Proceedings of the 2nd International Conference on Electronic Engineering and Renewable Energy Systems: ICEERE 2020, 13-15 April 2020, Saidia, Morocco [1st ed.] 9789811562587, 9789811562594

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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 (UPM-CSIC), 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, Manawatu-Wanganui, New Zealand Cun-Zheng 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 Opto-electronics, Chinese Academy of Sciences, Beijing, China Junjie James Zhang, Charlotte, NC, USA

The book series Lecture Notes in Electrical Engineering (LNEE) publishes the latest developments in Electrical Engineering - quickly, informally and in high quality. While original research reported in proceedings and monographs has traditionally formed the core of LNEE, we also encourage authors to submit books devoted to supporting student education and professional training in the various fields and applications areas of electrical engineering. The series cover classical and emerging topics concerning:

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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 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-15-6258-7 ISBN 978-981-15-6259-4 (eBook) https://doi.org/10.1007/978-981-15-6259-4 © 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, #21-01/04 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, micro-grids, 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 state-of-the 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 Euro-Mediterranean 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

ENSA-Oujda, Mohammed First University, Morccco University of Picardy Jules Verne, France

General Co-chairs Adel Mellit Giuseppe Marco Tina Jerome Launay

University of Jijel, Algeria University of Catania, Italy LAAS-CNRS, 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. Temple-Boyer N. Msirdi F. Tadeo A. Migan-dubois 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. Jean-Yves 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 ENSA-Fès, Morocco University of Marmara, Turkey University of Madinah, KSA CRTEN, Tunis Université de Catane, Italy United Arab Emirates LAAS-CNRS, France ENIS, Tunisia University of Thrace, Greece LAAS-CNRS, France LSIS-UMR 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 TFSC-Instrument, 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 ENSA-Oujda, Morocco Universitéd’Aix Marseille, France EST-Salé, 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 Ed-Dahhak A. Mbarki Mohammed-Amine Koulali

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EST-Oujda, Morocco Universiti Sains, Malaysia Universiti Sains, Malaysia Jijel University, Morocco CDER, Algeria EST-Oujda, Morocco CDER, Algeria Jijel University, Algeria Med First University, Morocco ENSA-Oujda, Morocco ENSA Safi, Univ. Cadi Ayyad, Morocco FS-Tanger, Univ. Abdelmalek Essaadi, Morocco ENSA-Oujda, Morocco Blida University, Algeria ENSA-Oujda, Morocco University of Picardie Jules Verne, France ENSA-Oujda, Morocco AASTMT, Egypt Marmara University, Turkey ENSA-Oujda, Morocco Jijel University, Algeria Université de technologie de Compiègne, France USMBA, Fes, Morocco Université Amar Telidji de Laghouat, Algeria University of Blida, Algeria EST-Oujda, Morocco UATL, Algeria University Abdelmalek Essaadi, Morocco FST-Fes, Morocco Med First University, Morocco ENSA Oujda, University Med First, Morocco ENSA Safi, University Med First, Morocco EST-Oujda, Morocco University Cadi Ayyad, Morocco Hauts-de-France Polytechnic University, Valenciennes, France FST Errachidia, Université My Ismail, Morocco EST Oujda,University Med First, Morocco EST-Oujda, Morocco ENSA Oujda, University Med First, Morocco EST University Moulay Ismail, Morocco FSO Oujda, University Med First, Morocco Moulay Ismail University, Morocco ENSA-Oujda, Morocco ENSA-Oujda, 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 El-Kaber 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 UMP-Oujda, Morocco ENSA-Kenitra, Morocco TecnologicoNacional de México ENSA-Oujda, Morocco EST-Oujda, Morocco ENSA-Oujda, Morocco EST-Oujda, Morocco ENSA-Safi, Morocco ENSA-Fes, Morocco ENSA-FES, Morocco ENSA-Oujda, Morocco Université Sidi Mohammed Ben Abdellah – Fès, Morocco FSO-UMP, Morocco ENSA-Oujda, Morocco. Sidi Mohamed Ben Abdellah University, Fes, Morocco ENSA-Oujda, Morocco University of Catania, Italy University of Laghouat ENSAK, Morocco Polymont Engineering, France Polymont Engineering, France Jijel University, Algeria ENSA-Oujda, 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 Grid-Connected 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 IoTScal-C: 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 WBAN-Based 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 Multi-Frequencies Filtering by a Defective Asymmetric Photonic Serial Loops Structure . . . . . . . . . . . . . . . . . . . . . 195 M. El-Aouni, Y. Ben-Ali, I. El Kadmiri, Z. Tahri, and D. Bria

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Effect of the Hydrostatic Pressure on the Electronic States Induced by a Geo-Material Defect Layer in a Multi-quantum Wells Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Fatima Zahra Elamri, Farid Falyouni, and Driss Bria Simulation and Optimization of Cds/ZnSnN2 Structure for Solar Cell Applications with SCAPS-1D 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 Djafari-Rouhani Experimental and Theoretical Study of Group Delay Times and Density of States in One-Dimensional Photonic Circuit . . . . . . . . . . 249 Soufyane Khattou, Madiha Amrani, Abdelkader Mouadili, El Houssaine El Boudouti, Abdelkrim Talbi, Abdellatif Akjouj, and Bahram Djafari-Rouhani Optical Properties of One-Dimensional Aperiodic Dielectric Structures Based on Thue-Morse Sequence . . . . . . . . . . . . . . . . . . . . . . 257 Hassan Aynaou, Noama Ouchani, and El Houssaine El Boudouti Numerical Simulation of Direct Carbon Fuel Cell Using Multiple-Relaxation-Time 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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Contents

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 Deae-Eddine 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 Dual-Junction 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 TG-43 Dosimetric Parameters for 192 Ir HDR Brachytherapy Source Using FLUKA . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Nacira Hanouf, Deae-eddine Krim, Mustapha Zerfaoui, Dikra Bakari, and Abdeslem Rrhioua

Contents

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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 (Si-NW) Field-Effect Transistor (FET) pH Microsensor . . . . . . . . . . . . . . . . . . . . 387 N. Ayadi, B. Hajji, H. Madani, A. Lale, J. Launay, and P. Temple-Boyer 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 K-Nearest 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 Model-Free Control for Altitude of a Mini-Drone 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 Three-Level T-Type Inverters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Abdelmalik Zorig, Said Barkat, Mohamed Belkheiri, and Abdelhamid Rabhi An Improved Sinusoidal (PWM) and Vector (SVPWM) Current Control for a Three-Phase Photovoltaic Inverter Connected to a Non-linear 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 Es-Sbai Adaptive Intelligent Control of the ABS Nonlinear Systems Using RBF Neural Network Based on K-Means 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 Micro-Grid 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 Income-Generating 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 (North-East 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 (North-East 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 Short-Term and Long-Term 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 Three-Dimensional Analysis of the Effect of Transverse Spacing Between Perforations of a Deflector in a Heat Exchanger . . . . . . . . . . . 719 Jamal-Eddine Salhi and Najim Salhi Analysis of a Building-Mounted Wind-Solar 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 Photovoltaic-Thermal 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 Convection-Diffusion 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, Abdel-illah 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 Micro-grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 Photovoltaic-Batteries 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 Bi-fluid 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 inter-vehicular 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 e-mail: [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/10.1007/978-981-15-6259-4_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 truck-mounted 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/.

Autonomous Vehicle Platooning and Motion Control ...

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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/promote-chauffeur-ii. 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 Mass-Spring-Damper [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 right-hand 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 (Inter-Vehicle 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:

Autonomous Vehicle Platooning and Motion Control ...

x¨i = u i

7

(1)

The vehicles are considered independent and linked only by the control. Let dr represents the desired relative Inter-Vehicle 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 Mass-Spring-Damper. The longitudinal motion of the vehicles is modeled by a second-order 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. LBM1-VID 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 (inter-vehicle distance). A constant distance is defined for the inter-vehicle 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 Anti-collision 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/67034-simple-animation-for-n-vehicles?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

9

(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 Inter-Vehicle 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).

Autonomous Vehicle Platooning and Motion Control ...

11

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 Bi-directional. 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 Inter-distance and Stability. Inter-Vehicle 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 inter-vehicle safety distance and collision margin, has been studied in [4, 6, 22]. The evolution of the minimum inter-distance is studied in [4] as a function of the braking capacities and the speed of the fleet. The author used a sub-optimal 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 Inter-vehicle 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 inter-distance 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 (RM-IVDR). 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 RM-IVDR are needed. In addition, note that the RM-IVDR (mass-spring) 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 built-in 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 so-called 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 Bio-chip 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 Bio-heat 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 Real-time 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 e-mail: [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/10.1007/978-981-15-6259-4_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), Ultra-Sound (US), and Radio Frequency (RF). Thermal therapy, known commonly as thermotherapy, uses heating sources as well as cooling sources [1, 2, 5]. Real-time 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, MRI-guided 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 real-time 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 real-time 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 real-time 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 bio-microchip technology. The proposed system can work in real-time 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 bio-probes 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 electro-microfluidic platform allowing users to develop and program their bio-applications. The platform is controlled by automated software with a simple graphical interface. This study proposes a new approach based on bio-implants for Real-time 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 field-programmable gate array (FGPA). The test results extracted from Xilinx Virtex-6 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 left-hand 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, short-time 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 Real-Time 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 real-time 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 iso-contour 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 real-time 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 real-time and locally measure the tumor parameters

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Fig. 2. Different parts of the biochip implant. It includes four components: bio-probes, temperature sensor, RFID module, and a smart unit. The temperature sensor and the bio-probes characterize the tissue locally in real-time. 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 space-dependent simple function or as a constant. We propose a biochip-based 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 real-time 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 6-axis 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 real-time 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 real-time 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 real-time 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 bio-probes. 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 real-time 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 real-time 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 bio-chip 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 bio-chip will measure the local parameters of the tissue (thermal conductivity and density). Using these accurate measured data, the bio-chip 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 bio-chip 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 real-time 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 1D-FDM 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 gen-rated 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 6-c, where amplitude and pulse duration are the highest. The lowest temperature was noticed in the case 6-b, 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 bio-chip, 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 bio-chip 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 FDM-FTCS. 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 real-time 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 biochip-based 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. Real-time 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 real-time. 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 bio-implant, 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 multi-tine electrode configuration in realistic hepatic RF ablative heating. Arch Electr Eng 68(3):521–533 4. Huang H-W, Liauh C-T (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, Lora-Michiels 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 bio-heat 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 Grid-Connected 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 self-sufficiency, 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 off-grid 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: grid-connected mode and stand-alone 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 24-h 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 e-mail: [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/10.1007/978-981-15-6259-4_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 bottom-up 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 non-renewable 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 two-layered 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 multi-agent 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;

Active and Reactive Power Regulation in Nano Grid …

41

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]: Grid-connected mode: the system is connected to the external AC grid and the Genset is always disconnected from the bus (IG closed, ID open); Stand-alone mode: the Genset is disconnected when the system PV+BEES can fully supply the load (IG open). In stand-alone 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 24-h 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 DC-AC converter interfacing the LV grid. The electronic converter is composed of a DC-DC boost converter and a DC-AC inverter. Accurate models of the PV system and the single-phase inverter are exposed in [26, 27], in which a one-diode 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 time-constants model of a single Li-ion 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 Grid-Connected Mode In grid-connected 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

Active and Reactive Power Regulation in Nano Grid …

43

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 grid-connected mode is limited to performing a peakshaving of the PV power output, obtained by a scheduling of the active power. PV Inverter Control in Stand-Alone Mode In stand-alone mode, the DC-AC 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 stand-alone 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 stand-alone mode (voltage droop control)

on the DC-DC 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 stand-alone 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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45

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

Line-to-line 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

Low-pass 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 h-simulations are run for both grid-connected and stand-alone operation modes and the obtained results are exposed.

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3.1 Grid-Connected Mode In grid-connected 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 grid-connected mode

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Fig. 8 Voltage profile in grid-connected mode

3.2 Stand-Alone Mode In stand-alone 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 grid-connected to stand-alone. (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 re-connected to the electrical bus (19:00 ca.).

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Fig. 9 Active power profiles in stand-alone 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 stand-alone 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 re-connected to the bus, it produces the reactive power needed by the load. Frequency and voltage profiles in stand-alone 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 stand-alone 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 stand-alone 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 grid-connected 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 stand-alone 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.

References 1. Burmester D, Rayudu R, Seah W, Akinyele D (2017) A review of nanogrid topologies an technologies. Renew Sustain Energy Rev 67:760–775 2. Han Y, Shen P, Zhao X, Guerrero JM (2017) Control strategies for islanded microgrid using enhanced hierarchical control structure with multiple current-loop damping schemes. IEEE Trans Smart Grid 8(3):1139–1153 3. Taghizadeh M, Hoseintabar M, Faiz J (2015) Frequency control of isolated WT/PV/SOFC/UC network with new control strategy for improving SOFC dynamic response. Int Trans Electr Energy Syst 25(9):1748–1770 4. Asghar F, Talha M, Kim SH (2018) Fuzzy logic-based intelligent frequency and voltage stability control system for standalone microgrid. Int Trans Electr Energy Syst 28(4):e2510 5. Kumar D et al (2019) Frequency regulation in islanded microgrid considering stochastic model of wind and PV. Int Trans Electr Energy Syst 29(9):e12049 6. Eid A, Abdel-Akher M (2017) Voltage control of unbalanced three-phase networks using reactive power capability of distributed single-phase PV generators. Int Trans Electr Energy Syst 27(11):e2394 7. Mahmoud MS, Alyazidi NM, Abouheaf MI (2017) Adaptive intelligent techniques for microgrid control systems: a survey. Int J Electr Power Energy Syst 90:292–305 8. Mousazadeh M, Yousef S et al (2018) Power quality enhancement and power management of a multifunctional interfacing inverter for PV and battery energy storage system. Int Trans Electr Energy Syst 28(12):e2643 9. Satapathy P, Dhar S, Dash PK (2018) A new hybrid firefly optimized P-Q and V-f controller coordination for PV-DG–based microgrid stabilization. Int Trans Electr Energy Syst 28(7):e2568 10. Lu X, Yu X, Lai J, Guerrero JM, Zhou H (2017) Distributed secondary voltage and frequency control for islanded microgrids with uncertain communication links. IEEE Trans Industr Inf 13(2):448–460 11. Merino J, Mendoza-Araya P, Veganzones C (2014) State of the art and future trends in grid codes applicable to isolated electrical systems. Energies 7(12):7936–7954 12. Bouffard F, Kirschen DS (2008) Centralised and distributed electricity systems. Energy Policy 36(12):4504–4508 13. Mhlambi BA, Kusakana K, Raath J (2018) Voltage and frequency control of isolated pico-hydro system. In: 2018 open innovations conference (OI). IEEE, pp 246–250 14. Ma T, Yang H, Lu L, Peng J (2014) Technical feasibility study on a standalone hybrid solarwind system with pumped hydro storage for a remote island in Hong Kong. Renew Energy 69:7–15 15. Bekele G, Palm B (2010) Feasibility study for a standalone solar–wind-based hybrid energy system for application in Ethiopia. Appl Energy 87(2):487–495 16. Notton G, Nivet ML, Zafirakis D, Motte F, Voyant C, Fouilloy A (2017) Tilos: the first autonomous renewable green island in Mediterranean: a Horizon 2020 project. In: 2017 15th international conference on electrical machines, drives and power systems (ELMA). IEEE, pp 102–105 17. Ma Y, Yang P, Wang Y, Zhou S, He P (2014) Frequency control of islanded microgrid based on wind-PV-diesel-battery hybrid energy sources. In: 2014 17th international conference on electrical machines and systems (ICEMS). IEEE, pp 290–294

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18. Jia H, Qi Y, Mu Y (2013) Frequency response of autonomous microgrid based on familyfriendly controllable loads. Sci China Technol Sci 56(3):693–702 19. Yin X, Ding M (2017) A new power coordination method for islanded micro-grid basing on multi-agent system. Sci China Technol Sci 60(6):946–952 20. Ma, Y., Yang, P., Zhao, Z., Wang, Y.: Optimal economic operation of islanded microgrid by using a modified PSO algorithm. Math Problems Eng 2015 (2015). 10 p 21. Sabat HK, Pradhan PC (2018) Simulation and control of a stand-alone PV-wind-battery-diesel generator hybrid power system. Simulation 5(05) 22. Zhu B, Tazvinga H, Xia X (2015) Switched model predictive control for energy dispatching of a photovoltaic-diesel-battery hybrid power system. IEEE Trans Control Syst Technol 23(3):1229–1236 23. Halabi LM, Mekhilef S, Olatomiwa L, Hazelton J (2017) Performance analysis of hybrid PV/diesel/battery system using HOMER: a case study Sabah, Malaysia. Energy Convers Manage 144:322–339 24. Italian Standard, CEI 0-21 (2012) Reference technical rules for the connection of active and passive users to the LV electrical utilities 25. Delfanti M, Merlo M, Monfredini G, Cerretti A, De Berardinis E (2011) Voltage regulation issues for smart grids. In: CIGRE 2011 Bologna symposium the electric power system of the future: integrating supergrids and microgrids, pp 1–7 26. Celsa G, Tina GM (2015) Matlab/Simulink model of photovoltaic modules/strings under uneven distribution of irradiance and temperature. In: 2015 6th international renewable energy congress (IREC). IEEE, pp 1–6 27. Tina GM, Celsa G (2015) A Matlab/Simulink model of a grid connected single-phase inverter. In: 2015 50th international universities power engineering conference (UPEC). IEEE, pp 1–6 28. Yao LW, Aziz JA, Kong PY, Idris NRN (2013) Modeling of lithium-ion battery using MATLAB/simulink. In: IECON 2013-39th annual conference of the IEEE industrial electronics society. IEEE, pp 1729–1734 29. Min C, Rincon-Mora GA (2006) Accurate electrical battery model capable of predicting runtime and IV performance. IEEE Trans Energy Convers 21(2):504–511 30. Garozzo D, Tina GM, Sera D (2018) Comparison of the reactive control strategies in low voltage network with photovoltaic generation and storage. Thermal Sci 22 31. Demirok E, Gonzalez PC, Frederiksen KH, Sera D, Rodriguez P, Teodorescu R (2011) Local reactive power control methods for overvoltage prevention of distributed solar inverters in low-voltage grids. IEEE J Photovoltaics 1(2):174–182 32. Mahmood H, Michaelson D, Jiang J (2015) Accurate reactive power sharing in an islanded microgrid using adaptive virtual impedances. IEEE Trans Power Electron 30(3):1605–1617 33. Theubou T, Wamkeue R, Kamwa I (2012) Dynamic model of diesel generator set for hybrid wind-diesel small grids applications. In: 25th IEEE Canadian conference on electrical & computer engineering (CCECE). IEEE, pp 1–4

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.), method-based 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 intra-hour forecasts (up to 1 h) are useful for grid quality and stability. Intra-day forecasts (up to 6 h) are essential and could be used for optimal integration. Forecasts up to one-day mainly used for unit commitment planning and dispatching power. Up to one-week 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 e-mail: [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/10.1007/978-981-15-6259-4_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 multi-step 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 pre-processing 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 short-term forecasting with a time horizon ranging from a few seconds to some minutes; short-term forecasting up to 48–72 h ahead; medium-term forecasting from a few days to one week ahead; long-term forecasting from a few months to a year or more. Each forecasting horizon has its specific application so that, for example, very short-term forecasters are used for the control and management of PV systems, in the electricity market, for the control of micro-grid. Short-term horizons are adopted for the control of power system operations, economic dispatch, unit commitment, etc. Medium and long-terms horizon are usually used for the maintenance and the planning of PV plants. In this talk we are very motivated by Machine learning-based 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 short-term applications [8]. Recently Deep learning (DL) is also investigated, in this topic, in order to improve some shortcoming of method-based 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

DC-Box Combiner AC-Circuit Breacker

AC-Circuit Breaker

Produced AC power

AC filter

AC-Circuit Breaker AC surge protection Lighting protection

Transform-

Grid

Fig. 1 Basic structure of a grid-connected 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 grid-connected 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 semi-supervised 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), k-Nearest Neighbors (k-NN), Linear Regression (LR), Neural Networks (NNs), Fuzzy-logic, Random forest (RF) and Extreme Machine learning

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Classification Supervised learning

Regression Unsupervised learning Anomaly detection Machine learning

Clustering Semi-supervised learning Dimensionality reduction

Reinforcement learning Control

Fig. 3 Machine learning classes

pt

Fig. 4 RNN for PV power forecasting at one step ahead

pt-1

pt+1

-1

Z

Z-n

pt-n

(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 NN-based 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, self-driving 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 short-term 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 long-term 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

ct-1

f

g

f

ct

o

ht-1 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 grey-box 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 k-NN-based method for forecasting the power produced by small-scale PV plants installed in three different regions: SanDiego, Braedstrup and Catania. They concluded that simple techniques such as k-NNs can produce relatively accurate forecasts (the nMAE was in fact 0.96%). In [18] the authors used a SVR-based method applied to a small-scale 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 multi-model 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 hierarchical-based 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, semi-statistical methods perform better in summer than in winter, while have similar performance in summer and autumn. The T–S fuzzy-based 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

k-NN

1 day ahead On-site 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

ML-H

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 -

On-site 1 point measurements: temperature, relative humidity, and aerosol

Beijing, China 1.2 kWp

MRE = 11.61%

[26] 2018

ELM-ANN 24 h ahead -

On-site 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, MLP-ANN, 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 MLP-based 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 small-scale 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 1-day estimation. SVM and a MLP have been used to for the ultra-short-term forecasting of a small-scale 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 on-site 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 24-h 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 (MR-ESN) 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, back-propagation neural networks (BPNNs), support vector regression (SVR-ANN), and wavelet transform (WT). A multi-model ELM-based 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 multi-model, 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 multi-model 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 learning-based techniques published during the period 2017–2019 for forecasting the power produced by PV plants. For example, in [29] five LSTM-based 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 six-layer feed-forward deep neural network (FFDNN) for one day-ahead 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 on-site 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 networks-based one-day 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 LSTM-based method has been designed for the hourly short-term 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

On-site 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

RNN-LSTM DNN

1 h ahead On-site 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 seasonal-ARIMA. 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 short-term regime (up to few days)— which is also the most requested and used. ML-based forecasters are well suited

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for this case, particularly when combined with appropriate algorithms—such as ANN-optimized Genetic Algorithm or Particle Swarm Optimization. Most ML and DL-based models perform well for sunny days, while for cloudy days the forecasting accuracy decreases significantly. In addition, the accuracy of ML and DL-models 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. One-step ahead forecasting performs best, and has been extensively investigated. Conversely, multistep-ahead 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 good-quality 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 short-term 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 Iea-PVPS T14–01: 2013. International Energy Agency, Paris, France 4. Antonanzas J, Osorio N, Escobar R, Urraca R, Martinez-de-Pison FJ, Antonanzas-Torres 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.; Prentice-Hall, 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 short-term 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 encoder-decoder 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) Day-ahead 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) Day-ahead power output forecasting for small-scale solar photovoltaic electricity generators. IEEE Trans Smart Grid 6:2253–2262 18. Baharin KA, Abdul Rahman H, Hassan MY, Gan CK (2016) Short-term forecasting of solar photovoltaic output power for tropical climate using ground-based 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) Takagi-Sugeno fuzzy model-based approach considering multiple weather factors for the photovoltaic power short-term 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) Data-driven 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) Ultra-short-term forecast of photovoltaic output power under fog and haze weather. Energies 11:528 26. Al-Dahidi 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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29. Abdel-Nasser M, Mahmoud K (2017) Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput Appl 31:2727–2740 30. Son J, Park Y, Lee J, Kim H (2018) Sensorless PV power forecasting in grid-connected buildings through deep learning. Sensors 18:2529 31. Wang K, Qi X, Liu H (2019) A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network. Appl Energy 251:113315 32. Lee D, Kim K (2019) Recurrent neural network-based hourly prediction of photovoltaic power output using meteorological information. Energies 12:215

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 Min-Sum 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 error-correcting performance. LDPC codes were initially introduced by Gallager [1] in 1962 and re-discovered by David Mckay [2] in 1996. LDPC codes have been adopted by many standards such as Digital Video Broadcasting–Satellite-Second Generation DVB-S2 [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 e-mail: [email protected] M. Razi e-mail: [email protected] A. Mansouri e-mail: [email protected] A. Ahaitouf e-mail: [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/10.1007/978-981-15-6259-4_5

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Their excellent threshold performance is achieved at large code lengths (64800 in DVB-S2 [3], between 576 to2304 in Wi-Max [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 parity-check matrix. In this work, we propose a software implementation of LDPC decoder, with a structured parity-check 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 error-correcting 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 quasi-cyclic (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 all-zeroes 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 n-th VN is connected to the m-th 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 n-th variable node, vn denotes the n-th 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 min-sum 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 no-null 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, z-1] 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, z-1] do 8: m=(rowxz)+((z-per)+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 level-one (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 phase-shift 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 last-level cache memory has decreased using the proposed method (80% in L1 and 70% in last-level). 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 IT-8:21–28 2. MacKay DJ (1999) Good error-correcting 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 (DVB-S2), ETSI EN 302 307, V1.2.1, April 2009 4. IEEE (2006) IEEE Std. 802.16e-2005 and IEEE Std.802.16-2004/Cor1-2005 5. Shih X-Y, Chou H-R (2018) Flexible design and implementation of QC-Based LDPC decoder architecture for on-line user-defined 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) Low-density-parity-check codes: Construction and implementation. Ph.D. thesis, The University of Adelaide, Australia, 18 November 2007 9. Jin H, Khandekar A, McEliece R (2000) Irregular repeat-accumulate 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 low-density parity-check 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 quasi-cyclic 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 quasi-cyclic low-density parity-check 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) Partially-parallel LDPC decoder based on high-efficiency message-passing algorithm. In: Proceedings International Conference Computer Design (ICCD), pp 503–510, October 2005 14. Ji W, Hamaminato M, Nakyama H, Goto S (2010) Self-adjustable offset minsum algorithm for ISDB-S2 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 e-mail: [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/10.1007/978-981-15-6259-4_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 non-contact and real-time 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 multi-antenna 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 trade-off 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, Mu-MIMO 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 fast-fading, 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 e-mail: [email protected] A. Alami Hassani e-mail: [email protected] Y. Garrouani e-mail: [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/10.1007/978-981-15-6259-4_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 signal-to-noise ratio (SNR) at the receiver or bit-error-rate (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 Occurrence-Threshold 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 ML-based antenna selection schemes for MIMO systems using

Fig. 3 CDF plot of system capacity for various values of n

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the k-NN 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 k-NN, 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 multi-antenna 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 trade-off 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 energy-efficiency 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) Two-step 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 flat-fading channels: theory and algorithms. IEEE Trans Inf Theor 6. Gharavi-Alkhansari M, Gershman AB (2004) Fast antenna subset selection in MIMO systems. IEEE Trans Sig Proces 7. Joung J (2016) Machine learning-based antenna selection in wireless communications. IEEE Commun Lett 8. Yao R, Zhang Y, Qi N, Tsiftsis TA (2018) Machine learning-based 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 narrow-band compact band pass filter using the technique of a rectangular-shaped linear resonator closed on a capacitive load and based on magnetic coupling. This filter is intended for wireless communication systems (Wi-Fi, 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 (Wi-Fi, 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 micro-strip 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 e-mail: [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/10.1007/978-981-15-6259-4_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 micro-strip 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

Compact Structure Design of Band Pass Filter ... Table 2 Dimensions of the proposed rectangular open-loop resonator

99

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 open-loop resonator

Once the geometrical dimensions of the metal track are determined (Table 2), it is folded on itself in order to have a rectangular open-loop 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 micro-strip filters [15]. The following table shows the characteristics of the designed resonator. The next step is the co-design of two identical resonators to size the filter and set its bandwidth.

2.2 Co-design 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 inter-resonators 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 micro-strip technology for use in wireless communication systems. The proposed band pass filter consists of two identical closed-loop 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 J-S, 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 U-shaped resonators for WLAN applications. TELKOMNIKA J 17(3) 4. Lee HM, Tsai CM (2005) Improved coupled-microstrip filter design using effective even-mode and odd-mode 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 open-loop resonators for wireless communications systems. Int J Instrum (JINST) 9. Hong J-S, Lancaster M (1999) Aperture-coupled microstrip open-loop 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 band-pass 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 quad-band 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, Abdel-Monem 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 tri-band band pass filter using two stub-loaded 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 dynamic-range (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 e-mail: [email protected] A. Mansouri · S. Bennani Dosse National School of Applied Sciences Fez, Sidi Mohamed Ben Abdellah University, Fes, Morocco e-mail: [email protected] S. Bennani Dosse e-mail: [email protected] Y. Ruichek Belfort-Montbéliard University of Technology, Montbéliard, France e-mail: [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/10.1007/978-981-15-6259-4_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 Multi-exposure Image Fusion Multi-exposure image fusion method fuses multi-exposure 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 best-exposed 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 multi-scale 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 well-exposedness. 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 well-exposedness 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 k-th exposure image, C = Contrast, S = Saturation and E = well-Exposedness, 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 Cortex-A53 (ARMv8) 64-bit 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) Artifact-free 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 COM-31: 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 vehicle-to-vehicle 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 position-based routing for VANET has been proposed that is efficient in terms of PDR, throughput and has low overhead. Moreover, the proposed protocol named DVA-GPSR 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 · DVA-GPSR · Angle direction · Speed · Density · Position-based routing

A. Bengag (B) · A. Bengag · M. Elboukhari MATSI Laboratory, ESTO, University Mohamed 1er Oujda, Oujda, Morocco e-mail: [email protected] A. Bengag e-mail: [email protected] M. Elboukhari e-mail: [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/10.1007/978-981-15-6259-4_10

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1 Introduction VANETs consist of a set of intelligent vehicles equipped with an on-board unit (OBU) to exchange information directly with other vehicles by using vehicle-tovehicle communication (V2V) or via vehicle-to-infrastructure 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 MV-GPSR, E-GPSR and GPSR-2P [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 Density-Velocity-AwareGPSR (DVA-GPSR) 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 DVA-GPSR 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 well-known 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 DVA-GPSR 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 DVA-GPSR 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 Next-Hop 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 DVA-GPSR 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 DVA-GPSR, 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 next-hop to get suitable results.

4 Simulation Results and Discussion In this section, the performance of DVA-GPSR 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 DVA-GPSR 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 DVA-GPSR 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 DVA-GPSR 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 DVA-GPSR 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 DVA-GPSR. In this routing, the procedure of selecting the next hop-relaying 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 DVA-GPSR 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) GSPR-MV: 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) ns-3 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

IoTScal-C: 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 service-level 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 · MAPE-K 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, FST-G, Cadi Ayyad University, Marrakech, Morocco e-mail: [email protected] A. Ghammaz e-mail: [email protected] M. N. Bahiri · A. Zyane (B) S.A.R.S. Team—ENSA Safi, Cadi Ayyad University, Safi, Morocco e-mail: [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/10.1007/978-981-15-6259-4_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 system-production 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 ITU-T 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 scalability-oriented 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 MAPE-K 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 real-world 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 ETSI-compliant 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 (ITU-T 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 (traffic-oriented 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

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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 traffic-oriented and resource-oriented 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 978-981-13-1405-6) 2. Bahiri MN, Zyane A, Ghammaz A, Chassot C (2017) A new monitoring approach with cloud computing for autonomic middleware-level scalability management within IoT systems. In: Advances in intelligent systems and computing book series (AISC) (ITCS2017), vol 640. Springer, pp 281–296 (ISBN 978-3-319-64719-7) 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) Cloud-things: 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 (WF-IoT), 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 non-contact, fast and wide-area 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 e-mail: [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/10.1007/978-981-15-6259-4_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, non-contact, 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 set-up

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 set-up 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 set-up 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

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6.42

29.4

33.9

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Tmax= 41.1°C

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Table 1 Temperature of the heated area in the thermograms for each condition

45 40 35 30 25 20 15 10 5 0

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Time (min)

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Fig. 3 a thermogram and b temperature variations versus time in healthy and defective state

condition the set-up 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 set-up

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 non-destructive 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 inter-turn 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) Data-driven 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) Non-destructive 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) Non-destructive testing of composite materials by means of active thermography-based 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 wide-angle 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, open-ended 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, El-Alia, Bab-Ezzouar, Algiers, Algeria e-mail: [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/10.1007/978-981-15-6259-4_13

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millimeter wave wireless communication systems. They are for instance implemented to replace uncompressed high-definition (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 meta-surfaces 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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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

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S12 magnitude (dB)

Fig. 5 Transmission magnitude and phase of the unit cell of the second structure

-100 -150 50

55

60

65

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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 half-opening 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

Enhancing Performance of a 60 GHz Patch Antenna …

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 3-D 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, Perruisseau-Carrier 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 dual-band wide-scan phased array in X-Band. 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 S-W, Feng P-Y, Yi H, Chen B, Ng KB, Chan CH, Wu G-B (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) Metamaterial-based 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 millimeter-wave multibeam transparent transmitarray antenna at Ka-Band. 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 cross-slot 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 high-efficiency 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 Coil-20 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 non-orthogonal 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 e-mail: [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/10.1007/978-981-15-6259-4_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 Coil-20 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)

Enhancing the Performance of Grayscale Image Classification …

 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 three-term 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 two-dimensional 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 Coil-20 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 keep-probability 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: Coil-20 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 Coil-20 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

• Coil-20 [14]: The Coil-20 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 Coil-20 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 Coil-20 and ORL datasets on left and right side respectively

157

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

D-SLSE [38] 95.60

Our

98.70

Our

99.00

References 1. Hu M-K (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8(2):179–187 2. Teague MR (1980) Image analysis via the general theory of moments. J Opt Soc Am 70(8):920– 930 3. Mukundan R, Ong SH, Lee PA (2001) Image analysis by Tchebichef moments. IEEE Trans Image Process 10(9):1357–1364 4. Yap PT, Paramesran R, Ong SH (2003) Image analysis by Krawtchouk moments. IEEE Trans Image Process 12(11):1367–1377 5. Zhu H, Liu M, Shu H, Zhang H, Luo L (2010) General form for obtaining discrete orthogonal moments. IET Image Proc 4(5):335–352 6. Yap PT, Paramesran R, Ong SH (2007) Image analysis using Hahn moments. IEEE Trans Pattern Anal Mach Intell 29(11):2057–2062 7. Lakhili Z, El Alami A, Mesbah A, Berrahou A, Qjidaa H (2019) Deformable 3D shape classification using 3D Racah moments and deep neural networks. Procedia Comput. Sci. 148:12–20 8. Lakhili Z, El Alami A, Mesbah A, Berrahou A, Qjidaa H (2019) 3D shape classification using 3D discrete moments and deep neural networks. In: Proceedings of the 2nd international conference on networking, information systems & security. ACM, p 64 9. El Alami A, Lakhili Z, Mesbah A, Berrahou A, Qjidaa H (2019) Color face recognition by using quaternion and deep neural networks. In: 2019 international conference on wireless technologies, embedded and intelligent systems (WITS). IEEE, pp 1–5 10. Lakhili Z., El Alami A, Mesbah A, Berrahou A, Qjidaa H (2020) Robust classification of 3D objects using discrete orthogonal moments and deep neural networks. Multimedia Tools Appl 1–25

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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 (COIL-20) 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: Human-centric machine vision, p. 31 17. Hmimid A, Sayyouri M, Qjidaa H (2015) Fast computation of separable two-dimensional 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) Feature-based 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 RGB-D 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 K-Means clustering. In: IJCAI, pp 2224–2230 25. Gao S, Zhou J, Yan Y, Ye QL (2016) Recursively global and local discriminant analysis for semi-supervised and unsupervised dimension reduction with image analysis. Neurocomputing 216:672–683 26. Ye J, Jin Z (2014) Dual-graph regularized concept factorization for clustering. Neurocomputing 138:120–130 27. Shang R, Zhang Z, Jiao L, Liu C, Li Y (2016) Self-representation based dual-graph 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 two-stage 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) Pose-invariant 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 appearance-based 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 multi-fluid 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 re-encryption 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 re-encryption

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 Smart-Grid Architecture Model (SGAM) which gives a three-dimensional 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 e-mail: [email protected] URL: https://www.mis.u-picardie.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/10.1007/978-981-15-6259-4_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 re-encryption. 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 all-trusted 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 re-encryption(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 re-encryption key and rely on a semi-trusted 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 re-encryption 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 re-encryption 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 DDos-attacks. 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 Re-encryption 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 re-encryption 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 re-encryption key and transmit it to the CSP. The latter calls the PREaaS to re-encrypt 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 pre-checked 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 CCA-secure in the standard model. But all these schemes uses pairing based solution which is expensive in terms of computation. We proposed the first CCA-secure unidirectional PRE in the standard model without pairings in [12] and present the result of its implementation below. Our scheme is based on the Cramer-Shoup encryption system which is by design CCA-secure 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 3072-bit, and the second one using NIST Standard ECC p-256 [13] thanks to SJCL [14]. Both correspond to the same security level that is 128-bit. 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 re-encryption functions consumes the most compared to keys generation and decryption. Practically, encrypt and key generation functions wont be so called. Generating re-encryption keys, depends on the number of delegations needed, but still not constraining in terms of time consuming. On the other side, re-encryption function is called for each new user, new delegation and changed session key. Having an independent service that do the re-encryption 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 CEN-CENELEC-ETSI. Group sgcg/m490. B_Smart Grid Report First set of standards Version, 2 (2014) 3. Boronat J-P (2017) Véritable énergie du territoire positif et modulaire 4. A compliance package for smart meters. https://www.cnil.fr/en/innovative-home-energymanagement-compliance-package-smart-meters 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 identity-based encryption. In: Annual international conference on the theory and applications of cryptographic techniques. Springer, pp 457–473 8. Boneh D, Franklin M (2001) Identity-based 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 A-A, 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: CYTED-RITOS 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 elliptic-curve cryptography with 256-bit 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 WBAN-Based 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 non-medical [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 e-mail: [email protected] A. Bengag e-mail: [email protected] O. Moussaoui e-mail: [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/10.1007/978-981-15-6259-4_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 sub-conditions 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, ICT-DM 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. Del-Valle-Soto C, Valdivia LJ, Rosas-Caro 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 millimeter-wave 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 Millimeter-wave 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 millimeter-wave 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 e-mail: [email protected] S. Dosse Bennani e-mail: [email protected] A. Belmajdoub e-mail: [email protected] J. Zbitou FST, HASSAN 1 University, Settat, Morocco e-mail: [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/10.1007/978-981-15-6259-4_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 millimeter-wave (MMW) bandpass filters due to its advantages of low-cost, compact-size, 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 millimeter-wave 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 CST-MS 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 Stepped-Impedance 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 millimeter-wave 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 full-wave 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 open-ended feed lines are used [18]. As described in [18], strong magnetic intensity attribute to high conduction current in the open-ended 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 one-dimensional 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 electro-magnetic wave which makes by the materials constituting the structure.

References 1. Ben-Ali Y, Tahri Z, Bouzidi A, Jeffali F, Bria D, Azizi M, Khettabi A, Nougaoui A (2017) Propagation of electromagnetic waves in a one-dimensional 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 omni-directional reflection in 1D photonic crystal heterostructures. Optik 122:2186–2190 3. Goyal AK, Dutta HS, Pal S (2017) Recent advances and progress in photonic crystal-based 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. Ben-Ali Y, Tahri Z, Bria D (2019) Electromagnetic filters based on a single negative photonic comb-like structure. Progr Electromagn Res 92:41–56 6. Ben-Ali 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, Djafari-Rouhani 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, Djafari-Rouhani 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, Djafari-Rouhani 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, Ben-Ali J, Bria D, Djafari-Rouhani 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. Ben-ali 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, Djafari-Rouhani B (2015) A tiny gas-sensor 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 one-dimensional 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 one-dimensional 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 Geo-Material Defect Layer in a Multi-quantum 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 geo-material 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 · Geo-material · 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 e-mail: [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/10.1007/978-981-15-6259-4_20

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electron-hole. 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 donor-related density of states and polarizability in a GaAs-(Ga, Al)As quantum-well 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 zinc-blende 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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205

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 spin-orbit 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 geo-material 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 geo-material 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 well-defined 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 well-defined 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 half-maximum 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

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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) Donor-related density of states and polarizability in a GaAs-(Ga, Al)As quantum-well 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. Segovia-Chaves F, Vinck-Posada H (2018) Simultaneous effects of the hydrostatic pressure and the angle of incidence on the defect mode of a one-dimensional photonic crystal of GaAs/Ga0.7Al0.3As. Optik 164:686–690 14. Elamri F-Z, Falyouni F, Kerkour-El Miad A, Bria D (2019) Effect of defect layer on the creation of electronic states in GaAs/GaAlAs multi-quantum wells. Appl Phys A 125(10):740 15. Elamri F-Z, Falyouni F, Tahri Z, Bria D (2018) Localized states in GaAs/GaAlAs multiquantum-wells. 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 SCAPS-1D 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 SCAPS-1D. 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 open-circuit voltage and (Jsc) the short-circuit 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, earth-abundant, non-toxic and inexpensive element. Keywords ZnSnN2 · High efficiency · Photovoltaic parameters

1 Introduction ZnSnN2 is an II-IV-V2 semiconductor material which composed of only of earthabundant, non-toxic and inexpensive elements [1–3]. The II-IV-V2 semiconductors are closely related to the wurtzite-structured III-N (III-V) semiconductors so they have similar electronic and optical properties, direct bandgaps and large optical absorption coefficients [4–6]. Inx Ga1-x 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 e-mail: [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/10.1007/978-981-15-6259-4_21

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[8], RF sputter deposition [9], plasma-assisted VLS (Vapor-Liquid-Solid) technique [10]. The optical direct-bandgap 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 SCAPS-1D 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 (Scaps-1D) is a one-dimensional 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 p-type absorber layer ZnSnN2 , the Al is considered as back contact deposited on a glass substrate [14], an n-type buffer Fig. 1 Structure of the ZnSnN2 solar cell

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5x105

4x105

(cm-1)

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 n-CdS and window layer made of n-ZnO: 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 III-V 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

1000-2000

10-80

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 (electron-hole 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.

Simulation and Optimization of Cds/ZnSnN2 ...

217

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 open-circuit 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

Simulation and Optimization of Cds/ZnSnN2 ...

219

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% (Shockley-Quiesser limit [21]), high absorption coefficient ≈ 105 cm-1 comparable to III-V semiconductors [23], earth-abundant, non-toxic 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 (Zn-IV-N2 ), which is an earth-abundant, non-toxic and inexpensive material, the photovoltaic parameters have been calculated under different parameters such as thickness and temperatures using SCAPS-1D. 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% (Shockley-Quiesser limit), high absorption coefficient ≈ 105 cm-1 , earth-abundant, non-toxic and inexpensive element.

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References 1. Chen S, Narang P, Atwater HA, Wang L-W (2014) Phase stability and defect physics of a ternary ZnSnN2 semiconductor: first principles insights. Adv Mater 26:311–315. https://doi. org/10.1002/adma.201302727 2. Arca E, Fioretti A, Lany S, Tamboli AC, Teeter G, Melamed C, Pan J, Wood KN, Toberer E, Zakutayev A (2018) Band edge positions and their impact on the simulated device performance of ZnSnN2-based solar cells. IEEE J Photovolt 8:110–117. https://doi.org/10.1109/JPHOTOV. 2017.2766522 3. Qin R, Cao H, Liang L, Xie Y, Zhuge F, Zhang H, Gao J, Javaid K, Liu C, Sun W (2016) Semiconducting ZnSnN2 thin films for Si/ZnSnN2 p-n junctions. Appl Phys Lett 108:142104. https://doi.org/10.1063/1.4945728 4. Cao X, Kawamura F, Ninomiya Y, Taniguchi T, Yamada N (2017) Conduction-band effective mass and bandgap of ZnSnN2 earth-abundant solar absorber. Sci Rep 7:1–10. https://doi.org/ 10.1038/s41598-017-14850-7 5. Alnjiman F, Diliberto S, Ghanbaja J, Haye E, Kassavetis S, Patsalas P, Gendarme C, Bruyere S, Cleymand F, Miska P, Boulet P, Pierson J-F (2018) Chemical environment and functional properties of highly crystalline ZnSnN2 thin films deposited by reactive sputtering at room temperature. Sol Energy Mater Sol Cells. https://doi.org/10.1016/j.solmat.2018.02.037 6. Narang P, Chen S, Coronel NC, Gul S, Yano J, Wang L-W, Lewis NS, Atwater HA (2014) Bandgap tunability in Zn (Sn, Ge) N2 semiconductor alloys. Adv Mater 26:1235–1241. https:// doi.org/10.1002/adma.201304473 7. Karim MR, Zhao H (2018) InGaN-ZnSnN2 quantum wells for high efficiency light emitters beyond green. In: 2018 conference on lasers and electro-optics (CLEO), pp 1–2 8. Feldberg N, Aldous JD, Linhart WM, Phillips LJ, Durose K, Stampe PA, Kennedy RJ, Scanlon DO, Vardar G, Field RL, Jen TY, Goldman RS, Veal TD, Durbin SM (2013) Growth, disorder, and physical properties of ZnSnN2. Appl Phys Lett 103:042109. https://doi.org/10.1063/1.481 6438 9. Lahourcade L, Coronel NC, Delaney KT, Shukla SK, Spaldin NA, Atwater HA (2013) Structural and optoelectronic characterization of RF sputtered ZnSnN2. Adv Mater 25:2562–2566. https://doi.org/10.1002/adma.201204718 10. Quayle PC, He K, Shan J, Kash K (2013) Synthesis, lattice structure, and band gap of ZnSnN2. MRS Commun 3:135–138. https://doi.org/10.1557/mrc.2013.19 11. Wang Y, Ohsawa T, Meng X, Alnjiman F, Pierson J-F, Ohashi N (2019) Suppressing the carrier concentration of zinc tin nitride thin films by excess zinc content and low temperature growth. Appl Phys Lett 115:232104. https://doi.org/10.1063/1.5129879 12. Fioretti AN, Pan J, Ortiz BR, Melamed CL, Dippo PC, Schelhas LT, Perkins JD, Kuciauskas D, Lany S, Zakutayev A, Toberer ES, Tamboli AC (2018) Exciton photoluminescence and benign defect complex formation in zinc tin nitride. Mater Horiz 5:823–830. https://doi.org/10.1039/ C8MH00415C 13. Mostefaoui M, Mazari H, Khelifi S, Bouraiou A, Dabou R (2015) Simulation of high efficiency CIGS Solar cells with SCAPS-1D software. Energy Procedia 74:736–744 14. Fioretti AN, Boccard M, Tamboli AC, Zakutayev A, Ballif C (2018) Nitride layer screening as carrier-selective contacts for silicon heterojunction solar cells. In: AIP conference proceedings no 1999, p 040007. https://doi.org/10.1063/1.5049270 15. Deng F, Cao H, Liang L, Li J, Gao J, Zhang H, Qin R, Liu C (2015) Determination of the basic optical parameters of ZnSnN2. Opt Lett 40:1282–1285. https://doi.org/10.1364/OL.40.001282 16. Wang T, Ni C, Janotti A (2017) Band alignment and p-type doping of ZnSnN2. Phys Rev B 95:205205. https://doi.org/10.1103/PhysRevB.95.205205 17. Coronel NC (2016) Earth-Abundant Zinc-IV-Nitride Semiconductors. https://resolver.caltech. edu/CaltechTHESIS:05252016–080726422 18. Heriche H, Rouabah Z, Bouarissa N (2017) New ultra thin CIGS structure solar cells using SCAPS simulation program. Int J Hydrog Energy 42:9524–9532. https://doi.org/10.1016/j.ijh ydene.2017.02.099

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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 CdS-n/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 physico-chemical 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, thin-film 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 e-mail: [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/10.1007/978-981-15-6259-4_22

223

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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 one-dimensional (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 non-collisional. 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)

Numerical Characteristics of Silicon Nitride SiH4 /NH3 /H2 Plasma Discharge …

225

∂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

227

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 inter-electrode of 2.7 cm. The one-dimensional spatio-temporal 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 non-zero 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 )

Numerical Characteristics of Silicon Nitride SiH4 /NH3 /H2 Plasma Discharge …

229

(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 plasma-enhanced 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, Clayton-Warwick 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 plasma-enhanced 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) Multi-model simulation of 300 mm silicon-nitride thin-film 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 high-and low-frequency 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+ -i-n+ 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 ultra-thin 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) · p-i-n 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 stress-balanced MQWs, into the undoped region of a p-i-n 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 strain-balanced 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 High-aspect-ratio 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 e-mail: [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/10.1007/978-981-15-6259-4_23

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their period number and their composition to keep them strain-balanced [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+ -i-n+ 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

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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

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( pn − pn 0 )(x) =

M+2 

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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 G-173-03 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 p-region (x p ) µm Length of i-region (xi ) µm Length of n-region (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 multi-scale, 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

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(a)

237

(b)

Fig. 4 Variation of IQE with wavelength of in i-MQW-region (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+ -i-n+ 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 ultra-thin GaAs spacers inserted between the QW and the barriers. It has been observed that the spectral response extends beyond the cut-off 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 high-efficiency multi-band-gap solar cells. J Appl Phys 67(7):3490–3493. https://doi.org/10.1063/1.345339 2. Ekins-Daukes 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) High-aspect-ratio 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 1-9. https://doi.org/10.1109/PVSC-Vol2.2012.6656741 4. Alonso-Álvarez D, Ekins-Daukes N (2016, March) Quantum wells for high-efficiency 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 multiple-quantum-well 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 strain-balanced multiple-quantum 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/S0955-7997(03)00106-1 8. Chinchapatnam PP, Djidjeli K, Nair PB (2007) Radial basis function meshless method for the steady incompressible Navier-Stokes 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 ultra-thin interlayers. J Cryst Growth 352(1):239–244. https://doi.org/10.1016/j.jcrysgro.2011.11.036 12. ASTM G173-03(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 ap-i-n quantum dot solar cell based on the InAs/GaAs system. Indian J Phys 91(10):1197–1203. https://doi.org/10.1007/s12648-017-1026-y 16. Alonso-Álvarez D, Wilson T, Pearce P et al (2018) Solcore: a multi-scale, Python-based library for modelling solar cells and semiconductor materials. J Comput Electron 17:1099–1123. https://doi.org/10.1007/s10825-018-1171-3

Plasmonic Demultiplexer Based on Induced Transparency Resonances: Analytical and Numerical Study Madiha Amrani, Soufyane Khattou, Adnane Noual, El Houssaine El Boudouti, and Bahram Djafari-Rouhani

Abstract We study both analytically and numerically the possibility to realize a simple plasmonic Y-shaped demultiplexer made of an input line and two output lines. Each line consisting of a metal-insulator-metal (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 closed-form 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 e-mail: [email protected] S. Khattou e-mail: [email protected] A. Noual e-mail: [email protected] E. H. El Boudouti e-mail: [email protected] B. Djafari-Rouhani Institut d’Electronique, de Microélectronique et de Nanotechnologie (IEMN), UMR CNRS 8520, Département de Physique, Université de Lille, 59655 Villeneuve d’Ascq, France e-mail: [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/10.1007/978-981-15-6259-4_24

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1 Introduction Electromagnetically induced transparency (EIT) is a well-known 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], solid-liquid 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, Metal-Insulator-Metal (MIM) structures have been widely used as nano-waveguides for filtering and demultiplexing light using different nano-cavities [15–20]. Among different papers dealing with the classical analogue of EIT resonances in plasmonics, the so-called 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 Y-shaped photonic demultiplexer based on such EIT resonances [21]. These circuits operate in the radio-frequency 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 one-dimensional (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 Comsol-Multiphysics 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 Y-shaped 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 2D-MIM 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 well-Known Drude-Lorentz 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 one-dimensional plasmonic system with two grafted resonators of lengths d1 and d2 on the same site. The whole structure is inserted between two semi-infinite 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 z-component 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 right-hand 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 left-hand 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 Y-shaped 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 double-double electromagnetically induced transparency. Phys Rev A 94:053832–0538311 5. Yang X, Yu M, Kwong DL, Wong CW (2009) All-optical 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. Al-Wahsh H, El Boudouti EH, Djafari-Rouhani B, Akjouj A, Mrabti T, Dobrzynski L (2008) Evidence of Fano-like resonances in mono-mode magnetic circuits. Phys Rev B 78:075401 10. Quotane I, El Boudouti EH, Djafari-Rouhani B (2018) Trapped-mode-induced Fano resonance and acoustical transparency in a one-dimensional solid-fluid phononic crystal. Phys Rev B 97:024304 11. Mouadili A, El Boudouti EH, Soltani A, Talbi A, Akjouj A, Djafari-Rouhani B (2013) Theoretical and experimental evidence of Fano-like resonances in simple monomode photonic circuits. J Appl Phys 113:164101 12. Mouadili A, El Boudouti EH, Soltani A, Talbi A, Djafari-Rouhani 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 plasmon-induced absorption in resonator-coupled 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, Djafari-Rouhani B (2019) Terahertz multiplasmon induced reflection and transmission and sensor devices in a graphene-based coupled nanoribbons resonators. Opt Commun 440:1–13 16. Noual A, Amrani M, El Boudouti EH, Pennec Y, Djafari-Rouhani B (2019) Terahertz plasmoninduced transparency and absorption in compact graphene-based coupled nanoribbons. Appl Phys A 125:184

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17. Huang Y, Min C, Veronis G (2011) Subwavelength slow-light 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 plasmon-induced transparencies in coupledresonator systems. Opt Lett 37:5133 20. Noual A, Akjouj A, Pennec Y, Gillet JN, Djafari-Rouhani B (2009) Modeling of twodimensional nanoscale Y-bent 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, Djafari-Rouhani B (2019) Photonic demultiplexer based on electromagnetically induced transparency resonances. J Phys D Appl Phys 52:075101 22. Vasseur JO, Akjouj A, Dobrzynski L, Djafari-Rouhani B, El Boudouti EH (2004) Surf Sci Rep 54:1 23. Lin C, Swillam MA, Helmy AS (2012) Analytical model for metal-insulator-metal mesh waveguide architectures. J Opt Soc Am B 29:3157 24. Zhu Q, Wang Z (2013) The Green’s function method for metal-dielectric-metal SPP waveguide network. EPL 103:17004 25. Zhu Q, Wang Z (2019) Analytical method for metal-insulator-metal 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 One-Dimensional Photonic Circuit Soufyane Khattou, Madiha Amrani, Abdelkader Mouadili, El Houssaine El Boudouti, Abdelkrim Talbi, Abdellatif Akjouj, and Bahram Djafari-Rouhani

Abstract We present a comparative study of density of states (DOS) and group delay times for a one-dimensional (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 e-mail: [email protected] M. Amrani e-mail: [email protected] E. H. El Boudouti e-mail: [email protected] A. Mouadili LPMCER, Département de Physique, Faculté des Sciences et Techniques de Mohammedia, Université Hassan II, Casablanca, Morocco e-mail: [email protected] A. Talbi Univ. Lille, CNRS, Centrale Lille, ISEN, Univ. Valenciennes, UMR 8520 -IEMN - LIA LICS/LEMAC, 59000 Lille, France e-mail: [email protected] A. Akjouj · B. Djafari-Rouhani Institut d’Electronique, de Microélectronique et de Nanotechnologie (IEMN), UMR CNRS 8520, Département de Physique, Université de Lille, 59655 Villeneuve d’Ascq, France e-mail: [email protected] B. Djafari-Rouhani e-mail: [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/10.1007/978-981-15-6259-4_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 semi-infinite 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 semi-infinite 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 semi-infinite 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 semi-infinite 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

−

and rN = −

A2N

B N2

2 j F BN − F 2 − 2 j F AN

A2N − B N2 + F 2 − B N2 − F 2 − 2 j F A N

(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 semi-infinite 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 solid-state 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, Djafari-Rouhani B, Aynaou H (2007) Two types of modes in finite size one-dimensional 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, Djafari-Rouhani 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, Djafari-Rouhani B (2013) Theoretical and experimental evidence of Fano-like 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 in-phase resonances in time-reversal 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, Djafari-Rouhani 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, Djafari-Rouhani 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. Djafari-Rouhani B, Dobrzynski L (1993) Acoustic resonances of adsorbed wires and channels. J Phys Condens Matter 5:8177. https://doi.org/10.1088/0953-8984/5/44/010 17. Sanchez-Merono A, Arias J, Sanchez-Lopez 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 One-Dimensional Aperiodic Dielectric Structures Based on Thue-Morse 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 Thue-Morse (T-M) substitutional rules. It is demonstrated that the T-M 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 one-dimensional T−M structures has been investigated by means of the Green’s function approach. The T-M structures could be of practical interest to design all-optical diodes, omnidirectional reflectors and optical filters. Keywords Optical properties · Aperiodic photonic crystal · Thue-Morse 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], all-optical 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 e-mail: [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/10.1007/978-981-15-6259-4_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 Thue-Morse (T-M) sequence, from the names of the first mathematicians to study its numerical properties [4]. The Thue-Morse multilayered structures have been the subject of intensive theoretical and experimental investigations [3, 5–13]. Liu [5] studied the localization properties of light in Thue-Morse sequences at the normal propagation of light waves. Qiu et al. [6] showed that the Thue-Morse 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 T-M dielectric multilayers is also reported by Qiu et al. [7]. In addition Dal Negro et al. [3] fabricated Si/Si O2 T-M multilayered structures to study the bandgap properties and omnidirectional reflectance at the fundamental optical bandgap. The same group also made silicon-rich light-emitting Si N x /Si O2 T-M 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 T-M 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 Thue-Morse dielectric multilayers. Different materials such as superconductors [11], graphene [12] and single negative metamaterials [13] have been considered as the constituent materials of the Thue-Morse 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 one-dimensional Thue-Morse structures to confirm these properties. In addition, the theoretical method adopted in these works for the analysis of the 1D T-M 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 quasi-periodic 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 Thue-Morse structures. The omnidirec-

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μ3 , ε 3

μ0 , ε 0 θ

A A B B A B AA A B B A A B A B B A

z

Fig. 1 Schematic representation of the fourth generation of the Thue-Morse 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 z-axis of the T-M system, has been investigated. Finally, a summary of this work is presented in Sect. 4.

2 Theoretical Model Thue-Morse (TM) sequence is one of the well known examples in one-dimensional aperiodic structures. The Thue-Morse 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 Thue-Morse 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 block-diagonal 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 T-M structure and a reference system formed out of the same volumes of the bulk semi-infinite 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 Thue-Morse 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 T-M 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 Thue-Morse multilayer structure in the near-infrared (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 T-M sequences (S3 , S4 and S5 ). In this illustration, we assume that the incident electromagnetic wave is launched normally (θ = 0 ◦ ) to the T-M 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 T-M 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 T-M 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 high-precision optical filter. The behavior of transmission optical wave shown in the first panel of Fig. 2 is reproduced in the DOS spectra for different T-M 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 T-M 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 Thue-Morse, Fibonacci, double period, and Rudin-Shapiro structures. In order to deduce other properties of the localized modes which appear in the band gap of different T-M 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 T-M 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 self-similar behavior around the main peak for every three generations. In a forthcoming work, we will investigate the behaviors of different localization properties of Thue-Morse 1D-layered structures. To widen the scope of our findings, we have also investigated the transmittance spectrum of the fourth generation T-M structure at different incident angles as shown in Fig. 4. The transmission amplitudes are calculated for both transverse electric (TE) and transverse-magnetic (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 T-M systems are due to the self-similarity in the internal T-M 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 T-M 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 one-dimensional aperiodic dielectric structure based on Thue-Morse sequence. The transmission and the density spectra of the optical modes have the same behaviors for different generations of T-M 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 T-M 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 quasi-periodic one-dimensional photonic crystal based on Si/SiO2. Superlattice Microstr. 97:429–438. https://doi.org/10.1016/j.spmi.2016.07.007 ISSNs 0749-6036 2. Biancalana F (2008) All-optical 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 Thue-Morse quasicrystals. Appl Phys Lett 84:5186–5188. https://doi.org/10.1063/1.1764602 4. Allouche J-P, Shallit J (2003) Automatic sequences: theory, applications, generalizations. Cambridge University Press, Cambridge ISBN 9780521823326 5. Liu NH (1997) Propagation of light waves in Thue-Morse dielectric multilayers. Phys Rev B 55:3543–3547. https://doi.org/10.1209/epl/i2003-00608-x 6. Qui F, Peng RW, Huang XQ, Hu XF, Wang M, Hu A, Jiang SS, Feng D (2004) Omnidirectional reflection of electromagnetic waves on Thue-Morse dielectric multilayers. Europhys Lett 68:658–663. https://doi.org/10.1209/epl/i2004-10261-y 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 Thue-Morse dielectric multilayers. Europhys Lett 63:853–859. https://doi.org/10.1209/epl/i2003-00608-x

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8. Dal Negro L, Yi JH, Nguyen V, Yi Y, Michel J, Kimerling LC (2004) Light emission in aperiodic Thue-Morse dielectrics. Symp F-Group IV Semicond Nanostruct 832:F1.3.1–F1.3.6. https:// doi.org/10.1557/PROC-832-F1.3 9. Lei H, Chen J, Nouet G, Feng S, Gong Q, Jiang X (2007) Photonic band gap structures in the Thue-Morse 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 one-dimensional Thue-Morse all-dielectric 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 one-dimensional ternary superconductor-dielectric photonic crystals based on a new Thue-Morse aperiodic structure. J Supercond Nov Magn 27:41–52. https://doi.org/10.1007/s10948-013-2255-8 12. Saleki Z, Entezar SR, Madani A (2016) Omnidirectional broadband THz filter based on a onedimensional Thue-Morse 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 one-dimensional Thue-Morse structure containing single-negative 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, Djafari-Rouhani B, Akjouj A, Velasco VR (2007) Electromagnetic wave propagation in quasi-periodic photonic circuits. J Phys Condens Matter 19:246217. https://doi.org/10.1088/0953-8984/19/24/246217 (20p) 15. El Hassouani Y, Aynaou H, El Boudouti EH, Djafari-Rouhani 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 Thue-Morse 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/10.1016/0167-5729(90)90003-V 18. Dobrzynski L, El Boudouti EH, Akjouj A, Pennec Y, Al-Wahsh H, Lévêque G, Djafari-Rouhani B (2017) Phononics. Elsevier. https://doi.org/10.1016/C2015-0-06989-1 19. Djafari-Rouhani B, Dobrzynski L (1993) Acoustic resonances of adsorbed wires and channels. J Phys Condens Matter 5:139. https://doi.org/10.1088/0953-8984/5/44/010 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, Djafari-Rouhani 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 Multiple-Relaxation-Time 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 e-mail: [email protected] M. Hasnaoui e-mail: [email protected] A. Amahmid e-mail: [email protected] A. El Mansouri e-mail: [email protected] M. Alouah e-mail: [email protected] Y. Dahani e-mail: [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/10.1007/978-981-15-6259-4_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−

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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 oxide-carbonate 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)

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    ∂Ci + div Ci V = ∇. Di,e f f ∇Ci + Si ∂t

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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 Bulter-Volmer equation presented as follows [8]:   j = j0 ex p(αn Fηact /RT ) − ex p(−(1−α)n Fηact /RT )

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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 DarcyBrinkman-Forchheimer 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

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The equation of species is solved using the D2Q5 arrangements and the corresponding MRT-LB 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 quasi-linear behavior at high voltage and low current densities.

272 Fig. 2 Validation of the numerical code against experimental results [6]

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4.3 Effect of the Porosity of the Anode In this sub-section, 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 over-potential 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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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 Lattice-Boltzmann 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. Physico-chemical 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 two-phases electrolyte. Int J Hydrogen Energy 38(20):8514–8523 7. Sahraoui M, Kharrat C, Halouani K (2009) Two-dimensional 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) 2-D 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 multiple-relaxation-time 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 finite-volume and cascaded lattice-Boltzmann 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 Solution-processed organic–inorganic hybrid perovskites have attracted attention as light-harvesting materials for solar cells and photonic applications. The present study focusses on cubic single crystal; microstructures of CH3 NH3 PbBr3 perovskite fabricated by a one-step solution based self-assembly 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 self-assembled 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 layerby-layer 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 X-ray 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 e-mail: [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 23955-6900, 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/10.1007/978-981-15-6259-4_28

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1 Introduction Organic-inorganic perovskites in the form of thin films, microcrystals, nanoparticles and bulk single-crystals exhibit outstanding optoelectronic properties [1]. They are attractive candidates in many cutting-edge 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, three-dimensional (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 trap-state density and carrier diffusion length have been investigated in bulk perovskite single crystal [21]. However, low-dimensional 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 surface-to-volume ratio, and anisotropic geometry [23]. Several synthesis methods were used to prepare single crystal CH3 NH3 Pb3 , such as top-seed solution growth [24], inverse temperature crystallization [25, 26], and anti-solvent vapor-assisted crystallization [27]. Recently, researchers were interested in the nucleation and growth mechanisms of perovskite structures prepared by inverse temperature crystallization method, using grazing incidence X-ray 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 in-depth analysis using first principles methods. In this work, CH3 NH3 PbBr3 microstructures were synthesized using a one-step solution self-assembly method. The morphology and the structure were analyzed using SEM, and X-ray 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 Organic-Inorganic Perovskite CH3 NH3 PbBr3 The hybrid organic-inorganic perovskite under this study is CH3 NH3 PbBr3 . The CH3 NH3 PbBr3 microstructures (hollow cubes, plates, cubes, and wires) were synthesized using a one-step solution self-assembly method, which has been reported in Ref. [30–32]. CH3 NH3 Br and PbBr2 were independently dissolved in N-Ndimethylformamide (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 dip-casted 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 X-ray 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. X-ray photoelectron spectroscopy (XPS) studies were carried out in a Kratos Axis Supra DLD spectrometer equipped with a monochromatic Al Kα X-ray source (hν = 1486.6 eV) operating at 45 W, a multi-channel 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 high-resolution 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 PerdewBurke-Ernzerhof (PBE) formalism [36]. A kinetic energy cut-off 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 cross-sections 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 self-assembled 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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(a)

Precursor solution (b)

Formation of the frame of the cube by self-assemble (c)

CH3NH3PbBr3 single crystal (d)

Layers

10 μm

(e)

Layer-by-layer growth model

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 layer-by-layer (Frank–Van der Merwe) growth model of CH3 NH3 PbBr3 single crystal in (001) direction (b)

12

DOS (States/eV)

(a)

__ Total __ Br __ MA __ Pb

10 8 6

2.3 eV

4 2 0 -10

-8

-6

-2

0

2

4

6

8

10

2

4

6

8

10

XPS-VB

500

Intensity (a.u.)

-4

E-Ef (eV)

(c) 400 300 200 100 0 -10

-8

-6

-4

-2

0

Binding Energy with respect to Ef (eV)

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 layer-by-layer 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 self-assembly 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 layer-by-layer 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 one-step solution self-assembly 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 layer-by-layer 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 CSC-IT, Finland for computational resources and Academy of Finland (# 311934).

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22. Ufuk E, Pablo SF, Hyun GJ, Keisuke S, Yung CL, Mina M, Kazu S, Susumu O, Kazunari M, Hiroki A (2019) Vapor phase selective growth of two-dimensional perovskite/WS2 heterostructures for optoelectronic applications. ACS Appl Mater Interfaces 11:40503–40511 23. Xing J, Liu XF, Zhang Q, Ha ST, Yuan YW, Shen C, Sum TC, Xiong Q (2015) Vapor phase synthesis of organometal halide perovskite nanowires for tuneable room-temperature nanolasers. Nano Lett 15:4571–4577 24. Liu Y, Yang Z, Cui D, Ren X, Sun J, Liu X, Zhang J, Wei Q, Fan H, Yu F, Zhang X, Zhao C, Liu SF (2015) Two-inch-sized perovskite CH3 NH3 PbX3 (X = Cl, Br, I) crystals: growth and characterization. Adv Mater 27:5176–5183 25. Nayak PK, Moore DT, Wenger B, Nayak S, Haghighirad AA, Fineberg A, Noel NK, Reid OG, Rumbles G, Kukura P, Vincent KA, Snaith HJ (2016) Mechanism for rapid growth of organic-inorganic halide perovskite crystals. Nat Commun 7:13303 26. Rao HS, Li WG, Chen BX, Kuang DB, Su CY (2017) In situ growth of 120 cm2 CH3 NH3 PbBr3 perovskite crystal film on FTO glass for narrowband-photodetectors. Adv Mater 29:1602639 27. Shi D, Adinolfi V, Comin R, Yuan M, Alarousu E, Buin A, Chen Y, Hoogland S, Rothenberger A, Katsiev K, Losovyj Y, Zhang X, Dowben PA, Mohammed OF, Sargent EH, Bakr OM (2015) Low trap-state density and long carrier diffusion in organolead trihalide perovskite single crystals. Science 347:519–522 28. Hu Q, Zhao L, Wu J, Gao K, Luo D, Jiang Y, Zhang Z, Zhu C, Schaible E, Hexemer A, Wang C, Liu Y, Zhang W, Gratzel M, Liu F, Russell TP, Zhu R, Gong Q (2017) In situ dynamic observations of perovskite crystallisation and microstructure evolution intermediated from [PbI6 ] 4- cage nanoparticles. Nat Commun 8:15688 29. Chen F, Xu C, Xu Q, Zhu Y, Zhu Z, Liu W, Dong X, Qin F, Shi Z (2018) Structure evolution of CH3 NH3 PbBr3 single crystal grown in N, N-dimethylformamide solution. Cryst Growth Des 18:3132–3137 30. Liao Q, Hu K, Zhang H, Wang X, Yao J, Fu H (2015) Perovskite microdisk microlasers self-assembled from solution. Adv Mater 27:3405–3410 31. Zhang Q, Ha ST, Liu X, Sum TC, Xiong Q (2014) Room-temperature near-infrared high-Q perovskite whispering-gallery planar nanolasers. Nano Lett 14:5995–6001 32. Kresse G, Furthmüller J (1996) Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B 54:1116 33. Qamhieh N, Najar A, Qamhieh ZN, Abdel Aziz B, Mansour A, Alghoul I (2018) Synthesis and characterization of a perovskite film for solar cells applications. Optik 171:648–651 34. Kresse G, Hafner J (1993) Ab initio molecular dynamics for liquid metals. Phys Rev B 47:558(R) 35. Blöchl PE (1994) Projector augmented-wave method. Phys Rev B 50:17953 36. John PP, Kieron B, Matthias E (1996) Generalized gradient approximation made simple. Phys Rev Lett 77:3865 37. Federico B, Keith TB, Aron Mark WVS (2014) Relativistic quasiparticle self-consistent electronic structure of hybrid halide perovskite photovoltaic absorbers. Phys Rev B 89:155204 38. Eric W, Luisa S, Alex Z (2016) Density functional theory + U modelling of polarons in organohalide lead perovskites. AIP Adv 6:125037 39. Peng L, Dutta A, Xie R, Yang W, Pradhan N (2018) Dot−Wire−Platelet−Cube: step growth and structural transformations in CsPbBr3 perovskite nanocrystals. ACS Energy Lett 3:2014–2020 40. Venables JA (2000) Introduction to surface and thin film. Cambridge University Press, Processes Cambridge 41. Burke SA, Topple JM, Grütter P (2009) Molecular dewetting on insulators. J Phys Condens Matter 21:423101 42. Noh JH, Im SH, Heo JH, Mandal TN, Seok SI (2013) Chemical management for colourful, efficient, and stable inorganic-organic hybrid nanostructured solar cells. Nano Lett 13:1764

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 e-mail: [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/10.1007/978-981-15-6259-4_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 (Particle-In-Cell) [9], and Fluid Modeling [5]. In this paper, the effects of applied voltage on the electron heating are studied using fluid modeling with drift-diffusion approximation in one-dimensional (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 parallel-plate 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 self-consistent 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 drift-diffusion approximation and can be written as Je,i = ∓n e,i μe,i E − De,i

∂n e,i ∂x

(2)

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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

(4)

where κe = 23 k B De n e is the electrons thermal conductivity coefficient. The third term on the left-hand 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

(5)

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 =−

∂V ∂x

(6)

(7)

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 energy-loss 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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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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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 low-pressure, high-frequency 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 low-pressure radio-frequency 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 radio-frequency oxygen discharge. Plasma Sources Sci Technol 28:115008. https://doi.org/10.1088/1361-6595/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 self-consistent fluid model. Phys Plasmas 26:125201. https://doi.org/10.1088/1674-1056/26/12/125201

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 e-mail: [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/10.1007/978-981-15-6259-4_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 self-corrective, 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 model-based 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 data-driven 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 first-order accuracy can be achieved by using first-order 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 18650-20R 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

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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) Sigma-point 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 18650-22FM Li-ion 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 self-adaptability and self-learning, 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 Filter-based 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

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– Accurately estimates states affected by – Don’t perform on nonlinear system external disturbances – Divergence if the model is inaccurate

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– Predicts a nonlinear dynamic state with good precision

– Limited robustness – Error could occur if the system is highly nonlinear

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– No Jacobian matrices calculation

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– 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 lithium-ion 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 model-based online state-of-charge estimation using Kalman filter family for lithium-ion 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 Li-ion 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 Li-ion batteries using neural network modeling and unscented Kalman filter-based 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 state-of-charge and state-of-health of batteries by fuzzy logic methodology. J Power Sources 80:293–300. https://doi.org/10.1016/ S0378-7753(99)00079-8 6. Zheng F, Xing Y, Jiang J et al (2016) Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion 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 lithium-ion batteries using the open-circuit 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 Deae-Eddine 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 phase-space folders are exploited to speed up computing time. The model developed includes the majority of the components of the patient-dependent 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 e-mail: [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/10.1007/978-981-15-6259-4_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 SLURM-cluster. 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 TG-51 protocol [4]. The experiment data were obtained using a cylindrical ionization chamber type 9732-2 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 V4-r2-30. Furthermore, with advantaging that the I.P.P components never seen changed throughout a real treatment. – Multi-Leaf 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 well-known 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)

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ψmax =

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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 (SLURM-CNRST Team Morocco) is used. Openmosix cluster platform is employed to split the main GATE code to 1000 sub-main 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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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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0 2 (c)

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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 build-up 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 on-the-fly 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 TG-51 protocol for clinical reference dosimetry of high-energy 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 synergy-S 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 meta-heuristic search method inspired by Canis-lupus. 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 · Meta-heuristic approach · Hybrid optimization · Optimization-based approach · E industrial series · Cadence Virtuoso

A. Lberni (B) · A. Ahaitouf FST, Sidi Mohamed Ben Abdellah University, Fez, Morocco e-mail: [email protected] A. Ahaitouf e-mail: [email protected] M. Alami Marktani ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco e-mail: [email protected] A. Ahaitouf FPT, Sidi Mohamed Ben Abdellah University, Fez, Morocco e-mail: [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/10.1007/978-981-15-6259-4_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, cut-off 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 fourth-order 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 fourth-order 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 meta-heuristics. The PSO is a widely used and well-known method [4, 5]. It can give good results in almost any real-world problem. The GWO algorithm, although relatively new in the literature, is also a meta-heuristic 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 low-pass Butterworth active filter. This fourth-order active filter can be designed by cascading two second-order Sallen-Key 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 low-pass 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 cut-off 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 fourth-order active low-pass 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 low-pass filter

Where ωc1 and ωc2 (Q 1 and Q 2 ) are the angular cut-off 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 cut-off 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 i7-7820HQ @ 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 x-axis indicates the response in decibels and the y-axis indicates the Table 1 Low-pass filter results and comparison using E96 series Parameters

CRPSO [11]

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frequency. Since ωc is the cut off frequency. It may well be noticed that the cut-off 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 low-pass 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 simulation-based 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, El-Asir 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 multi-layer 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 one-dimensional simulation program (SCAPS-1D). 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 double-graded 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 thin-film 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 e-mail: [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/10.1007/978-981-15-6259-4_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(In1-x 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 SCAPS-1D [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 p-type 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)

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−

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

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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

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750

900

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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. Three-bandgap 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 density-voltage 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 n-type 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 short-circuit 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 J-V 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 open-circuit voltage (Voc ) [14]. Figure 6 shows the power voltage P-V 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 current-voltage characteristic of our optimal structure. Comparing the simulated current-voltage 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.

Study of Graded Ultrathin CIGS/Si Structure …

323

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 P-V 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 bandgap-graded structures of copper-indium-gallium-diselenide (CIGS) absorber layer, are investigated using numerical simulation to optimize the performance of ultra-thin 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) Band-gap 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) CIGS-based 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 3-stage 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 Ga1-x 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 Ga1-x As/GaAs alloy promising for realization of mid-infrared 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 all-optical switches [1–5]. However, the GaAs-based 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 e-mail: [email protected] A. Aissat (B) · S. Ammi Faculty of Technology, University of Blida 1, Blida, Algeria e-mail: [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/10.1007/978-981-15-6259-4_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 Ga1-x 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 Ga1-x As/GaAs QWs as the strain, band gap Eg , the conduction band-edge 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 Ga1-x 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 Ga1-x 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 Ga1-x As/GaAs alloys

Fig. 2 Band gap Eg (x, T) of Inx Ga1-x As alloys

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Fig. 3 Conduction band offset Ec as a function of T and In concentration in the Inx Ga1-x As/GaAs alloy

Fig. 4 Conduction band offset ratio Qc as a function of T and In concentration in Inx Ga1-x 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 Ga1-x 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 Ga1-x 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

Investigation of Temperature, Well Width and Composition Effects …

331

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 Ga1-x As/GaAs alloy promising for realization of mid-infrared 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 inter-subband absorption of InGaAsSb/GaAs quantum wells structure. Superlattices Microstruct 129:115–123 4. Iizuka N, Kaneko K, Suzuki N (2006) All-optical 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 1.2-μm 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) Tunnel-coupled 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 non-linear 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 self-organized 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 Ga1-x As1-y Ny /GaAs quantum wells calculated by 10-band, 8band, and 6-band 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 model-solid 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 Dual-Junction 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 Dual-Junction solar cells such as current density-voltage (J-V), 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 current-voltage 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 III-V semiconductor like GaAs have the highest energy-conversion 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 multi-junction solar [2]. Recently, research exploring device characteristics and optoelectronic properties of GaAsPN layers for III-V photovoltaic applications has been accelerated. However, research A. Bahi azzououm · A. Aissat (B) Faculty of Technology, University of Blida 1, 09000 Blida, Algeria e-mail: [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 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/10.1007/978-981-15-6259-4_35

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into III-V-N 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 closed-loop 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 LMI-based 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 closed-loop response, and thus is very common in practical control design. An extended Lyapunov Theorem for the closed loop T-S model (15) is developed with above definition of an LMI-based circular pole region as below [23]. Theorem 2: The closed loop T-S model (15) is D-stable (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 state-feedback synthesis problem [22]. Theorem 3: The closed loop T-S 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 T-S 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 Takagi-Sugeno 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 T-S fuzzy model representation. The framework is based on T-S model and parallel distributed compensation (PDC) technique. Simulation results showed that the multi-objective 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, El-Ghaoui 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 type-quadrotor. 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 Takagi-Sugeno. 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 Takagi-Sugeno’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 T-S 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 takagi-sugeno 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 LMI-based 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 pole-placement 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 3D-Model based on a new approach integrating both the flight controller and the quadrotor CAD-model (Computer-aided 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 (Proportional-Integral-derivative) 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 · 3D-Model · 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 non-controllability in the lateral motions (x, y), its rotational and translational dynamics are under-actuated 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, Geo-Resources and Renewable Energies, Faculty of Sciences and Technology, Sidi Mohammed Ben Abdellah University, Fez, Morocco e-mail: [email protected] A. Mansouri Laboratory of Intelligent Systems, Geo-Resources 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/10.1007/978-981-15-6259-4_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 propeller-rotor, 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 propeller-Rotor 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 counter-torque between each pair propeller-rotor. 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 near-realistic 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 CAD-Models 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 CAD-models

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Fig. 3 MATLAB simscape CAD-models

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 near-optimal 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 (x-y 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 3D-visualization 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.

Mechanical Modeling, Control and Simulation of a Quadrotor UAV Roll displacement

Reference 0.1

Pitch displacement

0.2

θ (rad)

φ (rad)

0.2

Output

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Reference Output

0.1

0

0 0

1

2

4

3

5

0

1

Time (s)

4

3

5

Time (s) Z displacement

Yaw displacement

0.2

2

Z (m)

ψ (rad)

1

Reference

0.1

Output

0

Reference Output

0.5

0 0

1

2

3

4

5

0

Time (s)

1

2

3

4

5

Time (s)

Fig. 5 Drone-simulator attitude and altitude stabilization

Fig. 6 Quadrotor views in MATLAB simscape interface

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 3D-representation 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 3D-representation (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 under-actuation 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 double-granularity 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) Real-time trajectory tracking of an unmanned aerial vehicle using a self-tuning fuzzy proportional integral derivative controller. Int J Micro Air Veh 8(4):252– 268 4. Mian AA, Daobo W (2008) Modeling and backstepping-based 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 Model-Free Control for Altitude of a Mini-Drone Using PSO Algorithm Hossam Eddine Glida, Latifa Abdou, Abdelghani Chelihi, Chouki Sentouh, and Gabriele Perozzi

Abstract This paper presents a model-free controller based on particle swarm optimization algorithm (PSO-MFC) for the altitude systems of a Mini-Drone. 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 Mini-Drone platform. Results evaluation show that the proposed control scheme achieves good performance for altitude system compared to the controller without optimization. Keywords Mini-drone · Model-free 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 e-mail: [email protected] L. Abdou · A. Chelihi Department of Electrical Engineering, LI3CUB Laboratory, University of Biskra, Biskra, Algeria C. Sentouh · G. Perozzi Automatic Control, LAMIH-UMR CNRS 8201, Hauts-de-France 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/10.1007/978-981-15-6259-4_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 model-free controller (MFC) strategies with a basic controller along with an ultra-local model is proposed to compensate for systems uncertainties and disturbances [5], in [6] authors proposed MFC based terminal sliding-mode control for attitude system of a quadrotor, in [7, 17] a model-free controller based on the cascaded structure of the dynamic model for trajectory tracking of quadrotors, a sliding-mode-based model-free 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 model-free control based on PSO algorithm (PSO-MFC) for the altitude control of a quadrotor. Firstly, a model-free control law is proposed for the altitude model of a Mini-Drone without accurate knowledge of its nonlinear dynamics. Then, the PSO optimization algorithm is used to tune the MFC proposed controller such that null steady-state 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 Mini-drone 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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Fig. 1 Control structure

x˙ z = z x z + z (πz (x z , u z , t) + αz u z ), yz = C zT x z , 

with z =

    T 01 0 , z = , Cz = 1 0 00 1

(3)

(4)

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 closed-loop system governed by E˙ z = z E z + z ( y¨d − αz u z − πz (x z , u z , t)).

(5)

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

(6)

where K z = [k1 , k2 ]T > 0 will be defined by The PSO optimization algorithm to have a stable closed-loop system, i.e. all roots of (z − z K zT ) are in the open left half-plane 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 Model-Free Controller In this section, the objective is to design an optimal robust model-free 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 Model-Free 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

(7)

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 closed-loop 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)

(11)

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 closed-loop, 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 PSO-MFC 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 trial-and 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 model-free control (PSO-MFC) 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 Mini-Drone system using Simulink Template for Parrot Minidrone platform (see Fig. 2) [13]. The proposed PSO-MFC 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 PSO-MFC: k1∗ = 81.35, k2∗ = 10.05, αz∗ = 1.25 and βz∗ = 80. In hovering flight experiment, the PSO-MFC, 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 Mini-Drone to the given reference (see Figs. 5 and 6).

Optimal Robust Model-Free Control for Altitude ... Fig. 3 Altitude response of reference tracking at different setpoints

Fig. 4 Input signal response of the PSO-MFC

Fig. 5 Altitude response when disactivating/activating the control law

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Fig. 6 Input signal response of the PSO-MFC when disactivating/activating the control law

5 Conclusion In this paper, an optimal robust model-free control has been introduced to deal with hover flight mode for a Mini-Drone. 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 (PSO-MFC). 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 nature-inspired algorithms. Evol Intell 1–13 2. Perozzi G, Efimov D, Biannic J-M, Planckaert L (2018) Trajectory tracking for a quadrotor under wind perturbations: sliding mode control with state-dependent 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 model-free 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) Model-free-based 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 model-free 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 model-free 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) Model-free control of a quadrotor using adaptive proportional derivative-sliding 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 pwr-type 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 nature-inspired 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 model-free backstepping control for a quadrotor helicopter. Nonlinear Dyn 1–20. https://doi.org/10.1007/s11071020-05671-x

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 · DC-DC 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 e-mail: [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/10.1007/978-981-15-6259-4_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 model-bas 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 model-free 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 DC-DC converter, current and voltage sensors, solar irradiance sensor, resistive load, a dSPACE-1104, and a computer. A mono-crystalline PV module SW300 was used. The electrical characteristic of SW300 PV panel is presented in Table 1. A DC-DC converter is used for tracking the maximum power point (MPP), the chopper DC-DC 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 DC-DC converter can be found in [6]. As shown in Fig. 2, two current sensors are used for measuring the input current from chopper DC-DC converter and output current from resistive load, also we used two voltage sensors for measuring the input voltage for chopper DC-DC converter

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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 in-plane 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 DC-DC 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 DC-DC 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 DC-DC 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

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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

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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 low-level 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 DC-DC 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) MPPT-based 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 low-cost 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 Three-Level T-Type 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 three-level Space vector modulation and proposes a new strategy to eliminating the circulating current for paralleled three-level t-type inverters. Results obtained confirmed the performance and the effectiveness of the proposed circulating current control strategy. Keywords Parallel inverters · Circulating current · Three-level T-type inverter · Three-level 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], high-power motor controls [3] and power-factor-correction [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 e-mail: [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/10.1007/978-981-15-6259-4_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 two-level inverters [4–12]. However, multilevel inverters offer several advantages compared with two-level 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 zero-sequence modulation function in order to eliminate the low-frequency circulating current and to control neutral point potential. So far, investment on Space Vector Modulation (SVPWM) to eliminate circulating current in parallel three-level t-type 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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on the analysis of the average model of parallel three-level inverters, the mathematical model of circulating current is developed and then a circulating current control strategy is developed.

2 Modeling of Parallel Two Three-Level T-Type Inverters The structure of the parallel t-type inverters is shown in Fig. 1. The model of each t-type inverter in the three-phase 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 three-phase 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 zero-sequence 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 i-phase (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 Three-Level T-Type Inverters 3.1 Proposed Circulating Current Controller for Parallel Three-Level T-Type Inverters The switching states of the each t-type inverter are represented on a space-vector 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 i-phase 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 three-level t-type inverter

(6)

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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 three-level 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 t-type 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 zero-sequence 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 t-type inverters

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(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 Three-Level T-Type Inverters In order to validate the proposed circulating current controller two parallel three-level t-type 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 t-type inverters.

4 Simulation Results 4.1 Simulation of Parallel Three-Level T-Type 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 three-level t-type have the same values as listed in Table 2, while inductors and ESR values of the second three-level t-type inverter are decreased by 25%. The simulation results without circulating current controller are shown in Fig. 6.

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vd

iα 2

iq1

iq∗1

Fig. 5 Control diagram-included proposed circulating current controller for parallel three-level t-type inverters

Table 2 Simulation parameters

Switching frequency

5 kHz

Fundamental frequency

50 Hz

DC-source 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 three-phase currents. To gain better insight the a-phase, waveforms of each inverter are shown in Fig. 6(d). From this figure, one can see that the a-phase currents discrepancy and distortion are important and they may cause serious problems for the parallel inverters operation.

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a

b

c

d

Fig. 6 Performance of parallel three-level t-type inverters without circulating current control: (a) circulating currents (b) phase currents of inverter 1, (c) phase currents of inverter 2, (d) a-phase currents of each inverter

4.2 Simulation of Parallel Three-Level T-Type 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

b

c

d

Fig. 7 Performance of parallel three-level t-type inverters with the proposed circulating current control: (a) circulating currents (b) phase currents of inverter 1, (c) phase currents of inverter 2, (d) a-phase currents of each inverter

It can be seen from Figs. 7(b) and (c) that even the variation in the filters impedance value, the three-phase currents of each t-type inverter are consistent and balanced. These results validate the design and the effectiveness of the proposed circulating current controller. Figure 7(d) displays the a-phase currents of each t-type 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 t-type inverters.

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5 Conclusion This paper proposes a circulating current control strategy for parallel three-level t-type. The idea consists of an investment on the three-level 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 permanent-magnet direct-drive wind power generation system. IEEE Trans Ind Electron 60(4):1619–1629 2. Asiminoaei L, Aeloiza E, Enjeti PN, Blaabjerg F (2008) Shunt active power-filter 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 direct-drive permanent-magnet 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 three-phase 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 space-vector modulation. IEEE Trans Power Electron 14(5):982–989 6. Chen TP (2009) Common-mode ripple current estimator for parallel three-phase inverters. IEEE Trans Power Electron 24(5):1330–1339 7. Mazumder SK (2005) Continuous and discrete variable-structure controls for parallel threephase boost rectifier. IEEE Trans Ind Electron 52(2):340–354 8. Pan C-T, Liao Y-H (2008) Modeling and control of circulating currents for parallel three-phase boost rectifiers with different load sharing. IEEE Trans Ind Electron 55(7):2776–2785 9. Zhang D, Wang F, Burgos R, Boroyevich D (2011) Common-mode circulating current control of paralleled interleaved three-phase two-level voltage-source converters with discontinuous space-vector modulation. IEEE Trans Power Electron 26(12):3925–3935 10. Hou CC (2013) A multicarrier PWM for parallel three-phase active front-end 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 parallel-connected 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 three-level T-type converter for low-voltage applications. IEEE Trans Power Electron 28(2):899–907 14. Shao Z, Zhang X, Wang F, Cao R (2015) Modeling and elimination of zero-sequence circulating currents in parallel three-level T-type grid-connected 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 neutral-point-clamped 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 multi-paralleled three-level T-type 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 Three-Phase Photovoltaic Inverter Connected to a Non-linear Load Abdelhak Lamreoua, Anas Benslimane, Jamal Bouchnaif, and Mostafa El Ouariachi

Abstract After improving the electrical performance of a single-phase photovoltaic inverter (previous article), this article aims to model the three-phase 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 non-linear 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 d-q components would not be time-invariant 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 Phase-locked 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 Three-phase 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 Three-phase 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 single-phase 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 e-mail: [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/10.1007/978-981-15-6259-4_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 · Three-phase 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 micro-grid (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 non-linear charges (NLL) in small-scale 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 above-mentioned 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 Phase-locked 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 Three-Phase 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 phase-source 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 ∗

vk0

Fig. 1 Three-phase PV inverter connected to the grid

(1)

(2)

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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 



[1-1-1]

2U 3

− U3

− U3

[11-1]

U 3

U 3

− 2U 3

U √ 6

[-11-1]

− U3

2U 3

− U3

− √U

[-111]

− 2U 3

U 3

U 3

[-1-11]

− U3

− U3

2U 3

− √U

− √U

[1–11]

U 3

− 2U 3

U 3

U √ 6

− √U

[-1-1-1]

0

0

0

0

0

[111]

0

0

0

0

0

2 3U



2 3U

6

0

v¯ 1

U

2 3U

60

v¯ 2

0

2 3U

120

v¯ 3

-U

2 3U

180

v¯ 4

-U

2 3U

240

v¯ 5

0

2 3U

300

v¯ 6

U

0

0

v¯ 7

0

0

0

v¯ 8

0



U √ 2



0

U12

2 3U



U √ 2

6

−

0

v¯ s



2



2

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

V0

during (1-K0) × 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 grid-connected 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, all-available 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 three-phase 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 three-phase 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

An Improved Sinusoidal (PWM) and Vector (SVPWM) Current Control

  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 DC-AC 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 4-1 Current Control by Sinusoidal PWM (SPWM) Before Compensation: The output currents in the PV system are distorted due to the connection of the three-phase converter to the non-linear 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 three-phase 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 cut-off 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 non-linear load. The proposed control method includes the SPWM-SRF control method and the SVPWM-SRF method. When non-balanced, non-linear 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 non-linear 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 non-linear 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 single-phase and three-phase systems. The simulation results verify the feasibility and effectiveness of the new control method for a grid-connected 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 single-phase in-verter 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 connec-té 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, 26-28 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 Q-N, Lee H-H (2014) An enhanced grid current compensator for grid-connected 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 con-trollers for distributed power generation systems. Power Electron IEEE Trans 24:654–664 9. Farhood Sultani J (2013) Modelling, design and implementation of D-Q in single-phase 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 cur-rent control strategy for a photovoltaic grid-connected inverter under unbalanced and nonlinear load conditions. PLOS ONE. https://doi.org/10.1371/journal.pone.0164856 11. Vadlamudi SR (2016) Decoupled DQ-PLL 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 Es-Sbai

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 · Lithium-Ion 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. Es-Sbai Laboratory of Intelligent Systems, Georesources and Renewable Energies (LISGRE), Faculty of Sciences and Technologies, Sidi Mohammed Ben Abdellah University, Fez Box 2202, Fez, Morocco e-mail: [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/10.1007/978-981-15-6259-4_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 under-discharging or over-charging, 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 coulomb-counting [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 model-based state observers, such as Kalman filters [3], and sliding mode observers, these solutions, can self-adjust in case of any system change and overcome the accumulation of measurement errors. Moreover, artificial intelligence-based approaches were also proposed for SOC estimation due to their independence on the battery model, as they are data-oriented strategies [4, 5]. The correct choice of the best SOC estimation strategy for the targeted application depends on several criteria, including real-time 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 Processor-In-the 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 feed-forward 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 open-circuit 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 First-order Thevenin battery model

Table 1 Internal parameters of the battery

Parameters

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0.24

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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 model-based 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 self-adjust 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

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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 feed-forward 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 charge-discharge 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 Cortex-M4 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 state-of-charge determination and their applications. J Power Sources 96:113–120 2. Alzieu J, Smimite H, Glaize C (1997) Improvement of intelligent battery controller state-ofcharge indicator and associated functions. J Power Source 67:157–161 3. Sassi HB, Errahimi F, Es-sbai N, Alaoui C (2019) Comparative study of ANN/KF for on-board 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 Ni-MH 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 clustering-based multi-model support vector regression state of charge estimator for lithium-ion battery of electric vehicle. In: International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE, pp 392–396 6. Mohammad C, Mohammad F (2010) State-of-charge estimation for lithium-ion 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) State-of-charge estimation for lithium-ion 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, Es-Sbai 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 K-Means Clustering Hamou Ait Abbas, Abdelhamid Rabhi, and Mohammed Belkheiri

Abstract The anti-lock 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 K-average 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 · Anti-lock braking system · Adaptive output feedback control · Tracking error dynamics · Radial basis function neural network · K-means clustering algorithm

H. Ait Abbas (B) Laboratoire des Matériaux et du Développement Durable, University of Akli Mohand Oulhadj, 10000 Bouira, Algeria e-mail: [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/10.1007/978-981-15-6259-4_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 K-means 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 K-means clustering NN algorithm-based 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

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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 set-up 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 time-delay 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 K-Means Clustering Algorithm 4.1 Basis Function of Hidden Layer of RBF In the present paper, let the input vector be m-dimensional, the output vector be ldimensional, and the number of hidden neurons be h, we will use the Gaussian basis functions [5]

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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 k-clusters (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 k-cluster, 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 j-cluster 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

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To make clear the performance of the proposed adaptive controller in the presence of uncertainties, we consider the Anti-lock Braking System example (6). The following DComp: ψ˙ = −150ψ + e; L cD = −1000100ψ + 7501e, places the poles of the closed-loop 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 NN-based 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

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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 K-means clustering algorithm is introduced to eliminate terms of uncertainty. Computer simulations of the Anti-lock 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 Layer-Neural 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 anti-lock 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 K-means clustering in system identification. In: Fourth International Workshop on Advanced Computational Intelligence (IWACI). IEEE, pp 110-112, 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. ISBN-13: 978-0-13-147139-9 6. Hovakimyan N, Calise AJ, Kim N (2004) Adaptive output feedback control of a class of multiinput multi-output systems using neural networks. Int J Control 77(15):1318–1329 7. Lin JS, Ting WE (2007) Nonlinear control design of anti-lock 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 IEEE-14-bus system with constant loads. Firstly, we have analyzed the IEEE-14 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 IEEE-14 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 · IEEE-14 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 volt-ampere-reactive (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, Geo-Resources and Renewable Energies Laboratory (ISGREL), FST, Sidi Mohamed Ben Abdelah University, Fez, Morocco e-mail: [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/10.1007/978-981-15-6259-4_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 three-phase 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 voltage-sourced inverter, using Gate Turn-Off (GTO) thyristors inappropriate multi-phase 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 three-phase 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.

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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 fast-acting 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) Reliability-oriented design of modular multilevel converters for medium-voltage STATCOM. IEEE Trans Industr Electron 67:6206–6214 4. Zatsepina V, Zatsepin, E, Shachnev OY (2019) Improving eefficiency of high-power 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, Al-Haddad 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 · Soft-Switching

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 DC-DC 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 e-mail: [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/10.1007/978-981-15-6259-4_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 duty-ratio 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 duty-ratio α = 0.5 which is generated by the principle of pulse-width 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, so-called 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 Pseudo-random 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 flip-flops reveals an apparently random series of 1 and 0.The repetition period L is very large which justifies the name pseudo-random 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 PRBS-based 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 DC-DC 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 pseudo-randomized 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. Derrey-laCabuche. CH-1756 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 · DC-DC 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 e-mail: [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/10.1007/978-981-15-6259-4_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 Look-Up-Table 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 Look-Up-Table 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 short-circuit 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 Full-Bridge 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 back-end 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 I-V data in form of Look-Up-Table. 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 MATLAB-Simulink environment using the LUT characteristic generation technique and the implicit PV model described in Sect. 2. Sharp module NA-E135L5 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 I-V 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 NA-E135L5 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 I-V 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 I-V characteristic using implicit PV model

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Fig. 10 Reference and emulated P-V characteristic using implicit PV model

Fig. 11 Reference and emulated I-V 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 P-V 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, Slama-Belkhodja I (2016) Implementation of a PV panel model based on the look-up tables method for a PV generator emulator. 6 7. Koutroulis E, Kalaitzakis K, Tzitzilonis V (2009) Development of an FPGA-based system for real-time 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 inter-electrode distance causes an increase in the breakdown voltage of the oil, and that the point-plat 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 e-mail: [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/10.1007/978-981-15-6259-4_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 by-products 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 high-voltage 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) plane-tip

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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a) Hemispherical

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b) plane-tip

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 tip-plane configuration was lower than in the hemisphere-hemisphere configuration. The reason for the low breakdown voltage in the tip—plane configuration was due to the high non-uniformity 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 60156-1995 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 Sonatrach-DML’s laboratory-Laghouat 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 inter-electrode 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 non-uniform field. Indeed, the increase in the inter-electrode 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 physico-chemical 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):2450-1 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 on-board 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 multi-source 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 Villiers-Saint-Frédéric, France e-mail: [email protected] URL: https://www.polymont-engineering.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/10.1007/978-981-15-6259-4_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 real-time 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 (on-line or off-line). For a strategy to be applicable in a vehicle, it must comply with the following criteria: – Applicability: Real-time 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 logic-based approach as an real-time 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 electrode-electrolyte [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 R-C 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 (no-load 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 On-Line 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 real-time strategy. There is a very rich literature on the subject and many EMS are proposed. An EMS based on an off-line method is proposed in [1, 11]. Another is proposed in [1], which is based on dynamic programming as a real-time 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 twenty-four 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 off-line 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 real-time 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 hydrogène-oxygène 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 double-layer 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, Lemaire-Semail B (2013) Graphic formalisms for the control of multi-physical 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) On-line HEV energy management using a fuzzy logic. hal-00814204

Simulation of a Micro-Grid for Electric Vehicles Charging Station R. Bouhedir, A. Mellit, and N. Rouibah

Abstract This paper presents a simulation of a connected micro-grid (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 · Micro-Grid · Energy management

1 Introduction Nowadays the Micro-Grid (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 e-mail: [email protected] A. Mellit AS-ICTP, 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/10.1007/978-981-15-6259-4_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 over-availability 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/Simulink-based 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 Micro-Grid 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 Li-ion 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 Simulink-Based 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 MG-based 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 low-cost 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 algorithms-based 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, Mehrizi-Sani 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 network-based 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 plug-in 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 renewable-based microgrid. In: 2019 IEEE Milan PowerTech, pp 1–6. IEEE 7. Galiveeti HR, Goswami AK, Choudhury NBD (2018) Impact of plug-in 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 time-of-use 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 Fractional-Order (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 Fractional-Order 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 Fractional-order · 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 Anti-lock 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 e-mail: [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/10.1007/978-981-15-6259-4_60

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The lateral dynamic behavior of the vehicle can be represented by a nonlinear model [3]. The origin of this non-linearity 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 Takagi-Sugeno (T-S) 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 integer-order 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 T-S 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 

−2

⎞ 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 closed-loop 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 Takagi-Sugeno 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) Observer-based 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 Multi-Body 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 fractional-order sliding mode controller design for antilock braking systems. J Dyn Syst-T 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 observer-based fault tolerant control for vehicle lateral dynamics. Int J Veh Des 48(3-4):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 sliding-mode control, the super-twisting 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 single-actuator 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 e-mail: [email protected] M. Doumiati ESEO-IREENA 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/10.1007/978-981-15-6259-4_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 Multi-Input-MultiOutput (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 super-twisting 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 side-slip) 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 yaw-lateral-roll 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 yaw-roll 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 side-slip 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 single-input, singleoutput controller based on the Super-Twisting Sliding Mode (STSM) technique. Let us introduce an overview of the theory of Super-Twisting 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 Super-Twisting 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 Super-Twisting Sliding Mode algorithm, is given as:  u 1 = −α1 |s|τ 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”

A Decentralized Multilayer Sliding Mode Control Architecture … Fig. 2 Yaw rate comparison

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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 Super-Twisting 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 side-slip 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

Studio” is a simulator dedicated to vehicle dynamics simulations.

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4 2 0 -2 -4 -6

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Fig. 5 Lateral stability index comparison

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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 Lyapunov-based 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 Hauts-de-France 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 ANR11-IDEX-0004-02) and the Equipex ROBOTEX (Reference ANR-10-EQPX-44-01) 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. IFAC-PapersOnLine 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. Poussot-Vassal 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, Martinez-Molina J-J, 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 anti-roll bar control using electronic servo-valve 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 gain-scheduled 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 super-twisting 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 Li-Ion 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 low-frequency components of power demand to the fuel cell and the high frequencies to the battery and through a fuzzy logic controller, the low-frequency 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 Dynamo-meter 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 e-mail: [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/10.1007/978-981-15-6259-4_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, zero-emission 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 non-reversibility 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], rule-based strategy [5], Fuzzy Logic Controller [3], Optimal Control strategy [6] and frequency-separation strategy [4, 10, 13, 14]. In this work, we implemented a real-time energy management strategy for fuel cell hybrid electric vehicles powered by fuel cells as the main source and Li-Ion 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 Li-Ion battery as a secondary source connected to DC bus trough a bidirectional buck-boost 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 Non-linear battery model [16]

The battery State-Of-Charge 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 Lithium-Ion battery.

2.4 DC-DC 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 Lithium-Ion 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 Buck-boost 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 non-reversibility. Thus, two blocks are used. The first one rejects the negative signals while the second consists of a low-pass filter to obtain the FC reference power. The cut-off 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 Frequency-separation 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 real-time 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

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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 Dynamo-meter 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 fuel-cell hybrid electric vehicle (fuel-cell, battery, and ultra-capacitor) using optimized energy management strategy. Energy Convers Manag 160:74–84 2. Aouzellag H, Abdellaoui H, Iffouzar K, Ghedamsi K (2015) Model-based 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 frequency-separationbased 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 rule-based 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 multi-stack 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, Slama-Belkhodja 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 frequency-separation-based 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 multi-stack 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 Income-Generating 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 socio-economic 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 e-mail: [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/10.1007/978-981-15-6259-4_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 socio-economic 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 know-how 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 mechano-biological 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 Mohammadia-Benslimane 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 Mohammedia-Benslimane 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 socio-economic 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

A. Farhat et al. Component

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%

Glass-debris 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 mechanical-biological 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 income-generating 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 socio-economic 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 X30-445 - Household and similar waste - Consisting of a sample of household and similar waste in bulk 6. AFNOR (December 2013) Norm NF X30-408 - Household and similar waste - Characterization method - Analysis on gross product 7. AFNOR (December 2013) Norm NF X30-466 - 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 decision-making tools, table 25, p 120 12. Alda Y, et al (2013) Characterization of the household solid waste of the municipality of Abomey-Calavi 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 clean-gas-bus, a temperature field experimental system for LPG city bus (LPGB) engine compartment based on infrared imaging technology was built. The temperature field in the semi-enclosed space engine compartment was noninterfering, visualizing and continuously measured. At the same time, under the five working conditions of the LPG engine, 16-channel 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 high-temperature 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 e-mail: [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/10.1007/978-981-15-6259-4_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 full-field measurement. Single point measurement instruments mainly include hot-wire anemometers [1–3], hot-sphere 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 multi-point hot wire anemometers will interfere with the flow field [5]. Full-field 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 clean-gas-bus, 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 semi-enclosed space engine compartment is multi-operating, non-interfering, visualizing and continuously measured. At the same time, 16-channel 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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619

+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 rear-mounted 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 high-temperature 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 steady-state 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 high-temperature 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 counter-clockwise 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 high-speed 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

Experimental Analysis on Internal Flow Field…

a) Typical structure

623

b) Enhanced heat transfer structure

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 low-speed -high-torque 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 semi-enclosed 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 steady-state 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, low-speed and high-torque adverse conditions. 16-channel K-type 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, 16-channel K-type temperature sensors, NI USB-9213 data acquisition module, automotive power converter, power cord, high-temperature tape, clamp, and wire binding. K-type temperature sensors are installed on key components of the engine compartment before testing.

3.3 Experimental Results 3.3.1

Key Components Temperature

The steady-state 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 high-speed 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 steady-state temperature of high-temperature 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 high-temperature 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 above-mentioned 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 high-temperature components in typical structure(right) and enhanced heat transfer structure(left)

Experimental Analysis on Internal Flow Field…

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(e) 2000r/min Fig. 7 (continued)

the temperature in the typical structure was higher from low to high-speed conditions, especially in radiator assembly area and engine block area. Moreover, the temperature gradient of the heat transfer boundary of the high-temperature components in the typical structure was too small, which was detrimental to the enhanced heat transfer between the high-temperature 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 non-axisymmetry in a turbine rotor-stator 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 hot-wire 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 high-resolution boundary conditions and flow fields in the first-class cabin of an MD-82 commercial airliner. Atmos Environ 56:33–44 5. Hu B, Li XS, Fu YX et al (2019) Experimental investigation on the flow and flow-rotor heat transfer in a rotor-stator 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 propane-air 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 situation-Experimental 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 compartment-temperature 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 Navier-Stokes Methods with Computer Programs. Phoenix Lieb Press, New Haven 15. GBT12542-2009 (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 short-run and long-run. 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 e-mail: [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/10.1007/978-981-15-6259-4_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 short-run and long-run. The impact of population density is significant in long-run, but not in short run and economic growth and trade liberalization are negative and insignificant both in short-run and long-run. 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.

Trade Openness and CO2 Emissions in Morocco…

631 EC

.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 short-term 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 long-term 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 long-term 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 long-term dynamics and short-term adjustments. In the Table 2, the F-statistic (5.623), presented, indicates the existence of a long-term 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 long-term relationship between variables and then the conditional ARDL long-run 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 long-term 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 short-term dynamics are presented in the error-correction 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

t-statistic (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

t-statistic (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 long-term 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 K-Y, 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 the Oujda controlled landfill, with the aim of improving its economic and overall rate of return. For this purpose, the study begins with an analysis of the power generation and operating times of the station which showed a yield not exceeding 23%. These results are due to the control lack over the quantity of biogas and its biomethane content extracted directly from the burial dump of the landfill, and its fluctuation during the day. In this case, the throughput improvement is capped to ensure average production that cannot exceed 15 h of work per day. To achieve our goal, we have carried out three scenarios for the design of a batch digester methanation unit. The 1st scenario is the one where we add a second motor without affecting the performance of the first, in the 2nd scenario we add a second motor and we fill the missing difference of the first so that they both work at maximum capacity. In the third scenario, we only compensate the amount of biogas missing from the first motor without adding a second one. Keywords Anaerobic digestion · Biogas · Electrical efficiency · Discontinuous digesters

1 Introduction Landfills are the fastest alternative for governments to manage their household waste (HW). Nevertheless, their operation leads to environmental impacts such as the demand for large areas of soil, the generation of large volumes of leachates and the emission of gaseous pollutants such as methane. This activity contributes between A. Farhat (B) · K. Lagliti · M. Fekhaoui GEOPAC Research Center, Scientific Institute, Mohammed V University, Av. Ibn Batouta, B.P 703, 10106 Rabat, Morocco e-mail: [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/10.1007/978-981-15-6259-4_66

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6 and 18% of methane emitted [1], which is the third source of emission of this gaseous pollutant. This gas has a climate warming potential 25 times higher than that of carbon dioxide [2]. However, 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 program (NHWP) by 2022. Thus, the application of the anaerobic digestion process is an alternative for the management of household waste since the degradation conditions of the organic matter can be controlled, which makes it possible to reduce the environmental impacts resulting from the methane emissions but also from energy production that can be valued. Moreover, the use of biomass as a source of renewable energy is essential for the development of a sustainable energy policy. To this end, this work is geared to the choice of the most appropriate anaerobic digestion technology for the city of Oujda, where it produces annually approximately 156,362 tons/year of residual household waste that contains more than 78,181 tons/year of the organic fraction of this waste (OFHW), which are deposited directly for burial in the controlled landfill of the city. Thus, the objective is to improve the performance of the biogas plant in the landfill, size the methanization unit by calculating the number of batch digesters needed to ensure the continuous supply of biogas engines, in order to develop the basic unit that will be multiplied over time to contain all the waste arriving at the Oujda landfill. This study was carried out in collaboration with CSD-CRB, a specialist in the management, treatment and recovery of solid waste.

2 Problematic 2.1 The Study of the Existing At Oujda, residual HW goes directly to bury in the landfill. Following this, we can extract the biogas with biomethane rates that vary according to the season and time (see Fig. 1). In 2018, the highest value could not exceed 68% and the lowest value was 26% recorded on 21/05/2018 at 22:00. In contrast, this year since January 2019 so far, the highest value was 73% recorded on 12/02/2019 at 7:00 am and the lowest was 36% recorded on 14/06/2019 at 22:00 pm. Generally, after extraction from a new bin that was not used until then, the average at the beginning of the day is 68% and at the end of the day it drops to around 45%. These uncontrolled and independent variations in the biomethane content impose a mandatory reaction and adaptation for the use of the motor, in order to maintain a stable and continuous production rate during the day. The Table 1 shows the difference between the production of the motor and its production capacity that can be reached during the day.

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Fig. 1. Biogas extraction from landfills

Table 1. Difference between the production (P) of the motor and its production capacity (PC) Energy balance

P

PC

PD

Electric power produced

kWh

500

850

350

Electrical efficiency

%

23

39,2

39,2

Number of hours

h

15

16

16

Energy produced per day

kW/jour

7500

13 600

6 100

Gross energy upstream of the motor

kW/jour

32 609

34 694

15 562

OFHW needed to produce this energy Average methane content in biogas

%

50

60

Biomethane lower heating value

kWh/m3

4,98

5,98

60 5,98

The daily amount of biomethane

m3 /jour

6 548

5 802

2 602

Quantity of biomethane per ton of HW

m3 /tonne

100

90

90

Quantity of substrate needed per day

ton/jour

66

65

29

Quantity of substrate needed per year

ton/an

24 090

23 725

10 585

Added to this, the low workload imposed on the motor following maintenance stops and the obligation to allow time for biogas to accumulate the nights and afternoons of each Sunday. The Table 2 shows the Economic Rate of Return (ERR) and the Global Rate of Return (GRR) for 2018 and the first 8 months of 2019, by neglecting micro stops, rate differences and preventive maintenance, to get an idea

640 Table 2. ERR and GRR of the biogas plant at the Oujda landfill

A. Farhat et al. 2018

2019

Operating time

h

4 346

3 195

Opening time

h

5 475

3 645

Total time

h

5 840

3 888

Economic rate of return (ERR)

%

74,41

82,17

Overall rate of return (GRR)

%

79,37

87,65

about these indicators when production is at its peak (synthetic rate of return is equal to the global rate of return). It is clear that the ERR and the GRR improved in 2019 following the start of biogas extraction from a new bin that was not yet in use. We will establish three scenarios, the first (P) describes our basic unit to develop, that which will be multiplied in time to contain all the organic waste arrived at the landfill. The second scenario (PC) is our case study, that fills in the missing difference to improve the efficiency of the first motor already installed and add a second one by ensuring their optimal operation. The third (PD) only compensates for the amount of biomethane needed to maximize the efficiency of the first motor already installed.

3 Methanization of Household Waste and Technology Analysis The anaerobic digestion process is included in HW management systems to use its organic matter content as a substrate for biogas production, which helps to reduce the amount of waste that is deposited in landfills. Anaerobic digestion seems to be the most appropriate method for the treatment of household organic waste. The mechanisms and conditions of the methanization process inhibit the activity of the pathogens present in the waste, which reduces the risk of contamination of the solid by-product such as digestate [3]. The methanization of household organic waste can produce between 100 and 150 m3 of biogas per tons of this type of substrate [4]. As this process takes place in closed facilities, the methane does not escape to the atmosphere, which favors its capture and its application for the production of energy [5].

3.1 Technology Analysis A comparison of technologies makes it possible to make an objective analysis of the advantages and the disadvantages of their application for the management and the energetic valorization of household organic waste. Given the two main operating criteria of anaerobic digestion systems, which are the dry matter content and the

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Table 3. Methods grouped according to operating criteria Depending on matter content

According operation

Category

Process

Wet

Continued

Continuous wet

Wasaa

Dry

Continued

Continuous dry

Valorga Dranco Kompogas

discontinuous

Discontinuous dry

BEKON

biomass flow, it is possible to group the processes into three main categories as shown in Table 3, which will facilitate analysis of the choice of technology to use. The option of selecting a wet system is removed from the analysis, as this type of technology only accepts biomass with a maximum dry matter content of 15%, while the dry matter content of household organic waste is generally between 20 and 31% [4].

3.2 The Most Suitable Technology Selection In accordance with the criterion of Mata-Alvarez [4], discontinuous systems are the most suitable for developing countries, such as Morocco. This criterion can be reinforced by the fact that the transport of biomass with the use of a wheel loader, or the construction of an installation with the characteristics of discontinuous systems are not too technical processes, and which can adapt easily in the national context. Considering the anaerobic digestion of all the organic fraction produced in a city would require a large investment in the short term, which is a limiting factor for all municipalities in Morocco. Given the modular operation of a batch system, its initial installation capacity can be reduced and consequently its investment cost. The capacity of the facility can be increased gradually as the city’s curbside recycling system increases [4]. Based on the analyzes previously carried out, an anaerobic digestion system of the discontinuous type is presented as the most appropriate option for the energy recovery of household organic waste in the city of Oujda. BEKON technology is the discontinuous type of commercial technology known worldwide and is the basis of subsequent calculations.

4 The Biogas Unit Dimensioning The parameters for the waste biogas process must be in agreement with the selected technology. The Table 4 shows the values for these parameters, which are based on BEKON process operation information and also recommendations for similar projects [6].

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Table 4. Operating parameter of the anaerobic digestion process Parameter

Value

Residence time

28 days

Drain time

1 day

Temperature range

Mesophilic

Type of operation

Discontinuous

Fig. 2. Schematic of starting the methanation process similar to that of BEKON technology

4.1 Anaerobic Digestion According to the Chosen Technology According to the BEKON process, a quantity of green waste must be added to the OFHW to obtain fresh mater. The additional quantity of this green mater corresponds to 15% of organic waste [6]. Then, the fresh mater is inoculated with recirculated digestate of the same methanation process (see Fig. 2). The amount of the digestate added is equal to 40% of the substrate which is introduced into the digester [7]. Summaries of the amounts of mater used to start the methanization process are shown in the following Table 5.

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Table 5. Quantity of material for starting the methanation process Scenario 2

Scenario 1

Scenario 3

OFHW (ton/year)

34 310

23 725

10 585

Additional percentage of green waste (%)

15

15

15

Additional green waste (ton/year)

5 146

3 559

1 588

Total fresh material (ton/year)

39 456

27 284

12 173

Recirculated digestate rate (%)

40

40

40

Recirculated digestate (ton/year)

26 304

18 189

8 115

Substrate (ton/year)

65 760

45 473

20 288

Table 6. Digester dimensions based on BEKON technology Parameter

Value

Long (meters)

35

Width (meters)

7

High (meters)

5

Volume

(m3 )

1 225

Filling rate (%)

80

Useful volume (m3 )

980

Table 7. Calculating the number of days for filling one digester Scenario 2 Useful volume of the digester

(m3 )

Substrate quantity (ton/year)

Scenario 1

Scenario 3

980

980

980

65 760

45 473

20 288

Density (ton/m3 )

0,67

0,67

0,67

Substrate volume (m3 /year)

98 149

67 870

30 281

Substrate volume (m3 /day)

269

186

83

Digester filling time per day

4

6

12

4.2 The Installation Sizing To calculate the number of digesters required for the methanization of the substrate, we start from the dimensions of a BEKON process digester, which allows us to obtain its volume. We consider a filling rate of 80% which allows us to know the useful volume for the digester. The values shown in the Table 6 below correspond to those reported in references of this technology [8]. We take into account the value of 0.67 ton/m3 as the minimum density of the substrate to calculate its daily volume, what is added to the useful volume of the digester allows us to calculate the number of days for filling a digester (Table 7):

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Table 8. Calculating the number of cycles per year performed for one digester Scenario 2 Filling time (days)

Scenario 1

Scenario 3

4

6

12

28

28

28

1

1

1

Temp of a digestion cycle (days)

33

35

41

Number of cycles of a single digester

11

11

9

Residence time (days) Drain time (days)

Table 9. Calculating the number of digesters to treat 1 year of substrate Substrate volume

(m3 /year)

Scenario 2

Scenario 1

Scenario 3

98 149

67 870

30 281

Useful volume of a digester (m3 )

980

980

980

Number of cycles per digester

11

11

9

Number of digesters

9

6

4

Filling time =

Digester useful volume Substrate volume per day

The filling time of a digester calculated for each of the scenarios, added to that the residence time of 28 days and 1 day for the emptying of the digester give the time of a digestion cycle for a digester. The number of cycles that a digester can carry out for 1 year is calculated with the following formula (Table 8): Cycle number for 1 year =

365 days Time of a digestive cycle

Thus, to determine the number of digesters required, divide the total volume of the substrate for one year by the useful volume of a digester and the number of cycles that can be achieved during the same period. The results in Table 9 show the number of digesters required for each scenario. Number of digesters =

Substrate volume by one year (Useful volume of digester × Number of digester cycles per year)

5 Conclusion and Perspective The current biogas recovery at the Oujda landfill produced about 2.18 GWh/year of electricity in 2018, but its potential is estimated at 4.96 GWh/year. This work has shown the importance and the ability to control the biomethane flow upstream of the

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biogas unit engine in landfills with manageable and inexpensive technology. These facilities have a lifespan that exceeds 25 years and a return on investment that does not exceed 6 years (6 to 9 digesters per unit) including the purchase of a new motor. In terms of profits, we can distinguish 5 categories including the sale of electricity, cogeneration and the use of thermal energy (combined cycle), the sale of compost (processed digestate), the savings generated by each ton of OFHW not sent to landfill and the converted money from our participation to lower GHG rates. Our next work will focus on the sizing of a composting unit whose main goal is to reduce the rate of OFHW sent to landfill, and of course, develop a sales strategy for compost produced. This will enable a complete financial study and demonstrate the sustainability and effectiveness of our proposed household waste management strategy.

References 1. Pierini VI, Ratto S (2015) Yard trimming’s life cycle: composting vs landfilling. Ind J Sci Res Tech 3(3):43–51 2. EPA. Overview of Greenhouse Gases. Repéré à https://www.epa.gov/ghgmissions/overviewg reenhouse-gases#methane 3. Wellinger A, Murphy JD, Baxter D (2013) The Biogas Handbook: Science, Production and Applications. Elsevier 4. Mata-Alvarez J (2003) Biomethanization of the Organic Fraction of Municipal Solid Wastes. IWA Publishing, London 5. Harzevili FD, Hiligsmann IS (2017) Microbial Fuels: Technologies and Applications. CRC Press, Boca Raton 6. BEKON (2017). Le procédé BEKON - Installations de méthanisation innovantes pour la valorisation énergétique des déchets organiques et autres biomasses. Repéré à https://www.bekon.eu/ wpcontent/uloads/2017/12/BEKON_Broschuere_FR_web.pdf 7. BEKON (2018a) The BEKON Process Innovative biogas plants for energy production from organic waste. Repéré à https://www.bekon.eu/wpcontent/uploads/2015/11/BEKON_Brosch% C3%BCre_EN_Web.pdf 8. BEKON (2018b) Proveedor líder mundial de plantas de biogás en seco + Tecnología de compostaje. Repéré à https://www.ccc.org.co/bion/wp-content/uploads/pdf/27abril-2018/Ignaci oBenitezBekon.pdf

Heat Loss in Industry: Boiler Performance Analysis A. Meksoub, A. Elkihel, H. Gziri, and A. Berrehili

Abstract Boiler performance and efficiency evaluation is generally performed by direct and indirect methods as reported in three boiler performance evaluation codes which will be the subject of our study (ASME PTC4-2008, IS13979: 1994 and BS845-1: 1987 codes). On the one hand, ASME code PTC4-2008 describes in detail the indirect method with different types of losses; some of them are applied in certain exceptional cases. On the other hand, IS13979: 1994 and BS845-1: 1987 codes commonly introduce six types of losses. In literature, direct and indirect methods are widely used to calculate boilers efficiency, and it’s found that indirect method is more effective than direct method which is confirmed by the present study. The present work aims to compare boilers efficiency by direct and indirect methods, while matching the heat losses formulas proposed by the codes mentioned above with other formulas found in literature. Therefore, we will conduct the performance evaluation of a fire tube boiler burning fuel (HHV 9238,55 kcal/kg) through formulas retained from this study. Indirect method is found to be more effective and accurate than the direct method, and the major heat losses are dry combustion gases loss as well as water produced by hydrogen in fuel loss. Furthermore, some unavoidable losses occur for various reasons. However, energy lost could be reused by means of different energy recovery technologies. Keywords Performance evaluation codes · Industrial boiler · Heat loss · Direct & indirect method

A. Meksoub (B) Faculty of Sciences, Mohammed Premier University, B.P. 524, 6000 Oujda, Morocco e-mail: [email protected] A. Elkihel IAE Gustave EIFEEL ICAM PARIS SENART, Paris, France H. Gziri Faculty of Science and Technology Settat, Settat, Morocco A. Berrehili National School of Applied Sciences, B.P. 524, 6000 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/10.1007/978-981-15-6259-4_67

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1 Introduction The most common performance evaluation methods of boilers in literature are the direct and indirect method. Several international (CEN, ISO…) and national standards (AFNOR, ANSI, BSI, DIN…) recommend the indirect method (energy balance or loss method) to evaluate boiler efficiency and heat losses. The BS 845-1: 1987 and IS 13979: 1994 codes distinguish six types of losses, while ASME Code PTC42008 suggests an even more detailed energy balance method for energy loss and credits calculation. The losses are described in detail and some of them are applied in certain exceptional cases. This code provides yet a complete guide to set up balance sheet of losses dissipated over the entire heat production line, unlike other two codes which estimate a part of loss as “unaccountable”, it cannot be calculated but can be estimated. The present work highlights the boiler performance test and evaluation procedures with direct and indirect methods, by collecting and analyzing boiler efficiency formulas reported in the mentioned codes and comparing them with other formulas found in the literature (as reported in a previous work [1]). The aim is to establish boiler efficiency and heat loss formulas which we will retain for our calculation. Afterwards we will calculate efficiency of a fire tube boiler (4T/h) burning liquid fuel (HHV = 9238.55 kcal/kg) through direct and indirect method. Our aim is to point out the major heat losses occurring at boiler system level for eventual optimization.

2 Boiler Performance Evaluation In the literature, the indirect method reported by ASME code PTC4-2008 is compared to other thermal efficiency evaluation methods in accordance with the 1st and 2nd laws of thermodynamics. According to the 1st law, boiler efficiency does not depend on the variation of boiler parameters (since efficiency depends only on the flue gas exit temperature), while the 2nd law allows to observe the exergy efficiency variation as a function of vapor pressure and temperature [2]. The indirect method, used to assess coal consumption and to determine boiler heat input above 50% generator load (a pulverized coal power plant 140 MW) is more accurate than the design curve method [3], the direct method is less precise than the previous two methods, but it can be used to track coal consumption over time [3]. In the literature, boiler efficiency is calculated by both direct and indirect method, the calculations results are evaluated and the accuracy assessed. A coal-fired boiler efficiency (thermal power plant) calculated by direct method is 83.94% lower than indirect method efficiency (91.96%) according to ASME code PTC4-1 [4]. Efficiency of an atmospheric fluidized bed combustion boiler(AFBC 40T/h) calculated by direct method (81.98%) is lower than the indirect method efficiency (82.78%), and the major losses are due to moisture in fuel (4.87%), as well as radiation and convection loss (5.40%) [5]. Radiation and convection losses of a bagasse-burning boiler following

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ASME code PTC4-2008 are closer to values obtained from ABMA standard diagram of ASME code PTC 4.1 1964 (the values of six different boilers vary from 0.36 to 0.44%) [6]. As for the choice of efficiency evaluation method (direct or indirect method) it depends mainly on water and hydrogen rate in fuel (direct method if the rate is less than 10%, indirect method if not) [7].

3 Boiler Performance Evaluation Codes (Direct and Indirect Methods) Below we briefly describe the two methods used to conduct boiler performance tests.

3.1 Direct Method The direct method (Input-Output Method) defines efficiency as the ratio of heat output to heat input following formula (1) [8, 9]. This method depends on fuel flow measurement and on the fluid side conditions required to calculate the energy produced. The efficiency uncertainty calculated by this method is directly proportional to uncertainty associated to fuel flow determination, fuel analysis, and to steam generator power [10]. η=

mv ∗ (hv − hw ) * 100 HHV * mf

(1)

3.2 Indirect Method (Energy Balance) The indirect method presents a detailed balance of input and output energy needed to evaluate a steam generator performance [9–11]. The efficiency is obtained by subtracting the sum of losses from 100 according to BS 845-1: 1987 and IS 13979: 1994 codes. In the case of ASME code PTC 4-2008, energy losses and credits are used to calculate efficiency. This method is recommended for its results accuracy since it is based on detailed and complete information [9–11]. Theoretical Air Required (ATR %): In the literature, theoretical air is calculated by formula (2) which we will use for our calculation [8, 12, 13]. Excess air (XpA %): Formula (3) is widely used in the literature to determine excess air [8, 12, 13].

650 Table 1 Air parameters formulas

A. Meksoub et al. Sign

Formulas

ATR

=



XpA AAS

=

11,6.C + 34,8 *

 H2 −

O2 8

100



 + 4,35.S

(2)

O2 (21 − O2 )

* 100 (3)  = 1 + XpA 100 * ATR (4) 

Air Actually Supplied (AAS %): Air actually supplied is based on theoretical air required and excess air as expressed in formula (4) which we will use for calculation [8, 12, 13] (Table 1). Heat Losses: Dry Combustion Gas Loss ( P 1 ): ASME PTC4-2008 code [10] presents formula (5). On the one hand, IS 13979: 1994 code divide this loss under two types [9]: Loss by dry gases formula (6), and loss by hydrogen and moisture in fuel formula (17). On the other hand, BS845-1:1987 code proposes formula (7) based on Siegert constant [11]. For our calculations we retain formula (8) [8, 12, 14]. Loss Due to Hydrogen in Fuel ( P 2 ): Formula (9) [12, 15], is based on formula (10) [14]. IS 13979: 1994 and BS 845-1: 1987codes [9, 11] proposes formulas (16) and (17) of both losses by hydrogen and moisture in fuel. For our calculation, we will use formula (11) as reported in the literature [8]. Loss Due to Humidity in Fuel ( P 3 ): Formula (12) [12, 15] is based on formula (13) [14]. ASME PTC4-2008 code [10] suggests formula (14), and in the same way as hydrogen in fuel loss, we retain formula (15) for the calculation [8]. Loss Due to Humidity in Combustion Air ( P 4 ): ASME code PTC4-2008 [10] suggests formula (18) for loss by humidity in air. In the literature, we find a widely used formula (19) which we retain for calculation [8, 12, 13, 15]. Loss Due to Incomplete Combustion ( P 5 ): ASME Code PTC4-2008 [10] proposes formula (20) for loss by carbon monoxide, while BS845-1: 1987 code [11] suggests formula (21) for loss due to unburned gases. In the literature formula (22) is widely used which we will retain for calculation [8, 14]. Radiation and Convection Loss ( P 6 ): ASME code PTC4-2008 suggests formula (23). While BS845-1: 1987 code [11] proposes formula (24) similar to formula (25) of IS 13979: 1994 code [9]. For our calculation, we retain formula (26) as reported in the literature [8].

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Fig. 1 Boiler loss & efficiency

DIRECT METHOD

Fig. 2 Boiler efficiency direct method

η

Pi

84.76

0

20

15.24

40

60

80

100

INDIRECT METHOD

Fig. 3 Boiler efficiency Indirect method

0%

20%

40%

60%

P1

Efficiency & loss values % 3.15

P2

2.98

P3

0.57

P4

0.12

P5

0.14

P6

1.82

η

91.22

80%

100%

4 Boiler Performance Evaluation We present below calculations carried out as part of a measurement campaign, which took place within a leading beverage company. The studied boiler is multitubular fire tubes boiler (4T/h) burning fuel (HHV: 9238,55 kcal/kg). Steam is produced for manufacturing processes as well as for heating, spraying and conditioning the fuel. The boiler efficiency was calculated using direct and indirect methods, according to formulas previously presented (Table 2). The needed data for calculations (fuel mass, fuel analysis, flue gas analysis, etc.) and the results are presented in Fig. 1 and Table 3 (see Appendix). According to direct method, efficiency is 84.76% (see Fig. 2). On the

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Table 2 Efficiency and heat loss formulas Sign

Formulas

P1

= 100 * MqFg * Hg (5) = = =

P2

= = =

P3

= =

   1.866 * C + 38 ∗S - 0.023 * CVR *

A 100 -RC



* Cpg * (Tg −Ta )

HHV k * (Tg − Ta ) * [1 − 0.01 * (L4 + L5 )] CO2

* 100 (6)

(7)

m gaz * C pg * (Tg − Ta ) * 100 (8) H HV   9 * H2 2452.8+Cpv * (Tg − Ta ) * 100 (9) H HV   9∗H2 100 * Cpw * (100 − Ta ) + Cps * Tg

  − 100 + hfg (10)

9 * H2 * (584 + C pv * (Tg − Ta )) * 100 (11) H HV   M * 2452.8 + Cph * (Tg − Ta ) * 100 (12) H HV     M 100 * Cpw * (100 − Ta ) + Cps * Tg − 100 + hfg

(13)

= 100 * MF * (HS − Hwa ) (14) M * (584 + C pv * (Tg − Ta )) = * 100 (15) H HV

P2 & P3

=

(M + 9 * H) * (2488 − 4.2 * Ta + 2.1 * Tg ) H HV

(16)

  575 + 0.5 * Tg − Ta ) *H * 100 (17) = ( * M + 8.936 H HV 100 P4

= 100 * MM * MDA * HwL (18) A AS * F H * C pv * (Tg − Ta ) = (19) H HV

P5

= CO * M Fg * MCO * = =

P6

HHVCO HHV (20) k1 * CO * (l − 0.01 * (L4 + L5 )) (21) CO+CO2 CO * C 5744 CO + CO2 * H H V * 100 (22)

= C1 * (Hcaz + Hraz) * Afz * (Tz − Ta ) (23) =

6.7 * A1 * (TH −Ta ) QA * l1

+

53 * A2 * QA A * QR * (l2 + 1.3)

(24)  QA (25) = 5762 * AQ1 **(Tl1H − Ta ) + A *53Q* A*2(l*2 +1.3) A R 

4 4  (196.85 * Vm +68.9) Ts Ta 1.25 +1.957 * − − T * (26) (T ) s a 55.55 55.55 68.9 η

= 100 −

6 



Pi

i=1

other hand, indirect method efficiency is 91.22% (see Fig. 3) near nominal efficiency (which is greater than or equal to 90%). Major losses are dry combustion gases loss (3.15%) which depends on fuel, excess combustion air and flue gas temperature. Yet, exit flue gases carry away a big amount of energy, and affect boiler efficiency, reducing this loss rely on the optimal excess air amount provided for a stoichiometric combustion. Loss due to water produced by hydrogen combustion is important as

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well (2.98%) since a good part of heat is lost as water vapor carrying away enthalpy (or total heat) following the temperature and pressure conditions. This loss can be considerably reduced through a suitable condensing heat exchanger. In addition, other unavoidable losses occurs for various reasons, such as radiation, convection and conduction loss (1.82%) which can be optimized by insulating the distribution circuits and the boiler components external surfaces. Insulation thickness must be calculated basing on losses occurring at the system level and on installation cost [16]. The most effective recovering solution is to use boiler flue gases heat to preheat combustion air and feed water. Other expensive solutions exist, such as heat pumps [17, 18], Rankin organic cycle or Kalina cycle for electricity production [19], or absorption refrigeration cycle to produce air conditioning or cooling effect [20]. However, the choice of heat recovery technology depends on boiler flue gas temperature and flow rate [21].

5 Conclusion Both BS 845-1: 1987 and IS 13979: 1994 codes report six categories of losses for boilers burning various types of fuel (by dry combustion gases, by hydrogen in fuel, by moisture in fuel, by humidity in combustion air, incomplete combustion loss and radiation and convection loss), as well as some losses which are applied only in the case of solid fuel (such as solid unburned loss, residual ash and fly ash loss…). Other losses cannot be calculated according to these two codes but can be estimated. On the other hand, ASME code PTC4-2008 presents a more detailed methodology, and proposes other types of losses depending on the studied case (type of fuel, type of boiler, presence of boilers components such as: economizer, sprayer, auxiliary equipment…). In this paper, we used the most common formulas of boiler efficiency and loss calculations found in the literature. The calculation of loss and efficiency with direct and indirect methods was made, and the result displayed in Fig. 1 show that efficiency by indirect method (91,22%) is higher than efficiency by direct method (84,76) (see Fig. 2 and Fig. 3) although it is closer to nominal efficiency (which is greater than or equal to 90%). The major losses are dry flue gases loss (3.15%), hydrogen in fuel loss (2.98%) and radiation and convection loss (1.82%) (See Fig. 3). Dry combustion loss depends on fuel quality, excess air and flue gas temperature. This loss can be recovered and transferred to a heat sink or can be used to preheat combustion air and feed water. Loss by hydrogen in fuel which leaves boiler as water vapor after combustion can be reduced using a condensing heat exchanger. Radiation and convection loss, due to boiler external surfaces and naked steam piping system which transmit a significant amount of heat to the environment during boiler operation, can be reduced considerably using a proper insulation of external surfaces and of various boiler components. For coming studies, we will focus on radiation and convection loss to determine the economic thickness and to establish the choice of insulation material. For this

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purpose, we will conduct an audit of a steam production system (boiler assembly and steam piping system) using an infrared thermal camera to highlight heat losses occurring at the system level. We will study as well the excess air effect on boiler performance and on dry flue gas loss.

Appendix (See Table 3 and 4).

Table 3 Data for efficiency computation Fuel analysis_Immediate analysis

Unit

Value

C

Carbon

%

40

H2

Hydrogen

%

4,7

O2

Oxygen

%

8,5

S

Sulfur

%

0,5

M

Moisture

%

8,2

Unit

Value

Flue gas analysis O2

Oxygen

%

9

CO2

CO2 in flue gas

%

15,87

CO

CO in flue gas

%

0,094

Data for efficiency—indirect method

Unit

Value

mgaz

Flue gases mass per kg of fuel

kg/kg fuel

8,4

Cpg

Flue gas Specific heat

kcal/kg.°C

0,23

Cpv

Superheated steam specific heat

Kcal/Kg.°C

0,45

FH

Moisture factor

Kg/kg air

0,0163

HHV

Fuel higher heating value

kcal/kg

9238,55

Ta

Ambient temperature

°C

25

Tg

Flue gas temperature

°C

176

Ts

Boilers surface temperature

°C

52

Vm

Wind velocity

m/s

0,45

Data for efficiency-direct method

Unit

Value

mv

Quantity of steam

kg/h

4000

mf

Quantity of fuel

kg/h

300

hv

Enthalpy of steam ~240 °C

kcal/kg

692,46 (continued)

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Table 3 (continued) Fuel analysis_Immediate analysis

Unit

Value

hw

kcal/kg

105,13

Boiler efficiency-direct method

Unit

Value

η

%

84,76

Energy balance-Indirect method

Unit

Value

ATR

Formula 2

%

5,92

XpA

Formula 3

%

75

AAS

Formula 4

%

10,37

P1

Formula 8

%

3,15

P2

Formula 11

%

2,98

P3

Formula 15

%

0,57

P4

Formula 19

%

0,12

P5

Formula 22

%

0,14

P6

Formula 26  = 100 − Pi

%

1,82

%

91,22

Enthalpy of feed water ~105 °C Efficiency (formula 1)

η

Table 4 Abbreviations list Symbol

Description

A

Ash

Unit %

A1 /A2

Boiler surface heated with water/steam or by gas

m2

Afz

Flat projection surface of the envelope

m2

CO/CO2

Actual volume of CO and CO2 in gas dry basis

%

CVR

Calorific value of the residue

Kcal/kg

C1

Constant

0,293 W

Cph

Specific heat of humidity

kJ/kg.°C

CPw

Specific heat capacity of water (0.0042 MJ/kg.°C)

MJ/kg.°C

Cps

Specific heat capacity of steam (0.002 MJ/kg.°C)

MJ/kg.°C

Hraz

Radiation heat transfer coefficient

J/m2 .s.C

Hg

Enthalpy of dry gases at T ° of exit

J/kg

HHVCO

Higher heating value of CO (10111 kJ/kg)

kJ/kg

Hcaz

Convection heat transfer coefficient

J/m2 .s.C

Hs

Water vapor enthalpy (1 Psia and at T ° of gases)

J/kg

Hwa

Enthalpy of water at reference temperature

J/kg

HwL

Enthalpy of water vapor at the gas T °

J/kg (continued)

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Table 4 (continued) Symbol

Description

Unit

hfg

Water evaporation Latent heat (2,26 MJ/kg ~100°C)

MJ/kg

L4 /L5

Loss by residual and flying ashes

%

l1 /l2

Insulation thickness (surface heated water/steam or by gas)

mm

MM

Moisture mass in air by dry air mass

kg/kg fuel

MDA

Mass of dry air corresponding to excess air

kg/J

MFg

Moles of dry gas

moles/kg

MqFg

Mass of flue gases per unit of fuel

kg/J

MF

Moisture in fuel

kg/J

MCO

Molecular weight of CO

kg/mole

Qa

Heat input rate of boiler during test (HHV basis)

kcal/h

QR

Heat input rate at nominal boiler output (HHV basis)

kcal/h

Rc

Fuel in residues

%

TH

Steam or water temperature at boiler pressure

°C

Tz

Average temperature at location z

°C

References 1. Meksoub A, Elkihel B, Boulerhcha M (2019) Etude des Performances Énergétiques des Générateurs de Vapeur. In: 3rd international conference on mechanical materials structures, vol 2019, p 13 2. Sosa-Arnao JH, Nebra SA (2011) First and second law to analyze the performance of bagasse boilers. Int J Thermodyn 14(2):51–58 3. Hightower DA, Nasal JR (2005) A relative accuracy evaluation of various methods to determine long term coal-burned values for coal pile inventory reconciliation. In: EPRI heat rate improved conference, 25–27 January, p 18 4. Srinivas GT, Kumar DR, Venkata P, Murali V, Rao BN (2017) Efficiency of a coal fired boiler in a typical thermal power plant. Am J Mech Ind Eng 2(1):32–36 5. Kapre AS (2010) Energy auditing and scope for its conservation in textile industry: a case study. Maharana Pratap University of Agriculture and Technology, Rajasthan 6. Cortes-Rodríguez EF, Nebra SA, Sosa-Arnao JH (2017) Experimental efficiency analysis of sugarcane bagasse boilers based on the first law of thermodynamics. J Braz Soc Mech Sci Eng 39(3):1033–1044 7. Lang FD (2009) Errors in boiler efficiency standards. In: ASME 2009 power conference, no April, pp 487–501 8. Patro B (2016) Efficiency studies of combination tube boilers. Alex Eng J 55(1):193–202 9. IS 13979:1994 (1994) Method of calculation of efficiency of packaged boiler, New Delhi 10. ASME PTC4-2008 (2008) Fired steam generators performance test codes, New York 11. BS 845-1:1987 (1987) British standard assessing thermal performance of boilers for steam, hot water and high temperature heat transfer fluids, London 12. Krishnanunni S, Paul JC, Potti M, Mathew EM (2012) Evaluation of heat losses in fire tube boiler. Int J Emerg Technol Adv Eng 2(12):301–305 13. Ibrahim H, Qassimi M (2008) Matlab program computes thermal efficiency of fired heater. Period Polytech Chem Eng 52(2):61–69 14. Harimi M, Sapuan M, Ahmad M, Abas F (2008) Numerical study of heat loss from boiler using different ratios of fibre to shell from palm oil wastes. J Sci Ind Res 67(June):440–444

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15. Barma MC, Saidur R, Rahman SMA, Allouhi A, Akash BA, Sait SM (2017) A review on boilers energy use, energy savings, and emissions reductions. Renew Sustain Energy Rev 79(March 2016):970–983 16. S. public de Wallonie (2010) Economies d’énergie dans l’industrie, Gecinox 17. Sternlicht B (1982) Waste energy recovery: an excellent investment opportunity. Energy Convers Manag 22(4):361–373 18. ECN (2012) Heat powered cycles, Alkmaar-Netherlands 19. Lecompte S et al (2017) Case study of an organic Rankine cycle (ORC) for waste heat recovery from an electric arc furnace (EAF). Energies 10(5):1–16 20. Wakim M (2017) Etude des machines à absorption pour la valorisation de la chaleur fatale basse température, Paris 21. McKendry P (2002) Energy production from biomass (part 2): conversion technologies. Bioresour Technol 83(1):47–54

Numerical Simulation of the Flood Risk of the Deviation Hydraulic Structure at Saidia (North-East Morocco) Farid Boushaba, Abdellatif Grari, Mimoun Chourak, Youssef Regad, and Bachir Elkihel

Abstract In this paper, we study the impact of floods on the hydraulic flood protection structure. It is a development, consisting of a ditch collecting water from the western watersheds and drainage at the western edge of the city towards the Oued Moulouya. The present work therefore uses the software IBER which is based on a formalism finite volume. Several flood scenarios are applied in order to show the failures and propose solutions or improvements to the existing structure, in order to mitigate the damage caused by the floods. Keywords Flood · Hydraulic structure · Finite volume · Numerical simulation

1 Introduction The prediction of this risk, in view of the catastrophic damage it provokes, has become a priority for all state agencies. For this, in recent years, plans and methods of forecasting and prevention have been put in place. This study concerns the city of Saidia, located in the far north-east of Morocco, on the Mediterranean. It is located on the edge of the Wadi Kiss, which is the eastern border with Algeria. Its geographical coordinates are: 35◦ 6 Latitude North, 2◦ 15 Latitude West [8]. The Saidia plain is among the 391 sites subject to very high flood risk nationwide (National Flood Protection Plan 2003) [1]. The conditions for increasing the risk of flooding are related to the morphology of the terrain. In fact, the soil, partially or totally saturated with water by a superficial groundwater, will generate “saturation” runoff, in very particular cases, these types of runoff can occur at the same time and suddenly F. Boushaba · M. Chourak · Y. Regad (B) · B. Elkihel Department of Mechanic and Applied Mathematics, National School of Applied Sciences, First Mohamed University, Oujda, Morocco e-mail: [email protected] F. Boushaba e-mail: [email protected] A. Grari Department of Geology, Faculty of Sciences, First Mohamed 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/10.1007/978-981-15-6259-4_68

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produce catastrophic floods. This plain, belonging to the subsiding basin of Triffa, is characterized by a very flat topography and whose surface is very close to the water table (Sadki 1996, Melloul 2007) [7]. In addition, its situation, sandwiched between two wadis, the Kiss in the East and the Moulouya in the West and the plateau of Ouled Mansour in the South, considerably increases the risk of brutal flooding flood. Uncontrolled and growing urban and tourist developments aggravate these risks (Mouzouri et al. 2011). The modeling is based on impressed works like [2–6].

2 Mathematic Model and Numerical Method To describe the equations governing the propagation of floods, the model of SaintVenant will be used for these types of flows, it is one of the most used models which allows to describe the surface flows, it is established from the laws conservation of mass and momentum by making certain assumptions; hydrostatic distribution of pressure and average speed in vertical direction. Let’s recall briefly the basics of the two-dimensional hydrodynamic model of Saint-Venant. This can be presented in the form: ∂hU y ∂hUx ∂h + + = Ms ∂t ∂x ∂y

(1)

∂hU y Ux ∂hUx ∂hUx2 ∂ Zs τs,x τb,x g h 2 ∂ρ + + = −gh + − + + 2Ω sin(λ)U y ∂t ∂x ∂y ∂x ρ ρ ρ 2 ∂x (2) e e ∂hτx y ∂hτx x + + Mx + ∂x ∂y ∂hτxey ∂hU y2 τb,y ∂hU y ∂hU y Ux τs,y ∂ Zs g h 2 ∂ρ + + = −gh + − + + 2Ω sin(λ)Ux + ∂t ∂y ∂x ∂x ρ ρ ρ 2 ∂y ∂x e ∂hτ yy + My + ∂y

(3)

Where h is depth, Ux , U y are the depth averaged horizontal velocities, g is the gravity acceleration, Z is the free layer elevation, τs is the free surface friction due to wind induced friction, τb is the bed friction, ρ is the water density, Ω is the earth rotation e are the effective angular velocity, λ is the latitude of the studied point, τxex , τxey , τ yy horizontal tangential stresses, and Ms , Mx , M y are respectively the terms of mass source drain and momentum, which are used to model precipitation, infiltration and drainage. The bed friction can be expressed as: τb = ρC f U 2 , where C f is the bed friction coefficient, we use the Manning formula to present the roughness coefficient

Numerical Simulation of the Flood Risk at Saidia ...

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2

like: C f = g hn1/3 . The τs is the stress friction due to wind over the free surface, we 2 . use the Van Dron equation: τs = ρCv V10 Where ρ is the water density, V10 is the wind velocity at 10 m of height and Cv is the surface drag coefficient. The τiej are function of the viscous stresses, the turbulent stresses and the lateral dispersion terms. This equation model deals with flooding, which is an occasional phenomenon that can flood large areas of the floodplain or plain, following a particularly large flood. The method used is the finite volumes to solve the mathematical model, which is in the form of a system of partial differential equations because this method is one of the most widespread and reliable spatial discretization techniques. It was developed after the finite difference and finite element methods. The basic principle of this method is based on the integration of partial differential equations on cells, called “control volumes”, thus producing a weak solution of the system, unlike the finite difference method where the domain of computation is generally Subdivided into rectangular cells, in the case of the finite volume method, the mesh can be quadrangular, triangular or even hybrid, and can be structured or unstructured, which offers the possibility for this method to adapt to complex geometries.

3 Application The problem of floods in the city of Saïdia is related to the floods caused for the most part to flows generated by small wadis that cut the cliffs of Ouleds Mansour. The watersheds that dominate the city on the south side, have very steep slopes and generate flows and volumes that frequently cause flooding. The figure below presents the delimitation of the catchment areas of the city of Saidia [9]. The channel of this study drains the main wadis that are East to West: Oued Ben Arbia; Wadi Abdellah; Oued Bel Arbi; Oued Sebbak and Oued Chbak. Figure 1 represents the delimitation of the drainage basins draining the canal in the form of the pit object of the study, the channel leads to the Oued Moulouya. The figure above presents

Fig. 1 Delimitation based on the numerical model of the terrain DEM.

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a watershed breakdown, these are nine basins in features are presented in the table below. The outlet of the nine basins is full of Saidia. In all the hydrological scenarios tested, we will conserve the rain with the highest peak flow, in order to be safe in the determination of the flood zones. A hydrological calculation has made it possible to establish the hydrograms of each sub basin, the calculation of precipitation flows is based on decadal rainfall events. The hydrographies in Fig. 2 will constitute boundary conditions across the channel boundary. The field of study consists of a moat 14 km long and variable width between 4–11 m and part of Oued Moulouya. The profile across the channel is in the form of trapezoid reinforced concrete, The mesh is of unstructured triangular shape, the number of elements 48702 and nodes 26211, see Fig. 3 below. The main Oued and Chaiba feed the canal are grouped into five tributaries of flood flow and return period. The table below shows the flood flows for different return periods. The determination of flood zones can be done according to two approaches: – A hydrogeomorphological and historical approach, determining the middle and major beds of the watercourse;

Fig. 2 Simulation in the presence of the diversion canal

Fig. 3 Real simulation of the scenario without the diversion canal

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– A hydraulic modeling of the flows, determining the influence, velocities and ratings of the flood zone obtained for the reference flow (100-year flood). The latter method is used in sectors with many stakes and which need to characterize flooding at a frequency point, and at speed, so we use the second approach. We run a simulation for a duration of 16 h, we use 100 year return period floods. These flood flows are generated by the watersheds that dominate the city on the south side, have very steep slopes. To illustrate the flow of the flood wave, on the artificial channel, we adopted a triangular mesh T3 , the number of elements is 24603and the number of corresponding nodes is 24 101. The condition CFl is 0.45 with a dry bottom in the initial state. The maximum cumulated flow rate is 100 m3 /s (Table 1). It is proposed to simulate full of Saidia in two cases, in the presence and absence of the protection channel. It is based on a return period of 100 years. It is observed that the canal has managed to divert a large volume of flood towards the Moulouya

Table 1 Flows in m 3 recorded during the flood period (Hydraulic Basin Agency of Moulouya.H.B.A.M). Return period (year s)

Debit (m 3 /s) Oued Ben Arbia

Oued Bel Abdellah

Oued Arbi

10

5.2

16.3

12.8

20

6.3

20.6

50

7.6

25.5

100

8.3

29.6

Oued Sebbak

Oued Chbak

7.9

20.3

16.1

9.9

26.3

10.7

12.7

33.1

22.7

14.1

37.6

Fig. 4 Proposed solution to minimize the overflow of water in the city of Saidia

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River. The height of the waterbed is between 0.01 and 0.22 m (see Fig. 3). On the other hand, it is easy to reach a height of 1 m in the case of absence of the diversion channel (see Fig. 4), i.e. a decrease in the water height of 80%. To limit the remaining runoff a second ditch (channel) is proposed, see Fig. 4.

4 Conclusion The management of the water has become of a daily actuality, that is by rarity or by excess generated by the great floods. In this context, we study the influence of the flood propagation on the protective channel of the city of Saidia. We have chosen the Saint Venant model of equations for schallaw water equation which is a model based on the conservation equations of the mass and quantity of movements of Navier Stocks, this system of Saint-Venant is the model the most appropriate governing free-surface flows, it is established on average a certain number of hypotheses. Then we applied the method of volumes, which is one of the most widespread and reliable spatial discretization techniques, we have discretized the partial differential equations on an unstructured mesh. It is concluded that the present study is part of the overflow risk assessment framework in order to act for the prevention and protection against floods. – To implement global actions allowing the reduction of the contributions of floods and flood risks; – Take into account the risk of overflow and runoff flooding in the urban planning documents of the municipality.

References 1. Agence du bassin hydraulique de Moulouya (ABHM). http://www.abhmoulouya.ma/ 2. Benkhaldoun F, Elmahi I, Seaid M (2007) Well-balanced finite volume schemes for pollutant transport by shallow water equations on unstructured meshes. J Comput Phys 226(1):180–203 3. Benkhaldoun F, Elmahi I, Seaid M (2010) A new finite volume method for flux-gradient and source-term balancing in shallow water equations. Comput Methods Appl Mech Eng 199(49):3324–3335 4. Bermudez A, Dervieux A, Desideri V, Cendon M (1998) Upwind schemes for the two dimensional shallow water equations with variable depth using unstructured meshes. Comput Methods Appl Mech Eng 155:49–72 5. Bouguerra SA et al (2016) Transport solide dans un cours d’eau en climat semiaride : cas du bassin versant de l?Oued Boumessaoud (nord-ouest de l’Algérie). Revue des sciences de l’eau 29(3):20–27 6. Boushaba F, Chaabelasri EM, Elmahi I, Benkhaldoun F, Borthwick AGL (2008) A comparative Study of Finite Element and Finite Volume for problems of transcritical free surface flows on unstructured meshes. Int J Comput Methods 5(3):413–431 7. Boushaba F, Lakhlifi Y, Daoudi S (2009) Dam-break computations by a dynamical adaptivefinite volume method. J Appl Fluid Mech 1(4):120–126

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8. Mouzouri M et al (2013) Utilisation d’image satellitaire et d’un modele numerique d’altitude (MNA) pour la cartographie des zones à risque d’inondation sur le litoral mediterraneen de saïdia (nordest du maroc). Revue Française de Photogrammétrie et de télédétection, No 201 9. Regad Y, Elkihel B, Boushaba F, Chourak M (2019) Numerical study of the smoke plume of the Jerada thermal power plant Morocco. Pollack Periodica 14(2):131–142. https://doi.org/10. 1556/606.2019.14.2.12

Numerical Simulation of the Sediment Transport of the Hydraulic Diversion Structure in Saidia (North-East of Morocco) Farid Boushaba, Abdellatif Grari, Mimoun Chourak, Youssef Regad, and Bachir Elkihel Abstract In this paper we study the impact of sediment on the hydraulic flood protection structure built in the coastal city of Saidia. It is a development, consisting of a ditch collecting the waters of the western catchment basins and evacuation at the western limit of the city towards Oued Moulouya. The present work uses the IBER software which is based on a finite volume formalism. The study predicts the behavior of sediment deposition, erosion and sediment transport, which makes it possible to fight against the flaring of the canal based on the knowledge of the morpho-sedimentary dynamics of the streams. Keywords Flood · Sediment transport · Finite volume · Erosion

1 Introduction The study of sediment deposition is of paramount importance, since the most dramatic consequence of erosion, especially in the semi-arid countries, is probably the loss of useful volumes of water storage or diversion for water flood protection. One of the means to fight silting of canals is the knowledge of the sediment morphodynamics of rivers. Especially the heavy rainfall on steep slopes basins which is our case. To predict sediment deposition, erosion and sediment transport behavior, the IBER numerical model is used, which incorporates the diffusion-dispersion concept required to analyze sediment transport, deposition and erosion problems unsteady flow. It predicts the behavior of sediments in a hydrographic network as well as the morphological change of the flow sections to define the evolution of the capacity F. Boushaba · M. Chourak · Y. Regad (B) · B. Elkihel Department of Mechanic and Applied Mathematics, National School of Applied Sciences, First Mohamed University, Oujda, Morocco e-mail: [email protected] F. Boushaba e-mail: [email protected] A. Grari Department of Geology, Faculty of Sciences, First Mohamed 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/10.1007/978-981-15-6259-4_69

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of the hydraulic structure. Our study concerns the city of Saïdia, located in the far north-east of Morocco, on the Mediterranean. It is located on the edge of the Wadi Kiss, which is the eastern border with Algeria. Its geographical coordinates are: 35◦ 6 Latitude North, 2◦ 15 Latitude West [10]. The main objective of this article is to assess the risks of extreme storms on a diversion channel, used to protect the city of Siadia against submersion. It is a channel 14 km long and 11 m wide, which is the outlet of 9 sub basins. We mainly examine the physical impacts of the structure and the proposed solutions to improve it. The modeling is based on impressed works like [1, 3, 4, 6, 8, 12].

2 Mathematic Model and Numerical Method To describe the equations governing the propagation of floods, the model of SaintVenant equations will be used for these types of flows, it is one of the most used models that allows to describe the surface flows, it is established from laws of conservation of the mass and quantity of movement by means of certain hypotheses; hydrostatic distribution of pressure and average speed in vertical direction. Let’s recall briefly the basics of the two-dimensional hydrodynamic model of Saint-Venant. This can be presented in the form: ∂hU y ∂hUx ∂h + + = Ms ∂t ∂x ∂y

(1)

∂hU y Ux ∂hUx ∂hUx2 ∂ Zs τs,x τb,x g h 2 ∂ρ + + = −gh + − + + 2Ω sin(λ)U y ∂t ∂x ∂y ∂x ρ ρ ρ 2 ∂x (2) e e ∂hτx y ∂hτx x + + Mx + ∂x ∂y

∂hU y2 ∂hU y Ux τs,y τb,y ∂hU y ∂ Zs g h 2 ∂ρ + + = −gh + − + + 2Ω sin(λ)Ux ∂t ∂y ∂x ∂x ρ ρ ρ 2 ∂y (3) e ∂hτ yy ∂hτxey + + My + ∂x ∂y Where h is depth, Ux , U y are the depth averaged horizontal velocities, g is the gravity acceleration, Z is the free layer elevation, τs is the free surface friction due to wind induced friction, τb is the bed friction, ρ is the water density, Ω is the earth’s rotation e are the effective angular velocity, λ is the latitude of the studied point, τxex , τxey , τ yy

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horizontal tangential stresses, and Ms , Mx , M y are respectively the terms of: mass source/drain and momentum, which are used to model precipitation, infiltration and drainage. The bed friction can be expressed as: τb = ρC f U 2 , where C f is the bed friction coefficient, we use the Manning formula to present the roughness coefficient 2 like: C f = g hn1/3 . The τs is the stress friction due to wind over the free surface, we 2 . use the Van Dron equation: τs = ρCv V10 Where ρ is the water density, V10 is the wind velocity at 10 m of height and Cv is the surface drag coefficient. The τiej are function of the viscous stresses, the turbulent stresses and the lateral dispersion terms. We conjugate to the equation of hydrodynamics with the sediment transport equation: (1 − p)

∂qsb,y ∂qsb,x ∂ Zb + + = D−E ∂t ∂x ∂y

Where p is the porosity of the sediment that forms the bed layer, Z b is the bed level, qsb,x and qsb,y are the two solid flow components. The difference D − E is the balance between the bedload and suspended load. This model of equation deals with flooding, which is an occasional phenomenon that can drown large parts of the floodplain or the plain, following a particularly high flood and overflowing waters. The numerical resolution of the system of equations Eqs. 1 and 2, presents many numerical difficulties and is still the subject of much recent work, including research [7, 11]. On the one hand, the non-linear nature of this system combined with its hyperbolically precludes the use of analytical techniques for most practical problems, in addition, one can lead to discontinuous solutions (hydraulic jumps) even if the data initial is regular. On the other hand, the presence of stiff source terms from the irregularity of the domain background may make most of the existing classical schemas inappropriate. We use the IBER calculation code which is proving robust for this type of simulation [2, 6]. All the equation described so far, are solved using the finite volume method. In this section the numerical schemes used in IBER code. The characteristics of the used numerical schemes in IBER modules are: – Finite Volume Schemes, considered in an integral and conservative way; – Non-structured Mesh is used; – It solves the hydrodynamic equations using the upwind High Resolutsion extension of the Roe schemes; – Upwind treatment of the bed layer slope term; – Second Order schemes in space; – Time explicit schemes.

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3 Results and Discussion The modeling of sediment transport is in a unsteady mode and in two dimensions, one imposes a hydro-gram of 100 years return period flood liquid flow. These flood flows are generated by the watersheds that dominate the city on the south side, have very steep slopes. For the calculation of the solid flow an approach based on statistics of the samples has been elaborated. By reference [5]. Data control methods (Q L , Q S ), regression correlation methods are available [9]. The solid flow and the liquid flow generally evolve according to a power model according to an empirical relation Q S = a Q bL commonly called the solid transport curve [9]. For the calculation of the solid flow the following correlation is applied: Q S = 1.6Q 1.76 L This relationship has been successfully applied in the northwestern region of Algeria [9] which is similar to the area under study. In order to perform the sediment transport calibration, an iterative calculation was performed by varying the value of the sediment transport model diffusion coefficients until there was a satisfactory agreement between the observed sediment concentration and the concentration simulated sediment by the unsteady model IBER. The value of 0.0015 for the diffusion coefficient gives satisfactory results. Concerning the simulation, we adopted the following coefficients: – – – – –

Diffusion Coefficient 0.002 Smith number 0.7 Diameter suspsediment 0.001 m Porosity 0.4 Model Mayer Peter and Muller

The sediment deposit along the 14 km long channel is simulated. A sediment deposition of a 4 cm thick layer along the canal, from the mouth side to the section of the channel, is observed in Figs. 1 and 2 coordinates x = 7.8318105, y = 5.0241105

Fig. 1 Dot line: Bed elevation, line: Topography

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Fig. 2 Graph of the bed elevation and the topography

Fig. 3 Discharge and Sediment graph (canal outlet)

beyond this section towards the entrance of the canal, the sediment deposit decreases at the bottom of the canal bed, see Fig. 3. This can be justified firstly by the slope which is more low on the mouth side, so the speed is lower which will favor the deposit on the other hand upstream of the channel the slope is stronger which generates high speeds creating sediment transport downstream of the channel, which is in consultation with Chezy’s relationship. In addition, most of the tributaries are located on the south side of the canal. In order to determine the volume of sediment stored in the channel, two solid flow curves are cut at the inlet and the outlet of the channel the hydro-gram output see Fig. 3 is the same pace as the hydro-gram of the centennial flood, which is of the order of 25 h, but the peak flow rate of 70 m3 /s is reached quickly after 2.5 h which decreases then during 2.5 h, after this period of 5 h the variation of the flow becomes weak. As already mentioned, sediment discharge is mainly due to soil erosion, which in turn is a function of rainfall and run. Hence, the sediment discharge should be expected to be high when the discharge is high. Indeed, the sediment graphs follow a similar pattern with that of the hydrographs. From both graphs, we can estimate with a good approximation, the volume of sediment remaining in the channel, or 500 tons, with a return period flow of 100 years, the channel stores a volume of 252 m3 .

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4 Conclusion The gradual siltation of water transport channels has become a daily problem because it decreases the capacity of these structures, increases the cost of their maintenance, the cost of treating the water they transport and causes a malfunction in the water. This can be explained by the fact of the solid movements with the current of water, once this material arrived in the streams, it will be transported by the force of the current. Sediment concentrations are proportional to watercourses and the magnitude of floods. A significant proportion of these sediments will be trapped in the reservoirs of dams and canals. This settlement gradually decreases the capacity of the reservoir. To carry out this study, the hydraulic modeling of the flows was used, with the reference flow. We have chosen the Saint Venant model of equations for Schallaw Water, which is a model based on the Navier Stocks movement and mass conservation equations. This model of Saint-Venant is the model the most appropriate governing free-surface flows, it is established on average a certain number of hypotheses. The Saint-Venant model is then coupled with the Exner model for modeling sediment transport. Then we applied finite volume method, which is one of the most widespread and reliable spatial discretization techniques, we have discretized the partial differential equations on an unstructured mesh. Deposition zones along the Saidia City Protection Channel have been identified, the most exposed sections that will be subject to periodic maintenance and cleaning. The volume of sediment stored in the channel is in the order of 500 tons, with a return period flow of 100 years, the channel stores a volume of 252 m3 .

References 1. Agence du bassin hydraulique de Moulouya (ABHM). http://www.abhmoulouya.ma/ 2. Benkhaldoun F, Elmahi I, Seaid M (2007) Well-balanced finite volume schemes for pollutant transport by shallow water equations on unstructured meshes. J Comput Phys 226(1):180–203 3. Benkhaldoun F, Elmahi I, Seaid M (2010) A new finite volume method for flux-gradient and source-term balancing in shallow water equations. Comput Methods Appl Mech Eng 199(49):3324–3335 4. Bermudez A, Dervieux A, Desideri V, Cendon M (1998) Upwind schemes for the two dimensional shallow water equations with variable depth using unstructured meshes. Comput Methods Appl Mech Eng 155:49–72 5. Bois Ph, Obled Ch, Zin I (2007) Introduction au traitement de donn en hydrologie. 7e tion revue, cours polycopi de l’ENSHMG (ole Nationale Supeure d’Hydraulique et de Mnique de Grenoble), Grenoble, France, p 265 6. Bouguerra SA et al (2016) Transport solide dans un cours d’eau en climat semiaride : cas du bassin versant de l?Oued Boumessaoud (nord-ouest de l’Alge). Revue des sciences de l’eau 29(3):20–27 7. Boushaba F, Chaabelasri EM, Elmahi I, Benkhaldoun F, Borthwick AGL (2008) A comparative Study of Finite Element and Finite Volume for problems of transcritical free surface flows on unstructured meshes. Int J Comput Methods 5(3):413–431 8. Boushaba F, Lakhlifi Y, Daoudi S (2018) Dam-break computations by a dynamical adaptivefinite volume method. J Appl Fluid Mech 1(4):120–126

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9. Crawford CG (1991) Estimation of suspendedsediment rating curves and mean suspended sediment load. J Hydrol 129:331–348 10. Mouzouri M et al (2013) Utilisation d’image satellitaire et d’un modele numerique d’altitude (MNA) pour la cartographie des zones sque d’inondation sur le litoral mediterraneen de saa (nordest du maroc). Revue Franse de Photogrammie et de tdction, No 201 11. Patankar, S. V.: Numerical Heat Transfer and Fluid Flow. Series in Computational Methods in Mechanics and Thermal Sciences (1980) 12. Regad Y, Elkihel B, Boushaba F, Chourak M (2019) Numerical study of the smoke plume of the Jerada thermal power plant Morocco. Pollack Periodica 14(2):131–142. https://doi.org/10. 1556/606.2019.14.2.12

Industrial Energy Audit Methodology for Improving Energy Efficiency - A Case Study Ali Elkihel, Bouchra Abouelanouar, and Hassan Gziri

Abstract This study aims to highlight a methodology for implementing an energy audit plan within industries. Today, international competitiveness involves optimizing companies’ production costs. This necessarily requires the implementation of rigorous energy saving policies. These savings are measures taken to limit energy consumption or to avoid energy losses. The energy audit is a methodical review and analysis of the use and consumption of energy; which attempts to identify energy flows and information on all the company’s energy consumption patterns. According to the information obtained in audit potential improvements in energy efficiency are carried out. In this paper, we present the results of an energy audit conducted within a company operating in the agri-food sector in the eastern region of Morocco. It has been shown that, in this case, the database resulting from the energy audit has contributed significantly to the development of a systematic approach to decision-making and consequently to the improvement of the energy efficiency. Keywords Energy audit · Costs · Energy saving · Energy efficiency

1 Introduction Global warming is a global phenomenon of climate change caused mainly by emissions from the production of energy from fossil fuels. The unreasonable extraction and use of these fuels is also responsible for the pollution. Fossil fuels are an energy unevenly distributed on earth. According to the analysis of the Energy Information Administration (EIA 2019), the industrial sector accounts for the largest share of A. Elkihel · H. Gziri Laboratory of Engineering, Industrial Management and Innovation, Faculty of Sciences and Techniques Settat, University Hassan First, Casablanca, Morocco e-mail: [email protected] B. Abouelanouar (B) Laboratory of Industrial Engineering and Seismic Engineering, National School of Applied Sciences, University of Mohamed First, Oujda, Morocco e-mail: [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/10.1007/978-981-15-6259-4_70

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energy consumption compared to other sectors (more than 50% of energy consumption). In addition, the same study estimates that energy consumption in the global industrial sector increases by more than 30% from 2018 to 2050 [1]. Unlike some countries in Africa, Morocco is highly dependent on energy imports almost 90% of the energy used comes from abroad (EIA 2017). With the economic development and population growth, the country has become a net importer of fossil fuels, which makes it subject to global requirements. As a result, the energy bill becomes very heavy which has repercussions on the national economy [2]. Recent statistics published by EIA (2019) show a substantial increase in the demand for primary energy and electricity. Annual energy consumption was about 19.39 Mtoe in 2015 and dominated by petroleum (61%) and coal (29%) as primary resources [3]. The share of final energy consumption is currently estimated at 32% for industry, 24% for transport, 29% for residential, 6% for agriculture and 7% for the tertiary sector. Energy needs and the distribution between fossil fuels and electrical energy vary greatly from one industry to another. The Chemical and Para-chemical Industries, in Morocco, consume the large share of electricity and fuels. Total GHG emissions by industrial sector are about 5.99 million tones of CO2 according to statistics given by the Moroccan Agency for Energy Efficiency (AMEE) [3]. The cost of energy is an indicator allowing the measurement of the competitiveness of companies. Given this fact, and in order to reduce the energy bill, companies must set up a progressive and logical approach, which consists of carrying out managerial actions before moving on to more strategic decisions involving production processes and equipment. Energy can only be saved if it is known where and how it is used and when and where its efficiency can be improved, hence the need for energy audit. The energy audit, which is an element of an energy efficiency program, is undertaken to assess the overall energy situation (pre-diagnosis), to quantify the energy saving potentials (diagnosis) and to define the actions necessary to achieve these savings (action plan). Moreover, performing an energy audit will help identify actions to improve production systems and reduce energy costs, while helping to optimize production itself. There are several approaches (standardized or not) are proposed to conduct an industrial energy audit [4–8]. However, the whole purpose of any energy audit is to identify, quantify, describe and prioritize cost saving measures relating to energy use in the company. Based on literature research, there are few publications on the methods of the implementation of the energy audit in the Moroccan industries. The results obtained from the studies carried out so far have been fairly consistent and prove that there is a reduction in industrial consumption which is easily achievable and profitable thanks to the energy audit and good energy management practices [9–12]. It is in this context that this study is conducted to attract and convince industries of interest in energy audits to reduce the energy bill, improve energy efficiency and ensure competitiveness of the companies. The objectives of this study are: (1) to examine the different sources of energy consumption in the selected company, (2) to identify the sources of energy waste and (3) to propose appropriate solutions and recommendations for effective energy savings.

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2 Methodology

1. PRE-DIAGNOSIS

In this study, we are interested in implementing an energy audit procedure within a company specialized in the bottling and marketing of soft drinks and mineral water located in Oujda (Morocco). The procedure developed in this energy audit is described as shown in Fig. 1.

Questionnaire*

Energy Performance Indicator: Ratio**

*the questionnaire must contain: General information on the company, Plant installations, and Production and energy data

Validation

Production process

No Go

Go

Production per liter ** Ratio= Energy consumption in kWh

Preliminary Energy balance

2. DIAGNOSIS

*** Choice of measurement methods for each energy type/ Appropriate measuring instruments

Companion of measuring***

Monitoring Results

No Go

-

Pipe insulation Leakage assessment Electrical equipment Mechanical equipment

Validation

Go

3. ACTION PLAN

In-depth Energy balance

Proposal of solutions

No Go

Validation

Go

Audit report and closing meeting

Fig. 1 Schematic diagram of proposed methodology

- Technical improvement solutions - Management improvement solutions - Behavioral improvement solutions

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3 Results Analysis 3.1 In the Pre-diagnosis Phase After visiting the industrial site, carrying out the global energy mapping and the data obtained from the questionnaire. The preliminary energy balance indicates that: – According to the energy performance indicators, the consumption problem is mainly based on electricity and water; – According to the visit to the production systems and manufacturing circuits, leaks of compressed air and water were noticed.

3.2 In Diagnosis Phase In this phase, we will identify the sources of waste in terms of water and electricity by checking the water circuits, the electrical installation and mechanical equipment. In Fig. 2, we located the places of the water leaks in all the canalizations, after we evaluated the quantities for a precise period of time. The estimate of the losses relating to these leaks is grouped in Table 1. Figures 3, 4 and 5 show an extract from the infrared thermography diagnosis carried out in electrical pipe installations and mechanical equipment respectively.

Fig. 2 The location of water leaks on the circuit

Industrial Energy Audit Methodology for Improving Energy Efficiency... Table 1 Annual balance of annual losses of water leaks

Heading level

Annual losses in L

N1

620

4,15

N2

11,060

74,02

N3

4,030

26,92

N4

1,320

8,82

N5

710

4,75

N6

2,260

15,1

N7

2,960

19,77

N8

65.174,40

435,37

N9

299.592,00

2.001,28

N10

178.704,00

1.193,75

N11

122.640,00

819,24

679

Annual losses in MAD

Fig. 3 Lack of insulation in pipes carrying hot water to the boiler

Fig. 4 Diagnosis of electrical cabinets by infrared thermography

According to these thermograms, we notice thermal losses indicated by an increase in temperature, which lead to an increase in energy consumption.

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Fig. 5 Diagnosis of motors by infrared thermography

3.3 Action Plan In the light of the previous results and based on the budget allocated by the company to this project, the most appropriate solution is to adapt a preventive maintenance strategy. Among the solutions chosen, the company was convinced by the location of the energy management system based on continuous improvement (PDCA). In addition, we proposed to introduce an energy policy based on the behavior of its staff. It is for this reason that we have designed a simplified guide to the energy audit phases. This in order to allow staff to carry out diagnostics for themselves to know at all times the energy situation of the company.

4 Conclusion This paper presents the main results of the energy audit carried out for an industrial site based in the Moroccan city Oujda. The methodology adopted was detailed and the proposed action plan was described. The following conclusions can be drawn from this study: The analysis of the energy indicators (Ratio) showed a high consumption of electricity and water, which did not reach the company targets. The diagnosis of the installations indicated water leaks which generate estimated annual losses of MAD 7,760/year. As well as the problems of insulation of the pipes which present losses at the level of the transmitted thermal energy. Monitoring of electrical and mechanical equipment has shown that there is a high demand for electric due to thermal losses. Based on these results, an action plan containing solutions for energy efficiency improvement was discussed and validated by the managers of the company. In fact, it involves implementing a preventive maintenance strategy.

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References 1. International Energy Outlook 2019, Report. http://www.eia.gov/ieo. Accessed 05 Jan 2020 2. Atouk S (2013) Les énergies renouvelables et les populations rurales pauvres: le cas du maroc, Master in environment, University of Sherbrooke 3. Moroccan Agency for Energy Efficiency (AMEE), Report. www.amee.ma. Accessed 05 Jan 2020 4. Nikoli´c A (2015) Methodology for energy audits in power plants regarding analysis of electrical energy consumption. In: 5th regional conference industrial energy and environmental protection in south eastern European countries, IEEP, pp 1–9 5. Rosenqvist J, Thollander P, Rohdin P, Söderström M (2012) Sustainable energy, recent studies. Industrial energy auditing for increased sustainability – methodology and measurements. InTech 6. Patel T, Panchal K (2015) An effective implementation of energy audit methodology - a case study. Int J Appl Innov Eng Manag 3(4):260–268 7. Oyelaran A, Twada Y, Sanusi O (2016) Energy audit of an industry: a case study of fabrication company. Aceh Int J Sci Technol 5(2):45–53 8. Kanase D, Patil V (2019) An energy audit of an industry: a case study. Int J Electr Electron Eng Res 9(1):23–28 9. (2013) Am J Energy Res 3(1):36–44 10. Boharb A, Allouhi A, Saidur R, Kousksou T, Jamil A, Mourad Y, Benbassou A (2016) Auditing and analysis of energy consumption of an industrial site in Morocco. Energy 101:332–342 11. Chramate I, Assadiki R, Zerrouq F, Belmir F (2018) Energy audit in Moroccan industries. Asia Life Sci 1:213–221 12. Benmamoun Z, Hachimi H, Amine, A (2018) Green logistics practices. In: PROCEEDINGS 2017, international renewable and sustainable energy conference, IRSEC, vol 28. IEEE, pp 1–13

Prediction of Short-Term and Long-Term Hourly Global Horizontal Solar Irradiation Using Artificial Neural Networks Techniques in Fez City, Morocco Zineb Bounoua and Abdellah Mechaqrane Abstract The integration of renewable energy plants into the energy mix leads to serious problems in maintaining the balance of electricity grids. Indeed, renewable energy plants can produce electricity when there is not much need. Therefore, predicting renewable energy potentials and then the output of power plants can allow grid operators to prepare decision scenarios in advance. In this work, we are interested in predicting hourly short-term (h + 1) and long term (h + 48) global horizontal solar irradiation (GHI) by applying two types of Artificial Neural Networks (ANN): Multilayer Perceptron (MLP) and a Nonlinear AutoRegressive neural network with eXogenous inputs (NARX). Keywords Global horizontal irradiation (GHI) · Multilayer perceptron (MLP) · Nonlinear AutoRegressive neural network with eXogenous inputs (NARX) · Ahead prediction

1 Introduction Several researchers have developed different models and techniques to predict global horizontal solar irradiation including empirical models [1]. But according to the literature [2], it has been concluded that artificial neural network (ANN) techniques give the best precision for predicting solar irradiation compared to empirical models because of their ability to learn, from examples, the non-linear relationship between inputs and outputs. A review of different techniques using Artificial Neural Network to predict solar radiation is presented in [3]. According to the literature, studies based on Multilayer Perceptron neural network (MLP) techniques were used more than recurrent neural network methods (NARX) Z. Bounoua (B) · A. Mechaqrane Laboratory of Intelligent Systems, Georesources and Renewable Energies, Faculty of Sciences and Technologies, Sidi Mohamed Ben Abdellah University, PO. Box 2202, Fez, Morocco e-mail: [email protected] A. Mechaqrane e-mail: [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/10.1007/978-981-15-6259-4_71

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to predict solar irradiance at different scales. Therefore, the aim of this work is to predict hourly global horizontal irradiation in Fez city using commonly measured meteorological parameters in the short term (h + 1) and long term (h + 48) forecasting by applying the NARX and MLP models.

2 Methodology 2.1 Multi-layer Perceptron Artificial Neural Network (MLP-ANN) Artificial neural networks (ANNs) are simple models inspired by biological neural networks. They have a great ability to solve complex nonlinear problems by deducing the relationship between inputs and outputs in several domains of prediction, optimization and classification. In our case, we used a multilayer perceptron (MLP) feedforward neural network model with M inputs, one hidden layer with N hyperbolic tangent neurons, and one output linear neuron. This model is used to predict the outputs values y(t) for a given time, based on some inputs values x(t): y(t) = f (x1 (t), x2 (t), . . . , x M (t))

(1)

2.2 Nonlinear Autoregressive with Exogenous Input (NARX) Among the recurrent dynamic networks, we adopted the nonlinear autoregressive network with exogenous inputs (NARX), and feedback connections enclosing several layers of the network. The architecture of NARX is the same to that of the MLP to which delayed outputs are added. The degree of recurrence depends on the considered time horizon. The NARX model is based on the linear autoregressive network with exogenous inputs model (ARX), which is commonly used in time series modelling. The NARX model is characterized by the following equation: y(t) = f (x(t − 1), . . . , x(t − d), y(t − 1), . . . , y(t − d))

(2)

Where y (t) is regressed on its previous d values and the previous values of an independent input signal x(t).

Prediction of Short-Term and Long-Term Hourly Global Horizontal …

685

2.3 Performance Assessment Four statistical indicators are used to evaluate the performance of developed models: the coefficient of correlation (R), the normalised root mean square error (nRMSE), the normalised mean absolute error (nMAE) and the normalised mean bias error (nMBE) [4]. The nRMSE provides an indication of the dispersion of the prediction accuracy. A low value of nRMSE indicates a very good prediction model. n

− y¯e )(ymi − y¯m ) n ¯e )2 i=1 (ymi − y¯m )2 i=1 (yei − y 1 n i=1 |(yei − ymi )| n n M AE = y¯m   n 2 1 i=1 (yei − ymi ) n nRMSE = y¯m  n 1 (yei − ymi ) n M B E = n i=1 . y¯m

R =  n

i=1 (yei

(3)

(4)

(5)

(6)

With ymi and yei are the ith measured value and the ith estimated value, respectively. n  ymi ) and the y¯m and y¯e are the mean value of n measurements of ymi ( y¯m = n1 mean value of n estimations of yei ( y¯e =

1 n

n 

i=1

yei ).

i=1

3 Databases In this study, measurements of the two solar components (GHI and DHI) and meteorological data are carried out by a meteorological station placed on the roof of Faculty of Sciences and Technologies of Fez (Latitude: 33°59 58 N, Longitude: 4°59 22 W and Elevation: 450 m). Hourly global and diffuse horizontal irradiation measurements are made using two identical Kip & Zonen pyranometers (Model CM11), one of this pyranometers is equipped with a shading ring to measure diffuse radiation. This station also contains a thermohygrometer and an anemometer to measure air temperature, relative humidity and wind speed, speed direction, respectively and a photosynthetically active radiation sensor. The measurement period is from 01/01/2009 00:00:00 GMT to 11/06/2015 at 12: 00:00 GMT. Considering the good correlation between the hourly values of GHI and photosynthatically active radiation (PAR), we use this correlation to replace missing values of GHI from measured PAR values. A quality control procedure was applied in order to

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remove the eventual erroneous measurements and assure the accuracy and usefulness of the data [5]. All GHI data removed by quality control procedure are replaced using measured PAR values, ensuring that these replaced values are always accurate.

4 Results and Discussions In this study, six years of hourly data (2009–2014) were used to develop models for predicting a short-term forecast (h + 1) of global horizontal irradiation (GHI). Then, these developed models are used to predict a long-term forecast of global horizontal irradiation (h + 48) for the year 2015. To establish our models, 70% of historic data is presented to the network during training and the network is adjusted according to its error. Subsequently, 15% of the data is used to measure the generalization of the network and to stop training when the generalization stops improving. Finally, the remaining 15% of the data provides an independent measure of network performance during and after training, with no effect on training phase.

4.1 Selection of the Best Combination Inputs Variables The accuracy of a prediction model depends on the choice of relevant inputs that have a significant impact on the fluctuations of the output variables. For this, we performed a statistical analysis to test the correlation of each input variable with GHI by calculating the correlation coefficient. Table 1 shows the correlation degree found for each input variable. According to this table, five meteorological inputs (T, RH, TOA, h and SD) have an acceptable regression value compared to the other input variables (Ws, Wd and SF). In order to test the effect of grouping meteorological variables on the accuracy of global horizontal solar irradiation forecast, we propose to examine the possibilities of combining high and low performance input parameters. The NARX and MLP network architecture with Levenberg Marquardt training algorithm were trained, tested, and validated using ten significant combinations of eight meteorological variables shown in Table 1. Table 1 Correlation of hourly GHI with meteorological variables Input variables

R (%)

Input variables

R (%)

Temperature (T)

43.23

Rainfall (RF)

13.21

Relative Humidity (RH)

47.43

Top of atmosphere solar irradiation (TOA)

84.93

Wind speed (Ws)

11.37

Solar altitude (h)

79.78

Wind direction (Wd)

23.49

Sunshine duration (SD)

44.23

Prediction of Short-Term and Long-Term Hourly Global Horizontal …

687

Table 2 Statistical indicators obtained with various combinations of inputs for the validation dataset on 5 runs Combination NARX of inputs R (%)

MLP nMAE nRMSE nMBE

R (%)

nMAE nRMSE nMBE

T, RH, Ws, Wd, RF, TOA, h, SD

95.8251 0.1123 0.1723

−0.0005 91.6832 0.1753 0.2482

0.0016

T, RH, Ws, TOA

96.1556 0.1061 0.1667

0.0004 90.9817 0.1821 0.2621

−0.0021

T, RH, TOA, 96.0382 0.1082 0.1652 h, SD

−0.0003 92.0145 0.1682 0.2415

−0.0032

T, RH, TOA, 95.7922 0.1094 0.1735 h, RF

0.0007 90.1973 0.1854 0.2713

−0.0015

T, RH, Ws, Wd, TOA

95.7375 0.1115 0.1784

0.0005 90.074

0.1971 0.2662

−0.0014

T, RH, Ws, Wd, TOA, SD

96.0907 0.1072 0.1671

−0.0005 91.6293 0.1761 0.2481

0.0011

T, RH, TOA, 95.8533 0.1091 0.171 SD

0.0005 91.3195 0.1783 0.2562

0.0012

T, RH, TOA, 96.3714 0.0981 0.1607 h, SD, Ws

0.0002 92.2108 0.1650 0.2381

0.0010

T, RH, TOA, 96.2038 0.0426 0.1691 h, SD, RF

0.0003 91.7949 0.1713 0.2452

0.0013

T, TOA, h, SD

0.0004 90.9327 0.1825 0.2598

−0.0015

96.1682 0.0436 0.1713

Table 2 shows that the best combination is obtained using hourly data of temperature (T), relative humidity (RH), top of atmosphere solar irradiation (TOA), solar altitude (h), sunshine duration (SD) and wind speed (Ws) as inputs. The statistical indicators obtained are R = 96.37%, nMAE = 0.09, nRMSE = 0.16, nMBE = 0.0002 and R = 92.21%, nMAE = 0.16, nRMSE = 0.23, nMBE = 0.001 for the NARX and MLP models, respectively.

4.2 Selection of the Number of Neurons in the Hidden Layer In this part, the best-obtained combination of inputs is used with different numbers of neurons in the hidden layer in order to find the best architecture for NARX and MLP models. A constructive approach is used, by iteratively increasing the number of neurons in the hidden layer from 1 to 30. For each architecture, the performances were evaluated on 5 runs. The validation results show that the best performances obtained for NARX and MLP models are given with 12 hidden neurons with, R

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Fig. 1 Regressions for the NARX (left) and MLP (right) models for validation datasets

equal to 96.57% and 92.96%, nMAE equal to 0.095 and 0.159, nRMSE equal to 0.157 and 0.227 and nMBE equal to −0.0001 and −0.0009, respectively. Figure 1 shows, for the validation dataset, the regressions corresponding to the best-obtained performances for the NARX and MLP models. The R-value above each plot represents the correlation coefficient. To test the predictive ability of both models, Fig. 2 shows the randomly selected week forecast of hourly global solar irradiation (h + 1), corresponding to the best-obtained performances for the NARX (left) and MLP (right) models. We note that the NARX model has a very good accuracy in predicting the future values of GHI compared to the MLP model, which has acceptable accuracy. To explore the predictive ability in terms of long-term forecasts, the developed models are used to predict 2 days ahead solar irradiation of the year 2015. From Fig. 3, which shows the 2-days ahead global horizontal irradiation forecast for the year 2015 using the NARX and MLP models, it can be noted that the NARX prediction closely follows the measurements values against to the MLP prediction. According to this study, it can be concluded that the NARX model performs well in terms of short-term and long-term forecasts, with a lower nRMSE value than that of the MLP model.

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Fig. 2 Predicted and measured hourly future values of GHI for one randomly week using NARX (up) and MLP (down) models

Fig. 3 Two days ahead global horizontal solar irradiation forecast vs measured data on 1 and 2 January 2015

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5 Conclusion The objective of this work was to predict hourly global horizontal irradiation in Fez city using commonly measured meteorological parameters. Two ANN models are tested: MLP, which is feedforward architecture, and NARX, which is a recurrent one. The results show that the NARX model is able to produce accurate short-term (h + 1) and long-term (h + 48) forecasts, with an accurate nRMSE value of 0.157. Therefore, our model can provide accurate prediction of hourly global horizontal irradiation to use it in solar system planning. As perspective of the present work, we consider to test the NARX architecture using other Moroccan site data.

References 1. Chukwujindu NS (2017) A comprehensive review of empirical models for estimating global solar radiation in Africa. Renew Sustain Energy Rev 78:955–995 2. Benghanem M, Mellit A, Alamri SN (2009) ANN-based modelling and estimation of daily global solar radiation data: a case study. Energy Convers Manag 50(7):1644–1655 3. Yadav AK, Chandel SS (2014) Solar radiation prediction using artificial neural network techniques: a review. Renew Sustain Energy Rev 33:772–781 4. Marzouq M, Bounoua Z, El Fadili H, Mechaqrane A, Zenkouar K, Lakhliai Z (2019) New daily global solar irradiation estimation model based on automatic selection of input parameters using evolutionary artificial neural networks. J Clean Prod 209:1105–1118 5. Bounoua Z, Marzouq M, Mechaqrane A (June 2018) Assessment of a quality control procedure of hourly solar irradiations at Fez city, Morocco. In: IOP conference series: earth and environmental science, vol 161, no 1. IOP Publishing, p 012010

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, and Ahmed Mezrhab

Abstract The conservation of food products become more and more important in the last decades, especially because of the fast growth of the population around the world, which makes the drying method an interesting way to pre-serve the vegetal product for a long time. There is plenty of drying methods, such as electric drying, mechanic, solar …etc. this latter is so appealing due to its preservation of the environment. Our work is a contribution to the improvement of the drying process using ANSYS FLUENT, in which we use the meteorological data of Oujda city (eastern Morocco) to make this study more realistic. We investigate the trays effect in an indirect solar dryer, in order to find the optimum number of it as well their suitable position. The result showed that adding a second tray has improved both the velocity and the temperature distribution along the two trays as well for the chamber. But, adding a third one has decreased the velocity and the temperature along the two precedents trays as well it provides a bad distribution in the drying chamber. Regarding the position of the trays, we figure that the right position in our geometry is 0.35 m and 0.45 m for tray 1 and 2 respectively. Keywords ANSYS FLUENT · CFD · Trays · Solar dryer

1 Introduction The drying method is one of the most commonly followed methods of preservation for food products that use solar energy in the drying process. Furthermore, this method presents some advantages like reducing both, product volume and weight. This helps to minimize packaging, storage, and transportation costs [1]. The choice of dryer systems depends on the product to be dried because there are products that are sensitive to direct solar radiation [2]. For this reason, the only solar dryer that D. Chaatouf · M. Salhi · B. Raillani · N. Dihmani · S. Amraqui (B) · M. A. Moussaoui · A. Mezrhab Laboratory of Mechanics and Energetics, Faculty of Sciences, Mohammed First University, 60000 Oujda, Morocco e-mail: [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/10.1007/978-981-15-6259-4_72

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preserves the color and vitamins, as well the quality of the product is the indirect type. Thus, there are various numerical and experimental studies dealing with the problem of the indirect solar dryer. Among the early works that can be quote, the one of Sharaf-Eldeen et al. [3] that established an experimental model to determine the drying characteristics of fully exposed ear corn under three different temperature and for fixed air velocity and moisture content that ranged between 18 and 55%. In addition, they developed a mathematical model to predict the drying behavior. More recently, a numerical and experimental study was carried out by Demissie et al. [4] in order to predict the flow and temperature distribution within the drying chamber using the CFD method. To make this distribution uniform, Amanlou et al. [5] compared seven different geometries of cabinet dryer with a side mounted plenum chamber using FLUENT software. Then the best design was fabricated to compare the experimental results to those obtained by CFD data. They revealed a very good correlation coefficient of 99.9% for drying air temperature and 86.5% for air velocity in the drying chamber. This paper investigates the effect of trays on the dynamic and thermal behavior us-ing ANSYS FLUENT. Therefore, a parametric study is essential to optimize the quality of the drying process, such as the distance between the first tray and the chamber inlet, and the distance between the trays, which is going to lead us to the optimal number of trays for our geometry. To have a realistic study, a weather file of the Oujda region is used, to integrate the evolution of the temperature and solar irradiations as a function of the time.

2 Mathematical Formulation and Boundary Conditions The chosen geometry consists of a 1 m3 squared wooden chamber equipped with a 2 m2 solar air heater inclined with 34° (Oujda latitude). The trays are filled with figs as a product to be dried. The convective flow of the air inside the solar dryer is based on the Navier Stokes equations. Air movements are generally turbulent, and to model this turbulent pattern inside the dryer, the model K-eps standard is chosen, that characterized by 2 transfer equations, for the turbulent kinetic energy k (Eq. 1) and its rate of dissipation (Eq. 2). ρ( ρ(

∂(ku) ∂(kv) μt ∂ 2 k ∂ 2k ∂k + + ) = (μ + )( 2 + 2 ) + G k − ρε ∂t ∂x ∂y σk ∂ x ∂y

μt ∂ 2 ε ∂ε ∂(εu) ∂(εv) ∂ 2ε ε + + ) = (μ + )( 2 + 2 ) + (c1 G k − c2 ρε) ∂t ∂x ∂y σε ∂ x ∂y k

(1)

(2)

The boundary conditions of our geometry are regarding to atmospheric pressure in the outlet as well as the inlet of the solar dryer. Concerning the convective losses in the walls of the chamber a heat coefficient is defined by:

Trays Effect on the Dynamic and Thermal Behavior of an Indirect Solar Dryer …

h 0 = 2.8 + 3Vw

693

(3)

Where the wind velocity V w = 3 m.s−1 . To model the trays resistance to airflow, the product is modeled as a porous medium, the porosity and the coefficients of the power law model is given by Amanlou et al. [5].

2.1 Climate Data The meteorological data used in this paper are collected from our station with high precision, installed in the university of Mohamed first Oujda. The main data provide are pressure, humidity, temperature, wind velocity, direction, and the irradiation data. The two equations that have been used in the subroutine UDF (User Defined Function) that we wrote to integrate it in ANSYS FLUENT are: Tamb (t) = 25 + 6 cos( G sun (t) = 965 sin(

π (t − 14)) 12

t −6 π ), 14

6 < t < 20

(4) (5)

2.2 Validation Results To make sure that our results are correct, we validate our work, with experimental and theoretical results of Jyotirmay et al. [6] presented in Fig. 1. The results present

Fig. 1 Comparison of the average outlet temperature and average outlet velocity with those of Ref. [6]

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a good agreement. The difference rate does not exceed 1.47% for temperature and 16% for velocity, but it still better than the one calculated by the authors.

3 Results In order to optimize the drying process inside the chamber, we are going to study the effect of trays on the airflow distribution to find the optimum number of it. And to do so, studying the number of trays it’s not enough, because there is a lot of parameters related with the trays that affect the distribution, like the distance between the first one and the chamber inlet that has a significant effect on the air flow distribution which is going to be on the top list in our investigation. A test on the independency of the mesh was carried out, with seven different grids, the test showed that a mesh grid with a total number of nodes equal to 17,999 is fairly good to study the geometry. To make a comparative study, we are going to choose a specific time, and because the drying process is efficient between 11 h and 16 h, all the results are presented at 13 h.

3.1 Optimum Position of the Tray 1 In order to investigate the optimum position of the first tray in our dryer, 20 different positions were studied. Figure 2 presents temperature and velocity distribution for three different positions where the form is significantly different. We are looking for the position that gives us a good air deviation at the tray level. The three cases above show that the first tray has a crucial effect on the air distribution inside the chamber. A very low tray make the air pass directly from the right side, which produces recirculation in the upper part of the chamber that

(a)

(b)

Fig. 2 Distribution of velocity (a) and temperature (b) inside the chamber for one tray

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(a)

(b)

Fig. 3 Distribution of velocity (a) and temperature (b) inside the chamber for three different distances between the two trays

is occupied with hot air contrary to the lower part where the tray is placed. Even though the temperature is relatively homogeneous in the case of a very high tray, the velocity distribution is not good at all because the air act like there is no tray in the chamber, in which generate a flow recirculation. However, when the tray occupies the middle part of the chamber a high performance and a very good temperature form and velocity deviation are obtained, which make it the optimum position in our geometry.

3.2 Effect of the Distance Between Two Trays Figure 3 presents the impact of the distance between two trays on temperature and velocity distributions. 12 different positions were studied but we will present the effect of three of them that have a more obvious variation. The distance between the two trays affects air deviation. As can be seen, the air deviation is more interesting in case the tray is a little high (the middle picture in Fig. 3). The air crosses a large distance along the trays, which improves the temperature distribution inside the drying chamber.

3.3 Effect of Trays Number After knowing the right position for the two first trays, we are going to add a third one. Ten different positions of the third tray were studied in order to find the optimal distance between the second and the third tray. The Fig. 4 below presents three different positions that seem to have a different distribution. Adding a third tray affects negatively the two precedent trays. By comparing these 10 different positions of the third tray, the best seems to be the one in the position 0.55 m.

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(a)

(b)

Fig. 4 Distribution of velocity (a) and temperature (b) inside the chamber for three different distances between the three trays

3.4 Effect of Adding Trays on the Existing Trays To achieve a good investigation of trays impact on the airflow distribution, we investigate in Fig. 5 the effect of the second tray on the temperature and the velocity at the first tray, furthermore the same analyze is applied on the third one, to investigate its effect on the two other trays. By adding a second tray the velocity and temperature has increased in a big way on the first tray as shown in Fig. 5a. But when we add a third one a negative effect was seen at the two precedent trays (Fig. 5b).

Fig. 5 Comparison of velocity and temperature of: a the first tray without and after adding a second tray; b the first and the second tray without and after adding a third one

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4 Conclusion In this work, we presented an investigation of the trays effect on the distribution of the air inside the chamber, in order to find a uniform temperature and velocity along the optimal number of trays, as well a quiet variation of temperature between the trays in a various distance of height between them. The result showed that adding a second tray has improved both the velocity and temperature distribution along the two trays as well for the chamber. But, adding a third one has decreased the velocity and the temperature along the two existing trays. Regarding the position of the trays, we figure that the right position is 0.35 m and 0.45 m for tray 1 and 2 respectively.

References 1. Vintil˘a M, Ghiau¸s AG, F˘atu V (2014) Prediction of air flow and temperature profiles inside convective solar dryer. Food Sci Technol 71(2):188–194 2. Khaldi S, Korti AN, Abboudi S (2017) Improving the airflow distribution within an indirect solar dryer by modifications based on computational fluid dynamics. Int J Air-Conditioning Refrig 25(03):1750022 3. Sharaf-Eldeen YI, Blaisdell JL, Hamdy MYA (1980) Model for ear-corn drying. Trans ASAE 23(5):1261–1265 4. Demissie P, Kassaye MA, Hailesilassie A, Gebrehiwot M, Vanierschota M (2019) Design, development and CFD modeling of indirect solar food dryer. Energy Procedia 158:1128–1134 5. Amanlou Y, Zomorodian A (2010) Applying CFD for designing a new fruit cabinet dryer. J Food Eng 101:8–15 6. Jyotirmay M, Sanjay MA (2006) Summer performance of inclined roof solar chimney for natural ventilation. Energy Build 38:1156–1163

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, and Abdellatif Ghennioui Abstract The dust accumulation on solar mirrors is a complex and site-specific phenomenon. It strongly depends on the environment parameters such as wind, precipitation, ambient temperature and relative humidity. Therefore, finding the relationship between these parameters we can predict the impact of this accumulation on the mirrors optical efficiency. Currently the Artificial Neural Network (ANN) is one of the best solutions that can be used for a performant prediction of such problematic. In this paper, a new approach using ANN and different meteorological parameters is used to predict the soiling level for a solar mirror with a maximum accuracy. As first results we reached an accuracy value of 95% using only 7 months of daily measurements of the environment data in Green Energy Park (GEP) research facility. Keywords ANN model · Meteorological parameters · CSP soiling loss · Soiling prediction

1 Introduction Soiling is a natural phenomenon generated by a complicate process which depends on many parameters like, the localization, the environment factors, the dust and the mirrors surface characteristics. Picotti et al. [1] studied the dust life cycle and H. El Gallassi (B) · A. Ghennioui Research Institute for Solar Energy and New Energies (IRESEN), Green Energy Park, Benguerir, Morocco e-mail: [email protected] H. El Gallassi · M. Chourak Department of Mechanic and Applied Mathematics, ENSAO, University Mohammed 1st, Oujda, Morocco A. Alami Merrouni Materials Science, New Energies and Application Research Group, LPTPME Laboratory, Department of Physics, Faculty of Science, University Mohammed First, Oujda, Morocco e-mail: [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/10.1007/978-981-15-6259-4_73

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they arrange it in four steps: generation, deposition, adhesion and removal. Each one of these steps is influenced by weather parameters in a way or another. For instance, the dust generation mechanism depends mostly of wind velocity, contrarily, the dust adhesion depends of precipitation and humidity. In the literature many studies assessed the environment factors influencing soiling of solar systems, Figgis et al. [2] found that humidity, dew-point and the temperature difference are the most dominant factors influencing soiling adhesion on photovoltaic modules. This has been confirmed by Iles et al. in a similar study [3]. Since soiling is a meteorological dependent phenomenon, it is important to develop models in order to simulate and predict its impact on the solar plants efficiency. Conceição et al. [7] developed a first ANN model to predict soiling from meteorological factors and they compare its performance with the linear model and interactive model. As result, they found that the developed ANN model has a better accuracy of 62.9% in comparison to the interactive and the linear ones which have an accuracy of 27.9% and 15% respectively. Javed et al. [9] also used the ANN approach to model the relation between the soiling loss and the environment variables and they reached an accuracy value of 83.6%. Similarly, Wolfertstetter et al. [4] adapted the atmospheric dust transport models (ADTMs) to model mirrors soiling using two different data sets of meteorological parameters measurements. They found an RMSE of 0.46%/day for the set of Missour (Morocco) and 0.44% for the site of PSA (Spain). Other methods and parameters were used to model the soiling loss. Bouaddi et al. [5] used the Markov switching model to predict a soiling loss of Concentrated Solar Power (CSP) reflectors installed at PSA in the arid climate. And Pulipaka et al. [6] combined the dust particle sizes with the ANN to model soiling loss. In this paper, a new developed ANN model will be presented, in first section we will discuss the experimental setup and the characteristics of our field of study. The second section, is devoted to describe the new approach used to select and process the dataset, the architecture of the model will be explained with a result achieved using a different model’s configurations.

2 Materials and Method This work consists of the development of an Artificial Neural Network model for monitoring the CSP mirrors cleanliness drop using different meteorological parameters as wind, precipitation, relative humidity, and ambient temperature. For this, daily averages measurements of the mirrors cleanliness together with the already sited meteorological data were used. These data were collected from the 20th of April to 31st of October 2018 at Green Energy Park, Morocco.

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Fig. 1 TraCS device mounted on Solys2 tracker installed at GEP Benguerir

2.1 Equipment and the Field of Study The data used to train the ANN model were collected from April-20 to October31(7 months) by a high precision meteorological station installed at Green Energy Park (GEP). GEP is a solar energies research facility located in Benguerir, Morocco (32° 07 30.00 N, −7° 54 23.39 W). This site is characterized by a semi-arid climate. The training inputs selected to train the model are: wind speed, wind direction, precipitation, daily maximal temperature, daily maximal relative humidity, Aerosol Optical Depth 550 nm (AOD 550 nm), cleaning events and the initial cleanliness value. To collect measurements of these variables we used a Campbell CS215 to measure the ambient temperature and relative humidity, wind speed is measured using NRG 40H Anemometer, wind direction measured with NRG 200 Sensor. While precipitations are measured using Young 52202/03 Tipping Bucket Raingauge. To quantify the impact of soiling the TraCS sensor [10] was used to measure the cleanliness index. This sensor is mounted on the Solys2 tracker and it uses two pyrheliometers to measure the reflectivity of a solar mirror (see Fig. 1).

3 Results and Discussion 3.1 Meteorological Parameters and Model’s Inputs To select the features for our ANN model we firstly need to define and discuss the meteorological parameters measured at our field of study and used as inputs. Figure 2 presents the daily cleanliness measurements with the rain events during the experiment period. We need to mention that in order to avoid soiling saturation [11–13], the TraCS mirror was cleaned every two weeks, except for specific phenomenon’s like bird dropping… As it can be seen, GEP is a highly soiled site where the cleanliness

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Fig. 2 Cleanliness and precipitation at Green Energy Park

drop can reach 40%. The daily cleanliness drop is around 2% which considered as a high value and it can affect the durability of the solar plants [14]. The objective of this study is using these cleanliness values to train and validate our ANN model, however, before discussing this part we will present the measured data of the other meteorological parameters. As discussed above, wind has an impact on the soiling accumulation process as it participates to dust transportation, therefore, it should be taking as input for the ANN models. Figure 3 present the daily average of wind speed. Wind direction, is an important carecteristic orienting the dust in its distination. In fact, it is significant to consider this variable as input to the ANN model. The Fig. 4 show the wind rose of our site where the wind is oriented towards a north in the majority of days. Similarly, humidity and ambient temperature are two major parameters that increase the particles adhesion forces by dew formation and cementation process [3, 8]. At our field of study and as illustrated in Fig. 5, the relative humidity values are

Fig. 3 Wind speed daily averages at Green Energy Park

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Fig. 4 Wind rose measured at Green Energy Park

Fig. 5 Ambient temperature and relative humidity at Green Energy Park

high. This, if combined with the high temperature values (see Fig. 5), can contribute to the adhesion of the dust on the mirror surface, thus, a drop in its cleanliness. Other important parameters influence the cleanliness drop are the Aerosols concentration in the Atmosphere and the nature of dust deposition (dry or wet). In this study the AOD data were collected from SoDa (Solar radiation Data) services and they are illustrated in Fig. 6.

3.2 ANN Model Description After presenting the data inputs, this section will discuss the ANN model. In this study and to increase the Cleanliness/Soiling prediction accuracy we proposed a new

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Fig. 6 Aerosols Optical Depth daily averages at Green Energy Park

Fig. 7 Cleanliness classes

approach using the classification model. For this reason, we divided the cleanliness measurement data (measured in the field) into Four classes: (i) Clean, (ii) Semi-clean, (iii) Dirty and (iv) Very dirty, see Fig. 7. After that, the original inputs values were normalized, using the min-max scaler method (Eq. 1), to get unified data in the range between 0 and 1 for training the ANN model. X nor mali zed = X − X m / (X Max − Xmin)

(1)

The training outputs should be a numerical values those can be understood by the model. To transform our classes to numerical values we used one hot encoding to associate a numerical value with each class, Table 1 show the coded outputs. The model architecture is the most important step in the development of the ANN model. However, this architecture presents the topology of the neuron’s network and the number of layers and neurons in each layer. To set this architecture one need to variate randomly the number of layers and neurons in the way that improving the model accuracy. In this work we try different configurations (number of layers and neurons) and the best results was reached with the selected model shown in Fig. 8. Splitting dataset to training data and validation data is needed to run the model, for that, we used 75% of our measurements to train the model and 25% to validate it.

The Application of Artificial Neural Network to Predict Cleanliness Drop … Table 1 One hot encoding of classes

Classes

705

Associated code

Clean

1000

Semi-clean

0100

Dirty

0010

Very dirty

0001

Fig. 8 ANN model architecture

3.3 Optimization and Cost Function The loss function in Eq. (2) is a metric helping in the optimization of the parameters of the neural network models. A minimal loss function value can be found by optimizing the parameters weights. loss =

n 

  yo,k log po,k

(2)

k=1

n is a number of classes; y binary indicator depends in class label k if it is the correct classification for observation o and p is a predicted probability of observation o for class k. Another metric to increase the performance of the ANN model is the optimizer. It is used to minimize the cost function. In this model we compared two optimizers (Adam and Stochastic Gradient Descent) to select the performant one for our data set. From the results shown in Table 1 it is clear that the Adam optimizer is the best for our study and it’s the one that has been adapted (Table 2). For a better presentation of the model’s validation, the prediction accuracy of each class is presented in Fig. 9. In fact, from 48 cleanliness measurements tested 34 belong to the clean class, 12 to the semi-clean class and 2 to the dirty class. For the first class, the clean tests were predicted correctly with 89%, the other 11% were

706 Table 2 Optimizers comparison

Fig. 9 The model’s prediction accuracy by each class

H. E. Gallassi et al. Optimizer

Loss value

Accuracy

Adam

0.24

95.92

Stochastic Gradient Descent

0.28

93.88

100%

89% 75%

80% 60% 40% 20% 0%

25% 11% clean True prediction

semi-clean False prediction

wrong and they were predicted as semi-clean, the nearest class of clean. Similarly, the semi-clean tests were predicted correctly with 75% and 25% predicted as clean. For the “dirty” tests, they were both wrongly predicted as “clean” and as “semi-clean”. This is logical since we have only two data in this class, thus, more data are required for a better training of our Neural Network. Also, this is the reason why we neglect the “very dirty” class.

4 Conclusion In this paper, an ANN model was developed to predict the soiling/cleanliness status of solar mirrors using the meteorological parameters and the classification method. To do this, four classes were selected and seven months of daily measurements of weather factors measured at ground level, were used as inputs. Results show that the developed ANN model has reached a good accuracy values; 93.88 with SGD optimizer and 95.92 with Adam optimizer. For the classes accuracy prediction, the model can predict the Clean class with an accuracy value of 89% following by the semi-clean with 75%. So far, the model predicts the cleanliness situation in a good way, however, more measurement data are needed for a better class prediction especially for the dirty and the very dirty classes.

References 1. Picotti G, Borghesani P, Cholette ME, Manzolini G (2018) Soiling of solar collectors – modelling approaches for airborne dust and its interactions with surfaces. Renew Sustain Energy Rev 81:2343–2357

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2. Figgis B, Nouviaire A, Wubulikasimu Y, Javed W, Guo B, Ait-Mokhtar A, Belarbi R, Ahzi S, Rémond Y, Ennaoui A (2018) Investigation of factors affecting condensation on soiled PV modules. Solar Energy 159:488–500 3. Ilse KK, Figgis BW, Naumann V, Hagendorf C, Bagdahn J (2018) Fundamentals of soiling processes on photovoltaic modules. Renew Sustain Energy Rev 98:239–254 4. Wolfertstetter F et al (2019) Modelling the soiling rate: dependencies on meteorological parameters. In: AIP conference proceedings vol 2126, p 190018 5. Bouaddi S, Fernández-García A, Ihlal A, Ait El Cadi R, Álvarez-Rodrigo L (2018) Modeling and simulation of the soiling dynamics of frequently cleaned reflectors in CSP plants. Sol Energy 166:422–431 6. Pulipaka S, Mani F, Kumar R (2016) Modeling of soiled PV module with neural networks and regression using particle size composition. Sol Energy 123:116–126 7. Conceição R, Silva HG, Collares-Pereira M (2018) CSP mirror soiling characterization and modeling. Sol Energy Mater Sol Cells 185:233–239 8. Kim D-I, Grobelny J, Pradeep N, Cook RF (2008) Origin of adhesion in humid air. Langmuir 24(5):1873–1877 9. Javed W, Guo B, Figgis B (2017) Modeling of photovoltaic soiling loss as a function of environmental variables. Sol Energy 157:397–407 10. Wolfertstetter F, Pottler K, Alami A, Mezrhab A, Pitz-Paal R (2012) A novel method for automatic real-time monitoring of mirror soiling rates. In: Solar paces 11. Merrouni AA, Amrani AI, Mezrhab A (2017) Electricity production from large scale PV plants: benchmarking the potential of Morocco against California, US. Energy Procedia 119:346–355 12. Merrouni AA, Ouali HAL, Moussaoui MA, Mezrhab A (May 2016) Analysis and comparison of different heat transfer fluids for a 1MWe parabolic trough collector. In: 2016 international conference on electrical and information technologies (ICEIT). IEEE, pp 510–515 13. Merrouni AA, Mezrhab A, Ghennioui A, Naimi Z (2017) Measurement, comparison and monitoring of solar mirror’s specular reflectivity using two different reflectometers. Energy Procedia 119:433–445 14. Bouaichi A, Merrouni AA, Hajjaj C, Zitouni H, Ghennioui A, El Amrani A, Messaoudi C (2019) In-situ inspection and measurement of degradation mechanisms for crystalline and thin film PV systems under harsh climatic conditions. Energy Procedia 157:1210–1219

Comparative Study of Different Conical Receiver’s Materials of a Parabolic Solar Concentrator Raja Idlimam, Mohamed Asbik, and Abdellah Bah

Abstract The main objective of this research is to compare different conical receiver’s materials of a parabolic solar concentrator. For this reason, three types of metallic alloys and pure materials (Inconel 625, Inconel 718, and copper) are investigated in order to detect and analyze the most efficient material using ray-tracing simulation on Comsol Multiphysics. The results indicated that Inconel 625 gives better thermal performances compared to other materials. The maximum temperature on the surface of the absorber of 392 °C is obtained with a thermal power density of 393.15 kW/m3 . Consequently, the application of this material on the concentrator-receiver system will be suggested. Keywords Receiver material · Conical receiver · Parabolic solar concentrator · Comsol multiphysics · Inconel

1 Introduction Solar dishes are one of the most efficient solar concentrating technologies. They can reach high concentration ratios which are well suited to high-temperature applications. Therefore, a great deal of research has been focused on their structure to accomplish high-temperatures with high thermal and optical performances for a low cost [1, 2]. They are usually designed using a dish, an absorber, and a support system. Parabolic solar concentrators have two-axis tracking system intended to move them

R. Idlimam (B) · M. Asbik · A. Bah Thermal and Energy Research Team-ERTE, Energy Research Center-ENSET, Mohammed V University in Rabat, Rabat, Morocco e-mail: [email protected] M. Asbik e-mail: [email protected] A. Bah e-mail: [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/10.1007/978-981-15-6259-4_74

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in such a way that they continuously face the sun to maximize the irradiation received by the absorber. The receiver is a fundamental element designed to absorb the concentrated solar radiation and transfer the heat to the working fluid. It needs to operate at high temperatures to obtain high conversion performance. Besides, many researchers suggested the use of the conical receiver solar concentrators [3, 4]. Their results confirmed that the conical receiver absorbs higher quantity of energy compared to other shapes of receivers. On the other hand, it is fundamental to identify the effect of receiver’s material on a Parabolic Solar Concentrator (PSC) system thermal behavior by comparing and contrasting usual materials to determine the most efficient one. So, several studies are conducted to investigate the influence of receiver materials on the thermal and optical performances of the solar dish system. Wang et al. [5] examined the impact of three metals and two coating type materials of receiver surface on the concentrated solar flux distribution for the parabolic dish using ray-tracing methodology. They found that the different materials affect flux distribution and the optical efficiency. Lalau et al. [6] developed the measure and the control of the solar flux received by the sample. Recently, a wide variety of searches investigates refractory metals such us the Inconel, especially, Inconel 625 and Inconel 718 [7, 8]. Inconel 625 was used as a potential material for heat exchanger tubes in a solar dish concentrator [9]. In 2011, Rojas-Morin and Fernandez-Reche [10] conducted a theoretical and experimental study to simulate the thermal fatigue life for the Inconel 625lCF plate under concentrated solar radiation. They concluded that the lifetime of the plate is inside the high cycle fatigue region at the operational temperatures of both 650 °C and 900 °C. This work focuses on candidate materials that can be used as a receiver. This latter must be able to resist hostile conditions such us: excessive temperature level, thermal cycling, thermal stresses, and oxidizing atmosphere. These materials must have high absorptive capacity, low emissivity, and high thermal conductivity. Thus, our purpose is to identify the most efficient material of the receiver by rigorous and objective comparison of the materials (Copper, Inconel 625 and Inconel 718) by using Comsol Multiphysics software. This comparative study allows selecting a material as a potential candidate for the solar concentrator receiver.

2 Physical Model and Properties of Materials 2.1 Physical Model Figure 1 represents the physical model considered in this study. This is the solar parabolic concentrator whose reflector has a diameter of 2 m, a surface area of 3.2 m2 , and a rim angle of 45°. These specific dimensions give the highest concentration ratio, according to the reference [11].

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Fig. 1 Parabolic Solar Concentrator

Table 1 Dimensions of the parabolic dish and the conical receiver

Table 2 Illuminated surface

Parameters

Parabolic dish

Conical receiver

Diameter (m)

2

0.226

Absorber length (m)

–

0.387

Rim angle (°)

45

–

Properties

Value

Number of rays per release

100

Absorption coefficient

0.1

Surface slope error

3.5 mrad

PSC reflects and focuses incident solar radiation in the absorber, generating extreme heat. This receiver has a diameter of 0.226 m, and a length of 0.387 m as shown in Table 1. Additionally, Table 2 and Fig. 2 define the properties of illuminated surface and a graph of Direct Normal Irradiation (DNI) and also ambient temperature during the day of 06 July 2014. These data have been collected in the city Rabat (34°N, 6°49 ). Morocco. The maximum values of the ambient temperature and the solar flux density are in the range of 24 °C and 997 W/m2 .

2.2 Properties of Materials Nickel Superalloys. Anickel-based superalloy is generally used in high-temperature applications such as nuclear, chemical and petrochemical industries, marine and aerospace because of its excellent mechanical, chemical, and physical properties at elevated temperatures; it can also be used for heat exchanger and reactor-core. The most used alloys being Inconel 625 and Inconel 718. Indeed, Inconel 625 is a nickel-chromium alloy composed as solid-solution-strengthened. This material has excellent characteristics including a good yield, tensile strength and strong resistance to high-temperature corrosion on prolonged exposure to extreme situations. As for

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Fig. 2 Time variation of DNI and the ambient temperature

Table 3 Chemical composition in weight percentage of Inconel 718 and Inconel 625 [12, 13]

Components

Inconel 718

Inconel 625

Nickel

50–55

58 min

Chromium

17–21

20–23

Iron

Balance

5 max

Niobium

4.75–5.5

3.15–4.15

Molybdenum

2.8–3.3

8–10

Titanium

0.65–1.15

0.4 max

Aluminum

0.2–0.8

0.4 max

Cobalt

1 max

1 max

Carbon

0.08 max

0.1 max

Manganese

0.35 max

0.5 max

Silicon

0.35 max

0.5 max

Phosphorus

0.015 max

0.015 max

Sulfur

0.015 max

0.015 max

Boron

0.006 max

–

Copper

0.3 max

–

Inconel 718, it is a nickel-chromium superalloy age-hardenebale mainly because of the appearance of aluminum, niobium, and titanium. It is used at elevated temperatures too because of its high strength and good resistance to corrosion or oxidation. Their chemical compositions and physical properties are presented in Table 3 and Table 4 respectively. Figure 3 and Fig. 4 determine specific heat and thermal conductivity respectively of Inconel 625 and Inconel 718 versus temperature.

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Fig. 3 Specific heat of Inconel 625 and Inconel 718 versus Temperature

Properties

IN718

IN625

Melting range (°C)

1260–1336

1290–1350

Specific heat at 21°C (J/kg.K)

435

410

Thermal conductivity at 21°C (W/m.K)

11.2

9.8

Density (kg/m3 )

8190

8440

800

Specific heat (J/kg.K)

Table 4 Thermo-physical properties of Inconel 718 and Inconel 625 [12, 13]

713

700 600 500 400 300 200 100 0

Fig. 4 Thermal conductivity of Inconel 625 and Inconel 718 versus Temperature

Thermal conductivity (W/m.K)

Temperature (°C) Inconel 718 Inconel 625

30 25 20 15 10 5 0

Temperature (°C) Inconel 718

Inconel 625

Copper. This pure metal is a good thermal and electrical conductor, it has various useful qualities including corrosion-resistant, antibacterial, easily joined, easy to alloy and ductile. It is used in many heating applications because it doesn’t deteriorate and has a high melting point. Its physical properties are given in Table 5.

714 Table 5 Thermo-physical properties of Copper

R. Idlimam et al. Properties

Value

Unit

Heat capacity at constant pression

385

J/kg.K

Thermal conductivity

400

W/K.m

Density

8960

kg/m3

Melting point

1083

Elastic modulus

117

°C GPa

3 Numerical Solution 3.1 Mathematical Formulation The transient thermal behavior of the receiver is governed by the following energy equation:   ∂(ρcT )  k∇ T +Q =∇ ∂t

(1)

where ρ(kg/m3 ), c(J/kg.K), T(K), k(W/m.K), and Q are respectively the density, specific heat capacity, receiver temperature, thermal conductivity and the solar source term. Moreover, the Eq. (1) is associated with the appropriate boundary conditions at the receiver surfaces namely, heat convection QConv and thermal radiation Qrad. Note that hConv , Ar , ε and σ are convection coefficient (W/m2 K), receiver surface area, absorber emissivity and Stefan-Boltzmann constant (5.67·10−8 W/m2 K4 ) respectively. QConv = hConv Ar (T − Tamb )

(2)

  4 Qrad = Ar εσ T4 − Tamb

(3)

The initial temperature in the material is equal to the ambient temperature (293.15 K).

3.2 Numerical Solution Firstly, the solar source term is computed by using the ray-tracing method, and illuminated surface feature is considered (Table 2). So, this allows lights to be released from the surface of the dish in the direction of incident solar radiation. On the other hand, absorbed solar radiation is involved in complete analysis coupled with heat

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transfer. By the way, the obtained results are then used in Comsol Multiphysics as an input data. This commercial software is also the tool to solve the model Eq. (1).

4 Results and Discussions To highlight the impact of materials used for solar receiver, on parabolic dish concentrator performances, two types of metallic alloys (Inconel 625, Inconel 718), and the pure Copper were tested using ray-tracing methodology. Indeed, the temperature distribution on the receiver surface at 11 h is shown in Fig. 5. It is clear that the concentration ratio in the flat base of the cone is higher than that close to the vertex (the upper surface of the cone) even if both surfaces present some numerical noise. Additionally, receivers whose materials are alloys (Inconel 625 and 718) have a large temperature range (100 °C ≤ T ≤ 400 °C) whereas that corresponding to Copper is more low (100 °C ≤ T ≤ 250 °C). This difference results essentially from their different thermal properties (Tables 3, 4 and 5; and Figs. 3 and 4). Figure 6 represents the variation of the surface temperature for three receiver materials during the specific day (July 6th 2014). As expected, it confirms that for

Copper

Inconel 718

Inconel 625

Fig. 6 Time variation of temperature for the three receiver materials

Temperature (°C)

Fig. 5 Temperature distribution for the three receiver materials

400 350 300 250 200 150 100 50 0

Time of Day (hr) Copper

Inconel 718

Inconel 625

Fig. 7 Time variation of power density for the three receiver materials

R. Idlimam et al. Power density (kW/m³)

716 500 400 300 200 100 0 -100

Local time (hr) Copper

Inconel 625

Inconel 718

alloys especially for Inconel 625, the receiver reached high temperature values with a maximum value of about 392 °C at 11 h.  Figure 7 determines the variation of the receiver thermal power thdensity for three receiver materials during the day of July 6 2014. P kW/m3 = ρCT t It can be noticed that for alloys especially Inconel 625, the receiver can transmit energy better than for copper. The maximum thermal power density for Inconel 625 is of the order of 393.15 kW/m3 at 11 h compared with 378.75 kW/m3 and 196.73 kW/m3 for Inconel 718 and Copper respectively.For all these reasons, the Inconel 625 is chosen as the most efficient receiver’s material.

5 Conclusion In this research, the effects of the receiver’s material on the temperature and the thermal power density of solar receivers have been examined using Comsol Multiphysics. It was found that Inconel 625 gives higher thermal performances compared to other materials. For this material, simulation results show that the maximum temperature on the surface of the receiver is 392 °C and the power density is about 393.15 kW/m3 .

References 1. Hafez AZ, Soliman A, El-Metwally KA, Ismail IM (2017) Design analysis factors and specifications of solar dish technologies for different systems and applications. Renew Sustain Energy Rev 67:1019–1036 2. Stefanovic VP, Pavlovic SR, Bellos E, Tzivanidis C (2018) A detailed parametric analysis of a solar dish collector. Sustain. Energy Technol. Assess. 25:99–110 3. Idlimam R, Bah A, Asbik M, Malha M, Kazdaba H (2019) Effect of receiver shape on a parabolic solar concentrator system thermal behavior. In: Shaban AH, Salame CT, Aillerie M,

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5. 6.

7. 8.

9. 10.

11.

12. 13.

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Papageorga P (eds.) AIP conference proceedings 2019, TMREES, vol 2123. AIP Publishing, Beirut, pp 020048-1–020048-8 Xie WT, Dai YJ, Wang RZ (2011) Numerical and experimental analysis of a point focus solar collector using high concentration imaging PMMA Fresnel lens. Energy Convers Manag 52(6):2417–2426 Wang W, Laumert B (2017) Effect of cavity surface material on the concentrated solar flux distribution for an impinging receiver. Sol Energy Mater Sol Cells 161:177–182 Lalau Y, Faugeroux O, Guillot E, André D, Huger M, Proust A, Chotard T, Claudet B (2017) IMPACT: a novel device for in-situ thermo-mechanical investigation of materials under concentrated sunlight. Sol Energy Mater Sol Cells 172:59–65 Setien E, Fernández-Reche J, Álvarez-de-Lara M, Ariza MJ (2014) Experimental system for long term aging of highly irradiated tube type receivers. Sol Energy 105:303–313 Marchese G, Bassini E, Calandri M, Ambrosio EP, Calignano F, Lorusso M, Manfredi D, Pavese M, Biamino S, Fino P (2016) Microstructural investigation of as-fabricated and heattreated Inconel 625 and Inconel 718 fabricated by direct metal laser sintering: Contribution of Politecnico di Torino and Istituto Italiano di Tecnologia (IIT) di Torino. Met Powder Rep 71(4):273–278 Khalil I, Pratt Q, Spitler C, Codd D (2019) Modeling a thermoplate conical heat exchanger in a point focus solar thermal collector. Int J Heat Mass Transf 130:1–8 Rojas-Morín A, Fernández-Reche J (2011) Estimate of thermal fatigue lifetime for the INCONEL 6251CF plate while exposed to concentrated solar radiation. Revista de metalurgia 47(2):112–125 Idlimam R, Bah A, Asbik M, Malha M, Kazdaba H (20018) Impact of the parabolic solar concentrator’s rim angle on the quantity of reflected rays and concentrated flux on the receiver. 6th international renewable and sustainable energy conference (IRSEC). IEEE, Rabat, pp 1–5 Special metals Homepage. http://www.specialmetals.com/assets/smc/documents/alloys/inc onel/inconel-alloy-718.pdf Special metals Homepage. http://www.specialmetals.com/assets/smc/documents/alloys/inc onel/inconel-alloy-625.pdf

Three-Dimensional Analysis of the Effect of Transverse Spacing Between Perforations of a Deflector in a Heat Exchanger Jamal-Eddine Salhi and Najim Salhi

Abstract In this paper, we conducted a numerical study of three-dimensional laminar flow in forced convection, through a rectangular channel with twoperforation deflector fixed to its lower wall. To do so, the finite volume method based on the SIMPLE (Semi Implicit Method for Pressure Linked Equation) algorithm has been used to solve the Navier-Stokes, the energy equations and the for velocity-pressure coupling. Besides, we analyzed the effect of the transverse spacing between the perforations of the inserted deflector. The results presented as path lines and isotherms for the two planes XZ and XY, respectively, passing through the center of the deflector. Friction is studied as a function of the transverse spacing between the perforations and the Reynolds number. Furthermore, calculations of the average friction coefficient and the average Nusselt number are performed for Reynolds numbers between 100–700 and for different transverse spacings. The results show that the optimal design corresponds to the small spacing between perforations. Keywords Laminar flow · Numerical study · Deflector · Channel · Nusselt number · Friction factor · Reynolds number

1 Introduction Since decades, energy needs have been increasing in all sectors. Therefore, to meet all these needs, researchers and the scientific community have paid particular attention to the intelligent use of available energy. For instance, in the industrial sector, industrial process optimization has been the subject of several research topics. Indeed, heat exchangers, for example, have been an important development subject in the field because of their role in several applications such as thermal power plants, transportation, heating and air conditioning, refrigeration, petrochemicals, and other processes. J.-E. Salhi (B) · N. Salhi Laboratory of Mechanics and Energy, Faculty of Sciences, First Mohammed University, 60000 Oujda, Morocco e-mail: [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/10.1007/978-981-15-6259-4_75

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In practice, heat exchangers are designed to ensure the transfer of heat from a solid body, such as the wall of a channel, to the heat transfer fluid medium that flows through it. Besides, they are widely used in industrial processes such as car radiators, air conditioning systems, refrigeration, petrochemicals, and other technological fields. Also, the use of these systems with low efficiency in these applications increases the energy consumption. The engineers’ concern is, to optimize the design of these industrial processes in order to improve the thermal transfer rate with a minimum of energy. On way for improving the heat transfer rate in a heat exchanger is by increasing the heat exchange surface area or creating a flow disorder by inserting the fins or baffles into the system to ensure a high heat transfer rate. Also, heat exchangers with perforated baffles are widely used in industrial processes. The choice of the shape and position of these deflectors plays a crucial role in the intensification of heat transfer and a lower flow resistance. This passive technique has been the subject of several research studies. Karwa et al. [1] conducted an experimental study in a rectangular channel equipped with smooth and perforated baffles. They found that the friction coefficient for solid baffles is about 10 times higher than that of the smooth pipe. Besides, they found that the perforated deflectors decreases significantly as the perforation diameter increases. Therefore, the deflectors with the largest perforation diameter offers the best performance. Similarly, Dutta et al. [2] conducted an experimental study on the pressure drop and heat transfer behavior of a turbulent flow in a rectangular channel with uniform heat flow over the upper surface. Solid and inclined perforated deflectors have been installed in the channel with different sizes, positions, and orientations. They found that there is an optimal perforation to maximize heat transfer coefficients. Rouvreau et al. [3] combined an experimental and numerical study of laminar water flow around a rectangular block mounted a short distance from the first edge of a flat plate. They indicated that the upstream boundary should be placed at a sufficient distance from the obstacle to eliminating the interactions between the pressure disturbance associated with this object and the boundary. Huang et al. [4] Performed a detailed measurement of heat transfer in a square channel with a perforation deflector using transient liquid crystal thermography. The study parameters were the Reynolds number, the height of the deflector and the number of holes on the perforation deflector. The results showed that improvements in local heat transfer appearing in the leading edge of the baffle due to the collision effect, which was greater when the Reynolds number became larger or the height of the baffle became higher. Also, the eccentric heat transfer coefficients were better than those of the center downstream of the baffle. Heat transfer in the channels can be improved by oriented deflectors, as indicated by some authors [5–8]. Patankar [9] explored experimentally the turbulent flows and heat transfer in an air to water heat exchanger using perforated circular-ring. Their results show that the thermal performance increases while increasing the holes number, but it decreases when Reynolds number and pitch ratio increase. Micro heat sinks are adopted for heat removal from electronic devices. During the last decades, efforts have been made to understand the phenomenon of heat transfer to improve it in a heat exchanger. In the same direction, the purpose of this work consists in analyzing numerically in three-dimensional, the characteristics

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of heat transfer in the presence of a fluid in convective laminar flow through a heat exchanger equipped with solid or perforated deflectors. The deflectors are parallelepipedic in shape. The main objective is, therefore, to contribute to the development of industrial heat exchange and heat transfer processes by optimizing their geometric design. Likewise, the perforation effects were studied, not by increasing the number of perforations in the deflector, but on the variation in the transverse spacing between the perforations of the deflector. The work is completed by a comparison of the results calculated for different spacings in the case of a perforated deflector on the one hand and on the other hand with the results calculated for a full deflector.

2 Problem Description and Boundary Conditions 2.1 Physical Model In our model, the system studied is a horizontal rectangular channel equipped with a solid or perforated baffle, placed on the bottom wall (Fig. 1). The dimensions are given in Table 1 and are based on the experimental study of [3].

2.2 Governing Equations To simplify the model and to decrease the simulation time the following simplifying assumptions are adopted:

(a)

(b) Fig. 1 a System studied: b dimensions of the perforated baffle

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Table 1 Dimensions of the geometry

L 1 = 160 mm

Width of channel

Distance from the entered at the edge of L 2 = 120 mm attack Distance from leading edge at the exit Height of conduct

L 3 = 180 mm

Height of baffle

h = 18 mm

Width or length of baffle

D = 60 mm

Width of perforation

a = 16 mm

Height of perforation

b = 8 mm

Length of perforation

D = 60 mm

Transverse spacing between perforations Pi = 5–10–20 mm

• • • • •

Three-dimensional, laminar and stationary flow. The fluid is Newtonian and incompressible. Inlet velocity and temperature are assumed constant and uniform. The physical properties of the fluid (C p , μ, λf , ρ) and the solid (λs ) are constant. Viscous dissipation and radiation are negligible [10].

For three-dimensional steady state incompressible laminar fluid flow the governing equations are: ∂u i =0 ∂ Xi

Continuity equation:

Momentum equation:

uj

i = 1, 2, 3

∂u i 1 ∂P ∂ 2ui =− +ν ∂Xj ρ ∂ Xi ∂ X i2

Energy equation:

uj

∂T ∂2T =α 2 ∂Xj ∂ Xi

i, j = 1, 2, 3

i = 1, 2, 3

(1)

(2)

(3)

2.3 Boundary Conditions The bottom and upper walls are respectively subjected to temperatures T a = 343 K and T b = 298 K. Non-slip conditions were applied to the walls. • At the entrance: U int , V int = W int = 0, T int = 300 K and Flow Re is changed from 100 to 700. • At the output: Atmospheric pressure: P = Patm and

∂φ ∂x

= 0; φ = U, V, W, T .

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The aluminum is considered for the baffle wall (λs = 202W/(m.K)). Leung et al. [10] have reported that for polished aluminum fins with temperature differences around 40 and 77.5 K, radiation heat transfer rate is less than 5% and 8% of total heat transfer rate, respectively. Based on this conclusion, in our study we neglected the effect of radiation heat transfer because the maximum temperature difference in the present study is 45 K. The main parameters for the current investigation are Average friction coefficient (C f ) and average Nusselt number (N u) are computed by Eqs. (4) and (5) and results are illustrated in Figs. 6 and 7. Cf =

2τ w ρ.U 2

(4)

where τ w is the average shear rate at the wall, ρ the density of the fluid, and U the average axial velocity. Nu =

h Dh λf

(5)

where Dh is the hydraulic diameter, h is the average convection coefficient and λf is the thermal conductivity of the fluid.

2.4 Numerical Model The governing Eqs. (1)–(3) are discretized using finite volume code that controls the volume cells for velocity components are staggered with respect to the main control volume cells using the SIMPLE algorithm developed by Patankar [11]. The second order (Upwind) technique is used to calculate the momentum and the energy equations. The set of discretized equations are solved iteratively line-byline. Computation started by first solving continuity and momentum equations to determine the flow field and then the energy equations to find the thermal field in the computational region which include solid block. The residual target for all equations was set to 10-7. Calculations were performed on a machine with Intel Xeon CPU, 3.40 GHz of clock speed and 64 GB of RAM. The convergence was obtained after about 800–1200 iterations and from 3 to 5 h of CPU computational time. For quantification, a structured tetrahedral mesh is generated. Several tests were carried out to ensure that the results were independent of the mesh size adopted. The grid of 1,745,355 cells shows an early stabilization of the average Nusselt number and the average coefficient of friction (N u and C f ). For further calculations, a grid of 2,381,877 cells was chosen, which presents a good compromise between accuracy and computation time (see Fig. 2).

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Fig. 2 Grid independent studies for solid fin, (Re = 1000)

2.5 Validation of the Model Before considering the effect of the spacing between the perforations of the deflector on the dynamic behavior and heat transfer, we compared our numerical results with the numerical and experimental results obtained by Rouveau et al. [3] for a rectangular channel equipped with a solid deflector with a square cross-section of 60 mm side and 18 mm height, embedded with the lower wall. Authors in reference [5] reported that in numerical modeling the distance of the inlet boundary to obstacle must be sufficiently large to eliminate interactions between the pressure perturbation associated with the obstacle and the boundary. They solved this problem numerically by adding a development zone to upstream distance. The leading edge of the plate is 120 mm from the channel entrance Fig. 1a. A flow velocity of 1.8 cm/s is applied at the channel entrance, which corresponds to a Reynolds number of 1000. Figure 3, illustrates the very good agreement between our numerical results and numerical and experimental velocity profiles obtained by Rouvreau [3]. In addition, the validation of the experimental model by our numerical code can confirm the reliability of our numerical results.

3 Results and Discussion In Fig. 4, the current lines in the XZ plane passing through the centre of the deflector are shown: (a) case of a full deflector, (b) case of a perforated deflector, for Re = 200. As it can be seen, the flow is accelerating in the upper part of the channel with the presence of recirculation zones upstream and downstream of the deflector. It should also be noted that the recirculation length is different for both cases (a; b) due to the presence of both perforations and at a short distance between the perforations. Also, in the full case one current line passes through the surface of the deflector, while in

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Fig. 3 Dimensionless velocity profiles U/U0 at the leading edge of the plate for Y/D = 0. Numerical and experimental comparison with work of Rouveau et al. [3]

Fig. 4 Path lines [m/s] on both XZ plane for Re = 200, a deflector solid, b two-perforation deflector P1 = 5 mm

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Fig. 5 Thermal fields [K] on both XY plane for Re = 200, a deflector solid, b two-perforation deflector P1 = 5 mm

the other case there is a detachment with an accentuated presence of recirculation especially downstream. On the other hand, the presence of this downstream zone with the recirculation process contributes to the improvement of heat exchange, which increases the efficiency of the system. Figure 5 shows the phenomenon of heat transfer by the particles of the heat transfer fluid in the vicinity of the heated deflector (a) full and (b) through the perforations of the perforated deflector. In particular, it should be noted that in the case of the perforated deflector, the particles passing through the perforations are qualified to warm up much more than the others. This shows that a perforated deflector increases the efficiency of the system in terms of thermal transfer when it is considered as a heat source. Figure 6 shows the evolution of the average Nusselt number as a function of the Reynolds number for the solid case, and for different transverse spacings between the two perforations in the case of a perforated deflector. The variation curves show that for the whole cases, the average Nusselt number increases while Reynolds number is increasing. Besides, the highest values are observed for the full case. Also, it can be observed that the average number of Nusselt increases with decreasing transverse spacing between perforations. Figure 7 shows the evolution curves of the average friction coefficient as a function of Reynolds number for the all cases, and for different transverse spacings between

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Fig. 6 Average Nusselt number variation as a function of Reynolds number for different spacing

Fig. 7 Average friction coefficient of variation as a function of Reynolds number for different spacings

the two perforations for a perforated deflector. It can be seen that the average friction coefficient decreases with the increase in the Reynolds number for the whole cases. Furthermore, there is a difference between the different curves, especially, for low

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Reynolds values with a larger decrease in the coefficient of friction for the Reynolds number range from 100 to 400.

4 Conclusion This paper represents a three-dimensional study that was conducted on a channel equipped with a heated deflector (heat source) that can be solid or perforated in the presence of laminar flow. It is interesting to note that the rate of variation curves of the Nusselt number as a function of the Reynolds number is the same for all the cases studied. Also, it can be observed that the evolution of the coefficient of friction as a function of the Reynolds number is different for the Reynolds number range from 100 to 400, while it is the same for the higher values. The study shows that the technique of using a perforated deflector with less spaced perforations is more efficient to improve the hydrodynamic behavior and heat transfer intensification of an industrial process.

References 1. Karwa R, Maheshwari BK, Karwa N (2005) Experimental study of heat transfer enhancement in an asymmetrically heated rectangular duct with perforated baffles. Int Commun Heat Mass Transf 32(1–2):275–284 2. Dutta P, Dutta S (1998) Effect of baffle size, perforation, and orientation on internal heat transfer enhancement. Int J Heat Mass Transf 41(19):3005–3013 3. Rouvreau S, David L, Calluaud D, Joulain P (2005) Laminar junction flow at low Reynolds number : influence of the upstream region on the comparison between experiments and calculations 333: 265–272 4. Huang KD, Tzeng SC, Jeng TM, Wang JR, Cheng SY, Tseng KT (2008) Experimental study of fluid flow and heat transfer characteristics in the square channel with a perforation baffle. Int. Commun Heat Mass Transf 35(9):1106–1112 5. Tandiroglu A (2006) Effect of flow geometry parameters on transient heat transfer for turbulent flow in a circular tube with baffle inserts. Int J Heat Mass Transf 49(9–10):1559–1567 6. Tandiroglu A (2007) Effect of flow geometry parameters on transient entropy generation for turbulent flow in circular tube with baffle inserts. Energy Convers Manag 48(3):898–906 7. Tandiroglu A, Ayhan T (2006) Energy dissipation analysis of transient heat transfer for turbulent flow in a circular tube with baffle inserts. Appl Therm Eng 26(2–3):178–185 8. Salhi JE, Amghar K, Bouali H, Salhi N 2020) Combined heat and mass transfer of fluid flowing through horizontal channel by turbulent forced convection. Model Simul Eng 2020. Article ID 1453893, 11 pages 9. Patankar SV, Spalding DB (1972) A calculation procedure for heat, mass and momentum transfer in three-dimensional parabolic flows. Int J Heat Mass Transf 15(10):1787–1806 10. Leung CW, Probert SD (1989) Heat-exchanger performance: effect of orientation. Appl Energy 33(4):235–252 11. Patankar SV (1980) Numerical Heat Transfer and Fluid Flow. Series in Computational Methods in Mechanics and Thermal Sciences, Hemisphere, New York

Analysis of a Building-Mounted Wind-Solar Hybrid Power System in Urban Residential Areas: The Case Study of Istanbul B. Oral, S. Sa˘glam, and A. Mellit

Abstract In cities, buildings play an important role in electricity consumption and are one of the central problems of global warming. Therefore, buildings are good alternatives for micro-power generation facilities. In this paper, Göztepe, Istanbul is selected as a pilot region in Turkey. Solar and wind energy potential of this region was examined in order to study the feasibility of a hybrid wind-solar system. Technical analysis and cost analysis are performed for the electricity demand of the building. A computational fluid dynamics program is used to determine the most appropriate place for installation of the wind turbines on the top of the building. HOMER software is used to simulate and analyzing the performance of system. Keywords Building-mounted · CFD · HOMER · Hybrid system · Wind power · Solar energy · Residential areas

1 Introduction In recent years, renewable energy source applications have become widespread to meet the energy needs for the increasing world population. Research and development activities, especially in the wind energy, solar energy, biomass energy and geothermal energy fields, have accelerated. Wind energy and solar energy are considered clean, inexhaustible and the least environmentally harmful sources in many studies. During B. Oral · S. Sa˘glam Technology Faulty, Electrical and Electronics Engineering Department, Marmara University, Kadıköy, 34722 ˙Istanbul, Turkey e-mail: [email protected] S. Sa˘glam e-mail: [email protected] A. Mellit (B) Renewable Energy Laboratory, Jijel University, 18000 Jijel, Algeria e-mail: [email protected] AS-ICTP, Trieste, Italy © 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/10.1007/978-981-15-6259-4_76

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the production of electricity from wind energy and solar energy, there isn’t any contamination of water resources and no emergence of hazardous waste; this is an important advantage compared to the other sources, such as biomass, hydro and geothermal [1, 2]. Residential areas become more common to use applications like solar panels, micro combined heat and power and micro wind turbines for electricity production. The concept of on-site renewable energy generation is based on the idea of producing electricity in populated areas where such energy is required [3]. Hybrid renewable energy systems can be formed from a single renewable energy source and a single conventional energy source or from a combination of multiple renewable energy sources. These systems can work off grids mode or can operate in grid-connected mode. Hybrid systems have become popular for stand-alone energy production in crowded cities due to advances in power electronics and renewable energy technologies. Especially unregulated electric energy produced from renewable energy sources that can be successfully transferred to the grid due to the advances in power electronic converters. The most important feature of hybrid energy systems is using a combination of more than one renewable energy source by combining each of their characteristic features so they can achieve higher efficiency than the use of a single energy source [4–6]. Building-mounted micro wind–solar hybrid energy systems has significant benefits for both the electricity grid and the environment. These systems can offer the reduction of harmful gas emissions, minimization of transmission losses, and reinforcement of the grid and raised consciousness of sustainability and renewable technologies [7]. On the other hand, these hybrid systems have many technical problems. In [8] the authors explained that mounting micro wind turbines on the roof of the building is one of the most appropriate locations for on-site power generation. On the other hand, the building-mounted micro wind turbine system’s main problems are issues of safety, noise, vibration and visual impact and should not be depreciated. In [9] the authors summarized building-integrated photovoltaic systems developments and emphasized that the structural characteristics of the building are among the most important factors for photovoltaic (PV) system installation. In [10] the authors presented that micro wind generator performance criteria are quite different from the traditional wind generators, which are installed in rural areas on wind farms. Micro wind turbine (kW) energy production costs are so high that they are unable to compete with the large scale wind turbines (MW), which are installed on wind farms. Photovoltaic systems find worldwide application areas more than the micro wind systems, despite these energy production cost problems being same in urban environments. For that reason, building-mounted and integrated wind systems need the use of special design parameters to determine the most convenient generator types, to develop building forms that will improve their efficiency, and to predict expected power outputs [10]. In this paper, a pilot region has been chosen for a wind–solar hybrid system and wind–solar potential of the chosen place was analyzed carefully. The building is located at Göztepe region in ˙Istanbul it consists of 4 floors with 8 flats. Computational

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Fluid Dynamic (CFD) program is used to determine the best location place for wind turbines. HOMER (Hybrid Optimization Model of Electric Renewable) software is then used for optimizing, designing and evaluating the performance of the solar–wind hybrid system.

2 Building-Mounted PV-Wind Hybrid Systems Generally, renewable energy sources are known as inexhaustible, but sometimes power interruption occurs because of meteorologic or geographic conditions. Several different energy sources are used in hybrid systems to overcome this problem. Solar and wind energy systems are the most suitable for electrical energy conversion sources, considering their complementary characteristic advantages. Wind speeds are generally low during the time (under stationary high-pressure weather conditions) when the solar resource is at its best. On the contrary, the wind is usually stronger in seasons (especially autumn and winter) when there is less solar. These renewable energy sources depend on unpredictable weather conditions, which make the design of a hybrid system complicated. Other sources, such as fuel cells and diesel generators, can also be integrated to increase the reliability of PV-wind hybrid systems.

2.1 Hybrid System Modelling Figure 1 shows the location and top view of building. A model-building located at Göztepe region in ˙Istanbul, it that has 4 floors with 8 flats. The front of the building is located at the northern side and the rear side is located in the southern direction. The building in the east-west direction consists of adjacent

Fig. 1 Building location to design the hybrid system

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Table 1 Hourly electric energy consumption (KWh) T1 (06:00–17:00)

T2 (17:00–22:00)

T3 (22:00–06:00)

Hourly

Total

Hourly

Total

Hourly

Total

January

0.308

3.387

0.368

1.839

0.359

2.871

February

0.360

3.964

0.400

2.000

0.442

3.536

March

0.334

3.677

0.329

1.645

0.327

2.613

April

0.230

2.533

0.240

1.200

0.221

1.767

May

0.214

2.355

0.232

1.161

0.226

1.806

June

0.288

3.167

0.293

1.467

0.196

1.567

July

0.217

2.387

0.284

1.419

0.185

1.484

August

0.188

2.065

0.316

1.581

0.185

1.484

September

0.230

2.533

0.320

1.600

0.217

1.733

October

0.258

2.839

0.335

1.677

0.210

1.677

November

0.367

4.033

0.480

2.400

0.342

2.733

December

0.331

3.645

0.400

2.000

0.331

2.645

buildings. There is approximately one meter elevation difference between the front and rear faces of the building. The total height of the building, including the roof peak, is 14 m. The annual average electric energy consumption for one flat is given in Table 1. The average value of the electric energy consumption has been calculated from the electric bills for the last two-year period. As a result, the building’s electric energy consumption was found to be 2502 kWh/year. Two basic cases are considered: in the first case, the system is connected to the grid. If the energy produced from PVs and the wind turbine is not enough to feed to the building, the energy is imported from the grid. Otherwise, excess energy is stored in batteries. While, in the second case, the hybrid system works stand alone and there is no grid connection. The system must produce all the energy that the building needs. This case has some constraints because of the building’s physical structure. The PV panel number is limited with the roof area and the wind turbine capacity is limited with the building’s environmental factors. Global solar radiation values are some of the most important factors for the sizing the PV system component. Göztepe meteorological weather station values that are used for the PV system calculation and the average monthly wind speed are reported in Table 2. These wind speed values show that the average wind speed is 2.5 m/s for Göztepe [11].

Analysis of a Building-Mounted Wind-Solar Hybrid Power System … Table 2 Monthly solar radiation values and average wind speed for Istanbul, Göztepe

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Months

Solar radiation (kWh/m2 -day)

Average wind speed (m/s)

January

1.4

2.58

February

2.19

2.22

March

3.13

2.74

April

4.36

2.47

May

5.57

2.30

June

5.92

2.57

July

5.74

2.97

August

5.22

2.72

September

4.09

2.58

October

2.78

2.38

November

1.62

2.00

December

1.19

2.06

2.2 Modelling of the Hybrid System HOMER software has been chosen for this study considering its unique features for hybrid systems. HOMER has a deterministic input/output model that permits integrating the different type of renewable energy sources. For this reason, it is suitable for integration of renewable resources in residential area applications. The program is descriptive and has an analytically programmed computer model for timebased simulation of a hybrid renewable energy system. In particular, solar radiation and wind speed data can be entered into the program hourly by the user for the area that has been taken into consideration. As a result, HOMER is appropriate for the goal of study because the hourly time step capability of HOMER allows for a better evaluation of the non-programmable renewable energies production [12, 13].

3 Simulation and Results After examination of the model building’s location and architectural characteristics, the most suitable areas on the roof for the PV panels were determined. PV panels have been placed close to one another in order to use maximum space on the roof. When placing the PVs in the roof area, at least 0.5 m must be left between arrays to simplify installation and maintenance. According to the layout plan, 83 pieces of PV panel can be mounted, as shown in Fig. 2. The monthly average wind speed is 2.5 m/s in the region where the hybrid system is located. Therefore, a wind turbine selection criterion is taken into account for 2.5 m/s cut-in speed for the power generation of turbines. After the CFD analysis and wind speed data are analyzed, the selection of the wind turbine is placed on top

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Fig. 2 Placement of PV panels and wind turbines on the roof of the model building

Table 3 Selected wind turbine and technical specifications Wind turbine

Cut-in speed (m/s)

Rotor diameter (m)

Nominal power (w)

Nominal wind speed (m/s)

Zephyr1000

2.5

1.8

1000

12

Fig. 3 Grid-connected hybrid system HOMER results

of the building after considering the effect of noise on the environment. The selected wind turbine and technical characteristics are shown in Table 3. After modelling all elements of the building-mounted solar wind hybrid power system, data were entered into the HOMER program. The actual data entry for Göztepe is given in Fig. 3, which includes the average solar radiation 3.62 kWh/m2 /day and an average wind speed of 2.51 m/s for grid-connected hybrid system as well as the HOMER results obtained after the analysis of the region.

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Fig. 4 Cost summary results

The obtained results from the analysis program are examined, and it is determined that there is a case in which the optimum result is in the bottom row. The initial setup cost is $78425, the operating costs are $36692 and the total costs are $116813, which can be seen in Fig. 4 in the cost summary results window. The unit price of electricity that will be produced is calculated as 0.457 $/kWh. The annual electricity production capacity of solar cells 14650 kWh, for the wind turbines 2068 kWh and supplied energy from grid 13695 kWh. In this case PV’s 48%, Wind turbines 7% and grid have 45% percentage of energy production for the model-building (See Fig. 5). The hybrid system characteristics are found to be similar to other studies [14]. The actual data values for Göztepe include the average solar radiation of 3.62 kWh/m2 /day and an average wind speed of 2.51 m/s for a grid-connected hybrid system as well as the HOMER results obtained after the analysis with these meteorological data.

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Fig. 5 Electrical energy status summary

4 Conclusions A building-mounted wind–solar hybrid electric power system located in Istanbul (Göztepe) is presented and analyzed. Grid support is used when the wind–solar system couldn’t supply the entire electricity demand. In the summer season, the energy output of the hybrid system is primarily produced by the PV system. Solar radiation and sunshine duration are enough of a level for photovoltaic electricity production. However, in the winter season, the wind turbine’s electrical energy output becomes greater, while the PV output is not so high because of less available sunlight and shade from thick clouds and foggy weather. The optimized system consists of a 250W peak power 45 PV panel, 1 kW nominal power output for 5 wind turbines and 100 Ah capacity for 10 accumulators. The unit price of electricity generated on the system is about $0.47, and the percentage of the renewable energy production rate is 55%. The wind turbines are working inefficiently because the average annual wind speed in the region, where the analysis is performed, is very low. Therefore, only 5% of the system’s total energy production is made by the wind turbines.

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References 1. Yang H, Lou C, Sun L (2008) Building-integrated photovoltaics for maximum power generation. In 2nd Electronics system integration conference, Greenwich, UK. IEEE, pp 39–44 2. Chong WT, Fazlizan A, Poh SC, Pan KC, Ping HW (2012) Early development of an innovative building integrated wind, solar and rain water harvester for urban high rise application. Energy Build 47:201–207 3. Chong WT, Fazlizan A, Poh SC, Pan KC, Ping HW, Hew WP, Hsiao FB (2013) The design, simulation and testing of an urban vertical axis wind turbine with the omni-direction-guidevane. Appl Energy 112:601–609 4. Bajpai P, Dash V (2012) Hybrid renewable energy systems for power generation in stand-alone applications: a review. Renew Sustain Energy Rev 16:2926–2939 5. Rezzouk H, Mellit A (2015) Feasibility study and sensitivity analysis of a stand-alone photovoltaic–diesel–battery hybrid energy system in the north of Algeria. Renew Sustain Energy Rev 43:1134–1150 6. Roth A, Boix M, Gerbaud V, Montastruc L, Etur P (2019) A flexible meta model architecture for optimal design of Hybrid Renewable Energy Systems (HRES) e Case study of a stand-alone HRES for a factory in tropical island. J Clean Prod 223:214–225 7. Syngellakis K, Carroll S, Robinson P (2006) Small wind power: introduction to urban smallscale wind in the UK. Refocus 7:40–45 8. Dayan E (2006) Wind energy in buildings: power generation from wind in the urban environment – where it is needed most. Refocus 7:33–38 9. Parida B, Iniyan S, Goic R (2011) A review of solar photovoltaic technologies. Renew Sustain Energy Rev 15:1625–1636 10. Ayhan D, Sa˘glam S¸ (2012) A technical review of building-mounted wind power systems and a sample simulation model. Renew Sustain Energy Rev 16:1040–1049 11. Ayhan D (2011) Building mounted solar-wind hybrid power system analyses. Master’s thesis, Marmara University, Institute of Pure and Applied Sciences, Istanbul (2011) 12. Halabi LM, Mekhilef S (2018) Flexible hybrid renewable energy system design for a typical remote village located in tropical climate. J Clean Prod 177:908–924 13. Brandoni C, Bosnjakovic B (2017) HOMER analysis of the water and renewable energy nexus for water-stressed urban areas in Sub-Saharan Africa. J Clean Prod 155:105–118 14. Soysal AO, Soysal HS (2008) A residential example of hybrid wind-solar energy system: WISE. In: Power and energy society general meeting – conversion and delivery of electrical energy in 21st Century, Pittsburgh, USA. IEEE

Analysis of the Energy Produced and Energy Quality of Nanofluid Impact on Photovoltaic-Thermal Systems Stefano Aneli, Antonio Gagliano, Giuseppe M. Tina, and Bekkay Hajji

Abstract To limit climate change, the use of renewable energy is mandatory. PV/T systems generate renewable energy, simultaneously satisfy both the thermal and electrical energy requests. Usually, these systems have some limitations to fulfill the thermal energy needs; therefore, it is necessary to improve their efficiency with the aim to increase the enthalpy level of the energy produced. In this paper, the effects of changing the cooling fluid from pure water to a nanofluid composed by water and aluminum oxide (Al2 O3 ) in a PV/T system are studied. The analysis is based on the thermodynamics viewpoint, considering both the total energy produced and its quality. The thermal level achievable by changing the heat transfer fluid, as well as the electrical efficiency considering various input conditions has been calculated. Finally, the energy yield produced by a conventional PV/T plant, which use pure water (PV/T)w and the proposed improved PV/T plant, which use pure the nanofluid (PV/T)nf , under real climate conditions have been compared. Such comparison was developed taking into account the second law of thermodynamics as well as the exergy analysis. Keywords WISC PV/T collector, nanofluid · Numerical model

1 Introduction The world energy requirement shows a constantly increasing trend, where the necessary energy concerns both the electrical and thermal form. To produce the two forms of energy, photovoltaic and solar thermal systems must therefore be installed. Alternatively, there are PV/T systems able of simultaneously generating both forms of energy, thus producing a lot of renewable energy. Several comparative studies [1–3] S. Aneli · A. Gagliano (B) · G. M. Tina University of Catania, viale Andrea Doria 6, 95125 Catania, Italy e-mail: [email protected] B. Hajji 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/10.1007/978-981-15-6259-4_77

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show that PV/T systems are capable of producing more energy per unit area than any combination of conventional solar systems. Therefore, it is not surprising that in recent years important research has been dedicated to the various parameters that can increase energy production, such as the different geometric design and materials [4–7]. An alternative, concerns the use of heat transfer fluid with better performance than simple water. Despite the high thermal capacity of the water, which makes it a good heat storage vehicle, its thermal conductivity is low and therefore the heat transfer is limited. Starting from 1995 [8], many researchers have suggested the adoption of nanoparticles to be added to the base fluid to form a suspension with high thermal conductivity, i.e. nanofluids. Nanofluids are solid-liquid composite materials consisting of solid nanoparticles or nanofibers with sizes typically of 1–100 nm suspended in liquid [9]. Various studies have shown that nanofluids have substantially higher thermal conductivities than base fluids [10–13]. Practical studies have shown that nanofluids are preferred over base fluids, as they allow increasing electrical and thermal efficiency [14–17]. In this paper, the effects of changing the heat transfer fluid from pure water to a nanofluid composed by water and Al2 O3 are studied using a numerical model. Such model, developed with Matlab®, was validated using experimental data coming from the pilot plant installed at the University of Catania [18]. The first part of this study analyses the differences in terms of temperature of the fluid outlet from the PV/T panels, temperature of the photovoltaic cells, difference in electrical efficiency, considering different inlet temperature. In the second part, a comparison is developed in terms of thermal and electrical energy produced as well as the study of the total energy produced considering the first and second principles of thermodynamics, under real climatic data.

2 Methodology 2.1 PV/T Numerical Model The numerical model used in this paper was explained and validated in [19]. It works in dynamic state conditions and is based on the energy balance equations. The model is based on the PV/T system installed at University of Catania (IT) equipped with two commercially WISC panels DUALSUN Wave©, as presented in Ref. [18], with total surface of 3.32 m2 . The panels are connected in series and are connected with a storage tank, which has a volume of 170 l. The module consists of mono-crystalline (c-Si) cells, which provide a module efficiency of 15.4% at Standard Test Conditions, with electrical peak power of 250 W. The efficiency loss with temperature coefficient (β) is 0.44%.

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Table 1 Properties of the fluids Properties

Pure water

Nanoparticles

Nanofluid 3%/w.

ρ (density) [kg·m-3 ]

997.0

3970.0

1019.2

4177.0

765.0

4077.6

0.606

40.0

0.619

C (specific heat capacity)

[J·kg-1 ·K-1 ]

λ (thermal conductivity) [W·m-1 ·K-1 ]

2.2 Coolant Fluids The study on the performance of a PV/T system was carried out considering two different heat transfer fluids: pure water and nanofluid. The nanofluid is composed by pure water and nanoparticles of aluminum oxide (3% by weight), with a volumetric ratio (φ) calculated by Eq. (1). φn f =

m p /ρ p m p /ρ p + m f /ρ f

(1)

where m is the mass, ρ is the density and subscripts p, f and nf represent respectively: nanoparticles, fluid (pure water) and nanofluid. All the thermos-physical properties of the water and of the nanofluid are reported in Table 1, which have been calculated using the equations proposed by [20].

2.3 Key Performance Indicator In a PV or PV/T installation, the electrical performances depend by the working cells temperature, which in turn depends by the solar irradiation (G). Thus, it is worth of interest to introduce a KPi, namely T char,PV , which allows to evaluate the working temperature of the PV cells weighted by G. Tchar,P V =

∫ TP V · G · dt ∫ G · dt

(2)

The increase of Thermal Level (Tn+f ) of the cooling fluid trough the PV/T module is calculated by Eq. 3: Tn+f =

Tout,n f − Tout,w × 100[%] Tout−in

(3)

The overall performance of a PV/T system must be evaluated based on a thermodynamic approach from the viewpoint of the first and second laws. The KPi(s) useful for calculating the Overall Energy Yields are the primary energy produced (E T(I) ) [1, 21] and the Overall Exergetic Content (E χ T ) [22], calculated by Eq. 4 and 5:

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E T (I ) = Pe /η power + Pth

(4)

E χ T = E χe + E χth

(5)

3 Result and Discussions 3.1 Thermal Comparison During Day a Type In this section, the comparison of the working temperatures of the PV/T panel in case of using water or nanofluids as cooling fluid, are discussed. The simulations were carried out considering a typical summer day. The flow rate of 30 kg/(h·m2 ) was chosen in accordance with the solar collector fluid flow rates suggested by [23]. Figure 1 shows the comparison of the outlet temperatures for the two fluids analyzed and the increase of thermal level, varying the inlet temperatures (T in ) from 25 to 45 °C. It can be observed that the outlet temperature is greater in the case of use of the nanofluid. Moreover, when the inlet temperature increases, the difference between the two fluids is reduced, from about 0.29° to about 0.09 °C. This means that the use of the nanofluid increase the thermal level of about 1.9% respect to pure water. According to the outlet temperature, TPV increase with a maximum difference of about 0.08 °C. Therefore, the electrical efficiency decrease of 0.04% in the worst case.

Fig. 1 Outlet temperatures and increase of thermal level

Analysis of the Energy Produced and Energy Quality …

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3.2 PV/T System Under Real Weather Conditions In this section, the performances of the PV/T plant operating with nanofluid are analyzed during the period from 7 to 10 March 2019 as in this period experimental data are available for the PV/T plant operating with pure water [19]. Figure 2 shows the average temperature reached in the thermal storage for both the fluids analyzed. It is possible to highlight that the trend of temperature into the thermal storage are quite similar for both fluid. The exploitation of the nanofluid allows to slightly increasing the daily temperature into the thermal storage. The daily thermal and electrical energy produced and the overall energy calculated using Eq. 4, and the total exergy content are summarized in Table 2. The results show that the thermal energy produced undergoes a slight increase using nanofluids, while electricity losses are negligible. Indeed, the reference temperature of photovoltaic cells (Tchar,PV ), which can be assumed as an indicator of the temperature effects on the electrical efficiency, have a maximum increases of 0.05 °C, using the nanofluid (Nfluid).

Fig. 2 Average tank temperature during the simulation period for both fluids

Table 2 Daily results Day

03/07 Water

03/08 Nfluid.

Water

03/09 Nfluid.

Water

03/10 Nfluid.

Water

Nfluid.

Toutd,av

[°C]

18.42

16.52

17.58

16.34

GTOT

[Wh/m2 ]

6013

6070

6607

6794

Eχ,sun

[Wh/m2 ]

5781

5842

6356

Tchar,PV

[°C]

34.59

34.63

33.46

33.50

35.25

35.30

34.35

34.39

Eel

[Wh/m2 ]

780

780

792

792

855

855

883

883

Eth

[Wh/m2 ]

310

312

221

221

333

334

263

264

ET(I)

[Wh/m2 ]

2006

2007

1943

1944

2191

2192

2184

2184

EχT

[Wh/m2 ]

797

797

805

805

874

874

899

899

εT

[%]

13.79

13.79

13.78

13.78

13.75

13.75

13.75

13.75

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744

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4 Conclusions The paper shows the effects of changing the coolant fluid from pure water to a nanofluid composed by water and aluminum oxide in a PV/T system. The use of the nanofluid allows reaching higher thermal levels than the use of pure water. As the inlet temperature increases, the differences in outlet temperature between the two fluids decrease, while maintaining a relationship with the temperature difference between the inlet-outlet almost constant, with an increase in performance obtained by using the nanofluid of approximately 1.9%. Furthermore, the increase in the thermal level generates a small increase in the working temperatures of the PV, generating a negligible decrease in terms of electricity produced. Observing a complete system, the nanofluid slightly increases the thermal energy produced and the total energy produced, while the energetic content remains rather constant. Future study should evaluate to optimize all the operative parameters (e.g. the mass flow rate, the specific volume of the thermal storage) for a further improvement of the performance of the (PV/T)nf plant.

References 1. Gagliano A, Tina GM, Aneli S, Nižeti´c S (2019) Comparative assessments of the performances of PV/T and conventional solar plants. J Clean Prod 219:304–315 2. Boumaaraf B, Touafek K, Ait-cheikh MS, El Amine Slimani M (2020) Comparison of electrical and thermal performance evaluation of a classical PV generator and a water glazed hybrid photovoltaic–thermal collector. Math Comp Simul 167:176–193 3. Sakellariou E, Axaopoulos P (2017) Simulation and experimental performance analysis of a modified PV panel to a PVT collector. Sol Energy 155:715–726 4. Herrando M, Ramos A, Zabalza I, Markides CN (2019) A comprehensive assessment of alternative absorber-exchanger designs for hybrid PVT-water collectors. Appl Energy 235:1583–1602 5. Michael JJ, Iniyan S, Goic R (2015) Flat plate solar photovoltaic-thermal (PV/T) systems: a reference guide. Renew Sustain Energy Rev 51:62–88 6. Antonanzas J, del Amo A, Martinez-Gracia A, Bayod-Rujula AA, Antonanzas-Torres F (2015) Towards the optimization of convective losses in photovoltaic–thermal panels. Sol Energy 116:323–336 7. Zondag HA, de Vries DW, van Helden WGJ, van Zolingen RJC, van Steenhoven AA (2003) The yield of different combined PV-thermal collector designs. Sol Energy 74:253–269 8. Chol S, Estman J (1995) Enhancing thermal conductivity of fluids with nanoparticles. ASMEPublications-Fed 231:99–106 9. Sridhara V, Satapathy LN (2011) Al2 O3 -based nanofluids: a review. Nanoscale Res Lett 6:456 10. Minea AA (2017) Hybrid nanofluids based on Al2 O3 , TiO2 and SiO2 : numerical evaluation of different approaches. Int J Heat Mass Transfer 104:852–860 11. Ebrahimnia-Bajestan E, Moghadam MC, Niazmand H, Daungthongsuk W, Wongwises S (2016) Experimental and numerical investigation of nanofluids heat transfer characteristics for application in solar heat exchangers. Int J Heat Mass Transfer 92:1041–1052 12. HemmatEsfe M, Karimipour A, Yan WM, Akbari M, Safaei MR, Dahari M (2015) Experimental study on thermal conductivity of ethylene glycol based nanofluids containing Al2 O3 nanoparticles. Int Commun Heat Mass Transfer 68:248–251

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13. Yousefi T, Veysi F, Shojaeizadeh E, Zinadini S (2012) An experimental investigation on the effect of Al2 O3 –H2 O nanofluid on the efficiency of flat-plate solar collectors. Renewable Energy 39:293–298 14. Kasaeian A, Eshghi AT, Sameti M (2015) A review on the applications of nanofluids in solar energy systems. Renew Sustain Energy Rev 43:584–598 15. Alous S, Kayfeci M, Uysal A (2019) Experimental investigations of using MWCNTs and graphene nanoplatelets water-based nanofluids as coolants in PVT systems. Appl Thermal Eng 162, Article 114264 16. Al-Waeli AHA, Chaichan MT, Kazem HA, Sopiana K (2017) Comparative study to use nano(Al2 O3 , CuO, and SiC) with water to enhance photovoltaic thermal PV/T collectors. Energy Convers Manag 148:963–973 17. Sardarabadi M, Passandideh-Fard M, Heris SZ (2014) Experimental investigation of the effects of silica/water nanofluid on PV/T (phot. thermal units). Energy 66:264–272 18. Gagliano A, Tina GM, Nocera F, Grasso AD, Aneli S (2019) Description and performance analysis of a flexible photovoltaic/thermal (PV/T) solar system. Renew Energy 137:144–156 19. El Fouas C, Hajji B, Gagliano A, Tina GM, Aneli S Numerical model and experimental validation of the electrical and thermal performances of a pilot PV/T plant. Energy Conversion and Management (in press) 20. Avsec J, Oblak M (2007) The calculation of thermal conductivity, viscosity and thermodynamic properties for nanofluidson the basis of statistical nanomechanics. Int J Heat Mass Transf 50:4331–4341 21. Huang BJ, Lin TH, Hung WC, Sun FS (2001) Performance evaluation of solar photovoltaic/thermal systems. Sol Energy 70:443–448 22. Chow TT, Pei G, Fong KF, Lin Z, Chan ALS, Ji J (2009) Energy and exergy analysis of PV/T collector with and without glass cover. Appl Energy 86:310–316 23. Furbo L, Shah J (1996) Optimum solar collector fluid flow rates, EuroSun 1996 - Freiburg, Germany

Heat Transfer and Entropy Generation for Natural Convection in a Cavity with Inner Obstacles Jamal Baliti, Mohamed Hssikou, Youssef Elguennouni, Ahmed Moussaoui, and Mohammed Alaoui

Abstract Heat transfer and entropy generation for natural convection of a fluid inside a square cavity with inner adiabatic bodies, has been studied numerically with finite difference method. Calculations have been made for Rayleigh numbers Ra = 103 , 104 and 5.104 for two obstacles with a constant height h = 1/4. Results are presented as local Nusselt number, streamlines and isotherm contours. The results reveal the effects of relevant parameters on thermal fields, the fluid flow and heat transfer in the cavity. The outcome shows that the heat transfer rates generally rise with the increase of Rayleigh number. The entropy generation is higher at locations with large temperature gradients. Keywords Natural convection · Entropy generation · Cavity · Obstacle

1 Introduction Natural convection heat transfer phenomena in the cavities inner continue to be the matter of many studies over the last years. It has large applications in different industrial processes and environmental situations in a lot of engineering fields such like double-glazed windows, buildings ventilation using radiators, cooling of electronic equipment, thermal storages, solar collectors and drying technologies [1]. A lot of reviews are present in literature [2–4]. The view is persistently focusing in engineering methods to ameliorate the overall heat transfer efficiency in many applications by production of a lot of technics, from design optimization stade to the use of new materials one [5]. Furthermore, it is obvious that the reliability of thermal systems will be influenced by fluid flow and heat transfer irreversibility. In the purpose to optimize J. Baliti (B) Sultan Moulay Slimane University, Beni Mellal, Morocco e-mail: [email protected] M. Hssikou Ibn Zohr university, Agadir, Morocco Y. Elguennouni · A. Moussaoui · M. Alaoui Moulay Ismail University, Meknes, 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/10.1007/978-981-15-6259-4_78

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the system efficiency, the systems irreversibility should be minimized and controlled [6]. Many optimization methods allowing to achieve this goal are available, one of them is the entropy generation minimization [6, 9]. In this work the problem of natural convection heat transfer, fluid flow and heat generation in a square enclosure filled with air and equipped by two thin adiabatic bodies at its inside having a fixed temperature drop between the vertical walls, is studied using finite difference method. Results are presented as streamlines, isotherms, entropy rate contours and local Nusselt number plots for different Rayleigh numbers.

2 Description of the Physical Model A viscous fluid is confined inside a square enclosure having a length W and a heated in the left wall. The heated plate is taken to TH , and the right one is at to the environmental temperature TC . While, the two other surfaces are insulated. Two adiabatic obstacles, characterised by the height h and distant from the left plate by L, are fixed at the horizontal walls inside the enclosure (Fig. 1a).

3 Mathematical Modeling The governing equations expressing conservation of mass, momentum and energy are written in non-dimensional form as [7, 10]:

u

∂u ∂v + = 0, ∂x ∂y

(1)

  2 ∂u ∂u ∂p ∂ u ∂ 2u , +v =− + Pr + ∂x ∂y ∂x ∂x2 ∂ y2

(2)

Fig. 1 a Geometry and prescribed plate temperatures for the cavity, b Code verification with the results of a) Oztop et al. [8] and c Nusselt number as grid size function.

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  2 ∂v ∂p ∂ v ∂v ∂ 2v +v =− + Pr u + 2 + Pr Raθ, ∂x ∂y ∂y ∂x2 ∂y u

∂θ ∂θ +v = ∂x ∂y



 ∂ 2θ ∂ 2θ , + ∂x2 ∂ y2

(3)

(4)

where u, v are the two velocity components in the x and y directions, θ and p represents the temperature and pressure of the flow and Ra and Pr are respectively the Rayleigh and Prandlt numbers. Heat transfer rate across the enclosure walls was calculated by wall surface Nusselt number (N u). The local Nusselt number along the upper wall of the enclosure is given by [11]: N u loc

 ∂θ  =−  . ∂ x x=0

(5)

Some boundary conditions must be set in the objective to solve Eqs (1)–(4). Noslip boundary condition on the walls is envisaged, i.e. u = v = 0 on the four plates of ∂θ the enclosure and on the obstacles. Thermal boundary conditions are as: on the ∂y ∂θ horizontal walls, on the obstacles, θ = 1 on the left wall and θ = 0 on the right ∂x wall. The entropy generation, N S , for fluid flow is written in its dimensionless form as [6, 9]: (6) N S = W/Ω 2 S/k.  2  2   2   uθ ∂θ ∂θ ∂ u + + + +Φ 4 Where Ω = (TH − TC )/TC and S = ∂x ∂y ∂ x∂ y ∂y 2  vθ is the dimensional entropy generation with Φ is the irreversibility distribution ∂x ratio.

4 Numerical Details The governing equations are solved numerically by finite difference methods by use of the Gauss-Seidel technique. The governing equations are discretized by applying second order accurate central difference schemes and the discretized equations obtained were solved iteratively. In the objective of the validation of the used numerical method, the averaged Nusselt number results are compared with the data of Oztop et al. [8] for a gaz in a square enclosure with a heated obstacle inside. Its top and bottom walls are insulated, while the two others are taken at the environmental temperature. Good agreement is observed from this comparison (Fig. 1b).

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The relative change in the averaged Nusselt number from the upper surface for various mesh sizes is shown in Fig. 1c. The maximum deviation observed in terms of Nu is in the order of 0.23%, when the grid of 121 × 121 is considered. This justifies that the selected grid size of 121 × 121 was satisfactory.

5 Results Figure 2 depicts the isotherms and streamlines for the height h = 1/4 of the bodies positioned at the centre of the two horizontal plates, L = 1/2, for different Rayleigh numbers; Ra = 103 , 104 and 5.104 . The initial increase and decrease of air temperature near the heated plate is driven by conduction mechanism of heat transfer. Afterwards, due to the temperature difference apparition inside the enclosure, the heat transfer mechanism is governed by the natural convection that produces flow movement. Only one circulation is established in the left part of the cavity. Whereas, three other vortices are remarked in right cavity part; two near the top body and one abut the down obstacle. As Rayleigh number rises, the two, top and down, cells become bigger and the medium one becomes more and more weaker. For big Rayleigh number, Ra = 5.104 , the convection mechanism effect is very clear, where a forth eddy is produced near the obstacle of the bottom. The temperature next the obstacles is smaller than the fluid temperature in the right half of the cavity, which conducts the heat transfer to flow from the bulk air to the obstacles inside the cavity. While, at the left part of the enclosure, the heat transfer is directed inversely; i.e from the inside plates to the air. The Rayleigh number increase affects ennormely the heat penetration inside the cavity left part from the aperture produced between the two obstacles. Changes of local Nusselt number abut the left plate for different Rayleigh numbers Ra = 103 , Ra = 104 and Ra = 5.104 are dipected in Fig. 3a. By the Ra number increase, the fluid in the left-upper region of the cavity is heated significantly, which in consequence intensifies the fluid convection. The cold air, in the upper region of

Fig. 2 Streamlines and isotherms for h = 1/4, L = 1/2 and a Ra = 103 , b Ra = 104 , c Ra = 5.104

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Fig. 3 a Local Nusselt number for different Rayleigh numbers and entropy generation rate for b Ra = 103 and c Ra = 5.104 .

the left wall, which is brought by the recirculation eddies from the downward part of the cavity is heated up and consequently therefore the local Nusselt number becomes higher. The heated air from the left-upper part of the wall achieves the left cavity corners, mislaying heat to the inside cold air, and thereby the local Nusselt number becomes lower in these regions. As the inside bodies height increases, the heat is deprived to penetrate inside the cavity right side which improves the local Nusselt number [10]. The entropy generation rate contours inside a cavity with two adiabatic obstacles, for height h = 1/4 at Ra = 103 and Ra = 5.104 are plotted in Fig. 3bc. it should be pointed out that, by the Ra number increase a powerful mixing is created and hence there are significant temperature gradients at the top left corner and a high decrease in its gradient near the right side of the cavity. That is due essentially to the changes in the entropy generation rate (Fig. 3bc). It was established that, the active regions of entropy generation are sites in the left side of the cavity specifically those close to the adiabatic obstacles, due to the left heated plate considered as the heat source creating there highest flow movement and heat flux. The highest entropy generation rates are observed at locations where there is large temperature gradients. This is owing to heat transfer irreversibility because high heat transfer is created at these sites.

6 Conclusion This study shows that for the used height of the inner bodies, the Ra number increase creates two cells inside the main circulation and develops other vortices in the enclosure right part. Furthermore, The entropy generation, due to heat transfer irreversibility, is observed at locations where there is large temperature gradients.

References 1. Laguerre O, Benamara S, Remy D, Flick D (2009) Experimental and numerical study of heat and moisture transfers by natural convection in a cavity filled with solid obstacles. Int J Heat Mass Transf 52:5691–5700

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2. Bejan A (2005) Convection heat transfer, 3rd edn. Wiley, New York 3. Khramtsou PP, Martyneuko OG (2005) Free convection heat transfer. Springer, Heidelberg 4. Khalifa AJN (2001) Natural convective heat transfer coefficient-a review: II. Surfaces in twoand three-dimensional enclosures. Energy Convers Manag 42(4):505–517 5. Khan TZ, Parvin S (2019) Effects of Lewis number on two phase natural convection flow of nanofluid inside a square cavity with an adiabatic obstacle. In: AIP Conference Proceedings, vol. 2121. AIP Publishing (2019) 6. Rahimi A, Saee AD, Kasaeipoor A, Malekshah EH (2019) A comprehensive review on natural convection flow and heat transfer: "the most practical geometries for engineering applications". Int J Numer Methods Heat Fluid Flow 29(3):834–877 7. Bhave P, Narasimhan A, Rees DAS (2006) Natural convection heat transfer enhancement using adiabatic block: optimal block size and Prandtl number effect. Int J Heat Mass Transf 49(21– 22):3807–3818 8. Oztop HF, Dagtekin I, Bahloul A (2004) Comparison of position of a heated thin plate located in a cavity for natural convection. Int Commun Heat Mass Transfer 31(1):121–132 9. Famouri M, Hooman K (2008) Entropy generation for natural convection by heated partitions in a cavity. Int Commun Heat Mass Transfer 35(4):492–502 10. Baliti J, Hssikou M, Elguennouni Y, Moussaoui A, Alaoui M Heat transfer and entropy generation for natural convection by adiabatic obstacles inside a cavity heated in the left, submitted 11. Sheikholeslami M, Jafaryar M, Shafee A, Li Z, Haq RU (2019) Heat transfer of nanoparticles employing innovative turbulator considering entropy generation. Int J Heat Mass Transf 136:1233–1240

Behavior Study of a New Inverter Topology for Photovoltaic Applications Y. Amari, S. Labdai, M. Hasni, A. Rabhi, B. Hajji, and A. Mellit

Abstract In this paper, a photovoltaic (PV) system, with maximum power point tracking (MPPT) connected to a differential inverter is presented. The main characteristic of the used inverter is its potential ability to adapt to the permanent variation of the incident solar irradiation intensity that consequently imposes variable electrical quantities at the output of the PV panels, whereas our devices function with fixed electrical quantities. This set of related algorithms will identify the suitable duty ratio in which the DC/DC converter should be operated to maximize the power output, and suitable duty ratio in which the DC/DC/AC converter works according to the reference output signal. Results showed that this inverter is able to reach the output voltage reference despite the input voltage variation. Keywords Dynamic PV system · DC/DC/AC inverter · P&O with inverter

Y. Amari (B) · M. Hasni LSEI Laboratory, University of Sciences and Technology, H. Boumediene, BP.32 El- Alia16111, Algiers, Algeria e-mail: [email protected] S. Labdai Process Control Laboratory, National Polytechnic School of Algiers, El Harrach, Algeria A. Rabhi MIS Laboratory, University of Picardie Jules Verne, 3 Rue Saint Leu, Cedex 1 80039 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 A. Mellit RE Laboratory - University of Jijel – Mohamed Sedik Ben Yahya University Ouled AISSA, BP 98 -18000 Jijel, Algeria AS-ICTP, Trieste, Italy © 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/10.1007/978-981-15-6259-4_79

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1 Introduction Generating electricity with minimum environmental impact is the most important reason why PV is getting all this interest due to the environmental and economic benefits. Permanent variation of weather conditions affects the output power of photovoltaic systems significantly. Therefore, extracting maximum power from photovoltaic systems for major part of research activity in this field. Several maximum power point tracking (MPPT) algorithms for photovoltaic systems have been developed [1]. An algorithm called perturbation & observation (P&O) method identifies the maximum power point in the power–voltage graph or current–voltage graph. The P&O method is one of the well-known MPPT algorithm, and the most commonly used be- cause of its simplicity, it uses simple feedback and a few number of measured parameters to adjust the operating point. Despite its importance, this method still has many problems like the oscillations of the operating point and the divergence during fast changing weather conditions [2, 3] (Fig. 1). A new topology of boost inverter was recently proposed in [4], it will be used in the present study and it is the main contribution in this work. This inverter has a very big potential in terms of conversion quality and it responses to a permanently variable environment. The function of the used converter system is to keep the AC output voltage at the specified level in spite of the variation of the DC voltage with variation of solar irradiance G (W/m2 ) and ambient temperature T (°K). Each power electronic models represent subsystems within the simulation environment. These blocks have been developed so they can be interconnected in a consistent and simple manner for the construction of complex systems. The subsystems are masked, meaning that the user interface displays only the complete subsystem, and user prompts gather parameters for the entire subsystem. This paper is organized as follows: in the next section, we will present the structure of the photovoltaic system using in this study. In the third section, the new inverter topology is presented. This inverter imposes the way in which the whole system must be controlled. The simulation results are presented and discussed in the last section.

Fig. 1 Elements that constitutes an MPPT system

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2 Description of the Proposed System The proposed system is shown in Fig. 2. It consists of an integral PV system that contains a new topology of PV inverter [4] based on anti-parallel synchronization of the functioning of tow boost-stages where each feed the load in the opposite direction with regard to the other, this inverter is linked to DC/DC converter on each side “boost- stage”. The DC/DC converter is controlled by P&O algorithm that generates PWM signal to extract MPP from PV panels on each side. The reason of this combination are: • Dividing the photovoltaic array for better MPP tracking [5]. • The use of this inverter is because of its large domain of output voltage variation, in such a way the output voltage will be stable despite the permanent variation of input voltage due to the permanent variation of MPP tracked by P&O algorithm. This new topology of inverter is designed to absorb the permanent voltage variation that comes from the DC-DC converter controlled by MPPT algorithm in each side, as it will be explained.

2.1 Description of the Used Inverter A switched-mode power converter (SMPC) type boost inverter that insures the boosting and the rippling of input voltage in a harmonic way and gives a good quality of output quantities. This topology as shown in Fig. 3 is based on the complete electrical insulation between boost stages during the operation in such a way, every half-switch- ing period; the load forms a closed-circuit with one of the boost stages to appear like a simple DC-DC converter. The inverter function is the simple result of the periodic shifting of the boost that is connected to the load. The inverter function is ensured by alternating the load-connection to boost stages periodically using the following combination:

Fig. 2 Photovoltaic system that uses DC/DC/AC differential inverter

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Fig. 3 Electrical circuit of the DC-DC-AC converter



   K 1 , K 1 , K 2 = K 1, K 2 , K 2 

The output voltage of the inverter for each switching period is the average value V:  V  = VDC

2α − 1 α − α2

 (1)

This equation represents the output voltage of each commutation period. This equation is showing us an excellent property: it naturally generates an output voltage lower or larger than the input DC voltage, depending on the duty cycle.

2.2 MPPT-P&O Algorithm and Used to Feed Inverter The PV array produces electric energy under variable conditions of solar radiation and environmental temperature. This causes a permanent variation of maximum power point. Figure 4 shows used simulation of PV system environment with which our inverter is designed to interact. The purpose of this inverter topology is to respond quickly to micro-voltage fluctuations, which result from the permanent variation of environmental conditions.

2.3 Controller Design of the Inverter The controller design presented in Fig. 5 was used to regulate the output voltage by actively regulating the duty cycle of the generated PWM. The converter is supposed to generate output voltage with a sinusoidal shape. The PWM control algorithm was therefore designed to use the Eqs. (1) and (2) that appears in the mathematical expression of the open-loop gain and the value of duty cycle that corresponds each voltage level. The PI controller is used in the voltage control loop. The output of the controller

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Fig. 4 PV-system environment under the influence of variable parameters-(b) MPP Ttype P&O under variable environmental conditions- output voltage variation of DC-DC converter drived using P&O algorithm

Fig. 5 Proposed control structure for the proposed inverter system

is the duty cycle for the modulator. α =

√ (k − 2) + k 2 + 4 2k

(2)

out Where k = VVDC The modulator then operates in bipolar mode. That means each switching period generatesavoltagelevelaccordingtocomparatoroutput-voltageandreference-voltage. In the explanation, we can see that there is one PWM single to drive this converter but for practical reasons two PWM signals are required.

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3 Results The principle characteristic to take into account is that we have a permanent variable operational point unlike the DC-DC boost converter that works around a particular operation point. The PI controller is used to regulate the output voltage and follow the reference signal despite the permanent variation of input voltage and current, the simulation model is represented in Fig. 6. The converter used in the block named “converter” is a simulation model of the circuit that is shown in Fig. 3 (Table 1). The simulation results are presented in Fig. 7. The closed loop control model reaches the reference voltage evolution and as this reference vary with time and we have a non- linear system, the variation of input voltage that is caused by variation in environmental conditions destabilize the conversion system for a period. In Fig. 8 and according the functioning of our inverter in PV environment the variation of DC voltage under the effect of MPPT P&O algorithm is presented in Fig. 8a this permanent variation imposed a permanent variation of the rage of variation of duty cycle of the inverter in order to keep output voltage stable despite this permanent variation. Figure 8b presents this variation of duty cycle.

Fig. 6 Simulink model and functioning protocol of the DC/DC/AC converter in a closed-loop and PI controller in PV environment

Behavior Study of a New Inverter Topology for Photovoltaic … Table 1 Simulation parameters

Parameters

759 Values

Input DC voltage

Variable

Switching frequency

15 kHz

Modulation index

0.83

Inductances L1 = L2

0.009H

Capacitor C

420 µF ∼ = 470 µF

RL load

150 W

Temperatrue

Variable

Incident power

Variable (700–900)

(a)

(b)

(c)

(d)

Fig. 7 a-b DC/DC/AC output voltage variation compared to the reference. c-d shows how the output voltage approaches to the reference

What can be seen from this figure is very important in a way that the voltage values corresponds to the field of variation of duty cycle, this correspondence is throw the Eqs. (1) and (2).

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80

1

70

0.9

60

0.8

50

0.7

(a)

0.6

V

D

40 30

0.5 0.4

20

0.3

10

0.2

0

0.1

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(b)

0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

time

1

0

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18

time

Fig. 8 a permanent variation of DC bus under the effect of P&O MPPT algorithm, b duty cycle variation of DC/DC/AC converter in order to output a fixed sinusoidal from a variable DC bus

4 Conclusion The proposed inverter was designed to work in a permanent variable environment in order to produce a fixed voltage. Simulation results showed that the system is able to keep the output voltage stable despite the permanent variation of the input. This was because of the topology and the adapted control system. It has been observed that a delay to reach the reference after when the system vary suddenly. Further works will focus on the experimental validation as well as making the system regain the reference more rapidly.

References 1. Tsengenes G, Adamidis G (2011) Investigation of the behavior of a three phase grid-connected photo voltaic system to control active and reactive power. Electr Power Syst Res 81(1):177–184 2. Wilamowski BM, Li X (2002) Fuzzy system based maximum power point tracking for PV system. In: IEEE 2002 28th Conference Industrial Electronics Society, pp 3280–3284(2002) 3. Zhang K, Cheng P, He L (2014) A maximum powerpoint tracking method—combined with constant voltage tracking & variable step-size perturb. Sens. Transducers 175, 150–155 4. Amari Y, Hasni M, Menaa M (2018) Comparison between existing transformer-less PV inverter topologies. In: Conference: 2018 International—Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), pp 1–6. IEEE 5. Adinolfi G, Graditi G, Siano P, Piccolo A (2015) Multiobjective optimal design of photo voltaic synchronous boost converters assessing efficiency, reliability, and cost savings. IEEE Trans Ind 11(5):1038–1048

Application of the Random Walk Particle Tracking for Convection-Diffusion Problem Within Strait of Gibraltar Hind Talbi, Mohammed Jeyar, Elmiloud Chaabelasri, and Najim Salhi

Abstract A depth-averaged Lagrangian particle tracking method is used to examine the process of solute transport. The Random-Walk particle tracking (RWPT) method is based on the use of stochastic methods to describe the dispersive component of particle displacement, and it is practically exempt from the numerical dispersion. Its performance is examined by comparing it with the Eulerian method. Firstly, the model is used to simulate the strait of Gibraltar. Unlike the Eulerian methods, the Lagrangian (or particulate) methods require no meshing. The model hydrodynamic used for describing the free surface flow in the strait of Gibraltar was based on the Saint-Venant bidimensional equation for large scale simulation. Keywords Adepth-averaged · Lagrangian particle tracking method · Solute transport · Strait of Gibraltar · Eulerian method

1 Introduction Over the past decade, the simulation of pollutant transport of the strait of Gibraltar has attracted much attention from many researchers. This paper focuses on the Lagrangian approach compared with the Eulerian approach to solve pollutant transport. The Strait of Gibraltar is bounded to the north by the Iberian Peninsula, to the south by the continent of Africa, to the west by the Atlantic Ocean and to the east by the Mediterranean Sea. The basic circulation in the Strait consists of an upper layer of cold fresh water from the Atlantic and an opposite deep current of warm salt water flowing from the Mediterranean [1, 2]. The system is about 60 km in length between Barbate-Tangier in the West and Gibraltar-Sebta in the East. Its width varies between a minimum of about 14 km from Tarifa to Punta Cires and H. Talbi (B) · M. Jeyar · N. Salhi LME Laboratory, Faculty of Sciences, University Mohammed I, 60000 Oujda, Morocco e-mail: [email protected] E. Chaabelasri MENA Team, LPTPME, Faculty of Sciences, University Mohammed I, 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/10.1007/978-981-15-6259-4_80

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a maximum of 44 km from Barbate to Tangier. Various numerical simulation has been applied for the strait of Gibraltar using Eulerian approach (FV), this approach retains mass and is easy to implement. Use a fixed mesh and the spatial derivatives are approximated by using the variables in the meshes or the neighboring nodes. The strait of Gibraltar is highly employed by oil transport and shipping, be on of regions polluted [3]. Nearly all of these authors address these problems, the study uses the depth-averaged shallow water equations in the formulation of their models [4, 5]. In [6] the author has studied an adaptive Eulerian approach for simulate dispersion of the strait. The Lagrangian random walk particle tracking method (RWPT). In recent years, it has attracted much attention from researchers to simulate conservative and reactive transport in porous media [7–10]. The RWPT method is popular for solving convection-dominated problems. This method is the first to address the quantified representation of Brownian motion that leads to a successful microscopic image of the scattering mechanism [11]. The RWPT method was run to simulate the dispersion of passive tracers [12, 13]. Therefore, the Lagrangian (RWPT) technique has been expanded for the numerical simulation of pollutants in coastal zones and oil [14, 15]. By contrast, the random walk particle tracking employed discrete massless particles as indicators to represent solute or pollutant clouds, and these particles are tracked independently inflows. The remainder of this paper is organized as follows. Section 2 gives a general description of the equation governed for the transport pollutant model. Section 3 outlines the numerical method. The numerical results and discussion are devoted to Sect. 4. Conclusions are summarized in Sect. 5.

2 Depth-Averaged Pollutant Transport Equations The non-linear Saint-Venant equations govern free surface flows in shallow water, this class of equation is deduced from Navier-Stokes equations derived from the depth-averaged, using some simplifying hypotheses. The conservative form of the two-dimensional depth-averaged shallow water equations is written: ⎧ ⎨ ∂t (h) + ∂x (hu) + ∂ y (hv) = 0 ∂ (hu) + ∂x (hu 2 + 0.5gh 2 ) + ∂ y (huv) = −gh(Z ) ⎩ t ∂t (hv) + ∂x (huv) + ∂ y (hv 2 + 0.5gh 2 ) = −gh(Z )

(1)

Where h is the water depth, u and v are the depth-averaged velocities in the x and y directions respectively, g is the gravity constant. Z is the bed elevation above a fixed horizontal datum. The depth-averaged equation for the transport of pollutant is written in standard advection-diffusion: ∂t (hC) + ∂x (huC) + ∂ y (hvC) = D(∂x (h∂x C)) + (∂ y (h∂ y C)) Where C is pollutant concentration and D are pollutant diffusion coefficient.

(2)

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Fig. 1 Examples of initial randomly particles distribution inside a triangles

3 Numerical Methods 3.1 The Eulerian Hydrodynamic Finite Volume Method A two-dimensional finite volume model has been used to model the coupled hydrodynamic and transport diffusion of pollutants within the strait of Gibraltar. The model is based on solving the above depth-averaged shallow water equations coupled with the convection-diffusion equations. Incorporates upwind numerical fluxes and slope limiters in a Godunov-type finite volume scheme to provide a sharp resolution of steep bathymetric gradients that may develop in the approximate solution. The classical Eulerian numerical method is simple conserves mass and is easy to implement. For a more detailed description, the reader is referred to [16–19].

3.2 The Lagrangian Method Based of the RWPT In this work, the random walk particle method is employed to solve the convectiondiffusion processes as governed by the advection-diffusion Eq. (2). The RWPT is a statistical physics method based on tracking the motion of a particle cloud. The movement of the particle is expressed by the Itô-Taylor integration scheme [20] and is written in its traditional form given by: √ X p (t + t) = X p (t) + A(X p , t)t + B(X p , t).Z n t

(3)

X p (t) is the position of a particle at time t, A is a vector representing the convective velocity of the displacement of the particle, B is a tensor defining the direction and amplitude of the dispersive velocity, which is related to the dispersion tensor D.

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t is the time step, Z n is a vector having independent random components with zero mean value and unit standard deviation. Initial Random Points Distribution According to the concentration inside a selected triangle, i the particles are picking randomly. The particles number is calculated using the following formula: N p (i ) = N T

C(i )V (i )  Nele C T i=1 θ (i)V (i )

(4)

 Nele Where N T = i=1 N p (i ) is the total number of generated particles, C T =  Nele C( ) if the corresponding total concentration,V (i ) is the area of the selected i i=1 triangle and θ (i) is an indicator function defined as:  θ (i) =

1 i f C(i ) = 0 0 other wise

(5)

If a triangle i is allowed to contains particles, a set of N p (i ) particles are distributed randomly according to the following formula, that gives the position coordinates of each particle 2 2 x p (t = 0) = (xi (G) + dwx max dk , yi (G) + dw y max dk ) k=1,3 k=1,3 3 3

(6)

Where dk and (xi (G), yi (G)) are the medians length and the barycenter coordinates of the triangle i .

4 Numerical Results The strait of Gibraltar is an arm of the sea dividing Europe from Africa, with just 13 km that separate the two continents, this is the point of the crossing point in the Mediterranean. It lies between Spain and Morocco. This test, which focuses on the RWPT method to simulate the transport of pollutants in the strait of Gibraltar, shows the capacity of RWPT to treat irregular topography and complex geometry, compared to the Eulerian method. Equation (2) has been applied to simulate the flow within the strait and the Eq. (3) for simulating the transport of set of pollutant particles. We suppose here that the pollutant flows with a constant diffusion coefficient of Dx = Dy = 0.001 m2 /s. The Manning coefficient is set to n = 0.001 s/m1/3 . The bathymetry and mesh used in our calculations are shown in Fig. 2. Generally, the bathymetry can lead to major numerical difficulties that require careful treatment. Finally, a sequence of the pollutant concentration snapshots across the domain is calculated by the Eulerian method (VF) and the RWPT. The found results are presented in Fig. 3.

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Fig. 2 Location, bathymetries and mesh of strait of Gibraltar

Fig. 3 Pollutant concentration simulate by the Eulerian method (FV) (top row) and RWPT (bottom row) at different simulation times, from left to right t = 0.2,7.9 and 12 h

As plotted, the pollutant is propagated over the area by following the flow until the outlet of the computational zone. The maximum concentration, calculated by the Eulerian method, decreases and becomes less concentrated over time. Contrary to the concentration of particle clouds simulated with RWPT, which remains almost the same. It is intuitively obvious from the comparison of the two approaches that the Lagrangian approach keeps the concentration almost constant and over-comes spontaneously the numerical diffusion that affects the Eulerian methods.

5 Conclusion The Lagrangian method based on the RWPT is employed to solve a depth-averaged shallow water equation coupled with the advection-diffusion equation, for simulating pollutant transport of strain in Gibraltar. The RWPT has shown its capacity and performance to simulate a problem with irregular geometry with complex bathymetry.

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References 1. Gonzàlez M, Garcia MA, Espino M, Sàanchez-Arcilla A (1995) Un Modelo Numérico en Elementos Finitos para la Corriente Inducidapor la Marea. Aplicaciones al Estrecho de Gibraltar. Revista Internacional de Métodos Numéricos para Càlculo y Disenèo en Ingenieria., 11(3):383–400 2. Lafuente JG, Almazan JL, Castillejo F, Kharibeche A, Hakimi A (1990) Sea level in the Strait of Gibraltar: Tides. Int Hydrogr Rev Monaco LXVII(1):111–130 3. Gómez F (2003) The role of the exchanges through the Starit of Gibraltar on the budget of elements in Western Mediterranean Sea: consequences of Humain-induced modifications. Mar Pollut Bull 46:685–694 4. Tejedor L, Izquierdo A, Kagan BA, Sein DV (1999) Simulation of the semidiurnal tides in the Strait of Gibraltar. Journal of Geophysical Research, 104(C6):13, 541–13, 557 5. González M, Seaïd M (2005) Finite element modified method of characteristics for shallow water flows: application to the Strait of Gibraltar. Math Ind 8:518–522 6. Benkhaldoun F, Elmahi I, Seaid M (2009) Application of mesh-adaptation for pollutant transport by water flow. Math Comput Simul 79:3415–3423 7. Tompson AFB, Gelhar LW (1990) Numerical simulation of solute transport in threedimensional randomly heterogeneous porous media. Water Resour Res 26(10):2451–2562 8. Tompson AFB, Vomoris EG, Gelhar LW (1987) Numerical simulation of solute transport in randomly heterogeneous porous media: motivation, model development, and application. United Stetes: N.P. (1987). Web 9. LaBolle EM, Quastel J, Fogg GE (1998) Diffusion theory for transport in porous media: transition probability densities of diffusion processes corresponding to advection-dispersion equations. Water Resour 34(7):1685–1693 10. LaBolle EM, Quastel J, Fogg GE, Gravner J (2002) Diffusion process in composite porous media and their numerical integration by random walks: generalized stochastic differential equations with discontinuous coefficients. Water Resour Res 36(3):651–662 11. Scher H, Margolin G, Berkowitz B (2002) Towards a unified framework for anomalous transport in heterogeneous media. Chem Phys 284:349–359 12. Harms IH, Karcher MJ, Dethleff D (2000) Modelling Siberian river runoff—implications for contaminant transport in the Artic Ocean. J Mar Syst 27:95–115 13. Gomez-Gesteira M, Montero P, Prego R, Taboada JJ, Leitao P, Ruiz-Villareal M, Neves R, Perez-Villar V (1999) A two-dimensional particle-tracking model for pollution dispersion in A Coruna and Vigo Rias (NW Spain). OceanologicActa 22(2):167–177 14. Proctor R, Flather RA, Elliott AJ (1994) Modelling tides and surface drift in the Arabian Gulf: application to the Gulf oil spill. Cont Shelf Res 14(5):531–545 15. Ml Spaulding (2017) State of the art review and future directions in oil spill modeling. Mar Pollut Bull 115(1-2):7–9 16. Chaabelasri EM, Borthwick AGL, Salhi N, Elmahi I (2014) Balanced adaptive simulation of pollutant transport in bay of tangier (morocco). World J Model Simul 10(1):3–19 17. Chaabelasri EM, Amahmouj A, Jeyar M, Borthwick AGL, Salhi N, Elmahi I (2014) Numerical survey of contaminant transport and self-cleansing of water in Nador lagoon Morocco. Model Simul. Eng 4:1–8 18. Amahmouj A, Chaabelasri E, Salhi N (2012) Computations of pollutant dispersion in coastal waters of Tangier’s bay. Int Rev Model Simul 5(4):1588–1595 19. Chaabelasri EM, Salhi N, Elmahi I, Benkhaldoun F (2010) High order well balanced scheme for treatment of transcritical flow with topography on adaptive triangular mesh. Phys Chem News 53:119–128 20. Salamon P, Fernàndez-Garcia D, Gomez-Hernàndez JJ (2006) A review and numerical assessment of the random walk particle tracking method. J Contam Hydrol 87:227–305

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, and Smail Zouggar

Abstract In this paper, we will study the impact of the tilt angle of the photovoltaic panels PVP on the sizing of different photovoltaic energy systems. Two structures are studied in this paper, with a comparison of sizing results with and without a seasonal adjustment of the tilt angle. the first system is a PV/Battery and the second is a PV/Storage Tank for pumping water system, with a Well-detailed modeling of the solar radiation profile, containing the three components, the direct, diffuse and reflected, and a determination of the optimal tilt angle for each month of the year, are presented in this paper. For sizing, we are based on the Electric System Cascade Analysis, taking into account the climatic data of the site, and the technical and economic data of the system components. Keywords Photovoltaic panel · Tilt angle · Sizing system · Optimal configuration

1 Introduction Photovoltaic energy has become increasingly an optimal solution to the problem of high energy demands, caused by the increase in population, industry, and technology, because it is always renewable and accessible to everyone. And not only that, but it is also a clean energy, making it the new source of energy with no environmentally harmful residues and reducing emissions of gaseous pollutants, in accordance with international resolutions and laws in this regard. Like other sources of energy, a series of studies on photovoltaic energy has been conducted in the literature, to determine the factors that influence its productivity, in order to obtain the maximum energy and increase performance. As the tilt angle is an important factor, which influences the amount of solar radiation that falls on the surface of photovoltaic panels, and therefore their productivity. It has received M. Chennaif (B) · M. L. Elhafyani · H. Zahboune · S. Zouggar Laboratory of Electrical Engineering and Maintenance – LEEM, High School of Technology, University Mohammed 1st, Oujda, Morocco e-mail: [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/10.1007/978-981-15-6259-4_81

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its share of scientific interest. An estimation of the optimal tilt angle of the photovoltaic panels and a comparison with that of NASA were made by Kaddoura et al. [1]. Conceiçao et al. [2] have presented a study on the optimal tilt angle of taking into account the soiling, focusing on the maximization of energy production and the effect of soiling in the absence of a cleaning program. Ramli et al. [3] have developed a newly optimization algorithm called the vortex search algorithm is used to estimate the solar radiation on the tilted surface. The HDKR model (Hay, Davies, Klucher, Reindl) is analyzed to determine the solar radiation profile. The cell temperature of a photovoltaic panel is examined under standard test conditions (STC). The mathematical model of the optimal tilt angle is optimized by combining the correlation between dust density and tilt angle. A new model of the optimum tilt angle is proposed for a fixed photovoltaic generators Xu et al. [4]. Salim et al. [5] have come to the conclusion that the tilt angle can be calculated as (latitude ±15°). Asad Ullah et al. [6] have shown that four tilt angle adjustments per year allowed a ~6,6% increase in annual photovoltaic panel output at Lahore. In this paper, we will study the impact of tilt angle on the sizing results of autonomous photovoltaic systems, comparing these results without and with 4 tilt angle adjustments per year. The sizing is done by the Electric System Cascade Analysis. The ESCA is used in several studies by Zahboune et al. [7, 8] to obtain the optimal sizing of electrical systems based on renewable energy sources. This paper is organized according to the following structure: In the second section, a model of solar radiation is presented with its three components, direct, diffuse and reflected. The relationship between solar radiation and the tilt angle is discussed in Sect. 3. In Sect. 4, the sizing methodology is presented. In the fifth section, we presented a comparison between the sizing results of PV systems with and without adjustment of the tilt angle using meteorological data from the city of Oujda. Finally, the conclusions are presented in Sect. 6.

2 Solar System Model Since the amount of solar radiation that reaches the PV panel depends on various factors, it cannot be considered constant. After reaching the earth surface, the solar radiation that reaches the collector surface consists of three components (Fig. 1): RB , which is the direct beam component falling directly on the panel, RD is the diffused beam component falling at some angle on the collector area and RR is the reflected beam component falling on the collector surface after reflecting from the ground. IG (t) = IB (t) + ID (t) + IR (t)

(1)

• Direct beam radiation IB IB = (Hg − Hd) · Rb

(2)

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Fig. 1 Total solar radiations received by the PV generator

where Hg is the global radiation on a horizontal surface; Hd is the diffuse radiation on a horizontal surface; Rb is the beam radiation ratio between titled and horizontal surfaces [1]. For the northern hemisphere, Rb is defined as: Rb =

cos(ϕ − β) cos(δ) ∗ sinωss + ωss sin(ϕ − β)sinδ cos ϕ cosδ sinωss + ωss sin∅∗ sinδ

(3)

where ϕ is the latitude of the location; β is the tilt angle; δ is the azimuth angle; ∅∗ is the solar beam’s angle of incidence and ωss is the sunset angle for the titled surface. • Diffuse radiation ID It can be calculated as: ID = Hd · Rd

(4)

As suggested by Liu and Jordan [9], the diffuse radiation ration Rd can be calculated by: Rd =

1 + cosβ 2

(5)

• Reflected radiation IR It could be estimated as [9]: IR = Hg ρ where ρ is the ground albedo.

1 − cosβ 2

(6)

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3 Optimal Tilt Angle The ideal tilt angle of PV panel is the angle at which the panel must be tilted to capture as much solar radiation as possible throughout the year (Fig. 2), it is expressed by: β = 90 − β N

(7)

where βN is the Altitude angle of the sun at noon. Table 1 shows the variation of the altitude angle of the sun at noon and the tilt angle of the photovoltaic panels for each month of the year for the city of Oujda in northern Morocco (latitude: 34° 41 ; 1ongitude 1° 54 ). Based on the previous data, we will propose in Table 2 an optimal adjustment for the tilt angle of photovoltaic panels for each season of the year to maximize the amount of solar radiation captured by the PVP.

Fig. 2 The tilt angle of the PV generator

Table 1 Optimal tilt angles of each month of the year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Altitude angle at Noon ° 34

43

53

65

74

78

77

69

58

46

37

32

Optimal tilt angle °

47

37

25

16

12

13

21

32

44

53

58

56

Table 2 The proposed adjustement for the tilt angle of the PV panels Tilt Angle (4 adjustment per year)

Autumn

Winter

Spring

Summer

43°

53.5°

26°

15.5°

The Impact of the Tilt Angle …

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β=0° β=Latude-15° β 4 Adjs per year

β=Latude+15° β=90°

opmal for year (38°) β Adjusted thought year

8

Solar irradiaon

7 6 5 4 3 2 1 0

1

2

3

4

5

6

7

8

9

10

11

12

Month Fig. 3 Solar irradiation for different tilt angles

Table 3 The percentage of variation of solar irradiation for each tilt angle Horizontal 0°

Latitude + 15°

Optimal for the year (38°)

Latitude − 15°

Vertical 90°

Adjusted thought the year

4 Adjs

85,91%

90,10%

93,78%

92,07%

58,52%

100%

96,03%

In Fig. 3, we will present a comparative curve of the monthly solar irradiation for the different planes: horizontal angle 0°, vertical angle 90°, angle (Latitude + 15°) and (Latitude −15°), the ideal angle for the whole year 38°, seasonally adjusted angle (4 times a year) and for an angle adjusted thought the year. We can see clearly that the adjusted angle every month of the year gives us the best solar irradiance capture, it is immediately followed by the seasonally adjusted angle with a small difference, after them comes the ideal angle fixed for all the year, but comparing with the other tilt angles, we see a considerable difference, for example angles 0° and (Latitude −15°) give us a good solar radiation capture for summer and angles (Latitude +15°) and 90 for the winter, but a male catch for the other seasons. Table 3 summarizes these results by taking as a reference the solar irradiation captured by the tilt angle adjusted each month of the year (100%).

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4 Sizing Methodology In the sizing of photovoltaic systems, we used the algorithm of the Electric System Cascade Analysis method. The method is a tool that takes the Power Pinch Analysis as a guideline technique for the design and the optimization of the PV systems, to meet a specified load demand [7, 8] (Fig. 4). For the algorithm remain implemented correctly, we must respect certain conditions, which are: • Load, temperature, and solar radiation remain constant for each time step (1 h in our study). • Supplied the load before storing. • All Losses during load supply and storage periods should be considered. • Amount of accumulation in the storage units should always be positive. Start Data ExtracƟon & IniƟalisaƟon[11] & Tilt angle adjustement t=0 t = t+1 Calculate Epv(t)=npv*Apv*Ig(t) PV power – Load demand >0 Charging the storage units

Calculate the new PV panels number

Discharging from the storage units

Calculate the accumulaƟon in the storage units Qac (t) t=T Qac (t) < 0 Minimum

Qac (t) < 0

ExtarcƟon of Storage Units size & Number of PV panels End Fig. 4 The ESCA algorithm

Change the iniƟal accumulaƟon in the storage units Q0

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• Amount of storage at the starting and at the end of the time period (T) should almost remain approximately the same. The data needed for the ESCA method are: • • • • • • • •

Period of analysis. Hourly Load. Hourly Solar irradiation. Type of the PV panel. Power of a single solar PV module. Solar PV module efficiency. Inverters efficiency. Storage unit charging and discharging efficiency.

5 The Impact of the Tilt Angle on the Sizing of PV Systems In this part, we will present the impact of the tilt angle on the amount of solar irradiation reaching the PVP and on the size of the components of two different autonomous photovoltaic systems. The first system consists of PV panels and batteries and the second is a photovoltaic pumping system, composed of PV panels and a storage tank.

5.1 The Impact on the Amount of Solar Irradiation Reaching the PVP The Fig. 5 shows the difference between the solar irradiation arriving on the same photovoltaic panel for two different tilt angles, the first is the ideal angle fixed all year round for the city of Oujda (38°) and the second is the adjusted angle for the season that includes that day.

5.2 The Impact of the Tilt Angle on the ESCA Sizing Results of a PV/Battery System The seasonal adjustment of the tilt angle has led to increase the amount of solar irradiation reaching the photovoltaic panels, and therefore increasing the power produced by each panel, which explains the decrease in the number of PVPs NPV necessary to satisfy the same electrical load Fig. 6. We also see that the number of batteries NBAT has remained the same or decreased a little even if the number of PVPs has decreased clearly because their production has been increased (Table 4).

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Fig. 5 The impact of the tilt angle on the solar irradiation reaching the PVP 1200

Load

1000 800 600 400 0

1 31 61 91 121 151 181 211 241 271 301 331 361 391 421 451 481 511 541 571 601 631 661 691 721

200

Day

Fig. 6 The load profile Table 4 The impact of the tilt angle on the sizing of PV/Battery using ESCA method according to various Loss of Power Supply Probability (LPSP) values LPSP

NPV

NBAT

Fixed tilt angle

Proposed adjustment

Fixed tilt angle

Proposed adjustment

10%

27

20

2

1

5%

33

24

4

4

2%

39

29

9

8

0%

45

34

13

11

The Impact of the Tilt Angle …

775

5.3 The Impact of the Tilt Angle on the ESCA Sizing Results of a PV Pumping System As with the previous system, the optimal sizing configuration of the photovoltaic pumping system is modified thanks to the adjustment of the tilt angle, the considerable change is for the number of PVPs, as shown in the Fig. 7. There is a significant decrease in the number of PVPs for each value of LWSP (Loss of Water Supply Probability) and a small variation in the size of the storage tank, for example for LWSP = 0%, which includes the large variation of the Volume of ST, it went from 27974 to 28356 m3 (Fig. 8). Angle fix all the year

40

seasonal adjustment

Number of PV panels

35 30 25 20 15 10 5 0

0%

2%

4%

6%

8%

10%

Loss of Water Supply Probability

Fig. 7 The impact of the tilt angle on the capacity of the PV power according to the LWSP value

seasonal adjustment

Storage Tank size m3

Angle fix all the year

10%

7%

5%

2%

0%

LWSP %

Fig. 8 The impact of the tilt angle on the size of the storage tank according to different LWSP values for a PV pumping system

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6 Conclusion This paper studies the direct impact of the tilt angle on the amount of solar irradiation that reach the photovoltaic panels which implies a considerable variation in the sizing results of the PV systems for a site located in the city of Oujda of Morocco (latitude: 34° 41 ; 1ongitude 1° 54 ). The capacity of the different components of two different photovoltaic systems has been optimized considerably thanks to our proposed seasonal adjustment for the tilt angle. For a PV/battery system, the number of PVP is reduced from 45 PVP to 34 and for a photovoltaic pumping system is reduced from 38 to 35 for an LPSP value of 0%. It is noticed that the size of the storage units remains the same approximately. The sizing procedure is based on the algorithm of the Electric System Cascade Analysis method and used as an input the hourly climate data, hourly load demand and technical parameters of the components.

References 1. Kaddoura TO, Ramli MAM, Al-turki YA (2016) On the estimation of the optimum tilt angle of PV panel in Saudi Arabia. Renew Sustain Energy Rev 65:626–634 2. Conceição R, Silva HG, Fialho L, Lopes FM, Collares-Pereira M (2018) PV system design with the effect of soiling on the optimum tilt angle. Renew Energy 133:787–796 3. Ramli MAM, Bouchekara HREH (2018) Estimation of solar radiation on PV panel surface with optimum tilt angle using vortex search algorithm. IET Renew Power Gener 12:1138–1145 4. Xu R, Ni K, Hu Y, Si J, Wen H, Yu D (2017) Analysis of the optimum tilt angle for a soiled PV panel. Energy Convers Manag 148:100–109 5. Salim AA, Huraib FS, Eugenio NN (1988) PV power-study of system options and optimization. In: EC photovoltaic solar conference, pp 688–692 6. Ullah A, Imran H, Maqsood Z, Butt NZ (2019) Investigation of optimal tilt angles and effects of soiling on PV energy production in Pakistan. Renew Energy 139:830–843 7. Zahboune H, Elhafyani M, Zouggar S, Ziani E, Dahbi S (2016) Optimal design method for stand-alone solar system with LPSP technology by using electric system cascade analysis. In: IRSEC conference, November 8. Zahboune H et al (2016) Modified electric system cascade analysis for optimal sizing of an autonomous hybrid energy system. Energy 116:1374–1384 9. Liu B et al (1961) Daily insolation on surfaces tilted towards equator. Trans ASHRAE 10:53–59 10. Zahboune H et al (2016) Optimal hybrid renewable energy design in autonomous system using Modified Electric System Cascade Analysis and Homer software. Energy Convers Manag 126:909–922

Technical and Economic Analysis of Solar Hydrogen Production in Morocco Samir Touili, Ahmed Alami Merrouni, Youssef El Hassouani, Abdel-illah Amrani, and Samir Rachidi

Abstract The objective of this study is to conduct a technico-economic analysis of hydrogen production from solar energy via the water electrolysis process. To this end, four solar energy technologies were selected namely: fixed PV, 1-axis PV, 2axis PV, and Stirling dish and the simulation were carried in five different location in Morocco using 3-years average of high accurate meteorological data measured at ground level. The results show that the 2-axis PV is the technology that can produce the highest amount of hydrogen (around 4500 Tons per year), followed by the 1-axis PV, Eurodish and the fixed PV. Also, it was found that the lowest cost can be achieve by the fixed PV (~6 $/Kg), closely followed by the 1-axis PV, the 2-axis PV and finally the Eurodish. Keywords Hydrogen · Solar energy · Economic analysis · Morocco

1 Introduction Currently, fossil fuels are the main source for meeting the worldwide energy needs. Actually, coal, natural gas and oil provides 81% of the total primary energy and 66% of electricity production. However, such fuels release greenhouse gases in their processing and combustion which contribute largely to the global warming

S. Touili (B) · Y. El Hassouani · A. Amrani ENIM, Département de Physique, Faculté des Sciences et Techniques, Université Moulay Ismail, Boutalamine, BP 509, 52000 Errachidia, Morocco e-mail: [email protected] A. Alami Merrouni Materials Science, New Energies and Application Research Group, LPTPME Laboratory, Department of Physics, Faculty of Science, University Mohammed First, Oujda, Morocco A. Alami Merrouni · S. Rachidi Research Institute for Solar Energy and New Energies (IRESEN), Green Energy Park, Ben Guerir, 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/10.1007/978-981-15-6259-4_82

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[1]. Continuous utilization of fossil fuels can lead to irreversible damage to the environment [2]. Therefore, the use of sustainable and efficient alternative sources of energy has become a necessity. One of the most promising alternative to replace fossil fuels in the near future is hydrogen. This is due to its many advantages like high energy density, releasing only water during combustion and the possibility to be stored for long periods without losses. In addition to the fact that hydrogen can be used for most utilizations that requires fossil fuels, it has many industrial applications as well. In fact, the current global hydrogen production (50 million metric tons annually) is mainly used by chemical and petroleum industries [3, 4]. Also, hydrogen can be used in many other applications like metallurgical process, the glass purification, food industry, pharmaceuticals, semiconductor manufacturing, fuel in the aerospace industry, as well as, for hydrogen based cars, buses, submarines and ships [5]. However, hydrogen is not available in the nature as a chemical substance since its mostly attached to other molecules [5], thus the need for its production. Even though many methods exists, only water electrolysis can be considered as feasible process for large scale “green and clean” hydrogen production [6], especially if the electricity to drive this process was generated by a renewable source. Therefore, the aim of this study is to conduct a technical and economical comparison of electrolytic hydrogen production from four power plants with four different solar technologies in five different locations in Morocco. The selected technologies are fixed: PV, 1-axis PV, 2-axis PV, and Stirling dish. The steps followed to achieve this goal are described in the section below.

2 Methodology For this study, we selected four different solar technologies namely: the fixed PV, 1-axis PV, 2-axis PV and the solar Stirling Dish. In the case of the fixed PV, the panels are oriented in the direction of the equator, and tilted with an angle equal to the location latitude. For the 1-axis PV, a sun tracking system is added in order to follow the movement of the sun in accordance with the azimuth angle, and according to the azimuth and altitude angle for the 2-axis PV. As for the solar Stirling Dish, the Eurodish system is selected because it’s one of the most developed one. In order to conduct a fair comparison between the different technologies, we designed a solar plant with a capacity of 100 MWp for each technology. The electricity generated is then used for hydrogen production via the water electrolysis process. The simulation of the electricity production is carried by the Greenius software because it’s a powerful simulation tool for renewable energy systems. This software has been used for many studies, for instance we cite [7–12]. The comparison is based on three parameters, the amount of hydrogen generated, the system efficiency, and the cost of production. The hydrogen production is calculated by the following equation:

Technical and Economic Analysis of Solar Hydrogen Production …

M=

779

E × ηele H H VH2

(1)

E represent the electricity production (kWh), ηele is the electrolyser efficiency (75%) and H H VH2 is the hydrogen higher heating value (39.4 kWh/kg). The whole system efficiency from solar to hydrogen is calculate as follow: η S−H2 =

M H2 × H H VH2 ID A

(2)

With I D A represent the solar irradiation on the PV panel or the dish area (kWh). As for the economic analysis, the levelized cost of hydrogen production (LCOH2 ) is used as metric because its very useful when comparing various technologies in different sites. The LCOH2 is estimated by the following formula: N    (C E + Celec )(1 + T )−i

LC O H2 =

i=0 N  

Mi (1 + T )−i

(3)



i=0

CE and Celec represent the investment cost of the solar energy system and the electrolyser respectively, i is the year considered, while N and T represents the project lifetime (20 years) and the discount rate (6%) respectively. As inputs for the simulation, and since the accuracy of the data is very important for the electrical and economic analysis of solar power plants [13], we used the meteorological data gathered at ground level from five sites in Morocco. In addition, we used the 3 years average for higher accuracy starting from 2014. The sites are presented in Table 1. In general, the sites with high solar irradiance are considered as the most suitable to host solar power projects [14], and as it can be observed from Table 1, all sites are well irradiated, and therefore plenty of solar energy is available for hydrogen Table 1 Selected sites for simulation Latitude (°N)

Longitude (°E)

Annual average GHI (kWh/m2 )

Annual average DNI (kWh/m2 )

Mean annual ambient temperature (°C)

AinBeni Mathar

34.07

−2.1

2078.22

2065.56

16.54

Benguerir

32.12

−7.94

2049.84

2238.78

19.32

Erfoud

31.49

−4.21

2107.78

2378.99

22.33

Missour

32.86

−4.11

2074.39

2307.9

18.12

Zagora

30.27

−5.85

2228.48

2563.28

23.88

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production. Also, the site of Zagora is the one with that receive higher amount of solar irradiation for both GHI and DNI, however its also the site with the higher mean annual ambient temperature, which may impact the performance of the PV technologies because it has a high impact on the PV module open circuit voltage [15].

3 Results and Discussions

Hydrogen production (Tons)

Figure 1 represents the annual hydrogen production by the simulated technologies in all sites starting by the technology with the higher production. It can be observed that in all sites the PV with 2-axis sun tracking system generated the highest amount of hydrogen with an annual production that depending on the sites is in the range of 4300–4800 Tons, followed by the 1-axis PV with 4200–4500, the Eurodish with 3300–4510 Tons, and finally the fixed PV with 3200–3500 Tons. Also, it can be observe that the hydrogen production in Zagora from each technology is higher than all the other sites. This result was expected since Zagora is the site with the higher amounts of solar irradiation for both GHI and DNI (Table 1). The whole system efficiency to convert solar energy into hydrogen is presented in Fig. 2 starting from the technology with the lower efficiency. The first observation is that the efficiency of the Eurodish is remarkably higher than the rest of the technologies in all the sites. Indeed, depending on the site, the efficiency varies between 11.3% and 12.28% for the Eurodish while it is in the range of 9–8.74%, for both the 2-axis PV and the 1-axis PV, and 8.93–8.70% for the fixed PV. In addition, it can be noticed that the efficiency of the PV technologies is lower in site with higher mean annual ambient temperature, which is a results of the increase of the solar cell temperature that cause a decrease of the PV panel efficiency and consequently the whole system efficiency. 5000 4000 3000 2000 1000 0

AinBeniMathar

Benguerir

PV 2-axis

Erfoud

PV 1-axis

Eurodish

Missour

PV fix

Fig. 1 Yearly hydrogen production efficiency for all the technologies in each site

Zagora

Efficiency (%)

Technical and Economic Analysis of Solar Hydrogen Production …

12.5 12 11.5 11 10.5 10 9.5 9 8.5 8

AinBeniMathar

Benguerir

PV fix

PV-1 axis

Erfoud

PV-2 axis

Missour

781

Zagora

Eurodish

Fig. 2 The system efficiency for all the technologies in each site

14

LCOH2 ($/Kg)

12 10 8 6 4 2 0

AinBeniMathar

PV fix

Benguerir

PV 1-axis

Erfoud

PV 2-axis

Missour

Zagora

Eurodish

Fig. 3 Levelized cost of hydrogen production efficiency for all the technologies in each site

Regarding the cost of hydrogen production, the results are displayed in Fig. 3 starting from the technology with the lowest cost. As it can be seen, the fixed PV represent the technology with the lowest cost while the Eurodish the highest. For the fixed PV, the LCOH2 varies between 5.57 $/Kg and 6.05 $/Kg, followed by the 1-axis PV with 5.74–6.15 $/Kg, then the 2-axis PV with 7.86–8.65 $/Kg, and finally the Eurodish with 10.36–13.80 $/Kg.

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4 Conclusion In this paper, we presented the simulation results of electrolytic hydrogen production from four different solar technologies under the climate of five different locations in Morocco. The results show that among the simulated technologies, and in all the sites, the fixed PV is the one to produce hydrogen with the lowest cost with around 6 $/Kg. The 2 axis PV is the technology that produce the highest amounts with ~4500 Tons annually, and the Eurodish is the most efficient with an efficiency in the range of 11–12.5%. However, the 1-axis PV represent the cost effective choice for hydrogen production, because this technology is able to generate high amounts close to the ones generated by 2-axis PV at a low cost near the one achieved by the fixed PV.

References 1. Baykara SZ (2018) Hydrogen: a brief overview on its sources, production and environmental impact. Int J Hydrogen Energy 43:10605–10614 2. Mohsin M, Rasheed AK, Saidur R (2018) Economic viability and production capacity of wind generated renewable hydrogen. Int J Hydrogen Energy 43:2621–2630 3. Hanley ES, Deane JP, Gallachóir BPÓ (2018) The role of hydrogen in low carbon energy futures–a review of existing perspectives. Renew Sustain Energy Rev 82:3027–3045 4. Sinigaglia T, Lewiski F, Santos Martins ME, Mairesse Siluk JC (2017) Production, storage, fuel stations of hydrogen and its utilization in automotive applications-a review. Int J Hydrogen Energy 42:24597–24611 5. Abdalla AM, Hossain S, Nisfindy OB, Azad AT, Dawood M, Azad AK (2018) Hydrogen production, storage, transportation and key challenges with applications: a review. Energy Convers Manag 165:602–627 6. Proost J (2018) State-of-the art CAPEX data for water electrolysers, and their impact on renewable hydrogen price settings. Int J Hydrogen Energy 44(9):4406–4413 7. Merrouni AA, Ouali HAL, Moussaoui MA, Mezrhab A (2016) Integration of PV in the Moroccan buildings: simulation of a small roof system installed in Eastern Morocco. Int J Renew Energy Res 6:306–314 8. Touili S, Alami Merrouni A, El Hassouani Y, Amrani AI (2019) Performance analysis of large scale grid connected PV plants in the MENA region. Int J Eng Res Africa 42:139–148 9. Touili S, Merrouni AA, El Hassouani Y, Amrani AI, Azouzoute A (2019) A techno-economic comparison of solar hydrogen production between Morocco and Southern Europe. In: 2019 International conference on wireless technologies, embedded and intelligent systems, WITS 2019, pp 1–6 10. Merrouni AA, Ouali HAL, Moussaoui MA, Mezrhab A (2016) Analysis and comparison of different heat transfer fluids for a 1MWe parabolic trough collector. In: Proceedings of the 2016 international conference on electrical and information technologies, ICEIT 2016, pp 510–515 11. Merrouni AA, Amrani AI, Mezrhab A (2017) Electricity production from large scale PV plants: benchmarking the potential of Morocco against California, US. Energy Procedia 119:346–355 12. Touili S, Alami Merrouni A, El Hassouani Y, Bennouna EG, Ghennioui A, Amrani A-I (2018) A comparative study on hydrogen production from small-scale PV and CSP systems. In: Proceedings of the 1st international conference on electronic engineering and renewable energy, ICEERE 2018. Lecture notes in electrical engineering, vol 519. Springer, Singapore 13. Merrouni AA, Ghennioui A, Wolfertstetter F, Mezrhab A, Merrouni AA, Ghennioui A, Wolfertstetter F (2017) The uncertainty of the HelioClim-3 DNI data under Moroccan climate, p 140002

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14. Merrouni AA, Mezrhab A, Ghennioui A, Naimi Z (2017) Measurement, comparison and monitoring of solar mirror’s specular reflectivity using two different reflectometers. Energy Procedia 119:433–445 15. Hajjaj C, Merrouni AA, Bouaichi A (2018) Evaluation of different PV prediction models. Comparison and experimental validation with one-year measurements at ground level. In: Proceedings of the 1st international conference on electronic engineering and renewable energy, ICEERE 2018. Lecture notes in electrical engineering, vol 519. Springer, Singapore

Production of Hydrogen by Excess Energy Resulting from a Photovoltaic System Supplying a Load of Nominal Power Abdelhafid Messaoudi, Sanae Dahbi, Abdelhak Aziz, and Kamal Kassmi

Abstract The most conceivable systems, using photovoltaic solar energy (PV) with random behavior, are built using energy converters (converter and inverter). However, it is impossible to transfer the optimal PV power to a load and at the same time respect its nominal characteristics (voltage and current). In this article, we develop a PV system which supplies a main charge with specific nominal characteristics and a secondary load consisting of an electrolysis with concentric cylindrical electrodes and plays a fundamental role in the designed system. The results obtained show that the optimization and the transfer of power are always possible. Keywords Cylindrical electrolysis · Photovoltaic · Load

1 Introduction Photovoltaic solar energy (PV) from photovoltaic panels is difficult to implement so that it is consumed naturally under the conditions of the load (voltage and current). Even in the case of power optimization by adequate controls through energy converters, it is not obvious to consume the available PV power, in a load which requires power under nominal voltage and nominal current. In almost of the recent research [1–3], the optimization of PV power comes at the expense of non-compliance with the nominal load specification. When this happens, the load will malfunction. The two scenarios likely to occur in an environment where physical conditions (solar radiation, temperature) change are: – The insufficient PV power supplying the load. A. Messaoudi (B) Laboratory of Energy, Embedded Systems and Information Processing (LEESIP), National School of Applied Science Oujda, BP: 473, 60000 Oujda, Morocco e-mail: [email protected] A. Messaoudi · S. Dahbi · A. Aziz · K. Kassmi Laboratory of Electrical Engineering and Maintenance (LEEM), Higher School of Technology Oujda, BP: 473, 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/10.1007/978-981-15-6259-4_83

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– The excess PV power (the load is supplied with enough power and there is still excess energy left). So, we are going to operate a PV system where the energy supplied to the payload is always in excess. We thought of evacuating any excess of energy in a secondary load which accepts to store this surplus of energy under the conditions of tension and current imposed by the algorithms of optimization and not under the nominal conditions of the load itself. This means that this secondary load must accept variations in current and voltage across its terminals dictated by optimization. In this context, we are developing a clean storage system [4–6] with three advantages; which does not harm the environment [7–9], which accepts wide variations in current and voltage across its terminals and which does not disturb the optimization of PV power: it is the electrolysis which allows, by the phenomenon of water electrolysis, to transform any excess PV energy into a clean energy vector which is storable hydrogen. In this work, we give a primary interest to the type of electrolysis used as secondary charge.

1.1 System Overview Figure 1 shows the block diagram of the photovoltaic system which supplies a main load and divert the excess energy to an electrolysis. The system studied can be divided into four subsystems: – – – –

PV energy source (Mutsibichi 180 type) with peak power of 180 W, MPPT (maximal power point tracking) type adapter block (DC/DC converter), Electrolysis unit, PI (integral proportional) adaptation block (DC/DC).

Fig. 1 Schematic layout diagram of the proposed system PV/electrolysis/load

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2 Design, Modeling, Dimensioning and Production of the PV System 2.1 Electrical Model of the PV Source The basic element of a PV module is the PV cell, which is essentially composed of a PN junction with a threshold voltage of the order of 0.6 V, a short-circuit current generator ISC , resistors Rs series and Rp parallel [10]. The PV module used is modeled by putting 50 basic cells in series. 



   q · VP V + RS · I P V I P V = N P · I P H − I S · N P · exp · −1 − k·T · A



NP NV



· VP V + RS · I P V RP



(1)

2.2 Buck Converter Sizing and MPPT Control Algorithm This converter is a buck type adaptation quadrupole. Its elements are dimensioned in previous work [11] to operate at a switching frequency of 100 kHz. The electrical quantities of the buck converter (Vs , Is ) are linked to those of the PV (VPV , IPV ) with respect to the duty cycle D of the signal which controls the converter by: VS = D · V P V IS =

IPV D

(2) (3)

To follow the maximum power point of the PV source, we implemented the MPPT control algorithm known as the incremental conductance (InC) [4].

2.3 Sizing of the Boost Converter and Servo Loop This boost converter is introduced into the conversion chain to allow the load to work under its nominal conditions (fixed voltage at 24 V and variable current depending on the power of the load).

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The electrical quantities at the output of the boost converter (Us , I’s ) are linked to those of the output of the buck converters (Vs, Is) by the following relationships: US =

VS 1− D

I S = (1 − D)I S

(4) (5)

Note that the mission of this step-up voltage DC-DC converter controlled by a digital servo loop of the integral proportional type is maintain a constant voltage of 24 V when its input voltage is subject to variations.

3 Design of an Electrolyzer with Cylindrical Electrode 3.1 Electrolysis Process Electrolysis of water is the dissociation of water molecules into hydrogen and oxygen under the influence of an electrical current through the aqueous solution which fills the volume between the two metal electrodes. This process can be expressed by: At the anode: H2 O2 (l) →

1 O2 (g) + 2H + + 2e− 2

(6)

At the cathode: 2H + + 2e− → H2 (g)

(7)

Consequently, the global reaction of this decomposition can be written: H2 O2 (l) →

1 O2 (g) + H2 (g) 2

(8)

3.2 Electrolysis with Cylindrical Electrodes In this article, we design an electrolysis with cylindrical electrodes in stainless steel type 316L according to the diagram in (Fig. 2). The hydrogen production process is

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Fig. 2 Electrolysis with cylindrical electrodes

closely linked to the pipeline and the intensity of the electric field that prevails in the space between the two electrodes. Electrochemical Voltage of an Electrolysis Cell When the current flows through the solution, the voltage across the electrolysis can be represented by the sum of the Nernst Erev voltage, the activation overvoltage at the cathode őc and the anode őa and the interfacial overvoltage őI . E = Er ev + ηC + ηa + η + η L

(9)

The voltage and Energy to be Applied to the Electrolysis to Electrolyze nH2O Mole of Water The mass of water mH2O , of density ρ, which will undergo electrolysis is found in the volume V comprised between the two cylinders: V = π h( R22 − R12 )

(10)

Considering the approximation (e =0) (deg)

0

Fig. 7 Block diagram of the wind turbine with P and O and FL controller in Simulink

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Fig. 8 Evolution of power for a day with fuzzy logic

Fig. 9 Evolution of power for a day with P&O

S. Zouirech et al.

Maximum Power Extraction from a Wind Turbine Energy Source …

Fig. 10 Evolution of electromagnetic torque for a day with fuzzy logic

Fig. 11 Evolution of electromagnetic torque for a day with P&O

Fig. 12 Evolution of rotor speed for a day with fuzzy logic

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Fig. 13 Evolution of rotor speed for a day with P&O

4 Conclusion Extracting the maximum power out of the wind turbine array is a critical step in harvesting renewable energy. The goal of the MPPT technique is to extract the maximum power available in the wind turbine array. 3.7e3 W wind turbine T array is developed by using MATLAB/Simulink. The wind speed is a factor for which the wind turbine array output power varies. The paper presented an MPPT algorithm to obtain optimal power operation of a small wind turbine connected to DC Micro-grid. The system consisting of a Permanent Magnet Synchronous Generator (PMSG) driven by a SWT was interfaced to DC Micro-grid through a rectification stage and a buck converter. In this paper we have described the main elements of the Wind turbine system. Then we have cited the two techniques used for MPPT algorithms. Finally, we ended with a simulation.

References 1. Zammit D, Staines CS, Micallef A, Apap M (2017) MPPT with current control for a PMSG small wind turbine in a grid-connected DC microgrid. In: Research and innovation on wind energy on exploitation in urban environment colloquium. Springer, Cham, pp 205–219 2. Chen YK, Wu YC, Song CC, Chen YS (2012) Design and implementation of energy management system with fuzzy control for DC microgrid systems. IEEE Trans Power Electron 28(4):1563–1570 3. Xia Y, Ahmed KH, Williams BW (2012) Wind turbine power coefficient analysis of a new maximum power point tracking technique. IEEE Trans Industr Electron 60(3):1122–1132 4. Reddy DC, Narayana SS, Ganesh V (2018) Design of hybrid solar wind energy system in a microgrid with MPPT techiques. Int J Electr Comput Eng 8(2):730 5. Xiong YS, Qian SX, Liu QS, Zhan Y (2013) MPPT of wind generation for DC microgrid. Appl Mech Mater 448–453:1802–1805

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6. Ahmed M, Amin U, Aftab S, Ahmed Z (2015) Integration of renewable energy resources in microgrid. Energy Power Eng 7(01):12 7. Nayanar V, Kumaresan N, Ammasai Gounden N (2016) A single-sensor-based MPPT controller for wind-driven induction generators supplying DC microgrid. IEEE Trans Power Electron 31(2):1161–1172 8. Srinivasa Rao G, Harinadha Reddy K, Ravi Teja B, Devasahayam B, Khaleel S (2018) Matlab based simulation model of standalone DC microgrid for remote area power applications. Int J Eng Technol 7(1.8):153 9. Gite SS, Pawar SH (2017) Modeling of wind energy system with MPPT control for DC microgrid. In: 2017 second international conference on electrical, computer and communication technologies (ICECCT). IEEE, pp 1–6 10. Ito Y, Zhongqing Y, Akagi H (2004) DC microgrid based distribution power generation system. In: The 4th international power electronics and motion control conference, IPEMC 2004, vol 3. IEEE, pp 1740–1745 11. Liu X, Wang P, Loh PC (2011) A hybrid AC/DC microgrid and its coordination control. IEEE Trans Smart Grid 2(2):278–286 12. Gaud R, Vishwakarma D (2019) Optimization of renewable energy sources for DC microgrid. Optimization 6(06):1875–1881 13. Zammit D, Staines CS, Micallef A, Apap M (2019) Wind MPPT for a PMSG SWT in a grid-connected DC microgrid. In: 2019 6th international conference on control, decision and information technologies (CoDIT)

Management Strategy of Power Exchange in a Building Between Grid, Photovoltaic and Batteries Mohammed Dhriyyef, Abdelmalek El Mehdi, Mohammed Elhitmy, and Mohammed Elhafyani

Abstract This work presents a system managing electric energy in a building with a varying demand of energy with respect to the need of its occupants. The system uses renewable energy resources such as photovoltaic panels, the battery as an energy storage system and the grid. The objective of this work is to avoid the usage of the grid as much as possible and this is made in the purpose of minimizing the electricity bill. The objective is also to benefit from the photovoltaic energy and the storage system according to their availability in order to satisfy the energy demand by the building. An algorithm that manages these three resources is presented, it is dedicated to managing the energy production and consumption of the building, the surplus of renewable energy production is stored in the system storage or injected into the grid. The study is made in a period of two successive days according to the levels of the batteries SOCs with a varying energy demand and different solar irradiance. This study has succeeded to obtain the energy demand satisfaction, managing the storage system and planning of power exchange between the grid and the building. The results are obtained through simulation by the MATLAB and SIMULINK environments. Keywords Demand response · Home energy management · Renewable energy source · Smart grid · Smart home · Hybrid system

M. Dhriyyef (B) · A. El Mehdi Smart and Communication Technologies Laboratory, National School of Applied Sciences, Mohammed First University, Oujda, Morocco e-mail: [email protected] M. Elhitmy Electromagnetism, Signal Processing and Renewable Energy Laboratory, Faculty of Science, Mohammed First University, Oujda, Morocco M. Elhafyani Electrical Engineering and Maintenance Laboratory, Hight School of Technology, 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/10.1007/978-981-15-6259-4_87

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1 Introduction Photovoltaic energy nowadays is everywhere, the installation price has fallen and the efficiency has increased. The sun gives to the earth an amount of energy that is largely satisfying the demand in energy of all the humans. The photovoltaic energy is today used in many different fields such as agriculture [1] large areas of services in motorway, larges industries, electric power stations …etc. The photovoltaic panels are in general made by semiconductor materials such as silicon which is the most abundant material in earth. Buildings have most benefited from the photovoltaic systems [2, 3]. Battery phenomenon or technology is considered a most beneficial solution for transport vehicles and buildings [4], lithium ion batteries are very efficient and secure [5], they have a very good impact on renewable energy at world level scale. If the power generated by the photovoltaic and batteries exceed largely the demand of the users, the remaining power generally is injected into the grid [6]. In this work we present an electric system based on three components, photovoltaic, batteries and grid, the system supply electricity to a building in order to satisfy its demand we try to disconnect the grid as much as possible, the purpose is to reduce the electricity bill, the grid remains disactivated when the building demand is covered by the photovoltaic or the batteries. The exchange of electric energy between the three components of the system is made according to an algorithm that we present. The description of the studied system is described in Sect. 2 with the modeling of each component of the system, Sect. 3 describes the energy management algorithm used, the case studies and their final results are explained in Sect. 4, finally Sect. 5 describes the main conclusions of the work.

2 Presentation of the Studied System Figure 1 shows a global scheme of the studied system: The system contains photovoltaic panels as an essential electric energy source, it is combined with a boost converter controlled by MPPT algorithm in order to extract the maximum of power, the MPPT is based on the P&O method [7, 8], the other components of the system are the batteries together with the bidirectional converter, the converter operates as an adapter, if the batteries are in charging mode, the bidirectional converter operates as buck converter, it operates as a boost converter if the batteries are in discharging mode [9], the bidirectional converter line is regulated in voltage and current in the purpose to set in stable way the DC BUS to 311 V [10], The inverter is converting the DC voltage of the DC BUS to a sinusoidal voltage of the AC BUS of 311 V of amplitude and 50 Hz of frequency, the inverter is synchronized with the grid in order to ensure the power exchange between the different

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Fig. 1 Bloc diagram of studied system

components of the system [11] [12], the inverter is regulated through the PWM method ensuring a regulation in voltage and current [13]. The building contains an important number of home appliances such as the refrigerator, water heater, the Tv, Pc, washing machine, air conditioning etc., all this apparatus has been modeled by MATLAB & SIMULINK.

2.1 Model of PV Array A cell of a photovoltaic generator is modeled by the electric circuit of Fig. 2 I ph is a current source which is in parallel with a diode, the series resistance R S and the shunt resistance R Sh are accounting for dissipative phenomena of the cell [14]. The mathematical model of the current-voltage characteristic of an ideal photovoltaic cell is presented in Eq. (1):   qV I = I Ph.cell − I0.cell ex p a K T − 1

Fig. 2 Equivalent circuit of a solar cell

(1)

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I Ph.cell is the current generated by the incident solar irradiation, I0.cell is the inverse saturation current. q is the charge of the electron (1, 6.10−19 C), K is the Boltzmann constant (1, 3.10−23 J/K), a is the diode ideality factor and T is the absolute temperature in Kelvin. The output current of a photovoltaic panel is given by Eq. (2):  I = I Ph − I0 ex p

(V +R S I ) N S Vt

 (V + R S I ) −1 − R Sh

(2)

I Ph is generation current of a photovoltaic current, I0 is inverse saturation current of the diode, N S is the number of cells connected in series, Vt = AKq T is the thermodynamic voltage of the panel [15].

2.2 Model of the Battery Figure 3 shows an electric circuit equivalent to a battery, it contains a voltage source in series with an internal resistance and capacitor. The mathematical model of the battery is presented by Eq. (3): U B AT = E 0 − K B

∫ I B dt − R B .I B Q0

(3)

E 0 is the voltage across the battery in an open circuit, K B is a constant dependent on the nature of the battery, R B is the internal resistance of the battery, I B the is discharge current, Q 0 is the capacity of the battery in Ah [2]. The state of a battery is determined by its S OC state (state of charge) measured by Eq. (4) [16]:  S OC(t) = S OC(t − 1) −

t

t−1

Fig. 3 Electrical model of the battery

IB dt with S OC min < S OC < S OC max (4) Q0

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When the battery is in charge mode, I B current is negative and the charge power is negative, in case the battery is in discharge mode both I B and the battery power are positive. The capacity of the battery is given by Eq. (5): C Bat =

E daily .Daut U Bat .D O D.η Bat

(5)

Where U Bat is the battery’s voltage,Daut number of days of autonomy, E daily the energy to be supplied, D O D depth of discharge, η Bat is the yield of the battery.

2.3 Model of the Building Each home appliance in the building is modeled as a load on MATLAB & SIMULINK characterized by P, Q, and Cos(ϕ). P = U.I.Cos(ϕ)

(6)

Q = U.I.Sin(ϕ)

(7)

Where P is the active power in (w), Q is the reactive power in (v ar ) and Cos(ϕ) is power factor.

3 Description of Management Strategy The system managing the electrical energy operates according to an algorithm which will be described later. The data of the algorithm to be know in advance are, instantaneous power produced by the photovoltaic panels PP V , instantaneous power demanded by the building PD , the minimum value of the batteries SOC and its maximum value. the algorithm is entirely based on the difference of the power produced by the PV and that demanded by the building, P = PP V − PD , but it is also depended on the SOC value of the batteries. The PV is all the time connected to the system, the algorithm starts by calculating P and finds three different cases: P > 0, P < 0, P = 0. It is to be noted that the system operates with a yield knowing that there is a power loss on line, in the inverter, in the boost converter, in the bidirectional converter for example. This yield is of the order of 90%.

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Cases to be considered are: First case P = 0, in this case the electricity of the building will be supplied by both the PV and the grid is introduced in order to compensate for the losses. Second case P > 0, and this will give rise to three situations to be envisaged. – First situation: if S OC ≥ S OCmax there is then a surplus of power and the electricity supply of the building is by PV and the remaining of the power will be injected into the grid. – Second situation: S OC < S OCmax, if the S OC is lower than S OCmin then supply the electricity power of the building by the grid and charge the batteries by the PV. – Third situation: S OC < S OCmax, if the S OC is greater than S OCmin then both supply the electricity of the building and charge the batteries by the PV. Third case: P < 0, we make the test on the SOC of the batteries, If S OC > S OCmin, the building will be supplied by the PV and the batteries will be discharged. If S OC < S OCmin, we make test on PV, if PP V > 0 then the supply will be made by the grid and the batteries are charged by PV, if not the isolate the batteries and the supply the electricity demand of the buildings directly by the grid.

4 Results and Discussion The building input voltage is that of the grid and inverter voltage, it is around 311 V at amplitude and frequency 50 Hz. The characteristics of the photovoltaic panels (15 panels used) and batteries used are shown in Table 1 and 2 respectively. The profiles of power produced by PV and power demanded by the building are shown in Fig. 4, these profiles are taken on a period of two successive days. The profiles of the second day are different from those of the first day. Table 1 Pv characteristics

Parameters

Value

Power

349 W

V mp

43 V

I mp

8.13 A

V oc

51 V

I sc

9.4 A

Ns

80

Management Strategy of Power Exchange in a Building… Table 2 Batteries characteristics

Parameters

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Capacity

3 ∗ 100 Ah

Rated voltage

3 ∗ 48 V

Min Voltage

3 ∗ 43.47 V

Max Voltage

3 ∗ 55.87 V

Fig. 4 PV and demand power profile

Fig. 5 power behavior of PV and grid

The results are presented in three cases study: • case 1: System without batteries. Figure 5 shows the behavior of the system without batteries, it shows the power exchange between grid and the PV panels in order to satisfy the demand power by the building. It particularly shows that if the power produced by the PV panels is greater than that demanded by the building, then the difference between the two powers will be

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Fig. 6 power behavior of PV, batteries and grid (case 2)

injected into the grid. If the difference between the two powers is negative, the power produced by the PV panels is less than that demanded by the building, then the grid will intervene and will supply the lack of power. • Case 2: initial S OC batteries is a S OCmin = 25%, Figure 6 shows the behavior of the batteries and the grid for the SOC of the batteries at its minimal value, it is to be noted that the charge of the batteries is uniquely accomplished from the PV panels. This figure shows that in interval [0; 6 h] the buildings supplied by the grid because P < 0 and S OC of batteries is at its minimal value. The figure shows that in the intervals [39,24 h; 42 h], [42.63 h; 43 h] power is injected into the grid, because P > 0 and S OC = S OCmax. It also shows that in the intervals [6 h; 7 h], [7,4 h; 18 h], [30,41 h; 39.24 h], [42,28 h; 42,53 h] the batteries are in charge because P > 0 and S OCmin < S OC < S OCmax. It also shows that in the intervals [7 h; 7,4 h], [18 h; 30,4 h] and [43 h; 48 h] the batteries discharge because P < 0 and S OC > S OCmin. The variations in battery soc during these two days is shown in Fig. 7. the figure shows that the batteries was initially discharged and it has charged during functioning until it arrived to its maximal value in the intervals [39,24 h; 42 h] and [42.63 h; 43 h]. • Case 3: initial soc batteries is a S OCmax = 85%. Figure 8 shows the profile of the batteries operation and that of the grid, the figure shows that in the intervals [0 h; 6 h], [18 h; 30 h], [42 h; 42,26 h], [43 h; 48 h] the batteries are in discharge because P is negative and S OC > S OCmin. Its also shows that in the intervals [6,37 h; 7 h], [7,41 h; 8,28 h], [30,41 h; 40,48 h], [42,28 h; 42,56 h] the batteries are in charge state because in these intervals P > 0. Its also shows that in the intervals [8,28 h; 18 h], [35,65 h; 42,03 h] there is a surplus of power produced by the PV which will be injected into grid, the maximum of the injected power into the grid is 4900 W. The variations of the batteries SOCs is shown on Fig. 9.

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Fig. 7 SOC variation of batteries (case 2)

Fig. 8 power behavior of PV, batteries and grid (case 3)

Fig. 9 SOC variation of batteries (case 3)

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5 Conclusion All of the system components studied were simulated by MATLAB and Simulink, an electric model of all the system components were established, the models of the photovoltaic panels, batteries, boost and bidirectional converter, inverter, building and grid were performed. The results obtained by the management algorithm are good, power exchange between the different components of the system is made automatically and in perfect way exactly as expected by the algorithm.

References 1. Khiareddine A, Ben Salah C, Mimouni MF (2015) Power management of a photovoltaic/battery pumping system in agricultural experiment station. Sol Energy 112:319–338. https://doi.org/ 10.1016/j.solener.2014.11.020 2. Roumila Z, Rekioua D, Rekioua T (2017) Energy management based fuzzy logic controller of hybrid system wind/photovoltaic/diesel with storage battery. Int J Hydrogen Energy 42:19525– 19535. https://doi.org/10.1016/j.ijhydene.2017.06.006 3. Kim Y, Zhao J, Kim S, Harrington RJ (2018) Power management strategy for residential housing connected to the rooftop solar PV. In: 2017 IEEE conference technology sustainability SusTech 2017, pp 1–7. https://doi.org/10.1109/SusTech.2017.8333535 4. Hesse HC, Martins R, Musilek P, Naumann M, Truong CN, Jossen A (2017) Economic optimization of component sizing for residential battery storage systems. Energies 10. https://doi. org/10.3390/en10070835 5. Tervo E, Agbim K, DeAngelis F, Hernandez J, Kim HK, Odukomaiya A (2018) An economic analysis of residential photovoltaic systems with lithium ion battery storage in the United States. Renew Sustain Energy Rev 94:1057–1066. https://doi.org/10.1016/j.rser.2018.06.055 6. Jana J, Saha H, Das Bhattacharya K (2017) A review of inverter topologies for single-phase grid-connected photovoltaic systems. Renew Sustain Energy Rev 72:1256–1270. https://doi. org/10.1016/j.rser.2016.10.049 7. Kota VR, Bhukya MN (2017) A novel linear tangents based P&O scheme for MPPT of a PV system. Renew Sustain Energy Rev 71:257–267. https://doi.org/10.1016/j.rser.2016.12.054 8. Ahmed J, Salam Z (2018) An enhanced adaptive P&O MPPT for fast and efficient tracking under varying environmental conditions. IEEE Trans Sustain Energy 9:1487–1496. https://doi. org/10.1109/TSTE.2018.2791968 9. Iskak CA, Windarko NA, Rakhmawati R (2019) Design and implementation bidirectional DCDC converter for load sharing and charging battery. In: Proceedings - 2019 international seminar applied technology information communication industry 4.0 Retrosp Prospect Challenges, iSemantic, pp 455–459. https://doi.org/10.1109/ISEMANTIC.2019.8884344 10. Kondrath N (2017) Bidirectional DC-DC converter topologies and control strategies for interfacing energy storage systems in microgrids: an overview. In: 2017 5th IEEE international conference smart energy grid engineering SEGE 2017, pp 341–345. https://doi.org/10.1109/ SEGE.2017.8052822 11. Praiselin WJ, Edward JB (2017) Voltage profile improvement of solar PV grid - connected inverter with micro grid operation using PI controller. Energy Procedia 117:104–111. https:// doi.org/10.1016/j.egypro.2017.05.112 12. Mishra S, Pullaguram D, Buragappu SA, Ramasubramanian D (2016) Single-phase synchronverter for a grid connected roof top photovoltaic system. IET Renew Power Gener 10:1187– 1194. https://doi.org/10.1049/iet-rpg.2015.0224

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13. Munir MI, Aldhanhani T, Al Hosani KH (2017) Control of grid connected PV array using P&O MPPT algorithm. In: IEEE green technology conference, pp 52–58. https://doi.org/10.1109/ GreenTech.2017.14 14. Chauhan A, Prakash S (2019) Considering various equivalent circuits for solar PV array modelling. In: 2nd international conference energy, power environment towards smart technology ICEPE 2018, pp 1–6. https://doi.org/10.1109/EPETSG.2018.8658741 15. Kitson J, Williamson SJ, Harper P, McMahon CM, Rosenberg G, Tierney M, Bell K (2017) A photovoltaic panel modelling method for flexible implementation in Matlab/Simulink using datasheet quantities. In: IEEE international symposium industry and electronics, pp 946–951. https://doi.org/10.1109/ISIE.2017.8001373 16. Wijewardana S, Vepa R, Shaheed MH (2016) Dynamic battery cell model and state of charge estimation. J Power Sources 308:109–120. https://doi.org/10.1016/j.jpowsour.2016.01.072

Modeling, Simulation and Real Time Implementation of MPPT Based Field Oriented Control Applied to DFIG Wind Turbine Nabil Dahri, Mohammed Ouassaid, and Driss Yousfi

Abstract This paper aims to study the application of the maximal power point tracking (MPPT) to a variable speed wind turbine where a field-oriented control (FOC) is applied to the generator. Simulation in Matlab/Simulink environment is achieved first, then an experimental validation is conducted by means of a test bench containing a doubly-fed induction generator (DFIG) driven by a servomotor that emulates the behavior of the wind turbine, a back to back power converter and an RL filter. The implementation of the field-oriented control is realized using a digital signal processor (DSP) card and an acquisition interface. Keywords Wind energy conversion system · Wind turbine · MPPT · FOC · BLDC servomotor · DFIG · Back to back power converter · DSP

1 Introduction The increase of energy consumption, the exhaustion of fossil sources and the danger that threatens the environment by the atmospheric gas concentration produced by the burn of fossil fuel make the use of renewable energies a must. Hence, special attention is directed toward wind turbines, which are inexhaustible sources and harmless to the environment. Moreover, technological development and the low cost of power electronics enhance the potential of wind energy conversion systems. N. Dahri (B) · M. Ouassaid Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco e-mail: [email protected] M. Ouassaid e-mail: [email protected] D. Yousfi ESETI Laboratory, National School of Applied Sciences, Mohammed First University, Oujda, Morocco e-mail: [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/10.1007/978-981-15-6259-4_88

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Wind energy conversion systems (WECS) can work by fixed and variable speed. The variable-speed type is more desirable because of its ability to achieve maximum efficiency at all wind speeds [1]. For that reason, a lot of researches are made about the different methods of control to extract the maximum mechanical power from the wind. However, whatever the method chosen, the final goal is to control the generator electromagnetic torque to track the optimal speed ratio of the wind turbine. One of the most used methods to track the maximal power point in a WECS is the field-oriented control (FOC) based on PI controllers. It allows decoupling the active and the reactive power [2]. This method is known for its robustness and simplicity, however, it depends strongly on generator parameters [3]. To make up for this weakness, authors in [4] proposed to merge the PI controller with a Fuzzy algorithm to give the controller gains the ability to continuously being adjusted. Another method is shown in [5], where an artificial neuron network controller is combined with PI controller. This method estimates the controller parameters more prominently. The aim of this paper is to simulate and validate experimentally the tracking of the maximal power point (MPPT) using the field-oriented control based on PI regulators. The type of the generator used is a doubly-fed induction generator (DFIG). This paper is organized as follows: The wind energy conversion system model is presented in Sect. 2. Section 3 describes the principle behind the MPPT. Section 4 presents the field-oriented control of DFIG. The topology of the generator torque controller is presented in Sect. 5. The simulation results and the experimental validation are exposed in Sect. 6 and Sect. 7, respectively. Finally, the reported work is concluded in Sect. 8.

2 Modeling of the WECS A wind energy conversion system is a combination of three important parts: wind turbine, gearbox and DFIG. To control such a complex system, a mathematical model should be made for each part of them. Figure 1 presents the complete system with the inputs and the outputs of the MPPT algorithm and the controller (“*” means a reference value).

2.1 Modeling of the Wind Turbine The wind turbine of the WECS is the element that converts the kinetic power of the wind into mechanical power. The mechanical power extracted from the wind is modeled by the following equation: Pt =

1 ρ × π × R 2 × C p(λ; β) × Vwind3 2

(1)

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Fig. 1 Wind energy conversion system diagram

where R is the radius of the turbine, Cp is the power coefficient, λ is the speed ratio, β is the pitch angle and Vwind is the wind’s speed.

2.2 Modeling of the Gearbox The gearbox in the WECS is used to adapt the turbine speed to an admissible speed for the generator. It is modeled by the Eq. (2): Tt m = =G t Tm

(2)

where G is the multiplier gain of the gearbox.

2.3 Modeling of the DFIG The coupling of the active and reactive power with the inputs (voltages and currents) makes the control of the machine a complex task. To overcome this difficulty, the model of the DFIG is expressed in the Park reference frame (d, q) [6]. Stator and Rotor Voltages and Currents [V dq] = [Rsr ] × [I dq] +

d [ϕdq] + [ωsr ] × [ϕdq] dt

with [Rsr ] = [Rs 0 0 0; 0 Rs 0 0; 0 0 Rr 0; 0 0 0 Rr ] and [ωsr ] = [0 (−ωs) 0 0; ωs 0 0 0; 0 0 0 (−ωr ); 0 0 ωr 0]

(3)

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[I dq] =

1 × ([Lr s] × [ϕdq] − [Lm] × [ϕdq]) σ × Ls × Lr

(4)

2

Lm with: σ = 1 − Ls×Lr , [Lr s] = [Lr 0 0 0; 0 Lr 0 0; 0 0 Ls 0; 0 0 0 Ls] and [Lm] = [0 0 Lm 0; 0 0 0 Lm; Lm 0 0 0; 0 Lm 0 0]

Stator Active Power and Electromagnetic Torque Ps = T em =

3 × (V sd × I sd + V sq × I sq) 2

3 Lm × p× × (ϕsq × I r d − ϕsd × I rq) 2 Ls

(5) (6)

3 Maximum Power Point Tracking in a WECS As it is clear from Eq. (1), the power extracted from the wind depends on the power coefficient (Cp) of the wind turbine. One of the expressions used in [7] to describe the power coefficient is:  C p(λ, β) = (a1 − a2 × β) × sin

π × (λ + a3) a4 − a5 × β

 − a6 × (λ − a7) × β

(7)

where, a1 to a7 are parameters linked to the wind turbine’s construction. According to Eq. (7), the value of Cp depends on λ and β. The MPPT is used in the partial-load region of the generator. In this operation zone β = 0. The speed ratio λ is expressed by the following equation: λ=

R × t V wind

(8)

The maximum value of Cp(λ) is reached at a defined value of λ (λopt). Thus according to Eq. (8), in the MPPT the generator speed should be regulated for each value of the wind’s velocity in order to keep the value of λ fixed at λopt. Figure 2 represents the mechanical power of the turbine used in our case in terms of the generator angular speed for different wind’s velocity.

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Fig. 2 Mechanical power in terms of generator angular speed with β = 0

4 The Machine Field-Oriented Control The FOC consists of orienting the stator flux along the d axis: [ϕsd; ϕsq] = [ϕs; 0]

(9)

This method makes the asynchronous machine behaves similarly to a separately excited DC machine. Assuming that the stator flux is constant and by neglecting the stator resistance Rs, the stator voltages are expressed by the following equations [8]: [V sd; V sq] = [0; ωs × ϕs]

(10)

Even if the stator resistance is not negligible the regulator will compensate for the difference. By combining equations from (3) to (6) with (9) and (10), the following equations can be found: Lm 3 × V s × I rq Ps = − × 2 Ls T em =

p × Ps ωs

(11) (12)

As it is shown in Eq. (11) the decoupling of the active power from the direct axis has been made.

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Fig. 3 The controller’s topology

5 The Topology of the Controller The principal objective of the controller is the MPPT. Like it is explained in Sect. 3, the generator speed must be regulated to track the maximum power point. The speed of the generator is determined by the mechanical equation: J×

dm = T m − T em − f × m dt

(13)

Thus, the electromagnetic torque of the machine should be controlled in order to regulate its speed. The Fig. 3 summarizes the topology of the controller: As it is shown in Fig. 3, the regulator of the electromagnetic torque contains two cascade loops. The external loop calculates the reference value of the quadratic rotor’s current (Irq*). The inner loop calculates the reference value of the quadratic rotor’s voltage (Vrq*). A control of the stator reactive power can be added to the controller by acting on the value of the rotor’s direct voltage (Vrd*). The inverse Park transform of the voltages Vrd* and Vrq* gives the reference rotor’s voltages (Vra*, Vrb*, Vrc*). The three phase reference voltages are used in the Sinusoidal Pulse Width Modulation (SPWM) to generate the firing signals for the IGBTs of the rotor side converter. (ωr × σ × L r × I rd ) is a decoupling term used to compensate the coupling term added by the machine to the applied rotor’s quadratic voltage (Fig. 3).

6 Simulation Results The simulation has been achieved in MatLab/Simulink environment. A wind profile that changes every 5 s has been applied to the WECS and the ability of the controller

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to follow the angular speed reference is monitored. The results are represented in the figures below (Figs. 4, 5, 6, 7, 8, 9 and 10): As it is clear from Fig. 5, the generator angular speed follows its reference. As a result, the stator active power increases by the increase of the wind speed (see Fig. 4 and Fig. 7). This result is expected. Indeed, by applying correctly the MPPT, the value of the speed ration λ will stay fixed at its optimal value regardless of the wind speed. Consequently, the power coefficient Cp will be always equal to its maximal value Cpmax = Cp(λopt ) (see Fig. 10). Therefore, according to Eq. (1), the only parameter that will affect the mechanical power extracted from the wind is the wind speed. That is what explains why the changes of the stator active power take the same shape as the wind speed’s variation. In addition to the application of the MPPT, the controller aims to keep the stator reactive power at zero value in order to magnetize the machine only through its rotor (see Fig. 9). The stator currents are represented in Fig. 8.

Fig. 4 Wind profile

Fig. 5 Generator angular speed

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Fig. 6 Generator electromagnetic torque

Fig. 7 Stator active power

Fig. 8 Stator currents

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Fig. 9 Stator reactive power

Fig. 10 Power coefficient of the turbine

7 Experimental Validation To validate the simulation results experimentally, a 1.5 kW BLDC servo-drive is used as wind profile emulator. The servo-drive is coupled to 1.1 kW DFIG. The back-to-back converter used contains two controlled dual three phase converters and DC link capacitor. The FOC and the MPPT algorithms are implemented in real time via a TMS 320F28335 DSP. The test bench is shown in Fig. 11. The identification of the system’s parameters was the first step to validate the results experimentally. This step has been achieved through experimental tests. Table 1 presents the measurements used to calculate the system’s parameters. The parameters are presented in the Table 2. The stator and the rotor resistances are calculated using the method of the voltamperemeter. A DC voltage is applied to the stator phase and to the double rotor phases (the rotor phases are star-connected by construction) and resultant currents are measured. The following formulas give the values of the machine resistances:

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Fig. 11 Test bench used for the experiment Table 1 Measures taken to identify system parameters (SI) Parameter targeted

Formula followed

Measure 1

Rs

Equation 14

Vsa _dc

Rr

Equation 15

Ls

Equation 16

Lr’

Equation 16

Lm

Equation 17

m

Ur Us

Table 2 System parameters measured

Measure 2 3

Vsb _dc

Measure 3 9

Vsc _dc

6

Isa

0.43

Isb

1.28

Isc

0.86

Ur ab _dc

10

Ur bc _dc

11

Ur ca _dc

15

Ir ab

0.62

Ir bc

0.65

Ir ca

0.92

Usab

81

Usbc

145

Usca

66

Isab

0.29

Isbc

0.52

Isca

0.24

Ur ab

100

Ur bc

91

Ur ca

85

Ir ab

0.39

Ir bc

0.36

Ir ca

0.34

Ur ab _max

64

Ur bc _max

110

Ur ca _max

53

Isa

0.45

Isb

0.79

Isc

0.39

Ur ab

110

Ur bc

151

Ur ca

170

Usab

114.8

Usbc

157.3

Usca

177.3

Quantity

Value

Stator phase resistance Rs ()

6.990

Rotor reel phase resistance Rr ()

8.203

Stator inductance Ls (H)

0.509

Rotor reel inductance Lr (H)

0.466

Magnetizing inductance Lm (H)

0.3825

Transformation factor m

0.96

Moment inertia J (kg.m2 )

0.00564

Friction coefficient f (N.m.s)

0.007

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Rs =

V s_dc Is

(14)

Rr =

Ur _dc 2 × Ir

(15)

The determination of the inductions values needs to star-connect the stator phases. An AC voltage is applied to two stator and rotor phases and resultant currents are measured. The following formula is used to calculate the inductances (i ∈ {s; r }): 1 Li = × ωi



Ui √ Ii × 3

2 − Ri 2

(16)

The magnetizing inductance is calculated by applying an AC voltage to a stator phase and measuring the resultant current. A voltage is induced in the rotor phases. The machine shaft should be slowly rotated and the voltage between two of the rotor phases is monitored. The maximum value of the voltage induced is noted. The formula used to calculate the magnetizing inductance is: Lm =

Ur _max 3 × √ 2 ω × Is × 3

(17)

The transformation factor is calculated by applying a three-phase voltage to the rotor phases and measuring the induced voltages in the stator phases. The ratio between the rotor and the stator voltage gives the value of the transformation factor m. The mechanical parameters are calculated using the servo-drive.

8 Conclusion This paper presents a complete study about the control of a WECS using the FOC algorithm associated with PI controllers. In the first step, a model of the WECS based on a DFIG has been established. A combined MPPT and FOC were then synthesized. The complete system has been simulated in Matlab/Simulink environment. The simulation results show the effectiveness of the control strategy presented in this paper. Indeed, the system tracks the reference value of the angular speed, and the maximum power is extracted at all times despite the large variation of the wind speed. The power coefficient is kept near 1 due to the control of the stator reactive power. The experimental identification of the test bench parameters has been carried out and presented.

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References 1. Sheikhan M, Shahnazi R, Nooshad Yousefi A (2013) An optimal fuzzy PI controller to capture the maximum power for variable-speed wind turbines. Neural Comput Appl 23:1359–1368 2. Ihedrane Y, El Bekkali C, Bossoufi B (2017) Direct and indirect field oriented control of DFIGgenerators for wind turbines variable-speed. In: 2017 14th international multi-conference on systems, signals & devices (SSD). IEEE, Marrakech, pp. 27–32 3. Yao X, Liu Z, Cui G (2010) Decoupling control of doubly-fed induction generator based on fuzzy-PI controller. In: International conference on mechanical and electrical technology, vol 5 4. Jazaeri M, Samadi AA (2015) Self-tuning fuzzy PI-based controller of DFIG wind turbine for transient conditions enhancement: variable speed wind turbines. Int Trans Electr Energy Syst 25:2657–2673 5. Gopala VM, Obulesu YP (2015) A new hybrid artificial neural network based control of doubly fed induction generator. Int J Electr Comput Eng IJECE 5:379 6. Sattar AA, Marei MI, Badr AO (2010) Back-to-back converters with doubly fed induction generators for wind energy scheme. In: The 2010 international conference on computer engineering & systems, Cairo, Egypt. IEEE, pp 114–119 7. El Azzaoui M (2018) Approach of controling a doubly fed induction generator in order to integrate wind energy into the grid 8. Boujoudi B, Kheddioui E, Machkour N (2017) Comparative study between PI and the sliding mode control for the DFIG of a wind turbine. In: 2017 14th international multi-conference on systems, signals & devices (SSD), Marrakech. IEEE, pp. 49–58

Energy Management Strategy for an Optimum Control of a Standalone Photovoltaic-Batteries Water Pumping System for Agriculture Applications Mohammed Benzaouia, Bekkay Hajji, Abdelhamid Rabhi, Adel Mellit, Anas Benslimane, and Anne Migan Dubois Abstract Pumping water using multiple energy sources is the ideal solution for supplying potable water in isolated or arid areas where there is no supply of grid power. In this paper, an effective control, and energy management strategy for a stand-alone photovoltaic-batteries water pumping system for agriculture applications is presented. The system is composed of solar photovoltaic panels as a primary energy source, and Lead-Acid batteries as a seconder energy source to supply the BLDC motor that drives the centrifugal pump. The energy management strategy uses an intelligent algorithm to satisfy the energy demanded by the motor, also to maintain the state-of-charge of the battery between safe margins in order to eliminate the full discharge and the destruction of the batteries. Drift is a major problem in photovoltaic systems; this phenomenon occurs when solar irradiation changes rapidly. Classical power maximization algorithms do not solve this problem, for this reason, a Modified Perturb & Observe (MP&O) has been implemented. The obtained results show a fast convergence performance to the maximum power point compared to the conventional Perturb & Observe (P&O). Computer simulation results confirm the effectiveness of the proposed energy management algorithm under random meteorological conditions. Keywords Energy management strategy · PV panels · MPPT · Modified P&O · DC-DC converter · Lead-acid batteries · BLDC motor and centrifugal pump M. Benzaouia (B) · B. Hajji · A. Benslimane Renewable Energy, Embedded System and Data Processing Laboratory, National School of Applied Sciences, Mohamed First University, Oujda, Morocco e-mail: [email protected] A. Rabhi Laboratory of Modelization, Information and Systems, University of Picardie Jules Verne, 3 rue Saint Leu, 80039 Amiens Cedex 1, France A. Mellit Renewable Energy Laboratory, Faculty of Sciences and Technology, Jijel University, Jijel, Algeria A. M. Dubois GeePs | Group of Electrical Engineering-Paris CNRS UMR 8507, CentraleSupelec, Paris-Sud University, Sorbonne University, 3 & 11 Rue Joliot-Curie, 91192 Gif-sur-Yvette, 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/10.1007/978-981-15-6259-4_89

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1 Introduction Water pumping usually depends on conventional resources or a diesel generator. The use of these fossil energy resources (diesel generator, propane…) not only requires expensive fuels but also creates noise and air pollution and high maintenance costs [1]. Hence, to reduce the dependence on these resources, a lot of focus has been paid to renewable energy resources. Wind generators and photovoltaic (PV) systems are among the most widely used and exploited sources in last years. Combining these renewable energy sources with batteries and supercapacitors as storage systems have received recently considerable attention, especially for pumping water for agriculture or human process in isolated areas or remote hilly regions where there is no grid power [2] and [3]. These systems are environmentally friendly, require low maintenance with no fuel cost, and provide continuous energy whatever the variation of the load or the weather condition. However, these hybrid systems require a management strategy in order to satisfy the load demand and to manage the power flow while ensuring efficient operation of the different energy systems. In this context, some researchers have established many configurations and power management algorithms. In [4], the authors propose a system composed by a photovoltaic (PV) source and batteries storage. An algorithm of power management has been implemented in order to keep supplying the load demand whatever the solar irradiation condition is; two applications were presented throughout this paper. Firstly, the load is made variable while in the second it is considered constant in the case of water pumping. Other works have also developed an algorithm to manage a hybrid system in order to satisfy the energy demand of a home; the system has been evaluated under different meteorological conditions [5] and [6]. Moreover, some researchers have focused on developing the optimum sizing of the different components of the water pumping including PV panels and batteries to maintain the continuity of the energy produced and required by the loads [7] and [8]. In this research paper, a system composed by a PV panels and Lead-Acid batteries with an intelligent power management control is presented. The main aim of this system is to pump a constant water flow rate under random conditions (Solar irradiation and state of charge of batteries). In order to ensure maximum extraction of power from the photovoltaic panels a Modified Perturb and Observe (MP&O) have been used. In addition, the brushless DC motor is controlled by electronic commutation, which allows the motor to operate at the nominal performances. The paper is organized as follows: In Sect. 2, the modeling of each component is detailed; Sect. 3 is devoted to the extraction of the maximum power from the PV panels. In Sect. 4, a description of the proposed power management strategy is discussed, the different modes and possible cases of operation of the hybrid system were presented. The simulation results and discussion are provided in Sect. 5. Finally, the last section concludes this paper.

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2 Presentation and Modeling of Studied System The structure of the studied system is shown in Fig. 1; it contains a photovoltaic generator connected to a DC-DC converter, which is controlled by the Modified Perturb & Observe (MP&O) algorithm in order to extract the maximum power from the PV panels. The system also contains Lead-Acid batteries connected to a bidirectional converter to allow the current to flow in both directions, so the batteries are charged and discharged according to the user’s energy demand and the methodological conditions. An inverter is used as an intermediary between the two preceding components and the motor-pump. The control of the motor is done through electronic commutation that allows to operate the motor in the nominal capacities. The system is managed with an intelligent algorithm, which depends on the state of the three switches (K 1 , K 2 and K 3 ). The design of studied system is elaborated in following sections.

Fig. 1 Stand-alone water pumping system description

2.1 Photovoltaic Panels Modeling Several mathematical models of photovoltaic generators were developed to describe their nonlinear behavior and their operation. In this work, the following model (Fig. 2) is chosen [7, 8] and [9]. The PV module parameters used in this work are shown in Table 1. The output current (I pv ) of the photovoltaic cell under standard operating conditions (1000 w/m2 , 25 ◦ C) is given by the following Eqs. 1. and 2.

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Fig. 2 PV cell equivalent circuit

Table 1 The PV module parameters

Parameters

Values

Numbers of cells in a module, Nc

36

Open circuit voltage, VOC

21.8 V

Short circuit current, I SC

7.24 A

Maximum voltage at MPP, VM P P

17.2 V

Maximum current at MPP, I M P P

6.69 A

Maximum Power at MPP, PM P P

115 W

Fig. 3 P–V and I–V characteristics for different values of solar irradiation

Fig. 4 P–V and I–V characteristics for different values of temperature

I P V = I ph − Id − Ish 

I P V = I ph − Is e

q (V P V +I.Rs ) nkT



−1

−

(V P V + I.Rs ) Rsh

(1) (2)

where: Is is the saturation current, q is the electron charge (1, 6.10−19 (C)), k is Boltzmann constant (1, 38.10−23 (J.K−1 )), n is the diode ideality factor, T is the PV cell temperature (K).

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In this simulation, fourteen (14) panels has been used. The simulated I–V and P–V characteristics (Figs. 3 and 4) present the effect of irradiation and temperature on the behavior of the adopted PV generators.

2.2 Storage Modeling The purpose of using the batteries is to store the excess power and to satisfy the load demand in bad weather conditions or in night periods. Many varieties model of batteries exist in the literature. In this work, the proposed model is shown in fig. 5. It contains two electrical element: a voltage source (E B ) and an internal resistance (Ri ) [10–12] and [13]. The adopted model of battery is defined by the following equations: VBatt = E B ± I Batt Ri

(3)

where: E B is the voltage source, Ri the internal resistance and I Batt is the current of the battery. The battery capacity C Batt is given by [14]: C Batt =

E d .Nd VBatt .D O D.η Batt

(4)

where: E d is the daily electrical energy required by the load (Motor-pump), Nd number of autonomy days, VBatt is the battery voltage, D O D is the depth of discharge and η Batt is the battery performance. The state of charge S OC depends on the current of the battery (I Batt ). If it is positive (battery discharge) then the state will decrease and discharge if negative (battery charging) then the charge will increase [15]. This is given by the following equation: I Batt dt C t−1 Batt t

S OC(t) = S OC(t − 1) − ∫

(5)

where: C Batt is the capacity of battery and I Batt represents the current that flow to or from the battery. After sizing calculations, ten (10) batteries of 12 V, with a capacity unit C Batt = 50 Ah, have been considered.

Fig. 5 Electrical model of the battery

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3 Maximum Power Point Tracking (MPPT) Various algorithms were developed in order to extract the maximum power from photovoltaic panels. Perturb & Observe (P&O), Incremental conductance (Inc.) and Fuzzy logic (FL) are the most used algorithms in research [16] and [17]. In this work, a Modified Perturb & Observe (MP&O) is implemented to avoid the failure of P&O algorithm under fast changes of solar irradiation (Drift phenomenon) [18].

3.1 Perturb & Observe (P&O) The P&O algorithm is widely used to extract the maximum power from PV panels. The simple structure and small measured parameters are among the reasons why this algorithm is chosen [19, 20]. The entire P&O algorithm is given in Fig. 6.

Fig. 6 Flowchart of the conventional P&O MPPT algorithm

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3.2 Modified Perturb & Observe (P&O) The P&O technique algorithm fails under rapidly changing of solar irradiation [21], the P&O algorithm takes improper decision at changing primary step of duty-cycle [22]. A Modified P&O technique is proposed to avoid the drift problem by incorporating the information of change in current (d I ) in the decision process in addition to change in power (d P) and change in voltage (d V ). The steps of the algorithm are presented in Fig. 7 [23]. The obtained results (Fig. 8) show clearly the efficiency

Fig. 7 Flowchart of the drift-free modified P&O MPPT algorithm

Fig. 8 Comparison between conventional & modified P&O algorithm for one panel

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of the Modified Perturb & Observe (MP&O) algorithm compared to conventional P&O.

4 Energy Management Strategy The power management for 48 h is illustrated in the flowchart of Fig. 9. The strategy is based on the evaluation of daily demand of water (Pdemand ) by the user and on the availability of the power of solar (PP V ). The algorithm consists in generating three control signals K 1 , K 2 and K 3 starting from four inputs: power of PV generators (PP V ), the power demand (Pdemand ) which is given by user, the measured power (PLoad ) and the battery state of charge (S OC). For the beginning, it is necessary to set the maximum and minimum limits of the batteries S OC. In this work, the minimum S OCmin is fixed to 20% and the maximum S OCmax to 80%. The energy management strategy is described below: • If P demand = P Load : Mode 1: This operating mode occurs in the following case: if the power generated by PV panels is greater than the power required by the load and the state of charge is less than S OCmax , the excess power is stored by the batteries. Mode 2: This operating mode occurs in the following case: if the power generated by the PV panels is less than the power demanded by the load, the batteries are used to add the necessary power as long as the state of charge is not less than the S OCmin . Mode 3: This operating mode occurs in the following case: if the power generated by PV panels is equal to zero and the S OC > S OCmin , this mode may occurs during the night and the beginning and end of the day. The batteries cover the power demanded by the load until the intervention of the PV generators.

Fig. 9 Flowchart of energy management strategy

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Mode 4: This operating mode occurs in the following case: if the power generated by PV panels is equal to the power demanded and the S OC = S OCmax , so the disconnection of the batteries is necessary in order to protect them. Mode 5: This operating mode occurs in the following case: if the power generated by the PV panels is equal zero and the S OC is less than S OCmin , the load is disconnected. • If P demand = 0 : Mode 6: This operating mode occurs in the following case: the PV generators produce power, and the state of charge of the batteries is max. In this case, the batteries are diconnected. Mode 7: This operating mode occurs in the following case: the power of PV generators is PP V > 0. In this case, PP V is stored in batteries, as long as their state of charge of the batteries does not reach maximum (S OC < S OCmax ). Mode 8: This operating mode occurs in the following case: there is no load demand and the PV generators do not produce power so the system is disconnected.

5 Simulation and Results In the studied system, the renewable PV power is taken as primary source, while the batteries are used as a backup and storage system. The priority is given to the PV generators, not only to exploit the entire renewable energy but also to increase the battery’s life cycle. To test and to verify the effectiveness of the energy management strategy applied to the studied system (Fig. 1), the simulation under MATLAB/Simulink over a period of two different days has been performed. Figures 10 and 11 present the solar irradiation and temperature profiles. In the 1st day (00 h–24 h) the maximum irradiation value reaches 1000 w/m2 , while in the 2nd day (24 h–48 h) it reaches 600 w/m2 . The power profile produced by the PV panels is shown in Fig. 12, taking into account the variations of solar irradiation and temperature. Figure 13 shows the different powers supplied by the sources (PV generators and batteries), in addition to the load profile desired by the user. This profile (Pload ) has been chosen in different periods (night, day, sunrise and sunset). Based on these results, we can differentiate four cases, which are given as follows: Case 1: During the intervals [0 h, 3 h] and [23 h, 26 h], the load power demand is 1320 W. The deficient power is only supplied by the batteries because PP V equal zero (PP V = 0) and the state of charge (S OC) of batteries is not at the minimal value.

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Fig. 10 Profile of the solar irradiation

Fig. 11 Profile of ambient temperature

Fig. 12 Power of PV generator

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Fig. 13 Power profiles (PBatt , PP V and PLoad )

Case 2: During the intervals [5 h, 6 h], [10 h, 12 h], [14 h, 20 h], [30 h, 33 h] and [41 h, 44 h], the photovoltaic generators produce Power (PP V > 0) so the batteries are in charge mode since there is no demand of power (PLoad = 0), and the state of charge is between the maximum and the minimum (S OCmin < S OC < S OCmax ). Case 3: During the intervals [6 h, 10 h] and [33 h, 41 h], The load requires a power of 1320 W but the photovoltaic generators do not produce sufficient power. The compensation of power is done by the batteries taking in consideration that the S OC is not under S OCmin , (S OC < S OCmin ). Case 4: During the interval [12 h, 14 h], there is a surplus of power produced by PV generators, and the load requires a power of 1320 W, so the excess of power is stored in the batteries as long the S OC < S OCmax . The evolution of the state of charge (S OC) is given in Fig. 14. The (S OC) varies between the minimum (S OCmin = 20%) and the maximum (S OCmax = 80%). Therefore, the batteries are protected against complete discharge and overcharging. The objective was to pump a fixed water flow over two successive days and in different periods. Figure 15 shows clearly that the pumped flow is fixed at 30 m3 /h.

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Fig. 14 State of charge of batteries

Fig. 15 Water flow

6 Conclusion In this paper, the control and management of a photovoltaic system with storage batteries for agricultural applications have been presented. The system configuration, the MPPT algorithm and the proposed management strategy were simulated in MATLAB/Simulink environment. The obtained results show the effectiveness and the reliability of the system during two successive days under different weather conditions. The proposed management strategy allows to ensure optimal operation of the system without human intervention, which makes the system more intelligent.

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Mass Flow Rates Effect on the Performance of PV/T Bi-fluid Hybrid Collector (Single and Simultaneous Modes) Oussama El Manssouri, Chaimae El Fouas, Bekkay Hajji, Abdelhamid Rabhi, Giuseppe Marco Tina, and Antonio Gagliano Abstract A hybrid photovoltaic/thermal collector (PV/T) is used to produce simultaneously electrical and thermal energy from absorbed solar irradiation. The research to date has tended to focus on either bi-fluids (water and air) as the working fluid to supply energy needs for different applications. The purpose of this work is to test the performances of PV/T at different operating modes of fluid e.g. the air mode, the water mode, and the simultaneous mode (water& air). Furthermore the effect of mass flow rate as a key parameter for better electrical and thermal performances has been investigated. The PV/T performances were assessed based on a dynamic numerical model. An energy balance equations have been established for each layer, then implemented in MATLAB software. The results show that thermal efficiency in the simultaneous mode (air & water) is better compared to others modes. The thermal efficiencies for independently fluid condition have ranged from approximately 20 to 48%, and increased to a maximum efficiency of near to 68% for the case of the simultaneously fluids. This result indicates that the optimum mass flow rates for air and water are 0.035 kg/s and 0.007 kg/s respectively. Therefore, the theoretical model developed of the independently and simultaneously operational modes is validated, evidencing a good fit between simulation results and the experimental data available in literature experimental. Keywords PV/T collector · Bi-fluid · Electrical power · Thermal power

O. El Manssouri (B) · C. El Fouas · B. Hajji Renewable Energy, Embedded System and Data Processing Laboratory, National School of Applied Sciences, Mohamed First University, Oujda, Morocco e-mail: [email protected] A. Rabhi Modelization, Information and Systems Laboratory, Picardie Jules Verne University, Amiens, France G. M. Tina · A. Gagliano DIEEI: Department of Electric, Electronic and Computer Engineering, University of Catania, Catania, Italy © 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/10.1007/978-981-15-6259-4_90

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1 Introduction A PV/T solar collector is a new technology that overcome the problem of PV degradation performances with temperature increasing. In fact it is reduces the temperature of the PV module, which in turn increases efficiency. As well, its allows electricity and heat to be produced simultaneously from a solar radiation [1]. it is reported that the most fluid used to extract heat and transform it into thermal energy is air or/and water [2]. As the air is used, Koech et al. [3] presented the effect of operative conditions (mass flow, ambient temperature, irradiance) on the performance of a PV/T air system. A steady state thermal model has been developed and validated by experimental results. They concluded that increasing the air mass flow significantly increases the overall system performance when the design parameters are optimized. [4] studied the effect of flow rate on the performance of a single-pass, double-pipe PV/T collector. Results have shown that when the collector operates at high mass flow rates, electrical, thermal and overall performances are increased. As the water is used, the water-type PV/T collector is considered to be more effective than that using air due to it is thermo-physical-properties and its higher rate of heat extraction [5]. A (PV/T) water system is studied by [6], with constant inlet temperature and fluid flow. The analysis was performed from thermal and electrical point of view. The thermal and electrical performances of the PV/T water modules have been evaluated at different mass flow rates values (0.03 kg/s and 0.06 kg/s). They concluded that the mean thermal and electrical efficiencies for the first configuration are 40.7% and 11.8%, respectively, and for the second are 39.4% and 11.5% respectively [7]. On the other hand, when two fluid are used (air&water), the collector is known as a bi-fluid PV/T solar collector. It’s was first introduced by Tripanagnostopoulos and followed by Assoa et al. [8, 9]. Othman et al. [10], evaluated the bi-fluid PVT system performances at various irradiance, water and air flow rate. It’s found that at 800 W/m2 of solar irradiance and at optimum air and water flow rate respectively of 0.05 kg/s and 0.02 kg/s, a maximum electrical and thermal efficiencies can be reached. Moreover, a water-air based bi-fluid PV/T combined system is studied and tested by Roonak et al. [11] under weather case conditions of Sanandaj, Iran at air and water flow rate of 0.01 kg/s and 0.003 kg/s respectively, The obtained maximum overall efficiency is 0.49 and 0.69 for air and water mode respectively. An improved bi-fluid water-air based system model using a finite difference numerical method is proposed by Jarimi et al. [12]. It’s noticed that satisfactory electrical and thermal energy gain is achieved in separated fluid cooling mode and higher in simultaneous cooling mode. Su et al. [2] numerically analyzed the performance of dual channel PVT system using four different fluid configurations. It was verified that, water-water PV/T is excellent in terms of electrical as well as thermal performance. An optimum electrical and overall efficiencies were found around 7.8% and 84.2% respectively at mass flow rate of at 0.15 kg/s.

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Fig. 1 The bi-fluid PV/T hybrid collector concept

In this paper, a mathematical model of a bi-fluid PV-T solar collector that is cooled by two fluids (air and water) is developed. This model was then used to evaluate the performance of the PV-T collector at different operational modes. First, the simulations were conducted considering each fluid (air and water) operating individually (single fluid mode/Mode A and Mode B). Then, the bi-fluids mode (simultaneous fluid mode/Mode C and Mode D) was simulated. The thermal, electrical and performances of the PV-T collector using different modes were studied and compared. The mathematical model was then compared the experimental results published in the literature [12].

2 PV/T Design The PV/T hybrid collector studied in this work is a bi-fluid type as shown in Fig. 1. This PV/T is unglazed type; mainly formed by a monocrystalline photovoltaic cells, a back surface of Tedlar attached to a cooling unit and a thermal isolation. The cooling unit integrates a serpentine shaped tube for the water flow and a single pass air channel with set of fins.

3 Theoretical Analysis The proposed PV/T model is developed in order to determine the PV/T electrical and thermal performances. Then, It’s tested under a three different flow modes. At air stagnation, the water cooling mode is defined. Conversely, at water stagnation, the air cooling mode is defined. Indeed, the mode air& water is born at air and water simultaneous flow. For the different cooling modes reported, the following assumptions have been considered: • The sky can be assimilated to a black body with equivalent temperature calculated. • The thermo-physical proprieties of materials are assumed to be constant. • The effects due to the bends of the serpentine copper tube are negligible [13].

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• For stagnant air, the air layer is considered as an insulation layer. Hence, the heat transfer between the air channel inner wall surfaces is due to heat conduction. • The fluid entering the duct is at ambient temperature and the fluid temperature in the duct is an arithmetic mean of the inlet and outlet temperatures.

3.1 Energy Balance Equations The developed PV/T mathematical model is based on the equations of the energy balances written for the various nodes of the bi-fluid PV/T system can as presented: For the PV Module: ρc δc Cc

dTc = Ui .(Ta − Tc ) − Kcb .(Tc − Tb ) + G.αc .F.τg − G.η.αc .τg .F dt

(1)

For the Tedlar: dTted = dt Kcb .(Tc − Tted ) − Kba .(Tted − Ta ) + G.αted .τc .τg + αted .τg .(1−F).G ρted δted Cted

(2)

For the Air Flow: ρa δa Ca

dTa = hf .(Tted − Ta ) − 2.m ˙ a Ca .(Ta − Ta−in ) − hf .ηf .(Ta − Tiso ) dt

(3)

For the Water Flow: ρw δw Cw

dTw = hf .(Tted − Tw ) − 2.m ˙ a Ca .(Tw − Tw−in ) dt

(4)

For the Fins:   dTfin Nfin −Kfin .Afin = hcvfin,air Nfin Ac,fin .(Tfin − Ta ) dz

(5)

For the Thermal Insulation: ρiso δiso Ciso

dTis = hf .ηfin .(Tiso − Ta ) dt

(6)

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3.2 Heat Transfer Coefficients Different heat transfer coefficient formulation has been established for the developed PV/T configuration as function of heat transfer process. The correlations for the radiative and convective heat transfer coefficients and the thermo-physical properties of the air and water were obtained from the literature [14]. Ui = hw + hr−c−a

(7)

hw = 5.7 + 3.8.W

(8)

hr−c−a = σ.εv .

  4 Tc4 − Tsky (Tc − Ta )

(9)

Where εv is the glass emissivity; σ is the Stefan-Boltzman constant; Tg, is the glass temperature; Ta is the ambient temperature; W is the wind speed; Tsky is the equivalent sky temperature calculated from: Tsky = 0.005520.Ta1.5

(10)

The forced convective heat transfer coefficient in the channel is determined as function of fluid thermal conductivity λf , hydraulic diameter Dh and applying the correlation of [14] for Nusselt number Nu according to regime fluid flow: hf = Nu

λf Dh

(11)

where λf and Dh are the thermal conductivity and hydraulic diameter of the flow channel, respectively. Nu is the Nusselt number for forced convection employed by J.A. Duffie and Beckman [14].

3.3 Energy Analysis The electrical and thermal production of the PV/T is evaluated for the different considering configuration as noted bellow: Qe = G.αc .τ2g .F.A

(12)

    Qth = Cw .m ˙ w . Tw,out − Tw,in + Ca .m ˙ a . Ta,out − Ta,in

(13)

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The electrical and thermal efficiencies can be obtained by: ηe = ηref [1 − βref (Tc − Tref ) ηth =

Qth A.G

(14) (15)

4 Results and Discussion Fluid mass flow rate is a key parameter playing a very important role in energy production from the PV/T solar collectors [15]. The influence of fluid mass flow rate in getting better electrical and thermal performances is determiner in this section. Then, the features parameters of the bi-fluid PV/T studied in this work are indicated in [12]. Four cooling modes have been studied (Mode A, Mode B, Mode C and Mode D), considering an average solar radiation of 700 W/m2 and an average wind speed of 1 m/s as operative conditions. Such as, mode A and B defined the single water flow and air flow modes, in which mass flow rate of water and air respectively was set at 0 kg/s. The mode C and D defined the simultaneous flow of air and water. In mode C, water mass flow rate was fixed in the flow regions while the air mass flow rate is variable. Otherwise, in mode D, the air flows was fixed in flow region, while the water mass flow rate is varied.

4.1 Single-Fluid Cooling (Mode A and Mode B) The PV/T performances evolution in single-fluid cooling modes as function of mass flow rate in shown in Fig. 2a, b. The experimental and simulation values of electrical and thermal efficiencies and fluid temperature rise, during water flow (ModeA) and air flow (mode B) are illustrated in this figure. From obtained results, a similarly trend between experimental and simulated results is observed. An increase in fluids’ mass flow rate results an increase in the collector thermal efficiency. This is due to the great heat extracted from the PV module. In case of water mode cooling, the increase in water mass flow rate enhances heat transfer by convection in the cooper pipe. When in air cooling mode, the convection mechanism is favoured with the inner wall of the air channel. A decrease in fluid temperature rise with mass flow rated increase is also observed. This could be explained by the less time spent by fluids for each cooling mode with flow fluid speed increasing (mass flow rate increasing). In contrast to thermal efficiency, a marginal increase in electrical efficiency as tested and theoretically and experimentally.

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(a) (b) MODE A: water cooling , (a) Thermal efficiency &water rise temperature versus watermass flow rate., (b) Electrical efficiency & cell temperature versus water mass flow rate.

(a) (b) MODE B: air cooling , (a) Thermal efficiency &air rise temperature versus air mass flow rate., (b) Electrical efficiency & cell temperature versus air mass flow rate.

Fig. 2 Simulated and experimental bi-fluid PV/T performances at single mode (Mode A: water cooling; Mode B: air cooling)

Obviously, as the mass flow rate increase, thermal and electrical efficiency, tends to stabilize. As results, an optimal fluid mass flow rate could be determined about 0.035 kg/s and 0.007 kg/s respectively for water (mode A) and air (modeB).

4.2 Dual-Fluid Cooling (Mode C and Mode D) In this section, behavior of each fluid at simultaneous cooling operation, as function of fluid mass flow rate is assessed. The fluids flows were in the two channels, where the working fluids are not physically in contact. By the use of both fluid simultaneously, performances of fluid could be influenced by the other fluid. Therefore, an analysis is made using water at mass flow rate of 0.007 kg/s with different air mass flow rate values (mode A). Unlike, using air at mass flow rate of 0.035 kg/s with different water mass flow rate values (mode B) (Fig. 3a, b). As noticed, an increase in air or water mass flow rate would imply an increase in the thermal efficiency (single mode). From Fig. 3 a, b, when water mass flow rate

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(a)

(b)

MODE C :Simultaneous cooling at water mass flow rate of 0.007 kg/s , (a) Thermal efficiency &air rise temperature versus air mass flow rate., (b) Electrical efficiency &cell temperature versus air mass flow rate. Fig. 3 Simulated and experimental bi-fluid PV/T performances at simultaneous mode (water& air cooling Mode C)

(a) (b) MODE D :Simultaneous cooling at air mass flow rate of 0.035 kg/s , (a) Thermal efficiency &water rise temperature versus water mass flow rate., (b) Electrical efficiency &cell temperature versus water mass flow rate. Fig. 4 Simulated and experimental bi-fluid PV/T performances at simultaneous mode (water& air cooling ModeD)

remains fixe at 0.007 kg/s with variation of air mass flow rate, the thermal efficiency is evaluated as a total efficiency of the two fluid. Therefore, its found higher than that in single mode. On the other hand considering each fluid, a smaller amount of thermal energy is extracted by present of the other fluid. This was clearly observed when a fluid mass flow rate was fixed. As can be seen in Fig. 4a, b, when the air mass flow rate was fixed, the increase in the water mass flow rate has an inconsiderable effect on the amount of energy extracted by the air flow. This is due to the favored convection of the air, covering the entire back surface of the PV module and including the outer surface of the copper pipe. While, water convection is carried-out only inside the interior wall of

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the serpentine copper pipe. From, this figure, a good agreement is noted between theoretical and experimental results.

5 Conclusion This study introduced a theoretical assessment of a unglazed bi-fluid PV/T hybrid collector, with a single pass air-channel as an air heating component and a serpentineshaped copper pipe as water heating component. As well, the thermal behavior of this PV/T collector’s type is determined, based on energy balance equations established for each PV/T layer. A numerical model is built, taking into account the dynamic regime, where the Runge-Kutta (RK4) numerical is used to solve the set of obtained equations in MATLAB software. An attempt is made to predict the bi-fluid PV/T hybrid collector performances. In more detail, PV/T performances assessment is done when the air and water were operated independently (Mode A and Mode B respectively) or simultaneously (Mode C and Mode D respectively). Then, for better PV/T electrical and thermal efficiencies, developed model is tested at different mass flow rate values. Furthermore, the test the accuracy of the developed bi-fluid PV/T model is performed by comparing simulation results with experimental data reported in literature by [12]. Accordingly, obtained results considering the four modes (A, B, C, D) could be summarized as follow: • At simultaneous air & water mode (mode C and D), the electrical power is increased by providing a lower PV panel temperature. • Optimum mass flow rates for air and water are 0.035 kg/s and 0.007 kg/s respectively (single mode). • At simultaneous cooling, the equivalent thermal efficiency in mode C and mode D could be around 68%. • The developed model fit well with experimental data reported by [12].

References 1. Fuentes M, Vivar M, Casa J De, Aguilera J (2018) An experimental comparison between commercial hybrid PV-T and simple PV systems intended for BIPV. Renew Sustain Energy Rev 93:110–120. https://doi.org/10.1016/j.rser.2018.05.021 2. Su D, Jia Y, Huang X, Alva G, Tang Y, Fang G (2016) Dynamic performance analysis of photovoltaic-thermal solar collector with dual channels for different fluids. Energy Convers Manag 120:13–24. https://doi.org/10.1016/j.enconman.2016.04.095 3. Koech RK, Ondieki HO, Tonui JK, Rotich SK (2012) A steady state thermal model for photovoltaic/thermal (PV/T) system under various conditions 1:1–5 4. Bambrook SM, Sproul AB (2012) Maximising the energy output of a PVT air system. Sol Energy 86:1857–1871. https://doi.org/10.1016/j.solener.2012.02.038

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5. Touafek K, Khelifa A, Adouane M (2014) Theoretical and experimental study of sheet and tubes hybrid PVT collector. Energy Convers Manag 80:71–77. https://doi.org/10.1016/j.enc onman.2014.01.021 6. Mishra RK, Tiwari GN (2013) Energy and exergy analysis of hybrid photovoltaic thermal water collector for constant collection temperature mode. Sol Energy 90:58–67. https://doi.org/10. 1016/j.solener.2012.12.022 7. Dubey S, Tay AAO (2013) Testing of two different types of photovoltaic-thermal (PVT) modules with heat flow pattern under tropical climatic conditions. Energy Sustain Dev 17:1–12. https://doi.org/10.1016/j.esd.2012.09.001 8. Assoa YB, Gaillard L, Ménézo C, Negri N, Sauzedde F (2018) Dynamic prediction of a building integrated photovoltaic system thermal behaviour. Appl Energy 214:73–82. https://doi.org/10. 1016/j.apenergy.2018.01.078 9. Tripanagnostopoulos Y (2007) Aspects and improvements of hybrid photovoltaic/thermal solar energy systems. Sol Energy https://doi.org/10.1016/j.solener.2007.04.002 10. Othman MY, Hamid SA, Tabook MAS, Sopian K, Roslan MH, Ibarahim Z (2016) Performance analysis of PV/T Combi with water and air heating system: an experimental study. Renew Energy 86:716–722. https://doi.org/10.1016/j.renene.2015.08.061 11. Daghigh R, Khaledian Y (2017) Design and fabrication of a bi-fluid type photovoltaic-thermal collector. Energy 135:112–127. https://doi.org/10.1016/j.energy.2017.06.108 12. Jarimi H, Abu Bakar MN, Othman M, Din MH (2016) Bi-fluid photovoltaic/thermal (PV/T) solar collector: experimental validation of a 2-D theoretical model. Renew Energy 85, 1052– 1067. https://doi.org/10.1016/j.renene.2015.07.014 13. Abu Bakar MN, Othman M, Hj Din M, Manaf NA, Jarimi H (2014) Design concept and mathematical model of a bi-fluid photovoltaic/thermal (PV/T) solar collector. Renew Energy 67:153–164. https://doi.org/10.1016/j.renene.2013.11.052 14. Duffie JA, Beckman WA (2013) Wiley: solar engineering of thermal processes, 4th edn. (2013) 15. Mara UT (2011) Fluid mechanical and heat/mass transfer

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, and A. Gagliano

Abstract Hydrogen production from water electrolysis process is used worldwide as an alternative to conventional fuels. Using photovoltaic thermal system (PV/T) for electrolysis energy supply could be an attractive and innovative option. The PV/T systems are a genuine power production technology, providing simultaneously electricity and heat. In this paper, an attempt is made to investigate the possibility of hydrogen production from PV/T solar plant. Assessment of electrical and thermal performances of this plant is performed based on thermo–electrical dynamic model. Such model was developed taking into account energy balance equations and validated through an experimental data from PV/T power plant installed at the University of Catania, Italy. The results clearly show a good fitting between simulated and experimental results (RMSE% less than 7.00%, and R-squared more than 0.99). At 12 pm, the mean maximum electrical and thermal energy provided by the PV/T solar plant is found about 0.84 kWh/m2 and 0.548 kWh/m2 respectively. In terms of hydrogen production, a considerable amount of hydrogen about 60 ml/min can be provided. Keywords PV/T solar plant · Hydrogen production · Electrical power · Thermal power · Water electrolysis

1 Introduction Currently, solar energy is most promising technology for energy supply in both electrical and thermal form. Different technologies have been allowed for solar energy conversion. Photovoltaic systems (PV) were used for producing electricity and solar thermal system for producing heat. Due to PV modules performances degradation C. El Fouas (B) · O. El Manssouri · B. Hajji Laboratory of Renewable Energy, Embedded System and Information Processing, National School of Applied Sciences, Mohammed First University, 60000 Oujda, Morocco e-mail: [email protected] G. M. Tina · A. Gagliano Departments of Electric, Electronics and Computer Engineering, University of Catania, Catania, Italy © 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/10.1007/978-981-15-6259-4_91

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with its temperature increasing, a new technology is introduced as photovoltaic - thermal collector PV/T [1]. Where, using a cooling fluid performances allows enhancing PV performances and ensured a simultaneously co-generation of electricity and heat. Usually, the most common cooling fluid used in PV/T systems is the water, due to its thermo-physical features compared to air [2]. A huge research works has been investigating the PV/T systems and looking for further performances improvement [3–5]. Thermal absorbers designs and their integration play a crucial role and could be a decisive factor, affecting the energy conversion efficiency. Aste et al. [6] tested experimentally a dynamic model for performance prediction of roll-bond PV/T system. Accordingly, a good agreement has been noted between simulated and experimental daily performances. Bombarda et al. [7] compared and tested at SolarTechLAB three PV/T systems with different designs: insulated and non-insulated roll-bond system‘s design and sheet-and tubes system‘s design. It’s observed that insulated PV/T roll-bond allows a good thermal performance by 8% higher than the PV/T sheet-and-tubes. On the other hand, as hydrogen is a good alternative to conventional fuels, different technologies have been allowed in field of hydrogen production [8]. The use of solar energy as renewable energy for hydrogen production is widely investigated in many research works [9–11]. Photovoltaic- water electrolyzer system is the most studied for hydrogen production. Ahmad et al. [12] built an experimental prototype based on photovoltaic generator for hydrogen production. The energy required by water electrolyzer is supplied, with and without using MPPT. Accordingly, electrolyzer and overall system efficiencies are found respectively as 60% and 2.3% with MPPT, while 50% and 1.5% without MPPT. Ghribi et al. [8] assessed the produced hydrogen by PV/proton exchange membrane (PEM) electrolyzer system. They estimated the hydrogen production at seven locations in Algeria. Then, they found that Adrar, Ghardaia, Bechar and Tamanrasset have a highest hydrogen production. The annual hydrogen amount estimated at this location is between 20 and 29 m3 . To further improve hydrogen production from PV generator. Dahbi and al [13] proposed a method to adapting electrolysis to PV generator. As results, using DC/DC buckconverter with MPPT control can ensure a better adaption between the two systems. Then a maximum amount of hydrogen will be produced. From above literature review, different studies have been conducted on PV/T system performance and hydrogen production from photovoltaic system. Recently, a few research works have been done on hydrogen production based on PVT hybrid systems. [14] analyzed the performance of PV/T system coupled with Hoffman’s Voltameter for hydrogen production. Therefore in the present work, we are interesting by producing hydrogen from PV/T solar plant, providing simultaneously electricity and heat under real operative conditions of Catania, Italy.

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2 PV/T Plant Numerical Model In this work, the developed model concerns a PV/T solar plant, installed at University of Catania (IT) [15], equipped with two Roll-band DUALSUN Wave© PV/T panels connected in series and hydronic circuit as described in [16]. Coupling this solar plant with hydrogen production unit (water-electrolyzer system and hydrogen storage tank), the wool system could ensures, heat, electricity and hydrogen production as illustrated in Fig. 1. For hot water demand, cold water at temperature of 15 °C flows into the tank, then heated and drawn from the tank at temperature T t .

2.1 PV/T Thermal Model Governing Equations In this section, a thermal modeling of the PV/T panel is developed based on heat balance equations, established for each PV/T panel component (Table 1). Furthermore, the thermal model is built, taking into account, effect of environmental variables (irradiance G and ambient temperature Ta), temperature of solar tank and mass flow rate and PV/T system features [15]. A very different task is the estimation of the heat exchange coefficients, especially by convection. As well know, there is lot of empirical correlations which allow calculating the convective heat transfer for both forced and free convection [16–18] Heat transfer governing the be written considering the heat  tank could  ˙ f Tt,out − Tt , the heat delivered to the user demand supplied by the panels ε H mC

Fig. 1 Schematic diagram of PVT based hydrogen production system

Fluid

Tf =

Tout +Tint 2

(ρabsl δabsl Cabsl )

  dTabsl = hcabsl, f T f − Tabsl + (1 − PC)hcabsl,absh (Tabsh − Tabsl ) + PC· dt       hrabsl,absh · (Tabsl − Tabsh ) + hc f,absh T f − Tabsh + hrsky,absl Tsky − Tabsl + hr gr,absl Tgr − Tabsl     dT ˙ f (Tout − Tin ) (ρf δf Cf ) dtf = hc f,absh Tabsh − T f + hcabsl, f Tabsl − T f + mC

absh = (ρabsh δabsh Cabsh ) dTdt   hcabsh,T ed (TT ed − Tabsh ) + (1 − PC)hcabsl,absh (Tabsl − Tabsh ) + PC · hrabsl,absh (Tabsl − Tabsh ) + hc f,absh T f − Tabsh

Absorber

Absorber

(ρTed δTed CTed ) dTdtT ed =   (1 − P F)τg αTed G + (1 − P F)hcT ed,g Tg − TT ed + P F · hcT ed,P V (TP V − TT ed ) + +hcabsh,T ed (Tabsh − TT ed )

Equations   dT ρg δg Cg dtg =           αg G + hr g,sky Tsky − Tg + hr g,gr Tgr − Tg + hvg,a Ta − Tg + hc P V,g P F TP V − Tg + +hcT ed,g (1 − P F) TT ed − Tg      P F(ρPV δPV CPV ) dTdtP V = τg αPV − ηe G + hc P V,g Tg − TP V + hcT ed,P V (TT ed − TP V ) P F

Tedlar

PV cells

Glass

Layer

Table 1 PV/T energy balances equations [3, 16, 22]

(7)

(6)

(5)

(4)

(3)

(2)

(1)

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  m˙ l Cw Tt − Tsup and the heat losses with the outside environment St (Tt − Ta ) [16]: (8) (9)

Thermal Performance The thermal power (Pth ) can be estimated by applying the thermal balance equations to the fluid passing through the panels:   ˙ f Tc,out − Tc,in Pth = mC

(10)

And the thermal efficiency as: ηth =

Pth Aabs G

(11)

2.2 PV/T Electrical Model The electrical model of the studied PV panel is built, based on the single diode model. Then, the PV/T electrical behavior could be derived from a set of electrical parameters, affected deeply by the solar irradiance intensity G and cell temperature T pv . The current–voltage characteristics (I pv –V pv ) can be written as follows:   V +I .R  pv S V pv + I pv .R S q pvN nk.T −1 − I pv = I L − I0 e Rsh

(12)

I L and I0 are light current and diode reverse saturation current respectively R S , Rsh , q, N , n, K and are respectively series resistance, shunt resistance, electronic charge, he number of cells connected by series in PV module, ideality factor, Boltzmann constant (1,38 × 10−23 JK−1 ) [18]. The different electric parameters of the PV panel at STC conditions (AM = 1.5, G r e f = 1000 × W/m2 and T pv,r e f = 25 °C) are reported in [15]. At variable solar irradiance intensity G and cells temperature T pv , the new values of the current I(G, T pv ) and the voltage V (G, T pv ) at load could be given by the following equations [19–21]: 



I G, T pv = Ir e f + I = Ir e f



 G G T + +α − 1 I SC,r e f Gr e f Gr e f

(13)

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T = T pv − T pv,r e f

(14)

  V G, T pv = Vr e f + V = βT − Rs I

(15)

  G  αT + I SC,r e f Gr e f 

  G Voc G, T pv = Voc,r e f + βT + a ln Gr e f   I SC G, T pv =



(16) (17)

where, α and β are respectively the current and the voltage temperature coefficients; a is the modified ideality factor determined as function of reference conditions as reported by [20, 21]. Electrical Performance The electrical power (Pe ) produced by a PV/T plant could be determined, from the irradiance on the collector plane G, the total surface of the PV cells A P V and the electrical efficiency by: Pe = ηe A P V G

(18)

The electrical efficiency is calculating using the following equation:    ηe = ηr e f 1 − β TP V − Tr e f

(19)

Where ηref is the efficiency at STC condition, β is a characteristic of the PV cell and T PV obtained from the energy balance of the PV layer

3 Hydrogen Production Assessment The objectif of the current study is assisted and analyzed the hydrogen production rate, provided by the water- electrolyzer system. As well know, occurs water electrolysis process, the water (H2 O) is split into hydrogen (H2 ) and Oxygen (O2 ) gases. In fact, this process could be done by applying electrical energy, obtained from the PV/T solar plant. A high voltage applied to electrochemical cell in presence of water, allows oxygen gas (O2 ) bubbles evolve at cathode (negative electrode. While, the hydrogen gas (H2 ) bubbles evolve at anode (positive electrode) (Fig. 2). Accordingly, the water electrolysis process could take place, considering the following chemical reaction [14]. H2 0 + 2F −→ H2 + 1/2 O2

(20)

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Fig. 2 The principle of water electrolysis process

The volume of produced hydrogen could be determined using the following equation: VH2 =

R I e T at (ml) FPZ

(21)

Where: R: is the gas constant (8.314 J/mol.K) Ie: is the input current to the electrolyzer (A) Ta: is the ambient temperature (K) F: is the Faraday’s constant (96485 C/mol) P: is the atmospheric pressure (1.01325 105 N/m) Z: is the excess number of electrons (2) By replacing these values in the Eq. (22), the volume flow rate of hydrogen produced can be evaluated by [14]: QH2 =

VH2 = 7.445.I e (ml/min) t

(22)

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4 Results and Discussion Validation Setup The objectif of this section is the test accuracy of proposed PV/T solar plant numerical model. Hence, the simulated results obtained by MATLAB software are compared with the experimental results derived from this real plant. Collecting data has been done considering the test period from 7 to 10 March 2019. The environmental variables e.g. solar irradiance and ambient temperature were shown in Fig. 3. Figures 4 and 5 Show evolution of simulated and measured temperatures: inlet and outlet fluid temperature. A good fitting between compared set temperatures is observed, with some differences in period of sun-set to the sunrise. Such differences could be related to thermal losses in the hydronic circuit, neglected in the simulated model. Furthermore, the water temperature at the inlet and outlet of the PV/T panel achieved respectively average maximum values of 40.2 °C and 43.0 °C at midday. Figure 6 show a comparison between numerical and observed voltage in open circuit condition. As a result good agreement has been noted over central part of the day. Otherwise, a significative difference is shown in the period after the sunrise and before the sunset. Accordingly, presented differences could be explained by the limitation of the numerical model in predicting the electrical behavior at low values of solar irradiation, lower than 200 W/m2 . Figure 7 Shows the evolution of short circuit current at real operative condition, during the same monitoring period. Average, maximum values of about 38.5 V for short circuit current at 8:00 and at 18:00. The trend of open circuit voltage and short circuit current are mostly affected by solar irradiance and photovoltaic module temperature. Indeed, PV temperature is a favorable factor for short circuit current. Otherwise, the open circuit voltage is affected by PV temperature increasing (an increase in temperature allows reducing the open circuit voltage).

Fig. 3 Weather conditions: air temperature (Ta) and solar irradiance (G)

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Fig. 4 Trend of simulated and observed inlet temperatures in the PV/T panel

Fig. 5 Trend of simulated and observed outlet temperatures in the PV/T panel

Fig. 6 Simulated and observed open circuit voltage of the PV/T panel

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Fig. 7 Simulated short circuit current of the PV/T panel

Table 2 Daily results Days

G (kWh/m2 )

Eel (kWh/ m2 )

Eth (kWh/ m2 )

Etot (kWh/m2 )

07/03

6.01

0.79

0.56

2.27

08/03

6.07

0.80

0.45

2.19

09/03

6.61

0.87

0.65

2.54

10/03

6.79

0.89

0.54

2.48

Average

6.37

0.84

0.548

2.372

For evaluating the performance of PV/T power plant over a period, the energy values in a certain time (day, month, year) have to be evaluated. Table 2 shows the electrical (Ee ) and the thermal performance (Eth ) as well as the total performance (ETot ) of the PV/T plant during the specified period. Data summarized in this give a comprehensive outlook of the performances of the PV/T power plant. Hydrogen Production The hourly hydrogen produced from direct coupling PV/T- water electrolyzer system is plotted in Fig. 8. Obviously, there is linear relationship between hydrogen flow rate and the electrolyzer current (Eq. 25). As outcome, a great amount of hydrogen produced by the PV/T solar plant is observed. An average maximum daily production of hydrogen is estimated to be 60 ml/min occurs at 12:00.

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Fig. 8 The daily variations of hydrogen production rate

5 Conclusion In objective of hydrogen production from water electrolysis, a new solar technology is proposed for electricity supply of the electrolyzer device. In this work, a photovoltaicthermal PV/T solar plant is introduced as main energy generator for water- electrolyzer system. Hence, two type of energy can be provided simultaneously: electricity for energy requirement of the electrolyzer and heat that could be used in other application. Assessment of electrical and thermal performances of this PV/T plant is performed based on improved detailed thermo- electrical model. Description of the PV/T power plant behavior is performed, taking into account heat balance equations, PV/T system features, effect of environnemetal variables, temperature of solar tank and mass flow rate. Accordingly, the set of results is presented as numerical results obtained from in MATLAB software and experimental data, noted from PV/T solar plant mounted at the University of Catania, Italy. On the basis of present works, the following conclusions have been drawn: • The results show good agreement of numerical results with the experimental measurements derived from the real PV/T solar plant. • The water temperature at the inlet and outlet of the PV/T panel achieved • Respectively an average maximum values of 40.2 °C and 43.0 °C at midday. • Average, maximum values of about 38.5 V is noted at 8:00 and at 18:00 for open circuit current. While, it’s observed around 8.15 A for short circuit current. • Using PV/T solar plant as electrical energy generator, an average maximum daily amount of hydrogen could be produced about 60 ml/min at 12:00.

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Author Index

A Ababou, Noureddine, 141 Abbas, Hamou Ait, 503 Abd Rahim, Nasrudin, 531 Abdou, Latifa, 451 Abouelanouar, Bouchra, 81, 135, 675 Aboutni, R., 353 Abzi, Imane, 573 Ahaitouf, Abdelaziz, 309 Ahaitouf, Ali, 71, 309, 441 Aissat, A., 211, 317, 325, 333, 347 Ajaamoum, Mohamed, 531 Akjouj, Abdellatif, 249 Al Ghaithi, Asma O., 275 Alami Hassani, Aicha, 89 Alami Marktani, Malika, 309 Alami Merrouni, Ahmed, 699, 777 Alaoui, Mohammed, 747 Alouah, M., 267 Amahmid, A., 267 Amari, Y., 753 Amine, A., 231 Ammi, S., 325, 347 Amrani, Abdel-illah, 777 Amrani, Madiha, 239, 249 Amraqui, Samir, 691 Amzi, Mohamed, 175 Aneli, Stefano, 739 Asbik, Mohamed, 709 Assa Aravindh, S., 275 Ayadi, N., 387 Aynaou, Hassan, 257 Azghiou, Kamal, 183 Aziz, A., 353 Aziz, Abdelhak, 785

B Bah, Abdellah, 709, 809 Bahi azzououm, A., 333 Bahiri, Mohamed Nabil, 123 Bahhous, Karim, 361 Bakari, Dikra, 301, 367 Baliti, Jamal, 747 Barazane, L., 461 Barkat, Said, 469 Belaidi, Bilal, 429, 555 Belarabi, Ahmed, 531 Belkheiri, Mohamed, 469 Belkheiri, Mohammed, 503 Belmajdoub, A., 97 Belmajdoub, Abdelhafid, 175 Ben Abdellah, Abdellatif, 809 Ben Sassi, Hicham, 495 Ben-Ali, Y., 195 Benali, Abdelhamid, 183 Benbrahim, Mohammed, 573 Bengag, Amina, 115, 169 Bengag, Asmae, 115, 169 Benhayoun, Mhammed, 71 Benlghazi, Ahmad, 183 Bennani Dosse, Saad, 105 Bennani, S., 97 Benslimane, Anas, 481, 855 Benzaouia, Mohammed, 855 Berrehili, A., 647 Berrehili, Abd al Motalib, 135 Bikrat, Youssef, 183 Billard, Herve, 429 Billard, Hervé, 555 Blaacha, J., 353 Boubakeur, M., 317

© 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/10.1007/978-981-15-6259-4

894 Bouchnaif, Jamal, 481 Bougataya, Mohammed, 21 Boudouti, El Houssaine El, 239, 249, 257 Bouhedir, R., 461, 565 Bounoua, Zineb, 683 Boushaba, Farid, 659, 667 Bouzi, Mostafa, 293 Bria, D., 195 Bria, Driss, 203 C Chaabelasri, Elmiloud, 761 Chaatouf, Dounia, 691 Chaker, Mohammed, 531 Chatei, Hassan, 285 Chelihi, Abdelghani, 451 Chenini, L., 325, 347 Chennaif, Mohammed, 767 Chokor, Abbas, 583 Chourak, Mimoun, 659, 667, 699 D Dahani, Y., 267 Dahbi, Sanae, 785 Dahri, Nabil, 843 Derri, Mounir, 293 Dequen, Gilles, 161 Dhriyyef, Mohammed, 831 Dib, Mohamed, 421 Dihmani, Nadia, 691 Djafari-Rouhani, Bahram, 239, 249 Dosse Bennani, Saad, 175 Doumiati, Moustapha, 583 Drocourt, Cyril, 161 Dubois, Anne Migan, 855 E El Alami, Abdelmajid, 151 El Fouas, C., 879 El Fouas, Chaimae, 869 El Hassouani, Youssef, 777 El Maakoul, Anas, 809 El Mansouri, A., 267 El Manssouri, O., 879 El Manssouri, Oussama, 869 El Markhi, Hassane, 513 El Mehdi, Abdelmalek, 831 El Mouhib, Manal, 183 El Moussaoui, Hassan, 513 El Ougli, Abdelghani, 819 El-Aouni, M., 195 Elamri, Fatima Zahra, 203 Elboukhari, Mohamed, 115 Elguennouni, Youssef, 747

Author Index Elhafyani, Mohamed Larbi, 767, 797 Elhafyani, Mohammed, 831 Elhitmy, Mohammed, 831 Elkaouini, Morad, 285 Elkihel, A., 647 Elkihel, Ali, 81, 135, 675 Elkihel, Bachir, 659, 667 Elmarghichi, Mouncef, 293 Erraghroughi, Fatima Zahrae, 809 Errahimi, Fatima, 495 Essoufi, M., 593 Es-Sbai, Najia, 495 Ettalabi, Naoufal, 293 F Falyouni, Farid, 203 Fannakh, Mhamed, 797 Farhat, Akram, 609, 637 Feddi, Kawtar, 809 Fekhaoui, Mohammed, 609, 637 Filahi, I., 267 Fouzar, Youcef, 21 G Gadonna, Katell, 429, 555 Gagliano, A., 879 Gagliano, Antonio, 739, 869 Gallassi El, Hicham, 699 Garrouani, Yassine, 89 Ghammaz, Abdelilah, 123 Ghennioui, Abdellatif, 699 Glida, Hossam Eddine, 451 Grari, Abdellatif, 659, 667 Grari, Meryem, 223 Guidoum, Feriel, 141 Gziri, H., 647 Gziri, Hassan, 81, 675 H Hafyani, Mohamed Larbi El, 411 Hajji, B., 387, 461, 593, 753, 879 Hajji, Bekkay, 531, 739, 855, 869 Hajji, Moine El, 401 Hamal, Mohammed, 361 Hamdan, Ali, 583 Hamied, A., 461 Hamli, Abdelkader El, 361 Hanouf, Nacira, 301, 367 Harrak, Abdelkhalak, 339, 375 Hasnaoui, M., 267 Hasni, M., 753 Hassani, Hamid, 441 Hedhili, Mohamed N., 275 Hssikou, Mohamed, 747

Author Index I Idlimam, Raja, 709 J Jabri, A., 629 Jaddar, A., 629 Jeyar, Mohammed, 761 Jorio, M., 97 K Kabbaj, Mohammed Nabil, 573 Kadmiri El, I., 195 Kassmi, Kamal, 785 Kengne, Emmanuel, 21 Khathyri, Fatima, 81, 135 Khattou, Soufyane, 239, 249 Kinani, M. A., 231 Kourchi, Mustapha, 531 Krim, Deae-Eddine, 301, 367 L Labdai, S., 753 Lagliti, Kaoutar, 609, 637 Laidouci, A., 211 Lakhili, Zouhir, 151 Lakhssassi, A., 97 Lakhssassi, Ahmed, 21 Lale, A., 387 Lamreoua, Abdelhak, 481 Launay, J., 387 Lberni, Abdelaziz, 309 Li, Lifu, 617 M M’barki, Zakaria, 523 M’Sirdi, Nacer K., 3 Madani, H., 387 Mahmoudi, Hassane, 401 Mansouri, Anas, 71 Mansouri, Anass, 105, 441 Mazzi, Yahia, 495 Mechaqrane, Abdellah, 683 Meksoub, A., 647 Mellal, Idir, 21 Mellit, A., 461, 565, 729, 753 Mellit, Adel, 55, 855 Messaoudi, Abdelhafid, 785 Mezrhab, Ahmed, 691 Mir, Y., 231 Miry, Camelea, 361 Missaoui, Abdelhak, 285 Mouadili, Abdelkader, 249 Moufid, Ismail, 513 Moussa, Abdelilah, 361

895 Moussa, Labbadi, 401 Moussaoui, Ahmed, 747 Moussaoui, Mohammed Amine, 691 Moussaoui, Omar, 169 Mrabti, Fatiha, 89 N Naimi, Salah Eddine, 339, 375 Najar, Adel, 275 Nejmi, Ali, 421 Ng, Tien Khee, 275 Noual, Adnane, 239 Nour, Mhamed, 21 O Ooi, Boon S., 275 Oral, B., 729 Ou, Jiajie, 617 Ouachani, Iliass, 429, 555 Ouariachi El, Mostafa, 481 Ouassaid, Mohammed, 843 Ouchani, Noama, 257 P Perozzi, Gabriele, 451 Q Qjidaa, Hassan, 151 R Rabhi, A., 461, 593, 753 Rabhi, Abdelhamid, 469, 503, 543, 855, 869 Rachidi, Samir, 777 Raillani, Benyounes, 691 Ramzi, Mohamed, 421 Razi, Mouhcine, 71 Rechem, David Van, 555 Regad, Youssef, 659, 667 Rouibah, N., 461, 565 Ruichek, Yassine, 105 Rrhioua, Abdeslem, 301, 361, 367 S Sağlam, S., 729 Salhi, Jamal-Eddine, 719 Salhi, Mourad, 691 Salhi, Najim, 719, 761 Sbai, Anass, 161 Seghier, Tahar, 543 Seghir, Mohamed, 543 Sejai, Mohamed, 105 Selvaraj, Jeyrage, 531 Senhaji Rhazi, Kaoutar, 523 Sentouh, Chouki, 451

896 T Tahri, Z., 195 Talbi, Abdelkrim, 249 Talbi, Hind, 761 Talj, Reine, 583 Temple-Boyer, P., 387 Tidhaf, Belkassem, 819 Tijani, Lamhamdi, 513 Tina, G. M., 879 Tina, Giuseppe M., 739 Tina, Giuseppe Marco, 39, 869 Touili, Samir, 777 Tounsi, Mohamed Lamine, 141

V Vilcot, J. P., 211, 317, 325, 333

Author Index Y Yagoub, Mustapha C. E., 141 Yousfi, Driss, 531, 843 Z Zahboune, Hassan, 609, 637, 767, 797 Zazoui,M., 231 Zbitou, Jamal, 175 Zegnini, Boubakeur, 543 Zerdani, Sara, 411 Zerfaoui, Mustapha, 301, 361, 367 Zerouali, Mohammed, 819 Zoheir, CifAllah, 223 Zorig, Abdelmalik, 469 Zouggar, Smail, 411, 767, 797 Zouirech, Salaheddine, 819 Zyane, Abdellah, 123