergy.2016.09.010 7. Xing Y, He W, Pecht M, Tsui KL (2014) State of charge estimation of lithiumion batteries using the opencircuit voltage at various ambient temperatures. Appl Energy 113:106–115. https://doi. org/10.1016/j.apenergy.2013.07.008
GATE Simulation of 6 MV Photon Beam Produced by Elekta Medical Linear Accelerator DeaeEddine Krim, Abdeslem Rrhioua, Mustapha Zerfaoui, Dikra Bakari, and Nacira Hanouf
Abstract The previous Monte Carlo codes offer the most powerful engines to study the processes physic of particles including their interactions in Radiation Therapy. In this task, we take benefit of GATE 8.2 to simulate the linear accelerator system, IAEA phasespace folders are exploited to speed up computing time. The model developed includes the majority of the components of the patientdependent part using in Elekta 6M V platform. This model is used accompanied by a homogeneous water phantom with dimensions 50 × 50 × 50 cm3 , placed at an SSD of 100 cm. The comparisons of our results are performed with experiment data respecting the similar aspect. The Percentage Depth Dose (PDD) and transverse profiles, for field size of 10 × 10 cm2 , are accurately calculated. Besides, the beam quality such as D10 (%), dmax (cm), d80 (cm), T P R20/10 , the two relative differences in dose were derived on ψi , ψi,max and the ratio i are calculated. Once and for all, we typically take a good agreement between simulation MC GATE 8.2 and the experiment data with an error less than 2%/3 mm. Keywords Simulation · GATE · IAEA phase space · Radiotherapy · Grid
1 Introduction Monte Carlo manner is considered among the most efficient and typical techniques, it is intensively used to simulate particle interaction and transport in various fields. In our case, it is proven that this approach can be employed to determine accurately the dose in complex volumes, notably after the improvement of Variance Reduction Techniques (VRTs) [1], which allow achieving a suitable exactness in the simulation, despite in geometries as complex as realistic tumor shapes. Moreover, many D.E. Krim (B) · A. Rrhioua · M. Zerfaoui · N. Hanouf Laboratory of Physics of Matter and Radiation Faculty of Sciences, Mohammed First University, Oujda, Morocco email: [email protected] D. Bakari National School of Applied Sciences, Mohammed First University, Oujda, Morocco © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_31
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codes were developed from the generic Monte Carlo code ‘Geant4’ [2] for special purposes (GATE, GAMOS). The acronym GATE [3] stands for “Geant4 Application for Emission Tomography.” it is, consequently, a Monte Carlo simulation software as well based on the Geant4 toolkit. In this respect, all Geant4 libraries are summed up in the software GATE, with benefit to let an advanced user to add modern functionality in various fields. Accordingly, this paper is organized as follows, we model the dependent patient part of the ELEKTA platform accelerator, where all steps used in our simulation strategy are fully described. Additionally, in the tierce section, we examine the results obtained with the experiment data by the use of Percentage Depth Dose (PDD) and delivered dose profiles employing the SLURMcluster. Finally, in the fourth part, conclusions are drawn from this work.
2 Materials and Methods 2.1 Hardware Requirements The energy photon beam (X − 6M V ) was utilized in this investigation with a reference dose rate of 400MU/min, delivered by the ELEKTA platform. Moreover, the dosimetry calculation was carried out according to AAPM’s TG51 protocol [4]. The experiment data were obtained using a cylindrical ionization chamber type 97322 with an active volume of 0.125 cm2 , mounted over a motorized guide in a resistance temperature detector 3D water phantom.
2.2 Implementation of Linac Geometry Count on detailed information that was cited in the latest papers published [5, 6], we simulate the linac head through the usage of GATE. What is more Fig. 1 shows the global different structure of the employed technique that can be utilized to simulate the linear accelerator arrangement in QT mode. Simulation parts can be summed up in four steps – Phase space IAEA  Green box: The IAEA phase space storing millions of particles, by the simulation of the independent patient part I.P.P. with all components based on the vendor detailed information and by the use of EGSnrc version V4r230. Furthermore, with advantaging that the I.P.P components never seen changed throughout a real treatment. – MultiLeaf Collimator MLC: The definition of physical characteristics of the MLC leaves included the tungsten alloy material, Tongue and Groove T&G and the rounded part in the last of each leaf were performed. – Secondary collimators X, Y: They are made of tungsten alloy about 7.5 cm of thickness with curved part appearance in the last of each Jaw.
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Fig. 1 The view of geometry technique used to simulate LINAC accelerator
– Phantom: It is a box of water with dimensions 50 × 50 × 50 cm3 located at a source surface distance 100 cm from the target related to the information cited in the header of IAEA.
2.3 Validation Tests Many tests are performed in order to validate simulation against measurement data. Each of them has advantages and restrictions, but generally, they present the best standard and the most wellknown tests in dose computation. First, the results obtained are compared with the experimental data using the parameter ψ. Allowing to construct the standard mean error between each point measured experimentally and that calculated by GATE, with equation: ψi =
n 1  (Di − Dr e f i )  n i Dr e f i
(1)
In the low dose, the use of ψ error could lead to high overall errors. Furthermore, in the case of lateral profiles, the agreement procedure with measurements was calculated roughly by:
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ψmax =
n 1  (Di − Dr e f i )  n i Dr e f max
(2)
where, the (Di − Dr e f i ) describes the difference of the dose among the points measured and calculated. Nevertheless, Dr e f max represents the dose of maximal measured experimentally. The proportion among doses measured and calculated with the GATE was utilized like a second test. This parameter defined for each measurement point dm by: Dc (i) (3) i = Dm (i) where Dc and Dm represent the calculated and the measured doses severally, for each point (i) inside the distribution related to distance.
2.4 Clustering Methods The cluster computing way (SLURMCNRST Team Morocco) is used. Openmosix cluster platform is employed to split the main GATE code to 1000 submain codes, on 100 jobs (100 nodes in parallel: 15 CPUs, 2 threads per core). Throughout a simulation, the ROOT [7] folders arising from the parallelized simulations will be merged to provide a single output file.
3 Results and Discussion To validate properly the quality of photon beam taken by the simulation GATE against the real measured data and according to international recommendations (IAEA TRS398). The index of quality tissue phantom ratio TPR in water for the square field 10 × 10 cm2 [4, 8]. The D10 (%), dmax (cm) and d80 (cm) are reported and compared as shown in Table 1 T P R20/10 = 1.2661 × P D D20/10 − 0.0595
Table 1 Comparison of calculated and measured parameters of beam quality Parameters GATE simulation Measured data Error estimation D10 (%) dmax (cm) d80 (cm) T P R20/10
0.68411 1.417 6.8 0.58980
0.6741 1.5 6.5 0.59636
1.4% 0.83 < 3 mm 3 mm 1.1%
(4)
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1.02
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According to the Table 1, Deviation among measurement and GATE data for The relative dose at the depth of 10 cm was discovered to be less than 1.4% by computation. We can also perceive that the deviation attached to the depth of greatest dose dmax was found to be less than 1 mm, d80 (cm) was found also 3 mm, eventually, the deviation attached to T P R20/10 was less than 1.1%. To sum up, the dissimilarity observed at this comparison among GATE computations and measurements is down then 2%. Transferring immediately to examine the produced results with tests more powerful, the simulated and experimental PDD is plotted in Fig. 2(a) [a] for a 6 Mev photon beam ELEKTA and a fixed field dimensions 10 × 10 cm2 . All curves (measured and calculated by GATE) are normalized to the maximum dose Dmax also compared through the use of ψi and ψi,max parameters Fig. 2(a), (b) and (c). Furthermore, the ratio and the statistical histograms of dose differences are shown in Fig. 2(a), (b) and (c). Awarding to the last Fig. 2(a), it can be concluded that the approach exploited in this Monte Carlo simulation model is very matched with the
3 2 1
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Fig. 2 Comparison of calculated and measured PDD and transversal profiles ranging from 1.5 to 10 cm for 6 MV photon beam for field sizes 10 × 10 cm2 by the use of statistical parameters of tests
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measured data. It can be observed that the curve of the GATE MC simulation (blue curve) is very confused with the experiment data (red curve). Notwithstanding, the Fig. 2(a) shows that the part according to depth among 0 and 1.5 cm, near to the SSD surface, and more particularly in the buildup region (Earlier than the maximum ionization dose at z max ) the parameters ψi , ψi,max , and i recording the high overestimation in dose dissimilarities near to 3% compared with all the rest of interval depth z. Furthermore, This experimental overestimate is expected for cylindrical ionization chambers. This case constitues the proof. Otherwise, Fig. 2(a), (b) and (c) show the evaluation of dose differences with ψi indicate that experimental and simulated curves vary in more than −1% in depth between 30 and 35 cm, this big diffusion demonstrated the law cited in the second part, that ψi has a limitation in evaluation of low dose errors compared with ψi,max , that ranging in interval between −1% and 1% in low dose interval. Referring to Fig. 2(a) and (d), we can observe that the histogram of statistics based on i parameter about the differences for the PDD curves is close to a Gaussian distribution (the curve red taken by a Gaussian fit function). However, these results indicate the systematic dissimilarities around the unit “1”. Figure 2(b), (c) and (d) show lateral dose profiles at 1.5, 5 also 10 cm in depth. The evaluation of dose differences parameters ψi , ψi,max , and i show that experimental and simulated curves differs in no more than ±3% for the ψi,max parameter, but, in the case of the use of ψi , and i parameters, the differences were increased extremely, about of ±15%. Whereas, about the dissimilarity inside the interval among −5 to 5 cm which represent all signify importance part of the profile, is closed to 2%. The conclusion that can be drawn from Fig. 2(b) (c) (d) is that the greatest dissimilarities for the field shape, can be presented at the penumbra area. Additionally, we can prove this increases of differences through the high gradient dose of the quick decrease at the border of the beam.
4 Conclusion To improve the simulation time of ELEKTA LINAC, the new approach of the “fluence engine” based on IAEA phase space implementation. Moreover, the last version of GATE software v8.2 and the grid parallel calculation were exploited. The quality of the achieved results, for the investigated parameters T P R20/10 , D10 (%), dmax (cm) and d80 (cm) when compared with the measurements, confirms the accurateness of the proposed theoretical model. The agreement of the percent dissimilarity is less than 2%. Regarding the validation tests used in this task ψi,max , ψi and i , a good agreement of 98% between simulation MC GATE and experimental dose distributions is observed, in the same field this result is in good agreement (with percent difference less than 3%) with the theoretical value read of Grevillot [9]. Finally, this study demonstrates that the method followed to simulate a linear accelerator can be used to simulate other more complex beams by adjusting the primary parameters.
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Which can be exploited in oncology centers to reduce calculation time and improve treatment plans.
References 1. Yu Z, Yuefeng Q, Peng L, Yixue C (2019) An improved onthefly global variance reduction technique by automatically updating weight window values for Monte Carlo shielding calculation. Fusion Eng Des 147:111–238 2. GEANT4 collaboration (2003) Nucl Instrum Methods Phys Res A 506:250–303. IEEE T Nucl Sci 53:270–278 (2006). Nucl Instrum Methods Phys Res 835:186–225 (2016) 3. OpenGATE collaboration (2004) Phys Med Biol 49:4543–4561. Phys Med Biol 56:881–901 (2011). Med Phys 41–6 (2014) 4. Almond P, Biggs P et al (1999) AAPM’s TG51 protocol for clinical reference dosimetry of highenergy photon and electron beams. Med Phys 26:1847–1870 5. Li J, Zhang X et al (2016) Clinical feasibility of leakage and transmission radiation dosimetry using multileaf collimator of ELEKTA synergyS accelerator during conventional radiotherapy. Med Imaging Health Inform 6:409–415 6. Abou Taleb WM, Hassan MH, El Mallah EA, Kotb SM (2018) MCNP5 evaluation of photoneutron production from the Alexandria University 15MV Elekta Precise medical LINAC. Appl Radiat Isotopes 135:184–191 7. Antcheva I, Ballintijn M et al (2009) ROOT a C++ framework for petabyte data storage, statistical analysis and visualization. Comput Phys Commun 180(2):499–512 8. Teixeira MS, Batista DVS et al (2019) Monte Carlo simulation of Novalis Classic 6 MV accelerator using phase space generation in GATE/Geant4 code. Prog Nucl Energ 110:142– 147 9. Grevillot L, Frisson T et al (2011) Simulation of a 6 MV Elekta Precise Linac photon beam using GATE/GEANT4. Phys Med Biol 56:903–918
Application of HPSGWO to the Optimal Sizing of Analog Active Filter Abdelaziz Lberni, Malika Alami Marktani, Abdelaziz Ahaitouf, and Ali Ahaitouf
Abstract This paper discusses the optimal design and optimization of an analog active filter using the hybrid HPSGWO optimization algorithm, which is a combination of Particle Swarm Optimization Algorithm (PSO), and Grey Wolf Optimizer (GWO) algorithms. The PSO is a stochastic research method based on population, however, GWO is a recently introduced metaheuristic search method inspired by Canislupus. The values of the active filter components are selected from various standard industrial series (E series). Obtained results are compared with those obtained by PSO and GWO, as well as with other optimization methods, namely ACO, CRPSO and SOS. The Virtuoso Cadence tool was used to validate the optimization results obtained by the proposed approach. Moreover, it has been shown that this approach gives very robust results compared to the cited methods. Keywords Analog active filter · Butterworth filter · Metaheuristic approach · Hybrid optimization · Optimizationbased approach · E industrial series · Cadence Virtuoso
A. Lberni (B) · A. Ahaitouf FST, Sidi Mohamed Ben Abdellah University, Fez, Morocco email: [email protected] A. Ahaitouf email: [email protected] M. Alami Marktani ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco email: [email protected] A. Ahaitouf FPT, Sidi Mohamed Ben Abdellah University, Fez, Morocco email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_32
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1 Introduction Analog active filters are widely used in the areas of signal processing, communications and in biomedical instrumentation amplifiers [1]. All analog active filters circuits use amplifiers, capacitors and resistors in their design. The correct and efficient choice of the passive components of the circuit is very essential and important in the design of a feasible analog active filter. Conventionally, the capacitors and resistors used in the active filter circuit are selected directly from the standard industrial series (E series) by using a coding technique for each passive element [2]. This procedure produces acceptable results. In optimization methods, components are supposed to be any value in the search space (continuous variables) and, therefore, reliability and accuracy are affected when the filter is implemented. This requires the optimization of the filter with the usage of discrete preferred values. Hence, analog filter component values are calculated according to the minimization of cost function to meet the design criteria mainly, cutoff frequency (ωc ) and quality factor (Q). In this work, we propose an adaptation of the global optimization method recently introduced by Senel ¸ et al. in [3], for the optimization of the fourthorder analog filter. This method called HPSOGWO, which uses the concept of hybridization. It is used here to determine the optimal values of the capacitors and resistors used in the proposed fourthorder active filter. A comparison with other optimization algorithms is provided, and validation by simulations is given to demonstrate the good performances of the proposed approach.
2 HPSGWO Optimization Algorithm The hybrid approach proposed in this work uses the PSO and GWO metaheuristics. The PSO is a widely used and wellknown method [4, 5]. It can give good results in almost any realworld problem. The GWO algorithm, although relatively new in the literature, is also a metaheuristic approach that is reported to give successful results like the PSO algorithm [6–8]. A hybrid approach HPSGWO has been introduced without changing at all the general functioning of these two algorithms, gives good results. In this hybrid approach, the GWO method is used to support the PSO method in order to reduce the probability of falling into the problem of the local minimum [3, 9]. Indeed, the PSO algorithm moves certain particles to random positions with limited ability in avoiding local minima. These directives may entail some risks of moving away from the global minimum. The exploration capability of the GWO method is used to avoid these risks by directing certain particles to positions, which are partially enhanced by the GWO method instead of being directed to random positions. In this hybrid approach, PSO updates the initial population and then the updated solutions are updated by the GWO [10]. Algorithm 1 gives the pseudo code of HPSGWO method.
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3 Adaptation of HPSGWO to Optimal Sizing of Analog Filter The proposed HPSGWO algorithm has been adapted to optimize the active analog circuit, namely the lowpass Butterworth active filter. This fourthorder active filter can be designed by cascading two secondorder SallenKey blocks [10]. This type of filter is among the major building blocks in signal processing circuits and is widely used in signals demodulation and separation, noise signal estimation [11]. The objective of our optimization is to choose passive component values (resistors and capacitors) in order to obtain a specific lowpass filter. To do so, it is necessary to optimize the values of the components of the active filter to achieve the goal such as the quality factor and cutoff frequency in a way that they are compatible with the E series. The specific values of the filter design chosen here are given by: Q 1speci f ic = 1/0.7654, Q 2speci f ic = 1/1.8478 and ωc1speci f ic = ωc1speci f ic = 103 rad/s Figure 1 gives the circuit schematic of the fourthorder active lowpass filter. The transfer function of this filter is given by: H ( p) =
2 2 ωc1 ωc2 Vout = 2 × 2 2 Vin p + p ωQc11 + ωc1 p 2 + p ωQc22 + ωc2
(1)
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Fig. 1 4th order Butterworth lowpass filter
Where ωc1 and ωc2 (Q 1 and Q 2 ) are the angular cutoff frequencies (quality factors) of the two blocs, from where the 4th order filter is made up, and p = jω. Q1 = ωc2 =
√
R1 R2 C 1 C 2 , R1 C1 +R2 C1
Q1 =
√
R3 R4 C 3 C 4 , R3 C3 +R4 C3
ωc1 =
1 , R1 R2 C 1 C 2
1 R3 R4 C 3 C 4
(2)
To make it compatible with the E series, the expressions of the passive components are represented as follows (3): R1 = 100.x9 .10x1 R2 = 100.x10 .10x2 R3 = 100.x11 .10x3 R4 = 100.x12 .10x4
⎫ ⎪ ⎪ ⎬ ⎪ ⎪ ⎭
C1 C2 C3 C4
= 100.x13 .10x5 nF = 100.x14 .10x6 nF = 100.x15 .10x7 nF = 100.x16 .10x8 nF
⎫ ⎪ ⎪ ⎬ ⎪ ⎪ ⎭
(3)
Where xi are the variables of the optimization problem, the boundaries of those variables depend on the industrial series: For E96 series used here: 2 < xi < 4 for i = 1, 2, . . . 8. And 0.1 < xi < 0.976
for i = 9, 10, . . . 16.
The fitness function is considered as a total design error between the cutoff frequency and the quality factor, with the same importance. Thus, the purpose is to use the proposed approach to find the passive component values (resistors and capacitors) that minimize the fitness function given by (4), which leads to the smallest design error. The fitness function is given by: Fitness = 0.5.ωcerr or + 0.5.Q err or Where
(4)
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ωc1 − ωc1 ωc2 − ωc2 speci f ic speci f ic ωcerr or = 0.5 × + 0.5 × ωc1speci f ic ωc1speci f ic Q1 − Q1 Q2 − Q2 speci f ic speci f ic Q err or = 0.5 × + 0.5 × Q 1speci f ic Q 2speci f ic
(5)
(6)
4 Optimization Results In this work, we have used the parameters of the E96 series, which have a wider range of passive component values. To make a logical and acceptable comparison with the literature, all simulations performed by the proposed algorithm and the other algorithms used for the comparison we have chosen the same population size = 20 and a maximum number of iterations = 1000. The programming tool used here is Matlab 2018b software on a computer with an Intel Core i77820HQ @ 2.90 GHz processor. Table 1 shows the passive component values and fitness values obtained by other optimization algorithms in previously published work, namely CRPSO [12], ACO [13], SOS [2], and also our proposed HPSGWO algorithm and PSO and GWO methods. Figure 2 presents the plot of the log10 (Fitness) as a function of the iteration cycle obtained by the proposed HPSGWO algorithm. The minimum log10 (Fitness) is obtained by HPSOGWO is −3,9866, this value corresponds to 1.031 × 10−4 of fitness as indicated in Table 1. The filter frequency response obtained by the proposed algorithm is illustrated in Fig. 3, the analog active filter is made with E96 compatible results HPSGWO method using the Cadence Virtuoso tool. The Cadence Virtuoso simulation shows that the proposed method provides an extremely flat response in the bandwidth. In this figure, the xaxis indicates the response in decibels and the yaxis indicates the Table 1 Lowpass filter results and comparison using E96 series Parameters
CRPSO [11]
ACO [12]
SOS [2]
GWO This work
PSO This work
HPSGWO This work
R1 (k)
8.45
4.7
4.87
1.84
11.35
9.28
R2 (k)
13
5.13
9.76
1.8
10.46
9.76
R3 (k)
24.9
0.98
7.68
3.56
21.37
7.72
R4 (k)
13.3
2.3
4.02
4.34
33.56
1.08
C1 (nF)
3.57
7.76
5.23
57.7
8.52
9.7
C2 (nF)
25.5
53
40.2
52.3
9.91
11.36
C3 (nF)
4.87
55.5
15.8
9.78
1.39
8.74
C4 (nF)
6.19
77.7
20.5
67.44
9.97
138
Fitness
0.0028
0.001
0.000299
0.032
0.0021
0.000103
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Fig. 2 Convergence curve for HPSGWO algorithm results in Table 1
0,5
Log10(Fitness)
1,0 1,5 2,0 2,5 3,0 3,5 4,0
0
200
400
600
800
1000
Iteration
Fig. 3 Simulation results of the proposed algorithm
HdB
10
HdB obtained by HPSGWO
0 2,9
10
3,0
20 30
3,1 1570
1580 Frequency(Hz)
1590
Frequency3dB = 1578Hz
40 50
zoom
HdB
10
100
1000
10000
Frequency(Hz)
frequency. Since ωc is the cut off frequency. It may well be noticed that the cutoff frequency obtained by the simulation is very accurate and equal to the desired value, which is 10000 rad/s.
5 Conclusion In this work, the performance of the proposed approach on the design of the analog filter have been comprehensively studied. The HPSGWO algorithm was used for the fourth order low pass filter and was studied for directly selecting the passive components (Capacities and resistances) from the E96 series. Selection of optimal
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parameters is essential to minimize the total design error value, which affects filter performance. The performance of the proposed algorithm has also been compared with other optimization algorithms presented either in this paper as GWO and PSO or in the literature as ACO, CRPSO and SOS. The comparison results are very efficient. Cadence Virtuoso simulations give exactly the desired analog lowpass filter design specifications, which again reflects the good performance of the proposed approach. The automation process of optimized analog ICs is a very difficult task. The design of active filters with high precision and short runtime is successfully carried out using optimization algorithms. Currently, we are working on the application of these approaches to other analog IC topologies with specific industrial design constraints. The target is to develop an optimization methodology which can deal with all the specified constraints with a high accuracy and within a reasonable runtime. An extension of this study could be the optimal optimization of the parameters of analog circuit transistors using simulationbased optimization techniques.
References 1. Lberni A, Ahaitouf A, Marktani MA, Ahaitouf A (2019) Sizing of second generation current conveyor using evolutionary algorithms. In: 2019 international conference on intelligent systems and advanced computing sciences, pp 1–5 2. Dib N, ElAsir B (2017) Optimal design of analog active filters using symbiotic organisms search 3. Senel ¸ FA, Gökçe F, Yüksel AS, Yi˘git T (2019) A novel hybrid PSO–GWO algorithm for optimization problems. Eng Comput J 2(5):1359–1373 4. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN 1995 international conference on neural networks, vol 4, pp 1942–1948 5. Ghasemi E (2017) Particle swarm optimization approach for forecasting backbreak induced by bench blasting. Neural Comput Appl 28:1855–1862 6. Kaveh A, Zakian P (2017) Improved GWO algorithm for optimal design of truss structures. Eng Comput 34:1–23 7. Mirjalili S (2015) How effective is the Grey Wolf optimizer in training multilayer perceptrons. Appl Intell 43:150–161 8. Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61 9. Singh N, Singh SB (2017) Hybrid algorithm of Particle Swarm Optimization and grey wolf optimizer for improving convergence performance 10. Jiang M, Yang Z, Gan Z (2007) Optimal components selection for analog active filters using clonel selection algorithm. In: Proceedings of ICICI, LNCS 11. Paarman LD (2007) Design and analysis of analog filters. Kluwer, Norwell 12. De BP, Kar R, Mandal D, Ghoshal SP (2015) Optimal analog active filter design using crazinessbased particle swarm optimization algorithm. Int J Numer Model Electron Netw Devices Fields 28:593–609 13. Fadloullah I, Mechaqrane A, Ahaitouf A (2017) Butterworth low pass filter design using evolutionary algorithm. In: International conference on wireless technologies, embedded and intelligent systems (WITS)
Study of Graded Ultrathin CIGS/Si Structure for Solar Cell Applications M. Boubakeur, A. Aissat, and J. P. Vilcot
Abstract This paper aims to improve the performance of graded ultrathin CIGSbased solar cells using the onedimensional simulation program (SCAPS1D). In this context, we have assessed the effect of the graded bandgap and the thickness of the absorber layer (CIGS) on solar cell performance. We have also examined the impact of different graded bandgap profiles by varying the gallium concentration. Notably, the increase of the gallium concentration (xGa ) and the CIGS thickness (dCIGS ) have degraded the conversion efficiency η. The optimization of these parameters gives a considerable solar yield when dCIGS = 1 μm and xGa in the range 0.1–0.3. For the graded cell, we have mentioned that the doublegraded profile improves significantly the conversion efficiency up to 22.21% compared to the uniform profile with η = 21.43%. Keywords Materials · Ultrathin CIGS · Thickness · Bandgap gradient · Solar cell
1 Introduction In the last three years, the efficiency of CIGS solar cells has increased from 20% to 22.6% [1]. The speed of this development shows that CIGS is an ideal material for thinfilm solar technologies. However, the cost of production of this technology must be further lowered for better competitiveness of the sector. In order to reduce the cost and improve efficiency, it is necessary to reduce the thickness of the CIGS absorber and maintain its high efficiency. For this reason, many research works are performed to overcome this problem by reducing the thickness of the absorber layer M. Boubakeur · A. Aissat (B) Faculty of Technology, University of Blida 1, 09000 Blida, Algeria email: [email protected] A. Aissat · J. P. Vilcot Institut d’Electronique, de Microelectronique et de Nanotechnologie (IEMN), UMR CNRS 8520, Université des Sciences et Technologies de Lille 1, Avenue Poincaré, BP 60069, 59652 Villeneuve D’Ascq, France © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_33
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Fig. 1 Schematic view of ZnO/CdS/CIGS/Si/Mo structure [7]
and increasing the conversion efficiency using the bandgap grading process for the CIGS material. The CIGS represented as Cu(In1x Gax )Se2 is a compound of Cu, In, Ga, and Se2 elements with high optical properties due to the properties of its components [2]. The dependence of the CIGS bandgap on the Gallium rate (x) [3] leads to absorber’s bandgap ranging from 1.04 to 1.67 eV [4]. This propriety can be used to get different gaps at different depths in the CIGS layer that means the gap gradient absorbers can be achieved by changing the ratio [Ga]/[In] during the deposition process [5]. In this work, we focus on modeling an ultrathin CIGS structure using SCAPS1D [6]. We study ZnO/CdS/CIGS/Si/Mo structure to show the influence of the gallium concentration and the thickness of CIGS on our structure. Additionally, we use several bandgap gradient profiles to show its impact on solar cell properties. Figure 1 presents the simulated structure. It demonstrates a CIGS ultrathin solar cell made from zinc oxide ZnO:Al as a window layer, zinc sulfide CdS as a buffer layer, CIGS ptype and Si as absorber layers and molybdenum as a back contact.
2 Materials and Methods This section describes the physical models and the empirical equations used in this simulation: In uniform layers, SCAPS solves fundamental semiconductor equations in a single dimension (the continuity equation for holes and electrons and the Poisson’s equation) [8, 9]. dn 1 d Jn + G − Rn (n, p) − =0 q dx dt
(1)
Study of Graded Ultrathin CIGS/Si Structure …
−
1 d Jp dp + G − R p (n, p) − =0 q dx dt
319
(2)
Where G is the generation rate R is the recombination rate Jn and Jp are respectively electron and hole current density. d dΦ q 1 + − ε(x) = − . −n + p + N D − N A + ρdef (n, p) dx dx εo q
(3)
With ε is the dielectric constant, Φ is the electrostatic potential, p and n are the free carrier concentrations for hole and electron, N p− and N D+ are the density of ionized acceptors and donors and ρdef is the defect distributions. When grading is present, additional driving terms should be taken into consideration: • The electron and hole continuity equations are modified by the presence of a mobility gradient ∇μn or ∇μp. .
dn 1 d 2 E Fn 1 dμn dE Fn = μn + G(x) + R(x) + 2 dt q dx q dx dx
(4)
• The Poisson’s equation is modified by a gradient ∇ε in dielectric constant.
ε
d 2Φ ρ(x) dε dΦ =− + dx2 dx dx ε0
(5)
• The CIGS bandgap energy is defined as [10]:
E gC I G S = x E gC I S + (1 − x)E gC G S − 0.246.x(1 − x)
(6)
Where EgCIS and EgCGS are the bandgap energies of CuInSe2 and CuGaSe2 , respectively. Their bandgap energies used in our simulation are 1.035 and 1.68 eV. • The external quantum efficiency EQE, and it is expressed as [11]:
E Q E(λ) = (1 − R(λ)).exp(−α(λ).xi )
(7)
with R(λ) is the spectral reflection, α(λ) is the absorption coefficient and xi is the total intrinsic region.
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Efficency(%)
20 19 18 x=0 x=0.1 x=0.2 x=0.3 x=0.4 x=0.5
17 16 15 0,2
0,4
0,6
0,8
1,0
1,2
1,4
1,6
1,8
2,0
Thickness(µm)
Fig. 2 Conversion efficiency as a function of CIGS thickness for different gallium concentrations
Fig. 3 Bandgap profiles. a Uniform bandgap b grad1 (front graded) c grad2 (back graded) d grad3 (double graded bandgap)
3 Results and Discussion In this study, the simulation has been carried out using the simulation package SCAPS 1D [12]. All simulations were performed at room temperature under AM1.5G solar irradiance at one sun. During the first phase, we study the effect of the CIGS thickness and the Gallium concentration to determine the optimal values for the graded structures. Taking into consideration the last two factors, we chose three different graded shapes of CIGS absorber and comparing them with the uniform one (Fig. 3). Finally, we examine the effect of the bandgap grading on our solar cell performance.
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100 90 80
EQE (%)
70 60 50
uniform grad1 grad2 grad3
40 30 20 10 0 300
450
600
750
900
1050
1200
1350
1500
wavelength(nm) Fig. 4 External quantum efficiency as a function of the wavelength for uniform and different graded structures
Figure 2 illustrates the variation of the efficiency as a function of CIGS thickness for different Gallium concentrations. In order to identify the best grading structure, we should study the effect of the CIGS thickness dCIGS and the gallium concentration xGa . From the graph, it should be noted that the efficiency of our structure increases with increasing dCIGS and decreases when xGa increases. According to this, we can suggest that the optimal values of dCIGS and xGa are in the range 0.5–1 μm and 0.1–0.3, respectively. A study of the effect of the CIGS thickness and the gallium concentration on the efficiency of the solar cell (Fig. 2), conducted us to choose the appropriate profiles. Threebandgap profiles are considered comparing to the uniform CIGS gap, as shown in Fig. 3. The external quantum efficiency (EQE) data for the different graded and uniform bandgap are presented in Fig. 4. We have noticed that the graded bandgap profiles produce a high level of absorption range comparing to the uniform one, which is extended from 1100 to 1220 nm, due to the absorption of low energy photons by the graded profiles that will absorb photons with energies greater than 1.10 eV (x = 0.15). Besides, the uniform profile is less absorbent than the graded profiles which decrease the absorption (bandgap energy equals 1.17 eV for x = 0.3). Figure 5 describes the current densityvoltage characteristics. We have clearly noted that the graded 2 (back contact graded) has the best Jsc . This is explained by the difference potential which facilitates the transport of electrons to the space charge zone [13]. In addition, the recombination is reduced due to the presence of a larger gap at the back contact [14]. On the other hand, as seen in Fig. 5 and Table 1 the graded 3 (double graded structure) has the optimal value of Voc . This structure increases the effective gap at the heterojunction and limits the recombination at the interface with the ntype layer [15]. Thus, in the double gradient configuration, the value of the minimum gap will be decisive for the absorption of photons and therefore the shortcircuit current
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T
Current density (mA/cm2)
38 36 34 simple grad1 grad2 grad3
32

30 28 0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
Voltage(V)
Fig. 5 The current density JV variation for uniform and different graded structures
Table 1 Characteristic parameters of the uniform solar cell and different graded structures Graded CIGS
Jsc (mA/cm2 )
Voc (V)
FF%
η%
uniform
38.64
0.6950
79.80
21.43
grad1
39.32
0.6983
79.76
21.90
grad2
39.62
0.6963
79.84
22.03
grad3
39.16
0.70
80.93
22.21
(Jsc ). Similarly, the maximum gaps will affect the opencircuit voltage (Voc ) [14]. Figure 6 shows the power voltage PV characteristics for graded bandgap cases and the baseline profile. We note that the best value of Pmax obtained for the third graded structure (double graded profile) comparing to the other profiles. Table 1 represents the different solar cell characteristic parameters. From the table, it is clear that the optimal efficiency value obtained for the double graded structure comparing to the baseline solar cell. Figure 7 depicts the currentvoltage characteristic of our optimal structure. Comparing the simulated currentvoltage result with Rajan’s work [16], we notice the increase of the short current density (−40 mA/cm2 ) and the enhancement of the conversion efficiency up to 22.21% thanks to the graded process used in the simulation of ~1 μm CIGS film.
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24 22 20
Power (mW/cm2)
18 16 14 12 10 8 simple grad1 grad2 grad3
6 4 2 0 0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
Voltage(V)
Fig. 6 Power voltage PV characteristics for uniform and different graded structures 45
Current density(mA/cm2)
40 35 30 25 20
ref[16] grad3
15 10 5 0 0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
Voltage(V)
Fig. 7 Validation of simulation results with the experimental results [16]
4 Conclusion In this work, various bandgapgraded structures of copperindiumgalliumdiselenide (CIGS) absorber layer, are investigated using numerical simulation to optimize the performance of ultrathin CIGS solar cells. The study of bandgap grading in ultrathin CIGS cells is nowadays recommended to improve the solar efficiency and to reduce the amount of material used. In our simulation, we study the effect of the bandgap and the thickness of the CIGS absorber layer on our structure. Finally, we
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have improved the efficiency from 21.43% for the uniform structure to 22.21% for the double graded profile.
References 1. Jackson P, Wuerz R, Hariskos D, Lotter E, Witte W, Powalla M (2016) Effects of heavy alkali elements in Cu (In, Ga) Se2 solar cells with efficiencies up to 22.6%. Phys Status Solidi (RRL) Rapid Res Lett 10(8):583–586 2. Saji VS, Lee SM, Lee CW (2011) CIGS thin film solar cells by electrodeposition. J Korean Electrochem Soc 14(2):61–70 3. Hegedus SS, Luque A (2003) Status, trends, challenges and the bright future of solar electricity from photovoltaics. In: Handbook of photovoltaic science and engineering, pp 1–43 4. Huang CH (2008) Effects of Ga content on Cu (In, Ga) Se2 solar cells studied by numerical modeling. J Phys Chem Solids 69(2–3):330–334 5. Seyrling S, Chirila A, Güttler D, Blösch P, Pianezzi F, Verma R, Tiwari AN (2011) CuIn1 − xGaxSe2 growth process modifications: influences on microstructure, Na distribution, and device properties. Solar Energy Mater. Solar Cells 95(6):1477–1481 6. Degrave S, Burgelman M, Nollet P. (2003) Modelling of polycrystalline thin film solar cells: new features in scaps version 2.3. In: Proceedings of 3rd world conference on photovoltaic energy conversion, May 2003, vol 1. IEEE 7. Heriche H, Rouabah Z, Bouarissa N (2017) New ultrathin CIGS structure solar cells using SCAPS simulation program. Int J Hydrogen Energy 42(15):9524–9532 8. Movla H (2014) Optimization of the CIGS based thin film solar cells: numerical simulation and analysis. Optik 125(1):67–70 9. Gloeckler M, Sites JR (2005) Bandgap grading in Cu (In, Ga) Se2 solar cells. J Phys Chem Solids 66(11):1891–1894 10. Paulson PD, Birkmire RW, Shafarman WN (2003) Optical characterization of CuIn 1 − x Ga x Se 2 alloy thin films by spectroscopic ellipsometry. J Appl Phys 94(2):879–888 11. Benyettou F, Aissat A, Djebari M, Vilcot JP (2017) Electrical properties of InAsP/Si quantum dot solar cell. Int J Hydrogen Energy 42(30):19512–19517 12. Niemegeers A, Burgelman M, Decock K, Verschraegen J, Degrave S (2014) SCAPS manual, University of Gent 13. Turcu M, Kötschau IM, Rau U (2002) Composition dependence of defect energies and band alignments in the Cu (In 1–x Ga x)(Se 1 − y S y) 2 alloy system. J Appl Phys 91(3):1391–1399 14. Kemell M, Ritala M, Leskelä M (2005) Thin film deposition methods for CuInSe 2 solar cells. Crit Rev Solid State Mater Sci 30(1):1–31 15. Nakada T (2012) CIGSbased thin film solar cells and modules: unique material properties. Electron Mater Lett 8(2):179–185 16. Rajan G, Aryal K, Karki S, Arya P, Collins RW, Marsillac S (2018) Characterization and analysis of Ultrathin CIGS films and solar cells deposited by 3stage process. J Spectrosc 2018:9
Investigation of Temperature, Well Width and Composition Effects on the Intersubband Absorption of InGaAs/GaAs Quantum Wells L. Chenini, A. Aissat, S. Ammi, and J. P. Vilcot
Abstract Optical properties of the ternary Inx Ga1x As/GaAs alloys including strain, band gap energy, band offsets, and intersubband absorption coefficient in the conduction band (CB) are theoretically investigated. Effect of temperature, T, and Indium composition, In, of InGaAs on all these parameters is verified. The calculations show that the insertion of indium in the host material, varying the well width, L w , and changing temperature has pronounced effects on the optical intersubband coefficient of the InGaAs quantum well (QW) structure. These results make the Inx Ga1x As/GaAs alloy promising for realization of midinfrared devices. Keywords Intersubband absorption · InGaAs/GaAs · Quantum well · Band offset
1 Introduction Over the past few years, the intersubband transitions have attained much interest in semiconductor quantum wells owing to their high potential which will open great possible applications such as quantum cascade lasers, laser diodes, infrared photodetectors and alloptical switches [1–5]. However, the GaAsbased laser structures have attracted a much of attention in comparison to conventional InP based lasers. This type of lasers benefits from better temperature characteristics due to larger CB offsets. A large number of theoretical and experimental works have already been devoted to the investigation of the optical properties of the InGaAs/GaAs QWs in order to L. Chenini · S. Ammi Faculty of Sciences, University of Blida 1, Blida, Algeria email: [email protected] A. Aissat (B) · S. Ammi Faculty of Technology, University of Blida 1, Blida, Algeria email: [email protected] A. Aissat · J. P. Vilcot Institut d’Electronique, de Microélectronique et de Nanotechnologie (IEMN), UMR 8520, Université des Sciences et Technologies de Lille 1, Avenue Poincaré, CS 60069, 59652 Villeneuve d’Ascq, France © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_34
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obtain devices with high performances [6–15]. The investigation of the temperature effect also makes a large part of these studies [16–19]. However, the intersubband coefficient is less studied using temperature dependency in the literature [20]. The presence of strain in quantum well systems offers the possibility to improve device performance and it is the reason that it will be taken in our study. In this study, the influence of the In composition noted x (ranged from 0 to 0.4), the well thickness and the temperature on the absorption coefficients of the intersubband transitions (ISBTs) in Inx Ga1x As/GaAs QWs have been studied by solving the Schrödinger equation. The ISBTs E12 have been calculated between the first (1) and the second (2) conduction band levels. For this purpose, we try firstly to investigate the electronic and optical properties of the Inx Ga1x As/GaAs QWs as the strain, band gap Eg , the conduction bandedge discontinuity Ec and the conduction band offset Qc . Then we will verify the effect of three factors (In, L w and T) on the absorption coefficient.
2 Theory The wave functions and energy levels are calculated from the Schrödinger equation given by the following expression:
2 ∂ 2 − ∗ 2 + V(z) ψk (z) = Ek ψk (z) 2m ∂z
(1)
where Ek is the energy of the bound state k and ψk its envelope function, the reduced Planck’s constant, m* is the electron effective mass in the CB and V (z) is the conduction band potential. The parameters used for this study are obtained from reference [21] and are listed in Table 1. These parameters are interpolated according to a linear interpolation: Q(InGaAs) = (1 − x).Q(GaAs) + x.Q(InAs) Table 1 Parameters for binary compounds used in the band structure calculation
(2)
Quantity
InAs
GaAs
a (Å)
6.058
5.653
E g (eV)
1.424
0.356
0 (Å)
0.390
0.341
C11 (GPa)
83.29
122.1
C12 (GPa)
45.26
56.6
ac (eV)
−5.08
−7.17
av (eV)
1.16
1.00
α (meV/K)
0.504
0.276
β (K)
240
93
Investigation of Temperature, Well Width and Composition Effects …
327
where, x is the indium concentration in the ternary alloy. The strain has two components hydrostatic ε and biaxial ε⊥ one, which are calculated respectively as: a − a a a − a C12 ε⊥ (InGaAs) = −2 C11 a ε (InGaAs) =
(3) (4)
The lattice constants for the GaAs and InGaAs layers are noted in Eqs. (3) and (4) as a and à, respectively. The relationship between temperature and the band gap energy can be described by Varshni’s expression [22]: Eg(InGaAs) (T) = Eg(InGaAs) (0) −
α T2 T + β
(5)
where E g(I nGa As) (0), β and α are material constants. The CB discontinuity (Ec ) and the CB offset ratio Qc are described by the model solid theory [23] and are given by Eqs. (6) and (7) respectively. Ec(InGaAs) = Ec (InGaAs) − Ec (GaAs) Qc(InGaAs) =
Ec (InGaAs) Eg (InGaAs)
(6) (7)
The absorption coefficients of the ISBTs in Inx Ga1x As/GaAs quantum well can be obtained by the following formula [24]: ω α(ω) = Lw
μ0 (/τ) Mlk 2 (Nk − Nl ) l>k ε (El − Ek − ω)2 + (/τ)2
(8)
where Lw is the well width, ω is the photon frequency, μ0 is the vacuum permeability, ε is the permittivity given by ε = ε0 εr , ε0 is the vacuum dielectric constant and εr is the relative dielectric constant, Mkl is the dipole matrix, Nk and Nl are the electron densities residing in the subbands k and l, respectively, Ek and El are the energy levels of subbands k and l respectively, è is the reduced Planck constant, in our calculation τ is assumed to be 0.1 ps [25].
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3 Results and Discussion The hydrostatic ε// and biaxial ε⊥ strain as a function of the indium composition are shown in Fig. 1. It is clear that growing the InGaAs on the GaAs substrate leads to a compressive strain. The dependency of the band gap energy on the indium content x and the temperature can be shown in Fig. 2. Increasing both coefficients leads to decrease the band gap energy. Figure 3 shows the corresponding conduction band offset Ec as a function of T and In concentration of the Inx Ga1x As/GaAs alloy. The introduction of indium into GaAs and increasing the temperature increase the conduction band offset Ec . Figure 4 shows the variation of the conduction band offset ratio Qc as a function of temperature and indium composition. The addition of indium has been found
Fig. 1 Hydrostatic ε// and uniaxial ε⊥ strain as a function of the indium composition of Inx Ga1x As/GaAs alloys
Fig. 2 Band gap Eg (x, T) of Inx Ga1x As alloys
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Fig. 3 Conduction band offset Ec as a function of T and In concentration in the Inx Ga1x As/GaAs alloy
Fig. 4 Conduction band offset ratio Qc as a function of T and In concentration in Inx Ga1x As alloys
to decrease the CB offset ratio, while the temperature has the effect to increase the Qc . Figure 5 illustrates the intersubband absorption coefficient as a function of wavelength emission for the 70 Å strained In0.25 Ga0.75 As/GaAs quantum wells for several temperatures. Figure 5 clearly shows that the maximum peak absorption for InGaAs/GaAs decreases when the temperature value increases and the range wavelength shifts to shorter wavelengths. Figure 6 shows the evolution of intersubband absorption spectra as a function of wavelength for the Inx Ga1x As/GaAs structure with Lw = 70 Å at room temperature and several Indium values. Increasing In concentration increases the intersubband absorption coefficient. The wavelength of emission shifts to shorter values. We have plotted in Fig. 7 the behavior of intersubband absorption spectra as a function of wavelength for the In0.15 Ga0.85 As/GaAs structure for different well thickness (Lw ) ranging between 30 and 100 Å at T = 300 K (noted that for x =
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Fig. 5 Intersubband absorption coefficient for In0.25 Ga0.75 As/GaAs alloys as a function of emission wavelength for several T values
Fig. 6 Intersubband absorption coefficient for Inx Ga1x As/GaAs alloys as a function of emission wavelength for several In values
Fig. 7 Intersubband absorption coefficient for In0.15 Ga0.85 As/GaAs alloys as a function of emission wavelength for several Lw values
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0.15, the strain is 1%). Figure 7 reveals that increasing the well thickness, leads to decrease the intersubband absorption. We can also see, that the wavelength redshift for widths less than 60 Å but for values greater than this latter value, it blueshift.
4 Conclusion For InGaAs quantum well heterostructure grown on GaAs substrate, the intersubband optical absorption and the corresponding wavelength emission have been modeled and simulated. In addition, the temperature, well width and composition effects have also been done. The simulated results indicate that the well width has played a major role in modifying the intersubband absorption. Increasing the temperature and the indium mole fraction redshift the wavelength. The increase of well width leads to redshift the wavelength emission but, beyond 60 Å it’s blue shift the corresponding wavelength. These results make the Inx Ga1x As/GaAs alloy promising for realization of midinfrared devices.
References 1. Chakraborty T, Apalkov VM (2003) Quantum cascade transitions in nanostructures. Adv Phys 52(5):455–521 2. Alves FDP, Karunasiri G, Hanson N, Byloos M, Liu HC, Bezinger A, Buchanan M (2007) NIR, MWIR and LWIR quantum well infrared photodetector using interband and intersubband transitions. Infrared Phys Technol 50(2–3):182–186 3. Chenini L, Aissat A, Vilcot JP (2019) Optimization of intersubband absorption of InGaAsSb/GaAs quantum wells structure. Superlattices Microstruct 129:115–123 4. Iizuka N, Kaneko K, Suzuki N (2006) Alloptical switch utilizing intersubband transition in GaN quantum wells. IEEE J Quantum Electron 42(8):765–771 5. Chenini L, Aissat A, Vilcot JP (2019) Theoretical study of intersubband absorption coefficient in GaNAsBi/GaAs quantum well structures. In: Hajji B (eds) ICEERE 2018, LNEE 519. Springer Nature Singapore Pte Ltd, pp 216–224 6. Mogg S, Chitica N, Schatz R, Hammar M (2002) Properties of highly strained InGaAs/GaAs quantum wells for 1.2μm laser diodes. Appl Phys Lett 81(13):2334–2336 7. Khazanova SV, Baidus NV, Zvonkov BN, Pavlov DA, Malekhonova NV, Degtyarev VE, Bobrov IA (2012) Tunnelcoupled InGaAs/GaAs quantum wells: structure, composition, and energy spectrum. Semiconductors 46(12):1476–1480 8. Khatab A, Shafi M, Mari RH, Aziz M, Henini M, Patriarche G, Troadec D, Sadeghi M, Wang S (2012) Comparative optical studies of InGaAs/GaAs quantum wells grown by MBE on (100) and (311)A GaAs planes. Phys Status Solidi C 9(7):1621–1623 9. Vainberg VV, Pylypchuk AS, Baidus NV, Zvonkov BN (2013) Electron mobility in the GaAs/InGaAs/GaAs quantum wells. Semicond Phys Quantum Electron Optoelectron 16(2):152–161 10. Baidus N et al (2018) MOCVD growth of InGaAs/GaAs/AlGaAs laser structures with quantum wells on Ge/Si substrates. Crystals 8(8):311 11. Greg J, Lan F, Hao FL, Hark HT, Chennupati J (2012) The role of intersubband optical transitions on the electrical properties of InGaAs/GaAs quantum dot solar cells. Prog Photovoltaics Res Appl 21(4):1–11
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12. Yan X, Zhang X, Li J, Wu Y, Cui J, Ren X (2015) Fabrication and optical properties of GaAs/InGaAs/GaAs nanowire core–multishell quantum well heterostructures. Nanoscale 7(3):1110–1115 13. Huang X, Song Y, Masuda T, Jung D, Lee M (2014) InGaAs/GaAs quantum well lasers grown on exact GaP/Si (001). Electron Lett 50(17):1226–1227 14. Li Y, Wang P, Meng F, Yu H, Zhou X, Wang H, Pan J (2018) Investigation of InGaAs/GaAs quantum well lasers with slightly doped tunnel junction. Semiconductors 52(16):2017–2021 15. Mary´nski A, Mrowi´nski P, Ryczko K, Podemski P, Gawarecki K, Musiał A, Misiewicz J, Quandt D, Strittmatter A, Rodt S, Reitzenstein S, S˛ek G: Optimizing the InGaAs/GaAs Quantum Dots for 1.3 μm Emission. Acta Physica Polonica A 132(2):386–389 16. Bafna MK, Sen P, Sen PK (2006) Effect of temperatre on nonlinear optical properties, of InGaAs/GaAs single quantum dot. Indian J Pure Appl Phys 44:152–156 17. Martini S, Quivy AA, Tabata A, Leite JR (2001) Influence of the temperature and excitation power on the optical properties of InGaAs/GaAs quantum wells grown on vicinal GaAs(001) surfaces. J Appl Phys 90(5):2280–2289 18. Norris TB, Kim K, Urayama J, Wu ZK, Sing J, Bhattacharya PK (2005) Density and temperature dependence of carrier dynamics in selforganized InGaAs quantum dots. J Phys D Appl Phys 38(13):2077–2087 19. Musiał A, S˛ek G, Mary´nski A, Podemski P, Misiewicz J, Löffler A, Höfling S, Reitzenstein S, Reithmaier JP, Forchel A (2011) Temperature dependence of photoluminescence from epitaxial InGaAs/GaAs quantum dots with high lateral aspect ratio. Acta Phys Pol, A 120(5):883–887 20. Sahu T, Subudhi PK, Patra JN, Sarkar CK (2007) Effect of dielectric screening and intersubband coupling on low temperature electron mobility in a AlGaAs/InGaAs/GaAs asymmetric quantum well structure. In: 2007 international workshop on physics of semiconductor devices 21. Ng ST, Fan WJ, Dang YX, Yoon SF (2005) Comparison of electronic band structure and optical transparency conditions of Inx Ga1x As1y Ny /GaAs quantum wells calculated by 10band, 8band, and 6band k·p models. Phys Rev B 72(11), 115341 22. Sattler KD (2016) Hand book of nanophysics: nanoparticles and quantum dots. CRC Press 23. Van de Walle CG (1989) Band lineups and deformation potentials in the modelsolid theory. Phys Rev B 39(3):1871–1883 24. Ahn D, Chuang SL (1987) Calculation of linear and nonlinear intersubband optical absorptions in a quantum well model with an applied electric field. J. Quantum Electron 23(12):2196–2204 25. Suzuki N (2007) Intersubband optical switches for optical communications. In: Nitride semiconductor devices: principles and simulation, pp 235–252
Theoretical Modeling and Optimization of GaAsPN/GaAs Tandem DualJunction Solar Cells A. Bahi azzououm, A. Aissat, and J. P. Vilcot
Abstract This paper presents an optimization and simulation of optical and electrical properties of GaAsPN/GaAs tandem DualJunction solar cells such as current densityvoltage (JV), external quantum efficiency (EQE), with an AM1.5 solar spectrum. We comparing the simulated performance of various N fractions and we will show that the use of N = 0.01 improve the performances of external quantum efficiency (EQE) and currentvoltage characteristics. Our results have been shown that an optimal efficiency of about 26.19% was obtained with P composition x = 0.37 and N fractions y = 0.01. In addition, a doping of 2.1018 cm−3 of the GaAs0.62 P0.37 N0.1 base top cell boosts the efficiency from 25.73% to 26.19%. Keywords Tandem junction cell · Nitride · Efficiency · External quantum · Efficiency
1 Introduction Compared with other solar cells materials, solar cells made from IIIV semiconductor like GaAs have the highest energyconversion efficiency. In addition, they have higher power density mechanically flexible, offer superior heat rejection and have low temperature coefficient that enable up to twice as much energy production [1]. With these properties, solar cell can be fabricated to absorb various spectra of light by adjusting the elemental compositions. Furthermore, stacking solar cells with different band gaps using tunnel junction, so called multijunction solar [2]. Recently, research exploring device characteristics and optoelectronic properties of GaAsPN layers for IIIV photovoltaic applications has been accelerated. However, research A. Bahi azzououm · A. Aissat (B) Faculty of Technology, University of Blida 1, 09000 Blida, Algeria email: [email protected] A. Aissat · J. P. Vilcot Institute of Microelectronics, Electronics and Nanotechnology (IEMN), UMR CNRS 8520, University of Sciences and Technologies of Lille 1, Poincare Avenue, 60069, 59652 Villeneuved’Ascq, France © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_35
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into IIIVN alloys, including those based on GaAs and GaP, has mainly focused on 0 ⎛
S11 · · · ⎜ .. . . ⎝ . .
⎞ S1n .. ⎟ > 0 . ⎠
Sn1 · · · Snn
X AiT + Ai X − MiT BiT − Bi Mi + Sii < 0, ∀i ∈ I8 X AiT + Ai X + X A Tj + A j X − MiT BiT − Bi M j − MiT B Tj − B j Ni + 2Si j ≤ 0, ∀(i, j) ∈ I82 , i < j
Where X = P −1 , K i = Mi P −1 and Si j = X Q i j X , ∀ ∈ {1…,8}, are symmetric matrix. 3. Pole placement In the synthesis of control system, meeting some desired performances should be considered in addition to stability. Generally, stability conditions (Theorem 1) does not directly deal with the transient responses of the closedloop system. In contrast, a satisfactory transient response of a system can be guaranteed by confining its poles in a prescribed region. This section discusses a Lyapunov characterization of pole clustering regions in terms of LMIs. For this purpose, we introduce the following LMIbased representation of stability regions [22]. Motivated by Chilali [23] and Gutman’s theorem for LMI region, we consider circle LMI region D Dq,r = x + j y : (x + q)2 + y 2 < r 2
(19)
Centred at (−q,0) and with radius r > 0, where the characteristic function is given by: f D (z) =
−r z ∗ + q z + q −r
(20)
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Fig. 2 Circular region (D) for pole location
As shown in Fig. 2, if λ = ξ wn ∓ jwd is a complex pole lying in Dq,r with damping ratio ξ , undamped natural frequency ωn , damped natural frequency wd , then ξ = 1 − r 2 /q 2 , ωn < q + r and ωd < r. Therefore, this circle region puts a lower bound on both exponential decay rate and the damping ratio of the closedloop response, and thus is very common in practical control design. An extended Lyapunov Theorem for the closed loop TS model (15) is developed with above definition of an LMIbased circular pole region as below [23]. Theorem 2: The closed loop TS model (15) is Dstable (all the complex poles lying in LMI region D) for some state feedback ki if and only if there exists a positive symmetric matrix X such that
q X + X (Ai + Bi K j −r X −r X q X + Ai + Bi K j
T (21)
These inequalities are not convex; a simple change of variables Mi = K i X yields a convex LMI in Mi and X. This pole placement design problem can be recast as an LMI feasibility problem.
−r X q X + X AiT + MiT BiT q X + Ai X + Bi Mi −r X
< 0, i = j
(22)
By combining Theorems 1 and 2 leads to the following LMI formulation of two objectives statefeedback synthesis problem [22]. Theorem 3: The closed loop TS model (15) is stabilizable in the specified region D if and only if there exists a common positive symmetric matrix X and Mi such that the following LMI condition holds
X >0
Fuzzy Control Techniques Applied for Stabilization of a Quadrotor
⎛
S11 · · · ⎜ .. . . ⎝ . . Sn1 · · ·
437
⎞ S1n .. ⎟ > 0 . ⎠ Snn
X AiT + Ai X − MiT BiT − Bi Mi + Sii < 0, ∀i ∈ I8
(23)
X AiT + Ai X + X A Tj + A j X − MiT BiT − Bi M j − MiT B Tj − B j Ni + 2Si j ≤ 0, ∀(i, j) ∈ I82 , i < j
−r X q X + X AiT + MiT BiT q X + Ai X + Bi Mi −r X
< 0, ∀i ∈ I8
By solving these two kinds of LMI constraints directly leads to a state feedback controller, such that the resulting con troller meets both the global stability and the desired transient performance simultaneously.
4 Simulation Results To illustrate the proposed method, the control law is tested by the considered TS Model of the quadrotor system and the controllers are tested by simulation. This section shows the efficiency of designed control system and our design approach through computer simulations. Software packages (MATLAB, SIMULINK) are used for the simulations. The details of the following parameters used are listed in the table below [23]: Kt
0.28 μ N .m/rad/s
K f ax
0.00056 N .m/rad/s
K f ay
0.00056 N.m/rad/s
K f az
0.00064 N.m/rad/s
Ix x
0.0104 kg.m2
I yy
0.0104 kg.m2
Izz
0.0284
kg.m2
l
0.24 m
m
1.05 kg
Kd
0.0019 N/rad/s
Simulation results for Quadrotor based on the proposed design algorithm are shown in Figs. (3, 4). For comparison purposes, we present in Figs. (3, 4), Euler Angles and angular velocity. For fuzzy controller, when constraint for the pole placement is neglected (considering only stability condition, Theorem 1) and for pole placement (Theorem 2). The simulation result shows that the error obtained in our approach with pole placement is better than without.
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Fig. 3 Simulation results of the Fuzzy controllers of the pitch roll and yaw angles
Fig. 4 Angular velocity with the fuzzy controller and pole placement
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5 Conclusion This paper proposes the TakagiSugeno model of Quadrotor, which is developed using multiple model approach, Firstly, we start by the development of the dynamic model of the quadrotor taking into account the different physical phenomenon which can influence the evolution of our system in the space and represented in a nonlinear state form, in order to use it for the TS fuzzy model representation. The framework is based on TS model and parallel distributed compensation (PDC) technique. Simulation results showed that the multiobjective nonlinear controller (calculated from pole placement conditions) yields not only maximized stability boundary but also better tracking performance than single objective controller (calculated from only stabilizations conditions). For future prospects, we will test this control law on tested that we are currently developing in our laboratory.
References 1. Musial M (2008) System architecture of small autonomous UAVs. VDM Verlag, Saarbrücken, Germany 2. Changhong J, Haiwei W (2010) Backstepping control of each channel for a quadrotoraerial robot. In: International conference on computer, macaronis, control and electronic engineering (CMCE), pp 403–407 3. Boyd S, ElGhaoui L, Feron E, Balakrishnan V (1994) Linear matrix inequalities in system and control theory. SIAM, Philadelphia 4. Madani T., Benallegue A (2006) Backstepping sliding mode control applied to a miniature quadrotorflying robot. In: IEEE conference on industrial electronics, pp 700–705 5. Mokhtari A, Benallegue A, Belaidi A (2005) Polynomial linear quadratic Gaussian and sliding mode observer for a quadrotorun mannedaerialvehicle. J. Rob. Mechatron. 17(4):483–495 6. Craig J (2009) Introduction to Robotics Mechanics and Control. Prentice Hall, Pearson 7. Stevens B, Lewis F, Aircraft L (2003) Control and simulation. Hoboken, USA 8. Pettersen R, Mustafic E, Fogh M (2005) Nonlinear Control Approach to Helicopter Autonomy. Department of Electronics System, Aalborg University, Denmark 9. Bouabdallah S (2007) Design and control of quadrotors with application toautonomousflying. Lausanne Polytechnic University 10. Bouadi H, Cunha SS, Drouin A, Camino FM (2011) Adaptive sliding mode control for quadrotor attitude stabilization and altitude tracking. In: IEEE international symposium on computational intelligence and informatics, pp 449–455 11. Bouadi H, Bouchoucha M, Tadjine M (2007) Sliding mode control based on backstepping approach for an UAV typequadrotor. Int J Appl Math Comput Sci 4(1):12–17 12. Ichalal D (2009) Estimation et diagnostic de system non lineairesdecrits par un modèle de TakagiSugeno. Institut National Polytechnique de Lorraine, Frensh 13. Guerra T, Kruszewski A, Vermeiren L, Tirmant H (2006) Conditions of output stabilization for non linear models in the TakagiSugeno’s Form. Fuzzy Sets and Systems 157:1248–1259 14. Xiaodong L, Qingling Z (2003) New approaches to H∞ controller designs based on fuzzy observers for TS fuzzy systems via LMI. Automatica 39:1571–1582 15. Tuan H, Apkarian P, Narikiyo P, Yamamoto Y (2001) Parameterized linear matrix inequality techniques in fuzzy control system design. IEEE Trans. Fuzzy Syst. 9:324–332 16. Sugeno M, Kang GT (1986) Fuzzy modeling and control of multilayer in cinerator. Fuzzy Sets Syst 18(3):329–346
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17. Tanaka K, Sugeno M (1992) Stability analysis and design of fuzzy control systems. Fuzzy Sets Syst 45(2):135–156 18. Wang HO, Tanaka K, Griffin M (1995) Parallel distributed compensation of nonlinear systems by takagisugeno fuzzy model. In: International joint conference of the 4th IEEE fuzzy system and the 2nd international fuzzy engineering symposium, pp 531–538 19. Rabhi A, Chadli M, Pégard C (2011) Robust fuzzy control for stabilization of a quadrotor. In: Proceedings of the 15th international conference on advanced robotics Tallinn, Estonia. https:// doi.org/10.1109/icar.2011.6088629 20. Tanaka K, Ikeda T, Wang HO (1998) Fuzzy regulators and fuzzy observers: relaxed stability conditions and LMIbased designs. IEEE Trans Fuzzy Syst 6(2):250–256. https://doi.org/10. 1109/91.669023 21. Boyd S, Ghaoui LE, Feron E, Balakrishnan V (1994) Linear matrix inequalities in systems and control theory. SIAM, Philadelphia 22. Hong SK, Nam Y (2003) Stable fuzzy control system design with poleplacement constraint: an LMI approach. Comput Ind 51:1–11 23. Chilali M, Gahinet P (1996) H_Design with pole placement constraints: an LMI approach. IEEE Trans Autom Control 41:358–367
Mechanical Modeling, Control and Simulation of a Quadrotor UAV Hamid Hassani, Anass Mansouri, and Ali Ahaitouf
Abstract This paper presents the development of a quadrotor 3DModel based on a new approach integrating both the flight controller and the quadrotor CADmodel (Computeraided design). The quadrotor design is performed using CAD modelling environment, then imported to MATLAB Simscape for the design of the control scheme based on the PID (ProportionalIntegralderivative) controller. The effectiveness of the offered flight simulator system is tested using several predefined trajectories and the simulation results of each trajectory emphasize the accuracy of the proposed simulator. Keywords Quadrotor · 3DModel · Flight controller · MATLAB/simscape · PID
1 Introduction Quadrotor or quadcopter, known also as drone, has become one of the most attractive research topics. Mainly due to their ability to achieve autonomously multitude tasks, even in cluttered places. Recently, the use of this robot has been widened to cover new missions involving autonomous delivery, mapping and image acquisition [1]. Generally, quadrotor is an unstable flying robot suffering from the noncontrollability in the lateral motions (x, y), its rotational and translational dynamics are underactuated and strongly coupled [2]. Several stabilization approaches have been introduced to deal with these limitations. Among them, the PID controller has taken more attention, it has proven by many researchers especially in the practical implementation [3]. Additionally, nonlinear techniques such as sliding mode and backstepping controllers have been successfully proved in both simulations and experiments [4]. H. Hassani (B) · A. Ahaitouf Laboratory of Intelligent Systems, GeoResources and Renewable Energies, Faculty of Sciences and Technology, Sidi Mohammed Ben Abdellah University, Fez, Morocco email: [email protected] A. Mansouri Laboratory of Intelligent Systems, GeoResources and Renewable Energies, School of Applied Sciences, Sidi Mohammed Ben Abdellah University, Fez, Morocco © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_47
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Recently, simulation platforms have been used in a great number of application domains such as, automotive systems, autonomous underwater robots and aerial applications [5]. There is a real need to visualize the quadrotor behavior in a virtual environment before performing physically the flight experimentation where the fail is forbidden. Herein we discuss the development of a quadrotor flight simulator based on a new approach that involves both the mechanical model and the flight controller. This simulator will be used as a virtual prototyping for drone model validation, it can be also adopted to simplify the challenging task when tuning the flight controller. To scrutinize the contribution of this paper, we organize it as follows. In the second section, a general overview on the quadrotor system as well as the nonlinear dynamic model computation are discussed. The core of the flight simulator is presented in the third section. The simulator results are presented in section 5. Finally, the last section concludes the work and introduces some future directions of this study.
2 Quadrotor System 2.1 Quadrotor Description Quadrotor is an aerial robot powered by four identical rotors arranged in a plus or cross configuration. It consists of two pair’s propellerrotor, as shown in Fig. 1. The first pair (M1 , M3 ) spin in the counter clockwise direction, while the second pair (M2 , M4 ) rotates in the clockwise direction. This configuration makes the reactive force produced by each propellerRotor being cancelled. Quadrotors are controlled by the thrust forces produced by four identical rotors. However, the rotation around x axes called roll motion can be achieved by inversely changing the speed of the pair (M2 , M4 ), also the pitch movement is obtained by Fig. 1 Quadrotor configuration
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creating an unbalance between the generated force by rotors M1 and M3 . Yaw motion is screamed by changing the countertorque between each pair propellerrotor. The vertical flight is obtained by simultaneously varying the speed of all rotors.
2.2 Quadrotor Model The obsession behind the quadrotor modeling studies is the design of a nearrealistic model. This can be achieved using the Newton Euler methodology [6] or the Lagrange Euler formula. In this work, the quadrotor system is modeled using the Newtonian formalism, and the modeling phase is based on the following assumptions: • The quadrotor body frame and the propellers are rigid. • The quadrotor structure is symmetrical. • The thrust and drag forces are proportional to the square of the rotor speed. The quadrotor translational and rotational equations of motions are as follows: ⎛
⎞
⎛
U1 U m x U1 U m y
x¨ ⎜ ⎜ y¨ ⎟ ⎜ U1 ⎜ ⎟ ⎜ ⎜ ⎟ ⎜ m (cosϕ cosθ ) − g ⎜ z¨ ⎟ ⎜ ⎜ ⎟ = ⎜ θ˙ ψ˙ I y I−Iz − IJr θ˙ Ω + UI 2 x x ⎜ ϕ¨ ⎟ ⎜ y ⎜ ⎟ ⎜ Jr U3 x ⎝ θ¨ ⎠ ⎜ ϕ˙ ψ˙ Iz −I + ϕΩ ˙ + I y Iy ⎝ I y I −I ψ¨ ϕ˙ θ˙ x Iz y + UIz4
⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠
(1)
With ⎧ ⎪ ⎪ U1 ⎨ U2 ⎪ U ⎪ ⎩ 3 U4
2 2 = b ω12 + ω22 + ω + ω 3 4 = bl ω42 − ω22 = bl ω32 − ω12 = d ω12 − ω22 + ω32 − ω42
⎧ ⎨ Ux = cos ϕ sin θ cos ψ + sin ϕ sin ψ U = cos ϕ sin θ sin ψ − sin ϕ cosψ ⎩ y Ω = ω4 + ω3 − ω2 − ω1
(2)
(3)
Where: • • • •
(x, y, z) and (ϕ, θ , ψ) denote the position and orientation coordinates. I (Ix , I y , Iz ) is the diagonal matrix of inertia and m is the quadrotor total mass. Jr is the rotor inertia, b and d symbolize the thrust and drag constant. ωi and Ui are respectively the rotors speed and the input signals.
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The vertical movement and the quadrotor orientation (ϕ, θ, ψ) are directly controlled by the control efforts U1 , U2 , U3 and U4 . In contrast, the horizontal displacements (x, y) is indirectly controlled by selecting the appropriate roll and pitch angles. Based on Eq. (3), the desired pitch and roll angles are calculated as: θd = arctan
Ux sin(ψ) − U y cos(ψ) Ux cos(ψ) + UY sin(ψ) ; ϕd = arctan cos θd Uz Uz
(4)
With Uz = (cos(ϕ) cos(θ ))U1 m
(5)
3 Proposed Flight Simulator The quadrotor mechanical model is designed and validated using SolidWorks CAD. The design phase is done based on specifications listed in reference [7]. The designed components are imported to Simscape tools to generate the compatible file with MATLAB environment. Finally, the design of the control scheme is done using MATLAB/Simulink.
3.1 Quadrotor CADModels Design The idea is to build each component of the quadrotor system in SolidWorks tools, this phase involves the 3D drawing, the visual appearance and the material specification. Next, the designed components must be correctly connected to shape the quadrotor assembly as shown in Fig. 2. Fig. 2 Quadrotor SolidWorks assembled CADmodels
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Fig. 3 MATLAB simscape CADmodels
Table 1 Quadrotor flight controller parameters Quadrotor motions
P
I
D
Attitude (ϕ; θ; ψ)
(1.5;1.1;6.5)
(0.005;0.008;0.9)
(0.04;0.037;0.025)
Position (X ; Y )
(340; 350)
(60; 60)
(0.001; 0.001)
Altitude Z
10
15
1.9
Once the quadrotor model is properly designed, then correctly assembled and verified using the SolidWorks motion analysis. We use Simscape multibody link to generate the compatible files with MATLAB environment as illustrated in Fig. 3. The generated files involve the quadrotor mechanical model, the gravitational force and the system parameters (mass and inertial properties).
3.2 Control Strategy The control strategy adopted in this work is a hierarchical controller having three loops, a full actuated inner loop that controls roll, pitch and yaw movements. An under actuated outer loop that controls the lateral position (x, y), and an altitude loop which controls the vertical flight. The PID controller is adopted for the control of the three loops using the configuration listed in Table 1. The synoptic scheme of the control strategy is shown in Fig. 4. The nearoptimal gains of the PID controller valid in our case, are selected based on empirical tests inspired by work [5], where a thoroughly description of the PID parameters is presented.
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Fig. 4 Drone simulator structure
4 Simulator Environment The main concept of the proposed flight simulator is illustrated in Fig. 4. However, the drone user defines the desired trajectory and then the control process begins. First, the position controller manages the quadrotor lateral behavior (xy plane), and produces the desired roll and pitch angles which serve as a setpoint for the attitude controller Eq. (4). The altitude controller produces the sufficient thrust allowing the quadrotor to reach the desired z position. The controller’s outputs (U1 , U2 , U3 and U4 ) are used to calculate the speed of each rotor based on the inverse transformation of Eq. (2). The propulsion system generates the needed forces to achieve the expected flight. The drone localization is obtained through six sensors, position (x, y, z) and orientation (ϕ, θ, ψ), added to mimic the role of the Inertial Measurement Unit (IMU).
5 Results and Discussions Two different simulations were introduced to highlight the accuracy of the developed simulator. The fist, concerns the problem of stabilization, while the second simulation evaluates the ability of the quadrotor in path tracking missions. Case 1: Hovering Mode The simulation is carried out during 5 s which is sufficient to reach the desired altitude 1 m, with an initial configuration for the quadrotor attitude fixed at (0.2, 0.2, 0.2) rad. As depicted in Fig. 5, the PID controller is able to push the quadrotor to achieve the desired altitude 1 m within less than 1 s. Also, the quadrotor orientation is maintained to zero rapidly. Furthermore, the offered simulator provides a 3Dvisualization of the quadrotor motions. Figure 6 illustrates the Front, Top and Isometric views of the quadrotor system in the simulator environment when performing the hovering flight.
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Case 2: Path Tracking In order to check the trajectory tracking ability of the offered simulator, four flight tests have been performed. Figure 7 shows the 3Drepresentation of the real and the desired trajectories in the simulator environment using square trajectory (a), cross trajectory (b), Lemniscate trajectory (c) and circular trajectory (d).
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Fig. 7 Real and desired path for the square, cross, lemniscate and circular trajectory
As seen in the 3Drepresentation (Fig. 7), the quadrotor is well controlled and follows accurately the planned path. Even if the reference trajectory is suddenly changed, the quadrotor is able to accomplish the flight mission successfully. It should be mentioned that the adopted hierarchical methodology based on the PID controller has effectively controlled the quadrotor UAV in different flight tests.
6 Conclusion In this paper, based on the quadrotor mechanical design a quadrotor simulator is developed. A hierarchical methodology based on the PID controller is adopted to solve the underactuation and to steer the quadrotor behaviors in the simulator environment, contributing promising results in both stabilization and path following. The present simulator can be used as test platform to evaluate the quadrotor behaviors under several external conditions. It can be also used to configure the flight controller before moving on to the real time experimentation. In future work, robust adaptive controller will be used to enhance the flight ability in the presence of external perturbations.
References 1. Calì M, Ambu R (2018) Advanced 3D photogrammetric surface reconstruction of extensive objects by UAV camera image acquisition. Sensors 18:2815 2. Wang Y, Jiang B, Ningyun L, Pan J (2016) Hybrid modeling based doublegranularity fault detection and diagnosis for quadrotor helicopter. Nonlinear Anal Hybrid Syst 21:22–36
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3. Demir BE, Bayir R, Duran F (2016) Realtime trajectory tracking of an unmanned aerial vehicle using a selftuning fuzzy proportional integral derivative controller. Int J Micro Air Veh 8(4):252– 268 4. Mian AA, Daobo W (2008) Modeling and backsteppingbased nonlinear control strategy for a 6 DOF quadrotor helicopter. Chin J Aeronaut 21:261–268 5. Yasmina B, Mansouri A, Ahaitouf A (2018) Quadrotor flight simulator modeling. In: International conference on advanced intelligent systems for sustainable development, pp 665–674. Springer, Heidelberg 6. Hassani H, Mansouri A, Ahaitouf A (2019) Control system of a quadrotor UAV with an optimized backstepping controller. In: 2019 International conference on intelligent systems and advanced computing sciences (ISACS), pp 1–7 7. Flame wheel F450 manual (2015). http://dl.djicdn.com/downloads/flamewheel/en/F450_User_ Manual_v2.2_en.pdf
Optimal Robust ModelFree Control for Altitude of a MiniDrone Using PSO Algorithm Hossam Eddine Glida, Latifa Abdou, Abdelghani Chelihi, Chouki Sentouh, and Gabriele Perozzi
Abstract This paper presents a modelfree controller based on particle swarm optimization algorithm (PSOMFC) for the altitude systems of a MiniDrone. A modelfree control (MFC) is applied to improve both trajectory tracking and robustness of quadrotor in the presence of external uncertainties and disturbance. The problem of Tuning MFC parameters designed is formulated as an optimization problem according to time domain objective function that is solved by PSO algorithm to find the most optimistic results. In order to prove the robustness of the proposed algorithm, an extensive set of numerical results are presented using a real Simulink Template for Parrot MiniDrone platform. Results evaluation show that the proposed control scheme achieves good performance for altitude system compared to the controller without optimization. Keywords Minidrone · Modelfree controller · Metaheurestic algorithm · Particle Swarm Optimization Algorithm
1 Introduction In recent years, Unmanned Aerial Vehicles (UAVs) have attracted significant attention due to their wide range of uses, such as military reconnaissance, disaster management and various agricultural applications. Their model is more complicated because it present highly coupled nonlinear dynamic, unstable system, underactuation, as well as parameter uncertainties and external disturbances. So, the control of a quadrotor is difficult in particular with unknown dynamic model. Several approaches H. E. Glida (B) Department of Electrical Engineering, LMSE Laboratory, University of Biskra, Biskra, Algeria email: [email protected] L. Abdou · A. Chelihi Department of Electrical Engineering, LI3CUB Laboratory, University of Biskra, Biskra, Algeria C. Sentouh · G. Perozzi Automatic Control, LAMIHUMR CNRS 8201, HautsdeFrance Polytechnic University, Valenciennes, France © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_48
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were studied in order to have a satisfactory performance control of UAVs, such as linear control, nonlinear control, optimal control, adaptive control, robust control and intelligent control methods, have been proposed in the literature [1–3], and some of them have been reviewed in [4]. Therefore, in order to improve the effectiveness and robustness tracking of the UAV nonlinear systems, a modelfree controller (MFC) strategies with a basic controller along with an ultralocal model is proposed to compensate for systems uncertainties and disturbances [5], in [6] authors proposed MFC based terminal slidingmode control for attitude system of a quadrotor, in [7, 17] a modelfree controller based on the cascaded structure of the dynamic model for trajectory tracking of quadrotors, a slidingmodebased modelfree control in [8, 9]. Unfortunately, a shortcoming of this proposed controller is that it is difficult to determine the best values of the parameters which conduct to an optimal behavior where the use of the trial and error method doesn’t lead generally to the desired result. To deal with these issues, Particle Swarm Optimization (PSO) algorithm, since its simplicity and suitability to manage for the variety of objective functions, is introduced to tune parameters values of the control scheme in several works [10–12]. This paper proposes an optimal robust modelfree control based on PSO algorithm (PSOMFC) for the altitude control of a quadrotor. Firstly, a modelfree control law is proposed for the altitude model of a MiniDrone without accurate knowledge of its nonlinear dynamics. Then, the PSO optimization algorithm is used to tune the MFC proposed controller such that null steadystate error tracking is achieved. The stability of closed loop system is proven by the Lyapunov theory. Interestingly, PSOMFC is able to obtain robust and comparable results with MFC using the same test scenario for Parrot Minidrone platform [13].
2 Dynamical Model The mathematical model of the UAV describing the altitude dynamics is given in [14], which is as follows: z¨ = (cθ (t)cφ(t))u z − mg /m,
(1)
where z represents the altitude of quadrotor assumed to be available for measurement, m is the quadrotor mass, g is the gravity acceleration, u z is the altitude input signal, φ(t) and θ (t) are the roll and pitch angles respectively, two rotations enable the quadrotor to move and to position in its operational space. For control reasons, the altitude dynamic model (1) could be represented in a generalized nonlinear state equation described in the compact form y¨z = f z (x z , u z ) + h z (t).
(2)
where x z = [z, z˙ ]T is the state vector, yz denotes the output of system which is the quadrotor altitude and h z (t) is the added unknown disturbance function to the quadrotor model which presents the effect of external forces. After some manipulation, we can rewrite (2) in state space representation
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x˙ z = z x z + z (πz (x z , u z , t) + αz u z ), yz = C zT x z ,
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where πz (x z , u z , t) = −αz u z + f z (x z , u z ) + h z (t) with αz > 0 will be designed by z ,u z ) z ,u z ) − 1 < 1, with ∂ f z (x = 0. the PSO optimization algorithm and verifies  α1z ∂ f z (x ∂u z ∂u z T T The tracking error vector is E = [e, e] ˙ = [yd − yz , y˙d − y˙z ] , where yd is the desired reference. We obtain the closedloop system governed by E˙ z = z E z + z ( y¨d − αz u z − πz (x z , u z , t)).
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If the nonlinear functions f z (x z , u z ) and h z (t) are well known, i.e. the term πz (x z , u z , t) is known, the control objective is achieved, by the following ideal control law u˙ z =
1 ( y¨d + K zT E z − πz (x z , u z , t)), αz
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where K z = [k1 , k2 ]T > 0 will be defined by The PSO optimization algorithm to have a stable closedloop system, i.e. all roots of (z − z K zT ) are in the open left halfplane and lim e(t) = 0. However, f z (x z , u z ) and h z (t) are unknown and the t→∞ ideal controller (6) cannot be implemented.
3 Optimal Robust ModelFree Controller In this section, the objective is to design an optimal robust modelfree controller for the altitude of Mini Drone system with unknown dynamic function f z (x z , u z ) and bounded disturbance h z (t) and where the output yz tracks the desired smooth
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and bounded reference yd keeping bounded all the other signals in the closed loop system. This object is achieved by developing an estimator πˆ z for the unknown term πz and by using the estimation to compute the control law (6), see [15]. Then, the PSO optimization algorithm is introduced in order to find the best values of the design parameters.
3.1 ModelFree Controller Considering the unknown nonlinear function πz (x z , u z , t) and πˆ z (x z , u z , t) its estimation, the control law (6) becomes u˙ z =
1 ( y¨d + K zT E z − πˆ z (x z , u z , t)). αz
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Substituting of the control law (7) into (5), the dynamics of the tracking error can be written as E˙ z = (z − z K zT )E z + z π˜ (x z , u z , t), (8) where π˜ z (t) = πz (x z , u z , t) − πˆ z (x z , u z , t). The Lyapunov function candidate, introduced in order to develop the estimation law for πˆ z guaranteeing the stability of the closedloop system related to (8), is V =
1 1 T E Pz E z + π˜ z2 , 2 z 2
(9)
where Pz is a positive definite symmetric matrix. The time derivative of V along the error trajectory (8) is V =
1 1 ˙T E Pz E z + E zT Pz E˙ + π˜ z π˙˜ z , 2 z 2
V˙ = 21 E zT ((z − z K zT )T Pz + Pz (z − z K zT ))E z + 21 π˜ z (t)zT Pz E z + 21 E zT Pz z π˜ z (t) + π˜ z (t)π˙ˆ z (t) − π˜ z (t)π˙ z (t).
(10)
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Considering that the disturbances function could be written as πz (t) = y¨z − αz u z , πˆ z (t) obtained from the filtering of the unknown term πz (t) using 1/(1 + βs), where βz > 0 will be tuned also by PSO algorithm and s is the Laplace variable [15]. Using Lyapunov equation (z − z K zT )T Pz + Pz (z − z K zT ) + 4βz Pz z zT Pz = −Q z for positive symmetric matrix Q z , after simplification, we can find the following inequality V˙ ≤ 21 E zT ((z − z K zT )T Pz + Pz (z − z K zT ) + 2βz Pz z zT Pz )E z − 43z π˜ z2 − π˜ z π˙ z ,
(12)
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Fig. 2 Simulated flight screen
V˙ ≤ −γ V + ϑ,
(13)
where γ and ϑ are constants. This implies that all signals in the closedloop, i.e. e, e˙ and u z , are bounded. Moreover, by using Barbalat’s lemma, the tracking error and its time derivative be small by choosing appropriately the designed parameters K z , αz and βz . From Eq. 2, the estimator function becomes πˆ z (s) =
1 ( y¨z (s) − αz u z (s)). 1 + βs
(14)
Substituting (13) into (7) yields the following control law: u˙ z =
1 1 ( y¨d + K z E z (t)) + αz αz βz
t
( y¨d + K z E z (t))dτ −
0
1 y¨z (t). αz βz
(15)
It is evident that from (15) the control law is well defined and it leads to the optimal behavior of quadrotor if the design parameters αz , βz and K z have the best values. In the next section, the PSO algorithm is employed to achieve this objective where the scheme of the proposed PSOMFC is shown in Fig. 1.
3.2 Optimal Control Based on PSO In the previous section, the MFC law (15) is designed to stabilize the altitude system without knowing the dynamic model (3). The control parameters αz , βz , k1 and k2 need to be positive to satisfy the Lyapunov stability. Usually, the parameters of the control law are selected by a trialand error. Even if the parameters are properly chosen, it is not guaranteed that optimal parameters are selected. For this reason, we propose
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Particle Swarm Optimization (PSO) algorithm inspired from the social behavior of animals evolving in swarms. The best positions correspond to the positions pi (t) of the particles that have better values for each generation i th and each particle is moved with velocity vi (t) to the current position using global best position G as pi (t + 1) = pi (t) + vi (t + 1),
(16)
vi (t + 1) = wvi (t) + γ1 π1 ( pibest (t) − pi (t)) + γ2 π2 (G − pi (t)),
(17)
where w is the inertia weight, γ1 and γ2 are the cognition and the social learning factors respectively, π1 and π2 ∈ [0, 1] indicate the uniformly generated random numbers [16]. The proposed algorithm is used to find the optimal modelfree control (PSOMFC) parameters against the minimization of the Root Mean Square Error (RMSE) as a fitness function J . N 2 j=1 (yd − yz ) . (18) J= N where j th is sampling time, N is the sampling size.
4 Numerical Result In this section, the proposed control scheme is applied to the MiniDrone system using Simulink Template for Parrot Minidrone platform (see Fig. 2) [13]. The proposed PSOMFC is compared with the MFC controller using a different parameters chosen arbitrarily for two sets: Set 1 and Set 2. Their corresponding numerical results are provided to verify the effectiveness of the proposed controller. The parameters values of the proposed controller are tuned via the PSO algorithm in search space: k1 ∈ [1, 100], k2 ∈ [1, 100], α ∈ [0.1, 100] and β ∈ [1, 50]. In this part two simulation scenarios are indicated: Hovering flight, Deactivating/activating hovering control. The controller parameters of the proposed controllers are listed as Set 1: k1 = 200, k2 = 50, αz = 1 and βz = 5. Set 2: k1 = 50, k2 = 30, αz = 2 and βz = 10. e PSOMFC: k1∗ = 81.35, k2∗ = 10.05, αz∗ = 1.25 and βz∗ = 80. In hovering flight experiment, the PSOMFC, Set 1 and Set 2 were simulated in hovering mode in order to analyze their performance (see Figs. 3 and 4). In the deactivating/activating hovering control scenario it is introduced a robust test to include the switching of the controllers on/off in order to prove if the controllers have the ability to return the MiniDrone to the given reference (see Figs. 5 and 6).
Optimal Robust ModelFree Control for Altitude ... Fig. 3 Altitude response of reference tracking at different setpoints
Fig. 4 Input signal response of the PSOMFC
Fig. 5 Altitude response when disactivating/activating the control law
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Fig. 6 Input signal response of the PSOMFC when disactivating/activating the control law
5 Conclusion In this paper, an optimal robust modelfree control has been introduced to deal with hover flight mode for a MiniDrone. The MFC is designed for the altitude system without accurate known model. Then, in order to ensure the best behavior of the proposed controller, the PSO algorithm was adopted to get an optimal controller (PSOMFC). Finally, simulation results have demonstrated advantages of the proposed control strategy for the drone hovering. In future work, we would look to go on the experimental validation in real time giving a comparison with other metaheuristics methods.
References 1. Hasseni SEI, Abdou L, Glida H (2019) Parameters tuning of a quadrotor pid controllers by using natureinspired algorithms. Evol Intell 1–13 2. Perozzi G, Efimov D, Biannic JM, Planckaert L (2018) Trajectory tracking for a quadrotor under wind perturbations: sliding mode control with statedependent gains. J. Franklin Inst. 355(12):4809–4838 3. Labbadi M, Cherkaoui M (2019) Robust adaptive backstepping fast terminal sliding mode controller for uncertain quadrotor UAV. Aerosp. Sci. Technol. 93:105306 4. Zulu A, John S (2016) A review of control algorithms for autonomous quadrotors. arXiv preprint arXiv:1602.02622 5. Al Younes Y, Drak A, Noura H, Rabhi A, El Hajjaji A (2016) Robust modelfree control applied to a quadrotor UAV. J. Intell. Rob. Syst. 84(1–4):37–52 6. Wang H, Ye X, Tian Y, Zheng G, Christov N (2016) Modelfreebased terminal smc of quadrotor attitude and position. IEEE Trans. Aerosp. Electron. Syst. 52(5):2519–2528 7. Bekcheva M, Join C, Mounier H (2018) Cascaded modelfree control for trajectory tracking of quadrotors. In: 2018 international conference on unmanned aircraft systems (ICUAS), pp 1359–1368 8. Bouzid Y, Siguerdidjane H, Bestaoui Y (2018) Generic dynamic modeling for multirotor VTOL UAVs and robust sliding mode based modelfree control for 3d navigation. In: 2018 international conference on unmanned aircraft systems (ICUAS), pp 970–979
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9. Li Z, Ma X, Li Y (2018) Modelfree control of a quadrotor using adaptive proportional derivativesliding mode control and robust integral of the signum of the error. Int J Adv Rob Syst 15(5):1729881418800885 10. Mohammadi V, Ghaemi S, Kharrati H (2018) Pso tuned flc for full autopilot control of quadrotor to tackle wind disturbance using bond graph approach. Appl Soft Comput 65:184–195 11. Mousakazemi SMH, Ayoobian N (2019) Robust tuned pid controller with pso based on twopoint kinetic model and adaptive disturbance rejection for a pwrtype reactor. Prog Nucl Energy 111:183–194 12. Aghbashlo M, Tabatabaei M, Nadian MH, Davoodnia V, Soltanian S (2019) Prognostication of lignocellulosic biomass pyrolysis behavior using anfis model tuned by pso algorithm. Fuel 253:189–198 13. Bello Guisado Á (2019) Diseño de controladores de vuelo para un dron modelo parrot mambo minidrone. Ph.D. dissertation 14. Hasseni SEI, Abdou L, Glida HE (2019) Parameters tuning of a quadrotor pid controllers by using natureinspired algorithms. Evol Intell 1–13 15. Boubakir A (2014) Contribution à la commande sans modèle des systèmes non linéaires avec applications. Technical Report, Alger, Ecole Nationale Polytechnique 16. Tharwat A, Gaber T, Hassanien AE, Elnaghi BE (2017) Particle swarm optimization: a tutorial. In: Handbook of research on machine learning innovations and trends. IGI Global, pp 614–635 17. Glida HE, Abdou L, Chelihi A, Sentouh C, Hasseni SEI (2020) Optimal modelfree backstepping control for a quadrotor helicopter. Nonlinear Dyn 1–20. https://doi.org/10.1007/s1107102005671x
Experimental Assessment of Perturb & Observe, Incremental Conductance and Hill Climbing MPPTs for Photovoltaic Systems N. Rouibah, L. Barazane, A. Rabhi, B. Hajji, R. Bouhedir, A. Hamied, and A. Mellit Abstract This paper presents a simulation and hardware implementation of maximum power point tracking (MPPTs) algorithms. The investigated algorithms are: perturb and observe (P&O), Incremental conductance (InCond) and Hill climbing (HC). Firstly, the algorithms have been simulated and tested under Matlab/Simulink environment. Subsequently, the simulated algorithms have been verified experimentally at the MIS Laboratory of Picardie Jules Verne, University, (France). All steps to implement the controllers into the dSPACE are presented in detail, as well as the development hardware. The experimental test was done under a cloudy sky (solar irradiance = 100 W/m2 , air temperature 6 = degrees). The obtained simulation and experimental results proved an acceptable performance of 0.8, 0.83 and 0.85 for P&O, InCond and HC respectively. A slow convergence time is observed for all examined algorithms, particularly at low solar irradiation level. Keywords Photovoltaic systems · MPPT algorithms · DCDC converter · dSPACE
1 Introduction Photovoltaic (PV) energy is gaining its place in alternative energy sources, despite the development in manufacturing technologies PV panels suffer from a relatively low N. Rouibah (B) · L. Barazane Electric and Industrial Systems Laboratory, Faculty of Electronics and Informatics, USTHB, Algiers, Algeria email: [email protected] A. Rabhi Modelization, Information and Systems Laboratory, University of Picardie Jules Verne, 3 Rue Saint Leu, 80039 Cedex 1 Amiens, France B. Hajji Renewable Energy, Embedded System and Data Processing Laboratory, National School of Applied Sciences Mohamed First University, PO.BOX 669, Oujda, Morocco R. Bouhedir · A. Hamied · A. Mellit Renewable Energy Laboratory, Jijel University, 18000 Jijel, Algeria © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_49
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energy conversion efficiency. The efficiency can be even lower if the PV generator does not work around a point called maximum power point (MPP). The continuation of this point, which changes position with weather conditions, is a very important step in the design of a PV system. The first maximum power point tracking technique (MPPT) was designed in 1970 by the research Centre “NASA” and “Honeywell” company [1]. This tracking technique is considered as an important task in photovoltaic control system. After a short period, several MPPT methods were designed. These methods can be classified for three groups: offline method, online method and hybrid method [2]. The first group also known as modelbas methods, the most popular MPPTs are: open circuit voltage method (OCV), Short circuit current method (SCC), Artificial intelligence (AI) [3]. Generally, the physical parameters of the PV module are utilized to create the control signals. These MPPTs are mainly used for PV systems. The second, also named as modelfree methods, contain the most popular MPPTs are: Perturb and observe method (P&O), Incremental conductance method (InCond), Hill Climbing method (HC) [4]. These MPPTs methods will be tested experimentally in this work. The third group can be defined as a combination of online method with offline method, for example in [5] the authors combine the open circuit voltage method (OCV) and Perturb and observe (P&O) MPPT methods. The main objective of this work is to verify experimentally the performances of some MPPT algorithms (e.g., P&O, In Cond and HC) under low solar irradiance. The hardware implementation was achieved and tested at MIS Laboratory of Picardie Jules Verne University, France. The paper is organized as follows: Sect. 2 presents an overall system description. A detailed description of the hardware platform and development process are provided in Sect. 3. Finally results and discussion are given in Sect. 4.
2 Overall System Description Figure 1 shows the block diagram of the overall system. The used elements are: PV panel, chopper DCDC converter, current and voltage sensors, solar irradiance sensor, resistive load, a dSPACE1104, and a computer. A monocrystalline PV module SW300 was used. The electrical characteristic of SW300 PV panel is presented in Table 1. A DCDC converter is used for tracking the maximum power point (MPP), the chopper DCDC converter contains the following components: switching frequency (fs) of 25 kHz, self (L) of 1 mH, capacitors (Cint , Cout ) of 220 uF. More details about the chopper DCDC converter can be found in [6]. As shown in Fig. 2, two current sensors are used for measuring the input current from chopper DCDC converter and output current from resistive load, also we used two voltage sensors for measuring the input voltage for chopper DCDC converter
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Table 1 Electrical characteristic of SW300 at STC Specifications
Values
Maximum power (Pmax)
300 W
Rated voltage (Vmp)
31.9 V
Rated current (lmp)
9.4 A
Shortcircuit current (Isc)
10.23 A
Opencircuit voltage (Voc) Technology
40.10 V Monocrystalline
Fig. 1 Block diagram of overall system description
and the output voltage from the load resistive. We used also a solar sensor type (Spektron 320) for measuring the inplane solar irradiance. In this paper, we are not intended to present a detail explication and the theoretical background of the implemented algorithms: (Perturb and Observe (P&O), Incremental Conductance (InCond), Hill Climbing (HC) [4]). So, we will focus more on the experimental implementation.
3 Experimental Setup This section gives a detailed description of the hardware implementation platform, see Fig. 2. A PV panel is connected to the chopper DCDC converter with 25 kHz switching frequency. According to the measured PV power and PV voltage, the MPPT
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Fig. 2 Hardware platform under test
controller computes the required duty cycle. The dSPACE converts the MPPT’s duty cycle to a PWM signal which turns on and off the chopper DCDC converter’s power switch. Varying the on/off time of switch allows varying the output voltage and hence the operating point of the whole PV system. The resistive load of the chopper DCDC converter having the value of 48 Ohm.
4 Results and Discussion 4.1 Simulation Result Figure 3 shows the simulation results of the three MPPT algorithms. The simulation has been done at the following weather conditions (solar irradiance level = 100 W/m2 , temperature = 6 °C) in order to compute the static efficiency (3.4 W, 3.5 W, and 3.6 W for P&O, In Cond and HC MPPTs methods respectively). It can be observed that the three MPPT algorithms have converged to the exact MPP, but with slow time of convergence.
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Fig. 3 Curves of output power for three algorithms methods
Table 2 Evaluated parameters for MPPT algorithms under low solar irradiance level
Evaluated parameters
P&O
In Cond
HC
Power production (W)
3.30
3.3
3.40
Efficiency (%)
80
83
85
Sensors used
(current, voltage)
(current, voltage)
(current, voltage)
Response time(s) Slow
Slow
Slow
Algorithm’s complexity
Low
Low
Low
4.2 Experimental Results The experimental tests were carried out on 06th December 2019 (cloudy sky, and there were some raindrops). The system parameters (PV current, PV voltage, duty cycle, output current output voltage, radiation solar and power) are observed and recorded by using Matlab/Simulink environmental. In this subsection, we present only the output power of the implemented MPPT algorithms. Figure 4 shows the output power, it can be noticed that these algorithms have relatively low performances, and slow response time in this specific climatic condition (solar irradiance 100 W/m2 ). Table 2, summarized the evaluation parameters: power production, response time, efficiency, sensors used, complexity. According to Table 2, it can be seen that the three MPPT algorithms present a relatively average efficiency and slow response time, in this specific climatic conditions.
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Fig. 4 Output power for the implemented algorithms under low solar irradiance level
Fig. 5 Curves of output power for three MPPTs algorithms under partial shading
To evaluate the performance of the MPPT algorithms under partial shading conditions (PSCs), we partially covered the PV panel in three stages, see Fig. 5. The irradiance solar starts with G = 100 W/m2 , stepped down to G = 60 W/m2 at 40 s, then stepped down to G = 40 W/m2 at 94 s, and finally stepped down to G = 20 W/m2
Experimental Assessment of Perturb & Observe, Incremental Conductance …
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at 140 s. According to the curves plotted in Fig. 5, it can be clearly seen a poor performance of these MPPTs, particularly with very lowlevel solar irradiance.
5 Conclusion In this paper, the efficiency of MPPT algorithms (P&O, InCond and HC) is simulated and tested under real environmental conditions (solar irradiance level 100 W/m2 , air temperature = 6 °C). The investigated algorithms have been implemented into dSPACE controller by using a chopper DCDC converter. Simulation and experimental results confirm that these algorithms do not perform well, in terms of response time and efficiency, under low irradiance conditions especially P&O method. However, in the literature, most result obtained under high or average solar irradiance level. Generally, they proved good results in terms of precision, stability, and speed in the MPP tracking. Moreover, as a future work, we will try to investigate other MPPT methods such as offline methods and hybrid methods.
References 1. Cherdak AS, Douglas JL (1971) Maximum power point tracker. Google Patents 2. Reisi AR, Moradi MH (2013) Jamas, classification and comparison of maximum power point tracking techniques for photovoltaic system: a review. Renew Sustain Energy Rev 19:433–443 3. Mellit A, Kalogirou SA (2014) MPPTbased artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: review of status and future perspectives. Energy 70:1–21 4. Verma D, Nema S, Shandilya A, Dash SK (2016) Maximum power point tracking (MPPT) techniques: recapitulation in solar photovoltaic systems. Renew Sustain Energy Rev 54:1018– 1034 5. Yang C, Hsieh C, Feng F, Chen K (2012) Highly efficient analog maximum power point tracking (AMPPT) in a photovoltaic system. IEEE Trans Circ Syst 59:7 6. Rouibah N, Mellit A, Barazane L, Hajji B, Rabhi A (2019) A lowcost monitoring system for maximum power point of a photovoltaic system using IoT technique. In: International Conference on Wireless Technologies. Embedded and Intelligent Systems (WITS), pp 1–5. IEEE
Circulating Current Control for Parallel ThreeLevel TType Inverters Abdelmalik Zorig, Said Barkat, Mohamed Belkheiri, and Abdelhamid Rabhi
Abstract Parallel inverter is one of the most interesting topology to achieve high power level, overcame current limitation on the switching devices and also to enhance the output current waveforms. However, the circulating current results from the common connection of both AC and DC sides directly can increase the current stresses and conduction losses of the switching devices and reduces inverters efficiency. This paper provides an investment on the threelevel Space vector modulation and proposes a new strategy to eliminating the circulating current for paralleled threelevel ttype inverters. Results obtained confirmed the performance and the effectiveness of the proposed circulating current control strategy. Keywords Parallel inverters · Circulating current · Threelevel Ttype inverter · Threelevel space vector modulation
1 Introduction Because of its capability to achieve high power level with lower output current ripple and AC side harmonic, parallel inverter topology becomes widely integrated in different applications like renewable energy systems [1], shunt active powerfiltering [2], highpower motor controls [3] and powerfactorcorrection [4]. In addition, parallel inverter topology has attractive advantages such as high reliability, modularity, and reconfigurability. A. Zorig (B) · S. Barkat Laboratoire de Génie Electrique, Université de Mohamed Boudiaf – M’sila, M’Sila, Algérie email: [email protected] M. Belkheiri Laboratoire de Télécommunications, Signaux et Systèmes, Université Amar Telidji, Laghouat, Algérie A. Rabhi Laboratoire de Modélisation, Information et Systèmes, Université de Picardie Jules Verne, Amiens, France © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_50
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However, the main problem in parallel inverters operation is the circulating current which produced from differences between the hardware and/or the control parameters such as, impedance filters, switching device, switching frequency, dead time…etc. And because the paralleled inverters shared both dc and ac sides (see Fig. 1) this undesired current can circulate and result in current distortion, harmonic loss and unbalanced load sharing. Several contributions have been presented to deal with this problem in parallel twolevel inverters [4–12]. However, multilevel inverters offer several advantages compared with twolevel inverter, mainly, they are able to generate voltage waveforms with less distortion and lower electromagnetic interference [13]. A few papers have been addressed circulating current between parallel multilevel inverters [14–16]. Shao et al. [14] have developed a circulating current control loop for parallel threelevel inverters when two distribution factors are introduced into the zerosequence modulation function in order to eliminate the lowfrequency circulating current and to control neutral point potential. So far, investment on Space Vector Modulation (SVPWM) to eliminate circulating current in parallel threelevel ttype inverters has not been presented yet in publications. Hence, the primary aim of this paper is to show how this can be done. Based
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Fig. 1 Structure of paralleled threelevel ttype inverters
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on the analysis of the average model of parallel threelevel inverters, the mathematical model of circulating current is developed and then a circulating current control strategy is developed.
2 Modeling of Parallel Two ThreeLevel TType Inverters The structure of the parallel ttype inverters is shown in Fig. 1. The model of each ttype inverter in the threephase stationary coordinate can be represented as: ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ i an va vcan i an d L f n ⎣ i bn ⎦ = −R f n ⎣ i bn ⎦ − ⎣ vb ⎦ + ⎣ vcbn ⎦, n = 1, 2 dt i cn i cn vc vccn ⎡
(1)
where, va , vb , vc are the phase voltages at the point of common coupling (PCC); vcan , vcbn , vccn and i an , i bn , i cn are the phase voltages and currents, respectively of inverter n; L f n , R f n represent the inductance and equivalent series resistances of the inductors of inverter n, respectively. In two threephase inverters in parallel, there is a two circulating current have same magnitude and opposite direction, these currents can be defined as: i 0 = i 01 = i a1 + i b1 + i c1 = −i 02 = −(i a2 + i b2 + i c2 )
(2)
The model represented in (1) can be transformed into the synchronous reference frame (dq0) as: ⎤⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ 0 −R f n ω i dn vd vcdn i d ⎣ dn ⎦ ⎣ L fn i qn = −ω −R f n 0 ⎦ ⎣ i qn ⎦ − ⎣ vq ⎦ + ⎣ vcqn ⎦ dt i 0n i 0n v0 vc0n 0 0 −R f n
(3)
where vd , vq , v0 , vcdn , vcqn , vc0n and, i dn , i qn are the components in the dq0 synchronously rotating frame of PCC voltage, output voltage, and current of inverter n, respectively. By using (2) and subtraction the two equations of (3) the equation which describes the dynamic of the circulating current is obtained as: (L f 1 + L f 2 ) di 0 /dt + (R f 1 + R f 2 ) i 0 = vc01 − vc02 = (d01 − d02 ) vdc /2
(4)
where, d0n is zerosequence duty ratio of inverter n, defined by: d0n = (da1n + da2n ) + (db1n + db2n ) + (dc1n + dc2n )
(5)
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di1n and di2n are the duty ratios of the two switches si1n and si2n in the iphase (i = a, b or, c) of inverter n, respectively. Based on Eq. (4), the circulating currents would be effectively restrained when d0 = d01 − d02 is controlled and maintained close to zero.
3 Control of Parallel ThreeLevel TType Inverters 3.1 Proposed Circulating Current Controller for Parallel ThreeLevel TType Inverters The switching states of the each ttype inverter are represented on a spacevector diagram as shown in Fig. 2. The diagram is divided into six sectors and each sector consists of four triangle regions. The switching states are coded by 2, 0, and 1 as shown in Fig. 2. It denotes that the iphase inverter i = {a, b, c} is connected to the positive (P), negative (N), or the common NP of the power source; with voltage levels corresponding to vdc , 0, and vdc /2, respectively with respect to the negative DC rail. Then, the reference vector v ∗ in Fig. 2 can be synthesized as:
v ∗ = d x v x + d y v y + dz vz d x + d y + dz = 1
Fig. 2 Switching state vectors of threelevel ttype inverter
(6)
vβ Sector 2 120
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dc 21 d y1 d z1 d y1 d x1 d z1 d y1 d x1 d z1 d d y1 d z1 d d z1 d +k + k x1 − 2k z1 − 2k + k x1 +k 6 4 4 6 4 4 6 4 4 4 6 6 4 6
Fig. 3 Adjusting of redundant vectors with the introduced variable k, Region 1 of sector 1
where dx , d y , dz are the duty cycles of the nearest voltage vectors vx , v y , vz respectively. Figure 3 shows the PWM pattern of the threelevel SVPWM scheme for region 1 of sector 1. By introducing a control variable k in the duty cycles of redundant vectors, the d0n of each ttype inverter can be expressed in region 1 of sector 1 as: d01 = (da11 + da21 ) + (db11 + db21 ) + (dc11 + dc21 ) = 3 − dx1 /2 + d y1 /2 − 18k (7) Consequently, the difference d0 between the zero sequence duty ratios can be calculated in function of the new variable k. In addition, with the same current sharing, dx1 , d y1 and dx2 , d y2 are equals. So, d0 can be calculated as: d0 = d01 − d02 = 3 − dx1 /2 + d y1 /2 − 18k − 3 − dx2 /2 + d y2 /2 = −18k (8) Similarly, the difference between the zerosequence duty ratios can be calculated in all regions and sectors as shown in Table 1. Then the average model of the circulating current expressed in (4) can be rewritten as: (L f 1 + L f 2 ) di 0 /dt + (R f 1 + R f 2 ) i 0 = λkvdc /2, λ ∈ {−18, −12, −6}
(9)
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Table 1 Values of d0 in different sectors and regions
Fig. 4 Circulating current loop control for parallel two three level ttype inverters
d0
Sector
Region 1
−18k
all sectors
Region 2
−12k
all sectors
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i0*
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k
i0
λ vdc ( s ) / 2 ( L f 1 + L f 2 )s + ( R f 1 + R f 2 )
i0
A PI controller can be used to suppress the circulating current by acting upon k as: k = 2/λvdc P I (i 0∗ − i 0 )
(10)
In this case, the control block diagram of circulating current loop can be derived, as shown in Fig. 4.
3.2 Overall Control of Parallel ThreeLevel TType Inverters In order to validate the proposed circulating current controller two parallel threelevel ttype inverters feed a RL load via an LC filters are considered. Figure 5 shows the overall control including proposed circulating current controller. The control consists composed mainly on the proposed circulating current controller (highlighted with the dashed area) and two cascaded loops in the synchronous reference frame (dq). The first loop controls the desired load voltages; while the second is designed to ensure equal current charring between the two ttype inverters.
4 Simulation Results 4.1 Simulation of Parallel ThreeLevel TType Without Circulation Current Controller To show the effect of circulating current on the system performance, differences in the filters impedances are introduced where the inductors and equivalent in series resistances (ESR) values of first threelevel ttype have the same values as listed in Table 2, while inductors and ESR values of the second threelevel ttype inverter are decreased by 25%. The simulation results without circulating current controller are shown in Fig. 6.
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Fig. 5 Control diagramincluded proposed circulating current controller for parallel threelevel ttype inverters
Table 2 Simulation parameters
Switching frequency
5 kHz
Fundamental frequency
50 Hz
DCsource voltage
80 V
Line impedance
R f x = 1 mΩ, L f x = 1 m H, C f x = 330 μF
Load
Rlx = 8.4 Ω, L lx = 0.4 m H
From Fig. 6(a), one can see that the difference in the filters inductors and ESR values causes a large circulating current. This current flows through the paralleled ttype inverter and as it can be observed in Figs. 6(b) and (c), it provokes asymmetrical and imbalances inverters threephase currents. To gain better insight the aphase, waveforms of each inverter are shown in Fig. 6(d). From this figure, one can see that the aphase currents discrepancy and distortion are important and they may cause serious problems for the parallel inverters operation.
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a
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Fig. 6 Performance of parallel threelevel ttype inverters without circulating current control: (a) circulating currents (b) phase currents of inverter 1, (c) phase currents of inverter 2, (d) aphase currents of each inverter
4.2 Simulation of Parallel ThreeLevel TType with Proposed Circulation Current Controller Figure 7 show the results obtained in the same conditions as the previous subsection but the proposed circulating controller has been used. Figure 7(a) presents the obtained circulating currents waveforms. By comparing this results with that obtained in Fig. 6(a), it can be noted how the circulating current problem was effectively mitigated.
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a
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Fig. 7 Performance of parallel threelevel ttype inverters with the proposed circulating current control: (a) circulating currents (b) phase currents of inverter 1, (c) phase currents of inverter 2, (d) aphase currents of each inverter
It can be seen from Figs. 7(b) and (c) that even the variation in the filters impedance value, the threephase currents of each ttype inverter are consistent and balanced. These results validate the design and the effectiveness of the proposed circulating current controller. Figure 7(d) displays the aphase currents of each ttype inverter. It can be seen how the currents become consistent and the imbalances in Fig. 6(d) are successively overcome, which means a good load current sharing accuracy between the ttype inverters.
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5 Conclusion This paper proposes a circulating current control strategy for parallel threelevel ttype. The idea consists of an investment on the threelevel Space Vector Modulation proprieties where, the circulating current controller is realized by introducing a control variable adjusting the duty cycles of the redundant vectors. A simple PI controller is used to determine the amount of duty ratios that must be adjusted to eliminate the circulating current between the parallel inverter. The effectiveness of the proposed circulating current controller has been evaluated under differences in the impedances filters values. The obtained results have shown an excellent circulating current suppression.
References 1. Li R, Xu D (2013) Parallel operation of full power converters in permanentmagnet directdrive wind power generation system. IEEE Trans Ind Electron 60(4):1619–1629 2. Asiminoaei L, Aeloiza E, Enjeti PN, Blaabjerg F (2008) Shunt active powerfilter topology based on parallel interleaved inverters. IEEE Trans Ind Electron 55(3):1175–1189 3. Xu Z, Li R, Zhu H, Xu D, Zhang C (2012) Control of parallel multiple converters for directdrive permanentmagnet wind power generation systems. IEEE Trans Power Electron 27(3):1259– 1270 4. Ye Z, Boroyevich D, Choi JY, Lee FC (2002) Control of circulating current in two parallel threephase boost rectifiers. IEEE Trans Power Electron 17(5):609–615 5. Xing K, Lee FC, Boroyevich D, Ye Z, Mazumder S (1999) Interleaved PWM with discontinuous spacevector modulation. IEEE Trans Power Electron 14(5):982–989 6. Chen TP (2009) Commonmode ripple current estimator for parallel threephase inverters. IEEE Trans Power Electron 24(5):1330–1339 7. Mazumder SK (2005) Continuous and discrete variablestructure controls for parallel threephase boost rectifier. IEEE Trans Ind Electron 52(2):340–354 8. Pan CT, Liao YH (2008) Modeling and control of circulating currents for parallel threephase boost rectifiers with different load sharing. IEEE Trans Ind Electron 55(7):2776–2785 9. Zhang D, Wang F, Burgos R, Boroyevich D (2011) Commonmode circulating current control of paralleled interleaved threephase twolevel voltagesource converters with discontinuous spacevector modulation. IEEE Trans Power Electron 26(12):3925–3935 10. Hou CC (2013) A multicarrier PWM for parallel threephase active frontend converters. IEEE Trans Power Electron 28(6):2753–2759 11. Zorig A, Belkheiri M, Barkat S, Rabhi A, Blaabjerg F (2018) Sliding mode control and modified SVM for suppressing circulating currents in parallelconnected inverters. Electric Power Compon Syst 46(9):1061–1071 12. Gohil G, Maheshwari R, Bede L, Kerekes T, Teodorescu R, Liserre M, Blaabjerg F (2015) Modified discontinuous PWM for size reduction of the circulating current filter in parallel interleaved converters. IEEE Trans Power Electron 30(7):3457–3470 13. Schweizer M, Kolar JW (2013) Design and implementation of a highly efficient threelevel Ttype converter for lowvoltage applications. IEEE Trans Power Electron 28(2):899–907 14. Shao Z, Zhang X, Wang F, Cao R (2015) Modeling and elimination of zerosequence circulating currents in parallel threelevel Ttype gridconnected inverters. IEEE Trans Power Electron 30(2):1050–1063
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15. Zou Z, Hahn F, Buticchi G, Günter S, Liserre M (2018) Interleaved operation of two neutralpointclamped inverters with reduced circulating current. IEEE Trans Power Electron 33(12):10122–10134 16. Zhang Z, Chen A, Xing X, Li K, Du C, Zhang C (2017) Modeling and suppression of circulating currents for multiparalleled threelevel Ttype inverters. In: 2017 IEEE Energy Conversion Congress and Exposition (ECCE), Cincinnati, OH, pp 708–713
An Improved Sinusoidal (PWM) and Vector (SVPWM) Current Control for a ThreePhase Photovoltaic Inverter Connected to a Nonlinear Load Abdelhak Lamreoua, Anas Benslimane, Jamal Bouchnaif, and Mostafa El Ouariachi
Abstract After improving the electrical performance of a singlephase photovoltaic inverter (previous article), this article aims to model the threephase photovoltaic inverter of voltage connected to the grid, and the comparison of two improved methods of controlled of this inverter by the vector control PWM (SVPWM) and sinusoidal (SPWM) under nonlinear load conditions (NLL). For this and after modeling the converter, we wish to apply the vector and sinusoidal control in order to minimize the losses of the current injected by this converter in the grid. After application of the Park transformations, the dq components would not be timeinvariant in situations where harmonics, resonances or unbalance is present. Control allows indirect control of the active and reactive powers injected into the grid. This strategy is based on decoupling the output currents of the inverter into active and reactive currents using the Park transformation. The PI controllers are implemented in the dq frame (synchronous reference frame SRF) to adjust the grid currents in the synchronous dq frame. To generate the reference current and maintain synchronism between the inverter and the grid, a Phaselocked loop technique (PLL) can be used. The main advantage and objective of this method is to effectively compensate the harmonic current content of the grid current without and with the use of compensation devices. The main objective is to address, in terms of cost, efficiency, power management and power quality, the problems found with Threephase photovoltaic inverter connected to the grid controlled by SVPWM and SPWM, in order to compared the two methods and obtain a more reliable and flexible Threephase inverter. The results of simulations of the new SPWM and SVPWM algorithm demonstrate its superior performance compared to the simple sinusoidal pulse width modulation which is previously used with singlephase photovoltaic inverters (previous article [1–3]). After comparing the results of the two methods vector and sinusoidal commands, we notice that the current THDi of the current for the vector control (SVPWM) is lower than that
A. Lamreoua (B) · A. Benslimane · J. Bouchnaif · M. El Ouariachi (B) Laboratory of Electrical Engineering and Maintenance (LEEM) Higher School of Technology, University of Mohammed I, BP: 473, Oujda, Morocco email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_51
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obtained with the sinusoidal command (SPWM). The effectiveness of these techniques proposed in this article is demonstrated by the simulation results using the MATLAB/ SIMULINK environment. Keywords Modeling · Threephase inverter · Space vector PWM (SVPWM) · Sinusoidal PWM · Total harmonic distortion (THD) · Synchronous reference frame (SRF)
1 Introduction In recent years, photovoltaic production has become more and more important as a renewable resource because it does not cause fuel costs, pollution, maintenance and noise compared to other alternatives used in power applications that requires research on these alternative sources. Among the various renewable energy sources, photovoltaic (PV) is the most promising clean and renewable energy source, respectful of the environment and the fastest growing [4, 5]. The renewable energy sources (RESs) are connected to the distribution grid or to the microgrid (MG) by an interface converter. Power quality issues are a particular problem for PV systems, since harmonic distortion sources can represent a high proportion of total or nonlinear charges (NLL) in smallscale systems [6]. The current controller proposed in [7] uses the synchronous reference frame (SRF) and is composed of a proportional to integral (PI) controller. Several controllers, namely PI controllers implemented in the graphical framework (also constituting an SRF function), a resonant controller, a PI controller implemented in the abc frame and a predictive dead time (DB) controller, have been proposed in [8]. Unfortunately, conventional APFs (Active Power Filter shunt) have several drawbacks, including higher cost, larger size and higher number of power switches, as well as complex control algorithms and interface circuits to compensate for unbalanced and unbalanced loads linear [9]. Due to the abovementioned problems, this study presents a new inverter control method for harmonic compensation. The proposed control strategy based on space vector (SVPWM) or sinusoidal control (SPWM) [10], which proposed to control the power injection into the grid, provide harmonic current compensation and correct the unbalanced system. The control makes it possible to indirectly control the active and reactive powers injected into the grid, by decoupling the output currents of the inverter into active and reactive currents using the Park transformation. In addition, a Phaselocked loop (PLL) technique can be used to generate the current reference current and to maintain the synchronism between the inverter and the grid [11–13]. This document mainly focuses on the reduction of total harmonic distortion (THD) of the current in the grid. In addition, simulation results are presented, discussed and analyzed.
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This document is organized as follows. The proposed control scheme and the modeling of the photovoltaic inverter is presented in Sect. 1. In the 2nd section, the details of the entire sinusoidal and vector control structure, including the active current control unit and reactive, the PLL technique and the calculation of vector application times are explained. The results of the simulation with this study are presented in Sect. 3. Finally, the conclusions are presented in Sect. 4.
2 ThreePhase Inverter Modeling See (Fig. 1)
2.1 Inverter Modeling The load being balanced, the phase voltage vkN (k = 1, 2 or 3) is expressed by Eq. (2) as a function of the bus voltage U and of the control function hk from Eq. (1) linking the control function hk and the phasesource voltage vk0 of the inverter: U 2 2 −1 −1 h1 U 1 = −1 2 −1 h2 ∗ 3 2 −1 −1 2 h3 vk0 = h k ∗
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Fig. 1 Threephase PV inverter connected to the grid
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Table 1 Coordinates of each configuration in the coordinate system (α, β) h1 h2 h3 v1N v2N v3N vα vβ Phase(°) v2α + v2β
[111]
2U 3
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U √ 6
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− U3
2U 3
− U3
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− 2U 3
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− U3
− U3
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Eight switch configurations ([h1h2h3]) exist for this converter. A representation in the Concordia benchmark can transform these eight configurations into eight vectors (Table 1). Table 1 shows that the phase voltage VkN can be equal to five values (or voltage levels per phase): [2U/ 3; U/ 3; 0; U/ 3; 2U/ 3].
2.2 Calculation of Vector Application Times The control voltage vector → is approached, over the TPWM modulation period, by V ref
an average voltage vector → developed by applying the state vectors of the inverter V → and → and adjacent during times Tk, Tk + 1 respectively and null vectors → Vk
V k+1
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during (1K0) × T0 e → during K0 × T0. The PWM command signals will therefore V7
make it possible to recreate, as an average value over a period of TPWM, a voltage vector equal to that defined as a reference. Furthermore, the reference voltage vector → is sampled at the frequency fPWM = V ref 1/TPWM. The sampled value → n is then used to solve the following equations: V ref
−−→ Vr e f = Vn = n
1 TM L I
Tk 0
Vk dt+
T k+1 0
Vk+1 dt+
(1−k)T /2 0 0
With → = → = → : V0
V7
0
V0 dt+
K T /2 0 0 0
V7 dt
(3)
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−−→ 1 Vr e f = Vn = TK .Vk + TK +1 .Vk+1 n Te
(4)
T0 = TM L I − (Tk + Tk+1 )
(5)
T0 is the application time of the null vectors.
3 The Proposed Control Method 3.1 Active and Reactive Current Control Unit To improve the quality of the grid and the currents of our photovoltaic system, an advanced current control method for the gridconnected inverter (GCI) is presented. The proposed method includes two units: the active and reactive power control unit and the harmonic current compensation unit. Figure 2 presents a block diagram of the control strategy proposed for the GCI. This block diagram applies to reactive power control for sources of distortion and to the correction of system imbalance. In power control mode connected to the grid, allavailable power that can be obtained from the PV system is transmitted to the grid. In addition, reactive power compensation is possible. A functional diagram of the control configuration for the control mode connected to the grid is presented in Fig. 2. Some controllers, namely PI controllers, are implemented in the dq frame (method called SRF) to adjust th grid currents in the synchronous dq frame. This method uses
Fig. 2 Block diagram of the proposed control method
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a reference frame transformation module, abc to dq. The transformation dq can be used to convert the threephase currents injected by the inverter into three constant continuous components, defined as the direct, quadrature and zero components: Id, Iq and I0, respectively. In general, the threephase voltages and currents are transformed into coordinate’s dq0 by the Park transformation, as shown in the matrix [L]: ⎡
⎤
⎡
⎤
⎡
⎤
⎡
⎤
ud uA iA id ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ u q ⎦ = [L]⎣ u B ⎦And ⎣ i q ⎦ = [L]⎣ i B ⎦ With [L] = u0 uC i0 iC
sin α sin α − 2π sin α + 3 2⎢ ⎢ cos α cos α − 2π sin α + ⎣ 3 3 ⎡
√1 2
√1 2
√1 2
⎤
2π 3 ⎥ 2π ⎥ 3 ⎦
(6) The phase angles of the voltage and current signals are defined as the reference current, which makes it possible to reach the SRF as long as I*d = 0. The voltage frame with sinusoidal pulse width modulation (SPWM) is guaranteed. The design voltage reference and phase locked loop (PLL) synchronize the inverter with the grid. Thus, I*d and I*q as reference currents in the distance transformation are recalculated as follows: The reference currents in the line axis, I*d and I*q, can be obtained from following relationships: Vq=0
P = Vd Id + Vq Iq ⇒ Id∗ = Vq=0 ∗ Q = V I − V I ⇒ Iq = d q
q d
P∗ V∗d Q Vd
(7)
In addition, the inverter is able to supply P * and Q *, which are respectively the active power and the reactive power of reference. The simplified active and reactive powers are calculated as follows: P = Vd Id Q = Vd Iq
(8)
Reactive power is set to zero (I * q = 0). The reference current I * d is extracted from the dynamic analysis of the DC capacitor. The equation is as follows: 2 d 2 VDC = (Pin − Pout ) dt C
(9)
The reference current is extracted from the difference between the pins and the outputs using the PI controller. Id∗ =
1 k p (Pin − Pout + K I ∫(Pin − Pout )dt) Vd
(10)
From these parameters, the control voltages V*d and V*q of the SPWM gates can be obtained using
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Vd∗ = k p (Id∗ − Iq ) + K I ∫(Id∗ − Iq dt − ωL f Id + Vd Vq∗ = k p (Iq∗ − Iq ) + K I ∫ (Iq∗ − Iq dt − ωL f Iq + Vd
487
(11)
The intermediate circuit voltage in this structure is controlled by the output power, which is the reference for the active current controller. In general, the dq control methods are associated with PI controllers because they behave satisfactorily when regulating DC variables. Equation 12 gives the matrix transfer function in dq coordinates: 0 Kp + Ksi (dq) (12) GPI (s) = 0 Kp + Ksi Where Kp and Ki are the proportional and integral gains of the controller, respectively. The decoupled currents (active and reactive) are compared to the reference values. The active reference current is calculated from the output of the DC regulator, the reactive current reference is set to zero to ensure a unit power factor.
3.2 PLL Controller The PLL technique can be used to generate the current reference current and to maintain the synchronism between the inverter and the grid. To create the three current references, a PLL system was proposed. The diagram of the PLL is illustrated in Fig. 3: In this synchronization structure, f and ωg are respectively the fixed frequency and the estimated frequency of the grid. As illustrated in Fig. 3, the value of the nominal frequency normally consists of feedback f to improve the dynamics of the phase estimate θ, obtained by integrating f.
Fig. 3 Block structure of the proposed PLL
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Fig. 4 DC bus voltage regulation loop
Table 2 Parameters of the used system
Parameters
Value
PV Power
500 W
Vdcref
80 V
Imin
1A
fg
50 Hz
fpwm
10 kHz
3.3 DC Bus Voltage Regulation To reduce the variations and the instability of the DC bus voltage, a proportionalintegral (PI) regulator is proposed for the regulation of the DC bus voltage, as shown in Fig. 4:
3.4 Simulation Parameters Our system consists of a photovoltaic generator, a DCAC converter with its control strategy; the technical system parameters used in this application are presented in the Table 2: Simulation Results After recalling the operating principle of the SPWM and SVPWM command, we will model it under the environment of the MATLAB/SIMULINK software (Fig 5):
3.5 41 Current Control by Sinusoidal PWM (SPWM) Before Compensation: The output currents in the PV system are distorted due to the connection of the threephase converter to the nonlinear load (NLL). Negative and zero sequence harmonics, consisting of the 3rd, 5th, 7th, 9th, 11th and 13th harmonics, can cause power quality issues in the grid. The current and voltage spectrum obtained (Fig. 6 (a)) presents an output signal rich in harmonics of odd multiple
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Fig. 5 Current control diagram proposed by PWM sinusoidal (SPWM) and vectorial (SVPWM) of threephase PV inverter
order with fmli which are very far from the fundamental with a harmonic distortion rate is: THDi = 4.40% and THDv = 4.47% After Compensation. According to the frequency analysis (Fig. 6 (b)) we observe that all the harmonics of high frequencies (relative to the cutoff frequency of the filter) disappeared after filtering (THDisf = 4.40%), therefore the control of the inverter by the SPWM method allowed us to obtain a fairly significant performance especially obtaining a sinusoidal signal variable in amplitude and frequency (image signal of te reference signal), with a harmonic distortion rate to international standard: THDi = 0.68% and THDv = 0.05% < < 3%.
3.6 Current Control by Space Vector Pulse Width Modulation Without Compensation. The signal for output current and output voltage (Fig. 7 (a)), shows that the fundamental obtained whose frequency and amplitude depend on those of reference has harmonics of large amplitudes but of frequencies close to
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Fig. 6 Spectrum of the output voltage and current by the SPWM command (a) without harmonic filtering (b) with harmonic filtering
that of the very high carrier with a THDi = 4.39%, THDv = 4.47%, which allows the ease of filtering of the fundamental and guarantees a purely sinusoidal signal. With Compensation Device. Figures 7(b) show that the SVPWM control functions correctly after filtering, and the harmonic distortion rate of current and voltage is lower than that obtained with regulation without filter (THDisf = 4.39%) and of the order THDi successively = 0.22% and THDv = 0.04% < < 3% (international standard) therefore the proposed command allows the harmonics of high frequencies to be filtered and gives a purely sinusoidal signal.
3.7 Active and Reactive Powers in the Load The Fig. 8 shows the comparative waveforms of active and reactive power in the load and their references after compensation. The active and reactive power in the load in their references are presented in the Fig. 8, according to this figure the active power has an average value equal to the
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Fig. 7 Spectrum of the output voltage and current by the SVPWM command (a) without harmonic filtering (b) with harmonic filtering
reference power value (Pref = 500w) and the reactive power is almost maintained zero (Qref = 0 kV), for both SPWM and SVPM commands after compensation. Discussion of Result. This study indicates an improvement in the quality of energy with and without compensation devices in the grid for SPWM and SVPWM control. The main contribution of this study is the compensation of the harmonics of the output current in the grid. The analysis of the results obtained from monitoring the currents and voltage of the sinusoidal and vector control system, with or without compensation, are explained in Figs. 6 and 7. The proposed monitoring method can be applied to PV systems connected to the distribution grid with a linear load. Before the PV system connected to the grid with the proposed control method is compensated, the system current contains harmonics and is unbalanced. With filter connected to the system, the harmonics of the system and the unbalanced current are compensated. Figures 6 (b) and 7 (b) provide the results obtained from the simulation after connecting dedicated compensation devices which can reduce the THD in the system is from successively 4.68% to 1.14% for the SPWM command and 4.67% to 0.26% for the SVPWM order. By comparing the waveforms in Figs. 6 (a)–6 (B) and 7
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Fig. 8 Waveforms of active reactive power in the load and their references after compensation (a) SPWM control (b) SVPWM control
(a)–7 (b), the effects of both vector and sinusoidal control without and with filter can be clearly identified. Once the active filter is connected, the current source becomes balanced and sinusoidal. Regarding the power efficiency at the load terminal, the Fig. 8 shows that the active power has an average value equal to the reference power value and the reactive power is almost maintained.
4 Conclusion This study proposes a new harmonic current control and compensation strategy for photovoltaic inverters connected to the grid with a nonlinear load. The proposed control method includes the SPWMSRF control method and the SVPWMSRF method. When nonbalanced, nonlinear loads and generators are connected to the
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grid, the proposed strategy considerably and simultaneously improves the THD of the interface converter connected to the grid. After comparing the results of the two vector and sinusoidal commands, we notice that the current THDi of the current for the vector control (SVPWM) is 0.22% lower than that obtained with the sinusoidal command (SPWM) which is reduced to 0.68% after compensation. So from waveforms of the output current, it is remarkable that with the two control methods proposed, the 3rd, 5th, 7th, 9th, 11th and 13th harmonics in the current are substantially removed. With this method, the harmonic currents of nonlinear loads are completely compensated. The proposed command is responsible for controlling the injection of active and reactive power into the grid; it is also responsible for the compensation of harmonic currents due to the unbalanced load. The results of the simulation presented show that the harmonic currents due to unbalanced and nonlinear loads are compensated for the desired value. After comparing the results of the two vector and sinusoidal control methods, we notice that the performance of the vector control (SVPWM) is better than that obtained with the sinusoidal control (SPWM) and has the advantages of its output voltage is higher than regular SPWM, minimized total harmonic distortion (THD) and its lower switching losses. This strategy can be used for singlephase and threephase systems. The simulation results verify the feasibility and effectiveness of the new control method for a gridconnected converter in a photovoltaic system.
References 1. Lamreoua A, Benslimane A, Messaoudi A, Aziz A, El Ouariachi M (2018) Comparison of the different commands direct and indirect of a singlephase inverter for Photovoltaic. In: ICEERE international conference on electronic engineering and renewable energy, laboratory of electrical engineering and maintenance (LEEM), BP:473 Higher School of Technology, University of Mohammed I, Oujda, Morocco, pp 576–586, April (2018) 2. Lamreoua A, Benslimane A, El Ouariachi M (2018) Modélisation et simulation des commandes directes et indirectes d’onduleurs photovoltaïque triphasé 2 niveaux connecté au réseau. In: colloque international sur les mathematiques appliques et modelisation (CIMAM 2018), Oujda, 07–08 Décembre 2018 3. Lamreoua A, Benslimane A, Hajji B, El Ouariachi M (2019) Study of the performance of different topologies on inverters H4, H5, HERIC and H6 complete bridge for a photovoltaic system without transformer, with PR controllers. In: The first international conference on smart information & communication technologies (SmartICT2019), Saïdia, Morocco, 2628 September 2019 4. Wei J, Bai D, Yang L (2015) Polymer photovoltaic cells with rhenium oxide as anode interlayer. PLoS One 10, e0133725. https://doi.org/10.1371/journal.pone.0133725. Public Library of Science PMID: 26226439 2 5. Xu G, Moulema P, Ge L, Song H,Yu W (2016) A unified framework for secured energy resource management in smart grid. In: Smart grid. CRC Press, pp 73–96 6. Golovanov N, Lazaroiu GC, Roscia M, Zaninelli D (2013) Power quality assessment in small scale renewable energy sources supplying distribution systems. Energies 6:634–645 7. Trinh QN, Lee HH (2014) An enhanced grid current compensator for gridconnected distributed generation under nonlinear loads and grid voltage distortions. IEEE
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8. Timbus A, Liserre M, Teodorescu R, Rodriguez P, Blaabjerg F (2009) Evaluation of current controllers for distributed power generation systems. Power Electron IEEE Trans 24:654–664 9. Farhood Sultani J (2013) Modelling, design and implementation of DQ in singlephase grid connected inverters for photovoltaic systems used in domestic dwellings. Faculty of Technology De Montfort University Leicester, UK 10. Naderipour A, Guerrero JM (2017) An improved synchronous reference frame current control strategy for a photovoltaic gridconnected inverter under unbalanced and nonlinear load conditions. PLOS ONE. https://doi.org/10.1371/journal.pone.0164856 11. Vadlamudi SR (2016) Decoupled DQPLL with positive sequence voltage normalization for wind turbine LVRT control. Sands Expo and Convention Centre, Marina Bay Sands, Singapore, 25–27 October 2016 12. Bengourina MR (2017) Direct power control of a grid connected photovoltaic system, associated with an active power filter. Revue des Energies Renouvelables 20:99–109 13. Purushotham M (2019) Reinforced droop for active current sharing in parallel NPC inverter for islanded AC microgrid application. MDPI energies, 11 August 2019
Processor in the Loop Implementation of State of Charge Estimation Strategies for Electric Vehicle Applications Hicham Ben Sassi, Yahia Mazzi, Fatima Errahimi, and Najia EsSbai
Abstract In light of the recent emergence of Vehicle To Grid (V2G) technology, electric vehicles (EVs) are no longer viewed as just transportation tools. They could rather serve as energy sources available at disposal of the electrical grid for ancillary services provision. As a result, an accurate estimation of their battery state of charge (SOC) is now more crucial than ever. Knowing that the choice of the appropriate SOC estimation strategy must consider the computational aspects of each approach, in this paper we investigate the implementation of two advanced SOC estimation strategies; The Feedforward Neural Network (FFNN) and Adaptive Gain Sliding Mode Observer (AGSMO). To verify the performances of both strategies, Processor In the Loop (PIL) implementations were conducted using an STM32F429ZI discovery board. The obtained experimental results prove that both algorithms perform well in battery SOC estimation. However, due to its slight edge in terms of precision, we recommend the AGSMO over the FFNN for the targeted application Keywords Adaptive sliding mode observer · Feedforward neural network · Processor in the loop · Electric vehicle · State of charge · LithiumIon battery · V2G technology
1 Introduction Striving towards green and sustainable transportation system, most countries are now encouraging the vast adoption of electric vehicles in their territories. This transition will help reduce the contribution of the transportation sector in greenhouse gas emissions. Furthermore, with the emergence of Vehicle To Grid technology (V2G), EVs have gained more attention from both the scientific community and industrial sectors. Within the framework of V2G, the electrical grid could benefit from the H. Ben Sassi (B) · Y. Mazzi · F. Errahimi · N. EsSbai Laboratory of Intelligent Systems, Georesources and Renewable Energies (LISGRE), Faculty of Sciences and Technologies, Sidi Mohammed Ben Abdellah University, Fez Box 2202, Fez, Morocco email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_52
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EV storage unites for voltage regulation, or as spinning reserves. Henceforward, an accurate estimation of the EV’s battery state of charge (SOC) is of great importance. The correct value of SOC enables the battery management system (BMS) to protect the battery from any underdischarging or overcharging, which can lead to an overheat of the battery and thus its destruction. In this regard the proposed SOC estimation strategies can be categorized into three types [1]; Initially, the direct approaches, including coulombcounting [2], Open Circuit Voltage and impedance measurement were proposed. Although these approaches are straightforward and easy to implement, their accuracy depends highly on the physical sensors, as well as the initial value of SOC, which is not always known. As a result, other alternatives were proposed in the form of modelbased state observers, such as Kalman filters [3], and sliding mode observers, these solutions, can selfadjust in case of any system change and overcome the accumulation of measurement errors. Moreover, artificial intelligencebased approaches were also proposed for SOC estimation due to their independence on the battery model, as they are dataoriented strategies [4, 5]. The correct choice of the best SOC estimation strategy for the targeted application depends on several criteria, including realtime capability, accuracy, and implementation simplicity. As a result, in this paper, an adaptive sliding mode observer and Feedforward neural network are chosen due to their high performance, accuracy, and robustness against modeling uncertainties. Both strategies were implemented based on a ProcessorInthe Loop using an STM32F429ZI discovery board. Their performances were compared for an unused battery. Moreover, Thevenin electrical battery model was implemented and its internal parameters were identified using a hybrid nonlinear least square algorithm. The layout of this paper is as follows. In Sect. 2, a battery model is described to characterize the battery dynamics. Section 3, presents a theoretical study of both feedforward neural network and the adaptive sliding mode observer. In Sect. 4, simulation results of the PIL, as well as their interpretation, is presented, followed by a conclusion.
2 Battery Modeling Developing a battery model capable of reproducing the dynamic behaviors of the real battery, has been the subject of several studies in the literature. The proposed models include; artificial intelligence models [6], electrochemical models [7] and equivalent circuit models (ECMs) [8]. Owing to its implementation simplicity and superior performance, the first order Thevenin model is selected for our targeted application. As illustrated in Fig. 1, the model is composed of a nonlinear voltage source Voc (soc) reflecting the nonlinearity between the SOC and the opencircuit voltage, a shunt resistor R0 , and an R p C p branch representing the polarization effects. The internal parameters R0 ,R p C p and Voc (soc), are identified using a hybrid Levenberg Marquardt approach of the nonlinear Least Square algorithms. The obtained values are illustrated in Table 1. Finally, the mathematical representation of the selected
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Fig. 1 Firstorder Thevenin battery model
Table 1 Internal parameters of the battery
Parameters
R0 ()
R p ()
C p (F)
Values
0.24
0.1
920
model which will be used by the AGSMO is developed in Eq. (1). Where Vt and I L , are respectively the voltage and current at the terminals of the battery, while Ucp is the voltage across the R p C p .
U˙ cp =
−Ucp RpCp
+ I L C1p
Vt = Voc (soc) − Ucp + R0 I L
(1)
3 State of Charge Estimation 3.1 Adaptive Sliding Mode Observer Adaptive gain sliding mode observers (AGSMO) are an advanced version of the conventional sliding mode observers (SMO), which were inspired by the theory of sliding mode control (SMC). These modelbased observers rely on computing the appropriate feedback switching gain that can drive the estimation error to zero. For battery SOC estimation, this gain attracts and maintains the inner stats on a predefined region called sliding patch. While in this surface, it is possible for the dynamical observer system to exhibit sliding behavior. Once on the sliding patch, the observer produces inner stats estimates that are precisely commensurate with the actual output voltage of the reel battery. Unlike the conventional SMO, where the switching gain is a constant, in the proposed AGSMO, this gain can selfadjust in case of any unpredicted changes in operation conditions. As a result, the AGSMO is superior in terms of robustness against modeling errors, and uncertainties related to unknown initial SOC [9]. In the following the design process of the AGSMO is presented, where the state space model developed for this part is given by the Eqs. (2, 3 and 4): V˙t = −a1 Vt + a1 Voc (S OC) − b1 I L + f 1
(2)
˙ = a2 Vt − a2 Voc (S OC) + a2 V p + f 2 S OC
(3)
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V˙ p = −a1 V p + b2 I L + f 3
(4)
Where, a1 = C 1.R , a2 = C 1.R , b1 = C pR.Ri p + Ckn + C1p , b2 = C1p and f 1 , ( p p) ( p 0) f 2 , f 3 are modeling uncertainties. Cn is the nominal battery capacitor. In order to compute the derivative of Voc (S OC), we assume a piecewise linear relationship between the Voc and SOC in every 10% variation range. Accordingly, Voc (S OC) can be expressed as follows. Voc (S OC) = k.S OC + m
(5)
The values of k and m that corresponds for each 10% SOC variation interval are then determined. As a result, the time derivative of the open circuit voltage Voc (S OC) in that range can be expressed as follows.
Voc (S OC) = −k
IL Cn
where
t
S OC(t) = S OC(0) − 0
I L (τ ) dτ Cn
(6)
In the conventional SMO theory, the observer that can estimate the unknow state ˙ V˙ p , can be expressed by: vector X˙ = V˙t ; S OC; (7) (8) (9) The observer for the battery’s output voltage Vt in Eq. (2)is expressed in Eq. (7). However, inorder to reduce the chattering effect, the sgn eVt function is substituted by eVt /(eVt + λ) [9], where λ is a small positive scalar. The new observer for V˙t is then as follows: (10)
Where the adaptive switching gain is defined as: α is another small positive scalar that defines the adjusting speed of By subtracting Eq. (10) from Eq. (2), the dynamic of the error eVt is:
.
(11)
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According to the
Considering the Lyapunov function:
Lyapunov stability theory, for stable estimations, V˙1 must be negative, thus:
(12) Where V˙1 becomes:
. For eVt (t) > 2λ, the equation of
and Where
is initially set as a positive, as a
result V˙1 < 0. For small values of λ, and once on the sliding surface eVt = e˙Vt = 0. Thus, in a finite time we assume f 1 in (11) is rejected. Henceforward, The AGSMO is then obtained by following the same design process for the other unknown stats in Eqs. (3) and (4) [10]. With eVoc = Voc (S OC) − Voc S OC = ke S OC , and eV p = V p − Vˆ p are the error
represents their adaptive observer gains which dynamics of both states, while follows the same adaptive law as the batteries output voltage gain .
3.2 Artificial Neural Network Inspired by the complex structure of the biological neurons in the human brain, artificial neural networks (ANN) are considered the future of information processing. ANNs are widely known for their capability to approximate any nonlinear system if sufficient amount of data is available. As a result, a feedforward neural network for SOC estimation is selected in this paper to be implemented. The choice of FFNN over other ANN structures is due to its satisfying SOC estimation performances, as well as its implementation simplicity. Following the design process already presented in [3], the resulted FFNN structure is a combination of 4 layers: three neurons forming the input layer, connected to 24 hidden neurons forming two hidden layers with 11 hidden neurons in each, and finally the estimated SOC as the output layer. Based on the measured battery voltage, current and temperature the FFNN is trained offline using several functioning scenarios of the EV. Each hidden neuron is activated using the Leaky rectified linear unit (Leaky ReLu), while a sigmoid activation function is selected for the output layer
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4 Simulation Results and Discussion The performances as well as the implementation aspects of both the AGSMO and the FFNN were verified using PIL simulation tests during a chargedischarge cycle. Within the framework of PIL implementation, the battery model in Fig. 1 runs on the host computer. While the generated C code of both algorithms is executed and implemented on ARM CortexM4 microcontroller of the STM32F429ZI discovery board. The resulted estimations are then communicated back to the host computer for display and analysis. These results are presented in Fig. 2, and 3. The obtained results presented above, show that both strategies perform well, in SOC estimation. However, the AGSMO is superior in terms of accuracy with a max error of 12%, at the beginning of estimations, and an average error of 1%. While the FFNN, presents an average error of 6% and a maximum drift of 20%, due to the insufficient training data. The peaks displayed in the FFNN response are normal due to the instantaneous current transitions. From convergence stand point the FFNN is better than the AGSMO with an instantaneous convergence, compared to the AGSMO which is relatively slow. Fig. 2 SOC estimation curves
Fig. 3 FFNN and AGSMO SOC estimation error curves
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5 Conclusion This paper investigated a processor in the loop implementation of a FFNN and AGSMO for SOC estimation. The selected embedded platform in this paper was an STM32F429ZI discovery board. Based on the obtained results, and the available data, the authors recommend the AGSMO over the FFNN for the targeted application. This recommendation is supported by the superior accuracy of the AGSMO over the FFNN, and its independence on the training data.
References 1. Piller S, Perrin M, Jossen A (2001) Methods for stateofcharge determination and their applications. J Power Sources 96:113–120 2. Alzieu J, Smimite H, Glaize C (1997) Improvement of intelligent battery controller stateofcharge indicator and associated functions. J Power Source 67:157–161 3. Sassi HB, Errahimi F, Essbai N, Alaoui C (2019) Comparative study of ANN/KF for onboard SOC estimation for vehicular applications. J. Energy Storage 25:100822 4. Piao CH, Fu WL, Wang J, Huang ZY, Cho CD (2009) Estimation of the state of charge of NiMH battery pack based on artificial neural network. In: Intelec 2009–31st international telecommunications energy conference. IEEE, New York, pp 785–788 5. Hu X, Sun, F (2009) Fuzzy clusteringbased multimodel support vector regression state of charge estimator for lithiumion battery of electric vehicle. In: International Conference on Intelligent HumanMachine Systems and Cybernetics. IEEE, pp 392–396 6. Mohammad C, Mohammad F (2010) Stateofcharge estimation for lithiumion batteries using neural networks and EKF. IEEE Trans Ind Electron 57:4178–4187 7. Tang S.X, Wang Y, Sahinoglu Z, Wada T, Hara, S, Krstic, M (2015) Stateofcharge estimation for lithiumion batteries via a coupled thermal–electrochemical model. In: American control conference, pp 5871–5877 8. Chiasson J, Vairamohan B (2005) Estimating the state of charge of a battery. IEEE Trans Control Syst Technol 13(3):465–470 9. Kim IS (2008) Nonlinear state of charge estimator for hybrid electric vehicle battery. IEEE Trans Power Electron 23(4):2027–2034 10. Sassi HB, Errahimi, F, EsSbai N, Alaoui, C (2018) A comparative study of Kalman filtering based observer and sliding mode observer for state of charge estimation. In: IOP conference series: materials science and engineering, vol. 353, p 012012
Adaptive Intelligent Control of the ABS Nonlinear Systems Using RBF Neural Network Based on KMeans Clustering Hamou Ait Abbas, Abdelhamid Rabhi, and Mohammed Belkheiri
Abstract The antilock braking system (ABS) is an active safety system in road vehicles, which senses the slip value between the tyre and the road and utilizes these values to define the optimum braking force. Conventional control methods will not meet requirements due to uncertainties coming from vehicle dynamics and the high nonlinearity of the tyre and road interaction that are sources of instability. Therefore, we design an adaptive output feedback control methodology augmented via radial basis function neural network in order to force the slip dynamics to track a given smooth reference trajectory with bounded errors in the presence of high uncertainty. This result is achieved by extending the universal function approximation property of RBF NN together with the fast convergence of Kaverage clustering algorithm to model unknown system dynamics from input/output data. The effectiveness of the proposed control algorithm has been successfully verified through simulation results. Keywords Uncertain nonlinear systems · Antilock braking system · Adaptive output feedback control · Tracking error dynamics · Radial basis function neural network · Kmeans clustering algorithm
H. Ait Abbas (B) Laboratoire des Matériaux et du Développement Durable, University of Akli Mohand Oulhadj, 10000 Bouira, Algeria email: [email protected] A. Rabhi Laboratoire de Modélisation Information et Systémes, Université de Picardie Jules Verne, 33 rue Saint Leu, 80000 Amiens, France M. Belkheiri Laboratoire de Télécommunications, Signaux et Systémes, Université Amar Telidji, BP G37, Route de Ghardaia, 03000 Laghouat, Algeria © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_53
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1 Introduction Antilock braking system (ABS) is the most successful electromechanical product that aids the driver to keep directional control of the vehicle by preventing the wheels from locking during panic braking [8]. Today, a variety of nonlinear control methods which are significant for improving the performance of ABS is reported in the literature, namely, backstepping control strategy [7], sliding mode control [4], and feedback linearization control methods [2, 8] among others. Therefore, a common objective for these research efforts was the development of effective design schemes for controlling the nonlinear ABS system in order to have its wheel slip track given reference signal. Unfortunately, the mathematical model of ABS is partially known due to its nonlinearities which are reflected in nonlinear characteristics of braking dynamics and uncertain parameters of vehicle environment [2]. Nowadays, considerable research efforts have been focused on the use of computational intelligent controllers such as artificial neural networks, fuzzy logic, and evolutionary algorithms which are incorporated into control systems design in order to deal with high uncertainly [1, 3, 5]. Especially, the ability of the radial basis function neural network (RBF NN) to adapt well and fast even when the mathematical model is not accurate enough is a good reason for employing this intelligent technique [5]. Motivated by the above discussion and by the results of papers [1, 3], we propose to combine RBF NN based on Kmeans clustering algorithm that shows powerful potentials in approximating high uncertainty, with the output feedback linearization methodology in order to formulate a new control strategy in the context of Kmeans clustering NN algorithmbased adaptive output feedback control, that will be applied for the partially known ABS to eliminate the effect of modelling errors and unknown parameters. The proposed controller is tested, and satisfactory performances are achieved.
2 ABS System Modeling and Control Problem Statement As shown in Fig. 1, the simplified ABS system is schematized. The equations of motion of the system can be expressed
J1 x˙1 = F1 − M1 J2 x˙2 = F2
(1)
where F1 = Fn r1 μ(λ) − d1 x1 − M10 , F2 = −Fn r2 μ(λ) − d2 x2 − M20 . The variable x1 = ω1 denote the angular velocity of the upper wheel with radius r1 , and moment of inertia J1 , x2 = w2 represents the angular velocity of the lower wheel with radius r2 and moment of inertia J2 , λ is the slip which is the relative difference of the wheel
Adaptive Intelligent Control of the ABS Nonlinear Systems ...
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Fig. 1 Schematic diagram of ABS
velocities, M1 , represents the brake torque, it is the input signal of the model, μ denote the friction coefficient between the upper and lower wheels, Fn denote the normal force  the upper wheel acting on the lower wheel. The main objective is to force the slip λ to track a given set point λr e f with bounded error. Unfortunately, [1] confirm that a large range of physical systems and devices in practical engineering possess nonlinear and uncertain characteristics. Therefore, modelling errors, unmodelled dynamics and uncertain parameter variations should be explicitly considered in the control design to enhance robust control performance. For these reasons, the model (2) becomes
J1 x˙1 = F1 + δ F1 − M1 J2 x˙2 = F2 + δ F2
(2)
where δ F1 and δ F2 are perturbation terms. Assuming that there is a derived model for friction coefficient based on the following model: w4 λ p (3) + w3 λ3 + w2 λ2 + ω1 λ μ(λ) = a + λp in which wi are model parameters. The driving system of the brake is governed by the following equation: M˙ 1 = c31 (b1 u + b2 − M1 ). The dynamics of the driving system are very fast compared to those of the mechanical system, in the rest of this chapter we will consider it as a control gain, so we can write: M1 = K u u. For ABS laboratory setup the slip is defined as: λ=1−
r1 x 1 r2 x 2
(4)
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The slip dynamics can be expressed as: λ˙ = −
r1 x1 x˙1 − x˙2 r2 x 2 x2
(5)
Multiplying both sides of Eq. (5) by x2 x2 λ˙ = − rr21 x˙1 − xx21 x˙2 . From (4), we use the fact that rr21 xx12 = (1 − λ), then we get : x2 λ˙ = − rr21 x˙1 + (1 − λ)x˙2 t Defining the integrated slip as z 1 = h=0 (λ)dh, and the slip as z 2 = λ. Then, the slip dynamics takes the following form: ⎧ ⎨ z˙ 1 = z 2 ⎩ z˙ 2 = −
r1 (1 − λ) x˙1 + x˙2 x 2 r2 x2
(6)
Substituting for x˙1 , x˙2 and M1 in Eq. (6), then, we obtain
z˙ 1 = z 2 z˙ 2 = F + δ F + Gu
(7)
where F =−
r1 (1 − λ) r1 (1 − λ) r1 K u F1 + F2 ; δ F = − δ F1 + δ F2 ; G = J1 x2 r2 x2 J2 J1 x2 r2 x2 J2 J1 x2 r2
We aim to design a feedback controller in which the input signal u is selected to address the tracking problem ((λ → λr e f ) in the presence of uncertainties δ.
2.1 Controller Design and Tracking Error Dynamics 2.2 ABS Feedback Linearization Control The differential equation for z 2 still contain quite complicated nonlinearities in (7). To simplify this dynamics, we use nonlinear state feedback control u=
1 (−F + u) G
(8)
Then, the system dynamics (7) simplify to
z˙ 1 = z 2 z˙ 2 = u + δ F
(9)
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Note that in applying the feedback (8), there is some uncertainty in the knowledge of the ABS parameters and the state variables. For that, assuming that all neglected terms for the ABS system as an error signal δ. Therfore, the system dynamics defined by (10) will be expressed as y¨ = u + δ (10) where : δ(ξ, u) = h(z 1 , h −1 (z 1 , u)) − h(z 1 , h −1 (z 1 , u)) is the inversion error, and the h −1 (z 1 , u)) represents the best available approximation of function h(z 1 , h −1 (z 1 , u)). Let ξ = [z 1 z˙ 1 z¨ 1 ] for ease of notation. h(z 1 , Therefore, we change the control strategy by adding an adaptive NN component A N N in the expression of the control law u in order to deal with uncertainties δ. Consequently, the pseudocontrol u are chosen to have the form u = y¨r e f + L cD − AcN N
(11)
where y¨r e f is the 2nd derivative of the input signal yr e f , generated by a stable command filter, L cD is the output of a dynamic compensator, AcN N is the adaptive control signal designed to handle δ. With (11), the dynamics in (9) reduce to y¨ = y¨r e f + L cD − AcN N + δ
(12)
2.3 Dynamic Compensator (DComp) Design Defining the output tracking error as (e), and the dynamics in (12) becomes e¨ = −L cD + AcN N − δ, e = yr e f − y.
(13)
Notice that the adaptive neural network component AcN N will not be required when (δ = 0). Therefore, the error dynamics in (13) reduce to e¨ = −L cD . The following DComp is introduced to stabilize the ABS dynamics.
ψ˙ = G 1 ψ + G 2 e, L cD = G 3 ψ + G 4 e.
(14)
2.4 Tracking Error Dynamics Returning to (13), notice that the compensator state ψ mutually with the vector ˙ T will obey the following dynamics er = [e e]
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E˙ = A T E + bT [AcN N − δ] τ = CT E
(15)
referred to as tracking error dynamics, where τ is the vector of available measurements. Note that: A T = [A − G 4 bc − bG 1 ; G 2 c G 1 ]bT = [b; 0], cT = [c 0; 0 I ], and a new vector E d = [erT ψ T ]. We should design G 1 , G 2 , G 3 and G 4 in (14) such that A T is Hurwitz, where A = [0 1; 0 0]; b = [0; 1]; c = [1 0].
3 Adaptive Neural Network Control The model inversion error δ(ξ, u) can be approximated over D by a RBF NN [6] δ(ξ, u) = Ψ T φ(Θ) + (d, Θ),  < ∗ .
(16)
T
using the input vector : Θ(t) = [u (t) y T (t)]T ∈ D, Θ ≤ Θ ∗ , Θ ∗ > 0, T where: u (t) = [u(t) u(t − d) ... u(t − (n 1 − r − 1)d)]T , y T (t) = [y(t) y(t − d) ... y(t − (n 1 − 1)d)]T . with n 1 ≥ n, d > 0 denoting timedelay and Θ ∗ being a uniform bound for all (ξ, u) ∈ D. The adaptive signal is designed as follow T φ(Θ). AcN N = Ψ
(17)
is the estimate of Ψ that is updated according to the following adaptation where Ψ law: ˙ = −β [2φ(Θ)E T P2 bT + χΦ (Ψ − Ψ0 )]. (18) Ψ Φ in which Ψ0 is the initial value of of the NN weights, βΦ and χΦ are positive adaptation gains, P2 is the solution of the Lyapunov equation : A TT P2 + P2 A T = −Q 2 , for some is an implementable input vector to the NN on the compact set ΩΘ , Q 2 > 0, and Θ = [u dT (t) y dT (t)]T ∈ ΩΘ , y i = E i + yr(i−1) defined as Θ e f , i = 1, ..., r − 1.
4 RBF NN Based on KMeans Clustering Algorithm 4.1 Basis Function of Hidden Layer of RBF In the present paper, let the input vector be mdimensional, the output vector be ldimensional, and the number of hidden neurons be h, we will use the Gaussian basis functions [5]
Adaptive Intelligent Control of the ABS Nonlinear Systems ... p
φ j = f j (x p − C j ) = exp( p
p
509
x p − C j 2 ) 2b2j
p
(19) p
in which x p = [x1 , x2 , ..., xm ]T is the input vector of the p sample of NN, φ j in the output under the effect of input sample p of hidden layer of J neuron, the center vector of hidden layer node j of the networks is C j = [c j1 , c j2 , ..., c jm ]T , j = 1, 2, ..., h,   is norm 2 and denote the Euclidian distance, b j is base width parameter of hidden layer node j.
4.2 The Learning Algorithm Based on Clustering Method The learning procedure of this method is mainly described into two steps: first, the central vector and the base width parameter vector of hidden layer nodes of RBF NN are determined using an unsupervised learning method [5].
4.2.1
Unsupervised Learning Stage
The key idea of the KMC method is to divide input vector of intent training samples into many clusters and find out the central vector of RBF in each cluster and minimize the difference from all sample vectors to the central vector in each cluster [3]. Let i be the number of iterations, C1 (i), C2 (i), ..., Ck (i) be the cluster center of the n’s iterations. Then, we respect the following stages to define the central vector and the base width parameter vector of hidden layer’s of RBF NN via KMC algorithm: (1) Select k vectors randomly from the training set Samples of input vectors as the initial cluster (cluster) center; (2) Calculate the dissimilarity from all samples to the centers of kclusters (Euclidean distance); (3) Classify respectively sample vectors to the cluster of the lowest dissimilarity, if j (x p ) = min x p − C j (i), j
(20)
where j = 1, 2, ..., k, x p is classified to kcluster, x p ∈ w j (k). (4) Recalculate the center of each k cluster according to the clustering results. Notice that the calculation method is to take the arithmetic mean of the respective dimensions of all elements in the cluster; 1
x, (21) C j (i + 1) = N j x∈w (i) j
where j = 1, 2, ..., k in the above formula, N j is the number of contained samples in jcluster w j (k). (5) If C j (i + 1) = c j (i) cluster is end and turn to (21). (6) Define the base width of each hidden node parameters according to the distance between k clusters centers.
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b j = d j = min Ci − C j 
(22)
i= j
in which is overlap coefficient and it is a key factor that affects the quality of the RBF NN and it can be determined through learning and training.
4.2.2
Supervised Learning Stage
Now, we use supervised learning methods to obtain the weights and thresholds of output layer [3, 5].
5 Application
0.4
0.2
0.35
0.15
0.3
0.1
Control effort
Tracking performance
To make clear the performance of the proposed adaptive controller in the presence of uncertainties, we consider the Antilock Braking System example (6). The following DComp: ψ˙ = −150ψ + e; L cD = −1000100ψ + 7501e, places the poles of the closedloop error dynamics of the nonlinear systems at −50, −50 ± j. The adaptation gains were set to βΦ = 2.1, with χΦ = 0.09. The main feature of this paper is to design an adaptive output feedback control component using a RBF NNbased on KMC algorithm in order to compensate adaptively for the nonlinearities that exist in the nonlinear ABS model. First, we clearly demonstrating the almost unstable oscillatory behavior caused by the nonlinear elements (δ) for the ABS model in Fig. 2 that compares the system measurement y without NN augmentation (blue points) with the reference model output yr e f (solid line). While, the tracking of the slip λ (dashed red line) to its reference λr e f is very well in Fig. 2 after NN augmentation, what means that the effects of these nonlinearities are successfully cancelled. This is due essentially
0.25 0.2 0.15 0.1
y with RBF NN based KMC yref
0.05 0 0
y without RBF NN
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0.05 0 0.05 0.1 Without RBF NN With RBF NN based KMC
0.15 0.2
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Time [sec]
Fig. 2 Controller tracking performance & Control effort: without and with RBF NN based on KMC algorithm
Adaptive Intelligent Control of the ABS Nonlinear Systems ... 80
80 SpeedW without NN
60 50 40 30 20
SpeedWheel with NN based KMC
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SpeedV without NN
Speed W & Speed V
Speed W & Speed V
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SpeedVehicule with NN based KMC
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Fig. 3 Speed Wheel & Speed Vehicle (without and with NN)
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Fig. 4 a Identification of (δ) by NN b NN weight history
to the excellent identification of uncertainties (δ) (dashed line) by the adaptive NN component (AcN N ) (solid line), see Fig. (4a). The NN based adaptive output feedback controller (AcN N ) exhibits a steady state tracking error in Fig. 2 that compares the control efforts (yr e f − y) without and with adaptation. The weights history of the controller NN are shown in Fig. (4b).
6 Conclusion In this paper, we have clearly detailed an adaptive output feedback control design procedure for uncertain nonlinear SISO systems. Under the assumption that the system is feedback linearizable, a NN augmentation based on Kmeans clustering algorithm is introduced to eliminate terms of uncertainty. Computer simulations of the Antilock Braking System validate the theoretical results and demonstrate the practical potential of the proposed approach.
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References 1. Ait Abbas H, Belkheiri M, Zegnini B (2016) Feedback linearisation control of an induction machine augmented by Single Hidden LayerNeural Networks. Int. J Control 89(1):140–155. https://doi.org/10.1080/00207179.2015.1063162 2. Antic D, Nikolic V, Mitic D (2010) Sliding mode control of antilock braking system: an overview. FACTA UNIVERSITATIS Series Autom. Control Robot. 9(1):41–58 3. Ding S, Wu Q, Yang Y (2011) Research on the application of RBF neural network based on Kmeans clustering in system identification. In: Fourth International Workshop on Advanced Computational Intelligence (IWACI). IEEE, pp 110112, October 2011 4. Harifi A, Aghagolzadeh A, Alizadeh G, Sadeghi M (2008) Designing a sliding mode controller for slip control of antilock brake systems. Transp Res Part C Emerg Technol 16(6):731–741 5. Haykin S (2008) Neural networks and learning machines, vol 3. Pearson Education, Upper Saddle River. ISBN13: 9780131471399 6. Hovakimyan N, Calise AJ, Kim N (2004) Adaptive output feedback control of a class of multiinput multioutput systems using neural networks. Int J Control 77(15):1318–1329 7. Lin JS, Ting WE (2007) Nonlinear control design of antilock braking systems with assistance of active suspension. IET Control Theory Appl 1(1):343–348 8. Wei Z, Xuexun G (2015) An ABS control strategy for commercial vehicle. IEEE/ASME Trans Mechatron 20(1):384–392
The Best Place of STATCOM in IEEE 14 Bus System to Improve Voltage Profile Using Neplan Software Ismail Moufid, Hassane El Markhi, Hassan El Moussaoui, and Lamhamdi Tijani
Abstract In this paper, the static synchronous compensator (STATCOM) is used to improve the voltage of the IEEE 14 Bus power system network. We focus on the voltage level of the most majoring issues in the IEEE14bus system with constant loads. Firstly, we have analyzed the IEEE14 bus system under the standard test data, then we analyzed it with static synchronous compensator under the standard test data by changing its location overall buses. NEPLAN software was used to simulate the studied system. We have compared all the obtained results with the original power flow of the IEEE14 bus system in order to choose the optimal place of STATCOM to improve the voltage profile of all buses. Keywords Static synchronous compensator (STATCOM) · Improving a voltage level · IEEE14 bus system · NEPLAN software
1 Introduction Voltage ratings of the different buses in the electrical network, which includes generation buses, load bus, and bus that include both of them, must be permissible limits for satisfying operation of all equipment. The purpose of voltage control is exactly associated with fluctuating load conditions and corresponding requirements of reactive power compensation [1]. A static synchronous compensator (STATCOM) is a voltamperereactive (VAR)/voltage regulation equipment that is used in both electric transmission and distribution systems. The STATCOM utilizes voltage or currentsource, converters as well as its ability of being a source of reactive and real power in the energy system [2]. The application of flexible AC transmission system (FACTS) devices in a power system can probably overcome limitations of the present mechanically controlled medium voltages systems [3]. Through facilitating bulk energy transfers, these interconnected networks help reduce the need to increase power plants and I. Moufid (B) · H. El Markhi · H. El Moussaoui · L. Tijani Intelligent Systems, GeoResources and Renewable Energies Laboratory (ISGREL), FST, Sidi Mohamed Ben Abdelah University, Fez, Morocco email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_54
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facilitate neighboring utilities and regions to transfer power [4]. The stature of FACTS devices inside the bulk power system will continually increase as the industry moves via a more competitive posture in which power is bought and sold as a commodity [5]. As power wheeling becomes frequently prevalent, power electronic devices will be used more frequently to ensure system safety, stability and improving maximum power transmission along different transmission corridors. The static synchronous compensator, or STATCOM, is a shunt connected FACTS device [6]. It creates a balanced set of threephase sinusoidal voltages at the fundamental frequency, with a fast controllable amplitude and phase angle. This kind of controller can be realized using various topologies. However, the voltagesourced inverter, using Gate TurnOff (GTO) thyristors inappropriate multiphase circuit configurations, is directly considered the most practical for high power utility applications [7]. A common application of this type of controller is voltage regulating.
2 Static Synchronous Compensator (STATCOM) Static Synchronous Condenser or Compensator (STATCOM) operate as a shunt connected static VAR compensator whose capacitive or inductive output current can be controlled independently of the ac system voltage. STATCOM controls only one of these parameters i.e. voltage, phase angle, and impedance that determine the power flow in the AC power system. Moreover, it regulates the voltage at its terminal by controlling the quantity of reactive power injected or absorbed from the power system. STATCOM is the first generation of FACTS devices and it reacts as a reactive power compensator [8]. It control reactive power to bus voltage regulation. It continuously compensates the reactive power, so that Power factor and power quality both rise. When the voltage system is reduced under the limit, the STATCOM generates reactive power (capacitive). When system voltage is over the limit, it absorbs reactive power (inductive). The variation of reactive power is controlled by switching threephase capacitor banks and inductor banks connected on the secondary side of a coupling transformer. However, it is unable to exchange active power with the system [9].
3 Simulation of the IEEE 14 Bus System To investigate the performance of our proposed system the simulation technique is applied to the IEEE 14 Bus System using NEPAN software as shown in Fig. 1. The voltage profile of the buses is given from the load flow simulation as presented in Table 1 and Fig. 1. The results show that out of the fourteen buses for were over the limit (1, 6, 8 & 12), assumed to be around ±5%. Hence the need to implement STATCOM in the system to reduce the number of buses that overcome the limit.
The Best Place of STATCOM in IEEE 14 Bus System …
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Fig. 1 The IEEE 14 bus system simulation using NEPLAN software without STATCOM Table 1 Voltage profile for IEEE14 bus system without STATCOM
Node Name
U(kV)
U (%)
BUS_1 69.0
73,14
106
BUS_10 13.8
14,226
103,09
BUS_11 13.8
14,444
104,66
BUS_12 13.8
14,536
105,33
BUS_13 13.8
14,446
104,68
BUS_14 13.8
14,076
102
BUS_2 69.0
72,105
104,5
BUS_3 69.0
69,69
101
BUS_4 69.0
69,803
101,16
BUS_5 69.0
70,09
101,58
BUS_6 13.8
14,766
107
BUS_7 13.8
14,46
104,78
BUS_8 18.0
19,561
108,67
BUS_9 13.8
14,238
103,17
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4 Simulation Model and Results We used the NEPLAN software to simulate the IEEE14 bus system illustrated in Fig. 1 we placed the STATCOM in different locations to obtain the optimum voltage profile when moving the STATCOM location around the buses. 1 Firstly, we placed the STATCOM in the bus bar 14 as shown in Fig. 2. In this case, the bus voltage profile is presented from the simulation of the load flow as shown in Table 2. It was noticed that only tree bus bars (1, 6 & 8) are out of limit. 2 In this second part, STATCOM is connected to bus bar 7 as shown in Fig. 3 and the bus voltage profile is presented from the simulated load flow in Table 3. This position of the STATCOM provide a high improvement of the voltage profile since only the bus 1 “slack bus” who present the over limit value. 3 In this third section, STATCOM was dispatched to bus bar 11 as shown in Fig. 4. In this case, the voltage profile of the buses is presented from the load flow simulation as shown in Table 4. The voltage values of bus bars (1, 6, 12, and 8) are out of the limit. 4 In this section, as shown in Fig. 5 the STATCOM was connected to bus bar 10. In this case, and from the voltage profile of the buses presented in Table 5 we observe that we have return back to the case “1” with tree bus bars (1, 6 & 8) out of the limit.
Fig. 2 The IEEE 14 bus system simulation with STATCOM connected to bus 14
The Best Place of STATCOM in IEEE 14 Bus System … Table 2 Voltage profile for IEEE14 bus system with Statcom connected to bus 14
517
Node Name
U(kV)
U (%)
BUS_1 69.0
73,14
106
BUS_10 13.8
14,116
102,29
BUS_11 13.8
14,343
103,93
BUS_12 13.8
14,429
104,56
BUS_13 13.8
14,306
103,66
BUS_14 13.8
13,8
100
BUS_2 69.0
71,942
104,26
BUS_3 69.0
69,69
101
BUS_4 69.0
69,406
100,59
BUS_5 69.0
69,7
101,01
BUS_6 13.8
14,676
106,35
BUS_7 13.8
14,354
104,01
BUS_8 18.0
19,428
107,93
BUS_9 13.8
14,124
102,35
Fig. 3 The IEEE 14 bus system simulation with STATCOM connected to bus 7
518 Table 3 Voltage profile for IEEE14 bus system with Statcom connected to bus 7
I. Moufid et al. Node Name
U(kV)
U (%)
BUS_1 69.0
73,14
106
BUS_10 13.8
13,728
99,48
BUS_11 13.8
14,023
101,62
BUS_12 13.8
14,189
102,82
BUS_13 13.8
14,078
102,02
BUS_14 13.8
13,61
98,63
BUS_2 69.0
71,582
103,74
BUS_3 69.0
69,69
101
BUS_4 69.0
68,401
99,13
BUS_5 69.0
68,874
99,82
BUS_6 13.8
14,436
104,61
BUS_7 13.8
13,877
100,56
BUS_8 18.0
18,825
104,58
BUS_9 13.8
13,712
99,36
Fig. 4 The IEEE 14 bus system simulation with STATCOM connected to bus 11
The Best Place of STATCOM in IEEE 14 Bus System … Table 4 Voltage profile for IEEE14 bus system with Statcom connected to bus 11
519
Node Name
U(kV)
U(%)
BUS_1 69.0
73,14
106
BUS_10 13.8
14,175
102,72
BUS_11 13.8
14,361
104,07
BUS_12 13.8
14,504
105,1
BUS_13 13.8
14,413
104,44
BUS_14 13.8
14,04
101,74
BUS_2 69.0
72,073
104,45
BUS_3 69.0
69,69
101
BUS_4 69.0
69,695
101,01
BUS_5 69.0
69,981
101,42
BUS_6 13.8
14,735
106,77
BUS_7 13.8
14,427
104,54
BUS_8 18.0
19,519
108,44
BUS_9 13.8
14,2
102,9
Fig. 5 The IEEE 14 bus system simulation with STATCOM connected to bus 10
520 Table 5 Voltage profile for IEEE14 bus system with s connected to bus 10
I. Moufid et al. Node Name
U(kV)
U (%)
BUS_1 69.0
73,14
106
BUS_10 13.8
13,917
100,85
BUS_11 13.8
14,209
102,97
BUS_12 13.8
14,378
104,19
BUS_13 13.8
14,281
103,48
BUS_14 13.8
13,871
100,51
BUS_2 69.0
71,941
104,26
BUS_3 69.0
69,69
101
BUS_4 69.0
69,331
100,48
BUS_5 69.0
69,653
100,95
BUS_6 13.8
14,616
105,91
BUS_7 13.8
14,275
103,44
BUS_8 18.0
19,329
107,38
BUS_9 13.8
14,008
101,51
5 Conclusion In this paper, we clarified the usefulness of STATCOM which is it is a set of electrical devices for providing fastacting reactive power on high voltage electric transmission networks. We concentrated on its impact of integration at different positions into the IEEE14 bus power system. NEPLAN software was used to simulate the system. The results show the effectiveness of STATCOM to improve the voltage profile of the IEEE14bus when it connected to bus bars number 7. We conclude that the optimum position to connect STATCOM to IEEE14bus is at the bus bars number 7.
References 1. Khonde SS, Dhamse S, Thosar AG (2014) Power quality enhancement of standard IEEE 14 bus system using unified power flow controller. Int J Eng Sci Innovative Technol 3(5) 2. Canizares CA, Faur ZT (1999) Analysis of SVC and TCSC controllers in voltage collapse. IEEE Trans Power Syst 14(1):158–165 3. Farias JVM, Cupertino AF, Ferreira VDN, Pereira HA, Junior SIS, Junior Teodorescu R (2019) Reliabilityoriented design of modular multilevel converters for mediumvoltage STATCOM. IEEE Trans Industr Electron 67:6206–6214 4. Zatsepina V, Zatsepin, E, Shachnev OY (2019) Improving eefficiency of highpower plants through modernization STATCOM devices. In: 1st International conference on control systems, mathematical modelling, automation and energy efficiency (SUMMA), pp 673–678 5. Singh B, Saha R, Chandra A, AlHaddad K (2009) Static synchronous compensators (STATCOM): a review. IET Power Electron 2(4):297–324 6. Edwards C, Mattern K, Stacey E, Nannery P, Gubernick J (1988) Advanced state VAr generator employing GTO thyristors. IEEE Trans Power Delivery 3(4):1622–1627
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7. Menzies R, Zhuang Y (1995) Advanced static compensation using a multilevel GTO thyristor inverter. IEEE Trans Power Delivery 10(2):732–738 8. Kumar L, Dixit, A (2019) A review to improvement power quality to grid connected wind energy system by STATCOM 9. Mohanty AK, Barik AK (2011) Power system stability improvement using FACTS devices. Int J Modern Eng Res (IJMER) 1(2):666–672
Optimization of Electromagnetic Interference Conducted in a Devolver Chopper Zakaria M’barki and Kaoutar Senhaji Rhazi
Abstract This work presents an EMC modeling in conducted mode of a serial chopper designed for a photovoltaic application. Indeed, the frequency rise in semiconductor materials and the very short switching time of the switches promote electromagnetic interference and coupling with neighbouring environments. In order to overcome its drawbacks, effective methods have been adopted to reduce electromagnetic noise levels, such as Random Pulse Width Modulation (RPWM), which allows the electromagnetic spectrum to be spread over a wide range of frequencies, and the use of soft switching. Keyword Conducted EMC · Power electronics · Random PWM · SoftSwitching
1 Introduction In the last decade, photovoltaic energy has become more and more widespread. Therefore the use of conversion systems such as (chopper, inverter…) has become a necessity in order to adapt to different technologies and environments. However, these power converters based on semiconductors, which are ubiquitous in various fields, operate in a polluting manner due to the fast switching speed of their switches. The current and voltage differentials ( ddtV and ddtI ) generated induce disturbance emissions that propagate by conduction and radiation [1] in [10 kHz, 1 GHz]. Another factor is the high switching frequency, which leads to increased electromagnetic pollution (Conducted losses+HF spectrum transfer). Hence, the major interest devoted to the detailed study of these converters and their design in order to meet EMC (electromagnetic compatibility) standards. In this work, we will focus on the study of a DCDC conversion system used in photovoltaic applications, namely the Devolver Chopper [2]. As it is a polluting source of its electromagnetic environment, we will cite the various methods [3–5] Z. M’barki (B) · K. Senhaji Rhazi RITM Laboratory, Superior School of Technology, Casablanca, Morocco email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_55
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aimed at optimizing and reducing its electromagnetic emissions, especially those carried out to comply with the prescribed EMC rules.
2 Electromagnetic Disturbances Conducted in a Chopper Devolver The static converter (serial dutyratio chopper α) whose study will be carried out is interposed between the load Rch and the photovoltaic generator represented by a DC voltage source VDC (Fig. 1). For this application, we have chosen a chopper model consisting of a freewheeling diode and a MOSFET transistor (fast: operating at very high frequencies and used at powers of the order of 1KW). The MOSFET transistor is controlled by a logic signal Vcmd of fixed frequency (20 kHz) and dutyratio α = 0.5 which is generated by the principle of pulsewidth modulation “PWM” (Fig. 2). It is assumed that the voltage at the chopper input is constant and equal to VDC . In fact, the series chopper presents itself as one of the major sources contributing to electromagnetic interferences in conducted mode [3]; therefore, it is very useful to be able to quantify these interferences in order to reduce them by means of a device called a line impedance stabilization network “LISN” (Fig. 1).It is similar to a filter
Fig. 1 The structure of the serial chopper used
Fig. 2 Control signal generation “PWM”
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Fig. 3 Measurement of conducted disturbances
that is inserted between the device under test “chopper” and the network supplying the energy. Its role is to isolate the network on which common mode and differential mode disturbances may exist from the equipment under test (Serial chopper). It has a constant closing impedance (Z lisn = 50 ) with respect to the disturbances emitted by the device under test, both common mode and differential mode (Fig. 3).
3 Methods and Results 3.1 Hard Switching (Simple Scheme of the Chopper Devolver) From the simulation on the Powersim tool of the previous circuit given in Fig. 1, we were able to analyze the voltage Vlisn image of the disturbances in differential and common mode and see the spectral content it carries (Fig. 4), The main objective is to minimize the frequency content of this voltage (Power Spectral Density) in the range [10 kHz, 30 MHz].
Fig. 4 Spectral content of the voltage Vlisn for the hard switching method
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Fig. 5 Snubber circuit applied to a switching cell
3.2 Soft Switching (Switching Aid Circuit: Snubbers) The controlled switching of a semiconductor can place severe demands on electromagnetic compatibility. If the switching time is fast, as well as the switch operating frequency, current and voltage gradients can be significant. The frequency rise of static converters also leads to a significant increase in switching losses in the switches, socalled hard switching. In order to overcome these disadvantages, a snubber circuit [4] is used (Fig. 5), which is based on the principle of adding certain components such as a capacitor Cs in parallel with the switch for opening (reducing the rate of voltage rise) or an inductance L s in series with the switch for closing (reducing the rate of current rise). This considerably reduces current and voltage peaks as well as the propagation of conducted and radiated disturbances. This type of switching is called soft switching. Soft switching can be: – either switching on opening, in which case it is carried out at zero current: ZCS (Zero Current Switching) mode. – either switching on closingthis is then done at zero voltage: ZVS (Zero Voltage Switching) mode Now we will focus on the simulation results for the EMC measurements (Fig. 6). It is clear that the power spectral density of the Vlisn voltage has really decreased due to the use of the snubber circuit, which has the effect of reducing the current and voltage stresses as well as the switching losses. Hence its primary interest in terms of electromagnetic compatibility.
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Fig. 6 Spectral content of the voltage Vlisn for the method of soft switching
Fig. 7 Pseudorandom binary sequence (PRBS) principle
3.3 Chaotic Pulse Width Modulation Its principle lies in the generation of a chaotic binary sequence of 0 and 1 (PRBS) [5] from linear feedback shift registers. The theory behind these devices is based on algebraic computation in the Galois body (Fig. 7). The resulting binary sequence lasts L = (2 N − 1).TC L K .Where N is the toggle number and TC L K is the clock period. If N becomes large then the observation of one of the outputs of the N flipflops reveals an apparently random series of 1 and 0.The repetition period L is very large which justifies the name pseudorandom sequence. Thereafter we take in our work f C L K = 40 MHz and N = 8; so we will have a binary sequence of length L = 255.This technique will be used to realize a random pulse width modulation control. A multiplexer whose address input is the random sequence of 0 and 1 will direct two kinds of input signals (sawtooth) 180° out of phase to the output which will be considered as a carrier. Then comes the operation of comparing the reference signal Vr e f = 0.5 to this random carrier to generate the switch control signal (Fig. 8).
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Fig. 8 Generation of the PRBSbased control signal
Fig. 9 Spectral content of the voltage Vlisn for the RPWM method
Using the LISN, the measurement of the voltage Vlisn is collected. A spread of the frequency spectrum of this voltage with respect to the deterministic PWM control is clearly visible. As a result, the power spectral density is reduced (Fig. 9).
3.4 Combination of the Two Preceding Methods Subsequently, we will combine the two methods already mentioned, i.e. chaotic MLI associated with a snubber circuit. The results we have found further explain the usefulness of this process in reducing the disturbances conducted in the devolver chopper system. The power spectral density dropped well (Fig. 10). EMC measurements show that the levels of conducted electromagnetic interference are reduced.
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Fig. 10 Spectral content of the voltage Vlisn for the combination of the two previous methods
4 Conclusion In conclusion, static DCDC converters such as choppers are major sources of disturbance to their control circuits and surrounding environment. Hence the need to comply with the stringent electromagnetic compatibility regulations. In this work, two methods have been chosen which are capable of reducing the harmful effects of disturbances conducted in common and differential modes. The first method uses circuits (Snubber circuit) to soften the switching of the switches, either on or off, thus reducing the rate of voltage and current rise and achieving a large EMC gain. The second consists in using pseudorandomized control (PRBS), which contributes to spreading the power spectrum of the disturbances over a wide frequency range and consequently reducing the amplitudes or peaks of harmonics that come from the other control (deterministic PWM). The combination of these two methods has made it possible to considerably reduce the differential and common mode emissions of this device.
References 1. Richard R (1996) Power electronics and electromagnetic compatibility. ELF1 S.A. DerreylaCabuche. CH1756 Onnens, Switzerland. IEEE 2. Motahhir S, ElGhzizal A, Derouich A (2015) Modeling and control of a photovoltaic panel in the PSIM environment. International Congress of Industrial Engineering and Systems Management 3. Hong Li (2010) Design of analogue chaotic PWM for EMI suppression. IEEE Trans Electromagnet Compat 52(4) 4. Farhadi A (2008) Modeling, simulation and reduction of conducted electromagnetic interference due to a PWM buck type switching power supply. IEEE 5. Luo, FL, Hong Y (2003) Investigation of EMI, EMS and EMC in power DC/DC converters. IEEE
Design and Implementation of a Photovoltaic Emulator Using an Insulated Full Bridge Converter Based Switch Mode Power Supply Mohammed Chaker, Driss Yousfi, Bekkay Hajji, Mustapha Kourchi, Mohamed Ajaamoum, Ahmed Belarabi, Nasrudin Abd Rahim, and Jeyrage Selvaraj Abstract The study of renewable energies, such as photovoltaic generators, is still relevant until this day. As a result, PV Emulator is highly recommended. It allows to faithfully reproduce the characteristic of a panel, module or any photovoltaic field by taking into consideration the variation of the radiance, temperature and load. The PV Emulator proposed in this paper consists of an isolated switch mode power supply based on a full bridge converter. To force the current tracking the PV characteristic, PI controller and phase shift PWM are implemented via an F28335 platform. To duplicate PV module behavior, two modeling approaches are investigated and compared in simulation and confronted to experimental characteristics. Then, the PV Emulator is implemented using these modeling methods and the designed power supply. Both simulation and experimental results are presented at the end of this paper. Keywords PV emulator · DCDC converter · PV systems · SMPS · Phase shift
1 Introduction With the significant interest in renewable energies a great effort and a lot of investments are devoted to the development and research in renewable energy, specifically M. Chaker (B) · D. Yousfi · B. Hajji ESETI Laboratory, National School of Applied Sciences, Mohammed First University, Oujda, Morocco email: [email protected] M. Kourchi · M. Ajaamoum ESEM Laboratory, Higher School of Technology, Ibn Zohr University, Agadir, Morocco A. Belarabi Research Institute of Solar Energy and New Energies “IRESEN”, Green Energy Park Station, Benguerir, Morocco N. Abd Rahim · J. Selvaraj UMPEDAC, University of Malaya Power Energy Dedicated Advanced Centre, Kuala Lumpur, Malaysia © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_56
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photovoltaic systems. This has caused a significant demand for different equipment to test these systems [1]. The output of the photovoltaic panels is highly dependent on climatic conditions. Therefore, it is difficult to test the performance of photovoltaic energy conversion systems for different temperature and radiance values. A photovoltaic Emulator, which is essentially a DC Power Supply, offers the possibility of testing various photovoltaic systems (Inverter/MPPT Controller) while controlling through a software the climatic conditions [2]. Generally, the PV Emulator architecture which is widely used for research purpose, is based on the Buck converter [3–5]. However, despite the simplicity of this power topology and its control scheme, it is galvanically not isolated and it remains limited in terms of power that does not exceed a few hundred Watts. However, in this project, the high power and efficiency are the two most deterministic criteria in the choice of the power topology. It is a constraining challenge to realize a controlled supply that can reach 1KW of power and emulates perfectly the actual behavior of PV modules from short circuit to open circuit. In terms of characteristic generation, several approaches could be used to control the PV Emulator. In this work, two approaches will be investigated: The first approach consists of using a LookUpTable containing the data of a PV module and providing in real time the reference signal either for the current loop or the voltage loop [6, 7]. The second approach consists in replacing the LookUpTable generator with a mathematical model of the emulated PV panel [8, 9]. In this paper, an implicit model that does not require any identification test or any extra numerical method is presented. The model only uses the characteristics provided by the manufacturer datasheet.
2 Implicit Mathematical Model of PV Module The current of the photovoltaic module, which will be feed to the current controller, can be generated as an expressed of its voltage by the Eq. (1) [10]: V pv −1 I P V = Isc . 1 − C1 . ex p C2 .Voc
(1)
Where: I pv , V pv : Current and voltage supplied by module [A] −Vmpp Impp . ex p C1 = 1 − Isc C2 .Voc Vmpp −1 Voc C2 = I ln 1 − mpp Isc
(2)
(3)
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C1 and C2 depend on the following module parameters: Isc : Voc : Impp : Vmpp :
Module shortcircuit current [A] Module open circuit voltage [V] Module maximum power point current [A] Module maximum power point voltage [V]
These parameters can be expressed as follow: Isc (G, T ) = Iscs
G (1 + α(T − T s)) Gs
Voc (T ) = Vocs + β(T − Ts ) Impp (G, T ) = Impps
G (1 + α(T − T s)) Gs
Vmpp (T ) = Vmpps + β(T − Ts )
(4) (5) (6) (7)
Where α and β are respectively the current and the voltage temperature coefficient. Vocs , Iscs , Impps and Vmpps are defined under standard test conditions i.e. Gs = 1000 W/m2 and Ts = 25 °C. Compared to the LUT method, the major advantage of this implicit model is that all necessary parameters are provided by the manufacturer technical datasheet of the module. In LUT technique, the effort must be focused hardly on the measurement and implementation of current and voltage values that varies at each meteorological change. In addition, for more precision, it is necessary to collect a large number of data to fill the LUT; which means the need of a large storage space unlike for the model technique. On the other hand, the voltage in the presented mathematical model depends on the temperature only, which degrades the accuracy for certain PV panel technologies.
3 PV Emulator Power Circuit Description A PV Emulator consists of a switch mode power supply controlled by current or voltage taking into account climatic conditions and load as shown in Fig. 1. Different topologies of switch mode power supplies exist. In this work, a Full Bridge based topology is designed, as it can offer high powers of 1 KW order. For isolation, HF transformer is used with magnetizing and demagnetizing cycles under positive and negative voltage alternatively in order to gain two quadrants operation. Figures 2 and 3 respectively show the power circuit and the synoptic diagram of the emulator. The power part consists mainly of five cascade disposed stages i.e. Low frequency rectifier feeding a FullBridge inverter which is built with four MOSFETs to allow high commutation frequency. With such high switching frequency, the
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Fig. 1 General architecture of the PV Emulator
Fig. 2 PV Emulator power circuit
Fig. 3 Synoptic diagram of the proposed PV Emulator
seizing yields to a very small and light pulse transformer. Galvanic isolation is systematically gained as well, with very less cumbersome and low cost magnetic components. The backend stages are processing high switching frequency voltage. Consequently, the output rectifier is Schottky diode bridge and the LC filter is HF type filter which is seized according to ripple rate and slew rate requirements.
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The control circuit, built around an F28335 DSP Controller, takes in charge the PV characteristic generation using either the implicit model of the PV panel or the IV data in form of LookUpTable. Furthermore, the controller ensures the PV current tracking using a Phase Shift PWM Control technique and PI controller. The above special design results in high efficiency PV Emulator, which is a very critical design performance requirement.
4 Simulation Results The proposed power topology of the PV Emulator with the Phase Shift PWM Controller was simulated in MATLABSimulink environment using the LUT characteristic generation technique and the implicit PV model described in Sect. 2. Sharp module NAE135L5 is taken as a reference characteristic with a radiance E = 400 W/m2 and a temperature T = 15 °C. Table 1 shows the parameters of that Sharp module at STC. Realistic component parameters as well as experimental PV characteristic are used in order to predict the physical behavior of the PV Emulator. Figure 4 shows the reference IV characteristic together with the characteristic provided by the simulated PV Emulator in both generation technique cases. It is clear from the results above, that the characteristic provided by the simulated PV Emulator follows almost exactly the reference characteristic. Specifically, the simulation curves let see two particular zones. i.e. zone (A) where both generation techniques provide characteristics that perfectly merge with the reference. Then, zone B where the LUT based characteristic drift slightly from the reference compared to the model based technique. This is due to the spaced data used in LUT method in conjunction with a first order interpolation (Fig. 4 zoom). At the opposite, the implicit model calculates the (I,V) pairs at each sampling step resulting in better precision. Table 1 Electrical characteristics of the module Sharp NAE135L5 at STC
Maximum power
135Wp
Voltage at MPP
47.0 V
Current at MPP
2.88 A
Open circuit voltage
61.3 V
Short circuit current
3.41 A
Temperature Coef  V oc
−0.3%/K
Temperature Coef  Isc
0.07%/K
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Fig. 4 Reference and emulated IV characteristics using the LUT and the mathematical model of the PV module
5 Experimental Results For validation purpose, a PV Emulator (Fig. 5) was built around MOSFETs Full Bridge converter and HF transformer. The switching patterns are generated by Phase Shift PWM Controller following a current regulation. The voltage feedback feed the characteristic generator that is a LUT generator, in the first time, and an implicit model generator in the second time. The control part is implemented using an F28335 board.
Fig. 5 Photograph of the experimental test bench
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To emulate accurately the behavior of real PV modules, the designed switch mode power supply should present a fast dynamic response and a reduced output voltage ripple. In order to ensure these criteria, the LC filter is properly sized first; then special HF materials and components are used to build the prototype. Figure 6 shows the output circuit, which regroups the pulse transformer, the HF diode bridge and the LC filter. Figures 7 and 8 demonstrate respectively the ripple for an output voltage of 15 V and the response time of the voltage across the load. Fig. 6 Transformer, rectifier and LC filter
Fig. 7 Ripple of the voltage across the load at 15 V
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Fig. 8 Dynamic response time of the voltage across the load
According to these results the ripple rate is equal to 0.58% and the response time is 19.20 ms. These performances are within the range order of ripple rate and slew rate of a real PV module. The experimental results below (Figs. 9 to 12) represent the current and the power versus voltage characteristics provided by the PV Emulator when, successively, implicit PV model and LUT techniques are used. The PV Emulator is tested for two different reference PV modules presenting different Fill Factors 0.7 and 0.12 respectively. Figures 9 and 10 are associated with the implicit PV model method, while Figs. 11 and 12 are associated with the LUT method.
Fig. 9 Reference and emulated IV characteristic using implicit PV model
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Fig. 10 Reference and emulated PV characteristic using implicit PV model
Fig. 11 Reference and emulated IV characteristic using LUT
With both PV characteristic generation technics, the PV Emulator is able to reproduce accurately the reference characteristics and so to play the role of a real PV module. Particularly, the implicit model method demonstrates a perfect precision compared to the LUT method which confirm the simulation conclusion. However, the LUT method performances could be improved, at the price of large storage space, if large number of measurement points are used as reference characteristic.
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Fig. 12 Reference and emulated PV characteristic using LUT
6 Conclusion In this paper, a photovoltaic module emulator capable of reaching considerable power level has been proposed. The power circuit in this PV Emulator includes an isolated switch mode power supply built around a Full Bridge converter and a pulse transformer. Phase Shift PWM technique is used in conjunction with PI controller for the current loop. For the PV characteristic generation, a LUT technique is compared to an implicit model based technique. First, simulation study was carried out, in order to design the control scheme; then experimental tests have been conducted for validation purpose. For both techniques, within the power rang investigated so far, the PV Emulator demonstrates very good accuracy in reproducing real PV panel behavior. However, the model based method exhibits much better precision thanks to its fast sample real time update. In perspective, the power rang of the PV Emulator will be extended to cover large PV panels and advanced control techniques will be introduced to improve the performance. Acknowledgements This work is supported by the Research Institute for Solar Energy and New Energies  Morocco ‘IRESEN’.
References 1. Dolan DSL, Durago J (2011) Taufik: development of a photovoltaic panel emulator using labview. In: 37th IEEE photovoltaic specialists conference, Seattle, pp 1795–1800
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2. Rana AV, Patel HH (2013) Current controlled buck converter based photovoltaic emulator. JIII 1:91–96 3. Shinde, UK, et al (2016) Solar PV emulator for realizing PV characteristics under rapidly varying environmental conditions. In: 2016 IEEE international conference on power electronics, drives and energy systems (PEDES). IEEE, Trivandrum, India, pp 1–5 4. Rachid A, Kerrour F, Chenni R, Djeghloud H (2016) PV emulator based buck converter using dSPACE controller. In: 2016 IEEE 16th international conference on environment and electrical engineering (EEEIC). IEEE, Florence, Italy, pp 1–6 5. Erkaya Y, et al. (2015) Development of a solar photovoltaic module emulator. In: 2015 IEEE 42nd photovoltaic specialist conference (PVSC). IEEE, New Orleans, LA, pp 1–3 6. Hasnaoui M, Abdelghani A.B.B, SlamaBelkhodja I (2016) Implementation of a PV panel model based on the lookup tables method for a PV generator emulator. 6 7. Koutroulis E, Kalaitzakis K, Tzitzilonis V (2009) Development of an FPGAbased system for realtime simulation of photovoltaic modules. Microelectron J 40:1094–1102 8. Xiao W, Dunford WG, Capel A (2004) A novel modeling method for photovoltaic cells. In: IEEE 35th annual power electronics specialists conference, Aachen, Germany, pp 1950–1956 9. Sera D, Teodorescu R, Rodriguez P (2007) PV panel model based on datasheet values. In: IEEE international symposium on industrial electronics, Vigo, Spain, pp 2392–2396 10. Bellini A, Bifaretti S, Iacovone V, Cornaro C (2009) Simplified model of a photovoltaic module. IEEE, Pilsen, Czech Republic, p 6
Breakdown Voltage Measurement in Insulating Oil of Transformer According to IEC Standards Mohamed Seghir, Tahar Seghier, Boubakeur Zegnini, and Abdelhamid Rabhi
Abstract The current research paper deals with contribution to the worldwide problem of transformers which are essential parts to maintain the power flow in the electrical power system, the stability is significant for the reliability of the whole supply. The oil used in all transformers is used for insulating and cooling purposes. Degradation of transformer oil occurs because of the ageing, high temperature and chemical reactions such as the oxidation. It is also affected by contaminants from the solid materials. Therefore, the oil condition must be checked regularly and reclaimed or replaced periodically, to avoid the sudden. In this work is devoted to study the transformer oil behavior under AC voltage at industry frequency (50 Hz). The mineral used mineral oil Borak 22 is examined for different parameters such as, the distance between electrodes and geometry of electrodes. The experiment results concerning the evolution of the breakdown voltage into new oil and another old. The results showed that the spacing of the interelectrode distance causes an increase in the breakdown voltage of the oil, and that the pointplat electrode configuration was the worst form of the configurations. Finally, the used oil was better than new oil. He current research paper deals with contribution to the worldwide problem of transformers. Keywords Transformer · Mineral oil · Borak 22 · Ageing · Breakdown voltage (BDV)
1 Introduction An essential component of electrical networks which alters voltage levels and transforms energy is the transformer. The status and properties of insulation materials M. Seghir (B) · T. Seghier · B. Zegnini Laboratoire d’étude et de développement des matériaux semiconducteurs et diélectriques, Amar Telidji University of Laghouat, 03000 Laghouat, Algeria email: [email protected] A. Rabhi Laboratoire Modélisation Information and Systèmes, Université de Picardie Jules Verne, Amiens, France © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_57
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are very important for the functional reliability and lifetime of transformers. Oils that combine a high flashpoint with high dielectric strength have long been used as an insulating medium in transformers, switchgear and other electrical apparatus. To ensure that the dielectric strength of the oil does not deteriorate however, proper maintenance is essential, and the basis of proper maintenance is testing. The insulation fluid in power transformers performs two main functions; insulating and cooling. The highly refined mineral oils (transformer oils), typically used as insulating fluids, have low thermal conductivity and thus perform low cooling efficiency [1]. The electrical breakdown in transformer oil and characteristic properties of this process are very important for many applications. I bn bn insulating liquids such as transformer oils are critical components for high voltage and pulsed power system. The insulation liquid basically executes two primary actions in high voltage equipment: insulation and cooling. Insulating and thermal features of mineral oil typically restrain the minimal size and maximal transfer of power. One of the most significant parameters of liquid insulation is BDV. The BDV of oils is the value of voltage at which the oil is unable to oppose the flow of electricity and that the electricity will go through it. Determining the dielectric breakdown voltage of insulating liquids is important to understand the insulating liquid’s ability to withstand electric stress without failure. A low breakdown voltage value can be a clear indication of contamination within the liquid from the degradation processes that occur during the lifetime of a transformer. Publications indicated that dielectric breakdown is based on complex interactions of hydrodynamic and electronic phenomenon [2–4]. It is known that breakdown in transformer oil can be described by the bubble mechanism that leads to streamer propagation between the electrodes. The breakdown processes are also dependent on mechanisms, which play role on interface of the liquid and the surface of electrodes. The dielectric breakdown voltage test is a relatively quick and easy way of determining the amount of contamination in insulating oil. Usually the contaminant is water, but it can also be conductive particles, dirt, debris, insulating particles and the byproducts of oxidation and aging of the oil. In this investigation we present the experimental techniques carried out in the High Voltage Room, and the results obtained concerning the dielectric study insulating oil of transformer under 50 Hz AC voltage. The mineral oil Borak 22 is of naphthenic type, with a dielectric constant Er = 2 and dielectric strength Ec = 30 kV/mm which is generally used in power transformers and circuit breakers in the Algerian company SONELGAZ networks (Table 1). Two samples were carried out from the company SONELGAZ in Laghouat city the first is new and the second is old (used) from a transformer after a certain operating period. We were interested during our tests in the variation of the breakdown voltage according to the distance between electrodes, electrode system and the influence of ageing on the breakdown voltage.
Breakdown Voltage Measurement in Insulating Oil of Transformer … Table 1 New Borak 22 characteristics
Electrical properties
Unit
Dielectric strength kV (2.5 mm) Resistivity
G. . m
Value
545 Temperature (°C)
30–50 kV 20–2000
90
Dielectric losses
0,001–0,005 90
Permittivity
≤5,00 E−03 90
2 Experiment an Procedure The test circuit (Fig. 1) includes test transformer that can generate 100 kV (AC), test cell, measurement and protection elements. A testing transformer (HV9105) is connected as shown in Fig. 2. Single phase to earth; a measuring capacitor (HV9141), an oil test vessel (HV9137) and AC Peak voltmeter (HV9150) are connected on the highvoltage side. Computation of Critical F Put simply, a dielectric breakdown voltage test is a measure of the electrical stress that insulating oil can withstand without breakdown. The test is performed using a test vessel that has two electrodes mounted in it, with a gap between them. A sample of the oil to be tested is put into the vessel and an AC voltage is applied to the electrodes. This voltage is increased until the oil breaks down—that is, until a spark passes between the electrodes. The breakdown voltage should be measured using a standard testing vessel and alternating voltages of supply frequency. The spherical caps with spacing s = 2.5 mm shown in should be chosen as electrodes. Hemispheric geometry is part of the laboratory equipment, and other geometries such as tips and plans have been made in the turning workshop. The electrode size is shown in Fig. 3a,b . Fig. 1 AC circuit for testing the breakdown in insulating oil of transformer
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Fig. 2 Experimental set up
a) Hemispherical
b) planetip
Fig. 3 The dimensions of the electrodes (mm) plane and tip
The test voltage should be increased from zero at a rate of about 2 kV/s up to breakdown. Six breakdown experiments should be conducted for each 2nd to the 6th measurement may not be less than certain minimum values (Fig. 3).
3 Results and Discussion During the electrical breakdown tests, we found two modes of breakdown: “direct breakdown” and “burst breakdown”. This last mode of breakdown has also been observed by other authors [4, 5]. All tests are performed at atmospheric pressure and room temperature. Three different insulating oil breakdown parameters that can be
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Fig. 4 The electrode configurations
analyzed: The distance between electrodes, the electrode system configuration and ageing (oil condition). Results are shown in the Figs. 5 and 6. For both oil samples, we find that the increase in the distance between electrodes leads to an increase in the breakdown voltage. This is due to the decrease in the effect of the electric field. The results obtained are in good agreement with those found by other authors, under alternating voltage [6–8] Each value of the breakdown voltage shown in the following figures is an average value of six breakdown tests. Two different electrode configurations were used to study its effect on the breakdown voltage of the insulating oil. Figures 5 and 6 show that the breakdown voltage in the tipplane configuration was lower than in the hemispherehemisphere configuration. The reason for the low breakdown voltage in the tip—plane configuration was due to the high nonuniformity of the generated electric field compared to a uniform
Fig. 5 Variation of the breakdown voltage as a function of the distance between electrodes for tip—plane
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Fig. 6 Variation of the breakdown voltage as a function of the distance between electrodes for hemispheric profile
electric field in the hemispheric configuration. Both figures show that the breaking strength of the insulating oil was very high for a hemispherical configuration. The aging effect on dielectric strength was discussed, in order to study the importance of the oil condition, the difference between new oil and used oil. The breakdown of the transformer oil is complex enough to respond to this phenomenon because it depends on the condition of the oil, so it is quite possible that the sample of new oil that it contains water impurities or traces of moisture, in addition the samples are not from the same drum. In Figs. 7 and 8 we present the breakdown voltage values obtained at the LeDMaScD High Voltage laboratory, for a series of six tests and the average value of the breakdown voltages of the total series. The tests are carried out at atmospheric pressure and ambient temperature; the electrode profile and their spacing are fixed by the IEC 601561995 standard [9]. Fig. 7 New oil breakdown voltages according to IEC 60156
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Fig. 8 The breakdown voltages of used oil according to IEC 60156
These experimental values found are in good agreement with the breakdown voltage values obtained at SonatrachDML’s laboratoryLaghouat for the same new oil sample; using the BAUR DPA 75 automated tester [10]. These experimental results show that the breakdown voltage measurement confirms the reliability of our equipment. The voltmeter imbalance at the moment of breakdown, which makes it very difficult to measure the breakdown voltage accurately and especially for large values. The appearance of gusting breakdown for very high voltages of 50 kV and above. The decrease in voltage applied immediately following a breakdown is manual, which sometimes causes a burst breakdown due to the high temperature due to the long time of application of voltage during the increase and decrease lead to the appearance of the electric flashover effect (Fig. 9).
a) around the cell Fig. 9 Electric flashover effect
b) between the electrodes
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4 Conclusion This paper presents a contribution to the understanding of the dielectric behaviour of a mineral oil in tip plane and hemispheric geometry under AC voltage (50 Hz) based on the variation of the interelectrode distance for samples of new and used oil. The dielectric strength of the mineral oil is correlated to the electro geometric parameters of the system. In fact, the results showed that the electrode configuration in the tip plane was less than the hemispherical configuration, which reduced the breakdown voltage of the transformer oil due to the development of the nonuniform field. Indeed, the increase in the interelectrode distance leads to an increase in the breakdown voltage. The comparison of breakdown test results obtained from the BAUR tester ensures that our tests have been carried out in accordance with IEC 60156, and confirms the reliability of our equipment; In addition, the experimental results obtained show that the breakdown voltage of new oil is less than used oil, the breakdown of transformer oil is complex enough to respond to this phenomenon because it depends on the condition of the oil, so it is quite possible that the new oil sample it contains water impurities or traces of humidity, in addition the samples are not the same, in next study, we would have made the oil conditioning by heating to operating temperature about 80° C, in order to conduct physicochemical analysis. Acknowledgments This work was financially supported by Directorate General for Scientific Research and Technological Development (DGRST) Ministry of Higher Education and Scientific research Algeria. The authors gratefully thank the Laboratory of studies and development of the Semiconducting and Dielectric Materials, (LeDMaScD) Amar Telidji University of Laghouat, Algeria for their assistance in providing the high voltage equipment, and excellent discussion with member’s Lab.
References 1. Erdman HG (1996) Electrical Insulating Oils STP 998. ASTM Publ. Co., Philadelphia 2. Timoshkin V, Fouracre R, Given M, MacGregor S (2006) Hydrodynamic modeling of transient cavities in fluids generated by high voltage spark discgarges. J Phys D: Appl Phys 39:4808–4817 3. Jones H, Kunhardt E (1995) Development of pulsed dielectric breakdown in liquids. J Phys D: Appl Phys 28:178–188 4. Kúdelˇcík J (2007) Development of breakdown in transformer oil. ADVANCE 6:35–39 5. Tobazeon R (1997) Préclaquage et claquage des liquides diélectriques. Tech l’ingénieur 3(D2450):24501 6. Lesaint O, Saker A, Gournay P, Tobazeon R, Aubin J, Mailhot M (1998) Streamer propagation and breakdown under AC in very large oil gaps. IEEE Trans Dielectr Electr Insul 5 7. Berger N (2002) Liquides isolants en électrotechnique: présentation générale. Technique de l’Ingénieur (D2470):V1 8. Lezaint O, Tobazeon R (1998) Streamer generation and propagation in transformer oil under AC divergent field conditions. IEEE Trans Electr Insul 23(6):941–954
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9. IEC 60156 International Standard (1995) Insulating liquids—determination of the breakdown voltage at power frequency—test method. 2 edn. International Electrotechnical Commission 10. Seghir M (2019) Study of the breakdown phenomena of transformer oils  performance of breakdown tests. Master‘s thesis University Amar Telidji of Laghouat
Electric Vehicle
Energy Management Strategy for Hybrid Electric Vehicle Using Fuzzy Logic Bilal Belaidi, Iliass Ouachani, Katell Gadonna, David Van Rechem, and Hervé Billard
Abstract Faced with current energy and environmental challenges, electric vehicles represent an interesting alternative solution to vehicles powered by internal combustion engines. Our project focuses on finding a solution that combines several energy sources with complementary characteristics and low environmental impact, adapted to these vehicles. The two selected sources are a fuel cell and a supercapacitor for their complementarity in terms of power density and mass energy. Fuel cell systems have disadvantages, such as high cost, slow response and no regenerative energy recovery during braking. Supercapacitors have a low energy density. Hybridization can be a solution to these drawbacks. The Energy Management Strategy (EMS) based on fuzzy logic has been developed. A first dimensioning of an electric powertrain was made, modelling of sources and DC/DC converters were built. Energetic Macroscopic Representation (EMR) is used as a unified formalism for modelling, control, and EMS development. The results of robustness gathered using different types of driving cycles will be presented and compared. Keywords Fuel cell · Supercapacitor · Energy management strategy · Hybrid energy · Fuzzy logic
1 Introduction Limitations of electric vehicles (EV) are mainly related to the onboard energy sources used, the battery being the main source currently in use. The main weaknesse of the latter is the storable mass energy that limits the vehicle’s range. To overcome these limitations, one solution is to combine several sources with complementary characteristics. These are multisource vehicles. The fuel cell (FC) is an energy source based on the conversion of hydrogen into electrical energy, and represents an alternative solution to put back internal B. Belaidi (B) · I. Ouachani · K. Gadonna · D. Van Rechem · H. Billard Polymont Engineering, 15 rue de la gare, 78640 VilliersSaintFrédéric, France email: [email protected] URL: https://www.polymontengineering.fr/ © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_58
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combustion engines. However this solution has limitations because the FC system [1] has a slow dynamic range and degraded efficiency in the high load phases. For this reason the FC is never used alone in clean powertrains. The FC system hybridization with SC can be a solution to these drawbacks. The complementarity of the characteristics between the sources is provided by an Energy Management Strategy (EMS) whose role is to optimize the primary fuel while respecting the constraints and limitations related to each energy source. Several works have been developed for an optimal realtime strategy [1, 2]. Here the notion of real time has an impact on calculation time, optimality is directly related to the methods of solving optimization problems (online or offline). For a strategy to be applicable in a vehicle, it must comply with the following criteria: – Applicability: Realtime applicable, with an order calculation that follows the variation in demand – Safety: The EMS must involve energy sources while respecting the physical limitations associated with each energy source. – Performance: The EMS must always provide the optimal control solution and remain within the defined. The purpose of this paper is to present a fuzzy logicbased approach as an realtime energy management strategy.
2 Modelling and Representation System EMR, illustrated in Fig. 1 is used to organize the system model and to deduce the control scheme thanks to its ability to highlight the system energetic characteristics. It focuses on a systemic functional description of the elements of the system through the principle of interaction [10].
3 Modelling of Fuel Cell and Supercapacitors Using MATLAB/Simulink : 3.1 Dynamic Modelling of Fuel Cell (PEMFC) Proton Exchange Membrane Fuel cells (PEMFC) are power supplies that convert chemical energy of a reaction directly into electrical energy [3]. The PEMFC model used in this paper is realized with MATLAB and Simulink. This model has been built using the electrical circuit, show in Fig. 2 that describes the behaviour of a FC . The output voltage is given in relationship (1), the potential VN er nst is reduced by the three losses affecting the cell during operation, called: activation loss Vact expressed by
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Fig. 1 EMR of system study Fig. 2 Electrical circuit representing the simplified dynamics of the fuel cell
Tafel’s law (2), ohmic loss Vohm expressed in (3), and diffusion loss VDi f f expressed in (4). (1) V f c = E N er nst − Vact − Vohm − Vdi f f Vact = A ln(
i f c + in ) i0
Vohm = Relec .i f c Vdi f f = B. ln(1 −
i f c + in ) i0
(2) (3) (4)
Based on the reference [4], the potential of E N er nst can be expressed as follows: 1 E N er nst = [1, 229 − 0.85T −3 .(T − 298, 15) + 4, 3085 × 10−5 .T. ln(PH 2 ) + ln(PO2 ) ] 2
(5)
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Fig. 3 Dynamic model of the FC system
From where:
1 V f c = [1, 229 − 0.85T −3 .(T − 298, 15) + 4, 3085 × 10−5 .T. ln(PH 2 ) + ln(PO2 ) ] 2
(6)
− Vact − Vohm − Vdi f f ]
In (7), the relationship between the output voltage and the input current gives the power of the fuel cell [5, 6]. (7) P f c = U f c .i f c With: U f c = N .V f c
(8)
The FC model parameters used to obtain this model are as follows: – – – – – – – – –
A B E N er nst PO2 , PH2 T Relec i fc in i0
Tafel coefficient [V ] Diffusion constant [V ] Nernst instantaneous voltage [V ] Partial pressure of Oxygen and Hydrogen respectively Operating fuel cell temperature [K ] FC internal resistance [Ω] Current flowing through the fuel cell [A] Internal leakage current [A] Exchange current characterizing electrodeelectrolyte [A].
Figure 3 shows the detailed model of the PEMFC, realized with Simulink model, which is then integrated into the overall system. Finally, Fig. 4 represents the polarization curve of the PEMFC that is the variation in the overall real potential of the FC in function of the i f c input current from our
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Fig. 4 Polarization curve of the PEMFC
Fig. 5 Simplified circuit representing the dynamic modelling of the SC: Tow branches model [7].
simulation. In the absence of load current, the voltage is maximum. The passage of a current causes a voltage degradation until a passage current reaches a maximum estimated at 120 A, related to the sizing of the FC.
3.2 Dynamic Modelling of SC Figure 5 shows the chosen equivalent circuit of the supercapacitor. To be able to report on the use of supercapacitors in transient conditions, a model must be used to describe the rapid charging and discharging cycles. That’s why we used a simple RC model with 2 branches, based on the model of Zubieta et al. [7] The main branch reflects the energy behavior of the supercapacitor during both phases (charging and discharging). The capacity C1 of the main branch varies linearly according to the voltage at its terminals V1, and is expressed as follows:
V1 =
−C0 +
C02 + 2Cv Q 1 Cv
(9)
The second branch, called slow, describes the phenomena of internal redistribution of electrical charges after the charging and discharging phases expressed through the relationship (10) 1 Q2 V2 = (10) i 2 dt = C2 C2
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Fig. 6 Supercapacitors simulation model
We add to the model shown in Fig. 5, the voltage Vocv (noload voltage of the supercapacitor) which maintains a positive supercapacitor voltage. Moreover, it allows to be consistent with the convention positive current = discharge and negative current = charge. The voltage evolution of a single cell is expressed as follows: Vsc = (Vocv −
−C0 +
C02 + 2Cv Q 1 Cv t
S O E(t) = S O E 0 −
− R1 i sc )
(11)
i sc (t)
0
δ0
(12)
Figure 6 shows the SC Simulink model. The green block gives the voltage Usc from (11). The orange block calculates the state of energy S O E sc using equation (12). This model doesn’t take into account leakage current losses. Figure 7 shows the voltage evolution of our model with a current excitation profile whose maximum and minimum profile values are respectively 250 A and −250 A. It should be noted that a positive current on the SC allows to degrade the voltage level therefore the S O E sc of the module. We also note that a negative current, which represents a load, increases the voltage level of the module. To validate our model, we compare it with the model proposed in [8], for wich a perfect coherence between simulation and experience is observed. Our model is similar, the differences observed are related to neglected dynamics. On zooms in (A) and (B), we observe when the charge/discharge current is cancelled, the voltage
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Fig. 7 Dynamic behaviour of SC
is not constant for a relatively short time (5 s). This phenomenon is related to the redistribution of load [9].
4 OnLine Energy Management Strategy Using Fuzzy Logic The objective of the energy strategy is to control energy flows in such a way as to minimise vehicle consumption while respecting the constraints of each energy source, today the technological lock is on the functioning of the realtime strategy. There is a very rich literature on the subject and many EMS are proposed. An EMS based on an offline method is proposed in [1, 11]. Another is proposed in [1], which is based on dynamic programming as a realtime resolution method, the execution time of the latter is exorbitant compared to an electronic control unit (ECU). Fuzzy logic is established in such a way that imprecise variables can be processed on continuous values between 0 and 1 depending on their degree of a membership the verification of a condition. In addition, it is an appropriate technique for solving problems for which there are uncertainties about the available knowledge of a system. Fuzzy processing involves three important steps: fuzzification, inference processing and defuzzification. Fuzzification is considered to be the first step in fuzzy processing Fig. 8, consisting in determining the linguistic variables and membership functions of the fuzzy system
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Fig. 8 Fuzzy logic methode
Fig. 9 Membership functions of input and output variables.
at the input and output levels. In our application, the system contains three linguistic variables, the powertrain demand Ia , the state of energy of SC S O E SC as input variables and the current of the FC I pac as output variable. Each linguistic variable is defined by its triangular membership functions in order to reduce the complexity of the calculations and allow faster transitions. Figure 9 shows the membership functions of variables. The second step is the rules engine that allows the link between the input and the output variables using the operators IF, AND, OR to draw conclusions. In our application the rules engine contains twentyfour rules to implement in order to minimize hydrogen consumption while respecting the constraints of each energy source. Defuzzification is the final step in fuzzy processing, and consists in calculating the abscissa of output variable using the centroid method.The rules are based on the following constraints: (13) Ia = I pac + Isc S O E scmin < S O E sc < S O E scmax
(14)
I pacmin < I pac < I pacmax
(15)
S O E sc (t0 ) = S O E sc (t f )
(16)
The decision surface is shown in Fig. 10. Our study is interested in the capacity of the fuzzy logic method to respect constraints. Figure 11, Fig. 12 and Fig. 13 shows the evolution of the state of energy of SC on the city, preurban and highway parts of the WLTC cycle respectively. We notice that the constraints defined with red line S O E scmin , S O E scmax and green line S O E sc (t0 ) are well respected in the first and second parts. On the third part (highway) the implemented rules do not allow the total respect of the constraints 15 and 16, which is a major disadvantage of the fuzzy logic. Several studies have been developed on this topic as in [12] where an offline genetic algorithm is used to adapt the
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Fig. 10 Decision surface
Fig. 11 S O E sc trajectories in the first part of the WLTC cycle
Fig. 12 S O E sc trajectories in the second part of the WLTC cycle
Fig. 13 S O E sc trajectories in the third part of the WLTC cycle
degree of membership of each variable according to the GPS. For the rest of this work, we will introduce the electronic horizon as a system that predicts at a given distance the vehicle’s trajectory, in order to adapt the parameters of fuzzy logic to the improvement of EMS.
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5 Conclusion In this paper energy sources (FC) and (SC) have been dimensioned and modelled, a management system formulation with constraints has been presented under the EMR. Finally, an EMS based on fuzzy logic has been developed, simulations on the three WLTC cycle parts were presented and compared. Fuzzy logic is a well adapted method for realtime EMS if it is assisted by a system that predicts the vehicle’s trajectory, to optimize and correct offline membership settings.
References 1. Jiang Q (2018) Gestion énergétique de véhicules hybrides par commande optimale stochastique 2. Lahyani A, Venet P, Guermazi A (2011) Développement de méthodes de réduction de la consommation en carburant d’un véhicule dans un contexte de sécurité et de confort: un compromis entre économie et écologie. IEEE Trans Power Electron 94–114 3. Amrouche F, Mahmah B, Belhamel M, Benmoussa H (2005) Modélisation d’une pile á combustible PEMFC alimentée directement en hydrogèneoxygène et validation expérimentale. Rev Energ Ren 8:109–121 4. Luu HT (2003) Battery/supercapacitors combination in uninterruptible power supply (UPS). IEEE Trans Power Electron 28(4):1509–1522 5. Meddah S, Menasria A (2006) Etude d’un système énergétique á pile combustible destiné á une application résidentielle. Université de Bechar Algérie 6. Chatillon Y (2013) Méthodes électrochimiques pour la caractérisation des piles á combustible de type PEM en empilement 7. Zubieta L, Bonert R (2000) Characterization of doublelayer capacitors for power electronics applications. IEEE Trans Ind Appl 36(11):199–205 8. Lahyani A, Venet P, Guermazi A (2013) Battery/supercapacitors combination in uninterruptible power supply (UPS). IEEE Trans Power Electron 28(4):1509–1522 9. Lajnef W (2006) Modélisation des supercondensateurs et évaluation de leur vieillissement en cyclage actif á forts niveaux de courant pour des applications véhicules électriques et hybrides 10. Bouscayrol A, Hautier J, LemaireSemail B (2013) Graphic formalisms for the control of multiphysical energetic systems: COG and EMR 11. Liu C, Liu L (2015) Optimal power source sizing of fuel cell hybrid vehicles based on Pontryagin’s minimum principle. Department of Mechanical Engineering, The University of Kansas, Lawrence, KS 66045, USA 12. Gaoua Y, Caux S, Lopez P, Domingo Salvany J (2013) Online HEV energy management using a fuzzy logic. hal00814204
Simulation of a MicroGrid for Electric Vehicles Charging Station R. Bouhedir, A. Mellit, and N. Rouibah
Abstract This paper presents a simulation of a connected microgrid (MG) for electric vehicles (EV) charging station. An energy management system (EMS) is essential for the MG to operate in a coordinated way. Therefore a simple management strategy is adopted to ensure and maintain an adequate service. Solar energy is the main source of this MG, and this energy could be stored, delivered to the grid, or supplied the charging points through an appropriate interface board (IB). This kind of MG is installed in many countries around the world. The system includes: PV panels, the main grid, an inverter, rectifiers, batteries, EMS, and load. The MG has been simulated under different conditions and scenarios using Matlab/Simulink environment. Results showed that the adopted strategy for energy management performs well. Keywords Charging station · Electric vehicle · MicroGrid · Energy management
1 Introduction Nowadays the MicroGrid (MG) becomes widely used in the most of distributed energy systems (DES) through the world in connected mode, or used as the main source in islanded mode due to the advance of the technology, in addition to its ecological features. There is no specific definition of the MG, in literature there are many definitions, such as a MG is any power supply system independent of the main R. Bouhedir (B) · A. Mellit Renewable Energy Laboratory, Faculty of Sciences and Technology, Jijel University, Jijel 18000, Algeria email: [email protected] A. Mellit ASICTP, Trieste, Italy N. Rouibah Electric and Industrial Systems Laboratory, Faculty of Electronics and Informatics, USTHB, Algiers, Algeria © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_59
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grid, or it could be defined also like a power system that consists of different energy resources and loads [1]. There are many types of MG regarding its location and structure. The most common blocks are: converters, inverters, power electronic blocs, and it is very important in MG system to get an energy storage system (ESS), because the use of ESS makes the MG more reliable and it allows integrating intermittent renewable energy, and it turns non dispatchable units like PV system and wind system into dispatchable units by combining them with an ESS [2]. Even though the MG offers many advantages and benefits to the grid utility, customer, and ecology. At the same time it brings many challenges that can summarized on the distributed techniques, control strategies [3], coordination and management, security and protection for both main grid and MG [4]. Therefore there are several studies in this context aiming to enable the smooth use of the MG, and enhance the harvesting of power from renewable energies [5]. In order to reduce the pollution, one of the solutions except the adoption of the MG is the use of electric vehicles (EVs), which allows decreasing fossil fuel dependency and that lead to a decrease of CO2 emission, and at the present time the EVs are available in the market, also the charging points that are dedicated to charge this kind of vehicles [6, 7]. Recently the charging station undertakes to rely on renewable energies, which permit to improve the reduction of the dependency on the fossil fuel. In case of overavailability of power, it could injected to the grid. In addition of the amount of power that can be provided by the MG, the EV can inject its power to the main grid also, and this type of power circulation it called in the literature V2G [8]. Most of the recent studies of EVs charging station powered by solar energy are focusing on the energy management, impact of charging strategies, and optimization methods [9]. In this paper, we present a Matlab/Simulinkbased simulation of an EV charging station. As well as a simple algorithm for energy management of the MG, and determine the priorities of the power flow. The introduced algorithm ensures the energy coordination within the MG, and includes the transformation between grid connected and islanded mode. The simulated MG is evaluated under different scenarios and conditions. The content of this paper is organized as follows. Section 2 describes the structure of the investigated MG, and introduces the adopted strategy in EMS to coordinate and control the MG. Section 3 presents the Simulink model of the MG, as well as the obtained results. Conclusion and perspectives are provided in the last Sect. 4.
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Fig. 1 The structure of the investigated MG
2 MicroGrid 2.1 MG Structure The described MG is a charging station connected to the low voltage grid, see Fig. 1. This station is powered by solar energy source, and it is composed of a PV generator providing a power of 4 KWp. A storage block of 10 KWh based on Liion batteries with a management system of the energy stored inside the ESS, a 4.6 kVA inverter, a DC converter that guarantee a Maximum Power Point Tracking (MPPT), a data logger and displays. Two charging points, the connection between the power parts of the MG and the grid is performed through an interface board (IB).
2.2 EMS Strategy The proposed strategy consists to coordinate the flow of energy based on the status of each part of the MG, and guarantees the priorities of power circulation between EV, ESS, and the grid. As illustrated on the flowchart in Fig. 2. When a car is present at the charging point, and the state of charge of the EV battery (SOCv) is lower than a predefined value SOCvmin , if the solar power is available, that means PV power (PVp) is upper than the predefined threshold value Pmin , the charging points are powered by the PV generator and the flow of energy is from PV to EV (PV2V).
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Fig. 2 Flowchart of EMS strategy
However, if there is not enough solar power, it means that the captured PV power is lower than Pmin , in this case the battery feeds the EV (B2V). And if the battery power is not enough, which symbolized by the SOC of the battery is lower than the threshold value SOCbmin , in this case the grid will feeds the EV (G2V). When there is no demand, means that there is no EV or the EV is fully charged which means SOCv is upper than SOCvmin , in this case the power is stored in the storage block and the flow of the energy is from PV to the battery (PV2B). In case if the battery is fully charged, it signifies that SOC of the battery is upper than the predefined value SOCbmax , here the power is delivered to the grid (PV2G).
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Fig. 3 Simulated model of the charging station
2.3 SimulinkBased Model of the Whole System The introduced MG is simulated under Matlab/Simulink, as shown in Fig. 3 The model is composed of a mathematical PV module, a single phase inverter, DC converters, a battery of 10 kWh, a car battery (load) 30 kWh, the main grid, rectifiers, switchers, and an EMS block.
3 Results and Discussion Based on the values of SOC of EV (SOCv), solar irradiance (Ir), and SOC of the battery (SOCb), several scenarios have taken into consideration. We provide inputs data in sort that give us all possible cases as shown in Fig. 4. The EMS provides the accurate signals to the switchers of MG parts. With reference to Table 1, it can be clearly seen that the proposed strategy can works accurately. Range [0, 2]: there is a good irradiance, the EVb is not fully charged, and even the battery is not fully charged too, the EV is powered by the PV generator (P2V). Range [2, 3]: the solar irradiance is low and the PV power is not sufficient, in this case the battery feeds the EV (B2V). Range [3, 4]: it is the case when there is no good irradiance, and the battery is discharged, so the EVb is charged by the grid (G2V). Range [6, 7]: the irradiance is good and the EVb is charged, the battery is also fully charged, so the power of the PV generator is injected to the grid (PV2G). Range [7, 8]: the battery is not fully charged and the EVb is charged, the PV generator charges the battery (PV2B).
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Fig. 4 Inputs data and control signals
Table 1 Results of several cases Range
EV SOC (%)
Battery SOC (%)
Irradiance (W/m2 )
Power flow
[0,1]
60
80
800
PV2V
[1, 2]
60
90
800
PV2V
[2, 3]
60
90
200
B2V
[3, 4]
60
80
200
G2V
[6, 7]
99
99
700
PV2G
[7, 8]
99
70
700
PV2B
4 Conclusion In this study, a MGbased model has been developed and simulated under Matlab/Simulink. A simple energy management algorithm is presented with different scenarios. The investigated scenarios showed that the energy management system is stable and the simulation results support the claims of the algorithm. The main advantage of the presented algorithm is its simplicity, does not need complicated calculations, so it could be easily implemented for real time application, for example into a lowcost microcontroller. The results permit to observe clearly the behavior of the charging station in different conditions. Further work aiming to validate the obtained results in a real system, and improve the energy management system by using advanced algorithmsbased artificial intelligence techniques, and include other level of EMS. We will also integrate the V2G part and other scenarios by taking into consideration forecasted data, scheduling energy, and the pricing.
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References 1. Huang W, Lu M, Zhang L (2011) Survey on microgrid control strategies. Energy Procedia 12:206–212 2. Yazdanian M, MehriziSani A (2014) Distributed control techniques in microgrids. IEEE Trans Smart Grid 5(6):2901–2909 3. Chettibi N, Mellit A, Sulligoi G, Pavan AM (2016) Adaptive neural networkbased control of a hybrid AC/DC microgrid. IEEE Trans Smart Grid 9(3):1667–1679 4. Liu X, Wang P, Loh PC (2011) A hybrid AC/DC microgrid and its coordination control. IEEE Trans Smart Grid 2(2):278–286 5. Wikström M, Eriksson L, Hansson L (2016) Introducing plugin electric vehicles in public authorities. Res Transp Bus Manag 18:29–37 6. Pavan AM, Lughi V, Scorrano M (2019) Total Cost of Ownership of electric vehicles using energy from a renewablebased microgrid. In: 2019 IEEE Milan PowerTech, pp 1–6. IEEE 7. Galiveeti HR, Goswami AK, Choudhury NBD (2018) Impact of plugin electric vehicles and distributed generation on reliability of distribution systems. Eng Sci Technol Int J 21(1):50–59 8. Liu N, Chen Q, Liu J, Lu X, Li P, Lei J, Zhang J (2014) A heuristic operation strategy for commercial building microgrids containing EVs and PV system. IEEE Trans Industr Electron 62(4):2560–2570 9. Liu N, Zou F, Wang L, Wang C, Chen Z, Chen Q (2016) Online energy management of PVassisted charging station under timeofuse pricing. Electr Power Syst Res 137:76–85
Design of Fractional Order Sliding Mode Controller for Lateral Dynamics of Electric Vehicles Imane Abzi, Mohammed Nabil Kabbaj, and Mohammed Benbrahim
Abstract This paper focuses on FractionalOrder (FO) sliding mode control of the vehicle lateral dynamic. The objective is to force the vehicle to track the reference values of the yaw rate and the side slip angle. The use of the FractionalOrder Sliding Mode Controller (FOSMC) guarantees a high robustness against model uncertainties and external disturbances and reduces the chattering effect. The stability of the overall system is ensured by applying the Lyapunov’s theorem on the predefined FO sliding surface. Two simulation examples have been carried out based on different steering angle profiles to demonstrate the effectiveness of the proposed controller. The results obtained confirm the accuracy and the speed of the vehicle response compared to the dynamic of the desired yaw rate and side slip angle. Keywords Fractionalorder · Sliding mode control · Lateral dynamic · Yaw rate · Side slip angle
1 Introduction The control of the vehicle lateral dynamic has been a subject of significant interest over the previous decades. Recently, the integration of electric vehicles into the market has achieved an advanced level. Even if today’s electric vehicles are far to attain performances of the combustion engine based ones, but the replacement of some mechanical and hydraulic parts with electric systems increases the reliability and reduces the cost and the weight of the vehicle. Due to the great progress in both electrical and control engineering, the vehicle passengers safety and comfort are guaranteed through sophisticated electronic controllers such as the Direct Yaw Moment control system (DYC) and the Antilock Braking System (ABS) that enable adjusting the vehicle lateral and longitudinal dynamics, respectively [1, 2]. I. Abzi (B) · M. N. Kabbaj · M. Benbrahim Integration of Systems and Advanced Technologies Laboratory (LISTA), Faculty of Sciences, Sidi Mohamed Ben Abdellah University, BP 1796, 30000 Atlas, Fez, Morocco email: [email protected] © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_60
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The lateral dynamic behavior of the vehicle can be represented by a nonlinear model [3]. The origin of this nonlinearity is related especially to the lateral friction forces [4]. To deal with such a system, tremendous efforts have been devoted to design nonlinear control strategies of the yaw rate moment and the side slip angle [5–7]. Among them, schemes based on the TakagiSugeno (TS) fuzzy control technique [1]. Artificial intelligence (AI) control method has been applied on unmanned and intelligent vehicles [8]. Also, the Sliding Mode Control (SMC) has been used for its robustness. More recently, thorough studies have shown the superiority of the FOSMC over the classical SMC in terms of rapidity and accuracy [9, 10]. The general idea of the FOSMC consists of replacing integerorder derivatives or integrals with fractional order ones. The emergence of this mathematical formalism dates back three centuries. The contribution of this paper is the design of an FOSMC controller for electric vehicles lateral dynamic. The subsequent sections of this paper are organized as follows: Sect. 2 presents the mathematical model of the vehicle lateral dynamic. Section 3 describes the design procedure of the FOSMC controller. Section 4 analyses the performed simulation tests. While Sect. 5 adds some conclusions and perspectives.
2 The Mathematical Representation of the Vehicle Lateral Dynamic Figure 1 gives a graphical description of the vehicle lateral dynamic characteristics. The model derived from this representation neglects the roll, the pitch and the vertical suspension motions [1]. By assuming that the longitudinal speed Vx is constant and the steering angle δ f is small, the aforementioned model can be simplified and formulated as follows: mVx (β˙ + r ) = 2(Fy f + Fyr ) (1) Iz r˙ = 2(a f Fy f + ar Fyr ) + ΔMz where r indicates the yaw rate moment, while β designates the side slip angle, whereas Iz and m denote the yaw moment of inertia and the sprung mass, respectively. The Fy f and Fyr denote the front and rear lateral forces. They can be modeled by nonlinear functions of the tire slip angles. Amongst the tire forces models, the Semiempiric Pacejka majic formula is considered as the most accurate representation of the lateral forces nonlinear feature [4]: Fyr = Dr sin (Cr tan−1 (Br (1 − Er )αr ) + Er tan−1 (Br αr )) Fy f = D f sin (C f tan−1 (B f (1 − E f )α f ) + E f tan−1 (B f α f ))
(2)
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Fig. 1 Description of vehicle lateral dynamic
Di , Ci , Bi and E i are real coefficients that depend on the road surface conditions with i ∈ {r, f }. Whereas α f and αr are the front and the rear tire slip angles, respectively. They are written as follows [1]:
a r
α f = δ f − Vfx − β αr = aVrxr − β
(3)
In order to develop a vehicle model that can be handled easily at the design stage, the lateral forces are approximated by the following TS fuzzy multiple model [11]: ⎧ 2 ⎪ ⎪ μi α f  C f i α f ⎨ Fy f = i=1 (4) 2 ⎪ ⎪ ⎩ Fyr = μi α f  Cri αr i=1
Where C f i and Cri designate the front and rear lateral tire stiffness coefficients, respectively. μi are the membership functions, they verify the convex sum property. Their expressions are given as follows [3]:
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⎧ ωi α f  ⎪ ⎪ μi α f  = 2 ⎪ ⎪ ⎪ ⎪ ⎪ ωi α f  ⎪ ⎨ i=1 1 ⎪ ⎪ ωi α f  =
⎪ ⎪ α  − c 2bi ⎪ ⎪ f i ⎪ ⎪ 1+ ⎩ ai
(5)
The corresponding values of the coefficients ai , bi , ci , C f i and Cri are given in reference [3]. By plugging (3), (4) and (5) into (1), the vehicle lateral dynamic model can take the following compact form: X˙ = F(X ) + gU
(6)
Where: ⎞ ⎞ ⎤ ⎛ C f i a f − Cri ar C f i + Cri Cfi −2 − 1 2 ⎟ ⎟ ⎥ ⎢⎜ ⎜ mVx mVx2 ⎟ X + ⎜ mVx ⎟ δd ⎥ ⎜ F(X ) = μi (α f ) ⎢ ⎣⎝ C f i a f − Cri ar ⎝ C fia f ⎠ ⎦ C f i a 2f − Cri ar2 ⎠ i=1 2 −2 −2 Iz Iz Iz Vx ⎡⎛
2
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⎞ Cfi 0 2 ⎟ ⎜ mV x ⎟ g= h i (α f ) ⎜ ⎝ C fia f 1⎠ i=1 2 Iz Iz ⎛
2
and X = [β r ]T ; U = [Δδ f ΔMz ]T
3 The Design Method of the FOSMC Controller for the Vehicle Lateral Dynamic The control scheme of vehicle lateral dynamic using the FOSMC controller is depicted in Fig. 2 The first key step in the design of FOSMC controller is the selection of an appropriate FO sliding surface. The chosen surface is expressed by: α D e1 + k1 e1 S (7) S= 1 = S2 D α e2 + k1 e2
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Fig. 2 The control scheme of electric vehicle lateral dynamic
where the e1 = βr e f − β and e2 = rr e f − r are the tracking errors. k1 is a real positive parameter. The control law U is composed of two terms; U = Ueq + Ur
(8)
where Ueq indicates the equivalent control which is calculated by taking S˙ = 0, whereas Ur is the robust term. α+1 1 D e1 Ueq = g −1 −F(X ) + X˙ r e f + (9) k1 D α+1 e2 Ur =
1 −1 κ 0 g Λ sgn(S); Λ = ; κ>0 0κ k1
(10)
Theorem 1 let us consider the nonlinear model of the vehicle lateral dynamic described in (6) which is controlled by U given in (8), (9) and (10), then, the dynamic of the yaw rate moment and the side slip angle will converge to their desired reference values. Proof The selected Lyapunov function condidate can be written as follows: 1 V = ST S 2
(11)
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The time derivative of (11) leads to the following result : V˙ = S˙ T S
(12)
Putting S and S˙ in their developed forms and after simplification yields: V˙ = −Λsgn(S)T S
(13)
V˙ = −κ (S1  + S2 ) < 0
(14)
This is equivalent to :
Based on the Lyapunov’ theorem, knowing that V is positive definite, and according to (14) its derivative is negative, then the stability of the closedloop system is confirmed.
4 Simulation Results In this section, two simulation examples have been carried out under Matlab/Simulink. Various steering angle profiles have been considered to assess the performances of the proposed FOSMC controller. The vehicle parameters [11] and the controller gains are summarized in Table 1. The references values of the lateral motion are given by the following formulas [1]: ⎧ ⎪ ⎨βr e f = 0 ⎪ ⎩rr e f =
(15)
Vx δf (a f + ar )(1 + kus Vx2 )
where kus designates a stability factor. Table 1 The model and controller parameters
Parameters
Values
m Iz af ar k1 κ kus
1740 Kg 3214 Kg.m2 1.04 m 1.76 m 180 103 0.01
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Fig. 3 The lateral motion for the first scenario: a Steering angle profile b Yaw rate moment c side slip angle.
Figure 3 and Fig. 4 show the high performances of the FOSMC controller. It is noticed for both steering angle profiles that the vehicle yaw rate moment and side slip angle accurately track the references. Also, the figures exhibit the rapidity of the FOSMC controller. At this stage, we consider a test of robustness by adding a lumped disturbance that represents model parameters uncertainties and external perturbations to the model in (6). The vehicle lateral dynamic model becomes: X˙ = F(X ) + gU + d
(16)
with d = [d1 d2 ]T et d1 = d2 = 2 sin(t) The result obtained in Fig. 5 confirms the performances and robustness of the fractional order sliding mode controller over the conventional sliding mode controller.
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Fig. 4 The lateral motion for the second scenario: a Steering angle profile b Yaw rate moment c side slip angle.
Fig. 5 The lateral motion under disturbances and uncertainties: a Yaw rate moment b side slip angle.
5 Conclusion In this work, an FOSMC controller for the electric vehicle lateral dynamic has been proposed. The stability of the vehicle in cornering conditions has been guaranteed by fulfilling the Lyapunov’s Theorem. The effectiveness of our controller has been
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proved through two simulation examples. The concordance between the vehicle nonlinear model outputs and the input references asserts the interest of our proposal. Our future works could address the FOSMC adaptive for the lateral dynamics of the vehicle and why not a general fault tolerance control (FTC) scheme based on the elaborated controller combined with state observers to deal with sensor faults.
References 1. Jin X, Yu Z, Yin G, Wang J (2018) Improving Vehicle Handling Stability Based on Combined AFS and DYC System via Robust TakagiSugeno Fuzzy Control. IEEE Trans Intell Transp Syst 19(8):2696–2707 2. Moosapour SS, Fazeli Asl SB, Azizi M (2018) Adaptive fractional order fast terminal dynamic sliding mode controller design for antilock braking system (ABS). Int J Dyn Control 7:2195– 2698 3. Aouaouda S, Chadli M, Bouhali O (2013) Observerbased fault tolerant tracking control for vehicule lateral dynamics. 2013 International Conference on Control, Decision and Information Technologies (CoDIT), Hammamet, pp 051–056 4. Pacejka HB, Bakker E, linder L (1989) A new tire model, an application in vehicle dynamics studies. Presented at the Autotechnologies Conference Exposition, Monte Carlo, Monaco, 1989, SAE Paper 890089 5. Sazgar H, Azadi S, Kazemi R, Khalaji AK (2019) Integrated longitudinal and lateral guidance of vehicles in critical high speed manoeuvres. Proc Inst Mech Eng Part K J MultiBody Dyn 233(4):994–1013 6. Jiang J, Astolfi A (2018) Lateral control of an autonomous vehicle. IEEE Trans Intell Veh 3(2):228–237 7. Tagne G, Talj R, Charara A (2016) Design and comparison of robust nonlinear controllers for the lateral dynamics of intelligent vehicles. IEEE Trans Intell Transp Syst 17(3):796–809 8. Devineau G, Polack P, Altché F, Moutarde F (2018) Coupled longitudinal and lateral control of a vehicle using deep learning. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, pp 642–649 9. Tang YG, Wang Y, Han MY (2016) Adaptive fuzzy fractionalorder sliding mode controller design for antilock braking systems. J Dyn SystT ASME 138(4):041008 10. Tang Y, Zhang X, Zhang D, Zhao G, Guan X (2013) Fractional order sliding mode controller design for antilock braking systems. Neurocomputing 111:122–130 11. Oudghiri M, Chadli M, El Hajjaji A (2008) Robust observerbased fault tolerant control for vehicle lateral dynamics. Int J Veh Des 48(34):173–189
A Decentralized Multilayer Sliding Mode Control Architecture for Vehicle’s Global Chassis Control, and Comparison with a Centralized Architecture Ali Hamdan, Abbas Chokor, Reine Talj, and Moustapha Doumiati
Abstract This paper presents a decentralized Global Chassis Control (GCC) architecture. The objective of this global chassis controller is to improve the overall vehicle performance i.e maneuverability, lateral stability and rollover avoidance, by coordinating the Active Front steering, Direct Yaw Control and Active Suspensions in a decentralized architecture. The developed architecture is multilayer, and based on higher order slidingmode control, the supertwisting algorithm. The proposed GCC is validated by simulation using Matlab/Simulink, and a comparison is done with a centralized L P V /H∞ architecture that has been developed in the laboratory, to show the difference in behavior and performance of both strategies of control. Keywords Decentralized multilayer control architecture · Global Chassis Control · Active Suspensions · Direct Yaw Control · Active front steering · Sliding mode control
1 Introduction Active safety is an important feature into the intelligent vehicles. According to the “National Highway Traffic Safety Administration (NHTSA)” statistics, human’s faults cause almost 90% of road accidents as explained in [1]. Advanced Driving Assistance System (ADAS) influences on the behavior of vehicle on the road, and helps the driver in the driving process in order to avoid a dangerous situation. ADAS systems are formed by several singleactuator approaches that have been proposed and marketed, such as: Electronic Stability Program (ESP) or Direct Yaw Control (DYC) to enhance the vehicle lateral stability; Active Front Steering (AFS) to mainly A. Hamdan · A. Chokor · R. Talj (B) Sorbonne universités, Université de technologie de Compiègne, CNRS, Heudiasyc UMR 7253, CS 60 319, 60 203 Compiègne, France email: [email protected] M. Doumiati ESEOIREENA EA 4642, 10 Bd Jeanneteau, 49100 Angers, France © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_61
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improve the vehicle maneuverability or lane keeping; and (Semi) Active Suspensions (AS) to improve comfort, road holding and rollover avoidance [2]. Many advanced studies are developed in literature to improve the global performance of the vehicle in different driving situations. These studies suggest coordination between several ADAS systems known as Global Chassis Control (GCC). The GCC system deals with the complexity of control problems for MultiInputMultiOutput (MIMO) systems. The main idea of the GCC is the coordination between the AFS and the DYC to improve the vehicle maneuverability and lateral stability depending on the driving situation. Many advanced control approaches have been proposed for this issue. The authors in [3] applied a decentralized approach where they developed a DYC controller for lateral stability purpose and an AFS controller for maneuverability purpose, based on sliding mode technique, and then a monitor switches between the two controllers according to the driving situations. However, the overall stability of the system is not guaranteed in the decentralized approach, but it is simple to develop, implement and tune. In [4, 5], the authors propose several robust and optimal centralized controllers for the MIMO system based on the LPV/H∞ control technique, where the L P V /H∞ controller penalizes or relaxes the steering and braking to enhance maneuverability and lateral stability. By using this method, the overall stability of the system is guaranteed and a polytopic approach is used to actuate the different controllers. However, these controllers were synthesized while disregarding the roll motion; the deduced rollover enhancement was a consequence of the lateral stability control. Authors in [6, 7] have presented several centralized L P V /H∞ controllers, where AFS, DYC and AS are used to control the decoupled lateral and vertical vehicle dynamics. From the other side, authors in [8] and [9], have used the roll angle and its angular velocity to control the vehicle load transfer that leads to rollover avoidance. Moreover, authors deduced lateral stability improvement as a consequence of roll control. Centralized architectures are optimal in global performance, but are more complex to design and to implement, and could take an important amount of calculation. All these interesting research have motivated us to study the control of the vehicle yaw rate, the side slip angle and the roll angle in order to improve the overall vehicle performance. Thus, in our present work, a decentralized multilayer control structure, based on sliding mode supertwisting control approach, is developed to improve the maneuverability, lateral stability, and rollover avoidance using steering, braking actuators and active suspension system. A comparison between the proposed controller and a centralized architecture presented in [10] is done. The paper structure is as follows: Sect. 2 exposes the extended bicycle model of the vehicle based on the combination of the coupled lateral (yaw and sideslip) and roll motions. In Sect. 3, the proposed decentralized control architecture is detailed. Simulation validation of the proposed approach is reported in Sect. 4. Finally, the conclusions and the perspectives of this work are given in Sect. 5.
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2 Vehicle Model The vehicle is a group of interconnected mechanical and electrical systems that make the vehicle behavior nonlinear. The ADAS systems such as AFS (Active Front Steering), active suspensions, differential braking, etc, improve the vehicle’s performance (lateral motion, yaw motion, rolling motion, etc.). A complete nonlinear vehicle model has been developed in [11]. However, this model is a nonlinear model that does not respond to the formulation of control problems. For this reason, a linear simplified LTI vehicle model is used to develop the GCC controller. It is an extended bicycle model, with coupled lateral/vertical dynamics. For vertical dynamics, the rolling motion is considered, for being the most critical for stabilization problems and rollover avoidance. Hence, this LTI model is a coupled yawlateralroll linear vehicle model, inspired from literature [8], and is given by the following equations of “Plant P”: ⎧ Iz ψ¨ = Fy f l f + Fyr lr + Ix z θ¨ + Mz + Md,ψ˙ , ⎪ ⎪ ⎪ ⎪ ⎨ MV β˙ + ψ˙ = Fy f + Fyr + Ms h θ θ¨ + Fd,y , (1) Plant P : ⎪ Ix + Ms h 2θ θ¨ = Ms h θ V β˙ + ψ˙ + (Ms gh θ − K θ )θ ⎪ ⎪ ⎪ ⎩ −Cθ θ˙ + Md,θ , where the vehicle parameters and variables are given in Table 1. Fy f represents the lateral force of the front left and right tires merged together at the center of the front axle. Similarly, Fyr is noted for the rear axle. Fy f and Fyr are given as: Fy f = μC f α f , Fyr = μCr αr ,
(2)
and the tires slip angles as: α f = −β − αr = −β +
l f ψ˙ V lr ψ˙ . V
+ δt ,
(3)
The reference “bicycle model” used in the control layer is presented in [1] and is given in (4):
⎤ ⎡ l 2 c +l 2 c l c −l c lf cf −μ f Ifz Vxr r μ r r Iz f f ψ˙ r e f ψ¨ r e f μ I ⎦ z =⎣ + δd , c l c −l c c +c μ M Vf x β˙r e f −1 + μ r Mr V f2 f −μ Mf Vxr βr e f
(4)
x
where δd is the driver steer angle on the front wheels, ψ˙ r e f is the desired reference yaw rate, βr e f is the corresponding side slip angle, and Vx is the vehicle longitudinal
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Table 1 Parameters values for simulation Symbols Description ˙ Vehicle yaw rate ψ β θ Fyi δd V Ix Iz Ix z tf tr lf lr hθ Ms C f , Cr Kθ Cθ g μ
Parameters values
Vehicle side slip angle at CG Sprung mass roll angle Lateral forces at the i axle Driver steering angle Vehicle speed Roll moment of inertia of sprung mass Vehicle yaw moment of inertia Vehicle yawroll product of inertia Half front track Half rear track Wheelbase to the front Wheelbase to the rear Sprung mass roll arm Sprung mass Front, rear tire cornering stiffness Rolling suspension angular stiffness Rolling suspension angular damper Gravity constant Road adherence coefficient
[rad/s] [rad] [rad] [N ] [rad] [m/s] 534 [kg.m2 ] 1970 [kg.m2 ] 743 [kg.m2 ] 0.773 [m] 0.773 [m] 1.0385 [m] 1.6015 [m] 0.27 [m] 1126.4 [kg] 76776 [N/rad] 30000 [N.m/s] 10000 [N.m/s] 9.81 [m/s2 ] Dry surface = 1 [−]
speed. For security reasons, the authors in [1] propose to saturate βr e f and ψ˙ r e f below a threshold, as described in (5):  ψ˙ r e f ≤
0.85μg Vx

βr e f = arctan(0.02μg)
(5)
3 Decentralized Global Chassis Control Architecture The global decentralized multilayer control architecture of Fig. 1 is presented in this ˙ the sideslip angle β, and the section. The output variables i.e the vehicle yaw rate ψ, suspended mass roll θ are controlled independently by using the singleinput, singleoutput controller based on the SuperTwisting Sliding Mode (STSM) technique. Let us introduce an overview of the theory of SuperTwisting Sliding Mode. The STSM is a robust control technique that forces the states of the system to reach a sliding surface during a finite time (convergence phase) and to stay on this surface (sliding phase) in presence of perturbations.
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Fig. 1 Decentralized global chassis control architecture
Consider the second order system given as: x¨ = f (X, t) + g(X, t)u(t)
(6)
where X = [x, x] ˙ T ∈ 2 is the state vector, u is the control input, and f , g are continuous functions. X des is the desired state of X with X des = [xdes , x˙des ]T ∈ 2 . ˙ T ∈ 2 where e = x − xdes and The error vector is given by E = X − X des = [e, e] e˙ = x˙ − x˙des . Therefore, a sliding variable s with relative degree r = 1 w.r.t the control input, is defined as: s = e˙ + k e,
(7)
s¨ (s, t) = Φ(s, t) + ξ(s, t)u(t) ˙
(8)
The second order derivative of s is:
where Φ(s, t) and ξ(s, t) are the unknown bounded signals. The goal of the SuperTwisting algorithm is to enforce the sliding variable s to converge to zero (s = 0) in finite time. Assume that there exist positive constants S0 , bmin , bmax , C0 , Umax verifying for all x ∈ n and s(x, t) < S0 : ⎧ ⎨ u(t) ≤ Umax Φ(s, t) < C0 ⎩ 0 < bmin ≤ ξ(s, t) ≤ bmax
(9)
Thus, the control input based on the SuperTwisting Sliding Mode algorithm, is given as: u 1 = −α1 sτ sign(s), τ ∈]0, 0.5] u(t) = u 1 + u 2 (10) u˙ 2 = −α2 sign(s)
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α1 and α2 are positive gains. The following conditions guarantee the finite time convergence: ⎧ ⎨ α1 ≥ 4C2 0 (bmax α2 +C0 ) bmin (bmin α2 −C0 ) (11) ⎩ α > C0 2 bmin The analysis of convergence is presented in [12]. An approximation function used to smooth the sign(s) function, where > 0.
s s+
is
Let us define the three sliding variables for the three decentralized controllers as follows: sψ˙ = eψ˙ = ψ˙ − ψ˙ r e f , sβ = eβ = β − βr e f , (12) sθ = e˙θ + kθ eθ = (θ˙ − θ˙r e f ) + kθ (θ − θr e f ), The sliding variables sψ˙ , sβ and sθ have a relative degree equal to one w.r.t δc , Mz and Mθ respectively. Thus, in order to converge these variables to zero and the controlled states follow the desired ones, and based on the above discussion, the control inputs of AFS, DYC and AS applied to the system, are given by: δc = −αδ,1 sψ˙ τδ sign(sψ˙ ) − αδ,2
t
sign(sψ˙ )dτ, t Mz = −α Mz ,1 sβ τ Mz sign(sβ ) − α Mz ,2 0 sign(sβ )dτ t Mθ = −α Mθ ,1 sθ τ Mθ sign(sθ ) − α Mθ ,2 0 sign(sθ )dτ, 0
(13)
where αδ,i , α Mz ,i and α Mθ,i with i = [1, 2], are positive constants satisfying the conditions in (11). τδ , τ Mz and τ Mθ are constants between ]0, 0.5]. The decision layer monitors the driving situation based on S I (lateral stability index) and L T R (load transfer ratio) criteria, then it delivers the different gains λi in order to activate or deactivate the different actuators. These gains are given as follows: 1 , λβ = S I +S I 8 − (S I − ) 2 S I −S I 1+e (14) λψ˙ = 1 − λβ . λθ =
1 1+e
TR 8 − L T R−L (L T R− L T R+L ) 2 TR
,
(15)
4 Simulation Results In this section, the developed controller will be validated with a double lane change test at 110 km/h as initial speed. All simulations are done using Matlab/Simulink with a complete nonlinear model of the vehicle [11], validated on “SCANeR Studio”
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(OKtal)1 [13]. Then, a comparison is done between an uncontrolled vehicle, where no controller is used (“OL” as Open Loop) and controlled vehicle equipped with two different controllers, i.e, the decentralized controller (“STSM” as SuperTwisting Sliding Mode) and a centralized controller (“L P V /H∞ ”) developed in the laboratory Heudiasyc, and presented in [10]. During this test, The driver’s intention is to change the lane in a short time and then return to the same lane. Noting that in the two techniques of control, the active suspensions system AS aims to avoid rollover by decreasing the angle θ . Figure 2, 3 and 4 show the different control variables such as the yaw rate, the sideslip angle and the roll angle respectively. Figure 2 shows that the yaw rate tracks the reference yaw rate delivered by the bicycle model, and both controllers have almost the same behavior compared with the uncontrolled vehicle. Thus, the maneuverability objective is achieved. In order to improve the lateral stability and to prevent an undesirable driver situation, the sideslip angle should be reduced as shown in Fig. 3. Both control architectures have similar influence on this angle. On the other hand, the convergence of roll angle to zero allows the avoidance of rollover risk, by reducing the load transfer ratio L T R. The Fig. 4 shows that the L P V /H∞ controller is capable to diminish more the roll angle to zero compared to the STSM controller. Fig. 5 shows the lateral stability index (SI), and Fig. 6 presents the lateral load transfer ratio (LTR). Both SI and LTR are improved with both GCC architectures. Hence, lateral stability and rollover avoidance are enhanced.
1 “SCANeR
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5 Conclusion and Perspectives To conclude, a decentralized multilayer sliding mode control architecture has been developed to improve the overall vehicle performance. This enhancement is done by coordination of the Active Front Steering, Direct Yaw Control and Active Suspensions in a decentralized controller. The proposed controller is validated by using Matlab/Simulink and a comparison is done with a centralized approach based on the L P V /H∞ technique, presented in [10]. Results show an almost similar performance of the decentralized scheme with its centralized equivalent. However, decentralized architecture is simpler and easier to tune and implement than centralized controller. Hence, the decentralized architecture could be much more interesting. In future works, we will work on the proof of the global stability of the system with the decentralized global chassis controller, a Lyapunovbased analysis will be done to show the convergence and boundedness of the solution. Validation on the Scaner
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Studio simulator and on a real vehicle platform will also be investigated. In addition, other performance index will be also used to do the benchmark comparing against the other conventional control approaches. Acknowledgement The authors would like to thank the HautsdeFrance Region and the European Regional Development Fund (ERDF) 2014/2020 for the funding of this work, through the SYSCOVI project. This work was also carried out in the framework of the Labex MS2T, (Reference ANR11IDEX000402) and the Equipex ROBOTEX (Reference ANR10EQPX4401) which were funded by the French Government, through the program “Investments for the future” managed by the National Agency for Research.
References 1. Rajamani R (2012) Vehicle Dynamics and Control. Springer, USA 2. Chokor A, Talj R, Doumiati M, Charara A (2019) A global chassis control system involving active suspensions, direct yaw control and active front steering. IFACPapersOnLine 52(5):444–451 3. He J, Crolla DA, Levesley MC, Manning WJ (2006) Coordination of active steering, driveline, and braking for integrated vehicle dynamics control. Proc Inst Mech Eng Part D J Automob Eng 220(10):1401–1420 4. PoussotVassal C, Sename O, Dugard L (2009) Robust vehicle dynamic stability controller involving steering and braking systems. In: IEEE European Control Conference (ECC) 5. Doumiati M, Sename O, Dugard L, MartinezMolina JJ, Gaspar P, Szabo Z (2013) Integrated vehicle dynamics control via coordination of active front steering and rear braking. Eur J Control 19(2):121–143 6. Sename O, Gaspar P, Bokor J (2013) Robust control and linear parameter varying approaches: application to vehicle dynamics, vol 437. Springer, Heidelberg 7. Chen W, Xiao H, Wang Q, Zhao L, Zhu M (2016) Integrated vehicle dynamics and control. Wiley, New York 8. Van Vu T, Sename O, Dugard L, Gáspár P (2017) Enhancing roll stability of heavy vehicle by lqr active antiroll bar control using electronic servovalve hydraulic actuators. Veh Syst Dyn 55(9):1405–1429 9. Yao J, Lv G, Qv M, Li Z, Ren S, Taheri S (2017) Lateral stability control based on the roll moment distribution using a semiactive suspension. Proc Inst Mech Eng Part D J Automob Eng 231(12):1627–1639 10. Chokor A, Doumiati M, Talj R, Charara A (2019) Design of a new gainscheduled lpv/h ∞ controller for vehicle’s global chassis control. In: 58th conference on decision and control (CDC) 11. Chokor A, Talj R, Charara A, Shraim H, Francis C (2016) Active suspension control to improve passengers comfort and vehicle’s stability. In: 19th international conference on intelligent transportation systems (ITSC), pp 296–301. IEEE 12. Utkin V (2013) On convergence time and disturbance rejection of supertwisting control. IEEE Trans Autom Control 58(8):2013–2017 13. Chokor A, Talj R, Charara A, Doumiati M, Rabhi, A (2017) Rollover prevention using active suspension system. In: 20th international conference on intelligent transportation systems (ITSC), pp 1706–1711. IEEE
Energy Management Strategy Based on a Combination of Frequency Separation and Fuzzy Logic for Fuel Cell Hybrid Electric Vehicles M. Essoufi, B. Hajji, and A. Rabhi
Abstract Energy management of Hybrid Electric Vehicles (HEV) remains a concern and a challenge for many researchers. This paper presents, an energy management strategy for hybrid electric vehicles powered by fuel cell as a primary source and LiIon battery as a secondary one. Our proposed approach combines the frequency separation energy management strategy and a fuzzy logic controller. The principle of this strategy is based on routing the lowfrequency components of power demand to the fuel cell and the high frequencies to the battery and through a fuzzy logic controller, the lowfrequency component is corrected to control the battery state of charge. The models of the hybrid electric vehicle and the management strategy are evaluated under Matlab/Simulink for the New European Driving Cycle (NEDC) and the Urban Dynamometer Driving Schedule (UDDS). The simulation results show the good performances of the proposed strategy through respect of each source dynamics and maintenance of bounded battery state of charge (SOC). Keywords Fuel cell · Hybrid vehicle · Battery · Energy management · Frequency separation · Fuzzy logic
1 Introduction On our planet, around 1.2 billion vehicles circulate today and the manufacturers produce more than 90 million new vehicles each year. In their quasi majority, those vehicles are powered by Internal Combustion Engines (ICE). Phenomenal quantities
M. Essoufi (B) · B. Hajji Renewable Energy, Embedded System and Data Processing Laboratory, National School of Applied Sciences, Mohamed First University, Oujda, Morocco email: [email protected] A. Rabhi Modelization, Information and Systems Laboratory, Picardie Jules Verne University, Amiens, France © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_62
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of carbon dioxide are released into the atmosphere every day impacting our way of life and negatively altering the environment. Moreover, the increased energy consumption presents a risk for future generations. Hence, designing new vehicle technologies becomes a big challenge [8]. Nowadays, vehicles powered by fuel cell as the main source become one of the most promising alternatives. Low noise, zeroemission and high efficiency [17] constitute the main advantages of these technologies. Nevertheless, its slow dynamic response hinders the tracking of sudden power variations. Also, its nonreversibility limits the recovery of energy produced by regenerative braking. To overcome these problems, an energy storage device such as battery or supercapacitor must be used to supply the peak power of the motor and recover the energy produced by regenerative braking (Hybridization) [1]. Consequently, the combination of several energy sources requires the use of an energy management strategy to distribute the required power generation perfectly and effectively [9]. Thus, several energy management strategies have recently been proposed in the literature. Among of the most known we cite equivalent consumption minimization strategy [7], neural networks [11], rulebased strategy [5], Fuzzy Logic Controller [3], Optimal Control strategy [6] and frequencyseparation strategy [4, 10, 13, 14]. In this work, we implemented a realtime energy management strategy for fuel cell hybrid electric vehicles powered by fuel cells as the main source and LiIon battery as a secondary one. Our strategy combines the separation frequency method and the fuzzy logic controller. This paper is structured as follows: Sect. 2 describes the adopted vehicle architecture. The proposed energy management strategy was explained in Sect. 3. Afterward, the results of the simulation are presented and discussed in Sect. 4. Finally, we conclude our article and discuss further perspectives in Sect. 5.
2 Hybrid Electric Vehicle Modeling The vehicle studied in this article is presented in Fig. 1. It is propelled by an synchronous permanent magnet motor (PMSM) and supplied by DC bus through an inverter. The hybrid source used consists of two elements: Fuel cell as the main source connected to DC bus through a unidirectional buck converter, and LiIon battery as a secondary source connected to DC bus trough a bidirectional buckboost converter.
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Fig. 1 Architecture of the fuel cell hybrid electric vehicle
2.1 Vehicle Model The various forces affecting the vehicle in motion is presented in Fig. 2 [2]. According to Fig. 2 the Eq. (1) represents the dynamic of the vehicle. Mv
→ d− v −→ − → − → − → − → = Fair + Rr + Fr + P + Ft dt
(1)
−→ – Fair : The aerodynamic force acting to the vehicle during acceleration. 1 −→ → x Fair = − ρair V 2 AC x − 2
Fig. 2 Forces applied to the vehicle in motion
G
(2)
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− → – Fr : The resistance of the wheels on the floor. − → → x Fr = −PCr cos α −
(3)
− → → P = −Mv g sin α − x
(4)
− → – P : The gravitational force.
According to previous equations the mechanical tensile force Ft can given by Eq. (5). Ft = Mv
1 dv + ρair V 2 AC x + Mv g sin α + Mv gCr cos α dt 2
(5)
The motor power required to advance the vehicle is given by Eq. (7). Pm = v.Ft Pm = v(Mv
1 dv + ρair V 2 AC x + Mv g sin α + Mv gCr cos α) dt 2
(6) (7)
2.2 Fuel Cell Model The fuel cell is an electrochemical generator that converts a chemical fuel (Hydrogen) into electrical energy. Figure 3 illustrates the fuel cell model available in MATLAB/Simulink used in this work. This model consists of a controlled voltage source in series with a resistance. The parameters (E oc , i 0 , A) change depending on the flow rate of fuel and air. The controlled voltage source (E) and fuel cell voltage (V ) are expressed respectively by Eq. (1) and Eq. (1) [12]. E = E oc − N A ln (
ifc 1 ). i 0 sTd /3 + 1
V f c = E − Rohm .i f c
(8) (9)
The flow rates of oxygen and hydrogen are calculated respectively by Eqs. (10) and (11) [12]. 60000RT N I f c (10) U O2 = z F Pair Vlpm(air ) O2 % U H2 =
60000RT N I f c z F P f uel Vlpm( f uel) H %
(11)
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Fig. 3 Model of the fuel cell available in MATLAB/ Simulink [12]
The fuel cell used in this paper is a Proton Exchange Membrane Fuel Cell (PEMFC) with (400 cell, 288 V, 100 kW).
2.3 Battery Model For the simulations, the dynamic battery model available in MATLAB/Simulink environment is used. This model consist of a controlled voltage source in series with a constant resistance, as shown in Fig. 4. The value of (Vbatt ) is calculated by two equations [15]: – If the current is positive: Discharge mode (Eq. (12)). Vdischarge = E 0 − R.i − K
Q .(it + i ∗ ) + A exp(−B.it) Q − it
(12)
– If the current is negative: Charge mode (Eq. (13)). Vcharge = E 0 − R.i − K
Q Q .i ∗ − K .it + A exp(−B.it) it − 0.1Q Q − it
(13)
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Fig. 4 Nonlinear battery model [16]
The battery StateOfCharge is calculated by Eq. (14) [15]. S OCbatt = 100(1 −
i (t) dt ) Q
(14)
The battery used is a 13.9 Ah, 200 V LithiumIon battery.
2.4 DCDC Converter Model The fuel cell is connected to the DC bus through a unidirectional DC/DC buck converter, controlled by PI controller as shown in Fig. 5, the rapport cyclic which controls Q1 is generated by the error between the I f c and I f c−r e f . The LithiumIon battery is connected to DC bus via a bidirectional DC/DC buckboost converter, the two transistors Q2 and Q3 are controlled by two complementary pulse width modulation (PWM) signals which are calculated to keep the DC bus voltage near 288 V as shown in Fig. 6.
Fig. 5 Unidirectional buck converter
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Fig. 6 Buckboost converter
3 Energy Management Strategy Frequency energy management is based on the division of the power mission into several frequency channels. Each channel will be sent to a specific power source, taking into account the energy flow dynamics. The ratio between power and energy densities denoted respectively ρ power and ρenergy is termed: specific frequency and represents the relation between energy flow dynamics and the storage technologies. The specific frequency formula is given by Eq. (15).
Fig. 7 Ragone diagram [18]
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f c [H z] =
ρ power [W/Kg] ρenergy [J/Kg]
(15)
To determine the frequency range allowed by each source the Ragone diagram depicted in Fig. 7 is used. The proposed energy management strategy based on a combination of frequencyseparation strategy and the fuzzy logic controller is presented in Fig. 8. The first step is to find the reference power for the fuel cell taking into account its slow dynamic and its nonreversibility. Thus, two blocks are used. The first one rejects the negative signals while the second consists of a lowpass filter to obtain the FC reference power. The cutoff frequency of the filter is chosen from the Ragone diagram. Afterwards, for the purpose of controlling the battery state of charge (SOC), the filtered power will be corrected using a Mamdani Fuzzy Logic Controller (FLC). The main objective of FLC is to maintain the battery state of charge (SOC) bounded within the interval [40, 60%].
Fig. 8 Frequencyseparation principle
Fig. 9 Input and output membership functions
Energy Management Strategy Based on a Combination of Frequency Separation ... Table 1 Rules of fuzzy logic controller Pdem
N L M H
601
SOC L
M
H
ZE M H H
ZE ZE M H
NM NL ZE ZE
The proposed fuzzy logic controller takes as inputs the load power Pdem , the battery state of charge S OC and outputs the correction power Pc . The fuzzification is realized using triangular and trapezoidal membership functions as illustrated in Fig. 9. Finally, the fuzzy rules employed to assign the output values are listed in Table 1. These rules are proposed to correct the reference power of the fuel cell and ensure a reasonable range of the battery state of charge.
4 Simulation Results and Discussion To evaluate the performances of the proposed realtime energy management strategy, the model of fuel cell hybrid electric vehicle is simulated using MATLAB/Simulink environment for the UDDS drive cycle (Urban Dynamometer Driving Schedule) and NEDC drive cycle (New European Driving Cycle). The results of the simulation are presented with and without using the fuzzy logic controller to show the impact on the battery state of charge w.r.t traditional frequency management. Figure 10 and Fig. 11 present the simulated vehicle speed for NEDC and UDDS drive cycles. The vehicle speed follows the reference speed perfectly with small errors for the two drive cycle. These observations confirm the good functioning of our system. The DC bus voltage presented in Fig. 12 is well regulated to its reference (288 V) and remains constant with small oscillation in spite of the sudden variation of motor current.
Fig. 10 Speed vehicle for NEDC drive cycle (Reference, measured)
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Fig. 11 Speed vehicle for UDDS drive cycle (Reference, measured)
Fig. 12 DC bus voltage
Fig. 13 Powers of battery, motor and fuel cell during NEDC drive cycle without FLC
Fig. 14 Powers of battery, motor and fuel cell during UDDS drive cycle without FLC
Figure 13 and Fig. 14 illustrate the motor power, battery and fuel cell for NEDC and UDDS drive cycles respectively. These simulation results correspond to the frequency separation strategy. We observe the efficiency of the proposed energy management strategy to share perfectly the power between the sources while respecting the frequency domain of
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each source. Indeed, the battery provides the sudden requirement power, recover the energy produced by regenerative braking while the fuel cell provides the smoothed signals. Figure 15 and Fig. 16 show the evolution of the battery state of charge simulated under the NEDC and UDDS drive cycles respectively. For the NEDC drive cycle without using a fuzzy logic controller, a deep discharging occurs at t ≈ 1110 s, and this can affect the battery life. Also, a deep discharging occurs at t ≈ 300 s for the UDDS drive cycle without using a fuzzy logic controller. Its state of charge at the end of the cycle increases by 71%. For a long drive cycle and under the same conditions, the battery can be fully charged and this could lead to its destruction. The simulation results obtained by applying the combination of frequency separation and fuzzy logic controller are given in Fig. 17 and Fig. 18 for NEDC and UDDS drive cycles respectively. The battery state of charge is presented in Fig. 15 and Fig. 16: – Under NEDC drive cycle: 39% < S OC < 60% – Under UDDS drive cycle: 43% < S OC < 61% Figure 20 and Fig. 19 depict the behavior of each block of management strategy to obtain the fuel cell power reference. The effectiveness of the proposed approach is validated. Indeed the energy management splits perfectly the power between sources taking into account the flow dynamics of each one. The resulting states of charge are kept within the admissible ranges (40% < S OC < 60%) for the two drives cycles.
Fig. 15 Battery state of charge during NEDC drive cycle
Fig. 16 Battery state of charge during UDDS drive cycle
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Fig. 17 Powers of battery, motor and fuel cell during NEDC drive cycle with FLC
Fig. 18 Powers of battery, motor and fuel cell during UDDS drive cycle with FLC
Powers (W)
20000 Filtered power Correction power Reference power
15000 10000 5000 0 5000
0
200
400
600
800
1000
1200
Time (s) Fig. 19 Filtered, correction and reference power during UDDS drive cycle
Powers (W)
4
× 10
4
Filtered power Correction power Reference power
3 2 1 0 1
0
200
400
600
800
1000
Time (s)
Fig. 20 Filtered, correction and reference power during NEDC drive cycle
1200
1400
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5 Conclusion In this paper, an energy management strategy based on the combination of frequency separation and fuzzy logic has been proposed and simulated using Matlab/Simulink. The New European Driving Cycle (NEDC) and the Urban Dynamometer Driving Schedule (UDDS) are selected to evaluate the performance and effectiveness of the proposed energy management strategy. Firstly, only the frequency separation strategy is simulated, the obtained results illustrate the problem of bound violating battery state of charge. Afterward, the combination strategy is simulated. Simulation results performed by applying the proposed strategy show the efficiency to split perfectly the energy between sources, taking into account the different flow dynamics constraints, and keeping the battery state of charge bounded. This protects the vehicle from unsafe operating mode and increases the lifetime of the various components of the vehicle.
References 1. Ahmadi S, Bathaee S, Hosseinpour AH (2018) Improving fuel economy and performance of a fuelcell hybrid electric vehicle (fuelcell, battery, and ultracapacitor) using optimized energy management strategy. Energy Convers Manag 160:74–84 2. Aouzellag H, Abdellaoui H, Iffouzar K, Ghedamsi K (2015) Modelbased energy management strategy for hybrid electric vehicle. In: 2015 4th international conference on electrical engineering (ICEE), Boumerdes, Algeria, pp 1–6. IEEE, December 2015 3. Cui P, Ding A, Shen Y, Wang YX (2019) Hybrid fuel cell/battery power system energy management by using fuzzy logic control for vehicle application. In: 2019 IEEE 3rd international conference on green energy and applications (ICGEA), Taiyuan, China, pp 132–135. IEEE, March 2019 4. Florescu A, Bacha S, Munteanu I, Bratcu AI, Rumeau A (2015) Adaptive frequencyseparationbased energy management system for electric vehicles. J Power Sources 280:410–421 5. Hemi H, Ghouili J, Cheriti A (2013) A real time energy management for electrical vehicle using combination of rulebased and ECMS. In: 2013 IEEE electrical power & energy conference, Halifax, NS, Canada, pp 1–6. IEEE, August 2013 6. Hemi H, Ghouili J, Cheriti A (2014) An optimal control solved by Pontryagin’s Minimum Principle approach for a fuel cell/supercapacitor vehicle. In: 2014 IEEE electrical power and energy conference, Calgary, AB, Canada, pp 87–92. IEEE, November 2014 7. Li H, Ravey A, N’Diaye A, Djerdir A (2019) Online adaptive equivalent consumption minimization strategy for fuel cell hybrid electric vehicle considering power sources degradation. Energy Convers Manag 192:133–149 8. Li X, Wang Y, Yang D, Chen Z (2019) Adaptive energy management strategy for fuel cell/battery hybrid vehicles using Pontryagin’s Minimal Principle. J Power Sources 440:227105 9. Macias Fernandez A, Kandidayeni M, Boulon L, Chaoui H (2020) An adaptive state machine based energy management strategy for a multistack fuel cell hybrid electric vehicle. IEEE Trans Veh Technol 69(1):220–234 10. Marzougui H, Kadri A, Amari M, Bacha F (2019) Energy management of fuel cell vehicle with hybrid storage system: a frequency based distribution. In: 2019 6th international conference on control, decision and information technologies (CoDIT), Paris, France, April 2019, pp 1853– 1858. IEEE
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11. Muñoz PM, Correa G, Gaudiano ME, Fernández D (2017) Energy management control design for fuel cell hybrid electric vehicles using neural networks. Int J Hydrogen Energy 42(48):28932–28944 12. Njoya S, Tremblay O, Dessaint LA (2009) A generic fuel cell model for the simulation of fuel cell vehicles. In: 2009 IEEE vehicle power and propulsion conference, Dearborn, MI, pp 1722–1729. IEEE, September 2009 13. Slouma S, SlamaBelkhodja I, Mustapha SS, Machmoum M (2016) Frequency separation control of energy management system for building. In: 2016 7th international renewable energy congress (IREC), Hammamet, pp 1–6. IEEE, March 2016 14. Snoussi J, Elghali SB, Benbouzid M, Mimouni MF (2018) Optimal sizing of energy storage systems using frequencyseparationbased energy management for fuel cell hybrid electric vehicles. IEEE Trans Veh Technol 67(10):9337–9346 15. Tremblay O, Dessaint LA (2009) Experimental validation of a battery dynamic model for EV applications. World Electr Veh J 3(2):289–298 16. Tremblay O, Dessaint LA, Dekkiche AI (2007) A generic battery model for the dynamic simulation of hybrid electric vehicles. In: 2007 IEEE vehicle power and propulsion conference, Arlington, TX, USA, pp 284–289. IEEE, September 2007 17. Wang T, Li Q, Yin L, Chen W (2019) Hydrogen consumption minimization method based on the online identification for multistack PEMFCs system. Int J Hydrogen Energy 44(11):5074– 5081 18. Zhang S, Pan N (2015) Supercapacitors performance evaluation. Adv Energy Mater 5(6):1401401
Renewable Energy
Physicochemical Characterization of Household and Similar Waste, for Efficient and IncomeGenerating Waste Management in Morocco, City of Mohammadia Akram Farhat, Kaoutar Lagliti, Mohammed Fekhaoui, and Hassan Zahboune Abstract This publication focuses on the characterization physicochemical of household and similar waste in Mohammadia city, with the aim of understanding the mutation of its components compared to the socioeconomic evolution of the Moroccan citizen, and justify the need to propose other more adequate solutions to ensure achievement objectives of the national household waste program by 2022. For this purpose, the study was made by district (industrial zone, popular area with average habitats, villa zone and rural area). A manual and careful sorting (nine categories) is carried out for this study. Thus, the results of this characterization (organic matter 54,94%, plastic 15,18%, paper and cardboard 9,72%, textile 7,46%, sanitary textile 5,82%, metals 2,20%, glass 1,89%, Wood 1,82% and Others 1,28%) revealed a dominance of organic matter and an increase in plastic rate that did not exceed 8% in the past. Added to this, the results of the analysis of physicochemical parameters (loss on ignition of the order of 60,26%, humidity rate quite high 59,05%, a total organic carbon (TOC) of 33,47%, and a Lower Heating Value (LHV) of 1840,3 kcal/kg). From these data, we were able to demonstrate the inefficiency of the direct burying solution (large quantity of leachate produced and the possibility of recovering more than 80% of this waste). Also, the high LHV opens the way to another possibility that was not even considered in the past (waste stabilization and Solid Recovered Fuel production). Keywords Household and similar waste · Physicochemical characterization · Recycling and recovery matter · Organic matter
A. Farhat (B) · K. Lagliti · M. Fekhaoui GEOPAC Research Center, Scientific Institute, Mohammed V University, Av. Ibn Batouta, B.P 703, 10106 Rabat, Morocco email: [email protected] H. Zahboune Laboratory of Electrical Engineering and Maintenance – LEEM, High School of Technology, University Mohammed 1st, Oujda, Morocco © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_63
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1 Introduction The household and similar waste (HSW) management is one of the main challenges facing Morocco. The factors combination such as population growth, urban expansion, the development of socioeconomic and production activities, as well as the changes in lifestyles and consumption patterns, generates a growing field of waste, either average 0,75 kg/day [1]. The annual cost of the damage generated by the waste, all types combined, amounts to 1,7 billion Dirhams, for the case of municipal waste, this cost is 1,487 billion Dirhams or 0,4% of the GDP (World Bank Report, 2003) [1]. The annual cost of the damage generated by the waste, all types combined, amounts to 1,7 billion Dirhams, for the case of municipal waste, this cost is 1,487 billion Dirhams or 0,4% of the GDP (World Bank Report, 2003) [1]. The average cost is 65 DH per ton against 20 DH when the service was entrusted to the municipalities. The highest cost of around 120 DH per ton collected is recorded in Kenitra and Al Hoceima. For Casablanca, the city pays nearly 40 million DH per year to ensure the storage and processing of nearly a million tons (Ministry of Energy Mines Water and Environment, 2013) [2]. HSW management remains problematic almost for all local authorities in Morocco. The large quantities of waste produced, the financial shortfall, the organizational, institutional and managerial weaknesses, the shortage of qualified personnel, the insufficient infrastructure and the low level of environmental education constitute the important elements of this problem. Added to this, the inadequacy of Western solutions to local specificities following the report on Infrastructure Reform (Ministry of Energy Mines Water and Environment, 2013) [2]. Thus, in view of the acuteness of the waste problem and the importance of the stake on the environment and the health of the population, all municipalities in Morocco must engage in new approaches adapted to our needs, and responding to the diversity of our waste, in order to achieve the strategic objectives of the national household waste management program (NHWP) which essentially aims to attain: a collection rate of 100% in 2030–100% of urban centers must benefit from controlled landfills and close other landfills by 2022—a 20% recycling rate by 2022 [3]. Faced with this situation, and in order to bring our contribution and our knowhow in the valorization and the transformation of the HSW, this work represents the first step towards the realization of a complete study of feasibility and profitability of an autonomous unit for recycling and recovery of HSW in Morocco. To this end, and before proposing our process of mechanobiological treatment of HSW. It seems judicious to begin with a physicochemical characterization of this waste, in order to understand its compositions, to justify our choice of sorting process, transformation and recovery. This study was carried out at the MohammadiaBenslimane Technical Landfill Center (TLC) in collaboration with the ECOMED group specialized in solid waste management.
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2 Materials and Methods 2.1 Study Area: TLC MohammediaBenslimane The TLC receives household and similar waste from the prefecture of Mohammedia, the province of Benslimane and local companies. It occupies an area of 47 ha, with a storage capacity of 500 tons per day generated by a total population of 518 840 inhabitants [4], experiencing a growth rate of 3,06% per year in comparison with that of Morocco, which is in the order of 1,25% [4]. This population is spread over nine rural and urban communes (see Fig. 1). Sampling Our sampling approach is based on the level of socioeconomic and industrial development, following these four sectors were chosen: – Sector I: Mohammedia Industrial Zone. – Sector II: Neighborhoods Ennasr & Errachidia (popular area with average habitats). – Sector III: Neighborhoods of the Sun & La Siesta (villa zone). – Sector IV: Rural commune Sidi Moussa Ben Ali (rural area). Sorting by category has been achieved by applying the following steps: – Sectorization of the area according to the habitat type and the life way of the inhabitants. – Obtaining samples from different sectors in a random manner. – Manual sorting by samples category. – Measure the weight of each category (Fig 2). The minimum quantity of samples deemed representative for this approach is superior than 500 kg [5], (Table 1).
Fig. 1 Distribution of annual tonnage buried by zone (ECOMED Mohammedia)
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Fig. 2 Geographic location of the four sectors studied (ECOMED Mohammedia)
Table 1 Masses of samples sorted by sector Sectors
Sector I
Sector II
Sector III
Sector IV
weight (kg)
2315
519
1012
503
2.2 Chemical Analysis Methods for Household and Similar Waste The characterization can be supplemented by laboratory analyzes (Table 2). These analyzes can be relevant to complete compositional results from sorting.
3 Results and Discussions The HSW physicochemical characterization has given us the opportunity to set up the necessary reference data that can be used to set up a sorting, processing and recovery center for the management and treatment of waste in the controlled landfill.
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Table 2 Physicochemical methods of analysis used Formula used Density (T/m3 )a Humidity
(%)b
ρ =m/v [6] H = [(m0 − m1 )/ m0 ] × 100 [7]
Organic Matter (%)c
MO = [(m1 − m2 )/m1] × 100 [8]
Ash rate (%)
Ashes = 100 – MO% [8]
pH
pH meter with glass electrode in a suspension 1/10 [9]
TOC (%)
MO = TOC% × 1,8 [10]
LHVd
LHV = 40(P + T + B + F) + 90R − 46 W [11]
a m:
Masse de l’échantillon, v: Volume du camion ou casier
b m : Masse initiale avant séchage, m : Masse finale après séchage 0 1 c m : Masse initiale avant calcination, m : Masse finale après calcination 1 2 d P: paper and cardboard, T: total textiles, B: wood, F: fermentable, R: plastics
et W: average waste
humidity (%)
Table 3 Results of sorting Mohammedia’s HSW Categories
Sector I
Sector II
Sector III
Sector IV
Average
Fermentable waste
42,75%
60,51%
52,30%
64,20%
54,94%
Plastics and rubber
18,67%
13,74%
15,43%
12,88%
15,18%
Paper and cardboard
12,17%
8,25%
10,86%
7,60%
9,72%
Textiles
14,44%
5,24%
5,48%
4,68%
7,46%
Sanitary textiles
3,16%
7,52%
4,73%
7,87%
5,82%
Glasses
2,23%
1,72%
2,79%
0,84%
1,89%
Metals
2,80%
1,58%
2,88%
1,54%
2,20%
Wood
2,13%
0,55%
4,20%
0,40%
1,82%
Other
1,90%
0,67%
1,95%
0,60%
1,28%
3.1 Average Physical Composition of Mohammedia’s HSW The average sorting results obtained across the four sectors show that the fermentable matter generated during the sorting period is 54,94%, followed by plastic and rubber 15,18% then paper and paperboard 9,72% (Table 3). The comparison of the results obtained with those of the national scale shows a similarity in all the components of the sample (Table 4). With a percentage increase in plastic, mainly due to the high use of plastic bags. Recovery and Recycling of Recoverable Matters The average recovery of recoverable waste entering the TLC is 38,27% of the total quantity of sorted waste, which represents more than one third of the deposit total mass (Table 5).
614 Table 4 Comparison of Mohammedia HW Composition with Morocco (Department of Commerce, Industry, 1992 and Department of the Environment, 2002)
Table 5 Average Rates of Each Recyclable Fraction of the City of Mohammedia
Table 6 Results of the physicochemical characterization of HSW
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Mohammedia’s HSW
Fermentable waste 54,94%
Morocco’s HSW 50 à 70%
Plastics and rubber 15,18%
6 à 8%
Paper and cardboard
9,72%
5 à 10%
Metals
2,20%
1 à 4%
Glassdebris of ceramics
1,89%
1 à 2%
Various (wood, textile, others)
16,38%
16%
Recyclable matter
Average rates (%)
Plastics and rubber
15,18
Paper and cardboard
9,72
Textiles
7,46
Glasses
1,89
Metals
2,20
Wood
1,82
Total
38,27
Parameters pH Density in the TLC (T/m3 ) Humidity level (%)
Value 6,5 0,82 59,05
TOC (%)
33,47
Volatile matter (%)
60,26
Ashes (%) LHV (Kcal/kg)
39,74 1840,3
3.2 Physicochemical Compositions of Mohammedia’s HSW The results of the physicochemical characterization were obtained on a representative sample, taken at random, which contains all the fractions of the waste determined during sorting. The overall results are reported below (Table 6). As a result, the pH meter showed 6,5. These wastes are low acid or even neutral. The choice of a biological treatment technique becomes obvious. The average density in the TLC is in the order of 0, 82 T/m3 , this is due to the forced compaction mechanical treatment applied by the operating department.
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Table 7 LHV of waste by sector studied LHV (Kcal/kg)
Sector I
Sector II
Sector III
Sector IV
Average
1950
1803,1
1775,2
1832,9
1840,3
Otherwise, the average humidity has reached 59,05% because of the high presence of fermentable waste, this justifies the high rate of volatile matter which is in the order of 60,26%. Humidity and density levels are high, they prove the inefficiency and inadaptability of the burying solution in relation to the nature of our household waste, especially the high amount of leachate produced. Indeed, the dominance of organic matter that exceeds 54% of total waste confirms the durability and relevance of a choice of mechanicalbiological treatment. However, the rates of TOC and ashes are respectively 33,47% and 39,74%, confirming the quality of the digestate, and the possibility of its reuse as fertilizer or where we want to achieve energy and financial autonomy via a process of methanation. Though, the most unexpected result is that of the LHV which shows an average of 1840,3 kcal/kg (Table 7), this value far exceeds the average of developing countries and is positioned in the range of industrial countries (1500–2700 kcal/kg) [12]. This high LHV is due to a relatively high rate of plastic (15,18%). Today, the HSW follow the socioeconomic development of the Moroccan citizen, and opens the door and the possibility to consider the solution of the stabilization of waste, and the Solid Recovered Fuel production which was not even questionable in the past.
4 Conclusion The results show that our household and similar waste contains a real wealth. They prove that the direct burying solution is absolutely not the most optimal solution neither in time nor in space. Indeed, with the recovery and transformation of a rate of 54% of organic matter and 38% of recyclable matter, we largely exceed 80% of all waste received at the TLC. This implies the prolongation of the life of the landfill (over three times in minimum), a very significant reduction in the production of leachates, and the possibility of having a total energy and financial autonomy at the level of the landfill. Thus, in a spirit of eco and social entrepreneurship, we are currently working on an autonomous household waste management model, which will contribute to the change of the municipality’s strategy, and move from delegated management to incomegenerating strategies in one of the most difficult areas to manage in developing countries. A total energy and financial autonomy dedicated to our communes so that they can manage and not suffer from the parallel damage of a consumption evolution and the socioeconomic level of the Moroccan citizen.
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Our next actions will focus on the construction of an anaerobic digestion protocol adapted to the nature of our waste and our human and financial resources, in order to discuss the quantity and quality of the methane that can be produced. It will also clarify the mixture that will maximize the production of methane compared to other harmful gases, evaluate the need for use of a purification stage and the possibilities that go with it, discuss the possibility of processing the digestate residue to a fertilizer quality by specifying its composition. Also, we will discuss the possibility of increasing our electrical efficiency by using a combined cycle with a steam turbine.
References 1. Soudi B, Chrifi H (2008) Municipal waste management options adapted to the contexts of Southern countries, 80 p 2. GIZ (2014) Rapport sur la gestion des déchets solides au Maroc, Avril 2014 3. Ministère de l’intérieur (portail national des collectivités territoriales). http://www.pncl.gov. ma/fr/grandchantiers/Pages/PNDM.aspx 4. GCPH 2014: General Census of the Population and Housing of Morocco 5. AFNOR (June 2013) Norm NF X30445  Household and similar waste  Consisting of a sample of household and similar waste in bulk 6. AFNOR (December 2013) Norm NF X30408  Household and similar waste  Characterization method  Analysis on gross product 7. AFNOR (December 2013) Norm NF X30466  Household and similar waste  Characterization methods  Analysis on dry product 8. AFNOR (December 2011) Norm NF EN 13039  Soil improvers and growing media Determination of organic matter and ashes 9. AFNOR (May 2005) Norme NF ISO 10390 – Soil Quality Determination of pH 10. Moletta, R (2009) Waste treatment. p 309 11. Aloueimine SO (2006) Methodology of characterization of household waste in Nouakchott: contribution to waste management and decisionmaking tools, table 25, p 120 12. Alda Y, et al (2013) Characterization of the household solid waste of the municipality of AbomeyCalavi in Benin. E3 J Env Res Manag 4(11):0368–0378
Experimental Analysis on Internal Flow Field of Enhanced Heat Transfer Structure for Clean Gas Bus Engine Compartment Jiajie Ou and Lifu Li
Abstract Heat dissipation efficiency of clean gas bus cabin is undermined by both inappropriate design of air passages in the engine compartment and the excessively long paths for hot air to be discharged from the cabin. In order to verify the temperature field homogenization enhanced heat transfer method (TFH) in the engine compartment of cleangasbus, a temperature field experimental system for LPG city bus (LPGB) engine compartment based on infrared imaging technology was built. The temperature field in the semienclosed space engine compartment was noninterfering, visualizing and continuously measured. At the same time, under the five working conditions of the LPG engine, 16channel temperature sensors were used to collect the temperature of key components changing with time. The experimental results showed that compared with the typical structure, the temperatures of the radiator inlet water and the hightemperature exhaust manifold of the enhanced heat transfer structure decreased by 10.8% and 25.4% respectively. The engine compartment with the enhanced heat transfer structure had the following characteristic of “minimum temperature gradient in core flow region and maximum temperature gradient on the thermal boundary”, which conforms to the TFH optimization model which helped to strengthen the heat dissipation in the cabin. Keywords Infrared imaging technology · Heat transfer enhancement · Clean gas bus · Engine compartment · Structural design
J. Ou (B) Department of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, People’s Republic of China email: [email protected] L. Li Department of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, People’s Republic of China © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_64
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1 Introduction In China, most buses for public transport are running on clean gas engines, especially liquefied petroleum gas (LPG) and liquefied natural gas (LNG) engines. However, these buses suffer the problem of overheating in the engine compartment. The flow field condition and the thermal environment inside the engine compartment are very complex and the layout of components determines the flow of cooling air. The poor layout will cause overheating inside the engine compartment. Consequently, a series of problems will arise including the overheated engine, lower volumetric efficiency, abnormal combustion, poor lubricating performance, and vapor lock in the fuel system. Not only will overheating increase gas consumption, but it will also deteriorate dynamic performance. To avoid such problems, it is necessary and important to study the heat dissipation characteristics and flow field distribution inside the engine compartment. At present, there are two kinds of measurement technologies to study the air distribution in the closed cabin: single point measurement and fullfield measurement. Single point measurement instruments mainly include hotwire anemometers [1–3], hotsphere anemometers [4] and ultrasonic anemometers. When the threedimensional scale in the engine compartment of a clean gas bus reaches 3–4 m, the measurement of the velocity field by multipoint hot wire anemometers will interfere with the flow field [5]. Fullfield measurement technology generally refers to particle image velocity (PIV) [6]. It can obtain the overall velocity field, without interfering with the flow field in the cabin [7]. However, in order to measure the full velocity field in the clean gas bus cabin by PIV technology, it is necessary to make a transparent simulating model. And the engine compartment cannot be tested on the road, but be simulated on the engine bench. The above factors restrict the application of PIV technology on the velocity field detection of an engine compartment under real road driving conditions. The engine compartment is arranged at the rear of the clean gas bus, separated from the passenger cabin by a baffle. It is difficult to obtain the real and reliable flow field without damaging the boundary conditions of heat transfer in the compartment and affecting the airflow [8]. Therefore, it is necessary to design and develop a new experimental system for detecting the flow field of the engine compartment [9, 10]. In order to verify the temperature field homogenization enhanced heat transfer method (TFH) in engine block area for enhancing heat transfer in the engine compartment of cleangasbus, a temperature field experimental system of the LPGB engine compartment based on infrared imaging technology is built. The temperature field experimental method based on infrared imaging technology is proposed. Based on the five working conditions of LPG engine, the temperature field time series in the semienclosed space engine compartment is multioperating, noninterfering, visualizing and continuously measured. At the same time, 16channel temperature sensors are used to collect the temperature of key components changing with time. Comparing the heat dissipation performance of the typical structure and enhanced
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+Y
Z
+X
1.intake manifold 2.intake baffle 3..intercooler 4.radiator 5.fan 6.right outlet 7.air compressor 8.exhaust pipe 9.exhaust manifold 10.engine body 11.left inlet Fig. 1 The layout of the typical engine compartment
heat transfer structure, the guiding significance of TFH optimization model for engine compartment structure design and flow field optimization is verified. Figure 1 shows the typical structure of a rearmounted engine compartment of clean gas buses. As can be seen, the compartment can be divided into the radiator assembly area and the engine block area, each with different heat transfer principles between hightemperature components and cooling air in the engine compartment.
2 The TFH in the Engine Block Area This research proposed the temperature field homogenization enhanced heat transfer method in the core flow region for enhancing heat transfer of the engine block area, which was abbreviated as TFH. According to the thermoelectric analogy, Guo et al. introduced a physical quantity called entransy, which is half the product of heat capacity and temperature [11]. E vh =
1 Q vh T 2
(1)
where Q vh is heat capacity for constant volume and T denotes the temperature of the object. Air entransy is related to its ability to transfer heat. Such heat transfer ability, also called entransy dissipation, indicates the irreversible loss of entransy in the heat transfer process. For a steadystate fluid convective heat transfer process without the internal heat source, the energy equation can be expressed in the following vector form. ρc p U∇T = ∇(λ∇T )
(2)
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Multiplying T on both sides of Eq. (2) and then transforms into:
T2 ρc p U∇ 2
= ∇(λT ∇T ) − λ∇T 2
(3)
where ρc p U ∇ T 2 /2 represents heat transport caused by the movement of air microclusters in the process of convective heat transfer, ∇(λT ∇T ) represents the diffusion of entransy in the air, and −λ∇T 2 denotes entransy dissipation. In the equilibrium equation of entransy, the change in entransy is the sum of entransy flow and entransy dissipation [12, 13]. To enhance convective heat transfer, entransy dissipation of air in the core flow region must be small. The smaller the entransy dissipation, the lower the modulus of the temperature gradient, implying a more uniform air temperature field in the core flow region, and hence, smaller thermal resistance [14]. The above line of reasoning accords with the evaluation criterion of heat transfer intensity according to uniformity of temperature. For the engine compartment with multiple heat sources, complex passage structure, multiple inlets and outlets, and driving air resistance less affected by cabin structure, TFH is proposed. First, the TFH model in the engine block area is constructed with the functional variation method using the Lagrangian multiplier, with the aim to minimize the temperature gradient of air passages in the core flow region. Additional volume force constraint is added to the air momentum equation for changing the pure pressure driving the flow field and for obtaining the optimal vector field and flow path to enhance heat transfer in the cabin. In the engine block area, merely enhancing convective heat transfer is not enough, it is necessary to optimize the air velocity field and flow path in the cabin at the same time so that hightemperature air can be discharged out of the compartment in the shortest possible path. According to the theory of the fluid velocity boundary layer, the absolute value of temperature gradient in the core flow region of air passages in the engine block area is minimized. Coupled with the continuity equation and the energy conservation equation, a new Lagrangian functional model is constructed to achieve heat dissipation enhancement through TFH in the core flow region. The idea of TFH is shown in Fig. 2. ˚ J=
(∇T )2 + A∇ · U + B λe f f ∇ 2 T + Sτ − ρc p U · ∇T dV
(4)
Ω
Calculate the temperature variation for Eq. (4) and make it zero: −2∇ 2 T + ρc p U · ∇ B + λe f f ∇ 2 B = 0
(5)
Calculate the velocity variation for Eq. (4) and make it zero: −∇ A − ρc p B∇T = 0
(6)
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Fig. 2 The idea of TFH Entransy dissipation minimization
Convective heat transfer ability of limited cooling air in the compartment strengthened
The TFH model with heat source in the engine block area
Enhanced heat transfer structure design
The absolute value of temperature gradient in the core flow region of the air channel is minimized
Additional volume force constraints were added to the air momentum equation to change the pure pressure driven flow field
The optimal velocity vector field and flow path were obtained
A new Lagrangian functional model is constructed
Under given boundary conditions: 2∇T − ρc p U B − λe f f ∇ B δT + λe f f Bδ(∇T ) = 0, AδU = 0
(7)
The optimal velocity field of air convective heat transfer can be obtained when the temperature gradient in the core flow region of air passages is the smallest. Comparing Eq. (6) with the momentum conservation equation gives ∇ A = −ρ(U · ∇U) − ∇ p + μe f f ∇ 2 U
(8)
Then the momentum equation becomes ρ(U · ∇)U + ∇ p − μe f f ∇ 2 U = F, F = ρc p B∇T
(9)
Therefore, Eq. (9) is coupled with the continuity equation and the energy conservation equation, where the additional volume forces F and scalar B are determined using the constraint Eq. (5) and the boundary condition Eq. (7). The optimal velocity field corresponding to heat dissipation enhancement through TFH in the core flow region can be obtained.
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3 Experimental Analysis In order to verify the guiding significance of the TFH model for engine compartment structure design, an experimental system of the LPGB engine compartment temperature field based on infrared imaging technology was designed and developed, and the experimental results were analyzed.
3.1 The Enhanced Heat Transfer Structure Design The enhanced heat transfer structure design based on the TFH was applied in the experiment, including the improved radiator assembly azimuth structure, the setting of the deflector, the position of air inlets and outlets and the structure of cabin roof. (1) The improved radiator assembly azimuth structure In order to shorten the distance between the air inlet and the radiator assembly and improve the thermal environment around the radiator assembly, an improved radiator assembly azimuth structure is proposed. Compared with the typical structure seen in Fig. 3(a), the improved structural design, as shown in Fig. 3(b), has the radiator assembly system turned counterclockwise by 90° and the inlet baffle removed. These changes aim at enhancing the convective heat transfer at the heat transfer boundary of the radiator assembly and increasing the airflow rate to the engine block area. (2) The deflector behind the fan To avoid air leakage and ensure all cooling air entering the cabin from the left intake can flow through the intercooler and radiator, the left intake and radiator assembly are designed as a whole. At the same time, in view of the low air velocity near the exhaust manifold of the engine, which is not conducive to enhancing the convective heat transfer, a deflector is added behind the fan of the radiator assembly. As shown in Fig. 3(b), the deflector guides part of the highspeed airflow to the exhaust manifold, so as to improve the air velocity in this area and strengthen the heat transfer.
a) Typical structure
b) Enhanced heat transfer structure
Fig. 3 Radiator assembly improvement of the enhanced heat transfer structure
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Fig. 4 Compartment roof improvement of the enhanced heat transfer structure
(3) The position of air inlets and outlets The rear outlet of the enhanced heat transfer structure is set near the top of the hatch, as shown in Fig. 4(b). In order to avoid the formation of hot air vortices in the compartment roof, and to make full use of the characteristic of hot air rising and cold air sinking, two roof outlets are set on the top of the engine compartment.
3.2 Experiment Working Conditions and Equipment Considering the LPGB’s lowspeed hightorque road test condition, the vibration in the engine compartment is intense, which will affect the accuracy of continuous temperature field measurement in the compartment. At the same time, the engine compartment is a semienclosed space, which is not conducive to the fixing of the infrared imager. In order to obtain the sequence of continuous infrared imaging temperature field maps and analyze the poor performance of heat dissipation in the typical cabin, the engine is set at various steadystate conditions of 600 r/min, 1000 r/min, 1400 r/min, 1800 r/min and 2000 r/min [15]. When the water temperature of the engine is stable, the rear cabin door of the engine is opened, and the time series of infrared imaging continuous temperature field maps of the two structures are obtained from the +Y direction (looking forward from the rear of the car). To cooperate with the normal operation of LPGB and the experimental site, the ambient temperature in Guangzhou of the two experiments are 18–20 °C in autumn, which is far from the 39–40 °C of the worst working conditions in summer. However, it can be predicted that, if the experimental data obtained under cool conditions can reflect the difference of heat dissipation characteristics before and after structural improvement of the engine compartment, the difference will be more obvious in hot, lowspeed and hightorque adverse conditions. 16channel Ktype temperature sensors are arranged to test the temperature and its variation with time of key components under various working conditions. The locations and numbers of sensors in typical engine structure and enhanced heat transfer structure are shown in Table 1.
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Table 1 Location of sensors No.
Location
1
Intercooler intake pipe
9
2
Intercooler outlet pipe
10
Cabin bottom
3
Cabin roof
11
Engine body
4
Cabin left
12
Radiator intake pipe
5
Cabin right
13
Radiator outlet pipe
6
Cabin front
14
Exhaust pipe
7
Cabin rear
15
Exhaust manifold
8
Fan rear
16
Turbocharger pump wheel
100
1000r/min 1800r/min
C
600r/min 1400r/min 2000r/min
80
Temperature
Temperature
C
120
No.
60 40 20 0
1
2
3
4
5
6
7
8
9 10 11 12 13
Sensor number
a) Typical structure
Location Intake manifold
120 110 100 90 80 70 60 50 40 30 20 10 0
600r/min 1400r/min 2000r/min
1
2
3
4
1000r/min 1800r/min
5
6
7
8
9 10 11 12 13
Sensor number
b) Enhanced heat transfer structure
Fig. 5 Comparision for the stable temperature of 1 to 13 sensors under multiple conditions
The test instruments for road experiments include FLIR T640 infrared imager, 16channel Ktype temperature sensors, NI USB9213 data acquisition module, automotive power converter, power cord, hightemperature tape, clamp, and wire binding. Ktype temperature sensors are installed on key components of the engine compartment before testing.
3.3 Experimental Results 3.3.1
Key Components Temperature
The steadystate temperature distribution of No. 1–13 temperature sensors in typical structure and enhanced heat transfer structure under different engine conditions are compared in Fig. 5. According to sensors No. 4–8, in the typical engine compartment, from idle to high speed, the temperatures around the cabin was at a high level of 50–75 °C. The
Experimental Analysis on Internal Flow Field… 550
600r/min 1400r/min 2000r/min
500
625 500
1000r/min 1800r/min
400
Temperature
450
Temperature
600r/min 1400r/min 2000r/min
450
400 350 300 250
1000r/min 1800r/min
350 300 250 200 150
200 14
15
16
100
14
15
Sensor number
a) Typical structure
16
Sensor number
b) Enhanced heat transfer structure
Fig. 6 Comparision for the stable temperature of 14 to 16 sensors under multiple conditions
temperature around the cabin in the enhanced heat transfer structure was between 30– 60 °C. It showed that in the enhanced heat transfer structure, the cooling air in the core flow region of the engine cabin passages was smooth, which can effectively avoid the formation of air whirlpool and hot air retention. The hot air can be discharged out of the engine compartment in the shortest possible path to improve the heat dissipation performance of the cabin. The temperature of the intake manifold of typical structure exceeded 80 °C under high speed condition, while that of enhanced heat transfer structure was below 65 °C. The results showed that the cooling air from the fan was directly blown to the intake manifold for effective cooling after changing the azimuth of the radiator assembly. Though the experiment ambient temperature was 18–20 °C in autumn, which is far from the 39–40 °C of the worst working condition in summer, the water temperature of the typical structure reached 93 °C at high speed conditions, while that of the enhanced heat transfer structure still maintained the temperature range of 80–83°C under highspeed conditions and the engine worked normally. Sensors No. 14–16 measured the temperatures of the exhaust manifold, exhaust pipe, and exhaust gas turbocharger pump wheel. As shown in Fig. 6, the steadystate temperature of hightemperature components in enhanced heat transfer structure can be reduced by up to 25.4% under different working conditions, which indicated that the hot air in typical structure cannot be transferred out in time.
3.3.2
Infrared Imaging Temperature Field Maps
Under the 600 r/min, 1000 r/min, 1400 r/min, 1800 r/min and 2000 r/min steadystate conditions, the time series of infrared imaging temperature field maps of hightemperature components in typical structure (left) and enhanced heat transfer structure (right) are shown in Fig. 7, respectively. From the comparison of temperature field maps before and after the improvement of the engine compartment under the abovementioned conditions, it can be seen that
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(a) 600 r/min
(b) 1000 r/min
(c) 1400 r/min
(d) 1800 r/min Fig. 7 Time series of infrared imaging temperature field maps of hightemperature components in typical structure(right) and enhanced heat transfer structure(left)
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(e) 2000r/min Fig. 7 (continued)
the temperature in the typical structure was higher from low to highspeed conditions, especially in radiator assembly area and engine block area. Moreover, the temperature gradient of the heat transfer boundary of the hightemperature components in the typical structure was too small, which was detrimental to the enhanced heat transfer between the hightemperature components and the surrounding cooling air, affecting the heat transfer efficiency in the cabin. In enhanced heat transfer structure, the average temperature and the temperature gradient in the core flow region of air passages were lower, which was beneficial to enhancing the convective heat transfer at the thermal boundary. As shown in Fig. 7(e), we can judge the flow paths of the cooling air in the enhanced heat transfer structure from Fig. 7. Part of the cooling air entered the cabin from the right intake, cooling the engine intake manifold, engine body and engine exhaust manifold through the top channel, and then being transferred outside the cabin from the left outlet; the other part of air entered the compartment and cooled the intake manifold, engine cylinder side, skirt and oil pan, and then discharged from the bottom of the compartment. The heat was taken out of the cabin in the shortest path, and the heat dissipation performance of the engine cabin was obviously improved in this structure.
4 Conclusion The temperature field experiment system of the LPG engine compartment based on infrared imaging technology is designed and developed. The experimental results show that the water inlet temperature of the radiator and the temperature of the hightemperature components such as the exhaust manifold decrease by 10.8% and 25.4% respectively, compared with the typical structure. The time series of the infrared imaging temperature field in the cabin are analyzed, and it is found that compared with the typical structure, the engine compartment with the enhanced heat transfer structure has the following characteristic of “minimum temperature gradient in core flow region and maximum temperature gradient on thermal boundary”, which conforms
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to the TFH optimization model which helped to strengthen the heat dissipation in the cabin. Acknowledgements. This project is supported by National Natural Science Foundation of China (Grant No. 51605104) and Scientific Research Project of Guangzhou Municipal University (Grant No. 1201610296).
References 1. Tang ZQ, Guo Y, Cui XJ, Jiang N et al (2017) Turbulent characteristics in the near fields of gasper jet flows in an aircraft cabin environment: Intermittently energetic coherent structures. Build Environ 117:73–83 2. Kim Y, Song SJ (2019) Unsteady measurement of core penetration flow caused by rotating geometric nonaxisymmetry in a turbine rotorstator disc cavity. Exp Thermal Fluid Sci 107:118–129 3. Frantisek L, Ondrej P, Jan J, Jan T et al (2019) The automotive ventilation test case: Investigation of the velocity field downstream of a benchmark vent using smoke visualization and hotwire anemometry. Proc Inst Mech Eng Part D J Automob Eng 233(8):2146–2160 4. Liu W, Wen JZ, Chao JY, Yin WY, Shen C et al (2012) Accurate and highresolution boundary conditions and flow fields in the firstclass cabin of an MD82 commercial airliner. Atmos Environ 56:33–44 5. Hu B, Li XS, Fu YX et al (2019) Experimental investigation on the flow and flowrotor heat transfer in a rotorstator spinning disk reactor. Appl Therm Eng 162:14316 6. Souflas K, Perrakis K, Koutmos P (2020) On the turbulent flow and pollutant emission characteristics of disk stabilized propaneair flames, under inlet mixture stratification and preheat. FUEL, 260: UNSP 116333 7. Phuon NL, Quang TV, Khoa ND (2020) CFD analysis of the flow structure in a monkey upper airway validated by PIV experiments. Respir Physiol Neurobiol 271:103304 (2020) 8. Khaled M, Faraj J, Harika E, et al (2018) Impact of underhood leakage zones on the aerothermal situationExperimental simulations and physical analysis. Appl Thermal Eng 145:507–515 9. Khaled M, Hage HE, Harambat F et al (2015) Energy management in car underhood compartmenttemperature and heat flux analysis of car inclination effects. Heat Transfer Eng 36(1):68–80 10. Khaled M, Rab MGE, Hachem F et al (2016) Experimental study of the flow induced by a vehicle fan and the effect of engine blockage in a simplified model. Int J Autom Technol 17(4):617–627 11. Guo ZY, Zhu HY, Liang XG (2007) Entransy  a physical quantity describing heat transferability. Int J Heat Mass Transf 50(13–14):2545–2556 12. Chen Q, Liang XG, Guo ZY (2013) Entransy theory for the optimization of heat transfer  a review and update. Int J Heat Mass Transf 63:65–81 13. Zhao T, Liu D, Chen Q (2019) A collaborative optimization method for heat transfer systems based on the heat current method and entransy dissipation extremum principle. Appl Therm Eng 146:635–647 14. Cebeci T (2009) Computational Fluid Dynamics for Engineers: From Panel to NavierStokes Methods with Computer Programs. Phoenix Lieb Press, New Haven 15. GBT125422009 (2004) Road test method for automotive thermal balance capacity. China Standards Press, Beijing (in Chinese)
Trade Openness and CO2 Emissions in Morocco: An ARDL Bounds Testing Approach A. Jabri and A. Jaddar
Abstract This Study investigates the nexus between trade openness, energy consumption, economic growth, population density and Carbone dioxide CO2 emissions in Morocco CO2 during the period 1971–2014. Using the Autoregressive Distributed Lag (ARDL) bounds test, we find that there is a long term relationship between the variables of the model. The results show that energy consumption and economic growth have statistically significant positive effects on CO2 emissions both in the shortrun and longrun. The estimated coefficient for openness and population are positive and insignificant in the long term and these two variables are significant and respectively positive and negative in the short term. Economic growth has a positive impact on carbon emissions in both the long and short term. To conclude this research we suggest some recommendations for policy makers to undertake actions in order to develop alternative clean energies that emit less CO2 and contribute to more economic growth without damaging the environment by redirecting investment towards less polluting sectors. Keywords Trade openness · Energy consumption · Economic growth · Population density · Carbon emissions · ARDL
1 Introduction In recent decades, Morocco has adopted several structural reforms and has developed an economic model based on openness and trade liberalization. It is considered to be one of the most liberal and open countries in North Africa [1]. These political choices have different goals among which for instance to improve the balance A. Jabri (B) Team ERMATEFC of Laboratory, LARMATIF University of Mohammed Premier, ENCGO, Oujda, Morocco email: [email protected] A. Jaddar Team ROSA of Laboratory MAO, University of Mohammed Premier, ENCGO, Oujda, Morocco © Springer Nature Singapore Pte Ltd. 2021 B. Hajji et al. (eds.), Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems, Lecture Notes in Electrical Engineering 681, https://doi.org/10.1007/9789811562594_65
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of payments, create growth and employment and to attract foreign direct investments that generate foreign currency to support imports. However, these political and economic choices may affect negatively the quality of the environment through CO2 emissions. The relationship between openness of trade and environmental degradation has been highly discussed in economic literature. However, the results remain mitigated in totality. Several studies have been conducted since 1970 to test the relationship between these variables, but the results remain mixed in both developed and developing countries. In the case of Morocco, of course, the open economy policy adopted since the 1980s is expected to play an important role in promoting growth. To this end, Morocco has signed several free trade agreements with different countries in order to boost economic growth. But to support this growth, Morocco remained dependent on the use of polluting energy resources for several years. In the empirical literature, [2] examined the relation between trade openness and carbon emissions in the case of Sri Lanka during the period of 1960–2006 using the cointegration and the causality tests. He deduced a non long run relationship between trade openness and carbon emissions but rather a the short run equilibrium between these variables. [3] examined the relationship between economic growth, energy consumption, trade openness, population density, and carbon dioxide (CO2 ) emissions in Bangladesh for the period of 1975. By applying ARDL bounding tests, he found out that energy consumption has statistically significant positive effect on CO2 emissions both in the shortrun and longrun. The impact of population density is significant in longrun, but not in short run and economic growth and trade liberalization are negative and insignificant both in shortrun and longrun. This research paper first aims to fill in the literature gap in the case of Morocco using the ARDL approach and then tries to conclude with some recommendations for policy makers. However, in this paper we examine the relationship between trade openness, energy consumption, economic growth and population density in Morocco during 1971–2014. The rest of this paper is organized as follows: Sect. 2 discusses data and methodology. Section 3 presents empirical results and finally conclusion is presented in Sect. 4.
2 Econometric Techniques 2.1 Data and Empirical Specification The data used in this paper comes from the World Development indicators database (worldbank.org/indicator). The yearly data consists of openness, GDP per capita used as a proxy of economic growth, population density, energy consumption (EC) and CO2 emissions (metric tons per capita) for the sample period from 1971 to 2014. All variables were transformed into logarithms namely LnOP, LnGDP, LnPOP, LnEC and LnCO2 . In order to estimate the effects of our index variables on carbon dioxide emissions, our model estimated in this work takes the following form.
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.8
8.5
.6
8.0
LnEC, LnGDPC
LnCO2
.4 .2 .0 .2 .4 .6
GDPC
7.5 7.0 6.5 6.0 5.5
.8
5.0
1975 1980 1985 1990 1995 2000 2005 2010
1975 1980 1985 1990 1995 2000 2005 2010
Year
Year
(b)
(a) OP
POP
4.5
LnOP,LnPOP
4.0 3.5 3.0 2.5 2.0 1.5 1.0 1975
1980
1985
1990
1995
2000
2005
2010
Year
(c) Fig. 1 Evolution of CO2 emissions (a), Energy consumption and economic growth (b), Economic opening and population density (c)
Ln CO2t = β0 + β1 Ln GDPCt + β2 Ln OPt + β3 Ln ECt + β4 Ln POPt + εt
(1)
Where CO2 represents CO2 emissions, GDPC represents GDP per capita, EC represents energy consumption, FDI represents Foreign Direct Investment (% of GDP), and ε denotes stochastic error term, normally distributed with zero mean and constant variance. The stochastic error term is assumed to capture all other variables that may influence CO2 emissions that are not in the model. β1 , β2 , β3 and β4 are the slopes of the explanatory variables while β0 is the drift parameter (Fig. 1). To conduct this study, we used the ARDL (Autoregressive Distributed Lag) bounds testing approach to test the effect of economic openness, economic growth, energy consumption, population density on carbon dioxide emissions in Morocco. This approach has several empirical advantages and the ARDL model of our approach takes the following form: Ln CO2t =β0 + +
p i=1
q
β1i Ln CO2t−i +
β Ln ECt−i + i=0 4i
q
i=0 q
β2i Ln GDPCt−i +
q
β Ln OPt−i i=0 3i
β Ln POPt−i + β1 Ln GDPCt + β2 Ln OPt i=0 5i
+ β3 Ln ECt + β4 Ln POPt + εt
(2)
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An ARDL model is a general dynamic specification, which uses the lags of the dependent variable and the lagged and contemporaneous values of the independent variables, through which shortterm effects can be directly estimated, and the longrun equilibrium relationship can be indirectly estimated.
2.2 Unit Root Test In this research, we perform unit root tests with and without breaks: Augmented Dickey Fuller test [4] and Kwiatkowski, Phillips, Schmidt et Shin test [5]. Table 1 shows that all series are stationary in first difference, and therefore none of them are second order integrated. The KPPS test shows that all series are stationary in first difference except the Population density series which is stationary in level when the model with constant and trend is taken into account. Therefore, the ARDL approach is efficient because it allows to test the cointegration relationship between the variables regardless of the integration order I(0) or I(1). In the Next Step and in Table 1 Results of ADF (1979) and KPSS (1992) unit root tests Variables
ADF test statistics
KPSS tests statistics
Constant
Constant and trend
Constant
Constant and trend
Ln CO2
−1.42 (0.56)
−3.45* (0.058)
0.84 [0.46]
0.07 [0.14]
Ln GDPC
1.25 (0.997)
−2.909 (0.17)
0.83 [0.46]
0.11 [0.14]
Ln OP
−1.75 (0.399)
−2.586 (0.287)
0.66 [0.46]
0.16 [0.14]
Ln EC
−1.45 (0.546)
−2.80 (0.203)
0.83 [0.46]
0.100 [0.14]
LnPOP
−1.886 (0.335)
−2.684 (0.247)
0.66 [0.46]
0.10 [0.14]
Ln CO2
−7.57*** (0.000)
−7.84*** (0.000)
0.18 [0.46]
–
Ln GDPC
−11.21*** (0.000)
−11.09*** (0.000)
0.07 [0.46]
–
Ln OP
−6.72*** (0.000)
−5.28*** (0.000)
0.10 [0.46]
0.10 [0.15]
Ln EC
−6.15*** (0.000)
−6.173*** (0.000)
0.15 [0.46]
–
Ln POP
−6.62*** (0.000)
−6.55*** (0.000)
0.10 [0.46]
–
Notes: (*,***) denote the rejection of the null hypothesis of the existence of unit root at 10%, 5% level of significance respectively. The critical value only are in brackets
Trade Openness and CO2 Emissions in Morocco…
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order to test the longterm relationship between the variables, we apply the ARDL approach.
2.3 ARDL Bounds Test Results The ARDL model allows, on the one hand, to test longterm relationships on series that do not are not integrated in the same order and, on the other hand, to obtain best estimates on small sample sizes. In addition, the ARDL model offers the possibility to process simultaneously longterm dynamics and shortterm adjustments. In the Table 2, the Fstatistic (5.623), presented, indicates the existence of a longterm relationship between the underlying variables. Indeed, the observed values (5.623) exceed the critical value of the upper bound (at the 1% threshold for models 1 and 2, and at the 5% threshold for model 1). From Table 4, we conclude that there is a longterm relationship between variables and then the conditional ARDL longrun model can be written as: p q q Ln CO2t = β0 + β Ln CO2t−1 + β Ln GDPCt−1 + β Ln OPt−1 i=1 1i i=0 2i i=0 3i q q + β4i Ln ECt−1 + β5i Ln POPt−1 + εt i=0
i=0
(3)
Finally, we obtain the short term dynamics by estimating the model: p q q Ln CO2t = β0 + β Ln CO2t−1 + β Ln GDPCt−1 + β Ln OPt−1 i=1 1i i=0 2i i=0 3i q q + β4i Ln ECt−1 + β5i Ln POPt−1 + ∅ETCMt−i + εt (4) i=0
i=0
3 Estimation Results According to Table 3, in long run, economic growth and energy consumption variables have a direct, positive and significant longterm effect on CO2 carbon emissions. An increase of 1% in economic growth and energy consumption increases carbon emissions CO2 by 0.46% and 0.69% respectively. The shortterm dynamics are presented in the errorcorrection model in the Table 4. In the Table 4, the estimated lagged error term (E T C Mt−i ) is statistically significant at the 5% level with a negative sign. This confirms that our correction model is valid.
(3, 3, 6, 6, 6)
F Ln CO2t \Ln GDPCt , Ln OPt , Ln ECt , Ln POPt 5.623**
F statistics
(**) Rejection of the null hypothesis of no cointegration at 5% level of significance
Optimal lag length
Estimated ARDL model
Table 2 ARDL bounds tests for cointegration
2.45
3.52
Cointegration
Lower bound Upper bound Inference critical value at 5% critical value at 5%
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Trade Openness and CO2 Emissions in Morocco… Table 3 Estimated long run coefficients from ARDL model
Table 4 Error correction model for the selected ARDL model (ARDL (3, 3, 6, 6) selected based on Akaike info Criterion) R2 = 0.963. R2 Adj = 0.903
635
Variables
Coefficient
Standard error
tstatistic (Prob.)
Constant
−6.693
1.429
−4.683*** (0.000)
Ln GDPC
0.467
0.061
7.63*** (0.000)
Ln OP
0.088
0.845
0.104 (0.918)
Ln EC
0.695
0.068
10.132*** (0.000)
Ln POP
−0.824
3.478
−0.237 (0.817)
Variables
Coefficient
Standard error
tstatistic (Prob.)
Constant
−6.693
1.429
−4.683*** (0.000)
Ln GDPC
0.467
0.061
7.63*** (0.000)
Ln OP
0.088
0.845
0.104 (0.918)
Ln EC
0.695
0.068
10.132*** (0.000)
−0.824
3.478
−0.237 (0.817)
Ln POP
4 Conclusion and Policy Recommendations The purpose of this study is to examine the relationship between the trade openness, energy consumption, economic growth, population density and Carbone dioxide CO2 emissions in Morocco over the period 1971–2014. To test the longterm relationship between variables using the ARDL approach we conclude that in long run, economic growth and energy consumption have a positive and significant impact on carbon emissions and in short term, all estimated coefficients are significant and positive except the population density variable which is negative and significant. These results suggest that the Moroccan government should orient its industrial activities towards clean sectors using renewable and clean energies.
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References 1. Bouyoiyour J (2003) Trade and GDP growth in Morocco: short run or long run causality. https:// www.researchgate.net/publication/48376538 2. Naranpanawa A (2011) Does trade openness promote carbon emissions? Empirical evidence from Sri Lanka. Empir Econ Lett 10(10):973–986 3. Oh KY, Bhuyan MI (2018) Trade openness and CO2 emissions: evidence of Bangladesh. Asian J Atmos Environ 12(1):30–36 4. Managi S, Hibiki A, Tsurumi T (2009) Does trade openness improve environmental quality? J Environ Econ Manag 58:346–363 5. Kwiatkowski D, Phillips P, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? J Econ 54(1–3):159–178 6. World development indicators (2010) World Bank, Washington, DC
Sizing of a Methanation Unit with Discontinuous Digesters to Optimize the Electrical Efficiency of a Biogas Plant, City of Oujda Akram Farhat, Hassan Zahboune, Kaoutar Lagliti, and Mohammed Fekhaoui Abstract Our study focuses on optimizing the electrical efficiency of the biogas plant in th