Modeling, Simulation and Optimization: Proceedings of CoMSO 2020 (Smart Innovation, Systems and Technologies Book 206) 9811598290, 9789811598296

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Smart Innovation, Systems and Technologies 206

Biplab Das Ripon Patgiri Sivaji Bandyopadhyay Valentina Emilia Balas   Editors

Modeling, Simulation and Optimization Proceedings of CoMSO 2020

Smart Innovation, Systems and Technologies Volume 206

Series Editors Robert J. Howlett, Bournemouth University and KES International, Shoreham-by-sea, UK Lakhmi C. Jain, Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW, Australia

The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form. Volumes on interdisciplinary research combining two or more of these areas is particularly sought. The series covers systems and paradigms that employ knowledge and intelligence in a broad sense. Its scope is systems having embedded knowledge and intelligence, which may be applied to the solution of world problems in industry, the environment and the community. It also focusses on the knowledge-transfer methodologies and innovation strategies employed to make this happen effectively. The combination of intelligent systems tools and a broad range of applications introduces a need for a synergy of disciplines from science, technology, business and the humanities. The series will include conference proceedings, edited collections, monographs, handbooks, reference books, and other relevant types of book in areas of science and technology where smart systems and technologies can offer innovative solutions. High quality content is an essential feature for all book proposals accepted for the series. It is expected that editors of all accepted volumes will ensure that contributions are subjected to an appropriate level of reviewing process and adhere to KES quality principles. Indexed by SCOPUS, EI Compendex, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST), SCImago, DBLP. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/8767

Biplab Das Ripon Patgiri Sivaji Bandyopadhyay Valentina Emilia Balas •

•

•

Editors

Modeling, Simulation and Optimization Proceedings of CoMSO 2020

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Editors Biplab Das Department of Mechanical Engineering National Institute of Technology Silchar, Assam, India Sivaji Bandyopadhyay National Institute of Technology Silchar Silchar, India

Ripon Patgiri Department of Computer Science and Engineering National Institute of Technology Silchar Silchar, Assam, India Valentina Emilia Balas Aurel Vlaicu University of Arad Arad, Romania

ISSN 2190-3018 ISSN 2190-3026 (electronic) Smart Innovation, Systems and Technologies ISBN 978-981-15-9828-9 ISBN 978-981-15-9829-6 (eBook) https://doi.org/10.1007/978-981-15-9829-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed 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

Preface

National Institute of Technology Silchar, India, has organized an International Conference on Modeling, Simulation and Optimization (CoMSO 2020) during August 3–5, 2020. CoMSO is a premier annual international forum for Modeling, Simulation and Optimization researchers, Scientist, practitioners, application developers, and users. CoMSO 2020 conference aims to bring together researchers around the world to exchange research results and address open issues in all aspects of Modeling, Simulation and Optimization. CoMSO 2020 is an outstanding platform to discuss the key findings, exchanging novel ideas, listening to world class leaders and sharing experiences with peer groups. The conference provides the opportunities of collaboration with national and international organizations of repute to the research community. CoMSO 2020 witnessed a large number of participants and submissions from worldwide. CoMSO 2020 is organized virtually due to unavoidable situations all over the world. The conference aimed to consider unpublished original research works in the six different fields like: (i) Computational Simulation and Modeling; (ii) System Modeling and Simulation; (iii) Device/VLSI modeling and simulation (iv) Control Theory and Applications; (v) Optimization and its applications; and (vi) Modeling and simulation of the energy system. Apart from 61 accepted and presented papers, seven nos of internationally renowned speakers like Prof. Rajkurmar Buyya; Prof. Kalyanmoy Deb; Prof. Yanchook Choe, Prof. Uday S. Dixit; Prof. Tanmay Basak, Dr. Rituparna Dutta and Dr. Balaji Raghavan have shared their experience with the participants. This conference proceedings will be able to disseminate high quality research results in the relevant fields. Silchar, India Silchar, India Silchar, India Arad, Romania

Dr. Biplab Das Dr. Ripon Patgiri Prof. Sivaji Bandyopadhyay Prof. Valentina Emilia Balas

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Modelling and Forecasting of Solar Radiation Data: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Somila Hashunao, Hano Sunku, and R. K. Mehta

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Thermal Performance Study of Bamboo and Coal Co-gasification in a Downdraft Gasifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monoj Bardalai, Banabir Das, PP Dutta, and Sadhan Mahapatra

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Effects of Gurney Flap and Suction Slots on the Aerodynamics of a NACA0012 Airfoil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sumit Shankar Sarvankar, Anindita Apurbaa Phukan, Abhijeet Konwar, and Paragmoni Kalita Effects of Numerical Dissipation and Dispersion on Computing the Convection of a Sharp Scalar Cone . . . . . . . . . . . . . . . . . . . . . Shiv Bhawan Shivhare, Paragmoni Kalita, and Prabin Haloi Usage of Internet of Things in Home Automation Systems: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Suman Majumder, Sangram Ray, Chinmoy Ghosh, and Shrayasi Datta

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Dynamic Analysis of Rotating FRP Composite Cantilever Beam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diju Kumar Baro and Sachindra Mahto

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Online Tool Wear Monitoring Using Low-Cost Data Acquisition System and LabVIEW™ Program . . . . . . . . . . . . . . . . . . . . . . . . . Banarsi Pandey, Binit Kumar Jha, and Sachindra Mahto

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Product Priority Problem: A Multi-objective Optimization Approach for Product Development Based on Customers’ Priority . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Sidharth Sarmah and Dilip Datta

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Approximating Non-intersecting Closed Curves Through Four-Bar Linkage Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Dilip Datta, Chiranjeeb Deb, Abhishek Hafila, and Debajani Das

10 Optimization of Crude Oil Preheating Process Using Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Dimbalita Deka and Dilip Datta 11 Combined Crack and Unbalance Response Simulation for a Spinning Rotor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Pranjal Borah, Sandeep Singh, and Sutanu Samanta 12 Stability of Female Bicyclists on Sudden Braking . . . . . . . . . . . . . . 153 Dilip Datta, Arpeeta Saikia, and Zahnupriya Kalita 13 Stability of Bicycle at Low Speed . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Dilip Datta, Zahnupriya Kalita, and Sudipta Saikia 14 Impact of Helical Coil Insert in the Absorber Tube of Parabolic Trough Collector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Oveepsa Chakraborty, Biplab Das, and Rajat Gupta 15 Integral-Tilt-Derivative Controller Based Performance Evaluation of Load Frequency Control of Deregulated Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Sandhya Kumari, Gauri Shankar, and Biplab Das 16 Six Sigma Enablers for Incoming Material Quality Improvement and Their Interaction in Supplier Domain for Indian Manufacturing Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Sudeep Kumar Pradhan, Ravi Reosekar, and Srikanta Routroy 17 MLGARTC: Machine Learning Based Genetic Approach in RSSI Tree Climbing Path Improvisation of the Mobile Anchor’s Using K-Means Clustering of Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 P. Thilagavathi and J. Martin Leo Manickam 18 Modeling Performance and Power on Disparate Platforms Using Transfer Learning with Machine Learning Models . . . . . . . . . . . . 231 Amit Mankodi, Amit Bhatt, Bhaskar Chaudhury, Rajat Kumar, and Aditya Amrutiya 19 A Novel Effective Single Sensor MPPT Technique for a Uniform and Partially Shaded Solar PV System via MSCA Approach . . . . . 247 Manoja Kumar Behera, Lalit Chandra Saikia, Satish Kumar Ramoji, Biswanath Dekaraja, Sanjeev Kumar Bhagat, and Naladi Ram Babu

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20 Classification of Sequence Data Using LSTM: An Application on Chaotic Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 S. Shailesh, M. Anantha Krishnan, and M. V. Judy 21 Modeling and Simulation of a Multi-area Hydro-thermal Interconnected System Using FOPIµ Controller for Integrated Voltage and Frequency Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 Biswanath Dekaraja, Lalit Chandra Saikia, Satish Kumar Ramoji, Naladi Ram Babu, Sanjeev Kumar Bhagat, and Manoja Kumar Behera 22 Dye Sensitized Solar Cell Parameter Extraction Using Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Soumik Chakraborty, Ranjith G. Nair, and Lalu Seban 23 Modeling and Simulation of an Isolated CCGT and DSTS Plant Using BWO Optimized PIkDl Controller for Amalgamated Control of Voltage and Frequency . . . . . . . . . . . . . . . . . . . . . . . . . 297 Satish Kumar Ramoji, Lalit Chandra Saikia, Biswanath Dekaraja, Naladi Ram Babu, Sanjeev Kumar Bhagat, and Manoja Kumar Behera 24 Mathematical Analysis on the Behaviour of Tumor Cells in the Presence of Monoclonal Antibodies Drug . . . . . . . . . . . . . . . 311 Biplab Dhar and Praveen Kumar Gupta 25 LFC of a Solar Thermal Integrated Thermal System Considering CSO Optimized TI-DN Controller . . . . . . . . . . . . . . . . . . . . . . . . . 323 Naladi Ram Babu, Lalit Chandra Saikia, Sanjeev Kumar Bhagat, Satish Kumar Ramoji, Biswanath Dekaraja, and Manoja Kumar Behra 26 Maiden Application of Hybrid Particle Swarm Optimization with Genetic Algorithm in AGC Studies Considering Optimized TIDN Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Sanjeev Kumar Bhagat, Lalit Chandra Saikia, Dhenuvakonda Koteswara Raju, Naladi Ram Babu, Satish Kumar Ramoji, Biswanath Dekaraja, and Manoja Kumar Behra 27 Enhancement of Reactive Power Reserve Using Salp Swarm Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 Nibha Rani and Tanmoy Malakar 28 Weld Imperfection Classification by Texture Features Extraction and Local Binary Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Rajesh V. Patil and Y. P. Reddy 29 Simulation and Behavior of Vertically Oriented Planar Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 Vikram S. Singh and Keshav K. Sangle

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30 Automated Analysis and Classification of Sleep Stages Based on Machine Learning Techniques from a Dual-Channel EEG Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 Santosh Kumar Satapathy, D. Loganathan, and Rupalin Nanda 31 Optimal Controller Design for LFC in Power System . . . . . . . . . . . 405 Himangshi Changmai and Mrinal Buragohain 32 Comparative Study of Optimal Controller Application on Nonlinear Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Niku Borgohain and Mrinal Buragohain 33 Multi-class Weld Defect Detection and Classification by Support Vector Machine and Artificial Neural Network . . . . . . . . . . . . . . . . 429 Rajesh V. Patil, Y. P. Reddy, and Abhishek M. Thote 34 Time Series Forecasting Using Markov Chain Probability Transition Matrix with Genetic Algorithm Optimisation . . . . . . . . 439 Gurdeep Saini, Naveen Yadav, Biju R. Mohan, and Nagaraj Naik 35 Modeling Drivers of Machine Learning in Health care Using Interpretive Structural Modeling Approach . . . . . . . . . . . . . 453 Pooja Gupta and Ritika Mehra 36 Studies on the Optical and Structural Properties of Exfoliated Graphene Oxide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 Nipom Sekhar Das, Koustav Kashyap Gogoi, and Avijit Chowdhury 37 Deep Learning for Maize Crop Deficiency Detection . . . . . . . . . . . 473 Subodh Bansal and Anuj Kumar 38 Improvement in Fault Clearance Time of the Cascaded H-Bridge Multilevel Inverter Using Novel Technique Based on Frequency Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Hillol Phukan and Jiwanjot Singh 39 Impact and Scope of Electric Power Generation Demand Using Renewable Energy Resources Due to COVID-19 . . . . . . . . . 495 Manish Kumar, Muralidhar Nayak Bhukya, Anshuman, and Sachin 40 Demand Side Management-Based Load Frequency Control of Islanded Microgrid Using Direct Load Control . . . . . . . . . . . . . 503 Subash Chandra Sahoo, Abdul Latif, Satyajeet Naidu, Shruti Patel, Ranjan Kumar, and Dulal Chandra Das 41 Dynamics of a Class of Modified Leslie-Gower Predator-Prey Model with Strong Allee Effect on Prey and Non-monotonic Rational Functional Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 Udai Kumar and Partha Sarathi Mandal

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42 Mathematical Modelling, Design and Simulation of a Bipedal Walker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531 Randheer Singh, Vikas Kukshal, and Vinod Singh Yadav 43 A Simple Approach to Enhance the Performance of Traditional P&O Scheme Under Partial Shaded Condition by Employing Second Stage to the Existing Algorithm . . . . . . . . . . . . . . . . . . . . . 545 Muralidhar Nayak Bhukya, Manish Kumar, and Shobha Rani Depuru 44 One-Dimensional Model for Removal of Volatile Organic Compound Propane in a Catalytic Monolith . . . . . . . . . . . . . . . . . . 557 Umang Bedi and Sanchita Chauhan 45 Neural Machine Translation: Assamese–Bengali . . . . . . . . . . . . . . . 571 Sahinur Rahman Laskar, Partha Pakray, and Sivaji Bandyopadhyay 46 An Overview of Crossover Techniques in Genetic Algorithm . . . . . 581 Joseph L. Pachuau, Arnab Roy, and Anish Kumar Saha 47 Escalating Demand, Present and Future Status on Hybrid Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599 Manish Kumar, Muralidhar Nayak Bhukya, Anshuman, and Sachin 48 Analysis and Control of Civilian Aircraft Model Using Simulink [PECS] 2020 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 Utkarsh Sharma and Sudhir Nadda 49 Factors Affecting the Efficiency of Solar Cell and Technical Possible Solutions to Improve the Performance . . . . . . . . . . . . . . . 623 Muralidhar Nayak Bhukya, Manish Kumar, Vipin, and Chandervanshi 50 Order Reduction of Linear Time Invariant Large-Scale System by Improved Mixed Approximation Method . . . . . . . . . . . . . . . . . . 635 Pragati Shrivastava Deb and G. Leena 51 Seven Level Enhanced Modified T-type Multilevel Inverter (MLI) with Reduce Part Count . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645 Hillol Phukan, Tamiru Debela, and Jiwanjot Singh 52 Controller Design for Dynamic Stability and Performance Enhancement of Renewable Energy Systems . . . . . . . . . . . . . . . . . 657 Isha Rajput, Jyoti Verma, and Hemant Ahuja 53 A Note on Lyapunov Krasvoskii Funtional for Discrete Time Delayed Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671 Vipin Chandra Pal, Sudipta Chakraborty, Avadh Pati, and Gurpreet Singh

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54 Bidirectional DC-DC Buck-Boost Converter for Battery Energy Storage System and PV Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 681 Krishna Kumar Pandey, Mahesh Kumar, Amita Kumari, and Jagdish Kumar 55 Performance Comparison of DSTATCOM and PV Fed DSTATCOM for Mitigation of Power Quality Problems . . . . . . . . 695 Gurpreet Singh, Yash Pal, and Anil Kumar Dahiya 56 Numerical Simulation of Blocked Blood Vessel for Early Diagnosis of Coronary Artery Disease . . . . . . . . . . . . . . . . . . . . . . 711 Sandip Saha, Pankaj Biswas, and Sujit Nath 57 Green Supplier Selection: An Empirical Investigation . . . . . . . . . . 723 Sudipta Ghosh, Chiranjib Bhowmik, Madhab Chandra Mandal, and Amitava Ray 58 Solar-Driven Potassium Formate Liquid Desiccant Dehumidification System with Thermal Energy Storage . . . . . . . . . 737 A. Sai Kaushik and Satya Sekhar Bhogilla 59 Performance Studies with Trapezoidal, Sinusoidal and Square Corrugated Aluminium Alloy (AlMn1Cu) Plate Ducts . . . . . . . . . . 751 Partha Pratim Dutta, Hirakjyoti Kakati, Monoj Bardalai, and Polash P. Dutta 60 A Survey on Bloom Filter for Multiple Sets . . . . . . . . . . . . . . . . . . 775 Lilapati Waikhom, Sabuzima Nayak, and Ripon Patgiri 61 Topology Optimization of Structures Using Higher Order Finite Elements in Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791 Sougata Mukherjee, Dongcheng Lu, Subhrajit Dutta, Balaji Raghavan, Piotr Breitkopf, and Manyu Xiao Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 801

About the Editors

Dr. Biplab Das is presently working as an Assistant Professor in the Department of Mechanical Engineering, National Institute of Technology Silchar, India. Dr. Das completed his Ph.D. from NERIST, Itanagar, India, in the year of 2014. Later, he pursued his postdoctoral research from University of Idaho, USA. He is the recipient of the prestigious Bhaskara Advance Solar Energy (BASE) Fellowship from IUSSTF and DST, Government of India. He is also awarded with “DBT Associateship” by the Department of Biotechnology, Government of India. He has 12+ years of experience in teaching and research and published more than 60 nos. of refereed international/national journal/conference papers. Presently, Dr. Das is actively involved in 08 nos. of ongoing sponsored projects to develop a solar thermal system for North East India, worth 0.268 billion INR, sponsored by SERB, DST, Ministry of Power, and the Ministry of Climate Change, Government of India. He is guiding 06 nos. of Ph.D. scholars. He has ongoing research activities in collaboration with Jadavpur University, India, IIT Guwahati, India, University of Idaho, USA, and Ulster University, UK. Dr. Ripon Patgiri is an Assistant Professor at the Department of Computer Science & Engineering, National Institute of Technology Silchar. He has received his Ph.D. degree from National Institute of Technology Silchar. He has seven years of teaching and research experiences. Moreover, he has rich experiences in organizing conferences. He has published several journal articles, conference papers and book chapters. Also, he is editing several books. He is a senior member of IEEE. Prof. Sivaji Bandyopadhyay is Director of National Institute of Technology Silchar since December 2017. He is a Professor of the Department of Computer Science & Engineering, Jadavpur University, India, where he has been serving since 1989. He is attached as Professor, Computer Science and Engineering Department, National Institute of Technology Silchar. He has more than 300 publications in reputed journals and conferences. He has edited two books so far. His research interests are in the area of natural language processing, machine translation, sentiment analysis and medical imaging among others. He has xiii

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organized several conferences and has been the Program Committee member and Area Chair in several reputed conferences. He has completed international funded projects with France, Japan and Mexico. At the national level, he has been the Principal Investigator of several consortium mode projects in the areas of machine translation, cross-lingual information access and treebank development. At present, he is the Principal Investigator of an Indo-German SPARC project with University of Saarlandes, Germany, on Multimodal Machine Translation and the Co-PI of several other international projects. Prof. Valentina Emilia Balas is currently Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a Ph.D. in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is author of more than 300 research papers in refereed journals and international conferences. Her research interests are in intelligent systems, fuzzy control, soft computing, smart sensors, information fusion, modeling and simulation. She is the Editor-in-Chief to the International Journal of Advanced Intelligence Paradigms (IJAIP) and to International Journal of Computational Systems Engineering (IJCSysE), Editorial Board member of several national and international journals and is evaluator expert for national and international projects and Ph.D. Thesis. Dr. Balas is the Director of Intelligent Systems Research Centre in Aurel Vlaicu University of Arad and Director of the Department of International Relations, Programs and Projects in the same university. She served as General Chair of the International Workshop Soft Computing and Applications (SOFA) in eight editions 2005–2020 held in Romania and Hungary. Dr. Balas participated in many international conferences as Organizer, Honorary Chair, Session Chair and member in Steering, Advisory or International Program Committees. She is a member of EUSFLAT, SIAM, a senior member of IEEE, member in TC – Fuzzy Systems (IEEE CIS), Chair of the TF 14 in TC – Emergent Technologies (IEEE CIS), and member in TC – Soft Computing (IEEE SMCS). Dr. Balas was past Vice-President (Awards) of IFSA International Fuzzy Systems Association Council (2013–2015) and is a Joint Secretary of the Governing Council of Forum for Interdisciplinary Mathematics (FIM), A Multidisciplinary Academic Body, India.

Chapter 1

Modelling and Forecasting of Solar Radiation Data: A Case Study Somila Hashunao, Hano Sunku, and R. K. Mehta

Abstract Renewable source of energy plays an important role in meeting the world’s power demands. The major factor that makes this source of energy to be important is due to its clean energy generation methodology. Hydro, wind and solar are effectively harnessed in almost every part of the world. However, solar energy, although has less environmental impacts, remains behind when compared to hydro and wind. For effective solar energy generation, it is necessary to know the amount of solar radiation that it receives in a month or a year at that location to harness and install photovoltaic (PV) system in that particular location. Solar radiation at any place can either be measured directly using instruments or empirically determined from global solar radiation. However, these methods are either expensive or not accurate. Artificial neural network (ANN) method provides a convenient approach to overcome the drawbacks of conventional instrumental or other empirical methods. ANN is analogous to human nervous system and are widely used for solving linear and nonlinear problems. This paper has developed neural network model for prediction of monthly solar radiation considering two places in Arunachal Pradesh, i.e., Itanagar and Pasighat. Multilayer feed forward network with back propagation has been proposed for the ANN model. Five-year meteorological data was collected and used for training the proposed network model. The network performance was checked by considering mean square error (MSE) and R (coefficient of correlation) which gave the value very close to zero above 92% for the considered location, respectively. Thus, the predicted output is almost in agreement with the actual output making this model applicable for prediction of monthly solar radiation for considered places. Apart for this prediction, performance of different combination of meteorological parameter were also evaluated and found that parameters such as temperature, humidity, sunshine hour and wind speed are found to be more influencing parameter for solar radiation estimation. This paper demonstrates that ANN has the potential to overcome many of the drawbacks and challenges in estimating the solar radiation, which is essential for designing solar energy generation panels. The findings are particularly useful in data sparse regions and highly applicable in regions where adequate energy supply is still a herculean task. S. Hashunao (B) · H. Sunku · R. K. Mehta NERIST, Itanagar, Arunachal Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_1

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1.1 Introduction and Literature Review Many countries are finding solution to overcome the demands of electrical power by using available natural sources like wind, solar, thermal, hydro, etc. Since solar energy is pollution free and available in most part of the world, harnessing of solar energy to its maximum has been an interest of many countries in recent years. However, to build a reliable solar energy system, good forecasting and prediction methods for the solar radiation potential are required[18]. Generally, the estimation of solar insolation is done by installing pyranometers in many locations as possible. Hence, for many solar hotspots potential sites, global solar radiation (GSR) measured data is currently not so available due to cost of the overall installation of this instruments. Different methodologies for estimating the GSR have been proposed in the past years[20,2427]. Most of these methodologies used are greatly dependent on the meteorological parameters. Various models such as [1], in this paper the double-diode model, and single-diode model is used for prediction of solar irradiance for different climatic conditions. In [2] paper, a model based on multivariate adaptive regression spline (MARS) for predicting hourly global solar radiation on a horizontal surface is being developed to estimate the solar irradiance data with different approaches. Prediction of GSR using neural network is emerging as an important methodology to overcome some of the drawbacks in conventional approaches of measuring solar radiation data, particularly in remote locations where instrumental methods of recording solar radiation is difficult. The improved empirical equation [3, 4] with different combination of input parameters provides more accurate estimation of solar radiation. This paper has too developed an artificial neural network model to predict the solar radiation potential. Data are collected from two places in Arunachal Pradesh for the training of the neural network. The created network is trained by applying meteorological parameter as input and solar irradiance (w/m2 ) as target output. The performance of network is evaluated by considering mean square error (MSE) and R (coefficient of correlation) values. Different training algorithms are also applied to check the performance of the network. Also five different combinations of meteorological parameters are considered to check their influence on solar radiation prediction. Artificial neural network (ANN) is emerging potential widely applicable tool for various purpose which includes for estimation of solar radiations [19, 21-23]. A recent review of literature [5] reports that when compare to other empirical, statistical, conventional, linear, nonlinear and fuzzy logic system, ANN model is found to perform better. In reference [6], artificial neural network (ANNs) is created for the prediction of Turkey’s solar energy potential. The parameter like latitude, longitude, altitude, month, etc., for last three year (2000–2002) for 17 different stations in Turkey are used as meteorological and geographical input data in the propose ANN. Various types of learning algorithms and transfer function were applied in neural network so as to determine the best approach model. Here, the mean absolute percentage error (MAPE) and R2 value obtained are 6.735% and 99.893%, respectively, for testing stations. For the training station, these values are 4.398 and 99.965%. Data from 45 locations was used in proposed ANN model [7] for the prediction of solar radiation.

1 Modelling and Forecasting of Solar Radiation Data: A Case Study

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Thirteen input meteorological parameters were considered. But seven parameters was considered to be best for input data. Similarly in [8] and [9], ANN was developed with different combination of meteorological input to check the parameters that influence the most in solar radiation prediction. In [10], artificial neural network model was developed using only three meteorological parameter, i.e., temperature, RH and irradiation at top of the atmosphere. For this model, the coefficient of determination R2, relative root mean square error (RRMSE) and MAPE value is 97.16%, 18.79% and 21.77%. Two ANN models were developed and applied with four types of back propagation algorithms in [11]. Four type of BP algorithm used are gradient descent, Lavenberg–Marquadth, scaled conjugate gradient and resilient RP algorithm. Among the BP algorithm, model using LM gave the best result with R value in the range of 98–99%. ANN architecture was design with feed forward back propagation in [12]. The coefficient correlation R and root mean square (RMSE) obtained is 0.974 and 0.385 MJ/m2 . In another case [13], ANN model was presented for the estimation of solar irradiance for places with complex terrain located in between radio metric station. The RMSE and mean bias error (MBE) was obtained as 6% and 0.2. Various machine learning methods used for prediction of solar radiation was study in [14]. Using of ANN along with fuzzy logic in [15] shows only slight improvement in the prediction. Most paper discussions can be implemented if accurate meteorological and geographical data are provided. Although it requires a lot of memory and training time, most of them have predicted the solar radiation potential accurately to a great extent.

1.2 Theory and Methods Solar radiation falling on the surface are usually measured using instrument like pyranometer and pyrheliometer. They can also be obtained using empirical method. Here, two types of available empirical methods are discussed. Method 1 The total amount of solar radiation on a horizontal surface is obtained using equation below: GHI = DNI + DHI. cos(z) where GHI is global horizontal irradiance, DNI is the direct normal irradiance, DHI is the diffuse horizontal irradiance and z is known as the solar zenith angle.

(1.1)

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The monthly average daily global radiations is obtained by using Eq. (1.2) ref. [16].   Hga Sa =a+b H0a Smaxa

(1.2)

where Hga is the monthly average daily global radiation (MJ/m2 /day), H0a is the horizontal monthly average extra-terrestrial solar radiation (MJ/m2 /day), Sa is the monthly average daily sunshine hours and Smaxa is the maximum possible sunshine hours for a considered location Here, a and b are the constants. Method 2 Another empirical method uses air temperature for calculation of monthly average daily global solar radiation ref. [17].   Hga = α T 0.5 H0a

(1.3)

where T = Tmax −Tmin T max is the daily average maximum temperature and T min is the daily average minimum temperature. and α is given as  α = αa

 p 0.5 pa

(1.4)

where αa is taken as 0.17 for interior and 0.20 for coastal region.

1.2.1 Proposed Architecture of ANN Model ANN is an artificial intelligence (AI) technique inspired by the functioning of the human neuron. Just the way human nervous system consists of numerous neuron interconnections. ANN uses the similar structural properties. The basic artificial neuron model is shown in Fig. 1.1. In general, neural network has an input, hidden and output layer. The node in each layer is term as neuron, and the terms associated with the basic neuron model are input, weight, bias, activation function and summation node. These parameters decide the outputs of the neuron. The output of the neuron can be express as given below[8].

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5

Fig. 1.1 Basic artificial neuron model

y = F(net)

(1.5)

where net = b +

n 

xi ∗ wi

i=0

= w0 + x 1 w1 + x 2 w2 + x 3 w3 + · · · + x n wn

(1.6)

Here, x 1 , x 2 , x 3 and so on are the set of inputs applied to a neuron, and w1 , w2, w3 and wn are the weights associated with each corresponding input. These weights are nothing but the information possessed by the interconnection links. The product of input and its associated weight is then applied to the summing block to obtain a net input as shown in Fig. 1.1. In addition to weights, artificial neuron has a term called bias denoted by ‘b’. Bias is similar to weight which replace weight whose value is equal to 1. Finally, activation function ‘F’ is applied to the net input to obtain the neuron output ‘y’. Activation function is used so as to map the result in to desired range. There are linear as well as nonlinear activation functions. Some of the common known activation functions are identity, binary step, binary sigmoidal or logistic and bipolar sigmoidal or hyperbolic tangent function.

1.2.2 Multilayer Feed Forward Network There are many different types of architecture of neural network. In this study, the type of ANN model consider is multilayer feed forward network Fig. 1.2. with back propagation training algorithm. Multilayer feed forward neural network may have one or more hidden layer. ANN is well developed only if it is being well trained. It kind of gives the result based on its experiences. The training process may be supervised or unsupervised.

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Fig. 1.2 Functional diagram of multilayer feed forward neural network

Back propagation is considered as the most simplest and general method for supervised training. The weights are adjusted according to the error correction rule. The error signals are computed from the output layer by finding the difference between the actual and predicted output. This error signal are then propagated from output to hidden layer and used by each node to update the connection weights. This training cycles or iteration or epoch continues till required level of accuracy is reached.

1.2.3 Data Collection The meteorological data is collected for the two cities of Arunachal Pradesh namely Itanagar and Pasighat from Arunachal Pradesh Energy Development Agency (APEDA), Itanagar. The data collected consist of a monthly average of parameter like solar insolation, relative humidity, atmospheric pressure, temperature, rain and wind speed for approx. five years, i.e., 2014–2019.

1.2.4 Development of ANN Model Eight parameter has been considered for input, i.e., altitude (m), month of the year, monthly average value of temperature °C, atmospheric pressure (hpa), relative humidity (%), rain (mm), daily sunshine hour, and wind speed (m/s). Monthly average global horizontal insolation (w/m2 ) is considered as target output. Total of 8 × 109 data point are used for input and 1 × 109 data for target output. A multilayer feed forward network is created as shown in Fig. 1.3. Here, multilayer feed forward network with one input, one hidden and one output layer is considered. Lavenberg–Marquadth back propagation algorithm is applied

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Fig. 1.3 ANN model for solar radiation prediction

for supervise training purpose. Performances of commonly known BP algorithms are also compared on this neural network.

1.2.5 Normalisation All the data to be used for the input output set is first normalised before undergoing training algorithm. The output data is denormalised again to obtain the desired value. For normalisation, the equation is shown in (1.7)[8]. X normalized =

X actual − X min X max − X min

(1.7)

The data are normalised in the [0 1] range. The denormalisation applied in order to attain actual value from normalised value is given as X actual = X normalized ( X max − X min ) + X min

(1.8)

Training: The target outputs and the predicted outputs are compared to check if there is any difference between the actual and target outputs. The weights are then updated, and the process continues until it reaches the performance goal.

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Testing: In testing phase, the trained neural network is evaluated against the testing data to determine the suitability of trained ANN model, to satisfy the goal of accurate performance with minimum error.

1.2.6 Performance Evaluation Performance of the neural network can be evaluated using any of the following[8,11]: 1. Mean absolute error (MAE) =

1 n

n 

|Ai − Pi |

i=1

where Ai = Actual output, Pi = ANN predicted output and n = number of data n  |Ai − Pi |2 2. Mean square error (MSE) = n1 i=1  n  |Ai − Pi |2 3. Root mean square error (RMSE) = n1 i=1

4. Mean absolute percentage error (MAPE) =

1 n

n  i=1

|Ai −Pi | n

∗ 100

5. Coefficient of correlation R Here, MSE and R value are considered for a model so as to measure the statistical error between the predicted and target. The training process is stopped once the MSE is minimised. MSE gives the average of the square of the difference of actual output and ANN predicted output. Since the square of the difference is considered, the value of MSE is always positive. However, for a good prediction, the value of MSE should be as close to zero. On the other hand, coefficient of correlation ‘R’ indicates how well the change in one variable predicts the change in other. It has value between + 1 and −1. When R is 1, it indicates that the variable changes in the same direction, and negative value gives the vice-versa. Hence, solar irradiance prediction is good if R value is equal to 1.

1.3 Results 1.3.1 Determining Number of Hidden Neuron Through trial and error method, the number of hidden layer and neuron are selected. Basically, one hidden layer is enough to solve the lesser complex problem. Therefore, only one hidden layer is considered in this case as well. The number of hidden neuron is varied from 2 to 20. Out of which the combination that gives the best performance is selected. Considering the data from two places of Arunachal, the number of hidden neuron that is needed in order to estimate the monthly average solar insolation (w/m2 ) is found to be 15 after evaluation of different combination as given in Table 1.1. It

1 Modelling and Forecasting of Solar Radiation Data: A Case Study Table 1.1 Input–hidden–output neuron combination

Sl. no

Input–hidden–output

9

MSE

R

1

8−2−1

0.011675

0.65821

2

8−4−1

0.005921

0.85213

3

8−5−1

0.0036651

0.9128

4

8−8−1

0.0017249

0.95929

5

8−10−1

0.0001908

0.99556

6

8−12−1

0.00060803

0.98585

7

8−15−1

1.145 × 10–16

1

8

8−18−1

6.43 × 10–18

1

is observed that the performance of proposed network improves with increase in hidden neuron. However, after few combinations, the network becomes constant, and there is no further improvement in the performance even after we increase the hidden neuron (Fig. 1.4).

1.3.2 Performance of Different Back Propagation Algorithm The proposed network is train considering three types of back propagation supervised training algorithm namely Lavenberg–Marquardth (LM), scaled conjugate gradient (SCG) and resilient propagation (RP). Along with Itanagar and Pasighat, the proposed model is also cross-checked and verified for the place Pune. For all the considered places, the proposed network gave better performance when train with LM algorithm. It is also observed that RP algorithm gave almost same performance like that of LM. It can be concluded from the anlysis that among all the three types of back propagation supervised training algorithm, Scaled Conjugate Gradient(SCG) shows the lowest performance Table 1.2.

1.3.3 Determination of Most Influencing Meteorological Parameters for GSR Prediction Beside the combination regarding hidden neuron, the various combinations of meteorological parameter are also checked to find which combination of parameter gives the best predicted value. Five types of combinations considered are as follows (Table 1.3):where, Type I . Month of the year, altitude, monthly average temperature and sunshine hour and relative humidity Type II. Average sunshine hour, relative humidity, wind speed, atmospheric pressure and rain.

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Fig. 1.4 a, b & c is the regression plot with respect to increase in hidden neuron

1 Modelling and Forecasting of Solar Radiation Data: A Case Study Table 1.2 Performance of different back propagation algorithm

Place

Type of BP algorithm

MSE

R

Pune

LM

0.005369

0.93794

SCG

0.0066745

0.92224

RP

0.0058615

0.93205

LM

1.145×10–16

1

SCG

2.5×10–7

0.999

RP

7.47×10–5

1

Meteorological parameter combination

MSE

R

Type I

0.0036821

0.91088

Type II

0.002487

0.9284

Type III

0.008532

0.77807

Type IV

0.0014395

0.96615

Type V

0.0068171

0.82748

Itanagar and Pasighat

Table 1.3 Performance of different meteorological parameter combination

11

Type III. Wind speed, atmospheric pressure and rain. Type IV. Monthly average temperature and sunshine hour, relative humidity and wind speed. Type V. Average sunshine hour, relative humidity and wind speed.

1.4 Conclusion Many artificial neural network models have been proposed in the recent years to predict the solar radiation potential. In all cases, the meteorological and geographical data for more numbers of places were used for training and testing of network. Despite its requires lots of training time and memory , Most of them predicted the solar radiation potential accurately to a great extent. This report has developed multilayer feed forward network with a back propagation algorithm to estimate monthly average global horizontal irradiance. For training and testing of the proposed neural network, the solar radiation and other meteorological parameter data was collected for two places in Arunachal Pradesh. The data is normalised before applying it to the training. Normalisation is done so as to map the data with some particular range. Here, data was normalised between [0 1]. The type of back propagation used is Lavenberg–Maquardth. The number of hidden neuron was selected based on trial and error methods. The performance based on mean squared error (MSE) and correlation coefficient (R) was checked. It is observed that the proposed network works best with 15 hidden neuron. The MSE is very close to zero, and correlation coefficient is almost near to 1 for the considered places of Arunachal Pradesh. Beside this, the same ANN model has been considered with data for another place, i.e., Pune.

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Different training algorithm was also compared. The MSE achieved for Pune was within 0.005–0.006. The value of R was in the range of 0.922–0.937. Further, the paper has also discussed on how different combination of meteorological parameter predicts the solar radiation. Five type of combination was selected randomly, and it is observed that parameter like monthly average temperature, sunshine duration, relative humidity and wind speed has the most effect on prediction of the solar radiation.

References 1. Chaibi, Y., Allouhi, A., Malvoni, M., Salhi, M., Saadani, R.: Solar irradiance and temperature influence on the photovoltaic cell equivalent-circuit models. Sol. Energy 188, 1102–1110 (2019) 2. Li, D.H.W., Chen, W., Li, S., Lou, S.: Estimation of hourly global solar radiation using multivariate adaptive Regression spline (MARS)—a case study of Hong Kong. Energy 186, 1158575 (2019) 3. Antonopoulos, V.Z., Papamichail, D.M., Aschonitis, V.G., Antonopoulos, A.V.: Solar radiation estimation methods using ANN and empirical models. Comput. Electron. Agric. 160, 160–167 (2019) 4. Amrouche, B., Le Pivert, X.: Artificial neural network based daily local forecasting for global solar radiation. Appl. Energy 130, 333–341 (2014) 5. Qazi, A., Fayaz, H., et al.: The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review. J. Clean. Prod. 104, 1–12 (2015) 6. Sozena, A., et al.: Use of artificial neural networks for mapping of solar potential in Turkey. Appl. Energy 77(3), 273–286 (2000) 7. Federico, E., et al.: Artificial neural network optimisation for monthly average daily global solar radiation. Energy Convers. Manage. 120, 320–329 (2016) 8. Siva Krishna Rao K.D.V., Premalatha, M., Naveen, C.: Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: a case study. Renew. Sustain. Energy Rev. 91, 248–258 (2018) 9. Behrang, M.A., et al.: The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Sol. Energy 84(8), 1468–1480 (2010) 10. Marzouq, M., et al.: ANN-based modelling and prediction of daily global solar irradiation using commonly measured meteorological parameters. IOP Conf. Ser. Earth Environ. Sci. 161, 01 (2017) 11. Premalatha, N., Amirtham, V.A.: Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. J. Appl. Res. Technol. 14(3), 206–214 (2016) 12. Mubiru, J., Banda, E.J.K.B.: Estimation of monthly average daily global solar irradiation using artificial neural networks. Sol. Energy 82(2), 181–187 (2008) 13. Boscha, J.L., et al.: Daily solar irradiation estimation over a mountainous area using artificial neural networks. Renew. Energy 33(7), 1622–1628 (2008) 14. Voyant, C., et al.: Machine learning methods for solar radiation forecasting: a review. Renew. Energy 105, 569–582 (2017) 15. Sivaneasan, B., Yu, C.Y., Goh, K.P.: Solar forecasting using ANN with fuzzy logic preprocessing. Energy Procedia 143, 727–732 (2017) 16. Solanki, C.S.: Solar Photovoltaics: Fundamentals, Technologies and Applications, 2nd edn. PHI Learning Private Limited, New Delhi (2011) 17. Quansah, E., et al.: Empirical models for estimating global solar radiation over the ashanti region of Ghana. J. Sol. Energy 2014, 6 (2014). Article ID 897970

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18. Ramachandra, T.V., Jaina, R., Krishnadas, G.: Hotspots of solar potential in India. Renew. Sustain. Energy Rev. 15(6), 3178–3186 (2011) 19. Fadare, D.A., Irimisose, I., Oni, A.O., Falana, A.: Modeling of solar energy potential in Africa using an artificial neural network (2010) 20. Fadare, D.A.: Modelling of solar energy potential in Nigeria using an artificial neural network model. Appl. Energy 86(9), 1410–1422 (2009) 21. Rehmana, S., Mohandes, M.: Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36(2), 571–576 (2008) 22. Mellit, A., Pavan, A.M.: A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste Italy. Sol. Energy 84(5), 807–821 (2010) 23. Soares, J., et al.: Modeling hourly diffuse solar-radiation in the city of Paulo using a neuralnetwork technique. Appl. Energy 79(2), 201–214 (2004) 24. Mellit, A., et al.: An ANFIS-based forecasting for solar radiation data from sunshine duration and Ambient temperature. IEEE Power Eng. Soc. Gen. Meet. 24–28 (2007) 25. Iqdour, R., Zeroual, A.: A rule based fuzzy model for the prediction of solar radiation. IEEE Revue Des Energies Renouvelables 9, 113–120 (2006) 26. Almaraashi, M.: Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems. PLoS One 12(8), e0182429, (2017) 27. Alzahrani, A., et al.: Solar Irradiance forecasting using deep neural networks. Procedia Comput. Sci. 114, 304–313 (2017) 28. Sivanandam, S.N., Sumathi, S., Deepa, S.N.: Introduction to neural networks using Matlab 6.0. McGraw Hill Education (India) Private Limited (2006)

Chapter 2

Thermal Performance Study of Bamboo and Coal Co-gasification in a Downdraft Gasifier Monoj Bardalai, Banabir Das, PP Dutta, and Sadhan Mahapatra

Abstract In this study, bamboo and coal co-gasification is carried out in a downdraft gasifier at different blending ratios. The study mainly focuses on the flame front propagation rate (FFPR), bed movement, effective front propagation, tar concentration and particulate concentration in the producer gas. Three different air mass fluxes namely Φ1, Φ2, Φ3 were used to perform the gasification process. The experimental results show that flame front propagation increases upto 10 wt.% coal, after which it starts decreasing for further increase of coal percentages in the feedstock for all air mass fluxes. Rate of bed movement also shows the similar trend as flame front propagation except at Φ3. The effective propagation rate increases upto 10–20 wt.% coal beyond which the increment is insignificant for all air mass fluxes. In all air mass fluxes, the tar concentration decreases while the concentration of particulate increases by increasing coal percentages in the feedstock.

2.1 Introduction Coal has been one of the mostly utilised fossil fuel as the source of energy. The increased use of coal creates pollution by producing green house gases such as CO2 , CO etc, whcih has become the threat for the living beings in the earth. Moreover, coal is not a renewable source of energy and it is going to be finished in the near future. Therefore, total or partial replacement of coal by some other easily available renewable sources of energy has become the major concern for the researchers in the energy sector. Biomass is one of the easily available material which has the potential to replace coal upto a certain extent. As India is an agricultural based country, biomass is abundantly found in most of the places. The main components of biomass are the organic substances derived from animals and plants. The waste products obtained from the agro-industries and municipal are also considered as biomass [1]. The

M. Bardalai (B) · B. Das · P. Dutta · S. Mahapatra Tezpur University, Assam, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_2

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woody biomasses are composed by cellulose, hemicellulose and lignin and mainly taken as the feedstock for gasification process. As biomass contains low carbon and high moisture content,the energy value of biomasses is comparatively lower than any fossil fuel such as coal. Therefore, in order to improve the quality of the products by the thermal treatment of biomass called gasification, coal is added with the biomass upto certain amount as feedstock. The gasification of biomass blended with coal is known as co-gasification. The gasification of coal with biomass results in reducing the pollution and cost of the fossil fuel. Gasification is a complex engineering process and the gasification models are generally based on thermodynamic equilibrium, kinetics and artificial neural networks [1]. Many researchers have studied about the products of biomass coal co-gasification at different conditions. Boharapi et al. [2] performed the thermodynamic analysis of lignite coal and rice husk co-gasification with steam as gasifying medium. They investigated the effect of temperature between 500 and 1200 ◦ C on the product gas composition. Biomass char and coal char were taken as the feedstock for co-gasification by Ding et al. [3]. Zhang et al. [4] inspected the synergy of biomass gasification blended with coal in various percentages. Thengane et al. [5] studied high ash biomass and coal co-gasification in a downdraft gasifier. This study included the performance of catalytic effect due to presence of inorganic contents. Thermodynamic optimization of coal and biomass co-gasification process was studied by Das et al. [6] in order to examine the product gases for various coal-biomass ratios. More literature on coal-biomass co-gasification are found elsewhere [4, 7–10]. In the co-gasification process, the investigation of combustion front (i.e., reaction or ignition front) propagation rate against the stream of air flow is a common practice among the researchers. The flame front propagation in a fixed bed is either co-current or counter current based on the direction of fuel and air flow. If the direction of fuel and air flow is opposite, then it is called counter current configuration. On the otherhand, in co-current (downdraft) gasifier, air and fuel bed move in the downward direction, while flame front propagates in the upward direction. Mahapatra and Dasappa [11] analyzed the rate of propagation in a downdraft gasifier with woody biomass. The experimental study of Porteiro et al. [12] reported the combustion (ignition front propagation velocity) of a variety of biomasses with a wide range of air mass flow rate in a counter-current fixed bed tube reactor. Mahapatra et al. [13] studied the propagation front movement in a co-current downdraft gasification system. Their study revealed the increase of propagation rate by increasing air mass flux which attains a maximum value and then decreases in further increase of air mass flux and finally approaches the negative propagation rate. The investigation of the behaviour of co-pyrolysis and gasification of biomass and deoiled asphalt (DOA) was carried out by Zhang et al. [14]. The results of this study reveal that potassium and mineral content in biomass can promote the gasification rate and hence, co-gasification of DOA and biomass helps in disposing DOA. Formation of tar during gasification process have been a serious issue despite of many other advantages. Surjosatyo et al. [15] performed the evaluation of tar content gravimetrically before and after the venturi scrubber in the gasification of coconut shell in a downdraft gasifier. Prando et al. [16] presented a comparison of different

2 Thermal Performance Study of Bamboo and Coal …

17

approaches of tar analysis including gas chromatography-mass spectrometry (GCMS) and gravimetric approach. Hasler et al. [17] developed a new tar sampling method using several classes of tar components by allowing long duration sampling. In a downdraft gasifier, the volatile products and tars produced during pyrolysis move to the combustion zone. Since the temperature in the combustion zone is much higher which results the cracking of tar leading to reduce the tar content in gaseous products in downdraft gasifier. It is found that the tar production in biomass gasification process varies from 50 mg/Nm3 to 2 g/Nm3 in downdraft gasifier [15]. Martınez et al. [18] carried out gasification experiment in a moving bed downdraft reactor with the help of two stages of air supply in order to reduce the tar production. Jaojaruek et al. [19] performed the comparative study among three different approaches of stages in air supply. The study reported that in the innovative approach the tar cracking is improved and the producer gas can directly be fed into an IC engine application without further treatment. Similar studies on gasification and co-gasification of various bio-masses are reported in many other publications [20–28]. In the present work, the co-gasification is carried out with coal and bamboo using the parameters of air flux mass and blending ratio. The effects of bed temperature, tar concentration and flame propagation front movement are studied in this article.

2.2 Experimental and Methodology The details about the methodology and experiments are illustrated in the following sections.

2.2.1 The Reactor and Experimental Setup The experiment of gasification was carried out in an open top downdraft configuration with two stage air flow available in the Energy Department, Tezpur University. The schematic view of the set up of experiment is presented in Fig. 2.1. The major requirement for the experiment of co-gasification are reactor, cooling system, K type thermocouples and a data acquisition system. The inside diameter and height of the reactor 130 mm 650 mm respectively. A water seal is used in the top of the reactor with removable cover which is kept open during the operation in order to allow air inflow and biomass feeding. The reactor is a twin cylinder with inner wall made of SS 310 material while the outer wall made of SS 304 material, 50 mm ceramic fibre insulation in between them. The cooling system in the experimental setup is consist of hot gas cyclone and direct coolers. The cyclone is provided at the exit of the hot gas which removes all the large particles of dust in the gas. The direct cooling system contains some water spray arrangement at the top. Few K-type thermocouples were placed along the reactor in order to record the temperature of the biomass bed as seen in Fig. 2.2. The height of the thermocouple T1

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Fig. 2.1 Experimental setup of co-gasification (1: reactor, 2: screw conveyor, 3: ash collection, 4: gas outlet, 5: cyclone, 6: cooler, 7: duct, 8: scrubber, 9: trap1, 10: trap2, 11: filter, 12: valves, 13: blower, 14: flare,15: chiller with tank, 16: water tank)

Fig. 2.2 The reactor of co-gasification with thermocouples

from the nozzle is 150 mm, and the difference between the consecutive thermocouples is 50 mm. The temperature at different locations in an interval of 5 s were recorded with the help of a system of data acquisition (IOtech PDAQ56).

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19

2.2.2 Preparation of Sample and Experimentation The feedstock for the gasification was prepared by mixing the pieces of bamboo and coal (Fig. 2.3) in different ratios. The detail specification of the feedstock is shown in Table 2.1. The proximate analysis was performed based on the ASTM D-271-48 and ultimate analysis of the feedstock were carried out with the help of an Elemental analyser (EuroEA Elemental Analyser) at Biotech Park, Indian Institute of Technology, Guwahati. The results obtained from proximate and ultimate analysis are presented in Table 2.1. The feedstock was fed to a height of 550 mm from the reactor bed. A suitable air mass flow rate was provided in order to form the flame near ignition port which ignite the charcoal. Flaring was done for 10–15 mm after the ignition depending on the sample. The bed height and the equivalent biomass consumption after topping the reactor were noted in the experimental data sheet at an interval of 10–15 min depending on the sample.

2.2.3 Gasification Parameters Air mass flux This controlling parameter regulate the flame front and gasification process. The air mass flux can be calculate using the Eq. 2.1. Φ=

√ k H × + 1 1000 × Ar A F

A F

where A/F is air fuel ratio (1.5–1.8) H is height of the water column in mm Ar is reactor area in m2 .

Fig. 2.3 Feedstock of biomass and coal

(2.1)

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Table 2.1 Specifications, proximate and ultimate analysis of feedstock materials Properties and specification Bamboo Coal Particle dimension (mm) Particle density (kg/m3 ) Bulk density (kg/m3 ) Moisture content (wt.%) Surface area/volume (mm−1 ) Proximate analysis Ash content (wt.%) Volatile matter (wt.%) Fixed carbon (wt.%) Elemental analysis Carbon (wt.%) Hydrogen (wt.%) Sulphur (wt.%) Oxygen (by difference, wt.%)

Table 2.2 Different air mass flux

29.50 × 16.25 × 7.18 7603 4273 12.32 0.4140.04

38.83 × 11.89 × 10.27 2186.423 16367 1.2 0.4790.05

2.41 68.42 16.85

15.57 3.55 79.68

48.8 6.10 – 45.09

67.97 2.52 3.33 26.18

Air mass fluxes

Value (kg/m2 .s)

Φ1 Φ2 Φ3

0.100502 0.131169 0.164282

The inner diameter of the reactor is 130 mm from which cross-sectional area comes out to be 0.01327 m2 . Three different air mass fluxes are used in this study as presented in Table 2.2. Flame front propagation rate The time taken to reach a reference temperature between the two adjacent thermocouples is known as flame front propagation rate. The reference temperature in most cases is taken as 500 ◦ C due to identical temperature profile at various mass fluxes. Equation 2.2 can be used to estimate the rate flame propagation. d (2.2) Fr = t where is d the distance of two adjacent thermocouples in mm and t is time of reaching the given temperature in s.

Bed movement In downdraft gasifier (co-current), the fuel bed moved in downward direction against the flame propagation. Rate of bed movement can be determined by estimating the average distance travelled by the bed in a given span of time.

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21

Tar sampling The method of tar sampling used in this work was given by Mahapatra and Dasappa [11] and Prando et al. [16]. The procedure of tar sampling also followed standards of biomass tar and particulate analysis [20]. The experiments were carried out for three different air mass flow rates and for each air mass flow rate the experiment was repeated three times.

2.3 Results and Discussion 2.3.1 Proximate and Elemental Analysis of Feedstock The results obtained from both proximate and elemental analysis presented in Table 2.1 show that both fixed carbon and elemental carbon in coal are higher than biomass, while the oxygen content in coal is less than biomass. Therefore, the blending of coal with biomass (bamboo) in the feedstock is expected to give better gasification products as compared to only biomass gasification.

2.3.2 Temperature Profile of the Fuel Bed The typical temperature profile inside the reactor over 3 h period operation for 70% bamboo and 30% coal blend are shown in Fig. 2.4 for Φ2. The direction of propaga-

Fig. 2.4 Bed temperature profile in the reactor at different location during gasification

22

B. Das et al. 0.14 0.13

Propagation rate (mm/s)

0.12 0.11 Flamefront Bed Effective

0.1 0.09 0.08 0.07 0.06 0.05 0

5

10

15

20

25

30

Percentage of coal (wt.%) in feedstock

Fig. 2.5 Propagation rate versus feedstocks with various percentages of coal for Φ1

tion of the flame front as seen in Fig. 2.4 is towards top side of the reactor which can be compared with the study carried out by Mahapatra [21]. The temperature profiles in Fig. 2.5 indicates that all the profile gradients are almost identical. It is observed that time span between the temperature 500 and 900 ◦ C is very small. Hence, the 500 ◦ C temperature, where the temperature profile change is negligible has been considered for calculation of flame propagation in all sets of experiments.

2.3.3 Propagation of Flame Front, Effective Flame Front and Bed Movement Flame front propagation The rate of propagation at different air mass flux rates are presented in Figs. 2.5, 2.6 and 2.7 with air mass fluxes Φ1, Φ2 and Φ3 respectively. The propagation rate flame front in all Φ increase by increasing the coal percentage in the feedstock upto 10 wt.% and then decreases with further increase in coal percentages. The time taken by coal in glowing is relatively higher than bamboo. Upto 10 wt.% of coal, the sample is still dominated by bamboo and thus the burn time is less which leads to increasing the flame propagation rate. However, increased amount of coal percentages in the feedstock delay the burn time because of high ash content in coal [22]. Presence of more ash content in the sample resists the access the oxygen required for oxidation and increase the burn time. On the other hand, the rate of flame propagation is found to be increased slightly by increasing Φ for corresponding percentages of coal [21].

2 Thermal Performance Study of Bamboo and Coal …

23

0.3 Flamefront Bed Effective

Propagation rate (mm/s)

0.25

0.2

0.15

0.1

0.05 0

5

10

15

20

25

30

Percentage of coal (wt.%) in feedstock

Fig. 2.6 Propagation rate versus feedstocks with various percentages of coal for Φ2 0.35

Propagation rate (mm/s)

0.3

0.25

0.2

0.15

0.1

Flamefront Bed Effective

0.05 0

5

10

15

20

25

30

Percentage of coal (wt.%) in feedstock

Fig. 2.7 Propagation rate versus feedstocks with various percentages of coal for Φ3

24

B. Das et al. 0.35

Effective propagation rate (mm/s)

1 2 3

0.3

0.25

0.2

0.15

0.1 0

5

10

15

20

25

30

Percentage of coal (wt.%) in feedstock

Fig. 2.8 Effective propagation with coal percentages in feedstock

Bed movement The movement of bed indicates the consumption of biomass due to the consumption of char in pyrolysis. Figures 2.5 and 2.6 show that the rate of bed movement is not significantly influenced by coal percentages in feedstock. However, in Fig. 2.7, the movement of bed is found to be increased slightly with coal percentage due to higher value of Φ, i.e., Φ3. Effective front propagation In a downdraft gasifier, effective propagation is the net movement of bed and flame front. The trend of variation of effective propagation rate is similar to Figs. 2.5 and 2.6 while in Fig. 2.7, it sharply increases upto 10 wt.% coal after which the rate of increase is negligible. This is supposed to be due to the use of higher air mass flux. The effective rate of propagation (refer Fig. 2.8) is found to be increased at the initial stage and then decreases with the increase in coal percentages in feedstock for air mass flux Φ1 and Φ2. However, for higher air mass flux, i.e., Φ3, the effective propagation rate increases sharply with the addition of coal percentage up to 10 wt.% after which the changes of propagation rate is not significant. This reveals that the addition of a small amount of coal with the biomass is sufficient for increasing the rate of propagation using higher air mass flux such as Φ3. Peak bed temperature (PBT) The peak bed temperature for all the air mass fluxes is found to be increasing with the increase in coal percentages in the feedstock as seen in Fig. 2.9. The PBT also varies with mass flux of air because at higher Φ, the peak temperature of the bed is also higher. The increment of PBT is about 10–20% by increasing the coal percentage from 0 to 30 wt.%. Again, the PBT at Φ3 is estimated to be about 9–15% higher than at Φ1.

2 Thermal Performance Study of Bamboo and Coal …

25

1100

Peak bed temperature ( C)

1050

1000

950

900

850

1 2 3

800 0

5

10

15

20

25

30

Percentage of coal (wt.%) in feedstock

Fig. 2.9 Peak bed temperature with coal percentages in feedstock

2.3.4 Tar and Particulate Concentration in the Producer Gas Tar concentration The accumulation of tar and particulates in the producer gas and their separation is a common problem in co-gasification. Figure 2.10 shows the decreasing trend of tar concentration in the producer gas at all air mass fluxes with the increase in coal percentages in feedstock. This decreasing behaviour of tar concentration is in accordance with the trend observed by Bhoi et al. [23] in the co-gasification study. Figure 2.10 indicates that about 23–29% tar concentration is found to be decreased while increasing the coal percentages from 0 to 30 wt.% in the feedstock. The release of heat during gasification at higher amount of coal with the biomass helps in cracking the tar molecules leading to decrease the tar concentration. The presence of silica in coal also acts as a catalyst which is useful in cracking the complex molecules in tar. Further, the concentration of tar for Φ3 is about 49–53% of the tar concentration for Φ1. Particulate concentration The gases produced in the co-gasification of coal and bamboo increases with the increase of coal percentages (Fig. 2.11). The ash content in the coal is relatively higher in comparison to bamboo (refer Table 2.1) and this leads to increase the particulate concentration in producer gas. The particulate concentration increases by 1.2 to 2.3 times by increasing coal percentages from 0 to 30 wt.% in the feedstock. Further, the particulate concentration can be decreased by increasing higher air mass flux as seen in Fig. 2.11.

26

B. Das et al. 220 1 2 3

Concentration of tar (mg/m 3 )

200 180 160 140 120 100 80 60 0

5

10

15

20

25

30

Percentage of coal (wt.%) in feedstock

Fig. 2.10 Concentration of tar with coal percentages in feedstock 900

Particulate concentration (mg/m3 )

800

700

600

500

400 1 2 3

300

200 0

5

10

15

20

Percentage of coal (wt.%) in feedstock

Fig. 2.11 Particulate concentration with coal percentages in feedstock

25

30

2 Thermal Performance Study of Bamboo and Coal …

27

2.4 Conclusions The co-gasification was carried out for a mixture feedstock of coal and biomass in different mixing ratios with three various values of Φ. The FFPR increases by increasing coal percentages upto 10 wt.%, beyond which it starts decreasing in all the values of Φ. The percentage of coal in feedstock has negligible effects on the rate of bed movement for all Φ. At air mass fluxes Φ1 and Φ2, effective propagation rate increases upto 10 wt.% coal beyond which it decreases while for Φ3, the decreasing trend is not found. Peak bed temperature has been found to be increasing with the increase of coal percentages and at high value of Φ. Tar concentration decreases while concentration of particulate increases with the increase of coal percentages. However, both tar and particulate concentration are found to be lower at higher air mass flux.

References 1. Basu, P.: Biomass gasification and pyrolysis: practical design and theory, 1st edn. Academic Press, USA (2010) 2. Boharapi, A.B., Kale, G.R., Mahadwad, O.K.: Co-gasification of coal and biomass— thermodynamic and experimental study. Int. J. Res. Eng. Technol. 4, 2321–7308 (2015) 3. Ding, L., Zhang, Y., Wang , Jiejie, Z.H., Fang, Y.: Interaction and its induced inhibiting or synergistic effects during co-gasification of coal char and biomass char. Bioresource Technol. 173, 11–20 (2014) 4. Zhang, Y., Zheng, Y.: Co-gasification of coal and biomass in a fixed bed reactor with separate and mixed bed configurations. Fuel 183, 132–138 (2016) 5. Thengane, S.K., Gupta, A., Mahajani, S.M.: Co-gasification of high ash biomass and high ash coal in downdraft gasifier. Bioresource Technol. 273, 159–168 (2019) 6. Das, S., Sarkar, P.K., Mahapatra, S.: Thermodynamic optimization of coal-biomass cogasification process by using non-stoichiometric equilibrium modeling. Mater. Today Proc. 5, 23089–23098 (2018) 7. Jeong, H.J., Park, H., Hwang, J.: Co-gasification of coal-biomass blended char with CO2 at temperatures of 900–1100 C. Fuel 116, 465–470 (2014) 8. Li, C., Suzuki, K.: Tar property, analysis, reforming mechanism and model for biomass gasification—an overview. Renew. Sustain. Energy Rev. 13, 594–604 (2009) 9. Thunman, H., Leckner, B.: Co-current and counter-current fixed bed combustion of biofuel–a comparison. Fuel 82, 275–283 (2003) 10. Zhang, Y., Zheng, Y., Yang, M., Song, Y.: Effect of fuel origin on synergy during co-gasification of biomass and coal in CO2 . Bioresource Technol. 200, 789–794 (2016) 11. Mahapatra, S., Dasappa, S.: Experiments and analysis of propagation front under gasification regimes in a packed bed. Fuel Process. Technol. 121, 83–90 (2014) 12. Porteiro, J., Patiño, D., Collazo, J., Granada, E., Moran, J., Miguez, J.L.: Experimental analysis of the ignition front propagation of several biomass fuels in a fixed-bed combustor. Fuel 154, 94–99 (2015) 13. Mahapatra, S., Kumar, S., Dasappa, S.: Gasification of wood particles in a co-current packed bed: experiments and model analysis. Fuel Process. Technol. 145, 76–89 (2016) 14. Zhang, Q., Li, Q., Zhang, L., Yu, Z., Jing, X., Wang, Z., Fang, Y., Huang, W.: Experimental study on co-pyrolysis and gasification of biomass with deoiled asphalt. Energy 134, 301–310 (2017)

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15. Surjosatyo, A., Vidian, F.: Tar content evaluation of produced gas in downdraft biomass gasifier. Iranica J. Energy Environ. 3, 210–212 (2012) 16. Prando, D., Ail, S.S., Chiaramonti, D., Baratieri, M., Dassapa, S.: Characterization of the producer gas from an open top gasifier: assessment of different tar analysis approaches. Fuel 181, 566–572 (2016) 17. Hasler, P., Nussbaumer, T.: Sampling and analysis of particles and tars from biomass gasifiers. Biomass Bioenergy 18, 61–66 (2000) 18. Martínez, J.D., Lora, E.E.S., Andrade, R.V., Jaén, R.L.: Experimental study on biomass gasification in a double air stage downdraft reactor. Biomass Bioenergy 35, 3465–3480 (2011) 19. Jaojaruek, K., Jarungthammachote, S., Gratuito, M., Wongsuwan, H., Homhual, S.: Experimental study of wood downdraft gasification for an improved producer gas quality through an innovative two-stage air and premixed air/gas supply approach. Bioresource Technol. 102, 4834–4840 (2011) 20. Milne, T.A., Evans, R.J., Abatzoglou, N.: Biomass gasifier “tars”: their nature, formation, and conversion. Technical Report no. NREL/TP-570-25357, National Renewable Energy Laboratory, Golden, Colorado, USA, (1998) 21. Mahapatra, S.: Experiments and Analysis on Wood Gasification in an Open Top Downdraft Gasifier. Indian Institute of Science Bangalore, India (2016) 22. Sharma, M., Attanoor, S., Dasappa, S.: Investigation into co-gasifying Indian coal and biomass in a down draft gasifier—experiments and analysis. Fuel Process. Technol. 138, 435–444 (2015) 23. Bhoi, P.R., Huhnke, R.L., Kumar, A., Indrawan, A.: Co-gasification of municipal solid waste and biomass in a commercial scale downdraft gasifier. Energy 163 (2018) 24. Dutta, P.P., Baruah, D.C.: Gasification of tea (Camellia sinensis (L.) O. Kuntze) shrubs for black tea manufacturing process heat generation in Assam, India. Biomass Bioenerg. 66, 26– 38 (2014) 25. Dutta, P.P., Baruah, D.C.: Drying modelling and experimentation of Assam black tea (Camellia sinensis) with producer gas as a fuel. Appl. Therm. Eng. 63, 495–502 (2014) 26. Dutta, P.P., Pandey, V., Das, A.R., Sen, S., Baruah, D.C.: Down draft gasification modelling and experimentation of some indigenous biomass for thermal applications. Energy Procedia 54, 21–34 (2014) 27. Dutta, P.P., Baruah, D.C.: Possibility of biomass gasification in tea manufacturing industries. Int. J. Renew. Energy Technol. 5, 310–322 (2014) 28. Dutta, P.P., Das, A., Pandey, V., Devi, M.: Fuel characteristics of some locally available biomass as a potential gasification feed stock for thermal application. Ind. Eng. Chem. Res. 53(51), 19806–19813 (2014)

Chapter 3

Effects of Gurney Flap and Suction Slots on the Aerodynamics of a NACA0012 Airfoil Sumit Shankar Sarvankar, Anindita Apurbaa Phukan, Abhijeet Konwar, and Paragmoni Kalita Abstract The aim of this study is to carry out a computational study for the enhancement of lift-to-drag ratio of a NACA0012 airfoil. The enhancement is sought by optimizing the design parameters for gurney flap and suction slot. The flow past the airfoil is high-speed turbulent flow having Reynolds number 9 × 106 and Mach number 0.5. The simulations are carried out in ANSYS-FLUENT R19. A density-based solver is used along with the k-ω SST model for turbulence. The second-order upwind scheme is selected to discretize the convective terms. Solution steering is activated for better and smooth convergence of the problem. The present computational model is validated by comparing with established experimental results from the literature. The gurney flaps of various heights are studied having their heights from 0.5 to 2% of the chord length (c). The optimum results are obtained with 0.25%c when added to the plain airfoil. The maximum value of lift-to-drag ratio increases by 4% and maximum lift increases by 32.5%. The present studies also reveal the benefits of incorporating a suction slot in reducing the drag and delaying the separation. Detailed studies are carried to find the optimum location of suction slot for various angle of attack (AOA). The investigations indicate that a clear potential exists to improve the aerodynamics of flow over a NACA0012 airfoil by combining a gurney flap with a suction slot.

3.1 Introduction Airfoils are considered to possess the suitable shapes for least drag and known for maximum lift-to-drag ratio. Therefore, they are used in various industries oriented to fluid dynamics. Yet further design optimization of airfoils in order to enhance the performance of the airfoils is a promising area of research. Various methods are implemented to increase the lift and the lift-to-drag ratio (C l /C d ). The incorporation of gurney flaps, suction slots, synthetic jets, blowing holes, plasma actuators could enhance the ability of a plain airfoil. The present work carries out a computational S. S. Sarvankar · A. A. Phukan · A. Konwar · P. Kalita (B) Department of Mechanical Engineering, Tezpur University, Assam 784028, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_3

29

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study on the effects of gurney flaps, suction slots, and simultaneous application of both in order to improve the aerodynamic performance of a NACA0012 airfoil. The gurney flap is a small rectangular protrusion usually placed near the trailing edge of the airfoil. Typically, it is set perpendicular to the pressure side surface of the airfoil, and projects outward of the wing chord. This trailing edge device can enhance the performance of a plain airfoil to nearly the same level as a complex highperformance design. Normal uniform suction has been considered among the passive flow separation control in recent years. Most of studies have been concentrated on oscillatory suction or blowing near the leading edge. Experiments have demonstrated that suction can modify the pressure distribution over an airfoil surface and have a substantial effect on lift and drag coefficients. Several experimental and computational research works are available in the literature on the effects of design modifications on the aerodynamics of various airfoil shapes. Harris [1] conducted few experiments on NACA 0012 airfoil to determine its aerodynamic characteristics in the Langley 8 Foot transonic pressure tunnel. The transition was fixed at 5% chord. He carried out several experiments with the variation of Reynolds numbers and Mach numbers. He et al. [2] studied the effects of various gurney flap heights varying from 0.25 to 3%c on an SFT15thick airfoil. Jeffery and Zhang [3] carried out experiments to study the effects of gurney flaps and vortex shedding involved in comparison with a circular cylinder. Graham et al. [4] suggested that the lift augmentation due to the gurney flap increased with flap height and decreased with flap thickness; however, the zero-lift penalty increased with flap height and decreased with flap thickness. Jang et al. [5] performed some numerical investigations to determine the effects of gurney flap on a NACA4412 airfoil. Eleni et al. [6] studied the effects of different turbulence models on the aerodynamics prediction of a NACA0012 airfoil. The study suggested that the k-ω SST model was the most appropriate for the numerical simulation. Lutton [7] compared the performance of the O-grid and the C-grid in computing flows over airfoils and highlighted the advantage of one over the other in depending on the various cases involved. Yousefi et al. [8] studied the effects of suction on a NACA0012 airfoil. The study included the fluctuations of suction jet length and suction amplitude. The airfoil was provided higher angles of attack and the optimum location for suction was found to be 10%c. They also studied the effects of jet width, jet angle and suction amplitude to optimize the airfoil design with suction slot. The study suggested that tangential blowing improved the performance of the airfoil, whereas perpendicular blowing had some adverse effects. You and Moin [9] performed an LES to study the effects of synthetic jets to control the flow separation. The investigation showed that both blowing and suction slot could delay the separation and modify the boundary layer on the suction side of the airfoil. Dakka and Al-mawali [10] numerically investigated to enhance the performance of an airfoil by using gurney flap for UAV applications. The gurney flap was optimized using various heights, locations, and mounting angles. The study showed that 2%c gurney height was optimal. Further the study revealed the optimal location to be near the trailing edge within the range of 10%c.

3 Effects of Gurney Flap and Suction Slots …

31

The optimal mounting angles of gurney flap was found to be 45°. The present work performs a study on the effects of suction slot and gurney flap on the lift and drag of a NACA0012 airfoil. This paper is organized in five sections. Section 3.2 presents the governing equations and the design parameters for the present study. The methodology for grid generation and grid selection, boundary conditions, and numerical methods used are presented in Sect. 3.3. The validation of the present numerical results followed by the illustration of the results are discussed in Sect. 3.4 before making the concluding remarks in Sect. 3.5.

3.2 The Governing Equations and the Design Parameters The flow is governed by the compressible Navier-Stokes equations that are solved for the mass- and momentum-conservation equations in a coupled manner. The massconservation equation or the continuity equation is given by   ∂ρ + ∇ · ρ V = 0, ∂t

(1)

where ρ and V are the density and velocity vectors, respectively. The momentum equation is written in vector form as   ∂ ρ V ∂t

  + ∇ · ρ V V = −∇ p + ∇ · τ + ρ g,

(2)

where p, τ and g are the pressure, stress tensor, and body force vector per unit mass, respectively. The stress tensor is given by   2 τ = μ ∇ V + ∇ V T − ∇ · V I , 3

(3)

where μ and I are the molecular viscosity and unit tensor, respectively. The airfoil used for the present study is the NACA0012 variant with a cord length of c = 635 mm [1]. The free-stream parameters are stated in Table 3.1. The effect of gurney flap is studied for a flap thickness of 0.001c. The numerical simulations are carried out with different gurney flap heights h1 , h2 , h3 , h4 and h5 of 0.0025c, 0.005c, 0.01c, 0.015c, and 0.02c, respectively. Suction slots are incorporated at three different positions 0.1c, 0.4c, and 0.7c from the leading edge of the airfoil. The slots used are of three different depths, d 1 = 0.01c, d 2 = 0.02c and d 3 = 0.03c. The widths of the slots considered in the present study are w1 = 0.0025c, w2 = 0.005c, w3 = 0.01c, w4 = 0.025c and w5 = 0.05c. Different combinations of gurney

32

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Table 3.1 Free-stream parameters for flow over a NACA0012 airfoil Parameter

Value

Reynolds number, Reα

9 × 106

Mach number, Mα

0.5

Temperature, T α

311 K

Dynamic viscosity, μα

1.90 × 10−5 kg/(ms)

Velocity, U α

176.72 m/s

Density, ρ α

1.52 kg/m3

Pressure, pα

135,788.19 Pa

flap height, suction slot width, and suction slot depth are used to draw the conclusions from the present numerical investigation.

3.3 The Methodology Used This section presents the mesh generation and selection, boundary conditions and numerical methods used.

3.3.1 The Mesh Generation and Selection After creating the geometry of NACA0012 airfoil using the ANSYS Design Modeler, a suitable computational domain is created. Based on extensive numerical experiments, the left boundary of the domain is placed at a distance of 25c from the leading edge of the airfoil and the right boundary is placed at a distance of 30c form the trailing edge. The top and bottom boundaries are considered at 25c from the suction side and pressure side of the airfoil. The domain is then sliced into nine smaller parts as shown in Fig. 3.1, for the ease of creating the mesh. The idea behind slicing the Fig. 3.1 Division of geometry into subdomains

3 Effects of Gurney Flap and Suction Slots …

33

domain to smaller parts is to use different structured grids in different blocks in order to accurately resolve the flow features. In addition, rectangular gurney flaps of different heights are modelled at the trailing edge on the bottom side of the plain airfoil. The flaps are perpendicular to the airfoil. The entire domain is then sliced into several smaller parts for the ease of meshing. Figure 3.2 shows the typical geometry of a gurney flap modelled in the present work. Likewise, suction slots are also modelled. Figure 3.3 shows the geometry of a suction slot at a distance of 0.7c from the trailing edge of the airfoil. Figures 3.4, 3.5 and 3.6 show the meshes near the leading edge of a plain airfoil, near the gurney flap and near the suction slot without a gurney flap, respectively. The blocked-structured design of the mesh can be easily observed from Figs. 3.5 and 3.6. A mesh-independence study is carried out and Fig. 3.7 plots the variation of the ratio of lift coefficient to drag coefficient (C l /C d ) for the plain airfoil with the number of elements. The study reveals that mesh-independent solution is attained with 84,640 number of elements.

Fig. 3.2 Geometry of a typical gurney flap

Fig. 3.3 Geometry of a suction slot at 0.7c distance from the airfoil

34

S. S. Sarvankar et al.

Fig. 3.4 Mesh near the leading edge of a plain airfoil

Fig. 3.5 Mesh near the gurney flap

Fig. 3.6 Mesh near the suction slot

3.3.2 Boundary Conditions To solve the equation system, we need boundary conditions. In the present work, pressure far field condition has been applied on the inlet and domain by choosing

3 Effects of Gurney Flap and Suction Slots …

35

Fig. 3.7 Mesh-independence study on the plain airfoil

a reasonably large domain size. On the wall, no-slip boundary condition has been used.

3.3.3 Numerical Methods The density-based solver has been chosen to carry out the simulations, since the Mach number for the flow is 0.5. The density-based solver gives an advantage over pressure-based solver for high-speed compressible flows. The k-ω SST turbulence model is used as it has been found to yield good results in adverse pressure gradients and separating flows. The working fluid is air at standard conditions. The implicit scheme is used for the time-integration owing to its high numerical stability and the Roe-FDS scheme is selected for the numerical approximation of the inviscid fluxes. For accurate resolution of the flow and turbulent kinetic energy, second-order upwind scheme is selected for better accuracy. Solution steering is activated. For a few larger angles, first to higher order blending is used to obtain desirable results.

3.4 Results and Discussion The ANSYS-FLUENT model of the plain NACA0012 airfoil is validated first by comparing the computed pressure-coefficient profiles with the experimental results [1] for two different angles of attack. This is followed by studies on the effect of angle of attack on a plain airfoil and effects of angle of attack on airfoils with gurney flaps of different heights and suction slots of different widths, depths, and positions.

36

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3.4.1 Validation of the Numerical Model The pressure-coefficient profiles for AOA of 1.86° and 6.86° are plotted in Figs. 3.8 and 3.9, respectively, and compared with the corresponding experimental results. The numerical results are found in excellent agreement with the experimental ones, which validate the present numerical model.

Fig. 3.8 C p versus curve length plot comparing the experimental and numerical values for plain airfoil at AOA = 1.86°

Fig. 3.9 C p versus curve length plot comparing the experimental and numerical values for plain airfoil at AOA = 6.86°

3 Effects of Gurney Flap and Suction Slots …

37

Table 3.2 Variations of C l , C d and C l /C d with AOA for a plain airfoil AOA (in °)

Cl

Cd

C l /C d

0

0

0.004978

0

1

0.07926

0.005033

15.74806

2

0.15904

0.005196

30.60757

3

0.238355

0.005469

43.58053

4

0.31762

0.00586

54.20137

5

0.39721

0.006394

62.12716

6

0.47535

0.007087

67.07541

7

0.54256

0.008682

62.49035

8

0.58848

0.012596

46.71959

9

0.59316

0.01886

31.45069

10

0.57356

0.02822

20.32459

11

0.528995

0.040452

13.0771

12

0.4871

0.054271

8.975328

13

0.45333

0.066363

6.831066

14

0.42663

0.07715

5.529877

15

0.400405

0.085142

4.702791

16

0.376165

0.09584

3.924927

3.4.2 Effects of AOA on a Plain Airfoil Table 3.2 lists the variations of C l , C d and C l /C d with the AOA for a plain airfoil. It can be observed that C l increases with the AOA up to some extent, but so does C d . However, considering that the lift to drag should be the maximum, it is necessary to identify the condition for maximum C l /C d as the optimum one.

3.4.3 Effects of Gurney Flaps of Different Heights Table 3.3 shows the variations of C l /C d for different heights of gurney flaps. From the data presented in Table 3.3, the optimum gurney height can be considered as 0.0025c. The maximum value for C l /C d is 69.78388 with AOA of 5°. The present study further shows the gurney flap is effective for the smaller values of AOA. As the AOA exceeds 7°, the values of C l /C d decrease drastically. Also, gurney flaps with larger heights are much beneficial for smaller AOAs, since it can be observed that C l /C d increases drastically. Figure 3.10 plots the variation of C l /C d with the AOA for different gurney heights. The plot reaffirms the observations of Table 3.3 regarding the effects of gurney heights on the performance of the airfoil at different AOAs. It

38

S. S. Sarvankar et al.

Table 3.3 Variations of C l /C d with gurney height for different AOAs AOA (in °) C l /C d h0 = 0

h1 = 0.0025c h2 = 0.005c h3 = 0.01c h4 = 0.0015c h5 = 0.02c

0

0

18.03178

18.04012

28.22442

32.90174

34.67376

1

15.74806 33.1145

33.09471

40.90565

43.4215

43.38727

2

30.60757 46.24963

46.25256

51.58382

52.04824

50.54341

3

43.58053 56.76917

56.83583

59.86127

58.71706

56.08924

4

54.20137 64.65307

64.68706

65.76743

63.44591

60.00748

5

62.12716 69.78388

69.78082

68.65665

63.94163

58.11318

6

67.07541 67.59353

67.57317

59.8487

51.86115

45.53653

7

62.49035 51.7254

51.65748

42.53679

36.64779

31.04824

8

46.71959 39.89069

35.30827

28.26064

23.80354

20.55789

9

31.45069 26.25307

22.70878

18.8811

15.76137

13.7909

10

20.32459 16.83365

14.76108

12.12575

10.84199

9.763778

11

13.0771

11.13158

10.12068

8.919005

8.001634

7.469384

12

8.975328 8.046353

7.475227

6.894206

6.714531

6.039088

13

6.831066 6.276363

6.011188

5.497603

5.378599

5.095979

14

5.529877 5.215893

5.01786

4.773957

4.46967

4.470771

15

4.702791 4.441345

4.179995

4.181748

4.064374

3.94408

16

3.924927 3.878009

3.773132

3.712237

3.634692

3.527844

Fig. 3.10 C l /C d variations with the AOA for different gurney heights

3 Effects of Gurney Flap and Suction Slots …

39

is also to be noted from Fig. 3.10 that as the angle of attack increases, the maximum value of C l /C d shifts toward smaller and smaller gurney heights.

3.4.4 Effects of Suction Slots Suction slots of varying dimensions and at different positions are also incorporated in the design of the airfoil. For the ease of identifying the different combinations of gurney heights and suction slot sizes, a nomenclature style is adopted. For example, an airfoil with a gurney height of 1%c, slot width of 0.25%c, and slot depth of 2%c is designated as 1c-0.25c-2c, and so on. The effects of suction depth on the airfoil performance are studied. As an illustration, Fig. 3.11 shows the variation of C l /C d for suction width of 1%c, gurney height of 1%c, suction position 40%c and AOA = 5°. The study shows that suction depth has got marginal effect on C l /C d . Likewise, the effect of suction width is also studied. Figure 3.12 plots the variation of C l /C d for suction depth of 3%c, gurney height of 1.5%c, suction position 70%c, and AOA = 5°. The study shows that the maximum value of C l /C d obtained is for 2.5%c. However, its value decreases severely after 2.5%c width, as the airfoil shape starts distorting. Fig. 3.11 C l /C d variations with the suction depth

68.75

68.5

68.25 0

2

4

6

8

sucƟon depth (%c)

Fig. 3.12 C l /C d variations with the suction width

Cl/Cd

65 64 63 62 0

5

10

15

40

S. S. Sarvankar et al.

3.4.5 Search for Optimal Combination of Gurney Height and Suction Slot In order to study the combined effect of gurney flap and suction slot, a suction slot of w = 5%c and d = 3%c at 40%c and another suction slot of w = 0.5%c and d = 2%c at 40%c, along with a gurney flap of height h3 = 1%c are incorporated. The variations of C l /C d with the AOA for the two configurations are compared with the plain airfoil and airfoil with a gurney alone in Fig. 3.13. A suction slot of w = 1%c and d = 3%c at 70%c is also incorporated with a gurney flap of height h4 = 1.5%c, and Fig. 3.14 compares the results with the corresponding results of a plain airfoil and an airfoil with gurney flap only. The study shows that the incorporation of gurney with suction can be beneficial in some cases. However, gurney height beyond 1%c does not benefit the airfoil, as plain airfoil does have higher value of C l /C d than the design with a gurney height of 1.5%c. 80

Cl/Cd

60 plain

40

gurney, h3=1% c

20

1c-5c-3c 1c-0.5c-2c

0 0

10

20

AOA Fig. 3.13 C l /C d variations with AOA for plain airfoil, airfoil with gurney (1%c), and combined gurney (1%c) and suction slots

80

plain

Cl/Cd

60 gurney,h4=1.5%c

40 20

1.5c-1c-3c

0 0

5

10

15

AOA Fig. 3.14 C l /C d variations with AOA for plain airfoil, airfoil with gurney (1.5%c), and combined gurney (1.5%c) and suction slots

3 Effects of Gurney Flap and Suction Slots …

41

80

plain

Cl/Cd

60

gurney,h2=0.5

40 20 0

.5c-1c-3c 0

10

20

AOA Fig. 3.15 C l /C d variations with AOA for plain airfoil, airfoil with gurney (0.5%c) and combined gurney (0.5%c) and suction slots

Finally, numerical investigations are carried out to further improve the combined effect of gurney with the suction effect. It is found that maximum value of C l /C d increases for the design having gurney height of 0.5%c with suction dimensions 1%c × 3%c at 70%c. The results are compared in Fig. 3.15. It is found that the maximum value of C l /C d is 70.003 that is 4.364% higher than that of the plain airfoil. However, beyond AOA = 6°, the values of C l /C d start decreasing as compared with the plain airfoil. This study opens up an interesting avenue to explore the possibility of a system that can dynamically optimize the gurney height and suction slot widths in order to achieve the optimal performance of an airfoil.

3.5 Conclusions The present study successfully creates a numerical model for the simulation of turbulent flow over a NACA0012 airfoil on the ANSYS-FLUENT R19 platform. The model is validated by excellent agreement of pressure-coefficient profile computed by the model with the experimental results from the literature. Studies on the effect of angle of attack (AOA) on the aerodynamics of a plain airfoil reveal that although initially the lift force increases with the AOA, so does the drag force. Therefore, it is established that it is the ratio of lift to drag (C l /C d ) that needs to be maximized in order to optimize aerodynamic performance of the airfoil. The present investigations on the effect of gurney height reveal that the maximum value of C l /C d = 69.784 is obtained for a gurney height of 2.5% of the cord length (c) at AOA = 5°, and as the angle of attack increases, the maximum value of C l /C d shifts toward smaller and smaller gurney heights. Investigations on the effects of suction slot sizes reveal that suction depth only marginally affects the aerodynamic performance. On the other hand, the maximum value of C l /C d obtained is for the suction width w = 2.5%c. However, its value decreases severely after 2.5%c width, as the airfoil shape starts

42

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distorting. Studies on the combined effects of gurney with suction slots indicate that the better aerodynamic performance compared with the plain airfoil can be obtained only for gurney height less than or equal to 1%c. With a combination of gurney height = 0.5%c, suction width = 1%c, and suction depth 3%c at the location 70%c, the maximum computed value of C l /C d is 70.003 that is 4.364% higher than that of the plain airfoil. The present work opens up an interesting avenue to explore the possibility of a system that can dynamically optimize the gurney height and suction slot widths in order to achieve the optimal performance of an airfoil.

References 1. Harris, C.D.: Two-dimensional aerodynamic characteristics of the NACA 0012 airfoil in the Langley 8-foot transonic pressure tunnel. NASA (1981) 2. He, X., Wang, J., Yang, M., Ma, D., Yan, C., Liu, P.: Numerical simulation of Gurney flap on SFYT15thick airfoil. Theor. Appl. Mech. Lett. 6, 286–292 (2016) 3. Jeffrey, D., Zhang, X.: Aerodynamics of Gurney flaps on a single-element high-lift wing. Journal of Aircraft 37, 295–301 (2000) 4. Graham, M., Muradian, A., Traub, L.W.: Experimental study on the effect of Gurney flap thickness on airfoil performance. J. Aircr. 55, 897–904 (2018) 5. Jang, C.S., Ross, J.C., Cummings, R.M.: Numerical investigation of an airfoil with a Gurney flap. Aircr. Des. 1, 75–88 (1998) 6. Eleni, D.C., Athanasios, T.I., Dionissios, M.P.: Evaluation of the turbulence models for the simulation of the flow over a national advisory committee for aeronautics (NACA) 0012 airfoil. J. Mech. Eng. Res. 4, 100–111 (2012) 7. Lutton, M.J.: Comparison of C-and O-grid Generation Methods Using a NACA 0012 Airfoil. Air Force Institute of Tech Wright-Patterson AFB OH School of Engineering (1989) 8. Yousefi, K., Saleh, R., Zahedi, P.: Numerical study of blowing and suction slot geometry optimization on NACA 0012 airfoil. J. Mech. Sci. Technol. 28, 1297–1310 (2014) 9. You, D., Moin, P.: Active control of flow separation over an airfoil using synthetic jets. J. Fluids Struct. 24, 1349–1357 (2008) 10. Dakka, S., Al-Mawali, J.: Numerical investigation for the enhancement of the aerodynamic characteristics of NACA 0012 aerofoil by using a gurney flap. Int. J. GEOMATE 12, 21–27 (2017)

Chapter 4

Effects of Numerical Dissipation and Dispersion on Computing the Convection of a Sharp Scalar Cone Shiv Bhawan Shivhare, Paragmoni Kalita , and Prabin Haloi

Abstract This work presents a classical study on the numerical-dissipation and dispersion behaviors of upwind and central space-discretization schemes. For this purpose, pure convection of a sharp scalar cone is simulated by numerically solving the two-dimensional convection-diffusion equation with zero physical diffusion. The velocity field is so chosen that the scalar cone rotates about the origin. The numerical solutions are carried out on the finite-volume framework, where the interface fluxes are approximated by the first-order upwind (FOU), second-order central difference (CDS2), and Quadratic Upstream Interpolation for Convective Kinematics (QUICK) schemes. Although in the absence of physical dissipation, the cone should rotate without any decay of its amplitude, it is found that the different numerical-flux schemes result in different declining rates of amplitude. This is attributed to different levels of numerical dissipation of the schemes. In addition, it is seen that the numerical dispersions of the schemes result in the change of phase of the scalar being convected, yielding spurious numerical oscillations. The present study compares the FOU, CDS2, and QUICK schemes in the finite-volume computation of the convection-diffusion equation vis-à-vis their numerical dissipation and dispersion. The study demonstrates that among the lot, the FOU scheme is the most dissipative and the QUICK scheme is the least dissipative. On the other hand, the CDS2 scheme is the most dispersive. The aforementioned observations with the FOU and CDS2 schemes are substantiated with finite-difference computation of the governing equation using these schemes for approximating the space derivatives.

4.1 Introduction The generic convection-diffusion equation is a classic model equation for many fluid transport problems [1, 2]. Accordingly, the importance of the convection-diffusion equation as a model transport equation has attracted various research works related S. B. Shivhare · P. Kalita (B) · P. Haloi Department of Mechanical Engineering, Tezpur University, Tezpur, Assam 784028, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_4

43

44

S. B. Shivhare et al.

to its solution methodology, convergence, and accuracy [3–5]. In the numerical solutions of these equations, the accuracy of discrete approximations to the first-order spatial derivatives associated with the convection terms immensely influences the overall exactness of the computed solution itself. Further, it has been seen that the numerical stability and accuracy of computation greatly depend on the numerical dissipation and dispersion of schemes used to discretize the first-order space derivatives of the convection-diffusion equation [6–10]. Consequently, research works were also conducted to analyze the numerical dissipation and dispersion of finitedifference schemes for solving linear and nonlinear convection-diffusion equations [11, 12]. The present work carries out an explicit comparative study on the numerical dissipation and dispersion behaviors of three such schemes used for discretizing the convection terms in the Finite-Volume Method (FVM). Based on the direction of stencil selection the spatial-discretization schemes for convective terms can be classified as central and upwind. An upwind scheme chooses an asymmetric stencil with a higher number of points from the direction upstream of the associated convective velocity. On the other hand, the central schemes choose symmetric stencils irrespective of the direction of convection. In the Finite-Difference Method (FDM) method, an upwind approximation of a convection term contains an even-order derivative as the leading-error term. The even-order derivative in the leading error acts like artificial dissipation. Conversely, a central FDM approximation of a convection term contains an odd-order derivative in its leading error. This results in the tendency for higher numerical dispersion in the case of central schemes. These contrasting nature of the two types of schemes provides the motivation for the present work to carry out a quantitative comparison of the dissipative and dispersive nature of upwind and central schemes applied for finite-volume discretization of convective terms. The schemes chosen for the present study are the first-order upwind (FOU), second-order central difference (CDS2), and the Quadratic Upstream Interpolation for Convective Kinematics (QUICK) schemes. The FVM computations with the FOU and CDS2 schemes are also compared with the corresponding FDM results to demonstrate the consistency of the present findings. It is to be noted that the present results might be relevant in various applications of theoretical and practical applications [6, 12, 13]. The physical problem for the study involves a sharp scalar cone rotating about the origin in the absence of physical dissipation. Therefore, the cone must be ideally convected without any change of its amplitude. However, artificial dissipation of a numerical scheme results in the gradual diminishing of the amplitude. It is needless to mention that the most dissipative scheme results in the highest rate of decrease in amplitude. The present work shows a direct comparison of the dissipative nature of the various schemes in terms of the amplitudes computed at a given instant of time by each scheme. The difference in numerical dispersion caused by each scheme is also presented through three-dimensional (3D) contour plots of the cone profile computed at a given instant of time. This paper is organized into five sections. Section 4.2 presents the governing equations and the problem statement. The basic FVM discretization of the governing equation and the FOU, CDS2, and QUICK schemes to approximate the convective

4 Effects of Numerical Dissipation and Dispersion on Computing the…

45

flux across a cell-interface are presented in Sect. 4.3. The dissipative and dispersive natures of the schemes are compared in Sect. 4.4 before making the concluding remarks in Sect. 4.5.

4.2 The Governing Equation and the Problem Statement The two-dimensional (2D) convection-diffusion equation for any scalar ∅ is given by   2 ∂∅ ∂∅ ∂ ∅ ∂ 2∅ ∂∅ , +u +v =Γ + ∂t ∂x ∂y ∂x2 ∂ y2

(4.1)

where u and v are the velocity components along the x- and y-directions, respectively, and  is the physical-dissipation coefficient. The present work considers pure convection of the scalar ∅, i.e., the physicaldiffusion coefficient  = 0. It is evident that in the absence of physical viscosity, the cone should be convected undistorted. The initial distribution of ∅ is considered as a sharp cone with a Gaussian profile that is given by [6]    ∅(x, y, t = 0) = a × exp −η (x − xc )2 + (y − yc )2 ,

(4.2)

where a = 5, η = 1500, xc = −0.25, and yc = 0. Equation (4.1) is to be solved subject to the initial condition given by Eq. (4.2) in the domain −0.5 ≤ x ≤ 0.5 and −0.5 ≤ y ≤ 0.5. The velocity components are taken as u = −y and v = x, such that the initial profile rotates about the origin with an angular velocity of ω = 1 about the origin. From Eq. (4.2) one can find that the initial amplitude of ∅ is equal to a = 5 at x = xc and y = yc . The numerical solution is sought at the instant when the cone completes one complete revolution, i.e., at time t = 2π . For the numerical solution time integration is carried out by using first-order Euler-explicit scheme with a time step of t = 1 × 10−3 . The spatial discretization is done by various schemes in the FDM and FVM formulations presented in Sect. 4.3.

4.3 The Numerical Methods The governing equation is discretized in the FDM and FVM frameworks. Sections 4.3.1 and 4.3.2 present the discretization of Eq. (4.1) using the finitedifference and finite-volume approaches. The numerical schemes used for the spatial discretization are described within each subsection. It may be noted that in the absence of physical dissipation, Eq. (4.1) reduces to

46

S. B. Shivhare et al.

∂∅ ∂∅ ∂∅ +u +v =0 ∂t ∂x ∂y

(4.3)

4.3.1 Discretization Using FDM Using the first order Euler-explicit method for time integration, the semi-discrete form of Eq. (4.3) becomes ∅n+1 − ∅n + f n = 0, t

(4.4)

where the superscript n refers to any time level at an instant t and n + 1 is the time level at the instant t + t and f = u ∂∅ + v ∂∅ is the spatial function. With reference ∂x ∂y to Fig. 4.1, the fully-discrete form of Eq. (4.4) at any arbitrary node (i, j) in the computational domain is given by n ∅i,n+1 j − ∅i, j

t n ∅i,n+1 j = ∅i, j



   ∂∅ n ∂∅ n + vi,n j =0 ∂ x i, j ∂ y i, j   n n  ∂∅ ∂∅ − t u i,n j + vi,n j , ∂ x i, j ∂ y i, j + u i,n j

(4.5)

where u i,n j and vi,n j are the x- and y-velocity components at the node (i, j) at the time n

n ∂∅ level n, and ∂∅ and are the discrete approximations of ∂∅ and ∂∅ at the ∂ x i, j ∂y ∂x ∂y i, j

Fig. 4.1 Nodes for the FDM framework

4 Effects of Numerical Dissipation and Dispersion on Computing the…

47

node (i, j) at the time level n. The final form of Eq. (4.5) depends on the scheme used to discretize the spatial derivatives. In the present work, the

n and CDS2 schemes FOU

∂∅ n ∂∅ . It is to be marked are used for the discrete approximations of ∂ x i, j and ∂ y i, j

that for the present problem the convection terms are linear since u i,n j = −yi, j and vi,n j = xi, j . The FOU Scheme in FDM The discrete approximation of the time level n is given by 

∂∅ ∂x

∂∅ ∂x

with the FOU scheme at arbitrary node (i, j) at  ∅n

n

n i, j −∅i−1, j

x n n ∅i+1, j −∅i, j x

= i, j

if u i,n j > 0 otherwise

,

where x is the grid spacing along the x-direction. Similarly 

∂∅ ∂y

 ∅n

n

n i, j −∅i, j−1

= i, j

y ∅i,n j+1 −∅i,n j y

(4.6)

if vi,n j > 0

n ∂∅ ∂ y i, j

is given by

(4.7)

otherwise

where y is the grid spacing along the y-direction. Equations (4.6) and (4.7) can be used in Eq. (4.5) in order to obtain the fully-discrete form of Eq. (4.3) using the FOU scheme. The CDS2 Scheme in FDM  n n and ∂∅ are given by In the CDS2 approximation, ∂∅ ∂ x i, j ∂y i, j

 

∂∅ ∂x ∂∅ ∂y

n =

n n n ∅i+1, j − 2∅i, j + ∅i−1, j

2x

i, j

n

=

∅i,n j+1 − 2∅i,n j + ∅i,n j−1

i, j

2y

,

(4.8)

.

(4.9)

One obtains the fully-discrete form of Eq. (4.3) by using the CDS2 scheme after plugging Eqs. (4.8) and (4.9) in Eq. (4.5).

4.3.2 Discretization Using FVM For applying the FVM, Eq. (4.3) is first converted into conservation form by applying the continuity equation for an incompressible flow as ∂∅ ∂(∅u) ∂(∅v) + + =0 ∂t ∂x ∂y

48

S. B. Shivhare et al.



∂∅ + ∇. ∅V = 0, ∂t

(4.10)

− → ˆ ˆ Now Eq. (4.10) is integrated over a finite where ∇ = iˆ ∂∂x + jˆ ∂∂y and V = iu + jv. volume centered at any arbitrary node (I, J ) as shown in Fig. 4.2. The integration yields  I,J

∂∅ d + ∂t





∇. ∅V d = 0,

(4.11)

I,J

where I,J is the cell volume. After applying Gauss’s Divergence theorem to convert the volume integral associated with the divergence operator in Eq. (4.11) into a surface integral, the equation takes the following form:  I,J

∂∅ d + ∂t



− → ∅V .d S = 0,

(4.12)

S I,J

where S I,J refers to the control surface of the finite volume centered at the node (I, J ) − → and d S is an elementary surface vector on the control surface. Equation (4.12) is fully discretized on the FVM framework by introducing the concept of volume averaging with the first-order Euler explicit method of time integration as NF  ∅ I,J − ∅ I,J × I,J + ∅k (Vn )k Sk = 0 t k=1 n+1

Fig. 4.2 Finite volume cells, with (I, J ) representing the index of a cell centroid and (i, j) representing the index of a cell vertex

n

4 Effects of Numerical Dissipation and Dispersion on Computing the… Table 4.1 Normal velocities and surface areas at the cell-interfaces

Face number (k) 1 2 3 4

n+1 ∅ I,J

=

n ∅ I,J

49

Average velocity (Vn )k

Surface area (Sk )

u i, j+1 +u i, j 2 vi, j +vi+1, j 2 u i+1, j +u i+1, j+1 2 vi+1, j+1 +vi, j+1 2

−y

N F  t − × ∅k (Vn )k Sk , I,J k=1

−x y x

(4.13)

n

where ∅ I,J is the volume-averaged value of ∅ at any time level n over the cell, centred at the node (I, J ), NF is the number of faces of the finite volume, and Vn is the velocity normal to the cell-interface. With reference to Fig. 4.2, for the present problem NF = 4. The term ∅k (Vn )k is termed the flux across the kth cell-face. It is to be observed from Eq. (4.13) that while the cell-averaged values of ∅ are stored and updated at the cell-centroids, the fluxes need to be calculated based on the values of ∅ and Vn at the cell-interfaces. Accordingly, different numerical-flux schemes are used to approximate the fluxes across the cell-interfaces based on the cell-averaged values of ∅. The velocity (Vn )k and face-area terms (Sk ) for the cell centered at (I, J ) are listed in Table 4.1. It is to be noted that the width of control volume is taken as unity. The following discussion presents the FOU, CDS2, and QUICK schemes for computing the flux across an arbitrary cell-interface on the FVM framework. The FOU, CDS2, and QUICK Schemes in FVM In the FOU scheme, the interface-flux is obtained based on the cell-averaged value of ∅ from the cell located upstream of the cell-interface. The CDS2 scheme approximates the value of ∅ at a cell-interface as the average of the cell-averaged values of ∅ in the cells sharing that face. The QUICK scheme offers a higher-order accuracy by choosing an upwind-biased stencil. The formulae to evaluate ∅nk at the four faces of a finite-volume centered at the node (I, J ) are listed in Table 4.2.

4.4 Results and Discussion The profile of the scalar is computed by time marching after one complete revolution of the cone, i.e., at t = 2π . This section first presents the results with the FDM approach followed by the results with the FVM approach.

50

S. B. Shivhare et al.

Table 4.2 The expressions for scalar variable at the cell-faces using FOU, CDS, and QUICK schemes

 k Value of the scalar variable for the kth face ∅nk FOU If (Vn )k ≥ 0

CDS2 Else

1

∅nI−1,J

∅nI,J

∅nI−1,J +∅nI,J 2

2

∅nI,J −1

∅nI,J

∅nI,J −1 +∅nI,J 2

3

∅nI,J

∅nI+1,J

∅nI,J +∅nI+1,J 2

4

∅nI,J

∅nI,J +1

∅nI,J +∅nI,J +1 2

QUICK If (Vn )k ≥ 0

Else

6 n 8 ∅ I −1,J 1 n 8 ∅ I −2,J

+ 38 ∅nI,J −

6 n 3 n 8 ∅ I,J + 8 ∅ I −1,J 1 n 8 ∅ I +1,J

−

6 n 8 ∅ I,J −1 1 n 8 ∅ I,J −2

+ 38 ∅nI,J −

6 n 3 n 8 ∅ I,J + 8 ∅ I,J −1 1 n 8 ∅ I,J +1

−

6 n 3 n 8 ∅ I,J + 8 ∅ I +1,J 1 n 8 ∅ I −1,J

−

6 n 8 ∅ I +1,J 1 n 8 ∅ I +2,J

+ 38 ∅nI,J −

6 n 3 n 8 ∅ I,J + 8 ∅ I,J +1 1 n 8 ∅ I,J −1

−

6 n 8 ∅ I,J +1 1 n 8 ∅ I,J +2

+ 38 ∅nI,J −

4.4.1 Results with the FDM Approach The computations are carried out on a 101 × 101 grid. Figure 4.3 shows the initial profile of the scalar on the FDM framework. It is important to note that the figure plots the local values of ∅ calculated at the discrete nodes as shown in Fig. 4.1, which clearly shows the sharp cone with Gaussian profile. The profile of the scalar computed at t = 2π computed by using the FOU scheme is shown in Fig. 4.4. It can be observed by comparing Fig. 4.4 with Fig. 4.3 that the FOU scheme results in diminishing of the sharp cone almost into a flat surface after one revolution itself. It is known that for time-marching problems, numerical dissipation results in exponential decay of solution amplitude with time. The present observation is a clear indication of the highly dissipative nature of the FOU scheme. Further Fig. 4.4 shows that there is negligible numerical oscillation in the scalar profile computed by the FOU scheme, which suggests that the scheme is trivially dispersive. The scalar profile after one revolution of the cone computed by using the CDS2 scheme on the FDM approach is shown in Fig. 4.5. It can be observed from the figure that the CDS2 scheme does not cause a reduction of the amplitude as much as the FOU scheme. However, significant numerical oscillations are observed in the scalar profile, indicating the higher numerical dispersion caused by the CDS2 scheme compared with the FOU scheme

4 Effects of Numerical Dissipation and Dispersion on Computing the…

51

Fig. 4.3 The initial profile of the scalar on the FDM framework

Fig. 4.4 Scalar profile after one revolution computed by using the FOU scheme on the FDM framework

52

S. B. Shivhare et al.

Fig. 4.5 Scalar profile after one revolution computed by using the CDS2 scheme on the FDM framework

4.4.2 Results with the FVM Approach The computations are carried out on a grid with 100 × 100 cells. The initial profile will be similar to Fig. 4.3, with the only exception being that in this case the profile is plotted based on the cell-averaged values of ∅ over each cell compared with the FDM approach where the local values of ∅ are plotted. In fact, all the plots for the FVM formulation show the cell-averaged profile of ∅ over the computational domain. The present computations find that the scalar profile after one revolution of the cone computed by using the FOU scheme on the FVM approach is very much similar to Fig. 4.4 and hence not included in the paper for the paucity of space. Further, the scalar profile obtained by using the CDS2 scheme on the FVM approach is found to be analogous to Fig. 4.5 and hence not shown in the paper in order to avoid redundancy. Figure 4.6 shows the scalar profile after one revolution of the cone computed by using the QUICK scheme on the FVM approach. Figure 4.6 indicates that the QUICK scheme offers the least level of numerical dissipation among the three schemes and less numerical dispersion compared with the CDS2 scheme, albeit it offers higher numerical dispersion compared with the FOU scheme.

4 Effects of Numerical Dissipation and Dispersion on Computing the…

53

Fig. 4.6 Scalar profile after one revolution computed by using the QUICK scheme on the FVM framework

4.4.3 Quantitative Comparison of the Dissipative and Dispersive Effects A quantitative comparison of the dissipative and dispersive effects of the schemes is presented in Table 4.3 that shows the maximum and minimum values of the scalar after one complete revolution of the cone. The scheme that computes the highest value of the scalar after convection is the least dissipative. The scheme that computes the least value of the scalar after convection is the most dispersive. It may be noted that the initial maximum and minimum values of the scalar are 5 and 0, respectively. Table 4.3 shows that both the FOU and CDS2 schemes in the FDM framework perform significantly close to their FVM counterparts. In a strict sense, the FOU and Table 4.3 The maximum and minimum computed values of the scalar after one revolution

Framework Scheme Maximum value of Minimum value of ∅ ∅ FDM FVM

FOU

0.170665

0

CDS2

2.19567

−1.3924

FOU

0.17131

0

CDS2

2.204791

−1.3895

QUICK 2.637973

−0.58329

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CDS2 schemes are less dissipative than their FDM counterparts. The FOU shows no numerical dispersion, whereas the CDS2 on the FVM framework is less dispersive compared with its performance in the FDM framework. It is also noteworthy that the QUICK scheme shows to be the least dissipative and moderately dispersive among the three schemes tested on the FVM framework.

4.5 Conclusions The present study shows that while computing the pure convection of a sharp scalar cone, the FOU scheme results in the highest rate of amplitude reduction with time, thereby exhibiting the most dissipative behavior, but zero numerical dispersion in both the FDM and FVM frameworks. On the other hand, the CDS2 scheme is less dissipative compared with the FOU scheme, though it is more dispersive than the latter scheme on both the FDM and FVM frameworks. Both the schemes on the FVM formulations perform almost similar to their FDM counterparts. However, in a stricter sense, the FVM formulations of both FOU and CDS2 schemes are less dissipative than their FDM counterparts. Even the numerical dispersion exhibited by the CDS2 scheme on FVM framework is marginally less compared with the corresponding FDM result. The QUICK scheme that is used for higher-order accurate computation of fluxes in the FVM approach, exhibits significantly less numerical dissipation and dispersion than the CDS2 scheme.

References 1. Dehghan, M.: Weighted finite difference techniques for the one-dimensional advection–diffusion equation. Appl. Math. Comput. 147(2), 307–319 (2004) 2. Nazir, T., Abbas, M., Ismail, A.I.M., Majid, A.A., Rashid, A.: The numerical solution of advection–diffusion problems using new cubic trigonometric B-splines approach. Appl. Math. Model. 40(7–8), 4586–4611 (2016) 3. Wan, X., Xiu, D., Karniadakis.: Stochastic solutions for the two-dimensional advectiondiffusion equation. SIAM J. Sci. Comput. 26(2), 578–590 (2004) 4. Gu, X., Huang, T., Ji, C., Carpentieri, B., Alikhanov, A.A.: Fast iterative method with a secondorder implicit difference scheme for time-space fractional convection–diffusion equation. J. Sci. Comput. 72, 957–985 (2017) 5. Li, M., Zheng, Z., Pan, K.: An extrapolation full multigrid algorithm combined with fourthorder compact scheme for convection–diffusion equations. Adv. Differ. Equ. 2018, 178 (2018) 6. De, A.K., Eswaran, V.: Analysis of a new high resolution upwind compact scheme. J. Comput. Phys. 218, 398–416 (2006) 7. John, V., Knobloch, P.: On spurious oscillations at layers diminishing (SOLD) methods for convection–diffusion equations: part I-A review. Comput. Methods Appl. Mech. Eng. 196, 2197–2215 (2007) 8. Tian, Z.F.: A rational high-order compact ADI method for unsteady convection–diffusion equations. Comput. Phys. Commun. 182, 649–662 (2011) 9. Appadu, A.R., Djoko, J.K., Gidey, H.H.: A computational study of three numerical methods for some advection-diffusion problems. Appl. Math. Comput. 272, 629–647 (2016)

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10. Brandner, M., Knobloch, P.: Some remarks concerning stabilization techniques for convectiondiffusion problems. In: Programs and Algorithms of Numerical Mathematics, pp. 35–46. Institute of Mathematics CAS, Prague (2019) 11. Lantz, R.B.: Quantitative evaluation of numerical diffusion (truncation error). Soc. Petrol. Eng. J. 11, 315–320 (1971) 12. Suman, V.K., Sengupta, T.K., Prasad, C.J.D., Mohan, K.S., Sanwalia, D.: Spectral analysis of finite difference schemes for convection diffusion equation. Comput. Fluids 150, 95–114 (2017) 13. Silavwe, D.D., Brink, I.C., Wallis, S.G.: Assessment of some numerical methods for estimating the parameters of the one-dimensional advection–dispersion model. Acta Geophys. 67, 999– 1016 (2019)

Chapter 5

Usage of Internet of Things in Home Automation Systems: A Review Suman Majumder, Sangram Ray, Chinmoy Ghosh, and Shrayasi Datta

Abstract Home automation has drawn a massive attention from last few decades. The benefit of any home automation system is to diminish the hard effort, timings, and errors that generally take place due to inattention of individual. On the other hand, IoT is an infrastructure of numerous linked objects that is extensively used in HAS. Nowadays, the technology has been widened extremely and various applications like the smart home, smart phone, smart city, smart watch, smart shop, etc., have become obligatory applications for every person. As the inhabitation is mounting day by day, there is a big necessity to safeguard the energy and expenditure in every way possible. To save the energy and time, an access mechanism has to be build to control the smart appliances or any home application from inaccessible location. This paper presents technical comparison on IoT-based HAS using smart phone or any other smart applications and sensors.

S. Majumder (B) · S. Ray Department of Computer Science and Engineering, National Institute of Technology Sikkim, Ravangla, Sikkim 737139, India e-mail: [email protected] S. Ray e-mail: [email protected] C. Ghosh Department of Computer Science and Engineering, Jalpaiguri Government Engineering College, Jalpaiguri 735102, India e-mail: [email protected] S. Datta Department of Information Technology, Jalpaiguri Government Engineering College, Jalpaiguri 735102, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_5

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5.1 Introduction Automation is the system of directing a process by latest expertise and diminishes the effort of human. There has been a significant rise in efforts by the researchers and industrialists to built smart systems. Automation is needed not only at the corporate or industrial level but nowadays, automation has been designed to be used at the household level. This directs to the progress of simple home automation systems. Various implementations of systems are available including Bluetooth, GSM, Wi-Fi, etc., at the connectivity level. Different hardware implementations are also available like NodeMCU, ZigBee, Raspberry Pi, Arduino, and Intel Galileo. The common factor includes connecting various sensors that are spread around the home to a PC. The PC receives data from the sensors and updates a Web page accordingly on a server. The user can use a computer or a mobile phone to login anytime using the Web site and view the status of the home. This is very useful for elderly citizens for their home management. Home automation affords security, privacy, and relieve of use. The plan of this paper is to explore the latest advancements in the field of IoT. Different schemes have been analyzed, and a technical comparison has been done.

5.2 Literature Review In this review, different software and hardware-based home automation systems are discussed. Further, those systems are analyzed and the details are mentioned below.

5.2.1 Some Recent Applications of IoT in Home Automation Systems In this section, some recent home automation systems using IoT are presented. Real-Time Application Scheduling for IoT-Based Home Automation System: In 2020, Bhattacharyya et al. [1] proposed a novel communication protocol for realtime scheduling of IoT-based application of smart home automation system using Arduino Uno microcontroller, Wi-Fi connectivity, and mobile-based short message service (SMS), GSM SIM 300 ( TTL and RS232 interface) and SC 547 transistor to control various applications like lighting, entertainment system, or various smart home applications. For priority-based real-time scheduling, the SMS, or tasks of multiple users, an astronomic time and down counter clock is used to maintain the deadline of the tasks and shortest deadline first–real-time task scheduling (SDFRTTS) algorithm is used to short the SMS. In this application, an assumption is maintained that each user can send one task at a time and each task is managed by a unique processor and waits for the same processor if that task has several occurrences.

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Each task contains two parameters—(a) Signal with ON/OFF where ON indicates 1 and OFF is 0 and (b) Deadline of the task represents multiple binary bits. At any instance t, N numbers of tasks are allotted for the processors those are connected to M numbers of home automation appliances that is assigned an actuation time and this time is known to the assigned processor. The devices are further partitioned into two sets—(a) SET1 whose actuation time is less than the deadline and (b) SET2 whose actuation time is greater than the deadline. The algorithm checks if any task has reached at SET1 or not, if it arrives, then it starts the down counter clock whose value works till the deadline. On the other hand, SET2 devices are arbitrarily turned on when the process is free or within the space between actuation times of any two participating devices. Energy Saving of the Home Automation System Using Different Sensors: In 2019, Lohan and Singh [2] have proposed a HAS to save the energy of the home using various sensors like motion sensor, luminance sensor, temperature sensor, etc. Each room is enclosed with one motion sensor, one luminance sensor, and one temperature sensor. Kitchen and wash room are surrounded by one motion sensor and one luminance sensor. If the motion sensor M i contains the cost 1, then it indicates that a person or persons enter into the room. Depending on the threshold of the luminance, the light of the room is either on or off. Below the threshold value of the luminance (L i − δ), lights are necessary in the room. If the temperature is greater than 25 °C, then the AC machine is switched ON otherwise switched OFF. In the scheme, all total 7 numbers of motion sensors, 8 numbers of luminance sensor, and 4 numbers of temperature sensors are used. A detail analysis is done regarding the energy consumption of lights and AC machine, and it is found that total 79.59% energy of light and 20% energy of AC machine have been accumulated as well as all total 20% of energy has been set aside in a month [2]. E-monitoring-Based HAS—‘Thing Speak’ Web Server and ‘Blynk’ Android Application: Susheela et al. [3] have proposed an E-monitoring application for home automation using the Thing Speak Web server and Blynk Android application using Arduino board, and ATmega3208 microcontroller and NodeMCU Wi-Fi are used to control a gadget from the Android application. ATmega3208 microcontroller is a 28-pin DIP and the apparatus is activated between 1.8 and 5.5 V and the gadget is controlled indirectly through the Blynk Android application interface. Thing Speak is an opensource Web server and provides an API for IoT. It uses Arduino board and IP to connect with user and gets information through sensors from different IoT objects and provides information to host from sensors to screen as a cloud application. Alternatively, MCU Wi-Fi node is an application for IoT. In this scheme, one ATmega3208 microcontroller and two MCU Wi-Fi nodes are used. The microcontroller is attached with all the switches and sensors to supervise the data obtained from sensors and conveys to NodeMCU modules. One module conveys the sensor data to Thing Speak and other relocates to Blynk and acquires the data from the user end [3].

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Home Automation System Based on Voice Activity Using ‘Pocket Sphinx”: In 2019, Voice Activity Detection-Based Home Automation System is proposed by Jat et al. [4] where the system detects human speech and its presence or absence. Based on the human speech, an event is triggered and controlled using smart phones that control the home application and speech is controlled by ASR module like ‘Pocket Sphinx,’ an open-source application. It captures the respective speech in a Web form and splits it as utterances and acquires all the words based on probable combination to validate those with audio. On the other hand, ‘Pocket Sphinx’ also performs the noise suppression and works as speech-to-text conversion engine. HAS is implemented using Raspberry Pi 3 model B + that captures the spoken words and transfers the word, respectively, to the noise suppression module and speech-to-text convertor using the GPIO pin. If the speech contains any valid command, then the system triggers a process and sends the signal to actuator using LED infrared receiver and transmitter attached with board. Afterward, actuator performs the automation process. The LICR applications are used to organize the appliances and synchronize remote automation system. This structure is implemented using Python language [4].

5.2.2 Home Automation Systems with Raspberry Pi In this section, some recent HAS with Raspberry Pi are presented. Android Application and Web Service Using Raspberry Pi: Using the Android system, this type of application has been formulated [5]. To communicate with windows application, an interface card has been built. This card is required to encode/decode the signal using modem among the isolated user, server, the home appliances, and Raspberry Pi card using sensors or actuators. To control respective shutter and the Raspberry Pi card, the application is installed with the necessary equipments like an Android smart phone, a Web server, and Raspberry Pi. Home Automation Using Android Application and Raspberry Pi: Home automation can be built devoid of new wiring or without connecting with different buildings rather it follows a very simple structure [6]. However, the apparatus is used in home network which is still immature. Using this system, Android application and Raspberry Pi are put together with HAS and it can be used in different applications in diverse forms and the user can exchange different ideas of Raspberry Pi via Wi-Fi modem to set up the network. The main part of the HAS is to set up an Android connection using this Raspberry Pi board within the home network. It can be seen as a mini-knowledge processing machine able of doing many group events. So according to the user coming from different groups, they can control over and send in a way taken by electric current. The user is generally connected by different pins of the board and gives output as blinking electric light.

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Fig. 5.1 Face recognition using Raspberry

Internet Based Using Database and Raspberry Pi: A better performing arrangement of a HAS using a database is explained in this scheme [7]. It uses Intel Galileo board as a microcontroller to connect various sensors to the PC. Any device can be used to log in to a Web site designed for this purpose from anywhere around the world. Thus, it has higher range of operability. The board is connected to a central database in the Web site using Wi-Fi or LAN. The user can observe the status of the sensors and even send the control signals through the Web site. The board connects to a Raspberry Pi B module which is used to take necessary actions. The signals from the sensors are detected by SL driver to check for any changes are made. The database is updated periodically. The disadvantage is that there has to be a constant Wi-Fi connectivity which is not always feasible. Face Recognition System Using IoT: In this scheme, a face authentication is compulsory of the user to enter into the home (locked/unlocked) [8]. When an adversary strives to register into the system, his/her face is captured and sent a confirmation mail as an attachment. It also supports distant home control activity to monitor the movement during face recognition. These technology efforts are based on the principle of image processing activity. To authenticate the respective user, it generally utilizes Pi camera module that is attached to Raspberry Pi board to store the respective face of the user in the database. To enter into the room, the user needs to authenticate him standing in front of the camera and the board validates the respective face with the prestored image in the Labeled Faces in the Wild (LFW) database. Based on the successful validation, the door will be unlocked automatically otherwise a caution message will be fired to the owner. A block diagram has been shown in Fig. 5.1.

5.2.3 Home Automation Systems Using Arduino Board In this section, some recent home automation systems using Arduino board are presented.

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Fig. 5.2 Block diagram of arduino HAS

Arduino Uno-Based Home Automation System: The system shown in Fig. 5.2 starts the Arduino board as a knowledge processing machine. The system checks the parts of a greater unit as well as their connection [9]. If any kind of error is sensed then it is transferred to the error position. If no error is discovered then the system will consider the position as OK and the connection is made with the nearby Wi-Fi connectivity. Here, the system will again check whether the ESP8266-01 part of the greater unit is connected to the network or not. If there is no connection exists, then the system will transfer to error position or raises an event toward the house of the related IP. In this scheme, Arduino Uno is used as a chief organizing entity to control electric starting point apparatus and a Wi-Fi device is included to make connection with the Android system. Arduino-Based Smart Home Automation System: The output pins of Arduino Uno are connected to enough relay [10]. The connection is given according to schematics of Arduino. The program was uploaded to the board using Arduino Uno IDE to the respective USB host. Home appliances such as supporter and light are managed by new group of UNO board. Moreover, the safety is a kind of vital and essential issue if the system fails and goes to starting place. For this purpose, sensor participates a vital role for this work. In this case, LF sensor is used for the authorization purpose and will connect to the new group. If an unauthorized person enters into the room or going out from the room opening the door, then the sensor will activates a danger sign. Bluetooth-Based Home Automation Using IoT: The end user generally uses his cell phone to record into the system. Initially verification is performed whether the hardware apparatus is ON or not. The user identity is authenticated only if the hardware is ON [11] and then the user is capable of launching the control indication to the hardware machine. The unbroken network structure is maintained by the respective hardware device based on the requirement to monitor the wheel structure of the Bluetooth network. When a user changes the position for any of the apparatus that is currently in ON status, the data are sent to the network in a string format where the network is crowded and the status is stored in

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the knowledge base machine in their separate apparatus field. The page is refreshed in every 10 s and the new cookie values are changed for the fields like knowledge, sdhc sizes, etc. Smart Robotic Assistant Using IoT: In this scheme, a robotic assistant (RA) is introduced that makes human work more easily. Using the Android smart phone, human voice and gesture commands are offered remotely to make the RA to an operative state [12]. First the voice and gesture commands are altered into the structure of text. Later on, the text is communicated with the RA over Wi-Fi network that obtains and further throws the respective command to microcontroller. However, the DC motors used in the system for various movements are controlled by the microcontroller. Generally, Intel Edison Arduino microcontroller is used for this purpose. In the receiving end, the text is converted to speech using a special text-to-speech (TTS) converter. Bluetooth-Based Using Android: A HAS can be set up using Bluetooth [13]. The range of Bluetooth is up to 100 m. It consists of an Android phone that unites with a Arduino board (ATmega328P) through a Bluetooth module HC-05. This is shown in Fig. 5.3. The Bluetooth module acts as a serial data sender. The phone sends data to the microcontroller using this Bluetooth module. Various sensors like smoke sensor, temperature sensor, and motion sensor can be connected to the Arduino board. To see the status of the sensors, the user has to log in to the structure. He/she can even organize the sensors by the Android phone. However, the arrangement operates within the limited range like for Bluetooth about 100 m. Ubiquitous Smart Home System Using Android Application: In this scheme, a low-cost smart home system is used to organize as well as observe the whole set up remotely [14]. To control the individual hosts from an isolated spot, Arduino microcontroller is used with the smart home application by means of Fig. 5.3 Arduino uno board

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3G/4G network. This application is basically used to control the energy management systems. The Android Software Development Kit (ASDK) is used to as a plugin to develop the Android platform-based smart home application. To access the application, a response code ‘200’ for the received packet has to be received by the host from the micro-Web server as a response.

5.2.4 Home Automation Systems Using ZigBee In this section, some recent home automation systems using ZigBee are presented. Environmental Monitoring Using WSN Based on IoT: This type of system is mainly exercised to observe environmental-related risks of human and wildlife animals using wireless sensor network (WSN7). It monitors various physical or ecological circumstances such as temperature, pressure, and moisture [15]. A high-level communication protocols such as ZigBee is used to create wireless networks preserves a mesh network topology to send out the data over long distances. It broadcasts the data within the small transmission distance between 10–100 m depending on various conditions such as output and environmental characteristics. It is a wireless personal area networking (WPAN) that uses digital radio connection between nodes and related devices. ZigBee RF module is used for the wireless communication between sensor nodes and the gateway. However, using the USB cable and UART serial communication interface, ZigBee works as coordinator to unite the Raspberry Pi with XBee module. Voice Recognition Based Using ZigBee: A wireless voice recognition HAS system has been proposed in this scheme [16] to design a structure that assists elderly citizens to organize lights as well as electrical appliances using voice commands. Three modules are used—1. Handheld microphone module that captures voice with RF module (ZigBee protocol), 2. central microcontroller module (PC based) that reads and analyzes the digital version of signal and controls appliances by sending data, and 3. Appliance control module that is connected to appliances. Voice recognition is applied through Microsoft Speech API. For testing purpose, a mix voice signal containing of 35 numbers of males and females subjects with different English accents are tested. As a result, 79.8% of total commands were recognized correctly.

5.2.5 Home Automation Systems Using Wi-Fi In this section, some recent home automation systems using Wi-Fi are presented.

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Home Automation Using IoT Technology: In this scheme, a Wi-Fi wireless technology is used to monitor the respective devices [17]. An Android smart phone is used to build an interface for inbuilt switch of all the appliances separately. All the respective devices can be controlled and monitored individually by this interface. In this scheme, the respective command is obtained by the Wi-Fi module from mobile phone and passes the command to the relay circuit. As per the given signal from the user, ESP8266 relay circuit switches ON/OFF the respective devices. The procedure of implementation of the system is mentioned stepwise: First, the system is initialized. Second, Wi-Fi module is initialized. Third, set up the connection between Wi-Fi module and mobile application on Android Smart phone. Wi-Fi Based Using Arduino Microcontroller: In this scheme, HAS using Arduino microcontroller [18] is discussed. The system contains three main parts: (1) the network server—presents system middle part, (2) heart—starting points are managed and guided by users and (3) hardware connection module (Arduino PCB, Wi-Fi safety skin PCB, input danger signals PCB and output actuators PCB)—a permanent connection to sensors and actuator of HAS is afforded by this module. This system is very proficient associated to scalability and able to construct ready adjustment points than the commercially ready automation system. Based on the existing structure of the browser and network interface, user may be capable of login to the server for this scheme using Wi-Fi. Intel Galileo-Based Home Automation System: In this scheme, a connection is established to the network through Wi-Fi using different sensors like temperature, pressure, gas, motion, etc., through Intel Galileo HAS [19]. It is able to read the constraints of pressure sensors like p1, p2, etc., after the connection is established successfully. The edge limit for the needed sensors is grouped as t1, t2, and so on. The data are transferred to the network or the related terminal and are stored in the cloud server. If the values of sensor parameters are greater than the edge limit level, then separate danger sign a1, a2, and so on is raised and the necessary steps are performed to control the parameters. Later on, various parameters like temperature, pressure, gas loss, and motion in the house are captured. If the temperature becomes overlimit compared to the edge limit level, then the vessel is used to reduce the temperature until it becomes cold. Further, it is turned on automatically and stopped when the temperature comes under control. Similarly, if a situation can arise for the loss of gas in the house, then a danger sound/signal is lifted. Remote Healthcare Monitoring Systems of Patients Using IoT: This scheme helps doctors to diminish the recurrent checking of ECG, temperature, and pulse of Oxygen in the blood of patient every time [20]. This can be helpful to the doctors and hospitals using Wi-Fi technology to get the real-time data from cloud platform with the help of different bio-medical sensors like temperature sensor,

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pulse sensor, ECG sensor, etc., on the other hand, low power consumption as well as multi-tasking capability can be achieved using Intel Edison by this proposal. Wi-Fi Based Using Relay: A Wi-Fi-based HAS system has been discussed in this scheme [21]. For this purpose, a microcontroller is used along with Arduino (ATmega328P). Using a Wi-Fi module (ESP 8266 module) and the Arduino board connects an Android phone to control the system. Arduino obtains controls from the android phone and the data are exposed as an output. There are eight relays connected to the microcontroller to take the controlling action. To build the Android application, the MIT application inventor is used that is provided by Google. But, it restricts the user to be in the same network. Wi-Fi Based User Control: In this proposal [7], the microcontroller is united to the Web server exercising a Wi-Fi module. The driver program is installed at the hardware that continuously monitors the changes made in any sensor data. The user can select any sensor after successful authentication and the status of that sensor is retrieved from the database which is updated periodically at an interval of about 10–20 s. The user can send control command from the Web page to the PC using cookies. Then the PC sends the data to the microcontroller and from there the action events are triggered. The Web pages have been designed using HTML and JavaScript.

5.2.6 Other Applications in Home Automation System Other than the above-mentioned technologies-based home automation systems, some of the other technologies are listed here. Cloud Based Using Hadoop System: In this scheme, a home appliance-based observing and organizing system is developed using cloud-based application [22]. To assemble the metadata from the different home appliances, a home gateway is designed which sends the collected data to cloudbased data server to store the respective data on Hadoop Distributed File System (HDFS). Later on, MapReduce procedure is used to process and monitor data for remote user. Wireless Sensors-Based Mobile Technology: In this scheme, home network monitors the data of different appliances and sensors and further sends the data to cloud-based data server. The server manages and processes the data in its own data and provides information to user based on the commands provided by the user using the mobile application [23]. Both the features of good modularity and configurability are provided by this scheme with low power consumption.

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Using Dual-Tone Multi-Frequency (DTMF): DTMF is generally utilized in telephone lines [5]. It consists of three components— DTMF receiver and ring detector, IO interface unit and PC. The ringing of the line is detected by the PC and the user is authenticated as well as the keypad tones are used to control the devices. Stepper motor control is a good instance of such type of arrangement. This system is secure and follows international standardization and that is why DTMF tones are alike all over the globe. However, it consists of inadequate number of keys in the keypad for the appliances and shown in Fig. 5.4. Home Automation System Using ESP8266 Based on MQTT: In this scheme, the microcontroller is used that has some new features like USB type C plug in [6]. This scheme contains a home automation and security-based application utilizing IoT and shown in Fig. 5.5. MQTT supports message-based communication and it is implemented on a Wi-Fi-based development board called ESP8266. MQTT protocol sustains publish/subscribe model and occupies two kinds of connection agents: (1) MQTT client and (2) MQTT public broker or server. NodeMCU 8266 has advantages over Arduino board: (a) NodeMCU has greater flash memory than Arduino, (b) NodeMCU has on-board Wi-Fi support with ESP Wi-Fi 8266, (c) NodeMCU is preferred to IoT technology, and (d) NodeMCU has a USB type C power port which can be powered by our mobile charger.

Fig. 5.4 Working of DTMF

Fig. 5.5 MQTT-based HAS

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Internet Based Using Database and User Control: In this scheme [24], the system comprises of a Arduino board (ATmega32 Integrated Chip) as the main module. On the other hand, the connectivity is provided by the GSM module (SIM 300). The entire action is managed using a MySQL database. In this scheme, any signal is detected by the microcontroller and the corresponding messages are displayed by the Web page in the form of pop-ups. The user is notified via messages. The user may take necessary steps from the Web page by sending commands to the board. The board itself always monitors whether any action has been send to it or not through regular timing signals. This provides user more flexibility but also adds extra burden to manage action devices.

5.3 Summarization of Reviews In this work, a review is done based on various schemes of IoT projects and a detailed summarization has been done. A detailed discussion is done about different systems and its usage, communication interface, controller, its applications, benefits, and drawbacks. There are various advantages and drawbacks associated with each system. However, there is no perfect home automation system so far but it depends on the requirements and adaptability. The detailed references of most common systems have been tabulated in Table 5.1.

5.4 Conclusion This paper reviews and explains the idea of smart home automation systems. Various ideas are being put into use to create a stable system that can be installed on a large scale in an efficient way. We are on the border of considering home automation systems as the crucial requirement while building homes. However, security is the primary key of home automation system and that will be taken care in future work.

System and communication interface

Wi-Fi based using arduino unoboard and short message service (SMS) with TTL and RS232 interface

MQTT based node MCU ESP 8266

ARM7 LPC2148 having 2 UART ports

Intel Edison board and Wi-Fi module

Face detection using LFW database

Wi-Fi based using arduino board

Cloud based using hadoop

Intel Galileo through C

Arduino Atmega 2560

Raspberry pi, Esp-12 V

Voice recognition using ZigBee

S. No.

1

2

3

4

5

6

7

8

9

10

11

Table 5.1 Summarization of reviews Applications

Atmega

Raspberry pi

Arduino

Intel Galileo

Cloud based using hadoop

Wireless LAN, Wi-Fi shield

Raspberry Pi

Intel Edison arduino

Heartbeat sensor, temperature sensor

Node MCU ESP Wi-Fi 8266

Each user can send one task at a time and each task is processed by a unique processor

Drawbacks

Human speech through microphone

Light, fan, temperature

Fan, light, humidity, temperature

Temperature, gas leakage, light

Smart device

Web based application

Face recognition, door lock/unlock

ECG test, pulse graph

(continued)

Complex circuitry, hard to maintain

Network fail server down

Handles more hardware and sensor

Connection depends on WHAS

Semi structured and unstructured data

Permanent connection is required with sensors/actuator

Network failure causes server down

Problem with small data change

Heartbeat sensor, temperature Network down will stop the sensor system

Temperature sensor, gas and Module must be connected to light sensor those mainly used internet in IoT

Arduino uno microcontroller, Real-time application Wi-Fi connectivity, GSM SIM scheduling for IoT based 300, relay driver, BC 547 home automation system transistor

Controller

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Using database and Raspberry pi

Wi-Fi based and user control

Atmega3208 microcontroller and node MCU Wi-Fi

Voice activity detection of the user Raspberry pi using PocketSphinx application

Energy saving of the home automation system using different sensors

13

14

15

16

17

Different sensors

Arduino, node MCU Wi-Fi

Arduino

Intel Galileo

Arduino

Bluetooth based using android

12

Controller

System and communication interface

S. No.

Table 5.1 (continued)

Constant Wi-Fi not feasible

Limited operability range

Drawbacks

Temperature, luminance and motion sensor

‘Pocket Sphinx’ application, linux infrared remote control application (LICR)

Thing speak web server and Blynk android application

Module must be connected to internet

Cannot be used by speech-impaired people

Lack of management of environment where parameters are important

Sensors using microcontroller Reverse control is of little use

Various sensor using SL driver

Smoke sensor, temperature sensor

Applications

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References 1. Bhattacharyya, R., Das, A., Majumdar, A., Ghosh, P.: Real-time scheduling approach for IoTbased home automation system. In: Data Management, Analytics and Innovation, pp. 103–113. Springer, Singapore (2020) 2. Lohan, V., Singh, R.P.: Home automation using internet of things. In: Advances in Data and Information Sciences, pp. 293–301. Springer, Singapore (2019) 3. Susheela, K., Harshitha, E.S., Rohitha, M., Reddy, S.M.: Home automation and E-monitoring over thingspeak and android app. In: Innovations in Electronics and Communication Engineering, pp. 127–137. Springer, Singapore (2019) 4. Jat, D.S., Limbo, A.S., Singh, C.: Voice activity detection-based home automation system for people with special needs. In: Intelligent Speech Signal Processing, pp. 101–111. Academic Press, Cambridge (2019) 5. Koyuncu, B.: PC remote control of appliances by using telephone lines. IEEE Trans. Consum. Electron. 41(1), 201–209 (1995) 6. Kodali, R.K., Soratkal, S.: MQTT based home automation system using ESP8266. In: Proceedings of 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 1–5. IEEE (2016) 7. Sharma, M.L., Kumar, S., Mehta, N.: Smart home system using IoT. Int. Res. J. Eng. Technol. 4(11), 1108–1112 (2017) 8. Pawar, S., Kithani, V., Ahuja, S., Sahu, S.: Smart home security using IoT and face recognition. In: Proceedings of 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–6. IEEE (2018) 9. Satapathy, L.M., Bastia, S.K., Mohanty, N.: Arduino based home automation using internet of things (IoT). Int. J. Pure Appl. Math. 118, 769–778 (2018) 10. Pan, T., Zhu, Y.: Designing Embedded Systems with Arduino. Springer Nature Singapore Ltd., Singapore (2017) 11. Ullah, S., Mumtaz, Z., Liu, S., Abubaqr, M., Mahboob, A., Madni, H.A.: An Automated Robot-Car Control System with Hand-Gestures and Mobile Application Using Arduino (2019) 12. Raheem, A.K.K.A.: Bluetooth based smart home automation system using arduino UNO microcontroller. Al-Mansour J. 27, 139–156 (2017) 13. Kumar, S.: Ubiquitous smart home system using android application. arXiv preprint arXiv:1402.2114 (2014) 14. Jaladi, A.R., Khithani, K., Pawar, P., Malvi, K., Sahoo, G.: Environmental monitoring using wireless sensor networks (WSN) based on IOT. Int. Res. J. Eng. Technol. 4(1) (2017) 15. AlShu’eili, H., Gupta, G.S., Mukhopadhyay, S.: Voice recognition based wireless home automation system. In: Proceedings of 2011 4th International Conference on Mechatronics (ICOM), pp. 1–6. IEEE (2011) 16. Thoraya, O., Haliemah, R., Nour, A.A.E., Muhammad, R., Saleh, M.M., Mohammed, T.: ZigBee based voice controlled wireless smart home system. Int. J. Wirel. Mobile Netw. (IJWMN) 6(1) (2014) 17. ElShafee, A., Hamed, K.A.: Design and implementation of a WIFI based home automation system. World Acad. Sci. Eng. Technol. 68, 2177–2180 (2012) 18. Kumar, S.: Ubiquitous smart home system using android application. Int. J. Comput. Netw. Commun. 6(1), 33–43 (2014) 19. Keerthana, S., Meghana, H., Priyanka, K.R., Rao, S.V., Ashwini, B.N.: Smart home using internet of things. Perspect Commun. Embed. Syst. Sig. Process. PiCES 1(6), 86–89 (2017) 20. Salunke, P., Nerkar, R.: IoT driven healthcare system for remote monitoring of patients. Int. J. Mod. Trends Sci. Technol. 3(6), 100–103 (2017) 21. Shinde, H.B., Chaudhari, A., Chaure, P., Chandgude, M., Waghmare, P.: Smart home automation system using android application. System 4(04) (2017) 22. Cui, Y., Kim, M., Gu, Y., Jung, J.J., Lee, H.: Home appliance management system for monitoring digitized devices using cloud computing technology in ubiquitous sensor network environment (2014)

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23. Gabriele, T., Pantoli, L., Stornelli, V., Chiulli, D., Muttillo, M.: Smart power management system for home appliances and wellness based on wireless sensors network and mobile technology. In: Proceedings of 2015 XVIII AISEM Annual Conference, pp. 1–4. IEEE (2015) 24. Lamine, H., Abid, H.: Remote control of a domestic equipment from an android application based on Raspberry pi card. In: Proceedings of 2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp. 903–908. IEEE (2014) 25. Dhami, H.S., Chandra, N., Shrivastava, N., Pande, A.: Raspberry Pi home automation using android application. Int. J. Adv. Res. Ideas Innov. Technol. 3(2) (2017) 26. Sadhukhan, D., Ray, S., Biswas, G.P., Khan, M.K., Dasgupta, M.: A lightweight remote user authentication scheme for IoT communication using elliptic curve cryptography. J. Supercomput. (2020)

Chapter 6

Dynamic Analysis of Rotating FRP Composite Cantilever Beam Diju Kumar Baro

and Sachindra Mahto

Abstract The aim of the paper is to investigate the dynamic characteristics of a single link revolute-jointed flexible cantilever beam using finite element method. The system consist of a flexible composite material beam fixed at the rigid hub in one end and other end is attached with a payload. The mathematical model of the system is derived by using extended Hamilton’s principle. Dynamic analysis is performed using Newmark’s time integration scheme under sinusoidal excitation at the hub. Numerical simulation are carried out to investigate the effect of payloads and beam length on end point displacement and hub displacement at low as well as high speed of operation.

6.1 Introduction Dynamic analysis of lightweight and slender flexible beams made from composite materials are the hot topics of research interest for their excellent characteristics. Compare to the conventional heavy rigid beams, the light weight beams have advantages of greater payload-to-beam weight ratio, larger workspace, higher operation speed, small actuators and lower energy consumption. They are also more maneuverable and more transportable, so they are used in wide field of engineering applications. However, elastic deformation and tip vibration caused by structural flexibility are its major demerits. It is difficult to control tip position and orientation due to the long reach and slenderness. Accurate modelling of dynamic flexible beam-hub system is highly nonlinear and complex. The beam geometry and payload have great effects in system performance. Most of the studies in dynamic finite element analysis of flexible manipulator are related to the conventional materials. Cannon and Schmitz [1] have proposed D. K. Baro (B) · S. Mahto North Eastern Regional Institute of Science and Technology, Nirjuli, Arunachal Pradesh 791109, India e-mail: [email protected] S. Mahto e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_6

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a methodology for controlling of residual vibration of end point of a single-arm flexible manipulator fabricated from isotropic material. Kane et al. [2] included the effects of Coriolis forces and centrifugal stiffening on dynamics of robotic arms capable of moving and translating in predefined path. Yoo and Shin [3] derived a coupled linear partial differential equations of a uniformly rotating flexible beam. Both the Cartesian and non-Cartesian coordinate systems were used for stretching and bending motion. Chang and Gannon [4] proposed a new dynamic model of flexible manipulator by employing mode shape functions. The proposed model could predicts vibration characteristics at low and high frequency modes. Chung and Yoo [5] had used the stretch displacement instead of axial displacement in finite element formulation of governing equations for dynamics of rotating beam. Austin and Pan [6] carried out analytical and numerical investigation of an elastic rotating beam with tip masses. Usoro et al. [7] investigated dynamic characteristics of multilink manipulator system. Tokhi et al. [8] presented linear dynamics of single-arm manipulator using finite element method and its experimental validation without considering the payload mass. Mahto and Dixit [9] carried out shape optimization of a single arm flexible manipulator for small elastic deformation. Yang et al. [10] used a second order displacement fields for accounting dynamic stiffening effect in modelling of hub beam system. Scientific literature related to dynamic modelling manipulators constructed from composite materials is very meagre [11]. Use of composite materials for fabrication of robotic link is advantageous. Krishnamurthy et al. [12] used composite beam to investigate the dynamics and control of single-link flexible hub-beam. The link made of composite materials could be optimally tailored for better vibrational characteristics. Saravanos and Lamancusa [13] proposed a methodology for constructing tubular robotic arm from the composite material for high speed application. Material properties of composite such as ply thickness, ply angle were optimized using optimization algorithm for maximization stiffness and pay-load capacity. Choi et al. [14] performed the experimental and comparative study of flexible manipulator system. Flexible manipulator constructed from composite are superiors than the isotropic materials as it need smaller maneuvering torque, quick settling time and very less overshoots. Thompson and Sung [15] used variation approach and finite element method for deriving a numerical model of flexible manipulators. Chao [16] derived an optimal design strategy of fabricating flexible composite link and presented a sensitivity analysis. An objective of minimization tip deflection of the manipulator subject to thickness of ply, fiber angle, and fiber volume fraction was framed. Yavuz et al. [17] performed numerical simulation using ANSYS and validated the results experimentally. The Residual vibrations of end point were suppressed by controlling the motion of input profiles. Dubay et al. [18] presented a finite element model of a manipulator for active control of vibration by using actuator controller. From the above extensive previous literature studies, it is found that most of the studies are related to vibration of robotic link made of isotropic materials. Numerical studies dealing with vibrations of a composite robotic beam undergone prescribed torque are very limited. The effects of beam length and tip mass undergone large variations in rotational speed on the dynamics of robotic links have not received

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much attention. Therefore the aim of the present work are to investigate effects of tip mass, rotation motion of input torque and beam length on the residual tip displacement and hub displacement a revolute jointed single link flexible manipulator made of composite material. Numerical analysis performed by using finite element discretization of the system. The results of proposed dynamic model is compared with work of Cai et al. [19]. The model shows a good agreements and model is capable of predicting dynamics of composite flexible manipulator system.

6.2 Dynamic Modelling It is assumed that the system rotates in horizontal plain only. The system is consisting of a beam, a rigid hub and a tip mass. The beam is made of orthotropic composite materials with mid plane symmetry and it is fixed at hub and the free end is attached with a payload of mass as illustrated in Fig. 6.1. The hub which is subjected to external torque. The beam is straight and slenderer. In case of moderate speed, displacement in the chord-wise direction, beam undergoes small deformation. The beam vibrate flap-wise direction is neglected. Two frame of coordinate is being used. The coordinate frame P O Q is fixed for the global reference, and coordinate frame X O  Y is rotating for local reference. As we apply a torque at hub, an arbitrary point A on the undeformed beam located at distance x from the hub surface moves to point A on the deformed beam due to elastic deflection. The displacement fields of a beam using classical laminate beam theory is defined as u(x, t) = u(x, t) − z

∂w (x, t), w(x, y, t) = w(x, t), ∂x

(6.1)

where u and w represents displacement of mid-plane of composite beam along X and Y directions, respectively and the z represents the distance of any arbitrary point from the mid-plane. The stress and moment resultants. Fig. 6.1 Manipulator system

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t/2 N x x , Mx x =

σ1 (1, z)dz,

(6.2)

−t/2

where t represents the sum of ply thickness which is equal to thickness of beam and σ1 is the bending stress. 

Nx Mx



 =

A11 B11 B11 D11



∂u ∂x ∂2w ∂x2

 .

(6.3)

In the above Eq. (6.3), [B11 ] is equal to zero as we have considered that the beam is orthotropic and symmetrical about mid-plane. The in-plane displacement in X-direction is assumed to be negligible as compared to the displacement in Ydirection, hence [A11 ] = 0. The bending stiffness matrix D11 [20] can be defined as D11 =

 k=N k =N t3 1  3 (C 11 )k (z k3 − z k−1 )= (C 11 )k tk tk z 2k + k , 3 k =1 12 k =1

(6.4)

where t k denotes thickness of k th ply and zk denotes the distance from the mid-plane  to the centroid of the k th layer. The term C 11 k denotes the reduced stiffness of k th lamina and it is defined as ⎫ C 11 = C11 cos4 β + 2(C11 + 2C66 ) sin2 β cos2 β + βC22 sin4 β ⎬ , (6.5) E1 E2 ν12 E 1 , C22 = , C12 = , C66 = G 12 ⎭ C11 = 1 − ν12 ν21 1 − ν12 ν21 1 − ν12 ν21 where β is the fiber orientation angle of a ply, E 1, E 2 representing the Young’s modulus in longitudinal direction and transverse direction respectively, G12 denotes the shear modulus, and ν is the Poisson’s ratio. The system’s potential energy is defined as 

L P=

bD11 0

∂ 2w ∂x2

2 dx.

(6.6)

The system’s kinetic energy is defined as 1 K = 2

L

˙ 2 dx + m(w˙ + (x + r )θ)

0

The work done on the system is defined as

1 Ih θ˙ 2 . 2

(6.7)

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77

Wc = τ θ.

(6.8)

The governing partial differential equations of the system are derived by applying the extended Hamilton’s principle while neglecting axial deformation, centrifugal stiffening effects and structural damping as described by Krishnamurthy et al. [12] are obtained as bD11 

∂ 4w ∂ 2w ∂ 2θ + m 2 + m(x + r ) 2 = 0, 4 ∂x ∂t ∂t

 ∂ 2θ  mb  Ih + + (L + r )3 − r 3 + m p (L + r )2 3 ∂t 2

L m(x + r ) 0

(6.9) ∂ 2w d x = τ, ∂t 2 (6.10)

where b denotes the width of the beam, m = m b + m p δ(x − L); m b is measured as mass per unit length of beam, m p is the payload at the tip of beam, r is hub radius, τ is the torque applied at hub, L denotes un-deformed length of beam, Ih is the mass moment of inertia of hub, w is the displacement in cord wise direction and θ is the angular rotation of hub. The transverse displacement and slop at the fixed end is zero due to clamping of beam at the rigid hub. Also at the free end, bending moment and shear force are zero. The boundary conditions of the system are given as w(0, t) =

∂ 2w ∂ 3w ∂w (0, t) = (L , t) = (L , t) = 0. ∂x ∂x2 ∂x3

(6.11)

In the finite element formulation, the system is discretized into n number of elements. All the elements are equal in length and each element have five degree of freedom as shown in Fig. 6.2. The displacements at any point on the link is approximated using Hermitian interpolation functions. Here, w1 , w1 denotes the displacement and slop at the first node and similarly, w2 , w2 denotes the displacement and slope at the second node of the arbitrary ith element. Galerkin’s finite element formulation yields, the following elemental mass and stiffness matrix for ith element: ⎡

M1 ⎢M 2 mh ⎢ ⎢ Mi = ⎢ M3 420 ⎢ ⎣ M4 M5

M2 156 22h 54 −13h

M3 22h 4h 2 13h −3h 2

where the subscript i denotes the ith element and

M4 54 13h 156 −22h

⎤ M5 −13h ⎥ ⎥ ⎥ −3h 2 ⎥, ⎥ −22h ⎦ 4h 2

(6.12)

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Fig. 6.2 Typical finite element

⎫ mh 3 2 ⎪ ⎪ (3i − 3i + 1) ⎪ ⎪ ⎪ 3 ⎪ ⎬ mh mh 2 M2 = (5r h + h (5i − 3)) . (10r + h(10i − 7)), M3 = ⎪ 20 60 ⎪ ⎪ ⎪ ⎪ mh mh 2 ⎪ M4 = (10r + h(10i − 3)), M5 = − (5r + h(5i − 2))⎭ 20 60

M1 = mr h(r + h) + 2mr h 2 (i − 1) +

(6.13)

The contribution of payload mass and hub inertia are assimilated in the system dynamics by adding the terms Ih + m p (r + L)2 at the (1, 1) position, m p (r + L) at the position (1, 2n + 2) and (2n + 2, 1), m p at the position (2n + 2, 2n + 2) in the global mass matrix. Here n is counted from the hub side. The elemental stiffness matrix is computed as ⎡

0 ⎢0 bD11 ⎢ ⎢ Ki = ⎢0 h3 ⎢ ⎣0 0

0 12 6h −12 6h

0 6h 4h 2 −6h 2h 2

0 −12 −6h 12 −6h

⎤ 0 6h ⎥ ⎥ ⎥ 2h 2 ⎥. ⎥ −6h ⎦ 4h 2

(6.14)

All the elemental equations are assembled to obtain the equation of motion for the whole system, as given by   ¨ + [K]{X} = {F}, [M] X

(6.15)

where [M] =

i=n 

Mi ; [K] =

i=1

i=n 

 T Ki ; {F} = τ 0 0 . . . 0

(6.16)

i=1

 T    {X} = θ w1 w1 w2 w2 . . . wi wi wi+1 wi+i . . . . w2n+2 w2n+2 w2n+3 w2n+3 (6.17)

6.3 Result and Discussion The transient responses of the system are obtained by applying the Newmark’s scheme of time integration. Numerical simulation of the system is carried out using MATLAB software. In the first section, model is validated by comparing the dynamic

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responses with scientific literature. In the second and third section, effects of payloads and beam length are evaluated.

6.3.1 Model Validation For validation of proposed model, two cases are considered- flexible manipulator with isotropic beam and the composite material beam. The fiber orientation of the composite beam is composite beam is kept zero and the each ply of beam is made of isotropic material. The engineering properties and geometrical properties are obtained from the reference [19]. The geometric dimension of the system are as follows: L = 1.8 m; t = 0.0025 m; b = 0.1 m; ρ = 2766 kg m−3 ; r = 0.05 m; m p = 0.085 kg; I = 1.3021 × 10 m4 ; I H = 0.3 kg m2 . E = 6.9 GPa. A prescribe torque pulse applied at hub and it has the following profiles.  τ (t) =

 t 0 ≤ t ≤ T, τ0 sin 2π T 0 t>T

(6.18)

The torque (τ ) is varying with time and it is applied for T = 2 s. Results obtained in the simulation are plotted for end point displacement of beam and angular hub displacement with and without considering the payload. The end point displacement of beam and angular hub displacement for τ0 = 7 N m are displayed in Fig. 6.3. The observed results are found to be very close to that of Cai et al. [19]. Hence, the model is can be used for further dynamic analysis of flexible manipulator fabricated from composite materials.

Fig. 6.3 Dynamic response of composite manipulator [0◦ /0◦ /0◦ ] when τ0 = 7 N m. (a) Tip displacement, (b) hub rotation

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6.3.2 Effects of Payloads In this section, effects of payload on system’s characteristics are observed varying the tip mass and amplitude of prescribed torque. The system responses are presented in time domain with sinusoidal torque τ0 = 1 N m, and τ0 = 7 N m. It is assumed that beam is made of three layer graphite epoxy composite materials. All the ply have equal thickness and the fibers are orientated in [30◦ /60◦ /30◦ ]. The physical dimension of the system are L = 1.8 m; b = 0.1 m; t = 0.0025 m; and r = 0.05 m. The density of beam material is ρ = 1385 kg m−3 . Young’s modulus along the fiber direction is E 1 = 144.8 GPa, Young’s modulus in transverse to the fiber direction is E 2 = 9.65 GPa, Shear modulus G12 = 4.14 GPa, Poisson’s ration ν12 = 0.28. First consider the low speed of rotation of system with the input external torque of 1 N m as shown in Fig. 6.4a. There are significant variation of end point displacements with the increasing payload mass. The results shows that with rise in the magnitude of payload mass, there are differences in the system’s frequencies and end point deflections. The larger the payload, more the amplitude of vibration and lesser the vibration frequency. The external torque is applied for 2 s. When t > 2 s, the system frequency with the payload mass of 0.2 Kg, 0.3 Kg and 0.4 Kg are 1.4365 Hz, 1.3825 Hz and 1.3473 Hz respectively, which are fundamental frequency for that particular payload. The angular rotation of hub displacement in the time domain with the corresponding end point deflection as shown in Fig. 6.4b. The hub oscillation at the mean place of θ = 21.25◦ for payload mass 0.2 Kg. However, with the greater payload mass of 0.3 Kg, hub oscillates at θ = 17.5◦ and while with that the payload mass of 0.4 Kg, oscillation is restricted at θ = 15.24◦ . The oscillations is due to the residual vibration of beam is continued due to absence of damping in the system consideration. Then we consider the case with high speed of rotation of system with a high value of τ0 = 7 N m in Eq. (6.18). Figure 6.5 shows the end point responses of

Fig. 6.4 Effect of payload mass on a tip displacement; b hub displacement when τ0 = 1 N m

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Fig. 6.5 Effect of payload mass on (a) tip displacement, (b) hub rotation when τ0 = 7 N m

Table 6.1 Frequency of the system

Payloads 0.2 Kg

0.3 Kg

0.4 Kg

1.4365 Hz

1.3825 Hz

1.3473 Hz

the beam with the varying payloads. The trend of results are similar to the previous case of τ0 = 1 N m. From Fig. 6.5a, we observed that the amplitudes of vibration is maximum with the higher tip mass (0.4 Kg). Also we get minimum frequency of vibration with this payload. The frequency of vibration with the payload mass 0.2 Kg is 1.4365 Hz and that with considering the payload of 0.3 Kg is 1.3825 Hz and 0.4 Kg is 1.3473 Hz. Time domain of rotational displacement of the system for the large input torque 7 N m are shown in Fig. 6.5b. The beam swings at the hub rotation of θ = 147◦ , when with payload is 0.2 Kg, while with the payload of 0.3 Kg beam oscillate at θ = 124◦ . As we increase the payload mass to 0.4 Kg, beam oscillates at hub rotation of θ = 106◦ . Table 6.1 shows the fundamental frequencies system (hub + beam) with the variation of payloads. System frequencies decreases with the increase in the payload and it has not been affected by magnitude of input torques. Figure 6.6 shows the system resonances with the increasing order of payloads. Highest fundamental frequency is observed with the payload of 0.2 Kg (lowest payload).

6.3.3 Effect of Beam’s Lengths The effect of beam length on dynamics of tip deflection and hub displacement are observed by changing the beam’s lengths and torque. The system natural frequencies observed are 1.44 Hz, 1.36 Hz, and 1.30 Hz for the beam length of 1.8 m, 1.9 m and 2 m respectively. The torque applied are 1 N m and 7 N m for low and high speed

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Fig. 6.6 Fundamental frequency response of the system

of rotation respectively, while payload mass of m p = 85 g and fiber orientation [30◦ /60◦ /30◦ ] are kept same in analysis. The residual tip displacements and hub displacements are observed to be big differences as we change the beam length and torque as shown in Figs. 6.7 and 6.8. The amplitude of residual vibration is greater with the beam of 2 m length as compared to the shorter one. However, the angular displacement of hub is less with the higher beam length and vice-versa. Figure 6.7 shows the tip displacement when τ0 = 1 N m torque applied at hub. With the increase of applied torque τ0 = 7 N m, end point vibration and hub displacement changes are shown in Fig. 6.8. Its magnitude of displacements are greater than the low speed of operation. These are due the fact that the increase in beam’s length and torque enhances flexibility and inertial effects on the system. The phase diagrams of different beam lengths with the change in torque (τ0 ) are shown in Fig. 6.9.

Fig. 6.7 Effect of beam length on (a) tip displacement; (b) hub displacement when τ0 = 1 N m

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Fig. 6.8 Effect of beam length on (a) tip displacement; (b) hub displacement when τ0 = 7 N m

Fig. 6.9 Phase diagrams for (a) L = 1.8 m, (b) L = 1.9 m, (c) L = 2 m when τ0 = 1 N m, and (d) L = 1.8 m, (e) L = 1.9 m, (f) L = 2 m when τ0 = 7 N m

6.4 Conclusions In this numerical study, effects of payload mass of the beam on the system dynamics are investigated. The finite element method and Newmark’s time integration scheme are employed. Time-domain responses of end point displacements of beam and hub angular displacement due to the applied torque are observed at the low as well as high values. It has been observed that the payload and beam’s length has great effect on vibrational characteristics. With the increase in the payload and beam’s length, there are rise in the vibration amplitude and fall in the frequency of the system. The amplitude of vibration is maximum with the payload of 0.4 Kg, and

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minimum with the payload of 0.2 Kg. The input torque has significant effects on tip and displacements. The hub angular displacement decreases with the increase in payload mass and increase with the increase in the angular speed of hub. The payload and bream’s length has not only affecting amplitude of vibration and frequency of the system, but also the end point displacement. Phase diagram is different for different beam lengths and different torque profile as expected.

References 1. Cannon, R.H., Schmitz, E.: Initial experiments on the end-point control of a flexible one-link robot. Int. J. Robot. Res. 3(3), 62–75 (1984) 2. Kane, T.R., Ryan, R., Bannerjee, A.K.: Dynamics of a cantilever beam attached to a moving base. J. Guidance Control Dyn. 10, 139–151 (1987) 3. Yoo, H.H., Shin, S.H.: Vibration analysis of rotating cantilever beams. J. Sound Vib. 212(5), 807–828 (1998) 4. Chang, L., Gannon, K.P.: A dynamic model on a single-link flexible manipulator. J. Vib. Acoust. 112(1), 138–143 (1990) 5. Chung, J., Yoo, H.H.: Dynamic analysis of a rotating cantilever beam by using the finite element method. J. Sound Vib. 249(1), 147–164 (2002) 6. Austin, F., Pan, H.H.: Planar dynamics of free rotating flexible beams with tip masses. AIAA J. 8(4), 726–733 (1970) 7. Usoro, P.B., Nadira, R., Mahil, S.S.: A finite element/Lagrange approach to modeling lightweight flexible manipulators. J. Dyn. Syst. Meas. Control 108(3), 198–205 (1986) 8. Tokhi, M.O., Mohamed, Z., Shaheed, M.H.: Dynamic characterisation of a flexible manipulator system. Robotica 19(05), 571–580 (2001) 9. Mahto, S., Dixit, U.S.: Shape optimization of revolute-jointed rigid-flexible manipulator. Inst. Eng. (India) Ser. C 95, 335–346 (2014) 10. Yang, H., Hong, J., Yu, Z.: Dynamics modelling of a flexible hub-beam system with a tip mass. J. Sound Vib. 266, 759–774 (2003) 11. Dwivedy, S.K., Eberhard, P.: Dynamic analysis of flexible manipulators, a literature review. Mech. Mach. Theor. 41(7), 749–777 (2006) 12. Krishnamurty, K., Chandrashekhara, K., Roy, S., Mechanics, E.: A study of single-link robots fabricated from orthotropic composite materials. Comput. Struct. 36, 139–146 (1990) 13. Saravanos, D.A., Lamancusa, J.S.: Optimum structural design of robotic manipulators with fiber reinforced composite materials. Comput. Struct. 36(1), 119–132 (1990) 14. Choi, S.B., Gandhi, M.V., Thompson, B.S., Lee, C.Y.: An experimental investigation of an articulating robotic manipulator with a graphite-epoxy composite arm. J. Robot. Syst. 5(1), 73–79 (1988) 15. Thompson, B.S., Sung, C.K.: A variational formulation for the dynamic viscoelastic finite element analysis of robotic manipulators constructed from composite materials. J. Mech. Transmissions Autom. Des. 106, 183–190 (1984) 16. Chao, L.P.: Optimal design and sensitivity analysis of flexible robotic manipulators fabricated from advanced composite materials. J. Thermoplast. Compos. Mater. 8, 346–364 (1995) 17. Yavuz, S, ¸ Malgaca, L., Karagülle, H.: Vibration control of a single-link flexible composite manipulator. Compos. Struct. 140, 684–691 (2016) 18. Dubay, R., Hassan, M., Li, C., Charest, M.: Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator. Isa Trans. 53(5), 1609–1619 (2016)

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19. Cai, G.P., Hong, J.Z., Yang, S.X.: Dynamic analysis of a flexible hub-beam system with tip mass. Mech. Res. Commun. 32(2), 173–190 (2005) 20. Dwivedy, S.K., Srinivasulu, M., Narayana, K.S., Koushik, A.: Dynamic analysis of composite flexible robotic manipulator with harmonic drives using finite element method. Adv. Vib. Eng. 8(4), 313–320 (2009)

Chapter 7

Online Tool Wear Monitoring Using Low-Cost Data Acquisition System and LabVIEW™ Program Banarsi Pandey, Binit Kumar Jha, and Sachindra Mahto

Abstract This paper is based on designing of circuit diagram for the measurement of vibration signal and acoustic signal using accelerometer, acoustic sensors, NI DAQ and LabVIEW programs. LabVIEW is system engineer software used to conduct required tests and measurements, control of hardware and data insights. Here, an MEM’s accelerometer (GY-61 ADXL335 3-axis accelerometer module) is used to capture the vibration signal, and an Ahuja professional performance electrets condenser omnidirectional characteristic microphone is used for capturing acoustic signal. The integrated circuit is designed with LabVIEW software, and all the analytical data are captured. This whole assembly is used to analyze as a “online tool wear monitoring system”. The required prototype model is capable of measuring, monitoring and capturing the data in both the normal and abnormal operating conditions. The physical parameters monitored and analyzed are as follows: voltage (V), RMS voltage, frequency, dB, etc.

7.1 Introduction As the world economy is developed, the demand of very precise and automatic controlled systems is getting higher and higher. Automatic controlled systems like “Online tool monitoring system” play very important role in the production of highly precise surface finish product and taking care of tool failure as per data insights. Online tool monitoring system reduces the production cost as well as increases the surface quality efficiency of each and every machine. Hence, the demand of “Online B. Pandey (B) · S. Mahto Mechanical Engineering Department, NERIST, Itanagar, India e-mail: [email protected] S. Mahto e-mail: [email protected] B. K. Jha School of Manufacturing Skills, Bhartiya Skill Development University, Mahindra World City, Jaipur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_7

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tool monitoring system” becomes higher and higher. To develop the low-cost online tool monitoring systems, some MEM’s sensors (e.g., accelerometer, acoustic sensor, temperature sensor, piezometer, loads cell, etc.), myRIO 1900 DAQ, LabVIEW software are used. LabVIEW is the system engineer software for applications that requires tests, measurement and control with rapid access to hardware and data insights [1]. LabVIEW meant for laboratory Virtual Instrument Engineering Workbench, which is a very powerful programming language known as G-Language (graphical programming language) [2]. It is different from the other programming language, e.g., C, C++, Linux, Window, Mac OS X, etc. This programming language is user friendly and ideal for simulation work, presentation of ideas, data analysis and automated controlled systems. LabVIEW programming software is a more flexible and versatile for standard laboratory works. With help of graphical code, we do the design of a virtual system diagram through circuit diagrams. G-codes contain many more symbols, terminology, icons, brackets, etc. Each of them has its own physical significance and meaning. In this paper, circuit diagram has been designed with the help of graphical programming language (G-code) for myRIO 1900 data logger system. Separate circuit is designed for accelerometer as well as microphone. With the help of this designed circuit and hardware, I can take the vibration or noise signal and acoustic signals reading, while the CNC machine is in running condition. If we use these two circuits separately with its hardware, a time lagging factor is occurred. To avoid the time lagging, I have to merge these two circuits in single and used USB flash driver to store many more data. Circuit diagrams are merged and checked for simulation and analysis. The required circuit design is completed and is used in analytical and simulation works at low cost. Here, GY-61 ADXL335 3-axis accelerometer is used to measure the vibration signals and noise signals, which are fixed to the tool holder of a CNC machine. Accelerometer is attached to that position where we can get maximum noise and in the same fashion that the coordinate would be shown in same position and direction. Also, a microphone is attached to CNC machine tool post arrangement is in the similar way so the microphone received more cutting sound.

7.2 Related Work To sustain better position in global competitive market, the required quality of the product should be maintained at lower cost. Online tool monitoring gives an economical and automated controlled way to compete in global competitive market. The brief descriptions of work done by various researchers are appended below. Ma et al. [3] have developed a wireless monitoring system based on LabVIEW. Here, the data analysis work is done at a low cost. Al-Sahib and Hameed [4] have developed a hardware called Arduino-LabView control system for wireless monitoring power generation. Zhang et al. [5] have analyzed the transformer performance

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based on vibration analysis method with use of some sensors and LabVIEW. Singh et al. [6] have developed online monitoring of PMDC motor speed through Internet connection. Prakash et al. [7] have been developed an Arduino-based human body health monitoring system to measure temperature, heartbeat rate, SPO2 using available sensors and LabVIEW. Loganayaki et al. [8] have diagnosed the faults and analyzed the data through the Internet. A design is developed for small microgrid system that is capable of doing multifunction to measure various electrical parameters, power quality parameters and simulates and analyses the signals, records parameters and detects faults [9]. LabVIEW graphical programming platform provides a virtual environment to process and keep track of patient’s various physiological parameters like body temperature, ECG and heartbeat rate in real time [10]. A wireless solar power monitoring system is developed using LabVIEW Web publishing tool and data dashboard [11]. A Web-based lab is developed to do the related experiments on inverted pendulum with using a Java/MATLAB-based environment [12]. An Internetbased communication system is developed to electrical drive experiment using MATLAB/Simulink [13]. An online health monitoring system is developed for a boiler, its fuel supply and draft fan using a LabVIEW and DAQ [14–16]. Online monitoring system is not only used in the production or manufacturing plants. It is used in the entire field where the simulation, data analysis and automated controlled work are needed. In production or manufacturing plant, tool wear monitoring system is easily developed with the help of this paper circuit diagram and hardware. The data is stored in the USB pen driver, and the analysis is carried out on these with the help of different analyzing technique. This study describes the development and implementation of LabVIEW based “online tool wear monitoring system” on low-cost.

7.3 Proposed System Following hardwares and softwares are required to execute LabVIEW graphical programming. Hardware Requirement 1. 2. 3. 4.

NI myRIO 1900 DAQ GY-61 ADXL335 3-axis accelerometer Microphone USB pen drive.

Software Requirement 1. Windows Operating System 2. Web browser 3. National Instruments LabVIEW 2019 (32 bit).

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NI myRIO 1900 DAQ It is a portable and economical data logger device that can be used to design control system, robotics and mechatronics field. It is user friendly and having some of the feature included inside the device like accelerometer, LED’s bulbs and audio input and out jack, USB ports, etc. [17]. Some of the important technical specification is described below: Xilinx Zynq System on a Chip Analog i/p and o/p also in 3.5 mm audio jack. Analog Input (10 Channels) 6–16 V, 14 W power requirement. Analog Output (6 Channels) Accelerometer, LED’s and push button onboard 40 digital I/O lines; wireless enabled Compatible NI myRIO Module. GY-61 ADXL335 3-Axis Accelerometer This is used for the measurement of vibration and noise. It can measure all the 3-axis vibration and noise in static as well as dynamic condition. The technical specifications are as follows: Sensor Chip: Supply Current: Operating Temperature: Sensitivity of Accuracy (%): Operating Voltage Range: A Full Scale Range: Sensitivity: Pin Definitions: Fig. 7.1 NI myRIO 1900DAQ

ADXL335 400 µ −40 to +80 °C ±10 3–5 V ±3 g 300 mV/g (Figs. 7.1 and 7.2).

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Fig. 7.2 NGY-61 ADXL335 with pin diagram

VCC: 3.3 or 5 V; X,Y,Z-OUT: Analog O/P; GND: Ground. Microphone This is used to record the audible signal from the source point. Here, the used microphone is of electret condenser omnidirectional microphone. Technical Specification: Frequency Response: Sensitivity: Impedance: Operates on:

100–15,000 Hz; 5.0 mV/Pa; 1000 ; 1 × 1.5 V (UM-3) Pencil Cell.

USB Flash Drive This is used for storing or collecting the data outside of the PC hard disk. NI myRIO 1900 has a USB port with supporting operating system for USB flash drives to increase the ability of NI my RIO to work with large data sets and to performs the data logging task over a long period of time. USB flash driver is connected to NI myRIO 1900 and install the driver using Web browsers software [18].

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7.4 Software Implementation (Experimental Environments) The next step is software implementation so-called experimental environments connects all the hardware parts and wiring properly. If any types of errors and troubleshoots were raised, this must be solved. After that, software implementation or software run is needed to develop my vibration and sound monitor system. Following softwares are required to be installed in PC/laptop for the development of low-cost DAQ system 1. Windows Operating System 2. Web browser 3. National Instruments LabVIEW 2019 (32 bit). Here, we use Microsoft Window 10 pro 64-bit and × 64-based processor operating system in my laptop. Since this is easily available and user friendly, all the softwares are the easily interfaced with the operating system. Web client shared all the data within Web browser. An NI LabVIEW 2019 (32 bit) is installed into my laptop and building a Web service VI. VI is also known as virtual instruments, and it consists of three components a front’s panel, a block diagram and a connector pane. All the controller and indicators run Main.VI. With use of control units and indicators, we build a front panel that is used in appropriate purposes. Here, the control units are behaving like an input, and indicators are like output. It means all the control units are input and give permission to a user to share information to the Main.VI. Whereas indicators so-called outputs display the results based on inputs shared to Main.VI. All data is serially transmitted over serial port of LabVIEW. Web-page is used to display current vibration and acoustic signals reading. Here, freely available LabVIEW LINX is added first. This free open-source interfaces with available common embedded platforms by providing unified API such as chip kit and many more. The required LabVIEW front panel is designed and shown in Figs. 7.3, 7.4 and 7.5. Figure 7.3 describes the idea about how the sound monitoring front panel final.iv is designed and what are the results are shown at the end. Figure 7.4 shows the vibration monitoring front panel final. iv. Figure 7.5 shows the logging monitoring system. With the help of this panel, we give the address to device where to store the outcomes and what is his name virtual instrument software + architecture (VISA) drivers to establish serial communication is installed, and then, VISA control is used in block diagrams, for this, we need VISA open, VISA read, VISA close blocks [7] (Fig. 7.6). The wiring connection for accelerometer is done as mentioned in the block diagram. The VCC terminal is connected to A1 of pin no. 1 and the GND terminal to A1 of pin no. 6. The MIC is plugged in Audio in 3.5 mm jack and USB flash driver inserted into USB port. The connection for required circuit diagram is completed and ready to execute the program. The designed programmed having a numeric control is used for the sensitivity of nominal voltage output at zero-g acceleration. Here, we put the accelerometer sensitivity value in mv/g and nominal zero-g values. The

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Fig. 7.3 Sound monitoring final.vi front panel

Fig. 7.4 Vibration monitoring final.vi front panel

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Fig. 7.5 Logging data monitoring final.vi front panel

Fig. 7.6 Sound and vibration monitoring final.vi block diagram

waveform chart indicator shows the acceleration values, and other waveform chart indicator shows the microphone output in voltage. The following steps are involved in the configuration of the online tool wear monitoring system: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Be sure our NI myRIO 1900 is plugged in. Click on NI myRIO 1900 USB monitor. Move the cursor to launch the getting start wizard and click on it. Follows all the instruction as per displaying in the monitor. At the end move, the cursor goes to the LabVIEW 2019 and clicks on it. After a few second, LabVIEW 2019 myRIO tool kit window will be opened. Click on my RIO project and check the configuration then click on finish. When a project explorer window appears click on NI myRIO 1900, remove the main.iv, add the new NI myRIO block diagram and opened it. Check the all the wiring connections, hardware connection.

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Fig. 7.7 Accelerometer and MIC setup

10. Before running it, we have to ensure that all the properties of time and signals are available or not as per need. 11. Complete above steps and attach the accelerometer to the tool holder. 12. A microphone is attached nearer to cutting tool and specimen intersection zone. (Microphone is attached in such position that it could receive maximum audio signals). 13. Connect all the wiring according to the block diagram and plug in a USB pen driver to store the data at the end of each experiment. 14. Data analysis is done by applying the analysis tool as per need. After installation of software, hardware and wiring connection, monitoring system is build up as shown in Figs. 7.7, 7.8 and 7.9. Figure 7.7 shows an accelerometer and a mic setup to the tool holder in that cutting tool called inserts is assembled. Figure 7.8 gives the complete ideas about how a myRIO NI DAQ, sensors are attached. Figure 7.9 shows a monitoring system is assembled with an automatic 2-axis lathe for monitoring tool wear in a turning operation. All the experiments are done at different conditions and levels according to design of experiments sequences and the results in terms of signals display onto the front panel is stored in MS-Excel file for further analysis works. Now, the collected signal in term of data is converted into analog signals. While capturing the signals, some of noises are mixed with the vibration and acoustic signals, so they should be eliminated from the signals to get the actual signals. Filtration work is carried out for the same signals. After filtering, we can analyze the signals for cumulative probability of vibration signals and acoustic signals. Here, we cannot get the actual figure of monitoring process. Hence, some other signal processing technique is needed to analyze the signals.

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Lab Top

15V power supply port USB

ort 1

USB flash driver

3.5mm audio i/p jack

NI m RIO

MIC Wire connection

Tool holder insert 3-axis accelerometer

Fig. 7.8 Accelerometer MIC and pen drive with PC

Fig. 7.9 Complete experimental setup photo

7.5 Results 1. All the hardware parts and the adequate wiring connections are done. Now, it is available to use. 2. All types of errors and troubleshoots are completely eliminated so that the system device is easily used in monitoring systems. 3. Block diagram in LabVIEW is prepared as per our requirement and run successfully.

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4. All results in terms wave charts for vibration and sound sensors are displayed on the front panel in LabVIEW. 5. Wave charts and data files (in the forms of Excel sheet) are stored into external memory of PC called USB pen drive.

7.6 Conclusion Here, sound monitoring system is developed through acoustic or audible sound is captured. Secondly, a vibration monitoring system is designed to capture the noise or vibration signals easily. Finally, a logging monitoring system is developed to give address the outcomes or results. We also write the file name with specified format, so we can easily read the file and doing the analysis work easily by applying different analyzing tool as per our requirements. All the three monitoring systems are merged together, and a single monitoring system is designed that monitors both noise and acoustic signals at the same time and give name and location to the outcome files or results at the end of the experiments. Figure 7.10 shows the results of noise signal in voltage versus time (samples) for X-, Y- and Z-axis. The X-, Y- and Z-axis are shown in blue, red and green signals. By giving name to the signal like “logfile.V”, we can store all the data outcomes in Excel or other format. Figure 7.11 shows the outcomes of sound signals in voltage and decibel. The upper part of the graph shows microphone output in voltage versus time, while bottom portion of the graph shows microphone output in amplitude (dB) versus time index. The required hardware and their appropriate software programming design parts in LabVIEW are developed. All the available related sensors are interfaced with NI

Fig. 7.10 Output of X-, Y- and Z-axis

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Fig. 7.11 Output of mic in V and dB

myRIO 1900 DAQ are tested and used. NI myRIO 1900 acts as a data logger system, and GUI-based environment is provided to the user for continuous online tool wear monitoring. The system is designed efficiently and met all expectations as set earlier [7]. Low-cost DAQ system with LabVIEW is developed and tested for online tool wear monitoring system with many available sensors such as accelerometer, acoustic sensor (microphone) and USB flash driver. This system enables the improvement of the efficiency and productivity of machine tool. In future, many more sensors can be interfaced with this system for implementing it in different fields.

References 1. Travis, J., Kring, J.: LabVIEW for Everyone: Graphical Programming Made Easy and Fun, 3rd edn., pp. 3–6. Prentice Hall, Upper Saddle River (2006)

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2. NI equips engineers and scientists national instruments: products [Online]. Available from: https://www.ni.com/en-in/shop/labview.html (2019). Accessed 18 Dec 2019 3. Ma, T., Du, F., Fang, C.: Sensors state monitoring based on LabVIEW and wireless nodes. Procedia Eng. 15, 2639–2643 (2011) 4. Al-Sahib, N.K.A., Hameed, S.: Monitoring and wireless controlling of power generation by LabView. Control Theor. Inform. 4(2), 1–13 (2014) 5. Zhang, Z., Wu, Y., Zhang, S., Jiang, P., Ye, H.: Online monitoring research of transformer vibration based on LabView. In: CISAT 2018. IOP Conference Series: Journal of Physics: Conference Series 1168, pp. 1–8 (2019) 6. Kumar, N., Singh, N., Kulkarni, A.: LabVIEW Based Online Monitoring and Control of PMDC Motor Speed. IEEE (2014). 978-1-4799-6042-2/14/©2014 7. Prakash, M.M., Siva, R.M., Vasan, R.V.T., Vineeth, S.A., Nehru, S.A.: LabVIEW based health monitoring system using arduino. Int. J. Innovative Res. Sci. Eng. Technol. 6(3), 3407–3414 (2017) 8. Loganayaki, P.V., Keerthiga, S., Raj, S.S.: An on-line distributed power quality monitoring system based on internet and labVIEW. IJSER 4(5), 87–95 (2013) 9. Chinomi, N., Leelajindakrairerk, M., Boontaklan, S., Chompoo-Inwai, C.: Design and implementation of a smart monitoring system of a modern renewable energy micro-grid system using a low-cost data acquisition system and LabVIEW™ program. J. Int. Counc. Electr. Eng. 7(1), 142–152 (2017) 10. Babu, M., Raju, R.R., Sylvester, S., Mathew, T.M., Abubeker, K.M.: Real time patient monitoring system using LabVIEW. Int. J. Adv. Res. Comput. Commun. Eng. 5(3), 505–507 (2016) 11. Bhulakshmi Devi, O.: Solar power remote monitoring using labview. Int. J. Eng. Manage. Res. 6(5), 539–542 (2016) 12. Jose, S., Sebistien, D., Rafeal, R., Fernando, M.: A java/matlab-based environment for remote control system laboratories: illustrated with an inverted pendulum. IEEE Trans. Educ. 47(3), 321–329 (2014) 13. Bogosyan, S., Gokasan, M., Ali, T., Richard, W.W.: Development of remotely accessible matlab/simulink based electrical drive experiments. In: IEEE International Symposium on Industrial Electronics, Vigo-Spain, pp. 2989–2989 (2007) 14. Padhee, S., Singh, Y.: Data logging and supervisory control of process using LabVIEW. In: Proceeding of IEEE, Students’ Technology Symposium, Kharagpur, pp. 329–334 (2011) 15. Arun, P.R., Radhakrishnan, M., Periasamy, A., Muruganand, S.: Monitoring of fuel supply in power plant boilers using LabVIEW. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 3(9), 12168–12172 (2014) 16. Krishnan, P.H., Ramprasadh, V.: Controlling power plant boiler and draft fan using Labview. Int. J. Res. Eng. Technol. 3(7), 693–697 (2017) 17. User Guide and Specifications Ni Myrio-1900, ni.com 18. Doering, E.: NImyRIO Project Essentials Guide, pp. 165–168. National Technology and Science Press, Austin (2014)

Chapter 8

Product Priority Problem: A Multi-objective Optimization Approach for Product Development Based on Customers’ Priority Sidharth Sarmah and Dilip Datta Abstract Many companies emphasize on developing new products by adding or removing specific features from existing products according to customers’ demands or priorities. In this paper, an optimization-based procedure is proposed to depict how efficiently the same can be performed by conducting surveys among probable customers. This proposed optimization method is capable of solving selection-based problems in consent with customers’ priority and is aptly termed as the Product Priority Problem (PPP). Limiting the selection of features based upon their integrity and dis-integrity, maximization of customers’ satisfaction and minimization of a product’s price is considered as two conflicting objective functions to lead to a set of trade-off solutions that can be adopted by a company for new products to be developed.

8.1 Introduction A company’s corporate strategy of entering into a new market or expanding its current domain includes the development of entirely new products, as well as the modification of an existing product by adding or removing certain features [1, 15]. In this process, a company must work carefully to decide what changes are required in an existing product. Many companies come out with new products developed based on their research in isolation without taking into consent the views of probable customers. As a result, it is often found that many such products are not well accepted by customers, incurring a loss to the company [2]. Many factors contribute to the demand of a product, the priority, affordability, comparative price, assurance, similar S. Sarmah Department of Materials Science and Engineering, Indian Institute of Technology, Gandhinagar 382355, India e-mail: [email protected] D. Datta (B) Department of Mechanical Engineering, Tezpur University, Tezpur 784028, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_8

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existing products, the brand value of its earlier version (if any), reputation of the launching company, and so on [13]. All such factors depend on customers; hence, it is essential to have a viewpoint of customers of different categories on the onset of developing a product. Products based on customers’ demand may be developed in one of the following two ways [5]: 1. The best option would be to conduct survey among customers, estimate their present needs, and then develop a product accordingly. But this option may not work well due to two main reasons: (i) most of the customers would not be able to find their needs without knowing what the company can do, and (ii) the company also may not be able to develop the exact or a nearer product demanded by customers. 2. As an alternative, particularly in the case of an existing product not well prioritized by customers, the company may first conduct some research on how differently it can modify or improve the product. Then customers may be surveyed on merits and demerits of the product, along with the options of various features that can be added or removed in order to increase the demand for the product. For a particular product, customers may be surveyed on different aspects, such as their economic levels, demand or priority for the product, improvement of the product with available features, preference for other available similar products, priority for the company, use of other products of the company, etc. [12]. Based upon such information, the company may segregate the customers into different groups, assigning a ‘weight’ to each group against each feature and possibility of its acceptance. Since it may not be possible to add or remove all the features for which customers are surveyed. A company’s corporate strategy undoubtedly would be to increase profit. Since the amount of profit is usually proportional to the price of a product, indeed, the company would prefer to develop higher models of an existing product. However, it is also true that there might be many customers who prefer a lower model due to reasons such as budget limitation and redundant/non-prioritized features [4]. Accordingly, the company may plan to develop a lower model in such cases. Hence, a balanced position for the company would be to increase the sale in both the cases, which may be predicted from some satisfaction scores computed based on the size of surveyed customers and company’s ‘weight’ to them [9]. Based on price and satisfaction scores of customers (satisfaction here means preference, not technical features or comfort), prospective new products can be classified into the following four categories [10]: 1. Upgraded existing product: It is a slightly higher model with a modest rise in price developed by adding some new features to an existing product in order to increase its functionality or usability to a certain extent. 2. Degraded existing product: It is just opposite to upgrading an existing product. In this, some ‘not so’ important features are removed from an existing product by retaining its main functionality. 3. New higher level product: It is a product with a drastic rise in price, along with an increase in functionality, comfort, and performance.

Satisfaction score

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New higher level product New lower level product 1 2 3

4 5

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Upgraded existing product Existing product

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Fig. 8.1 Prospective new products developed based on price and satisfaction scores

4. New lower level product: It is just opposite to a new higher level product. A company may plan to bring an affordable cheap model of the product with its basic functionality only. In such an attempt, the Tata Motors in India developed the ‘Nano’ in the year 2009 as the world’s cheapest family car [14] (visit http:// nano.tatamotors.com for detail). The four types of products mentioned are illustrated in Fig. 8.1 through twelve new solutions around the existing solution. The new solutions are shown by hollow circles and marked by 1–12, while the existing solution is shown by a solid circle. The new solutions are taken in such a way that, those on the left side of the existing solution, i.e., solutions 1–6, trade-off with each other in terms of their prices and satisfaction scores of customers. Similarly, the solutions on the right side of the existing one, i.e., solutions 7–12, also trade-off with each other. Accordingly, the new solutions can be categorized into four types as follows: 1. Solutions 7–9 are slightly better than the existing solution at the cost of a little rise in price (upgraded existing products). 2. Solutions 4–6 are slightly degraded from the existing solution so as to reduce their prices by some amount (degraded existing products). 3. Solutions 10–12 are significantly improved over the existing solution at the cost of drastic rise in price (new higher level products). 4. Solutions 1–3 are significantly degraded from the existing solution so as to reduce their prices drastically (new lower level products). It may not be possible to implement all the suggested solutions of a particular category (as Fig. 8.1 shows three solutions in each category). Hence, based on demand marked by satisfaction score in Fig. 8.1, a company can plan to decide which out of the multiple solutions of each category is to be develop. Say, considering the benefit of customers, the decision-makers may select the solution having the maximum change in satisfaction scores to change in price, which can be expressed mathematically by Eq. (8.1).   |so − si | ; i = 1, 2, . . . , n (8.1) Selected solution = max i |Po − Pi |

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In Eq. (8.1), Po and so are respectively the price and satisfaction score of customers for the existing product (so may be set to zero), and Pi and si are respectively those for solution i, while n is the number of new solutions under consideration. As per Eq. (8.1), solutions 8, 5, 11 and 2 are shown in Fig. 8.1 as the selected upgraded existing product, degraded existing product, new higher level product, and new lower level product, respectively. The present work is motivated by the Next Release Problem (NRP) [3], which involves the selection of some features, from a given set, to be incorporated in the next release of a software package by maximizing the satisfaction of clients within a ceiling hike in cost of the software package. In contrast to NRP being confined to software package upgrading only, Product Priority Problem (PPP) is introduced here as a very generic concept for suggesting new optimal products that can be developed by a company to modify an existing product. The PPP considers both additions of new features and removal of existing features, to suggest a set of new trade-off products in terms of price and a satisfaction score of customers [9] as illustrated in Fig. 8.1. Further, a novel conditional multi-objective optimization formulation is proposed for the PPP, to arrive at a Pareto front of the pattern formed by solutions 1–12 in Fig. 8.1.

8.2 Problem Formulation According to the proposed Product Priority Problem (PPP), as explained in Sect. 8.1, a company plans to launch some improved models of an existing product by adding or removing features from a product. However, some features are constrained either as complementary sets to be added/removed simultaneously or as conflicting sets not to be added/present simultaneously. The job is to survey probable customers and subsequently suggest some effective product models, which would maximize customer satisfaction, leading to an increase in sales of the products. The entire formulation can be divided into four main parts: (i) survey probable customers for the development of new products by adding/removing various features, (ii) categorize the surveyed customers into various groups against each feature that can be added/removed, (iii) evaluate the overall satisfaction score of customers against each feature based on a company’s ‘weight’ to each customer, and (iv) then optimize the problem to obtain a set of trade-off optimal solutions (i.e., a Pareto front) by simultaneously minimizing the product cost and maximizing the overall satisfaction score of customers.

8.2.1 Indices and Parameters For formulating the proposed PPP, the following indices and known-valued parameters are considered:

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Number of features that can be added. Number of features that can be removed. Rise in price due to addition of ith feature; i = 1, 2, . . . , n. Cut in price due to removal of jth feature; j = 1, 2, . . . , n  . Price of existing product. Number of customers groups surveyed for addition of ith feature. Number of customers groups surveyed for removal of jth feature. Size of kth customer group opting addition of ith feature; k = 1, 2, . . . , m i . Size of lth customer group opting removal of jth feature; l = 1, 2, . . . , m j . Company’s weight toeach customer of the kth group for addition of the ith i feature; wik ≥ 0 and m k=1 wik = 1. Company’s weight to each customer of the lth group for removal of the jth m  feature; w jl ≥ 0 and l=1j w jl = 1. 

e1,ir =  e2, jr =  e3,ir =  e4,i j =

1 ; If both i th and r th features are added as a complementary pair. 0 ; otherwise. 1 ; If both j th and r th features are removed as a complementary pair. 0 ; otherwise. 1 ; If addition of both i th and r th features conflicts with each other. 0 ; otherwise. 1 ; If addition of i th feature conflicts with the existing j th feature. 0 ; otherwise.

8.2.2 Decision Variables The PPP seeks the determination of two sets of binary variables, X and X  , as given by Eq. (8.2) [6]. 

X = {xi |xi ; i = 1, 2, . . . , n} ;

X  = {x j |x j ; j = 1, 2, . . . , n  } ;

1 ; if ith feature is added 0 ; otherwise. (8.2a)  1 ; if jth feature is removed where x j = 0 ; otherwise. (8.2b) where xi =

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8.2.3 Constraints In terms of compatibility, a particular feature may be complementary of, or may even conflict with, another feature on some technical reasons. Accordingly, two constraints under each of these two types can be imposed on the PPP. Complementary constraints A pair of complementary features are those which always remain together, i.e., either both are to be added/removed together or none [6, 8]. 1. Complementary constraint on addition (g1 ): The addition of the ith feature requires the addition of the r th feature also, i.e., none of the ith and r th features can be added alone, even if customers opt the addition of one of them only. 2. Complementary constraint on removal (g2 ): The removal of the existing jth feature requires the removal of the r th feature also, i.e., none of the jth and r th features can be present alone, even if customers opt the removal of one of them only. Conflicting constraints A pair of conflicting features can never remain together, i.e., both cannot be added or present simultaneously [6, 8]. 1. Conflicting constraint on addition (g3 ): The addition of ith feature restricts the addition of r th feature, i.e., both the ith and r th features cannot be added simultaneously, even if customers opt the addition of both of them. 2. Conflicting constraint on removal (g4 ): The addition of the ith feature requires the removal of the existing jth feature, even if no customer opts the removal of the jth feature.

8.2.4 Objective Functions The customers’ satisfaction score si on addition of the ith feature, and s j on removal of the jth feature can be given by Eqs. (8.3a) and (8.3b) respectively [4, 6, 8]. si (X ) =

mi 

wik z ik xi

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Accordingly, the customers’ overall satisfaction score S on addition and/or removal of all the opted features, out of given n new features and n  existing features, can be expressed by Eq. (8.3c) [6, 8]. 

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Similarly, the overall price P of a new product on addition and/or removal of all the opted features, out of given n new features and n  existing features, can be expressed by Eq. (8.4) [6, 8]. 

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8.2.5 Optimization Problem Formulation The solution space of the proposed PPP is finite, and its size (i.e., the maximum n+n  (n+n  ) n+n  (n+n  )! number of solutions) is i=1 Ci = i=1 , which can easily be evali!(n+n  −i)! uated one by one. However, such evaluation is not only tedious for a large value of n + n  , but it would also be a difficult job for decision makers to select for implementation a limited number of solutions (may be only one or two at a time) out of a huge number of alternatives. Hence, the problem may be formulated as a multi-objective optimization problem for finding some efficient trade-off solutions in terms of the rise/cut in price and satisfaction score of customers. However, the problem cannot be formulated directly as an optimization problem for arriving at a Pareto front containing solutions (products) similar to those shown in Fig. 8.1. The solutions of Fig. 8.1 form an invalid Pareto front as shown by A-B-C-D in Fig. 8.2. It is invalid in the sense that the portion B-C is dominated by the portion A-B in terms of product price. Hence, an optimizer would find only A-B and C-D as a segmented Pareto front by skipping the portion B-C. In order to preserve the portion B-C, a simple novel approach is applied here. It is observed that only degraded solutions (i.e., new products having prices lower than that of the existing product) appear in the portion A-B, which may dominate some upgraded solutions because of lower prices of the former. Hence, without losing any generality, the degraded solutions (i.e., the portion A-B) can be reflected about the price-axis by negating their satisfaction scores of customers, so as to result a valid Pareto front. This is shown by the portion A -B in Fig. 8.2, giving A -B and B-C-D as the segmented

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Fig. 8.2 Modification of the Pareto front to take degraded products into account

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Pareto front without skipping any efficient solution. Accordingly, the optimization problem can be formulated as given by Eq. (8.5) [8]. Minimize  Maximize Subject to

f 2 (X, X  ) =

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S(X, X  ) ; −S(X, X  ) ;

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if

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g2 (X  ) ≡ x j = xr ; if e2, jr = 1 ; j, r = 1, 2. . . . , n  .

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g3 (X ) ≡ xi = xr ; if e3,ir = 1 ; xi = 1 or xr = 1 ; i, r = 1, 2. . . . , n.

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if

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8.3 Numerical Experimentation and Discussion The multi-objective optimization formulation of the PPP, expressed through Eqs. (8.2)–(8.5), can be solved by various approaches. However, since the main aim of the present work is to introduce the PPP, the genetic algorithm (GA) is arbi-

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trarily selected here without going through pros and cons of different applicable approaches [16]. The chosen GA is the {0,1} binary-coded nondominated sorting GA (NSGA-II) [7], which is a popular and widely applied multi-objective GA.

8.3.1 Case Study For numerical experimentation, the proposed PPP is illustrated here in detail with the help of a hypothetical case study. A hypothetical model as shown in Table 8.1 is generated for suggesting some new models of an existing product of price $1,000. There are five new features, marked by p1 – p5 , that can be added for improving the existing product in some aspects. Similarly, five existing features, marked by p1 – p5 , are identified that can be removed for degrading the existing product in some other aspects. The surveyed probable customers against each feature to be added or removed are categorized into three groups, namely poor, middle and rich. The sizes of the customer groups in different cases are fixed considering that no poor customer suggests to add any new feature, while no rich customer suggests to remove any existing feature (however, it does not have any connection with the definition and formulation of the PPP). It is also considered on obvious ground that more number of rich people than middle-income people suggest the addition of new features. Similarly, more number of poor people than middle-income people suggest the removal of existing features (an exception is also shown by making lesser number of poor people to opt the removal of p  ). The company’s weights to customer groups against a feature to be added or removed are fixed in such a way that their total is unity (i.e., 100%). These weights are also considered in a logical way that the company will generally give more preference to rich people in the case of adding new features, while to poor people in the case of removing existing features [11]. Further, one pair of features in the case study is considered to be subjected to each of the four types of constraints discussed in Sect. 8.2.3.

8.3.2 Results The numerical experimentation is performed in three parts. The first case considers only the addition of new features so as to suggest only upgraded models, while the second case considers only the removal of existing features so as to suggest only degraded models. On the other hand, the third case considers both addition and removal of features so as to suggest mixed models. The operators used in NSGA-II in the present work include binary tournament selection operator, single-point crossover operator, swaping mutation operator and elite-preservation operator. In all the studied three cases, the algorithmic parameter

p3 ) p3 ) p5 ) p5 )

0 2000 6000 0 0.2 0.8

Poor-group customer size Middle-group customer size Rich-group customer size Company’s weight to poor-group customer Company’s weight to middle-group customer Company’s weight to rich-group customer Complementary pairs on addition Complementary pairs on removal Conflicting pairs on addition Conflicting pairs on removal

0 6000 15000 0 0.3 0.7

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Table 8.1 Case study of the PPP with an existing product of price $1,000

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values for NSGA-II are fixed as follows—a population of size 60 is evolved over 500 generations with crossover probability of 80% and mutation probability of 1%. The obtained trade-off optimal solutions of all the three cases are shown in Fig. 8.3 along with listing the corresponding efficient models in Table 8.2. In the first case considering only addition of new features, 7 number of trade-off optimal solutions in terms of price and satisfaction score of customers are obtained as shown in Fig. 8.3a. If the company finds them applicable, one or two of them can be selected for implementation based on Eq. (8.1). Note in Table 8.2 that, though it is the case only of addition of new features, the existing feature p5 is to be removed in some models due to the addition of its conflicting new feature p4 , i.e., due to the satisfaction of constraint g4 given by Eq. (8.5f). In the second case also, 7 number trade-off optimal solutions, as shown in Fig. 8.3b, are found considering only removal of some existing features. In the third case, which considered both addition of new features and removal of existing features, a total of 13 trade-off optimal solutions are found as shown in Fig. 8.3c. Out of that, solutions 1–7 lying in Fig. 8.3a on the left side of the existing product are the same with those found under the case of removal only. However, the remaining 6 solutions, i.e., solutions 8–13 lying in Fig. 8.3a on the right side of the existing product, involve both addition of new features and removal of existing features. It is also seen that the satisfaction scores of customers in the latter part (i.e., in solutions 8–13) increase drastically against a little increase in price, thus implying

× × × × 

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 mark implies feature is to be added/removed, while ×mark implies not to be added/removed

Fig. 8.3c (both addition and removal)

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×

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the possibility of more sale if the company implements any of such solutions. It also depicts the fact why majority of companies prefer to develop products of higher levels instead of lower levels.

8.4 Conclusion A new problem, namely the Product Priority Problem (PPP), is introduced for suggesting some optimal higher/lower level models of an existing product. The proposed PPP would be beneficial for a company which plans to develop some new models of an existing product, either because of the non-prioritization of the existing product by customers due to some short-falls, or for expanding the company’s market domain with new products. In that case, the company will first survey probable customers with the proposal for adding some new features as well as for removing some existing features. Then based on the company’s weights to various groups of customers, the PPP will be formulated as a multi-objective optimization problem so as to identify a set of trade-off optimal models in terms of their price and satisfaction score of probable customers. Such a set of optimal models are also demonstrated by applying a multi-objective genetic algorithm to a hypothetical case study.

References 1. Ahonen, T., Reunanen, M., Kunttu, S., Hanski, J., Välisalo, T.: Customer needs and knowledge in product-service systems development. In: Proceedings of the 24th International Congress on Condition Monitoring and Diagnostics Engineering Management (COMADEM-2011). vol. 2, pp. 1632–1640. Stavanger, Norway (2011) 2. Alam, I., Perry, C.: A customer-oriented new service development process. J. Serv. Mark. 16(6), 515–534 (2002) 3. Bagnall, A.J., Rayward-Smith, V.J., Whittley, I.M.: The next release problem. Inf. Soft. Technol. 43(14), 883–890 (2001) 4. Baker, P., Harman, M., Steinhöfel, K., Skaliotis, A.: Search based approaches to component selection and prioritization for the next release problem. In: 22nd IEEE International Conference on Software Maintenance (ICSM’06) (2006) 5. Bhuiyan, N.: A framework for successful new product development. J. Ind. Eng. Manage. 4(4), 746–770 (2011) 6. Chaves-González, J.M., Pérez-Toledano, M.A., Navasa, A.: Software requirement optimization using a multiobjective swarm intelligence evolutionary algorithm. Knowl.-Based Syst. 83(1), 105–115 (2015) 7. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist Multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002) 8. del Sagrado, J., del Águila, I.M., Orellana, F.J.: Multi-objective ant colony optimization for requirements selection. Empirical Softw. Eng. 20(3), 577–610 (2015) 9. Durillo, J.J., Zhang, Y., Alba, E., Harman, M., Nebro, A.J.: A study of the bi-objective next release problem. Empirical Softw Eng 16(1), 29–60 (2011)

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10. Hepp, M., Leukel, J., Schmitz, V.: A quantitative analysis of product categorization standards: content, coverage, and maintenance of eCl@s, UNSPSC, eOTD, and the RosettaNet Technical Dictionary. Knowl. Inf. Syst. 13(1), 77–114 (2007) 11. Jiao, J.R., Chen, C.H.: Customer requirement management in product development: a review of research issues. Concurrent Eng.: Res. Appl. 14(3), 173–185 (2006) 12. Karlsson, J.: Software requirements prioritizing. In: Proceedings of the 2nd International Conference on Requirements Engineering (ICRE ’96). p. 110. ICRE ’96, IEEE Computer Society, USA (1996) 13. Lai, K.H.: Market orientation in quality-oriented organizations and its impact on their performance. Int. J. Prod. Econom. 84(1), 17–34 (2003) 14. Singh, B.K.: Nano-the people’s car. Int. J. Res. Manag. 2(2), 48–60 (2012) 15. Xu, W., Zhang, Q., Ma, J.: The relationship among customer demand, competitive strategy and manufacturing system functional objectives. J. Ind. Eng. Manage. 6(4), 1238–1254 (2013) 16. Zhang, Y., Harman, M., Mansouri, S.A.: The multi-objective next release problem. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation. pp. 1129–1136. GECCO ’07, Association for Computing Machinery, New York, NY, USA (2007)

Chapter 9

Approximating Non-intersecting Closed Curves Through Four-Bar Linkage Mechanism Dilip Datta, Chiranjeeb Deb, Abhishek Hafila, and Debajani Das

Abstract Many practical applications require to approximate curves, which can be done precisely through mathematical modeling or computer simulation. But manufacturing difficulty may arise if such curves become very complex. Moreover, exact methods are limited to approximate only a certain number of target points. Hence, the present work aims at approximating a non-intersecting closed curve by a coupler point of a four-bar mechanism. A simple numerical method is proposed for synthesizing the mechanism. Also, an interpolation-based technique is proposed for comparing two curves of unequal points. The mechanism is synthesized in a way to minimize the deviation of the coupler curve from the target curve. For minimizing the deviation, three evolutionary algorithms are investigated and their performances are compared statistically.

D. Datta (B) · C. Deb · A. Hafila · D. Das Department of Mechanical Engineering, Tezpur University, Tezpur 784028, India e-mail: [email protected]; [email protected] C. Deb e-mail: [email protected] A. Hafila e-mail: [email protected] D. Das e-mail: [email protected] C. Deb Department of Design, Indian Institute of Information Technology, Design and Manufacturing (IIITDM), Jabalpur 482005, India A. Hafila MBA, Indian Institute of Management (IIM) Shillong, Shillong 793014, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_9

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9.1 Introduction Many engineering applications require either to approximate the surface of an existing item or to trace a predefined trajectory. Approximating prototypes is one of such applications, known as the reverse engineering, which is usually performed by computer aided design or other software packages including computational fluid dynamics and finite element analysis [13, 19, 20]. However, it may be difficult in some cases to produce such a prototype due to various manufacturing limitations, if the approximated curve turns out to be a very complex one. Motivated by such issues, the present study aims at approximating a non-intersecting closed curve by a coupler point of a planar four-bar linkage mechanism. Approximating a curve by a coupler point requires to synthesize the geometry of a linkage mechanism in a way to generate a coupler curve with minimum deviation from the target curve. The geometry of a four-bar linkage mechanism involves the lengths of its four links and the location of the coupler point on the coupler link. Based on the geometry of the mechanism, different types of coupler curves can be generated, such as continuous and discontinuous, open and closed, intersecting and non-intersecting, etc. Accordingly, the exact mathematical formulations of general coupler curves become very complex due to the possible variations in the geometry of a mechanism. Moreover, there is no analytical solution to the general problem for tracing more than nine target points [12, 16, 17], which encourages to solve such problems by numerical methods. Therefore, instead of attempting any exact method, the present work proposes a very simple numerical method for tracing a non-intersecting closed coupler curve. For the purpose of approximation, the coupler curve traced using the proposed numerical method is to be compared with the target curve. The comparison is not easy as they, in general, may contain different number of points. Hence, a technique, based on numerical interpolation method, is proposed for comparing two such curves of unequal points. Finally, in order to have a good approximation, the coupler curve is to be generated by minimizing its deviation from the target curve, which is performed here using evolutionary algorithms (EAs). Further, due to the non-availability of similar works for comparison, three EAs are investigated and their performances are compared statistically. The investigated three EAs are genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO).

9.2 Literature Review Linkage mechanisms are usually synthesized by both exact and numerical methods. Many exact formulations of four-bar mechanism contain two quadratic equations making four alternatives for a coupler curve against every step of the crank of the

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mechanism [1, 2, 4, 16]. Bai [3] proposed even a more complex model, where the coupler-curve is formulated as a sixth-order bivariate polynomial. Bulatovi´c et al. [5] used a coupler curve to approximate a rectilinear motion, while Ganesan and Sekar [10] approximated a rectangular path by a coupler curve. For developing rehabilitation devices in medical science, Singh et al. [22] approximated five points of the hip trajectory by a coupler point. Kim et al. [12] proposed a model for approximating different types of curves by minimizing the deviations in the derivatives of a coupler curve from those of its target curve, which suffers from the major drawback that a practical target curve may hardly be differentiable. With the aim of reducing the number of design variables, Bu´skiewicz [7] synthesized a fourbar mechanism by taking the angular positions of the crank and the target curve as the only input. Cvetkovi´c et al. [8] classified four-bar linkages into 27 groups based on their geometric and kinematic characteristics as well as the type of the crank, which are expected to be good basis for analysing and visualizing motion in the field of three-dimensional modeling and synthesis of mechanisms. Sun et al. [24, 25] used Fourier series and wavelet theories to compile some numerical atlas comprising numerous planar and spherical four-bar mechanisms. Works on synthesizing fourbar mechanicsm for various pursposes can also be found in [6, 14, 15, 21], among others. For comparing two curves, many works similar to the present one are also found, where a target curve is approximated by a coupler curve. However, in all those works, keeping the number of points in both the curves same, the total of the Euclidean distances of pair-wise points from both the curves was minimized through different EAs, including GA, DE, and PSO [1, 16, 18].

9.3 Proposed Numerical Method for Tracing a Non-intersecting Closed Coupler Curve The schematic of a planar four-bar mechanism is shown in Fig. 9.1, where AB, BC, CD and AD are its four links, while A, B, C and D are four rotating joints. AD is the ground link, AB and CD connected to AD are the grounded links (one is the input link or crank and the other is the output link or rocker), and BC is the coupler link that connects the input to the output. P is a coupler point used to trace coupler curves, whose position is specified by the length of link BP and its angle α to BC. The mechanism is a system of single-degree freedom in terms of the rotational input angle θ of the crank (AB in Fig. 9.1). Point P will trace a closed coupler curve if one of the grounded links is made the shortest and fully rotational link of the mechanism. Firstly, this condition can be satisfied by applying the Grashof’s law, which states that the shortest link of a four-bar mechanism will rotate fully through 2π angle if the total length of the shortest and the longest links is smaller than that of the other two links. Then, the coordinate of P is to be determined, which depends upon those of B and C. For

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this, taking A as the origin of the x-y coordinate system, the fixed coordinates of A and D can be defined as (Ax , Ay ) = (0, 0) and (Dx , Dy ) = (d , 0), respectively. Now, making AB as the shortest link and the crank, the coordinate of B can be expressed as (Bx , By ) = (Ax + a cos θ, Ay + a sin θ ) as a function of the rotational angle θ of AB. Similarly, if CD becomes the shortest link and the crank, the coordinate of C can be expressed as (Cx , Cy ) = (Dx + c cos θ, Dy + c sin θ ) as a function of the rotational angle θ of CD. Determination of the coordinate of the other joint (i.e., joint C if AB is the crank, or joint B if CD is the crank) requires some geometric analysis of the mechanism. Taking AB as the crank with the conditions of (a + d < b + c) and (a < b, c, d ) to satisfy the Grashof’s law, the coordinate of C can be determined as shown in Fig. 9.2, which involves three cases of joint B: (i) B is vertically above the x-axis and below C (Fig. 9.2a), (ii) B is vertically above of both the x-axis and C (Fig. 9.2b), and (iii) B is vertically below the x-axis (Fig. 9.2c) (note that any other positional case of B will violate the Grashof’s law). Accordingly, the coordinate of C for the crank angle θi (i = 1, 2, · · · , S) can be expressed by Eq. (9.1a), where S is the number of steps of θ with θ1 = 0 and θS = 2π .

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    Cxi , Cyi = Ax + a cos θi + b cos γi , Ay + a sin θi + b sin γi (9.1a)   π Bθ + Bβ − 2 ; if θi  π  where, γi = π  (9.1b) − Bθ − Bβ ; otherwise 2 π Bθ = − Dθ (9.1c) 2   |v2 | Dθ = sin−1 (9.1d) z  2  21  (9.1e) z = (u2 − Dx )2 + v2 − Dy  subject to b sin Bβ = c sin Dβ (9.1f) Bβ = (π − β) − Dβ ; where Dβ ∈ Dβmin , Dβmax  2  b + c2 − z 2 β = cos−1 (9.1g) 2bc   2  2 2 2 2 2  min max

−1 c + (d + a) − b −1 c + (d − a) − b Dβ , Dβ = cos , cos 2c(d + a) 2c(d − a) (9.1h) In Eq. (9.1f), Dβ = Dβmin when θi = π and Dβ = Dβmax when θi = 0. Note that the lengths of the crank and the ground link cannot be equal, otherwise z in Eq. (9.1e) will be zero at θi = 0, which will make Eq. (9.1d) undefined. With the coordinates of B and C as obtained above, the coordinate of P can be determined as shown in Fig. 9.3, which involves two cases of B: it is either vertically below (Fig. 9.3a) or above of C (Fig. 9.3b). Hence, (Pxi , Pyi ) as the coordinate of P can be given by Eq. (9.2), where γi is obtained by using Eq. (9.1b). Pxi = Bxi + e cos φ Pyi = Byi + e sin φ

 α + |γi | ; if Byi < Cyi ; i = 1, 2, · · · , S ; φ = α − |γi | ; otherwise

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9.4 Proposed Technique for Comparing Two Curves of Unequal Points Comparison of two curves containing equal number of points is very easy, where simply the total of the Euclidean distances of pair-wise points from the two curves can be used to measure the difference between them. However, no work could be found in specialized literature addressing the job when the two curves contain different number of points. Hence, a simple technique is proposed here as the introductory work in this direction. The technique is divided into three parts—segmenting the target curve into a number of arcs, transforming the simulated curve (the coupler curve in the present work) in the window of the target curve, and finally comparing the two curves using interpolation method.

9.4.1 Segmenting the Target Curve Since a curve is mathematically nonlinear, it is likely to contain multiple points along a coordinate axis against a single value along the other coordinate axis. Such multiple points will obviously make the comparison of two curves complicated. Hence, the target curve in the proposed technique is segmented into a number of arcs avoiding the existence of such multiple points in any arc. It is done by enclosing each arc by the smallest rectangle, each side parallel to one of the x- and y-axes, in such a way that the arc will not extend inside the rectangle beyond its intersection (tangent) points with the rectangle. It is shown in Fig. 9.4 by segmenting a curve into six arcs:  BC,  CD,  DE,   EF  and FA. AB,

Fig. 9.4 Segmenting the target curve into a number of arcs

y

B

E

A

C

F

D

x

9 Approximating Non-intersecting Closed Curves …

121

9.4.2 Transforming the Coordinates of the Simulated Curve For the purpose of comparison of two curves, they should be shifted to the same window. In the proposed technique, this is done by transforming the coordinates of the simulated curve in terms of the coordinate of the left-bottom corner of the imaginary rectangle enclosing the entire target curve. Say, (xmin , ymin ) is the said coordinate of the target curve (e.g., in Fig. 9.4, xmin is the x value of point A and ymin is the y value of point D). Accordingly, the coordinates (Pxi , Pyi ) of the simulated curve can be transformed to (Qxi , Qyi ) as expressed by Eq. (9.3), where Pxmin and Pymin are respectively the minimum   x and y values of the simulated curve with Pxmin = min Pxj and Pymin = min Pyj for j = 1, 2, · · · , S. Qxi = Pxi − Pxmin + xmin Qyi = Pyi − Pymin + ymin

; i = 1, 2, · · · , S

(9.3)

9.4.3 Comparing the Two Curves As shown in Fig. 9.5, the proposed technique compares two curves of unequal points in three positional cases of the points of the simulated curve (points Q, Q1 and Q2 ) in respect to the rectangles segmenting the target curve: (i) Q lies in a rectangle and deviates along the x-axis (Fig. 9.5a), (ii) Q lies in a rectangle and deviates along the y-axis (Fig. 9.5b), and (iii) Q1 and Q2 do not lie in any rectangle (Fig. 9.5c). As shown in Fig. 9.5a, Q falls in rectangle Tj , and it lies in between points B and C along the y-axis, but not along the x-axis. Hence, its expected location Q along the x-axis is obtained first by linear interpolation between B and C, and then length QQ is taken as the deviation of Q from the target curve. The total of such deviations of all the points of the simulated curve can be computed by Eq. (9.4) as δx, where R is the total number of rectangles segmenting the target curve, Nj is the number of points of the target curve falling in Tj , (Tj,xmin , Tj,ymin ) and (Tj,xmax , Tj,ymax ) are respectively the coordinates of the bottom-left and top-right corners of Tj , (Tj,xk , Tj,yk ) and (Tj,xk+1 , Tj,yk+1 ) are respectively the coordinates of the kth and (k + 1)th points

y C

yk+1 yi yk

B

A x k

Q

E

D

y C

yk+1 Q

xk+1 xi

Tj

yk yi

x

(a) Deviation along x-axis

E

D

y B

A Q B

Tj

D C

Q

A x x x k k+1 i

F

Q1 Q2

x

(b) Deviation along y-axis

E

(c) Out of segmentation

Fig. 9.5 Comparison of two curves containing unequal number of points

x

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of Tj , (Qxi , Qyi ) is the coordinate of Q, and Qx i is the x-coordinate value of Q .



⎧ if Q ; Q ;  ∈ T , T ∈ T , T x j,x j,x y j,y j,y ⎪ i min max i min max

 ⎪ ⎪ ⎪ Q ; Q ∈ T , T < T or Q > T S yi j,yk j,yk+1  xi j,xk xi j,xk+1    ⎨ Qx − Q  ; Tj,xk+1 −Tj,xk (Qyi −Tj,yk ) δx =  i xi   ; Qxi = Tj,xk + ⎪ ⎪ i=1 Tj,yk+1 −Tj,yk ⎪ ⎪ ⎩ k = 1, 2, · · · , Nj − 1 ; j = 1, 2, · · · , R (9.4) Similar to the deviation along the x-axis as given by Eq. (9.4), the total deviation of the points of the simulated curve, falling in some segmenting rectangles of the target curve and deviating along the y-axis can be computed as δy. The computation of the deviations of those points of the simulated curve, which do not fall in any segmenting rectangle of the target curve, is a little bit different. In this case, as shown by Q1 and Q2 in Fig. 9.5c, the segmenting rectangle of the target curve nearest to Q along any of the x- and y-axes is first identified. Then, the shortest one out of the Euclidean distances from Q to the points of that rectangle is taken as the deviation of Q. The total deviation of all such outside points is computed by Eq. (9.5) as δo, where cxmin , cxmax , cymin and cymax are the indices of those segmenting rectangles of the target curve which are nearest to Q in terms of their minimum and maximum values along the x- and y-axes, while c is the index of the ultimate nearest rectangle.

⎫ 2  21  ⎪ + Qyi − Tc,yv ; v = 1, 2, · · · , Nc ⎪ ⎪ ⎪ ⎪ i=1 ⎪ ⎪ ⎪ if Q ∈ / Tj ; j = 1, 2, . . . ,R ⎪ ⎪ ⎪ ⎪ ⎪ where, c = min cxmin , cxmax , cymin , cymax ; ⎬  cxmin = k1 if |Qxi − Tk1 ,xmin | < |Qxi − Tj,xmin |  ⎪ ⎪ cxmax = k2 if |Qxi − Tk2 ,xmax | < |Qxi − Tj,xmax | ⎪ ⎪ ⎪ ⎪ cymin = k3 if |Qyi − Tk3 ,ymin | < |Qyi − Tj,ymin |  ⎪ ⎪ ⎪ cymax = k4 if |Qyi − Tk4 ,ymax | < |Qyi − Tj,ymax | ⎪  ⎪ ⎪ ⎪ ki ∈ {1, 2, · · · , R} ; ki = j ; i ∈ {1, 2, 3, 4} ; j = 1, 2, · · · , R ⎭ (9.5) Finally, the overall deviation of the simulated curve from the target curve is obtained as using Eq. (9.6). δo =

S 

min

 

Qxi − Tc,xv

2

= δx + δy + δo

(9.6)

9.5 Optimization Model In order to approximate a target curve by a coupler curve of a four-bar linkage mechanism, the geometry of the mechanism is to be evolved in a way to minimize the deviation of the coupler curve from the target curve, i.e., as given by Eq. (9.6). Hence, as

9 Approximating Non-intersecting Closed Curves …

123

per the notations used in Fig. 9.1, an optimization problem can be formulated as given by Eq. (9.7), where constraint  g1 is the Grashof’s law with lmin = min{a, b, c, d }, lmax = max{a, b, c, d }, and lm1 , lm2 = {a, b, c, d } \ {lmin , lmax }, while constraint g2 requires AB or CD (refer to Fig. 9.1) to be the shortest link and the crank, which will be made fully rotational through 2π angle. ⎫ Determine (a, b, c, d , e, α) ⎪ ⎪ ⎪ ⎪ To minimize f =

⎪ ⎪ ⎬ Subject to g1 ≡ lmin + lmax < lm1 + lm2 g2 ≡ (a < b, c, d ) or (c < a, b, d ) ⎪ ⎪ ⎪ ⎪ a, b, c, d > 0 ⎪ ⎪ ⎭ e, α  0

(9.7)

9.6 Numerical Experimentation The deviation of a coupler curve from its target curve, i.e., in Eq. (9.6), is not an explicit function of the decision variables of the optimization problem in Eq. (9.7), but a discrete function in terms of the point-wise difference between the two curves. Hence, classical optimization methods, particularly gradient-based methods which have convergence proof, would not be appropriate tools for solving the problem. As alternatives, evolutionary algorithms (EAs) are investigated here, which are usually independent of problem domains. Since no similar work (in particular comparing two curves of unequal points) could be found in specialized literature for comparison, the optimization model of Eq. (9.7) is studied with three real-coded EAs and their performances are compared statistically. The three EAs are genetic algorithm (GA) [9], differential evolution (DE) [23] and particle swarm optimization (PSO) [11], which are well-established EAs and the basis of many other EAs developed in recent years (being very fundamental for today, the details of the applied GA, DE and PSO are skipped here).

9.6.1 Experimental Setup For numerical experimentation, four random target curves are generated within the sizes of 100 mm along each of the rectangular coordinate axes. For a wider exploration of the search space, sufficiently large ranges are set for the decision variables of the optimization model as follows: range of each of a, b, c and d is (1, 500) mm, while those of e and α are (0, 500) mm and (0◦ , 180◦ ), respectively. Further, the coupler curve in each case is simulated by dividing the crank rotation into 360 divisions, i.e., S = (360 + 1) in Eq. (9.1a), diving the crank angle θ into 360 equal divisions with 1◦ interval.

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Finally, based on some trial runs, the user-defined values for various algorithmic parameters of GA, DE and PSO are fixed as follows: the population size and the maximum number of iterations for each EA are set as 100 and 500, respectively. The crossover probability for both GA and DE are 90%, while the mutation probability for GA is 1% and the scaling factor for DE is 0.8. The inertia constant, and cognitive and social factors for PSO are 0.75, 1.5 and 2.0, respectively.

9.6.2 Results and Discussion With the experimental setup stated in Sect. 9.6.1, each of the three EAs (i.e., GA, DE and PSO) for each of the four target curves is executed for 30 independent runs with different sets of initial solutions. The comparisons of the obtained best coupler curves with the corresponding target curves are shown in Fig. 9.6. In the case of target curve 1 in Fig. 9.6a, visual inspection shows almost similar performances of GA and PSO, while the performance of DE is marginally better than those of GA and PSO. However, the performances of all the three EAs are seen indifferent for target curves 2 and 3 shown in Figs. 9.6b and c, respectively. On the other hand, the performances of GA and DE are seen very similar for target curve 4 shown in Fig. 9.6d, while that of PSO is the worst. In fact, the visual inspection of Fig. 9.6 could not draw any clear conclusion. Hence, the statistical t-test is conducted for pair-wise EAs by taking their mean objective values and standard deviations obtained from 30 independent runs of each of the EAs. The obtained t-values are given in Table 9.1, where a negative t-value 100

100

80

80

60

20 0

60

Target curve GA DE PSO

40

0

20

40

60

Target curve GA DE PSO

40 20 80

100

0

0

(a) For target curve 1 100

100

80

80

60

60

Target curve GA DE PSO

40 20 0

0

20

40

60

80

(c) For target curve 3

20

40

60

80

100

(b) For target curve 2

Target curve GA DE PSO

40 20 100

0

0

20

40

60

80

(d) For target curve 4

Fig. 9.6 Comparison of the simulated best coupler curves with the target curves

100

9 Approximating Non-intersecting Closed Curves …

125

Table 9.1 The t-test values over 30 independent runs of the EAs for each target curve Target curve GA-vs-DE GA-vs-PSO DE-vs-PSO (1) (2) (3) (4) 1 2 3 4

4.424647 4.221406 2.193638 2.913774

−5.269332 −2.527113 −2.346499 −7.614347

0.050098 0.399233 0.134620 −2.008109

means that the second EA in the corresponding pair of a column is not better than the first EA. Considering a significance level of 5% for the degree of freedom of 58 [= (30 runs of EA 1) + (30 runs of EA 2) − 2], it is observed in columns (2) and (4) of Table 9.1 that in the case of all the four target curves, the performance of DE is better than those of both GA and PSO for t-value  2.0 (a negative t-value means that the performance of the second EA is not better than that of the first EA). On the other hand, it seen from column (3) that the performance of GA is better than that of PSO for target curve 4, while their performances remain indifferent for the first three target curves. Finally, the values of the decision variables (geometries) and the relationships among the four links of the four-bar mechanisms, corresponding to the best coupler curves in Fig. 9.6, are examined as given in Tables 9.2 and 9.3, respectively.

Table 9.2 Obtained best geometries of the four-bar linkage mechanisms Decision

Target curve 1

Variable

GA

DE

PSO

GA

Target curve 2 DE

PSO

GA

Target curve 3 DE

PSO

GA

Target curve 4 DE

PSO

a (mm)

46

46

39

459

50

51

310

49

49

43

42

499

b (mm)

320

387

358

164

120

164

227

401

215

71

91

462

c (mm)

80

78

81

49

497

432

50

500

381

63

86

47

d (mm)

352

417

365

408

478

380

158

297

275

51

47

163

e (mm)

104

121

168

47

149

120

177

499

462

17

16

177

α (◦ )

60.9

57.4

86.1

4.0

4.9

4.7

1.8

1.8

2.2

11.1

7.4

7.1

Table 9.3 Relationships among the links of the obtained best geometries EA

Target curve 1

Target curve 2

Target curve 3

Target curve 4

Crank

Relationship Crank

Relationship Crank

Relationship Crank

Relationship

GA

a

a 0) = 0, yielding T (t > 0) ≥ 0 under the condition T (0) ≥ 0. Thus, we proved the non-negativity for each of the state variables included in the model. Boundedness Let (T (t), E(t), C(t), U (t)) be a non-negative solution of the system (1)–(4) under the conditions (5). We consider a Lyapunov function L(t), defined by L(t) = T (t) + E(t) + C(t) + U (t).

(24.6)

24 Mathematical Analysis on the Behaviour of Tumor …

315

Now, we differentiate Eq. (6) along the system (1)–(4) with respect to t and this gives T E − μ1 E − pET L˙ = rT (1 − bT ) − dTE − k1 T U + α1 + g s+T     −k2 1 − e−U E + α2 − μ2 C − k3 1 − e−U C + v − γ U − k4 U T   1 2 s ⇒ L˙ = −rb T − − dTE − k1 T U − g E − (μ1 − g) E − pET 2b s+T     −k2 1 − e−U E − μ2 C − k3 1 − e−U C − γ U − k4 U T  r  + α1 + α2 + v + 4b ⇒ L˙ = σ1 + σ2 ,    1 2 s − dTE − k1 T U − g s+T E − (μ1 − g) E − pET − k2 1 where σ1 = −rb T − 2b      r . − e−U E − μ2 C − k3 1 − e−U C − γ U − k4 U T and σ2 = α1 + α2 + v + 4b ˙ ˙ Now if we impose (μ1 − g) > 0, we achieve that L > 0 if σ1 < σ2 and L < 0 for otherwise. So any solution initiating from a non-negative value ought to be bounded (see [9]). Hence, the theorem. From the above lemma, we can conclude the following theorem,  Theorem 1 Let  = (T , E, C, U ) ∈ R4+ |T > 0, E > 0, C > 0, M > 0 . Then  is a positive invariant set of the system (1)–(4) under the condition (5), if (μ1 − g) > 0.

24.4 Equilibrium Points and Their Stability By fixing all the eqautions involved in the system (1)–(4) to zero, we get two equilibrium points; namely, (i) Tumor-free equilibrium point S0 (T0 , E0 , C0 , U0 ), where T0 = 0, U0 = γv , C0 = α2 1 , and E0 = μ +k α1−e . −U0 μ2 +k3 (1−e−U0 ) ) 1 2( (ii) Coexisting equilibrium point S1 (T1 , E1 , C1 , U1 ), where U1 = γ +kv4 T1 , C1 = r (−γ −k4 T1 +bγ T1 +bk4 T12 )+k1 v α2 , and E = . 1 −U 1 (γ +k4 T1 )d μ2 +k3 (1−e )

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The value of T1 is determined by the roots of the expression F(t, v) mentioned below when it is set to zero,   gT1 r −γ − k4 T1 + bγ T1 + bk4 T12 + gT1 k1 v F(T , v) = α1 + (s + T ) (γ + k4 T1 ) d   μ1 r −γ − k4 T1 + bγ T1 + bk4 T12 + μ1 k1 v − (γ + k4 T1 ) d   pT1 r −γ − k4 T1 + bγ T1 + bk4 T12 + pT1 k1 v − (γ + k4 T1 ) d     −U1 r −γ − k4 T1 + bγ T1 + bk4 T12 k2 1 − e − (γ + k4 T1 ) d   −U1 v k1 k2 1 − e . + (γ + k4 T1 ) d

24.4.1 Biological Interpretation of S0 and S1 For the case of the tumor free equilibrium S0 , the tumor cell gets eradicated from the system and all other population of effector cells and circulating lymphocyte together with the concentration of drugs exists in the system. Hence the stability of this point is useful for the therapy. For the case of coexisting equilibrium S1 , all the state variables are present in the system. So if this equilibrium point is stable it means that population of tumor is constant and will be same as forever in the system. Hence the stability of this point is not useful for the therapy.

24.4.2 Local Stability of Tumor-Free Equilibrium Point S0 Theorem 2 The tumor free equilibrium point S0 is locally asymptotically sta

−v and Q2 = ble if Q1 v + Q2 < 0, where Q1 = −k1 μ1 + k2 1 − exp γ   

 −v γ rμ1 + rk2 1 − exp − d α1 . γ Proof The Jacobian of the system (1)–(4) under the condition (5) at S0 is given by ⎛

⎞ r− 0 0 0  gdE0 −k1 U0   ⎜ ⎟ − p E0 −μ1 − k2 1 − e−U0 0 −k2 e−U0 E0 ⎟ ⎜ ⎜ ⎟, s   ⎝ 0 0 −μ2 − k3 1 − e−U0 −k2 e−U0 C0 ⎠ −k4 U0 0 0 −γ

24 Mathematical Analysis on the Behaviour of Tumor …

317

v where U0 = . The eigen values of this matrix are given by: λ1 = r − dE0 − k1 U0 , γ     λ2 = −k2 1 − e−U0 − μ1 , λ3 = −k3 1 − e−U0 − μ2 and λ4 = −γ . Now as we have all the parameters as non-negative and 0 < e−U0 ≤ 1, so the eigen values λ2 , λ3 , λ4 are negative. Thus, the equilibrium point E0 is locally aymptotically stable if λ1 < 0 and this is equivalent to the inequality Q1 v + Q2 < 0. Hence, the theorem.

24.4.3 Global Stability of Tumor-Free Equilibrium Point S0 Theorem 3 The sufficient for tumor free equilibrium point S0 to be globally asymptotically stable is r − dEmin − k1 Umin < 0, where Emin and Umin are minimum effector cells and minimum concentration of targeted chemotherapeutic drug. Proof We form a Lyapunov function V (t) = T (t), which is continously differentiable and positive definite such that V (t = 0) = 0 and V (t > 0) > 0. Now derivating V w.r.t time t along the system (1)–(4), we get V˙ = rT (1 − bT ) − dTE − k1 T U ≤ T (r − rbTmin − dEmin − k1 Umin ) , where Tmin represents the minimum tumor cells population. According to the condition (5), we can easily get Tmin = 0. Eventually, we have V˙ ≤ 0 if and only if T = 0 under the condition r − dEmin − k1 Umin < 0. This gives lim T (t) = 0 under t→∞ the conditions (5) for the system (1)–(4). So, when t → ∞ we get the limiting equation U˙ = v − γ U . v Therefore, by using lim T (t) = 0 and lim U (t) = , we see that t→∞ t→∞ γ α1 α2       , lim E(t) = and lim C(t) = t→∞ t→∞ −v −v μ1 + k2 1 − exp μ2 + k3 1 − exp γ γ respectively. Thus, the tumor free equilibrium is globally asymptotically stable if r − dEmin − k1 Umin < 0. Hence, the theorem. Now we evaluate the expressions for Emin and Cmin with the help of calculations present by Valle (see [11]) by LCIS method. At first, we present some preliminaries, terminalogies and notations. Let us consider an autonomous system of the form y˙ = f (y), where f is infinitely continously differentiable vector function and y ∈ Rn is the state vector. Let h(y) : Rn → R be a localizing function and it is infinitely continously differentiable function. The restriction of h on any subset W of Rn is denoted by

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 ∂h ∂h f (y) = 0 , where f (y) is a Lie h|W . We also define a set A(h) = y ∈ Rn : ∂y ∂y derivative w.r.t f . We define hmin and hmax as; hmin = min {h(y) : y ∈ W ∩ A(h)} and hmax = max {h(y) : y ∈ W ∩ A(h)}. So, all compact invariant sets are contained in the localization set B(h) = {hmin ≤ h(y) ≤ hmax }. From Eq. (1), we can get T˙ ≤ 1 rT (1 − bT ). Now, Tmin = 0 ≤ T (t) ≤ Tmax = . b   3  Let us now study the system in non-negative invariant region  = R+ ∪ {0} × 1 0, . We take the Localizing function h1 = U and so we have b ∂h1 f (y) = v − γ U − k4 U T , ∂y and we get the set A(h1 ) ∩  = {(γ + k4 T ) U = v}, hence we get the inequality h1 |A(h1 )∩ ≥

v . γ + k4 Tmax

Eventually, we can get the infimum and supremum for the concentration for the chemotherapy drug, which is given by  B(h1 ) = U ≥ Umin =

v bv . = γ + k4 Tmax bγ + k4

 v This gives us the conclusion that B(h1 ) = Umin ≤ U ≤ Umax = . γ Now we find the supremum and infimum of the effector immune cells, for which we take the Localizing function h2 = E. Therefore we have   T ∂h2 f (y) = α1 + g E − μ1 E − pET − k2 1 − e−U E. ∂y s+T So, by keeping same logic as earlier we get the set    T −U E − μ1 E − pET − k2 1 − e E=0 . A(h2 ) ∩  = α1 + g s+T The infimum and supremum for the effector cells are thus given by Emin =

α1 b α1   and Emax =  . −U 0 b bμ1 + γ p + k2 1 − e (μ1 − g) + k2 1 − e−U1

This yields B(h2 ) = {Emin ≤ E ≤ Emax }.

24 Mathematical Analysis on the Behaviour of Tumor …

319

Now, with the help of the two sets B(h1 ) and B(h2 ), the inequality r − dEmin −



k1 Umin < 0 becomes Q1 v + Q2 < 0, where   

Q1 = − k1 μ1 b2 + k1 bp + k1 k2 1 − e−U0 b2   

Q2 = (bγ + k4 ) rbμ1 + rp + rk2 1 − e−U0 b − α1 bd .

24.5 Numerical Calculation For the system (1)–(4) under the conditions (5), we have considered large initial population of tumor cell taken as T0 = 107 . The initial population of effector cell is E0 = 3 × 105 and that of the circulating lymphocyte is C0 = 6.25 × 1010 for both the cases. The initial concentration of mAbs drug is taken as U0 = 0.5 [9]. As the value of v is not fixed, so with references to articles by Gupta [8] and Liu [9] we have taken some estimated values of v. Later we vary values of the kill rates to know their after-effects on respective compartments by setting a suitable value of v, which we will acheive from previous case. In Fig. 24.2, we see that if the value of v < 0.52 the large tumor remains violent in the system, whilst they can be supressed if v ≥ 0.53. Fixing v = 0.53, we get that tumor free equilibrium point is loaclly and globally asymptotically stable. So v = 0.53 will be the ideal dosage for eliminating large tumor cells in our model. With references to Liu [9] we take some estimated values of k1 , k2 , k3 and k4 to check the nature of the state variables. It is clear from Fig. 24.3 that changes in k1 , k2 and k3 only effect to respective cells they are linked to. The combination rate k4 due to mAbs drugs also play a significant role in removing tumor, and this suggest that mAbs drugs are useful in treating malignant tumors.

v 0.49 v 0.50

Tumor Cells

v 0.51

2.5 107

v 0.52 2.0 107 v 0.53 1.5 107 1.0 107 5.0 106

10

20

30

40

Time in days

Fig. 24.2 Time series plot for the population of tumor cells with different values of v

320

B. Dhar and P. K. Gupta k1 0.74 Tumor Cells

k1 0.76 k1 0.78

4 107

k1 0.80 3 107

k1 0.82

k1 0.74 Effector Cells

k1 0.76

300 000

k1 0.78

250 000

k1 0.80

200 000

k1 0.82

150 000

2 107

100 000 1 107 50 000 Time in days

Time in days 10

20

30

10

40

20

30

40

k1 0.74 k1 0.76

Circulating lymphocytes 6 1010 5 1010

k2 0.5

7

k1 0.78

1 10

k1 0.80

8 106

k1 0.82

4 1010

k2 0.4 Tumor cells

k2 0.6 k2 0.7 k2 0.8

6 106

10

3 10

4 106

2 1010 2 106

1 1010

Time in days

Time in days 10

20

30

10

40

20

30

40

k2 0.4

k2 0.4 k2 0.5

Effector cells 300 000

k2 0.6

250 000

k2 0.7

200 000

k2 0.8

150 000

k2 0.5

Circulating Lymphocytes

k2 0.6 6 1010 k2 0.7 5 1010 k2 0.8

4 1010 3 1010

100 000

2 1010

50 000

1 1010 Time in days

Time in days 10

20

30

10

40

20

30

40

k3 0.4

k3 0.4 Tumor cells

k3 0.5

7

1 10

8 106

k3 0.5

Effector cells

k3 0.6

300 000

k3 0.6

k3 0.7

250 000

k3 0.7

k3 0.8

200 000

k3 0.8

6 106 150 000 4 106

100 000

2 106

50 000 Time in days

Time in days 10

20

30

10

40

20

30

40

k3 0.4 k3 0.5

Circulating Lymphocytes

k3 0.6

6 1010

k3 0.7

5 1010

Tumor cells 1 107 8 106

k3 0.8

4 1010

k4 2.4 10

9

k4 3.1 10

9

k4 3.1 10

9

k4 3.4 10

9

k4 3.7 10

9

6 106

3 1010

4 106

2 1010 2 106

1 1010 Time in days 10

20

30

Time in days

40

Effector cells 300 000 250 000 200 000

10

k4 2.4 10

9

k4 3.1 10

9

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Time in days 10

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Fig. 24.3 Time series plot for the population of tumor cell, effector cell and circulating lymphocytes with different values of kill rates i.e., k1 , k2 , k3 , k4 and by setting v = 0.53

24 Mathematical Analysis on the Behaviour of Tumor …

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24.6 Conclusion We have done a qualitative analysis and found that all the solutions of the system (1)–(4) under condition (5) are non-negative and bounded if (μ1 − g) > 0. We have also acheived two kinds of equilibrium points, and see that the tumor free equilibrium point is asymptotically stable; locally and globally. Biological importance of tumor free equilibrium point shows that it is useful for therapy. The novelty of this paper lies in the fact that the model can eventually explain the role of mAbs drugs in removal of large tumor whereas Liu’s model [9] failed to do so. The reason behind this is   the term 1 − e−U . The kill rates also play a significant role in the whole process, especially the combination rate of drugs and tumor i.e., k4 . Thus, we conclude that by using mAbs drugs large size of tumor can be eliminated, but of course with adequate amount of drug dosage. This also helps us to understand the neccessity of drug.

References 1. de Pillis, L.G., Radunskaya, A.: A mathematical tumor model with immune resistance and drug therapy: an optimal control approach. Comput. Math. Methods Med. 3, 79–100 (2001). (Taylor & Francis) 2. de Pillis, L.G., Radunskaya, A.: The dynamics of an optimally controlled tumor model: a case study. Math. Comput. Modelling 37, 1221–1244 (2003). (Elsevier) 3. de Pillis, L.G., Radunskaya, A.E., Wiseman, C.L.: A validated mathematical model of cellmediated immune response to tumor growth. Cancer Res. AACR 65, 7950–7958 (2005) 4. de Pillis, L., Gu, W., Radunskaya, A.: Mixed immunotherapy and chemotherapy of tumors: modelling, applications and biological interpretations. J. Theor. Biol. 238, 841–862 (2006). (Elsevier) 5. de Pillis, L.G., Gu, W., Fister, K.R., Head, T., Maples, K., Murugan, A., Neal, T., Yoshida, K.: Chemotherapy for tumors: an analysis of the dynamics and a study of quadratic and linear optimal controls. Math. Biosci. 209, 292–315 (2007). (Elsevier) 6. Dhar, B., Gupta, P.K.: Numerical solution of tumor-immune model including small molecule drug by multi-step differential transform method. Int. J. Adv. Trends Comput. Sci. Eng. 8, 1802–1807 (2019) (World Academy of Research in Science and Engineering (WARSE)) 7. Ghosh, D., Khajanchi, S., Mangiarotti, S., Denis, F., Dana, S.K., Letellier, C.: How tumor growth can be influenced by delayed interactions between cancer cells and the microenvironment? Biosystems 158, 17–30 (2017). (Elsevier) 8. Gupta, P.K., Dhar, B.: Dynamical behaviour of fractional order tumor-immune model with targeted chemotherapy treatment. Int. J. Eng. Technol. 7, 6–9 (2018). (Science Publishing Corporation) 9. Liu, P., Liu, X.: Dynamics of a tumor-immune model considering targeted chemotherapy. Chaos Solitons Fractals 98, 7–13 (2017). (Elsevier) 10. Sharma, S., Samanta, G.: Dynamical behaviour of a tumor-immune system with chemotherapy and optimal control. J. Nonlinear Dyn. (2013) (Hindawi) 11. Valle, P.A., Starkov, K.E., Coria, L.N.: Global stability and tumor clearance conditions for a cancer chemotherpy system. Commun. Nonlinear Sci. Numerical Simul. 40, 206–215 (2016). (Elsevier) 12. Yafia, R.: Hopf bifurcation in differential equations with delay for tumorimmune system competition model. J. Appl. Math. SIAM 67, 1693–1703 (2007)

Chapter 25

LFC of a Solar Thermal Integrated Thermal System Considering CSO Optimized TI-DN Controller Naladi Ram Babu, Lalit Chandra Saikia, Sanjeev Kumar Bhagat, Satish Kumar Ramoji, Biswanath Dekaraja, and Manoja Kumar Behra Abstract This article presents the impact of various types of solar insolations on two thermal area LFC system. Area-1, 2 contain solar thermal plant (STP)-thermal systems. An initial effort was made to apply tilt integral minus derivative control with filter (TI-DN) as an ancillary controller with crow search optimization (CSO) as an optimization technique in LFC studies. Comparison of system dynamics with TI-DN controller in a thermal system are compared with PIDN, TIDN reveals the better performance. Moreover, it is observed that integration of HVDC with AC tieline improves system dynamics. Further case studies are carried with the integration of STP in area-1, 2 and in both areas and are observed that when STP is connected in both areas shows better dynamics over others. Furthermore, a new case study is carried on various combinations of solar insolations (i.e., combinations of fixed and random) in STP. It is witnessed that system with constant insolation in area-1 and haphazard insolation in area-2 shows better dynamics over other combinations.

N. R. Babu (B) · L. C. Saikia · S. K. Bhagat · S. K. Ramoji · B. Dekaraja · M. K. Behra Electrical Engineering Department, NIT Silchar, Silchar, India e-mail: [email protected] L. C. Saikia e-mail: [email protected] S. K. Bhagat e-mail: [email protected] S. K. Ramoji e-mail: [email protected] B. Dekaraja e-mail: [email protected] M. K. Behra e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_25

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25.1 Introduction The deviations in power and frequency are minimized by the governor’s automatic valve operation among the generator units and are termed as LFC [1, 2]. It assists in maintaining the alterations among the generating stations and the load demand. It expects to retain the systems scheduled parameters within prescribed values. The early LFC works started with one-area and further extended to multi-area [3]. The practical systems are created by considering GRC, droop, and GDB [4]. Authors in [5–7] demonstrated the idea of incorporating of geo-thermal, dish-stirling solar, STP with thermal of two-area. However, no studies are stated with integration of STP considering various insolations in all the two-areas. System dynamics gets worsened during faulty and abnormal conditions. Moreover in a longer distance transmission line, systems dynamics can also be weaken due to transient oscillations. This can be overcome by the amalgamation of HVDC with prevailing AC tie-lines. They have quick power controllability via voltage inverters thus eliminating transient oscillations [8, 9]. Authors in [10–12] presented the benefits of AC-HVDC. Authors in [10, 13, 14] demonstrated the LFC of two-area AC-HVDC only. However, the above LFC studies do not include renewable integration and its subjection to various solar insolations in STP. Nowadays, the LFC study is focused on the design of ancillary controllers. These controllers help in nullifying the control area errors. Controllers like integer [15, 16], fractional [17], cascading [8, 9], intelligent [18], and tilt controllers [19, 20] are available in literature. A new controller namely TI-DN with a sign change in TIDN is proposed with the advantages of both integer- and fractional-order controllers which provide extensive study. Classical method of controller tuning is laborious, time consuming, and achieves sub-optimal results. Various meta-heuristic algorithms like firefly [21], biogeography [22], particle swarm (PS) [23], coyote optimization (CO) [24], shuffled frog algorithm [25], etc., are available. A novel algorithm named by crow search optimization (CSO) [26] is available that works on the food search behavior of crows. Crow has ability of utilizing tools, storage, and stealing of food. Surprisingly, the application of CSO is to be utilized. From the above, the aims are: (a) To develop a two-area STP-thermal system incorporated with AC-HVDC. (b) Application of CSO algorithm for optimization of PIDN, TIDN, and TI-DN controller. (c) Performance comparison of the proposed CSO technique with CO and PS algorithms. (d) To demonstrate the impact of AC-HVDC system. (e) Applications of various combinations of solar insolations for the STP system among different areas.

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25.2 System Investigated The two-area STP-thermal systems with AC-HVDC are provided with 3%/ min GRC and droop of 4% for realistic approach. The STP system is provided with fixed insolation of 0.01 p.uW/m2 and random of 0–0.004 p.u W/m2 [8, 11]. The power equations of AC, HVDC, and both are given by Eqs. (25.1–25.3). 2T12 (F1 − F2 ) s

(1)

K DC (F1 − F2 ) 1 + sTDC

(2)

Ptie 12 AC = Ptie 12 DC =

Ptie 12 = Ptie 12 AC + Ptie 12 DC

(3)

Figure 25.1 is provided with the proposed TI-DN controller and are optimized by CSO. Investigations are carried with 1% SLP considering ISE in Eq. 25.4. T ηISE =

{(F1 )2 + (F2 )2 + (Ptie 1 - 2 )2 } · dt 0

Fig. 25.1 Investigated two-area STP-thermal system with AC-HVDC

(4)

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25.3 Proposed TI-DN Controller A new controller namely TI-DN is proposed whose structure is similar to the design of TIDN controller and its transfer function (TF) is given by Eq. 25.5. TFTIDN =K Tj (1/s) +K Ij nj





Nj s+K Dj · s s+N j

 (5)

where j is area number (1, 2), real number (n), K Tj , K Ij , K Dj and N j are the tilt, integral, derivative, and filter coefficients of TIDN controller. Due to the presence of tilt and derivative in feedback path, it has less disturbance rejection ratio. In order to avoid this, the industrial engineers have redesigned the structure of TIDN as TI-DN. The TF model of TI-DN controller is in Fig. 25.2a and its TF equation, constraints set are given by Eqs. 25.6, 25.7, respectively.  TFTI - DN =K Tj (1/s)n j +K Ij s−K Dj · s

0 ≤ K Dj



Nj s+N j

0 ≤ K Tj ≤ 1, 0 ≤ K Ij ≤ 1 ≤ 1, 0 ≤ N j ≤ 100 and 0 ≤ n j ≤ 7

 (6) 

Fig. 25.2 a Transfer function model of TI-DN controller, b Flowchart of CSO technique

(7)

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25.4 Crow Search Optimization Technique Crow search optimization (CSO) is a meta-heuristic population-based algorithm developed by Alireza [26]. It uses crow for finding the food. The properties of tools usage, recognizing faces, hiding, stealing, and storing the food make crow an intellectual bird. In a dimension (d), CSO evaluates the search agents in all the directions and are given by Eq. 25.8. y,iter

[X y,iter =X 1

y,iter

,X 2

y,iter

, . . . ,X d

]

(8)

where y is flock length (= 1, 2, …, n) and iter is iteration. Search agents trace and store the crow’s position. Suppose, if crow-z desires to visit the food storing domicile of crow-y, two possibilities are accessible. Possibility-1: Crow-y does not know that crow-z is approaching, it approaches to its optimum hiding place and is given by Eq. 25.9. X y,iter+1 =X y,iter + r y × FL y,iter × (m z,iter − X z,iter ) rb ≥ APz,iter a

(9)

where 0 < r y < 1. Less values of flight length (FL) gives local and global optimum is achieved by higher values of FL. With reduction in AP values, CSO provides best solution in iteration whereas with smaller step reduction in AP, intensification can be improved. Possibility-2: Knowing that crow-z is approaching, crow-y flew to a random position and its expression is given by Eq. 25.10. X a,iter + 1 = random position

(10)

In iteration, the optimum position is obtained by the crow having best fitness and is stored, gets updated till termination criteria are reached. The flowchart of CSO is shown in Fig. 25.2b.

25.5 Results and Analysis 25.5.1 System Dynamics of Two-Area Thermal System 25.5.1.1

Performance Comparison Among PIDN, TIDN and TI-DN Controller

The two-thermal area system is provided with controllers like PIDN [16], TIDN [19] and TI-DN controller and are optimized by CSO. Optimum values are tabulated in Table 25.1. The corresponding responses are plotted in Fig. 25.3. Careful observa-

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Table 25.1 CSO optimized values of two-area thermal system Area-1

Area-2

Gains

K Pj /K Tj nj

K Ij

PIDN

0.5161

–

0.6383 0.7868 10.6597 0.3299

K Dj

Nj

K Pj /K Tj nj

TIDN

0.6514

3.5842 0.5812 0.9150 19.5652 0.1096

6.3435 0.2622 0.3299 21.9706

TI-DN 0.3567

3.5647 0.9652 0.9364 36.4585 0.7714

6.0125 0.5378 0.4463 57.2450

–

K Ij

K Dj

Nj

0.0936 0.1096 21.9706

Fig. 25.3 System response comparison of various controllers like PIDN, TIDN, and TI-PDN, a F2 versus time and b Ptie 1–2 versus time

tions explore that the responses with TI-DN controller outperform over others (Table 25.1).

25.5.1.2

Convergence Characteristic Comparisons Among CO, PS and CSO Algorithms

In this section, the best controller obtained from the above study is considered for investigation. The proposed TI-DN controller gains are optimized by PS [23], CO [24], and the proposed CSO technique. The corresponding responses are compared in Fig. 25.4. Critical observations of Fig. 25.4 reveal that the system convergence characteristics with CSO technique outcomes over PS and CO.

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Fig. 25.4 Convergence characteristic comparison among PS, CO, and CSO technique

Table 25.2 Optimized TI-DN controller with AC-HVDC integration Gains

K Tj

nj

K Ij

K Dj

Nj

Area-1

0.4723

7.0251

0.6211

0.7347

20.1521

Area-2

0.2116

6.4990

0.8469

0.4351

64.1150

25.5.2 System Dynamics with HVDC Integration The two-area thermal system in Sect. 25.5.1 is integrated with HVDC and is provided with TI-DN controller. The controller values are optimized by CSO and are tabulated in Table 25.2. The corresponding responses are plotted in Fig. 25.5. It can be clearly seen that the responses with AC-HVDC show better dynamics over AC tie-line alone (Table 25.2).

25.5.3 System Dynamics with Integration of STP 25.5.3.1

Effect of STP in Different Areas

The system in Sect. 25.5.2 is integrated with renewable, namely STP. The STP system is provided with a constant solar insolation of 0.01p.u W/m2 and is shown in Fig. 25.6a. The STP system is considered in area-1, 2 and in both. The investigated system is provided with TI-DN and is optimized by CSO. The corresponding responses are shown in Fig. 25.6b. Careful observations reveal that when STP system connected in both areas shows better dynamics over area-1 and area-2.

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Fig. 25.5 System dynamics with AC-HVDC, a F 1 and b Ptie 1–2

25.5.3.2

Effect on System Dynamics Considering Various STP Insolations

The system in Fig. 25.1 is considered for investigations. The input for STP is solar insolation is shown in Fig. 25.6a. Combinations of various insolations are provided in area-1 and area-2. The investigations are carried by considering TI-DN controller and CSO. Its corresponding dynamics are shown in Fig. 25.6c. Critical observations of Fig. 25.6c reveal that the dynamics with fixed insolation to the disturbed area show better responses over random insolations.

25.5.3.3

Effect of STP Integrated System

The STP system is integrated in area-1, 2. It is provided with best insolations obtained from Sect. 25.5.3.2. The obtained responses are compared with the thermal system and are compared in Fig. 25.6d. It is observed that the responses with the integration of STP show better responses over the thermal system.

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Fig. 25.6 System dynamics vs. time with integration of STP, a Random and constant solar insolations, b F 1 considering STP in area-1, 2 and in both areas, c F 1 considering various solar insolations for STP and d F 1 considering with and without STP

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25.6 Conclusions An attempt was made to utilize TI-DN controller in LFC of two-area system. The controller values are optimized by algorithm named by crow search optimization (CSO). Investigations with TI-DN controller outperform over PIDN and TIDN controllers. Moreover, the convergence characteristic comparison with CSO technique converges faster than COA and PSO techniques. Further integration of ACHVDC shows better responses over AC tie-line alone. A comparative study is carried with fixed solar insolation of STP in individual and both the areas. Furthermore, the STP system is examined with various solar insolations in both the areas. Solar insolations of fixed in area-1 and random in area-2 are found to be best combinations over other. It is aslo concluded that the STP system with best combinations of insolations and AC-HVDC improves system dynamic over thermal and AC tie-line alone.

Appendix 1. Nominal system Parameters: F = 60 Hz, SLP of 1% in area-1, area capacity ratio = 1:2, T 12,AC = 0.086pu MW/rad, H j = 5 s, K pj = 120 Hz/MW pu, Dj = 8.33 × 10–3 pu MW/Hz, Bj = 0.425 pu MW/Hz, Rj = 2.4pu MW/Hz, T gj = 0.08 s, T tj = 0.3 s, T rj = 10 s, K rj = 0.5 s, T pj = 20 s, Ksj = 1.8, Tsj = 1.8 s, SLP = 1%, loading = 50%, Pr1 = 1000 MW, Pr2 = 2000 MW, K DC = 0.5, T DC = 0.03 s. 2. Algorithm parameters (a) Coyote optimization algorithm: Number of packs = 50, number of coyotes = 10, maximum genration = 1000. (b) Particle swarm optimization: swarm size = 50, C1 = 0.5, C2 = 0.3, mutation rate = 0.1, maximum generation = 100. (c) Crow search algorithm: Flock size = 50, flight length = 0.2, awareness Probability = 0.1, maximum generation = 100.

References 1. Elgerd, O.I.: Electric Energy Systems Theory: An Introduction. Tata McGraw-Hill, New Delhi (2007) 2. Ibraheem Kumar, P., Kothari, D.P.: Recent philosophies of automatic generation control strategies in power systems. IEEE Trans. Power Syst. 20(1), 346–357 (2005) 3. Tasnin, W., Saikia, L.C.: Performance comparison of several energy storage devices in deregulated AGC of a multi-area system incorporating geothermal power plant. IET Renew. Power Gener. 12(7), 761–772 (2018a) 4. Mobarak, Y.: Effects of the Droop speed governor and automatic generation control AGC on generator load sharing of power. Int. J. Appl. Power Eng. 4(2), 84–95 (2015)

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5. Sharma, Y., Saikia, L.C.: Automatic generation control of a multi-area ST–Thermal power system using Grey Wolf Optimizer algorithm based classical controllers. Int. J. Electr. Power Energy Syst. 73, 853–862 (2015) 6. Tasnin, W., Saikia, L.C.: Maiden application of an sine–cosine algorithm optimised FO cascade controller in automatic generation control of multi-area thermal system incorporating dishstirling solar and geothermal power plants. IET Renew. Power Gener. 12(5), 585–597 (2018b) 7. Saikia, L.C., Chowdhury, Shakya, A.N., Shukla, S., Soni, P.K.: AGC of a multi area gas-thermal system using firefly optimized IDF controller. In: Annual IEEE India Conference (INDICON), Mumbai, pp. 1–6 (2013) 8. Babu, N.R., Saikia, L.C.: Automatic generation control of a solar thermal and dish-stirling solar thermal system integrated multi-area system incorporating accurate HVDC link model using crow search algorithm optimised FOPI Minus FODF controller. IET Renew. Power Gener. 13(12), 2221–2231 (2019) 9. Babu, N.R., Saikia, L.C.: AGC of a multiarea system incorporating accurate HVDC and precise wind turbine systems. Int. Trans. Electrical Energy Syst. 1–18, e12277 (2019). https://doi.org/ 10.1002/2050-7038.12277 10. Sharma, G., Nasiruddin, I., Niazi, K.R.: Robust automatic generation control regulators for a two-area power system interconnected via AC/DC tie-lines considering new structures of matrix Q. IET Gener. Transm. Distrib. 10(14), 3570–3579 (2016) 11. Singh, O., Nasiruddin, I.: Optimal AGC regulator for multi-area interconnected power systems with parallel AC/DC links. Cogent Eng. J. 3(1), 1–17 (2018) 12. Sharma, G., Ibraheem, Niazi, K.R., Bansal R.C.: Adaptive fuzzy critic based control design for AGC of power system connected via AC/DC tie-lines. IET Gener. Transmission Distribution 11(2), 560–569 (2016) 13. Adeuyi, O.D., Cheah-Mane, M., Liang, J., et al.: Frequency support from modular multilevel converter based multi-terminal HVDC schemes. In: Power & Energy Society General Meeting. IEEE, pp. 1–5 (2015) 14. Rakhshani, E., Rodriguez, P.: Inertia emulation in AC/DC interconnected power systems using derivative technique considering frequency measurement effects. IEEE Trans. Power Syst. 32(5), 3338–3351 (2017) 15. Jagatheesan, K., Anand, B., Samanta, S., Dey, N.,Ashour, A.S., Balas, V.E.: Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm. IEEE/CAA J. Automatica Sinica. https://doi. org/10.1109/JAS.2017.7510436 16. Babu, N.R., Bhagat, S.K., Saikia, L.C., Chiranjeevi, T.: Application of hybrid crow-search with particle swarm optimization algorithm in AGC studies of multi-area systems. J. Discrete Math. Sci. Cryptogr. 23(2), 429–439 (2020) 17. Pan, I., Das, S.: Fractional order AGC for distributed energy resources using robust optimization. IEEE Trans. Smart Grid 7(5), 2175–2186 (2016) 18. Sharma, G., Ibraheem, Niazi, K.R., Bansal R.C.: Adaptive fuzzy critic based control design for AGC of power system connected via AC/DC tie-lines. IET Generation Transmission Distribution 11(2), 560–569 (2016) 19. Topno, P.N., Chanana, S.: Tilt integral derivative control for two-area load frequency control problem. In: 2nd International Conference on Recent Advances in Engineering & Computational Sciences, pp. 1–6 (2015) 20. Babu, N.R., Narrisetty, V., Saikia, L.C.: Maiden application of coyote optimizer algorithm with TIDN controller in AGC of a multi-area multi-source system. In: IEEE 16th India Council International Conference (INDICON), Rajkot, India, pp. 1–4 (2019) 21. Raju, M., Saikia, L.C., Sinha, N., Saha, D.: Application of antlion optimizer technique in restructured automatic generation control of two-area hydro-thermal system considering governor dead band. In: Innovations in Power and Advanced Computing Technologies, Vellore, pp. 1–6 (2017) 22. Rahman, A., Saikia, L.C., Sinha, N.: Load frequency control of a hydro-thermal system under deregulated environment using biogeography-based optimised three-degree-of freedom integralderivative controller. IET Gener. Transm. Distrib. 9(15), 2284–2293 (2015)

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23. Sahu, B.K., Pati. S., Panda, S.: Hybrid differential evolution particle swarm optimisation optimised fuzzy proportional–integral derivative controller for automatic generation control of interconnected power system. IET Generation Transmission Distribution 8(11), 1789–1800 (2014) 24. Pierezan, J., Dos Santos, C.L.: Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems, pp. 1–8. IEEE Congress on Evolutionary Computation, Rio de Janeiro (2018) 25. Babu, N.R., Sahu, D.K., Saikia, L.C., Ramoji, S.K.: Combined voltage and frequency control of a multi-area multi-source system using SFLA optimized TID controller. In: 2019 IEEE 16th India Council International Conference (INDICON), Rajkot, India, pp. 1–4 (2019) 26. Askarzadeh, A.: Novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. J. Comput. Struct. 169, 1–12 (2016)

Chapter 26

Maiden Application of Hybrid Particle Swarm Optimization with Genetic Algorithm in AGC Studies Considering Optimized TIDN Controller Sanjeev Kumar Bhagat, Lalit Chandra Saikia, Dhenuvakonda Koteswara Raju, Naladi Ram Babu, Satish Kumar Ramoji, Biswanath Dekaraja, and Manoja Kumar Behra Abstract This paper presents an application of hybrid PSO-GA (HPSO-GA) in automatic generation control (AGC) studies of multi-area systems for controller gains and other parameters. A new ancillary controller named by tilt-integral-derivative with filter TIDN is proposed for the system. The system responses with the TIDN controller are compared with various controllers like PI and PID and outperforms over others. Moreover, the system performance is also analyzed by TIDN controller with optimization techniques named as genetic algorithm (GA), particles swarm optimization (PSO), and HPSO-GA, and it is observed that the system performance with HPSO-GA optimization technique provides better dynamics. Further, the sensitivity analysis is executed to check the controller robustness at nominal conditions and explored that the optimum gain and other parameters yield at nominal are robust with wide changes in loading.

S. K. Bhagat (B) · L. C. Saikia · D. K. Raju · N. R. Babu · S. K. Ramoji · B. Dekaraja · M. K. Behra Electrical Engineering Department, NIT Silchar, Silchar, India e-mail: [email protected] L. C. Saikia e-mail: [email protected] D. K. Raju e-mail: [email protected] N. R. Babu e-mail: [email protected] S. K. Ramoji e-mail: [email protected] B. Dekaraja e-mail: [email protected] M. K. Behra e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_26

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26.1 Introduction The modern power system is highly complex in nature because of its day to day increase in size. Due to this, maintaining the system parameters like frequency, voltage profile, and tie-line power interchange between the control-areas at the nominal value becomes more challenging in the power system. These challenges can be addressed by the automatic generation control (AGC). The aim of AGC is to maintain the system parameters within the prescribed value during sudden load deviation. Preliminary studies of AGC presented by Concordia and Elgerd [1, 2]. Many works on the AGC have been reported with two-area or three-area system using thermal, hydro, and gas generating units. Although three-area systems have been reported, but very fewer studies are found with the integration of renewable like solar thermal power plant (STPP) [3], dish stirling solar thermal system (DSTS) [4], wind turbine system (WTS) [5, 6], etc., with conventional units. Therefore, further study can be carried out with three area with renewable integration which provides scope for further investigations. Many secondary controllers have been reported in AGC study such as PI [3], PID [4], fractional-order (FO) like FOPI [7], and FOPID [7]. A few works have been found in the past literature using a controller which has tilt characteristics [8, 9] with filter coefficient. This gives the opportunity to apply a special characteristic control approach in the AGC field. A numerous optimization technique have been developed such as differential evolution (DE) [9], bacterial foraging (BF) [10], cuckoo search algorithm [11], crow search [12, 20], firefly algorithm (FA) [13], gray wolf optimization (GWO) [14], genetic algorithm (GA) [15], grasshopper optimization algorithm [16], particles swarm optimization (PSO) [17, 18], and ant-lion optimizer technique [19]. Interestingly, it is found that the hybridization of two algorithms hardly used in the AGC studies. The hybridization of PSO and GA (HPSO-GA) optimization techniques gives the opportunity to apply in the AGC. The main aim to hybridization of GA and PSO is to obtain the best quality solution and faster converge within a few generations [24–27]. Sinha et al. [4, 19] demonstrated the sensitivity of the controller at varied conditions with BF and FA techniques. Moreover, the AGC study with HPSO-GA can be extended to perform sensitivity analysis at different loading conditions which provide further scope. From the above, the objectives are as follows: (a) To develop a three-area two-source system constituting thermal-wind generating units. (b) Performance comparison of (a) with HPSO-GA based optimized controllers such as PI, PID, and TIDN controller in order to find the best controller.

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(c) To compare the system dynamics with the best controller found in (b) by using various evolutionary algorithms such as GA, PSO, and HPSO-GA and in order to find the best optimization technique. (d) To perform a sensitivity analysis of the best controller under different loading conditions.

26.2 System Investigated The transfer function model of the investigated system is shown in Fig. 26.1. The system consists of thermal-wind units in all three areas with area capacity ratio as 1: 2: 4. The thermal system is provided with governor dead band (GDB) of 0.06%/minute and generation rate constraint (GRC) of 3%/minute. The area participation factor (apfij) of 0.5 is considered in each area for individual generating units. The nominal parameters of thermal and wind are taken from [6, 12]. Various optimization techniques like GA, PSO, and HPSO-GA are used to optimize the controller gains parameter with minimizing the cost function (z) in (26.1) using integral-squared-error (ISE) providing 1% step load perturbation in area-2. z=

t 

2   (Fi )2 + Ptiei− j dt

0

where area number (i, j = 1, 2, 3 and i = j).

Fig. 26.1 Transfer function model of three-area thermal-wind system

(26.1)

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Fig. 26.2 Transfer function model of TIDN controller

26.3 The Proposed TIDN Controller A new secondary controller named as TIDN controller is proposed. The TIDN controller is identical to the PID controller except the proportional coefficient is replaced with the tilt component which transfer function is 1/sˆ(1/n) [8, 9]. The advantage of TIDN over others is that it has a simple design process, better tuning ability, and it can reject the disturbance ratio and provides better relative stability with very less sensitive to variation in closed-loop plant parameter. The structure TIDN is shown in Fig. 26.2.

26.4 The Proposed HPSO-GA Technique 26.4.1 Genetic Algorithm (GA) In the 1960s, a popular biological evolution algorithm called a genetic algorithm (GA) was developed by John Holland. The advantage of GA over many traditional optimization techniques is that it can be used for various types of optimization technique such as stationary or non-stationary function, linear or nonlinear functions, as well as continuous or discontinuous. The working procedure for GA is follows: 1. Initialization of random population (suitable solutions) by chromosomes string [15] 2. Fitness evaluation of each chromosome in (step-1). 3. Production of new population from current iteration through selection, mutation, and crossover. In the selection process, the two chromosomes considered as a parent from a random initial population depending on their fitness value. Those who have best fitness value will have more probability to participate in the crossover. In the crossover operator, the most probabilistic parent is allowed to perform to generate the new child. The choosing of a parent is done by at random

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or a cutting point method at which each of the parents is divided into two parts, and in the final mutation, operator is applied to change the value randomly of a particular location of an individual. This process is continued until the desired value is achieved.

26.4.2 Particles Swarm Optimization (PSO) PSO is based on the social behaviors of birds flock. The main aim of this technique is to find the best solution in the entire multi-dimensions search space. It is achieved by allocating initial random positions of all the particles in the entire search space with small random initial velocity to each and every particle [17, 18]. Then, the simulation technique is implemented to improve each particle’s position turn based on the velocity. This technique helps to find out the best global position in the entire search space of particle. After each position update, the objective function is sampled continued until some of the good solutions have explored by particles and converge around the optimal point. Mainly, the PSO has only two populations called the best population and current positions. This provides better diversity and exploration over a single population which allows the faster convergence in search trajectories. But the main issue in PSO is that it has poor local search ability in the local search space which can overcome by hybridizing GA and PSO.

26.4.3 Hybrid PSO-GA Generally, the sensitivity of GA depends on the initial population. If the initial population is not chosen properly, the GA may not converge toward the desired results and other hands. The PSO sensitivity is independent of the initial population, and it has been observed that PSO converges fast at the initial phase of global search or toward global optimum value. But the search process becomes sluggish around the global optimum value. So in this paper, the hybridization is done in such a way that the issue in both algorithms can overcome and the performance of the HPSO-GA over GA and PSO. To make hybrid PSO-GA, first, the PSO algorithm will be created, and GA has to start the work to takes the initial population to solve the optimization problem continuously. Moreover the no of iteration for HPSO-GA, we set the sub-iteration of each algorithm [21–24], so that the PSO completes its sub-iteration first, then GA starts runs until it is given sub-iteration and overall the HPSO-GA finished the provided iteration. Schematically, the HPSO-GA can be summarized by flowchat in Fig. 26.3.

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Fig. 26.3 Flowchart of HPSO-GA

26.5 Results and Analysis 26.5.1 Dynamic Response Comparison of Various Controllers with HPSO-GA Optimization Technique The system in Fig. 26.1 is considered for investigation. The controllers like PI, PID, and TIDN are considered. The controller parameters are optimized by the HPSOGA optimization technique, and optimum gain values are presented in Table 26.1a. The dynamics response with the respective controllers are shown in Fig. 26.4a, b. Table 26.1 a Optimum gain parameters of the various controllers like PI, PID, and the proposed TIDN using HPSO-GA, b Comparison of system dynamics responses with various controller PI Gains

PID

TIDN

Area-1 Area-2 Area-3 Area-1 Area-2 Area-3 Area-1

KTi /KPi 0.0001 0.0005 0.0038 0.8258 0.2452 0.7469 –

–

–

–

Area-2 0.0011

Area-3 0.0004

ni

–

2.0780

2.0079

6.6617

KIi

0.0005 0.0001 0.8375 0.7368 0.7814 0.8457

0.0004

0.0001

0.6756

KDi

–

–

–

0.2540 0.9632 0.6578

0.0344

0.0034

0.0001

Ni

–

–

–

–

–

–

0.0011

–

87.63

86.47

99.65

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Fig. 26.4 Comparison of dynamic responses of the system with PI, PID, and TIDN. a Frequency deviation versus time in area-2. b Tie-power deviation versus time in area-2 and area-3

Parameters like over-shoot (OS), under-shoot (US), and settling-time(ST) are given in Table 26.1b. From Fig. 26.4 and Table 26.1b, we can clearly observe that the system with TIDN controller gives satisfactory performance than other controller in terms of US, OS, and ST. Characteristics

F 2

Ptie23

OS

US

ST

OS

US

ST

PI

0.0021

0.0099

68.130

0.0050

0.0020

NS

PID

0.0019

0.0098

25.400

0.0045

0.0015

85.770

TIDN

0.0005

0.0087

23.870

0.0031

0.0003

28.250

NS—Not settled

26.5.2 Dynamics Response Comparison Among GA, PSO and the Proposed HPSO-GA Considering TIDN Controller In this section, the best controller found in Sect. 5.1 is optimized with different algorithms such as GA, PSO, and HPSO-GA algorithms. The dynamic responses of the system is obtained, and its comparisons are shown in Fig. 26.5a, b. The optimized control gain parameters with GA, PSO, and HPSO-GA are noted in Table 26.2. Moreover, the convergence curve is also obtained and shown in Fig. 26.5c. After critical observation of Fig. 26.5a, b, the dynamics responses with HPSO-GA optimized controller provides better dynamics with lesser OS, US, and ST in comparison with GA and PSO. From Fig. 26.5c, it can be concluded that the HPSO-GA optimization is converging more faster than other optimization technique.

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Fig. 26.5 Comparison of dynamic responses of the system with GA, PSO, and HPSO-GA. a Frequency deviation versus time in area-2. b Tie-power deviation versus time in area-2 and area-3. c Cost values versus the number of evaluations in iteration of GA, PSO, and HPSO-GA

Table 26.2 Optimum gains parameter of the controller using various algorithms like GA, PSO, and the proposed HPSO-GA Gains GA Area-1

PSO Area-2

Area-3

Area-1

HPSO-GA Area-2

Area-3

Area-1

Area-2

Area-3

KTi

0.7888

0.9153

0.7312

0.1642

0.7182

0.6696

0.0011

0.0011

0.0004

ni

5.3233

2.0574

5.8120

6.0328

5.8247

3.0270

2.0780

2.0079

6.6617

KIi

0.4210

0.1810

0.9655

0.6313

0.5263

1.0000

0.0004

0.0001

0.6756

KDi

0.6835

0.9956

0.9999

0.9856

0.7923

2.9905

0.0344

0.0034

Ni Cost (z)

98.9233 4.14e-04

12.0574 98.8820 70.0328 4.02e-04

45.8247 77.9905 87.63

86.47

0.0001 99.65

3.94e-04

26.5.3 Sensitivity Analysis at Different Loading Condition Now in this section, dynamics responses of considered system is examined at different loading condition −20% and +20% from their nominal value such as 30%, and 70%. The dynamics responses are obtained with the best controller found in Sect. 5.1, and

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the best optimization technique found in Sect. 5.2. The obtained response is shown in Fig. 26.6a, b, and the optimum value of the controller parameter at nominal condition and varied condition area is noted in Tables 26.2 and 26.3. From Fig. 26.6, it can be seen that the obtained response with nominal value reflects better dynamics instead of varying loading condition; hence, it can be concluded that the proposed control techniques such as TIDN controller provides satisfactory performance at different loading condition, and therefore, it obeys the robustness of controller.

Fig. 26.6 Comparison of dynamic responses of the system at varied with nominal condition versus time, a Frequency deviation in area-1 at 30% loading condition and 70% loading condition, b Tiepower deviation among area-1 and area-2 at 30% loading condition and 70% loading condition

Table 26.3 Optimum gains parameter of the controller at different loading conditions Gains

70% loading Area-1

30% loading Area-2

Area-3

Area-1

Area-2

Area-3

KTi

0.7915

0.6992

0.6516

0.4125

0.9160

0.5789

ni

2.6291

1.8405

4.769

2.1475

5.4764

1.2740

KIi

0.2442

0.0954

0.7823

0.1484

0.1374

0.9972

KDi

0.3698

0.8942

0.9913

0.9936

0.9979

Ni

99.629

32.8405

100.000

97.147

5.4764

0.9977 100.000

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26.6 Conclusions In this study, a new optimization technique named by HPSO-GA along with the TIDN controller is successfully applied in three-area multi-source AGC system for the first time. The system responses considering PI and PID are compared with the proposed TIDN controller and outperforms over other controllers in terms of ST, US, and OS. Also, most frequently optimization approaches in AGC study such as GA and PSO are compared with the proposed HPSO-GA technique and are observed that the HPSO-GA optimization technique converges faster than others. Moreover, the sensitivity analysis is also conducted to examine the robustness of TIDN controller at different loading conditions, and it is observed that the responses at nominal loading conditions provide better dynamics than other varying conditions.

Appendix At nominal conditions: 1. Area number (i, j where i = j) = 1, 2 and 3, frequency (F) = 60 Hz, Loading = 50%, Tie-power (T ij ) = 0.086puMW/rad, Kpi = 120 Hz/pu, Ri = 2.4 Hz/pu, Bi = 0.425 MW/Hz, Tpi = 20 s, apfij = 0.5, Tgi = 0.08 s, Tti = 0.3 s, Kri = 0.5, Tri = 10 s, KHP2 = 1.25, TBP1 = 0.0415 s, KHP3 = 1.4, TBP2 = 0.041 s, KPC = 0.8. 2. Genetic algorithm (GA): Number population = 30, maximum number of iterations = 100, Parents (off springs) Ratio = 0.7, Mutants Population size Ratio = 0.2 3. Particles swarm optimization (PSO): Number population = 30, maximum number of sub-iterations = 100, PSO parameter C1 and C2 = 1.5, PSO momentum of inertia = 0.73. 4. Hybridization of PSO and GA (HPSO-GA): Number population = 30, maximum number of sub-iterations for PSO and GA = 20, Parents (off springs) Ratio = 0.7, Mutants Population size Ratio = 0.2, PSO parameter C1 and C2 = 1.5, PSO momentum or inertia = 0.73.

References 1. Concordia,C., Kirchmayer, LK.: Tie-line power and frequency control of electric power systems. Trans. Am. Inst. Electrical Eng. Part III: Power Apparatus Syst. 73(1), 562–572 (1954)

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2. Elgerd, O.I.: Electric Energy Systems Theory: An Introduction. Tata McGraw-Hill, New Delhi (2007) 3. Sharma, Y., Saikia, L.C.: Automatic generation control of a multi-area ST–thermal power system using Grey Wolf optimizer algorithm based classical controllers. Int. J. Electr. Power Energy Syst. 73, 853–862 (2015) 4. Rahman, A., Saikia, L.C., Sinha, N.: Load Frequency control of a hydro-thermal system under deregulated environment using biogeography-based optimised three-degree-of freedom integralderivative controller. IET Gener. Transm. Distrib. 9(15), 2284–2293 (2015) 5. Saha, A., Saikia, L.C.: Combined application of redox flow battery and DC link in restructured AGC system in the presence of WTS and DSTS in distributed generation unit. IET Generation Transmission Distribution12(9), 2072–2085 (2018) 6. Tasnin, W., Saikia, L.C., Saha, A., Saha, D., Rajbongshi, R.: Effect of different renewables and FACT device on an interconnected thermal system using SCA optimized fractional order cascade controllers. In: 2nd International Conference on Power, Energy and Environment: Towards Smart Technology (ICEPE), Shillong, India, pp. 1–6 (2018) 7. Pan, I., Das, S.: Fractional order AGC for distributed energy resources using robust optimization. IEEE Trans. Smart Grid 7(5), 2175–2186 (2016) 8. Topno, P.N., Chanana, S.: Automatic generation control using optimally tuned tilt integral derivative controller. In: IEEE First International Conference on Control, Measurement and Instrumentation (CMI), Kolkata, pp. 206–210 (2016) 9. Behera, S.P., Biswal, A., Samantray S.S., Swain, B.: Hybrid power system frequency regulation using TID based robust controller design and differential evolution (DE) algorithm. In: Technologies for Smart-City Energy Security and Power (ICSESP), Bhubaneswar, pp. 1–6 (2018) 10. Nanda, J., Mishra, S., Saikia, L.C.: Maiden application of bacterial foraging-based optimization technique in multiarea automatic generation control. IEEE Trans. Power Syst. 24(2), 602–609 (2009) 11. Dash, P., Saikia, L.C., Sinha, N.: Comparison of performances of several FACTS devices using Cuckoo search algorithm optimized 2DOF controllers in multi-area AGC. Electrical. Power Energy Syst. 65, 316–324 (2015) 12. Rambabu, N., Saikia, L.C.: Automatic generation control of a solar thermal and dish-stirling solar thermal system integrated multi-area system incorporating accurate HVDC link model using crow search algorithm optimised FOPI Minus FODF controller. IET Renew. Power Generation13(2), 2221–2231 (2019) 13. Yang, X.S.: Firefly algorithm for multimodal optimization, stochasticalgorithms: foundations and applications. Lect. Notes Comput. Sci. SAGA 5792, 169–178 (2009) 14. Seyedali, M., Mohammad, M.S., Andrew, L.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014) 15. Das, D.C., Roy, A.K., Sinha, N.: GA based frequency controller for solar thermal–diesel–wind hybrid energy generation/energy storage system. Electrical Power Energy Syst. 43, 262–279 (2012) 16. Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017) 17. Shayeghi, H., Jalili, A., Shayanfar, H.A.: Multi-stage fuzzy load frequency control using PSO. Energ Convers. Manage. 49, 2570–2580 (2008) 18. Barisal, A.K., Somanath, M.:Improved PSO based automatic generation control of multi-source nonlinear power systems interconnected by AC/DC links. Cogent Eng. 5 (2018) 19. Raju, M., Saikia, L.C., Sinha, N., Saha, D.: Application of antlion optimizer technique in restructured automatic generation control of two-area hydro-thermal system considering governor dead band. In: Innovations in Power and Advanced Computing Technologies Vellore, pp. 1–6 (2017) 20. Babu, N.R., Saikia, L.C.: AGC of a multiarea system incorporating accurate HVDC and precise wind turbine systems. Int. Trans. Electrical Energy Syst. e12277, 1–18 (2019)

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21. Martínez-Soto, R., Castillo, O., Aguilar, L.T., Type-1 and type-2 fuzzy logic controller design using a hybrid PSO-GA optimization method. Information Sci. 285, 35–49 (2014) 22. Tao, Y., Jiatian, X., Weiguo, J.: A load distribution optimization among turbine-generators based on PSOGA. IEEE Int. Conf. Intell. Comput. Technol. Autom. 4, 15–18 (2011) 23. Lenin, K., Ravindranath Reddy, B., Surya Kalavathi, M.: A new improved GA and PSO combined hybrid algorithm (HIGAPSO) for solving optimal reactive power dispatch problem.J. Industrial Intell. Information 2(3), 205–209, September 2014 24. Babu, N.R., Bhagat, S.K., Saikia, L.C., Chiranjeevi, T.: Application of hybrid crow-search with particle swarm optimization algorithm in AGC studies of multi-area systems. J. Discrete Math. Sci. Cryptogr. 23(2), 429–439 (2020) 25. Bhagat, S.K., Ram, N., Sakia, L.C., Raju D.K.: Maiden application of meta-heuristic techniques with optimized integral minus tilt-derivative controller for AGC of multi-area multi-source system. EAI Endorsed Trans. Scalable Information Syst., 1–9. May 2020 26. Babu, N.R., Saikia, L.C.: Automatic generation control of a solar thermal and dish-stirling solar thermal system integrated multi-area system incorporating accurate HVDC link model using crow search algorithm optimised FOPI Minus FODF controller. IET Renew. Power Gener. 13(12), 2221–2231 (2019) 27. Babu, N.R., Sahu, D.K., Saikia, L.C., Ramoji, S.K,Combined voltage and frequency control of a multi-area multi-source system using SFLA optimized TID controller. In: 2019 IEEE 16th India Council International Conference (INDICON), Rajkot, India, pp. 1–4 (2019)

Chapter 27

Enhancement of Reactive Power Reserve Using Salp Swarm Algorithm Nibha Rani and Tanmoy Malakar

Abstract In this paper, an optimal reactive power dispatch problem is solved with an aim to maximize the Reactive Power Reserve (RPR) of power systems. The RPR of a system is the additional Reactive Power (RP) that can be supplied by reactive power sources over and above their current RP generations. The voltage stability indicator is critically dependent on RPR and in fact, the RPR is the capability of the RP sources to boost the bus voltages during power system operation. The problem is formulated as a nonlinear, non-convex optimization problem with continuous control variables. A recently developed meta-heuristic algorithm called ‘Salp Swarm Algorithm (SSA)’ is utilized to solve the problem. The problem is programmed in MATLAB and experimented on the standard IEEE 30-bus system. Various case studies are carried out to authenticate the effectiveness of SSA algorithm over other contemporary methods. Moreover, the effect of practical nonlinear loads on the variations of RPR is also investigated in this paper. Parametric sensitivity analysis of SSA is performed for each case to declare optimal results.

27.1 Introduction Modern restructured power system operation is constrained by strict economic considerations. As a result, power networks are often operated close to their operating limits and this causes excessive stresses on power apparatuses. Such operation may result in voltage instability or to voltage collapse in extreme cases. The major reasons for voltage instability are low voltages at load buses and insufficient reactive power support [1]. In particular, voltage collapse phenomena occurs when a power system network is forced to operate with higher loading and sources do not have sufficient reactive power left with them to support. Hence, sufficient Voltage Stability Margin N. Rani (B) · T. Malakar Electrical Engineering Department, National Institute of Technology Silchar, Silchar, India e-mail: [email protected] T. Malakar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_27

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(VSM) must be kept to protect the system against such subsequent voltage fall or collapse. In order to maintain this, suitable control strategies should be adopted and maintained continuously from control sources [2]. VSM is closely related to the availability of the Reactive Power Reserve (RPR). In order to maintain sufficient VSM, reactive reserve in the system should be kept as high as possible. The introduction of renewable energy has further increased the complexities of power system. These complexities can be handled by maintaining an adequate amount of reactive power support services [3]. Moreover, the increasing competition in the deregulated market mandates the evaluation of the contribution of each reactive power sources during various loading pattern [4]. Also, voltage instability can be well predicted by considering the effective reactive power reserve of each generating unit [5]. Apart from maintaining system security in the deregulated market, the ISO’s major interest is to motivate the generating units for their maximum contribution in maintaining system stability. This is possible when the synchronous generators are operated in the proper pattern such that their RPR can be utilized immediately during critical loading conditions [6]. Therefore, Reactive Reserve Maximization (RRM) can be considered as an optimization problem. Many researchers have worked toward maintaining sufficient reactive reserve in the system so as to maintain the VSM. For example, Feinstein et al. [7] analyzed the reactive power reserve corresponding to the system load with the help of the capability curve of a synchronous generator. In [8], Avramovic et al. explained the need for reactive reserves to be matched with the contingent reactive requirements in different zones of the network in order to avoid voltage instability problems. Capitanescu [9] worked on the evaluation of reactive power reserves with respect to operating constraints and voltage stability. Yorino et al. [10] evaluated effective reactive power reserve considering the sensitivity of the load with respect to the generators’ VAr output. The authors present a methodology to determine the minimum RPR required to avoid voltage instability in critical scenarios. Amjady et al. [11] proposed a new sensitivity analysis framework by calculating linear sensitivity and eigenvalue analysis. Since the power system networks are very complex and nonlinear in nature, the networks equations are also nonlinear with varied degree of complexities involved in the problem variables. Hence, when considered as an optimization problem, RRM is a highly nonlinear, non-convex optimization problem involving a large number of constraints, thereby making the optimization problem extremely complex [12]. In general, power system variables include real power generations of thermal generators, generator bus voltages, tap positions of tap changers, and reactive power output of VAr sources. Out of these variables, tap positions of tap changers and reactive power output of VAr sources are discrete in nature while others are continuous. Non-continuous nature of control variables as well as objective functions restricts the classical optimization techniques to solve these problems accurately, as they are mostly based on gradient-based approach. Hence, population-based approaches such as Differential Evolution (DE) [13], Particle Swarm Optimization (PSO) [14], Artificial Bee Colony (ABC) [15], Gravitational Search Algorithm (GSA) [16], Adaptive Immune Algorithm (AIA) [17], Modified Ant–Lion Optimizer (MALO) [18], and

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similar other algorithms find their application in solving power system optimization problem [19–26]. In these, researchers have applied different population-based approaches and examined their search capabilities while solving active or reactive dispatch problems of power systems. From the literature, it can be said that both optimal active dispatch (where the objective is to optimize the fuel cost or environmental emissions) and reactive dispatch (where the objectives is to either minimize the real power loss or total voltage deviation or stability index) problems are highly nonlinear and non-convex optimization problem. As discussed above, RRM is an optimization problem and can be treated as an optimal reactive power dispatch problem [27], where the objective is to maximize the RPR present in the power system by simultaneously maintaining the other physical and operational constraints. In [27], Arya et al. worked on the scheduling of voltage control devices to improve the system voltage stability margin. The concept of Effective Reactive Reserve (ERR) is evaluated using the weighting sum method. In this method, the relative contribution of each generating unit toward the critical loading condition is evaluated for determining the weight of each generating unit. In this work, authors have applied DE as an optimization technique for maximizing the ERR. Results were more convincing in all the case studies which also prove the effectiveness of DE in solving such a complex optimization problem. Titare et al. [28] tried different combinations of mutation and crossover in the DE algorithm for maximizing the RPR. From the above literature, it is found that the study of RPR is very crucial in power system operation since inadequate RPR may lead to vulnerable situations. The synchronous generators and other VAr supply sources present in the power system network play significant role in deciding voltage stability of the system which is directly related to RPR. Hence, the present authors were also motivated enough to investigate the RPR of the power systems. In this paper, a comparatively new optimization technique called ‘Salp Swarm Algorithm (SSA)’ is utilized to maximize the RPR. The SSA is a novel optimizer developed by Mirjalili et al. [29]. The aim of this work is to investigate the performance of SSA to maximize the RPR for a given power system network, without sacrificing the voltage stability index. The algorithm is implemented to solve the proposed problem for a standard IEEE test system. Further, the effect of nonlinear loads is also investigated and results obtained from the proposed algorithm are compared with other cotemporary methods like PSO [26], Firefly Algorithm (FA) [22], Bat Algorithm (BA), and Cuckoo Search Algorithm (CSA). Results confirm the capability and proficiency of SSA in solving highly nonlinear optimization problems over other algorithms.

27.2 The Reactive Power Reserve The reactive power reserve of the power system is the ability of the generators to support the system voltage during any disturbances or stressed condition. In other words, the RPR represents the unutilized reactive power that can be drawn on demand.

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The RP injection by a generator depends on various factors such as generators’ location, field and armature current heating, and system loading condition. The maximum reactive power generation depends on its field and armature current heating limits. In the presence of only field current heating limit, the maximum reactive power injected by the k-th generator (Qmax k ) is mathematically expressed as [30]: Q max k

 2   2  VGk E Gk 2 2 + VGk ∗ 2 − PGk =− X dk X dk

(27.1)

Here,VGk is the kth generator’s terminal voltage, X dk is synchronous reactance of kth generator,E Gk is the maximum internal voltage of the kth generator corresponding to the maximum field current, and PGk represents the real power output of kth generator. in presence of only armature current heating limit is On the other hand, the Q max k expressed as [30]: Q max k

=



 2 2 2 VGk − PGk ∗ IGk

(27.2)

where IGk represents the maximum armature current of the kth generator. The is defined as the smaller of the two mentioned in Eq. (27.1) and Eq. (27.2) [30]. Q max k produced by a generator is fixed, the available (technical) RPR As the Q max k of a generator at a given loading condition is defined as the difference between the maximum RP generation and its current generation [27]. Mathematically it is expressed as:  gen  − Qk Q kRes = Q max k

(27.3)

where Qk gen is the current value of reactive power generation. As long as Qk gen is less than Qk max , the RPR is calculated using Eq. (27.3); however, if Qk gen reaches its maximum limit then the RPR is set to zero. However, this available RPR cannot be considered as Effective Reactive Reserve (ERR). This is due to the fact that the RP of a generator cannot be transmitted to remotely located loads and as a result, the RP is locally produced as well as consumed. This implies that the generators’ contribution toward RP supply is different and varies with load variations. Thus, the calculation of ERR is important and should be analyzed with greater clarity [27]. Considering the sensitivities of generators toward their loads, the ERR is expressed mathematically as given in Eq. (27.4).  gen  − Qk ERRk = Wk Q max k

(27.4)

where W k is the relative RP participation factor or weighting factor of the k-th generator, which is determined by measuring the RP sensitivity of the same generator [32]. The step-by-step procedure for ERR calculation is shown in Fig. 27.1.

27 Enhancement of Reactive Power Reserve Using Salp Swarm Algorithm

Obtain load flow solutions for the given loading pattern.

Determine participation factor of each generator.

Define available reactive power reserve.

351

Define effective reactive power reserve.

Fig. 27.1 Step-wise procedure for ERR calculation

The steps for obtaining ERR involves the calculation of available generators RPR from the base case voltage solutions, followed by evaluation of participation factor from QV sensitivity analysis and at last, determination of ERR using Eq. (27.4). In this process, determination of participation factors is very essential. The detailed steps involved in the calculation of the participation factor of each generator and ERR of the system is shown as. • Step 1: Obtain Newton–Raphson load flow solution, Jacobian matrix, and associated L-index for the base case condition. • Step 2: Calculate the reduced Jacobian matrix (a matrix that establishes a direct relation between incremental change voltage magnitude of each bus with reactive power injection at that bus) from the obtained Jacobian matrix as 

J11 J12 J21 J22



δ V





P = Q

(27.5)

Considering P = 0, the equation can be written as: J11 δ + J12 V = 0

(27.6)

J21 δ + J22 V = Q

(27.7)

Solving Eqs. (27.6) and (27.7) we get [J22 − J21 inv(J11 )J12 ]V = Q

(27.8)

J R = [J22 − J21 inv(J11 )J12 ]

(27.9)

• Step 3: Obtain the minimum eigenvalue of reduced Jacobian and the corresponding eigenvector ξi , to predict the next incremental loading pattern. Q = ξi

(27.10)

• Step 4: Obtain new voltage solutions and change in RP injection at each bus corresponding to the new loading pattern. Also, calculate L-index.

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V = ξi /λi

(27.11)

δ = −inv(J11 )J12 V

(27.12)

Thus, Vnew = Vold + V

(27.13)

δnew = δold + δ

(27.14)

• Step 5: From the new voltage and angle solutions, obtain a new RP injection at each bus. Also, calculate changes in RP injection at the generator buses. Q kinj = Q kinjnew − Q kinjold

(27.15)

• Step 6: Calculate participation factor or weighing factor of each generator using Eq. (27.16), 

Wk = Q kinj / max Q kinj

(27.16)

27.3 Problem Formulation In this work, the maximization of RPR for power system operation is modeled as an optimal power flow (OPF) problem and mathematically it is expressed as: Maximize F(x, u)

(27.17)

g(x, u) = 0 Subjected to h min ≤ h(x, u) ≤ h max

(27.18)

where F denotes the objective function to be maximized. g(x, u) & h(x, u) represent the set of equality and inequality constraints associated with the OPF problem. The vectors of independent (u) and dependent (x) variables are represented mathematically as in Eqs. (27.19) and (27.20), respectively. uT = xT =



VG1 , . . . , VGNPV , Tap1 , . . . , TapNT , Q C1 , . . . , Q CNC



PG1 , VL1 , . . . , VGNPQ , Q G1 , . . . , Q GNPV , SL1 , . . . , SLNTL

(27.19) 

(27.20)

27 Enhancement of Reactive Power Reserve Using Salp Swarm Algorithm

353

Here, (V G1 ,…V GNPV ), (Tap1 ,…TapNT ) and (QC1 , …QCNC ) in Eq. (27.19) represent the generators voltages, positions of tap changers, and the VAr output from switched capacitors, respectively. Since these variables are generated randomly within the given range, they are known as independent control variables. On the other hand„ PG1 and (QG1 ,…,QGNPV ) in Eq. (27.20) are the P and RP generation of the slack generator & other generators, respectively. The transmission line power flows (S L1 ,…,S LNTL ) and load buses’ voltage magnitudes (V L1 ,…,V LNPQ ) are dependent variables as mentioned in Eq. (27.20).

27.3.1 Objective Functions The proposed problem is formulated to maximize the ERR of generators present in the power system network with an objective (Fobj ) given in Eq. (27.21). Maximize Fobj =



ERRk

(27.21)

k

where ERRk represents the effective reactive reserve of the k-th generator.

27.3.2 Constraints 27.3.2.1

Equality Constraints

There are several constraints associated with the proposed problem; out of which, the Equality Constraints (ECs) are most important and they basically represent the real and reactive power balance Eqs. PGi − PDi − |Vi |

NB 

|Vk |{G ik cos(θi − θk ) + Bik sin (θi − θk )} = 0

(27.22)

k=1

Q Gi − Q Di − |Vi |

NB 

|Vk |{G ik sin(θi − θk ) − Bik cos (θi − θk )} = 0

(27.23)

k=1

where NB represents the no. of buses; PGi (Q Gi ) and PDi (Q Di ) are real (reactive) power generation and real (reactive) power demand, respectively, at the i-th bus. |Vi |&|Vk | represents voltage magnitudes at ith bus and kth bus, respectively. G ik and Bik are conductance and susceptance of the line connecting bus i and bus k, respectively. The voltage phase angles between buses i & k are represented as θik .

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N. Rani and T. Malakar

Inequality Constraints

The Inequality Constraint (IC) primarily appears to check the operational limitations of different power systems components and these are: (27.24) (27.25) (27.26) (27.27) (27.28) (27.29) Sij ≤ Sijmax

(27.30)

Here, Eqs. (27.24) and (27.25) represent the inequality constraints associated with generators active and reactive power output. NPV represents the number of generators. The generators excitation voltage limitations are represented by Eq. (27.26). The operating limitations of transformer tap changers and shunt capacitors are expressed mathematically in Eqs. (27.27) and (27.28), respectively, where NT & NC denote numbers of tap changers and shunt capacitors, respectively. The power system security-related constraints are mentioned in Eqs. (27.29) and (27.30), where the former represents the load bus voltage limitations and the later represents the line flow limitations. Here, NPQ represents the number of load buses or PQ buses. The overall objective function taking operational limitations of power system network into consideration is mathematically expressed as:    2 limit 2 − λVLi VLi − VLilimit F = Fobj − λG PG1 − PG1  2  2 − λQGi Q Gi − Q limit − λTL SL − SLlimit Gi

(27.31)

where F obj is the objective function. Here, (PG1 −PG1 limit ), (V Li − V Li limit ), (QGi − QGi limit ), and (S L −S L limit ) are the limit violations associated with real power output of slack bus, load bus voltages, generators RP generations, and transmission line loadings, respectively. For each violation, penalties are imposed to the objective function. The penalty factors associated to violations are chosen depending upon the nature of objective functions. PG1 limit ,V Li limit ,QGi limit , and S L limit are their limiting values and these are taken as follows:

27 Enhancement of Reactive Power Reserve Using Salp Swarm Algorithm

⎧ min min ⎨ PG1 if PG1 < PG1 limit max max PG1 = PG1 if PG1 > PG1 ⎩ min max PG1 if PG1 ≤ PG1 ≤ PG1 ⎧ min ⎨ VLi if VLi < VLimin limit VLi = VLimax if VLi > VLimax ⎩ VLi if VLimin ≤ VLi ≤ VLimax ⎧ min ⎨ Q Gi if Q Gi < Q min Gi limit Q Gi = Q max if Q Gi > Q max Gi Gi ⎩ max Q Gi if Q min ≤ Q Gi ≤ Q Gi Gi

27.3.2.3

355

(27.32)

(27.33)

(27.34)

Constraints Handling

Constraints handling is very crucial in an optimization problem. Efforts are made to obtain feasible solutions in optimization problems, where the associated constraints lie within their permissible ranges and reports no or tolerable violations. The OPF is a complex nonlinear optimization problem, and it demands efficient approach to satisfy its constraints as well. ECs are generally rigid and difficult to satisfy in an optimization problem. In this work, as the problem is formulated as an OPF, the Newton–Raphson load flow (NRLF) solution method is used to satisfy two ECs, i.e., Eqs. (27.22) and (27.23). The convergence of NRLF solution guarantees the solution of the power balance of the electric network. The IC in the OPF problem signifies various power system operational limitations. Among them, the IC related to independent variables shown in Eq. (27.24), Eq. (27.26–27.28) is generated randomly within their limit. However, the IC associated with dependent variables shown in Eq. (27.25), Eq. (27.29–27.30) is handled using the penalty function approach as discussed in Eq. (27.31–27.34)

27.4 Salp Swarm Algorithm 27.4.1 Overview of SSA The past few decades have witnessed the growing popularity of several natureinspired meta-heuristic techniques for solving various optimization problems. These meta-heuristics techniques are either evolutionary or swarm-based. Evolutionary techniques are inspired by nature’s concept of evolution and swarm-based techniques are motivated by the social behavior of different species for locomotion. Swarm intelligence techniques of salps for navigation and foraging inspired Mirjalili et al. [29] to devise an optimization technique named ‘salp swarm algorithm (SSA).’

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Salps are transparent barrel structured organism from the family of Salpiedae. The biological research on this species is at its early stage as they live in the deep ocean. However, it is proven by many researchers that they exhibit a peculiar swarming behavior. They form the largest salp chain on the planet for their smooth movement. The salp chain consists of only one leader and the rest followers in the swarm. The leader of the swarm explores the search space for food and directly or indirectly guides the follower salps to move toward the target. Similar to all other swarm-inspired algorithms, the salp swarm is first initialized randomly in m-dimensional search area, where m represents the no. of control variables in the problem. Thus, each salp’s position is stored in a two-dimensional matrix. Also, food is considered to be swarms’ prey/ultimate destination in the search area. In terms of position, each salp is in m-dimensional with x i = [x i1 , x i2 , . . . , x i m ]. After each iteration, the leader salp’s position is updated as x 1j

=

   F j + c1 U j − L j c2 + L j ; c3 ≥ 0.5 F j − c1 U j − L j c2 + L j ; c3 < 0.5

(27.35)

Here x 1j represents the leader’s position in the jth dimension, F j represents the position of food source in the jth dimension, U j and L j denotes the upper limit and lower limit of control variables in jth dimension, respectively. From Eq. (27.35), it is noted that the position of the leader gets updated only with respect to the food source in the search area. The coefficient c1 is the very essential in SSA as it maintains a balance between exploration and exploitation of search area and is represented as follows: c1 = 2e−( N ) 4n

2

(27.36)

Here, n denotes the value of present iteration & N denotes maximum no. of iterations. The coefficients c2 and c3 are randomly defined within the range of [0, 1]. Here, c2 determines the step size for the next salp position and c3 decides whether the next movement of salp in the j-th dimension would be toward positive or negative infinity. To update follower salps’ position, the following equation is utilized (Newton’s law of motion): x ij =

1 2 at + v0 t 2

(27.37)

where i ≥ 2, x ij represents the ith follower salp’s position in jth dimension, t 0 where v = x−x denotes time, v0 represents the initial speed, & a = vfinal v0 t As time is represented by iteration in optimization, thus consecutive change between iterations is 1. Also, by considering x0 = 0, this equation can be further simplified and expressed as follows:

27 Enhancement of Reactive Power Reserve Using Salp Swarm Algorithm

x ij =

 1 i x j + x i−1 j 2

357

(27.38)

This process of position updating continues until the convergence criterion is satisfied.

27.4.2 Flowchart The flowchart for maximizing ERR is shown in Fig. 27.2.

27.5 Results and Discussions The proposed problem is implemented for the standard IEEE 30-bus system and solved using the SSA. The data for the system is taken from [25] and the complete description of the system is given in Table 27.1. The proposed reactive power dispatch problem is programmed on MATLAB and various case studies are performed to analyze the search capability of the SSA. The active and reactive power control devices are taken as control variables to solve the optimization problems. The parametric sensitivity study is also carried out for all the case studies and the best results are compared with other swarm-based algorithms. Case 1: For Constant Power Load In this case, the system is considered to be operating at its base loading condition and all the generators are active with their initial generation [25] represented in Table 27.2, except the slack generator (PG1 ). With this initial state, the NRLF program is executed to calculate the ERR and L-index value at base. The values of ERR and L-index are found to be 146.8614 MVAr, 0.1583, respectively, as mentioned in Table 27.3. For optimization, generator voltages and tap changers positions are considered as control variables as mentioned in Section IV and the proposed problem is solved using the SSA. The optimization parameters of SSA are decided after parametric sensitivity analysis and for this, the control parameters such as ‘step size’ C 2 and swarm size (m) are varied over a wide range. The maximum no. of iteration is set to be 100 for the present case. As many as 10 trials are executed for each combination of these parameters and the best values obtained from these trials are shown in Fig. 27.3. It is clearly observed from the figure that the best value of the objective function, i.e., ERR is found to be 172.618 MVAr with a combination of C 2 = 0.4 and swarm size = 40. The required control settings correspond to this optimum result are mentioned in the last column of Table 27.3. It is observed that SSA is able to enhance the ERR from initial 147.8184 MVAr to 172.618 MVAr, while brings down the L-index values from 0.1583 to 0.1291 under base case and from 0.1641 to 0.1386 under incremental loading case to base to boost voltage stability. The contributions of generators for

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N. Rani and T. Malakar Start

Read Data: line data ,bus data, number of control variables, limits of control variables, maximum no. of iterations population size, Generate initial solution with random values of control variables i.e. generator voltages and turn ratio of tap changers.

Run Newton Raphson power flow and calculate ERR corresponding to each individual solution Iteration=1 Solution with best value of fitness i.e maximum ERR is considered as food source Itertion =Iteration+1

Update C1 using equation no.(36)

Update the position of leader and follower salps for every search agents using equation no.(35) and equation no. (38) respectively. Run Newton Raphson Power flow with updated values of salp position i.e. generator voltage and turn ratio of tap changing transformers.

Check Violations if any?

Yes

Add penalty

No Calculate Fitness

Save new population with better fitness i.e. better ERR

Is maximum iteration reached?

Return best value of ERR with corresponding solution

Stop

Fig. 27.2 Flowchart for ERR maximization using SSA

maintaining the ERR are shown in Fig. 27.5. To compare the performance of SSA over other contemporary swarm-based algorithms, the same problem is solved using BAT, CSA, FA, PSO, and CSO also, and the optimum results obtained by each of them along with their average computing time (CTavg) are shown in Table 27.3. The comparison of convergence characteristics of SSA with other algorithms is shown in

27 Enhancement of Reactive Power Reserve Using Salp Swarm Algorithm

359

Table 27.1 Description of the IEEE 30-bus system No. of buses

30

No. of transmission lines

41

No. of PV buses

6

No. of tap changers

4

No. of shunt capacitors

2 and (0.19 p.u. & 0.043 p.u.)

No. of PQ buses

24

Active power load at base case

283.4 MW

Reactive power load at base case

126.2 MVAr

Active power generation at base case

289.211 MW

Reactive power generation at base case

108.922 MVAr

Active power loss at base case

5.811 MW

Reactive power loss at base case

32.417 MVAr

Table 27.2 Generators’ operating points PG2 (MW)

PG3 (MW)

PG4 (MW)

PG5 (MW)

PG6 (MW)

80

50

20

20

20

Table 27.3 Comparison of ERR and L-index for Case 1 Initial

BAT

CSA

FA

PSO

CSO

SSA

V G1

1.05

1.0954

1.0830

1.0849

1.0781

1.0721

1.0977

V G2

1.04

1.0550

0.9878

1.0156

0.9613

0.9935

0.9766

V G3

1.01

1.0102

0.9749

0.9942

0.9958

0.9848

1.0628

V G4

1.01

0.9903

0.9584

0.9729

0.9813

0.9729

0.9801

V G5

1.05

1.0261

1.0435

1.0189

0.9849

1.0401

1.0996

V G6

1.05

0.9912

1.0379

1.0338

1.0036

1.0415

1.0999

Tap 6–9

1.078

0.9375

0.9162

0.9720

1.0201

0.9290

0.9159

Tap 6–10

1.069

1.0240

0.9675

0.9652

0.9487

0.9645

0.9358

Tap 4–12

1.032

0.9176

0.9176

0.9283

0.9565

0.9167

0.9062

0.9231

0.9116

Tap 28–27

1.068

0.9101

0.9022

ERR

146.861

170.617

171.0352

169.5156 170.76

171.7888

172.6189

%ERR

100

115.8

116.46

116.32

116.97

117.538

LI_il

0.1641

CTavg(sec)

0.9346

0.1407 1020.77

0.9203

0.1402 1137.21

0.1453

116.31 0.1411

1008.6950 993.786

0.1477

0.1386

522.8120 1013.408

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N. Rani and T. Malakar 173 Swarm size=20

ERR (in MVAr)

172.5 172

Swarm size=40

171.5 171

Swarm size=60

170.5 170

Swarm size=80

169.5 169

Swarm size=100 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Step size

Fig. 27.3 Parametric sensitivity for Case 1

Fig. 27.4. It is noticed that the SSA converges faster for a superior value over other methods. Case 2: For nonlinear loading condition Practical loads are mostly nonlinear and computation becomes challenging when nonlinearity is considered in the load modeling. A practical load is a function of

Fig. 27.4 Comparison of convergence characteristics for Case 1

32.21125845

19.00016911 32.93097802

19.66127261 60

8.815246775

ERR=172.6189 ERRG1 ERRG4

ERRG2 ERRG5

ERRG3 ERRG6

Fig. 27.5 Reserve sharing among generators for Case 1

27 Enhancement of Reactive Power Reserve Using Salp Swarm Algorithm

361

operating voltage and frequency. In view of this, the present section discusses the effectiveness of the SSA in maximizing the ERR under the presence of a nonlinear load. In this section, only Voltage-Dependent Loads (VDLs) are considered. The static VDL model depicts the relationship between the voltage and power using an exponential function and is mathematically expressed as PL = PLO V np

(27.41)

Q L = Q LO V nq

(27.42)

Here, PLO (PL ) & QLO (QL ) represents the P and RP demand at the nominal voltage (current operating state), respectively. The V represents the voltage magnitude of the load buses. The load exponents np and nq are 0 for constant power load (CPL), 1 for constant current load (CIL), and 2 for constant impedance load (CZL). However, practical loads are neither CPL nor CIL nor CZL but are combination of all the three components. In this section, the investigation is performed for two cases, namely ‘Case 2(a)’ as the case when all loads are CIL type and ‘Case 2(b)’ as the case when all loads are CZL type. As in Case 1, only reactive-related control devices are considered as control variables and maximum iteration is set as 100 for the present case. Here, SSA is applied to solve the problems and the best results obtained are declared after thorough parametric sensitivity studies as discussed previously. The best results obtained from Case 2(a) and Case 2(b) are mentioned in Tables 27.4 and Table 27.5, respectively. In order to decide the superiority of SSA over other methods, a comparison is drawn in Table 27.3 and Table 27.4. The comparison on convergence is shown from Figs. 27.6 and 27.7 for both the cases. It is revealed that the percentage improvement in the ERR values w.r.t the base cases are maximum for SSA, which are 118.2% and 118.14% for Case 2(a) and Case 2(b), respectively. Further, it is revealed that the CTavg obtained for SSA in all cases is at par with BAT, SSA, PSO, FA methods; however, the same is found to be lesser in case of CSO. The reason could be the process it follows to update the swarm. It is important to note that in case of CSO, only half of the swarm gets updated during each cycle. The generators’ contribution toward maintaining reactive power reserve at optimum operating states is shown in Figs. 27.8 and 27.9. The selection of convergence criterion is an important task while solving problems using general-purpose optimization methods. Since the SSA has not been explored well till date, the performance of the algorithm is critically assessed in this work on the basis of variations in its computation time w.r.t the change in convergence criteria. For this, the variations of ERR and CTavg are observed by changing the convergence criterion, i.e., the maximum number of iterations (max_itr) and observations are mentioned for all the cases in Table 27.6. It is worthy to note that when max_itr is varied from 100 to 200, the variations in ERR are found to be insignificant for all cases. However, the required CTavg for CPL and CIL cases get almost doubled and becomes about 2.5 times for CZL. In view of this, the convergence criterion, i.e., max_itr is set as 100 in each case.

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Table 27.4 Comparison of ERR and L-index for Case 2(a) Initial

BAT

CSA

FA

PSO

CSO

SSA

V g1

1.05

1.0431

1.0918

1.0593

1.0562

1.0628

1.0998

V G2

1.04

0.9723

1.0391

0.9748

1.0010

0.9703

0.9501

V G3

1.01

1.0499

0.9945

0.9914

1.0296

0.9601

1.0091

V G4

1.01

0.9735

0.9526

0.9615

0.9564

0.9571

1.0081

V G5

1.05

0.9911

1.036

0.9667

1.0306

1.0221

1.0984

V G6

1.05

1.0225

1.0437

1.0061

1.0390

1.0135

1.0904

Tap 6–9 1.078

0.9968

0.9265

0.9034

0.9392

0.9226

0.9000

Tap

1.069

0.9917

1.0995

0.9384

1.0010

0.9843

0.9118

1.032

0.9032

0.9009

0.9621

0.9077

0.9306

0.9067

1.068

0.9544

0.9456

0.9308

0.9387

0.9456

0.9108

170.5997

170.8766

169.30

170.59

171.23

171.4278

117.64

117.83

116.75

117.64

118.01

118.2

6–10

Tap 4–12

Tap 28–27

ERR

145.0125

%ERR 100 LI_il

0.1454

CTavg (s)

–-

0.1439 1183.75

0.1407 1309.402

0.1413 1107.51

0.1425

0.1496

0.1449

1106.49

804.75

1122.284

PSO

CSO

SSA

Table 27.5 Comparison of ERR and L-index for Case 2(b) Initial

BAT

CSA

FA

V g1

1.05

1.0451

1.0841

1.0907

1.0864

1.0846

1.0793

V G2

1.04

0.97375

1.0012

1.0126

1.0311

0.9935

0.9798

V G3

1.01

1.0509

1.0194

0.9779

1.0553

0.9872

1.0401

V G4

1.01

0.9744

0.9923

0.9715

0.9762

0.9500

0.9666

V G5

1.05

0.9921

1.0389

1.0541

1.0489

1.0034

0.9619

V G6

1.05

1.0246

1.0325

0.9531

1.0113

1.0038

0.9991

Tap 6–9 1.078

0.9960

0.9161

0.9029

0.9204

0.9667

1.0492

Tap

1.069

0.9916

1.0695

0.9844

1.0239

1.0147

1.0828

1.032

0.9022

0.9369

0.9039

0.9949

0.9518

0.9408

1.068

0.9526

0.9413

0.9010

1.0185

0.9862

1.0158

6–10

Tap 4–12

Tap 28–27

ERR

145.006

%ERR 100 LI_il

0.1403

CTavg (sc)

–

170.487

170.626

169.210

170.07

170.774

171.320

117.52

117.66

116.69

117.28

117.70

118.14

0.1411 1346.4

0.1414 1445.85

0.1453 1302.65

0.1511 1377.0

0.1477 936.518

0.1438 1387.01

27 Enhancement of Reactive Power Reserve Using Salp Swarm Algorithm

363

Fig. 27.6 Comparison of convergence for Case 2(a)

Fig. 27.7 Comparison of convergence for Case 2(b)

14.89223011 31.78628823 33.53355457 19.52369157 11.6919972 60

ERR=171.4278 ERRG1

ERRG2

ERRG3

ERRG4

ERRG5

ERRG6

Fig. 27.8 Reserve sharing among generators for Case 2(a)

Recapitulating the performance analysis of the SSA derived from the results section, the following observations have been noted. The percentage improvement of ERR w.r.t the base cases (Tables 27.3, 27.4 and 27.5) is found to be more in Case 2 as compared to Case 1, despite higher degree of

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N. Rani and T. Malakar

18.95098083 31.39407566 33.62062715

19.6244578

7.73018907 60

ERR=171.3203 ERRG1 ERRG4

ERRG2 ERRG5

ERRG3 ERRG6

Fig. 27.9 Reserve sharing among generators for Case 2(b)

Table 27.6 Variations of ERR and computing time on the convergence Type of load

Max_itr

ERR

%ERR

CTavg(in s)

Constant Power Load

100

172.6189

100

1013.4083

150

173.018

100.23

1507.1261

200

173.1261

100.29

2119.0431

Constant Current Load

100

171.4278

100

1122.2848

150

172.7340

100.17

1937.1821

200

172.8267

100.22

2436.8494

Constant Impendence Load

100

171.3203

100

1387.01765

150

171.5486

100.13

2348.49348

200

171.8697

100.32

3643.26336

nonlinearities associated with Case 2. This trend has been maintained by all other optimization algorithms used in this work. However, the search capability of SSA is superior and outperforms other methods. The optimum value of ERR is found to be more in Case 1 (172.6819 MVAr) using SSA in comparison to Case 2(a) (171.4278 MVAr) and Case 2(b) (171.3015 MVAr). This trend is consistent with other methods also. The reason could be the increase in overall load in nonlinear cases. More specifically, the total real power demands served by the generators are 284.3357 MW and 285.9029 MW under Case 2(a) and Case 2(b), respectively, against 283.4 MW under Case 1. The average computing time increases linearly with the increase in degree of nonlinearities in the optimization problems. For example, the CTavg is found to be 1013.4083 for CPL, 1122.2848 for CIL, and 1387.01765 For CZL using SSA. The same trend has also been noted for other methods.

27 Enhancement of Reactive Power Reserve Using Salp Swarm Algorithm

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27.6 Conclusion In this work, the ORPD problems of the power system are solved with an objective to maximize the RPR. The practical limitations in the form of equality and inequality constraints associated with the ORPD problems are taken into consideration in the formulation. The SSA is applied in this work to solve the said objective for IEEE 30bus system. Also, to assess the performance of the algorithm, results are reported for various cases. Further, the results are compared with PSO, CSA, BAT, FA, and CSO algorithms. The SSA performed quite efficiently in comparison to other methods while solving various cases of RPR maximization problems. But, proper tuning of the free parameters of the algorithm must be carried out to get the optimal solution. The proficiency of the SSA is further verified under complex nonlinear load in the power system. It is observed that the algorithm permits to accommodate more loads in the system without sacrificing at all on voltage stability index. Interestingly, it is observed that the trend is positive with the degree of nonlinearity.

References 1. Alizadeh Mousavi, O., Bozorg, M., Cherkaoui, R.: Preventive reactive power management for improving voltage stability margin. Electr. Power Syst. Res. 96, 36–46 (2013) 2. Momoh, J.A., Salkuti, S.R.: Feasibility of stochastic voltage/VAr optimization considering renewable energy resources for Smart Grid. Int. J. Emerg. Electr. Power Syst. 17(3), 287–300 (2016) 3. Reis, A., Moura, L.P., De Oliveira, J.C.: Computational studies of voltage regulation provided by wind farms through reactive power control. Int. J. Emerg. Electr. Power Syst. 19(2), 1–12 (2018) 4. Sun, Q., Cheng, H., Song, Y.: Bi-objective reactive power reserve optimization to coordinate long-and short-term voltage stability. IEEE Access 6, 13057–13065 (2018) 5. Dalali, M., Karegar, H.K.: Voltage instability prediction based on reactive power reserve of generating units and zone selection. IET Gener. Transm. Distrib. 13(8), 1432–1440 (2019) 6. Khandani, A., Foroud, A.A.: Design of reactive power and reactive power reserve market. IET Gener. Transm. Distrib. 11(6), 1443–1452 (2017) 7. Feinstein, J., Tscherne, J., Koenig, M.: Reactive load and reserve calculation in real-time computer control system. IEEE Comput. Appl. Power 1(3), 22–26 (1988) 8. Avramovic, B., Fink, L.H.: Real-time reactive security monitoring. IEEE Trans. Power Syst. 7(1), 432–437 (1992) 9. Capitanescu, F.: Assessing reactive power reserves with respect to operating constraints and voltage stability. IEEE Trans. Power Syst. 26(4), 2224–2234 (2011) 10. Yorino, N.: Reactive power reserve management tool for voltage stability enhancement. IET Gener. Transm. Distrib. 12(8), 1879–1888 (2018) 11. Amjady, N., Esmaili, M.: Application of a new sensitivity analysis framework for voltage contingency ranking. IEEE Trans. Power Syst. 20(2), 973–983 (2005) 12. Khazali, A.H., Kalantar, M.: Optimal reactive power dispatch based on harmony search algorithm. Int. J. Electr. Power Energy Syst. 33(3), 684–692 (2011) 13. Jithendranath, J., Reddy, K.H.: Differential evolution approach to optimal reactive power dispatch with voltage stability enhancement by modal analysis. Int. J. Engg. Res. App. 3(4), 66–70 (2013)

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14. Biswas, S., Mandal, K.K., Chakraborty, N.: Constriction factor based particle swarm optimization for analyzing tuned reactive power dispatch. Front. Energy 7(2), 174–181 (2013) 15. Abou El-Ela, A.A., Kinawy, A.M., El-Sehiemy R.A.„ Mouwafi, M.T.: Optimal reactive power dispatch using ant colony optimization algorithm. Electr. Eng. 93(2), 103–116 (2011) 16. Duman, S., Sönmez, Y., Güvenç, U., Yörükeren, N.: Optimal reactive power dispatch using a gravitational search algorithm. IET Gener. Transm. Distrib. 6(6), 563–576 (2012) 17. Lin, J., Wang, X.: Reactive power optimization based on adaptive immune algorithm. Int. J. Emerg. Electr. Power Syst. 10(4) (2009) 18. Rajan, A., Jeevan, K., Malakar, T.: Weighted elitism based Ant Lion optimizer to solve optimum VAr planning problem. Appl. Soft Comput. J. 55, 352–370 (2017) 19. Ramirez, J.M., Gonzalez, J.M., Ruben, T.O.: An investigation about the impact of the optimal reactive power dispatch solved by DE. Int. J. Electr. Power Energy Syst. 33(2), 236–244 (2011) 20. Duman, S., Sonmez, Y., Guvenc, U., Yorukeren, N.: Application of gravitational search algorithm for optimal reactive power dispatch problem. In: INISTA 2011—2011 Int. Symp. Innov. Intell. Syst. Appl., pp. 519–523 (2011) 21. Shaw, B., Mukherjee, V., Ghoshal, S.P.: Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm. Int. J. Electr. Power Energy Syst. 55, 29–40 (2014) 22. Rajan, A., Malakar, T.: Optimal reactive power dispatch using hybrid Nelder-Mead simplex based firefly algorithm. Int. J. Electr. Power Energy Syst. 66, 9–24 (2015) 23. Mokhtarifard, M., Mokhtarifard, H., Molaei, S.: Solution of reactive power dispatch of power systems using Bat search algorithm. Int. J. Tech. Phys. Prob. Engg. 7, 65–70 (2015) 24. Basu, M.: Quasi-oppositional differential evolution for optimal reactive power dispatch. Int. J. Electr. Power Energy Syst. 78, 29–40 (2016) 25. Rajan, A., Malakar, T.: Exchange market algorithm based optimum reactive power dispatch. Appl. Soft Comput. J. 43, 320–336 (2016) 26. Polprasert, J., Ongsakul, W., Dieu, V.N.: Optimal reactive power dispatch using improved pseudo-gradient search particle swarm optimization. Electr. Power Components Syst. 44(5), 518–532 (2016) 27. Arya, L.D., Singh, P., Titare, L.S.: Anticipatory reactive power reserve maximization using differential evolution. Int. J. Electr. Power Energy Syst. 35(1), 66–73 (2012) 28. Titare, L.S., Singh, P., Arya, L.D., Choube, S.C.: Optimal reactive power rescheduling based on EPSDE algorithm to enhance static voltage stability. Int. J. Electr. Power Energy Syst. 63, 588–599 (2014) 29. Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp Swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114 (2017) 30. Kundur, P.: Power System Stability and Control. McGraw Hill. Inc. (2007)

Chapter 28

Weld Imperfection Classification by Texture Features Extraction and Local Binary Pattern Rajesh V. Patil and Y. P. Reddy

Abstract Weld bead geometry inspection by non-destructive testing techniques is the major challenge of today’s welding industries. As compared to similar materials, application of dissimilar materials demanded all over. Generally, feature extraction by radiographic images sensed geometric features and categories for classification of weld imperfections. Whereas diverse understanding for human intelligence grasp beneficial evidence from non-geometric features of images. To overcome this difficulty by exploring features and recognize imperfections of two-dimensional digital image wherever geometric features state presence of weld. Mostly, classification accuracy significantly influences by weld imperfection region segmentation and imperfection texture feature extraction. The proposed techniques of local binary pattern in which local binary code describing region, generating by multiplying threshold with specified weight to conforming pixel and summing up by grey-level co-occurrence matrix to extract statistical texture features. At last, support vector machine and k-nearest neighbours compared to discover finest classifier and uppermost accuracy of 96% attained through grouping of local binary pattern features and support vector machine.

28.1 Introduction Detection of weld imperfection by non-destructive technique is the necessity of micro- to macro-joining industries. In non-destructive, radiographic testing is noteworthy for numerous industries. In this technique, X-rays or γ -rays are applied to irradiate workpieces, radiographic films that replicate inside structures and material defects are working to recognize defects. In weld examination, weld imperfections usually divided into many types such as Porosity (spherical, elongated), Penetration (Incomplete/ Insufficient /Excess), Crack (Longitudinal Cracks/Radiating Cracks/Transverse Cracks), Slag inclusion, Wagon tracks, Tungsten, Undercut, Lack of Fusion, Scattered Porosity, External Undercut, Root Undercut, Cavity R. V. Patil (B) · Y. P. Reddy Sinhgad College of Engineering, Savitribai Phule Pune University, Pune, Maharastra, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_28

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Fustian, Mismatch, Wormhole, Cold Lab (Spatter/Overlap/Spatter- Overlap), Burn through, Spatter, Under fill, Key Hole, Burn Through, Concavity/Conaxity, Misalignment, Reinforcement (Improper/Excessive), Shrinkage, Surface irregularity, Arc Craters,Weld spatter, Gas Pore. Here author selected good and bad type of weld as shown in Fig. 28.1. Conventional inspection mostly by physical detection and found idiosyncratic, unpredictable and laborious. Its imperfection classification methods generally comprise, image acquisition, image preprocessing, weld imperfection section segmentation, feature extraction and imperfection classification. The weld imperfection section segmentation and feature extraction are utmost perilous in weld imperfection classification. In apiece weld imperfection section is segmented from original image aimed at feature extraction and at last demonstrative features of weld imperfection section are planned for imperfection classification. In direction to measure accuracy and productivity of imperfection classification, numerous researchers developed automatic recognition and classification methods, such as Haodong et al. [1] proposed dual deep convolutional neural networks on the improved image groups via feature-extraction created transfer learning techniques to organize defects over a multi-model framework, targeting lesser incorrect detection rate. Li et al. [2] proposed deep learning network to classify welding defects. Created on examination of x-ray defect image features, convolutional neural network model and numeral layers calculated. Dong et al. [3] built SVM multi-classifier model aimed at slag, crack, gas hole, incomplete penetration and fusion and found defect structures for class created on defect record. Wenhui et al. [4] proposed automatic detection prototypical for weld flaws to maintain deep neural network extracts the central features of x-ray images. Tomas et al. [5] proposed camera calibration method via nozzle of computer numerical control machine plan for imaging system together with permitting approximation of camera position with relative to inspecting surface and its orientation. Zin et al. [6] presented expertise on correct time welding method sensor. The strong and stable images of weld pool found by the passive vision part, and pictures of laser stripe expected on weld bead found by vision portion at the matching time. Jesús et al. [7] presented an original system for actual time calculating of width and height of beads in gas metal arc welding via high-speed camera and long-pass optical filter in passive image structure. They found measurement method takes 3 ms for each image, which permits transfer amount of additional than 300 frames for each second. Hongquan et al. [8] proposed feature extraction and defect arrangement technique created on texture structures and principal component analysis. Furthermore, multiclass support vector mechanism used to categorize defects grounded on obtained principal components. Yinshui et al. [9] found system removing the feature opinions of weld seam shape which comprises, discovery of weld seam shape built on visual attention prototypical and extraction of feature facts of weld seam shape. Tou et al. [10] experienced reducing outcome volume when put on additional than six structures to classifier. They add issue with removing huge numbers of structures is necessity of additional disintegrating before classification, which needs computation period and additional resources. Mery et al. [11] described usage of texture structures when automatically sensing weld defects by grey-level co-occurrence matrix and Gabor filters. Patil et al. [15] determined net measured temperature by data

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1

2

3

4

5

6

Fig. 28.1 Specimen Imperfection 1. Excessive penetration, 2. Gas pores 3. Lack of fusion 4. Overlap 5. Porosity 6. Spatter 7. Warmhole. 8. Non-defect

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7

8

Fig. 28.1 (continued)

logger shown sensitive indicator of penetration depth and measured value of depth of penetration by experimentation, optimization and image processing technique satisfactorily. Patil et al. [16] projected technique for detection and cataloguing of imperfections in weld joint. This technique identify defects and differentiate weld images that look like to improper signs or deficiencies. Finally, support vector machine and artificial neural network classifiers confirmed accuracy performance of 92% and 87% by confusion matrix. Many researchers nominated 25 edge-based structures to achieve classification afterwards weld defect section segmentation and other selected features those on five edge-based and three region-based. Determination to segment weld defect section and explore their structures for final classification. The technique found exact shape of weld imperfection section through weld imperfection section segmentation. Though, due to inaccuracy of weld imperfection section segmentation, these techniques cannot precisely categorize imperfections whose profiles are alike. Consequently, conventional imperfection classification techniques, weld imperfection section segmentation sturdily disturbs imperfection classification accurateness. Numerous weld imperfection section segmentation methods available, such as background variance, grey outline curve and watershed methods, but these methods subtle to noise, laborious and inaccurate because of original images are comprised by significant noise, little contrast and edge distorting. Inappropriately, inaccurate weld imperfection section segmentation decreases efficiency of feature extraction and define to lesser amount of precise imperfection classification. In addition, weld imperfection section based on feature extraction studied data exclusive of weld imperfection section and profile of imperfection, it flouts grey-level circulation information concerning imperfection to its surrounds which similarly yield significant data nearby imperfection category. To resolve aforementioned issues, texture structures in weld seam section used for imperfection classification. Extraction of texture structure from weld seam section essentially segmented original image as substitute of specific weld imperfection section. Accordingly, segmentation method is modest and adverse influences on feature extraction outcome from inaccurate

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weld imperfection section segmentation circumvented. Furthermore, texture features define grey-level circulations that narrate pixels to their surrounds in original images. Therefore, by extracting texture structures in diverse directions of weld seam region, evidence about weld imperfection region and grey-level data relating weld imperfection section to its surrounds found. Through, additional imperfection evidence found accordingly, imperfection classification accurateness enhanced. Additionally, essential to explore texture features in diverse directions, quantity of features huge and severances between them. The projected technique used principal component analysis to lessen sizes of novel features. Determine structures fewer numerous and selfdetermining from one another. Therefore, principal component analysis decreases quantity of structures whereas enlightening their superiority, creating novel structures operative aimed at imperfection classification. Different types of defects as shown in Fig. 28.1.

28.2 Examination of Weld Seam Region In conventional imperfection classification methods, classification accurateness mainly focuses on weld imperfection region segmentation exactness. When weld imperfection region segmentation found inaccurate, the features extracted from weld imperfection region cannot precisely replicate imperfection evidence, resulting incorrect classification. To determine statement clearly, slag and porosity imperfections are considered as illustrations, the shape of slag imperfection is almost round, whereas porosity imperfection is more precisely round. Occasionally original image quality is poor, the weld imperfection section segmentation inaccurate and profiles of slag and porosity imperfections give the same idea after weld imperfection section segmentation. This case determines incorrect classification because of structures extracted from segmented weld imperfections region, such as roundedness, complexity and imperfection edge irregularity, which are essential to distinguish among slag and porosity imperfections inaccurate. Moreover, conventional classification techniques extract structures from specific segmented weld imperfections regions and do not find any evidence concerning weld imperfection regions to their surrounds, which is actual significant for imperfection classification. Therefore, imperfections classified using weld seam region information and then slag and porosity imperfections effortlessly notable. Consequently, imperfections remain classified by extracting texture structures of weld seam section in proposed work. This technique need not necessitate segmentation of individual weld imperfection section in an original image, so losses to classification triggered by inaccurate weld imperfection section segmentation evaded. Weld seam section is also convenient to segment than weld imperfection section. Additionally, this technique explored texture structures from weld seam section, it explores limited data about the weld imperfection section and evidence involving the imperfections to their surrounds. Therefore, imperfection classification accurateness can be significantly enhanced via technique. It is vital to understand that weld seam section usually encompasses only individual type of imperfection in

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Fig. 28.2 Examination of 1. Defected 2. Non-defect weld seam region

an original image in which circumstance texture structures of segmented weld seam section extracted without any obstacle. Though, if weld seam section comprises numerous types of weld imperfections, it can be separated into numerous areas and texture structures must explore separately to accomplish defect classification. Defected and non defected weld seam region as shown in Fig. 28.2.

28.3 Texture Feature Extraction by Grey-Level Co-occurrence Matrix and Local Binary Pattern Using grey-level co-occurrence matrix, feature matrix effectively signifies portrait with number of parameters by these properties. Texture is a significant quality of an image and distinct as the spatial form of colour or grey-level variations among image pixel. The texture feature meticulously connected to variations in grey level therefore grey-level co-occurrence matrix generally popular for texture feature extraction. GLCM built including frequencies by which sets of pixels having firm positions and grey-level associations occur. They also comprised all-inclusive grey-level circulation evidence around the images used to construct explicit direction, extent of variation and local surroundings. Assuming that dimensions of image I is M × N and grey level L, at that point GLCM definite as: distance among two pixel and θ is agreeing angle and magnitude of matrix P is L × L. In this technique, I (x, y) is grey level of pixel (x, y), d is distance among two pixels, θ is matching angle and magnitude of matrix P is L × L. Diverse GLCM by choosing additional values of d and θ. In general, texture feature portrayed by secondary statistical features of GLCM. This

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training selected four distinctive parameters such as contrast, correlation, energy and homogeneity. The formulations used to estimate these parameters are as follows. To get additional data approximately the defect type, d = 1 and θ = {0°, 45°, 90°, 135°} selected to estimate GLCM of images, which stayed normalized as normally completed. P(i, j, d, θ ) = P I (x, y) = i, I (x + d cos θ, y + d sin θ ) = j Energy ( f 1 ) =

L L   [ p(i, j)]2

(28.1)

(28.2)

i=1 j=1 L L   Contrast ( f 2 ) = (i, j)2 p(i, j)

(28.3)

i=1 j=1

  L L   (i − u x ) j − u y p(i, j) Coorealtion ( f 3 ) = σx σ y i=1 j=1 Homogeneity ( f 1 ) =

L L   i=0 j=0

Pi j 1 + (i + j)2

(28.4)

(28.5)

where u x ; u y ; σx ; σ y ar e as f ollows uX =

L L L L     i p(i, j); u y = j p(i, j) i=1

σX =

L  i=1

(i − u x )2

L  j=1

j=1

p(i, j); σ y =

j=1 L   j=1

(28.6)

i=1

j − uy

L 2 

p(i, j)

(28.7)

i=1

Local binary pattern is an overall explanation of a texture in local region and delivers binary code that defines texture form value of the centre pixel as threshold. The local binary code describing region generating by multiplying threshold with specified weight to conforming pixel and summing up. It is a technique intelligently work with region of diverse size and invariant to revolution of inputs. It describes texture of a local region in a colourless image. Round balanced region forms a loop around centre pixel to generate local region. If rate of region is not located in vital of pixel, interruption used for approximation. The grey value of region pixel approach as threshold to grey value in centre pixel, multiplication of threshold with given value for pixel and summing up outcome. A histogram with recognized local binary pattern patterns for apiece pixel formed to signify texture image. A pattern measured unvarying once fewer than two bitwise transitions assessed by Eq. (28.9). Rotation invariance accomplished by Eq. (28.10), which gathers the pairs of alike unvarying

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Fig. 28.3 Pixel pair to find grey-level co-occurrence matrix

patterns from diverse orientations in one container and others in another containers (Fig. 28.3). LBP P.R =

P−1 

S(g p − gc )2 p , s(x) =



1,x≥0 0,x Fmin + (m − 1) ∗ Df and n = 1, 2, ..., NS − 1 Sm|Q(f ) < Y min + m ∗ Df , for

(34.6)

Fmin is starting point of state S1 and (Fmin + Df) is ending point. The number of events for a specified state Sn that is the changes in the value F(f ) in nth interval of in a specific window NC candles represented as: Lms =



Fn ∈ Sm|Q(n)

(34.7)

n

m = 1,2,…,NS ; n =1,2,…,NC here Q(n) represents discrete moments f present in specific window (which have fix number of states and candles) for f = 1,2,3,4,5 ..., NC; and Fn represents the value of the F in instant like these. A salient characteristic of this algorithm is that before calculating the transition matrix we divide the time series in different time windows. Here NC denote the total number of candles in time windows, NW denote the total number of back windows and total number of windows taken from the starting point to the end point in the time series which is calculated by taking the ceiling of total number of candles divided by number of candles in one window. sum of events inside the time windows is used to calculate the transition matrix.  (Lms) (34.8) Lmw = w

w = 1,2,…,NW Let Q(n, l) denote we are in some state in n-th candle and in lth window. The mathematical representation is given as:

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Lmw =

 w

Fn ∈ Sm|Q(n, w)

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(34.9)

n

where w = 1,2,…,NW ; n =1,2,…,NC ; m = 1,2,3,4…,NS The thing which is less interesting in Markov model is no transition compare to transition from one state to another state it is because we remain in the same state in not useful in the investment.  [Fn, w ∈ Sm|Q(m, w)] ∧ [Fn + 1, w ∈ So|Q(m, w)] (34.10) Lmo = w

n

w = 1,2,…,NW ; m, n = 1,2,…,NS It is the total number of events in whole times series such that events change from one state Sm to So when the time change from n to n + 1. where Fn, w is the observed value of the variable at time fn and in the W -th window, and F(n + 1, w) is the observed value at time f (n + 1) and in the same window. We choose one particular state from all the possible states of the process (generally we choose state of last candle in the training data). The currently active state is the most interesting state prediction process and that particular state sm shall be expressed as Sm = S .  [Fn, w ∈ S ∗ |Q(n, w)] ∧ [Fn + 1, w ∈ So|Q(n + 1, w)] (34.11) Lo∗ = w

n

w = 1,2,…,NW : o = 1,2,…,NS above is the distribution of events per process by considering the windows where the previous state is known by using the markov model we can calculate the transition matrix explained by Eq. (34.11). The probability Pmo can be calculated by using the given formula Pmo = Lmo/Lo∗

(34.12)

The transition matrix in Eq. (34.12) is treated as the basis for decision making whether to open the forward position at a present instant here short position means the profit will go down and high position means profit will be high. If above state probability value is high as compare to below state then it is called open high position and if the below state probability is high as compare to above state value then it is called open short position. At every discrete interval in the Markov process we can take the investment decision by considering the closing value of the candle. As per the values in the transition matrix Pmo the decision basis prediction is made. It should be noted if Sm|Q(f ) is the instant at the present state then this is the probability of moving to the next state So|Q(f + 1). from the Eq. (34.12) we can say that total sum of the transition probabilities when we move from the state Sm|Q(f ) to all the states So|Q(f + 1), o = 1, 2, 3, 4, . . . , NS equal one.

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From the above discussion we can observe a the less interesting is the term of an investment. This is condition when So|Q(f + 1) = Sm|Q(f ). at this moment event has to stay in the present state. For making profit we are predicting it over time if it is found that the long positions are need to be opened when calculated value of the variable will raise , if not short positions are need to be opened. In a practical sense it can be identified as of little interest if it is found that the observed variable will stay or doesn’t change its current state then it is hard to make a decision on opening and closing the positions. Taking regard of the probability vector Pmo for o = 1,2,…, NS, by the essence of markov model whose sum of words according to (34.9) is equal to one, such that it is useful in differentiating the probabilities of achieving states So|Q(f + 1), Suppose the process i change from the state Sm|Q(f ), then to the stateSo|Q(f + 1) . Our objective is to achieve the probability of near by other states S(m − 1)|Q(f + 1) and S(m + 1)|Q(f + 1). According to predictive suggestions, We use the easiest investment strategy for opening the positions . If Pmo(m, m − 1) < Pmo(m, m + 1)

(34.13)

say long positions need to be opened Else Pmo(m, m − 1) > Pmo(m, m + 1)

(34.14)

say short positions need to be opened The variable Pmo(m,m-1) represents the probablity that at time f the process was in state S(m)|Q(f ) and at time f+1 the process will be in state S(m − 1)|Q(f + 1). Validation or reasoning with respect to investment is given as. If the state Sm|Q(f ) followed by So|Q(f + 1), is arranged above or higher then S(m)|Q(f ) in the space of the observe able variable F, then the raise of value between F(f ) and F(f + 1) is predicted. If it is like that then it is proper or suitable in opening of long position. As per Eq. (34.13) Probability of reaching from lower to higher states is checked. In such situation the probability of declining in F is smaller that probability of increasing in F. So the decision for opening of the long position is made so that after the end of the one candle we can close this position. As per Eq. (34.14) , As the case is quite varying, the short position has to be opened so that after the candle period we can close the position . While calculating the transition matrix from a less number of events at relatively low values of no of candles and no of windows or a high number of states NS, it is possible to get into a case where Pmo(m, m − 1) = Pmo(m, m + 1) (34.15) Then the opening of any position is not made. Clearly, each opening of a next position is decided after transition matrix calculation is done completely. For all investment strategies the idea is that the forecasting is done entirely according to data obtained from the past but not from upcoming data.

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34.3 Investment Strategy on the Bases of Markov Model Based on the three Eqs. (34.13–34.15) of investment strategy experiments are performed and the time series is taken Nifty50 Historical data where the candle size is one day (1d). The Nifty data contains 1239 candles up-to December 31, 2019. This Data-set is downloaded from [https://www1.nseindia.com/]. During this simulations, we have to find the values of given below parameters which in turn will reflect in the transition matrix that will be used to determine the transition probability matrix. Parameters which we have to determine are: NS—it define the number of state in the window. NC—it define the number of candles inside the window. Candle may be in the interval of 1 h ,1 day etc. NW—it define the total number of time window in the given time series . In this process we have take care of both risk and profit by closing all the open position short as well as long position . For the long position Wlong in each candle is calculated as given below. Here quality depends on both two parameters that is Wlong and Wshort. The criterion of quality in the parametric space depends on both the  (CLn + 1 − CLn)|Pr(m − 1, m) < Pr(m, m + 1) (34.16) W long(n) = n

W long(n) = 0 if above condition does not hold where CLn—n-th candle closing value. NC—it is the total candles taken during simulation. In the above equation it is indicated that if we can invest in the business we will get benefited. and the Profit from short positions, is calculated similarly: Wshort can be calculated as given below W short(n) =



(CLn − CLn + 1)|Pr(m − 1, m) < Pr(m, m + 1)

(34.17)

n

W short(n) = 0 if above condition does not hold Here it is indicating that it is time to sell your share because its value will go down. final profit is calculated by using the equation given below W (n)|(n = NC) = W long(n) + W short(n)

(34.18)

where n = 1, 2, ..., NC In the strategy sequence of candles from 1 to NC are used. According to the paper Young 1991 the calmar ratio is calculated by using profit and maximum drop down

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during the simulation Calmer Ratio = W (NC)/MaximumDropDown

(34.19)

Simulation contain both both factors and both are adjusted using the hyper parameter h1 and h2. C = h1 ∗ CalmerRatiolearn + h2 ∗ W (n)/Nlearn (34.20) where: h1 hyper parameter to adjust the Calmer component. h2 hyper parameter to adjust the profit component. CalmerRatiolearn—denote the calmer ratio at the end of learning process; W (n)—is the commutative profit at the end of learning. total numbers of candles are NC; we can select h1 and h2 so the we can balance profit and calmer ratio. We have to select the parameter in such a way that we can compromise between risk and profit. The factor of h2 was additionally changed so that final profit depend only one profit per candle. By doing this we can use this criteria in other simulation without worrying about NC. C = max(h2 ∗ W (NS, NC, N W ) + h1 ∗ CalmerRatiolearn(NS, NC, N W ) (34.21) function W(NS,NC,NW) is cumulative gains which is calculated first without using the genetic algorithm which is done by taking all the combination in three dimension space NS,NC,NW and then by using the genetic algorithm strategy are explained in the section and CalmerRatiolearn (NS,NC,NW) is the Calmer ratio which is learned by using the training data. Here simulation is realized two times first without using the genetic algorithm and second by using genetic algorithm which find the optimal parameter that we can use for the prediction of time series. In the reference paper author was using the all the combination of 3D space NS*NC*NW [4].

34.4 Profit Maximization Using Genetic Algorithm 34.4.1 Selection In the selection process the algorithm is going to select the individual which have maximum fitness value and we have various way to select fittest individual. After this individual is transfer to the next generation. In this paper we will select the the window (which have particular number of state and candles) which have greatest profit means which have greater calmer ratio , in our implementation we are selecting two best fit individuals for the next generation . For example too many pairs of states and candles are present in the one generation then select the pairs for the next generation which have higher profit.

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34.4.2 Crossover After selecting the individuals and these individual have to generate the offspring [3] and this is done by the process crossover. Simplest and efficient method is one point crossover in which the offspring take the number of state from one parent and number of candle from the other parents. Here we are using only single point crossover means here we can break the chromosome from one part. if one parents contains the pair (3,11) , (3,12) and (4,11) and another parents contains the pairs (5,12), (7,14) and (6,81) then child’s can be like these child 1: (3,11), (7,14), (6,81) child 2: (5,12), (3,12), (4,11) Above pairs denote the States and candles in one in one window respectively. Basic framework of genetic algorithm in given in Algorithm 1 Algorithm 1 Genetic Algorithm 1: while put the termination criteria as the number of iteration do 2: 3: calculate the Fitness Function by passing number of state and candles. 4: convert the the number of states and number of candle in integer by ceiling operator. 5: calculate the number of window dividing total number of candles by number of candles in one window after this take the ceiling of number of window. 6: select some window for the next generation which have high calmer ratio and some random window. 7: Crossover by taking some window from parents and some another parents as explained in crossover process in section 1. 8: Mutation process is presence or absence of particular window(which have numbers of state and of candles) for the next to generate the diversity. 9: Population update in each generation some individual are directly taken from the past generation which have high fitness and some are randomly selected as mention above in the genetic algorithm theory. 10: end while 11: take the mean of all the calmer ratio(profit). 12: fitness function value and number of state given by x1(take ceiling ) and number of candles given by x2 (take ceiling )

34.4.3 Mutation In each new generation we get the population of individual some set of state and candles are directly copied and some are generated either by taking the random set and rejecting the previous. Here the mutation probability means how many number of windows from the ten number of window we have to mutate. In each generation we select or deselect the individual means selecting and deselecting the window (which

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Table 34.1 Single point mutation Before After

00110100010110 00110000010110

have fix number of states and candles) from the parent, or we can mutate states or candles which mean taking new value in place of old states or candles value (Table 34.1). Mutation is also necessary to produce the genetic diversity in the population. In above example we have changed one to zero means we are rejecting this particular window. For example if (5,12) states and candles pair present in one chromosome then we can reject it to the next chromosome or can take new pair instead of this pair.

34.4.4 Fitness Function In this process we have to maximize the calmer ratio means we have to select the best number of candle and best number states. In each generation we take fifty pairs of candles and number of states. In each generation we calculate the mean of calmer ratio. If the calmer ratio of one selected pair is less then we don’t choose that pair for the next generation. In the genetic function we are passing the real value therefore inside the fitness function we take the ceiling of number of candles and ceiling of number of states because that can only be integer inside fitness function which take the two variable numbers of states and number of candles and it calculate the number of window and number of window is calculated by dividing the number of candles by number of candles in one window. Here profit function is to maximise Calmer ratio, so the below equation is same as Eq. 34.19 fitness function = W (NC)/MaximumDropDown

(34.22)

34.5 Performance Analysis We had run all the algorithm for different number of iteration and population size is fifty, mutation and crossover probability is 0.1 and 0.8 respectively, elitism is two. When we run the code using the genetic algorithm with only one iteration. The result observed are given below Calmar ratio obtain is 28.10321 States 17 Candles 94 Time taken to run the code using genetic algorithm is 47.39687 s but the time taken without genetic algorithm was 2.29 h with same parameter (here we are trying to find

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the best number of states from 3 to 20 and best number of candles from 8 to 100). Genetic algorithm answer is (17 states and 94 candles) and profit is 28.10321 (time taken 47.39687 s) Without genetic algorithm is (20 states and 95 candles) and profit is 26.45612 Genetic algorithm with 5 iterations.The results observed are given below Calmar ratio obtain is 28.28134 States 20 Candles 95, Time taken is 4.480167 min Genetic algorithm with 10 iterations. The results observed are given below Calmar ratio obtain is 28.1598 States 20 Candles 74 , Time taken is 8.502898 min. Genetic algorithm with 20 iterations. The results observed are given below Calmar ratio obtain is 28.31093 States 19 Candles 96 , Time taken is 16.22251 min As we increase number of iteration the profit function does not change much from this we can conclude that upto 5 iteration is enough to get better result which in turn save time (Fig. 34.2; Table 34.2). From this we can conclude that more number of iteration are not producing the good result and 1–5 iteration is enough to get the best result.

Fig. 34.2 Mean calmer ratio plot with 20 generation Table 34.2 Comparative result with GA Result name With GA Calmer Ratio States Candles Time taken

28.10321 17 94 47.39 s (if one iteration),16.22251 min(if ten iteration)

Without GA 26.45612 20 95 2.2942 h

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Fig. 34.3 Cumulative profit with 1239 candles

34.5.1 Cumulative Profit For every candle in each window the cumulative profit is calculated as (Fig. 34.3): If (Pr[m, (m − 1)] < Pr[m, (m + 1)]) Z = Z + CL(j + 1) − CL(j)

(34.23)

Z = Z + CL(j) − CL(j + 1)

(34.24)

Else Where, Pr[m, m − 1] probability such that at time f the process is in state Sm and at time (f + 1) the process will be in the state Sm −1; CL(j): closing value at J -th candle;

34.6 Conclusion From the above methodology we can easily conclude that forecasting based on the first-order Markov model with Genetic Algorithm produce impactive result with in short time. By using the genetic algorithm we have decreased the time by the factor of 18 and the calmer ratio is nearly 28 which is a better as compare to without genetic algorithm method. In the paper the calmer ratio shows that transition matrix also a good way to predict a time series. We can apply the similar technique in the present markets. Genetic algorithm playing a very important role to reduce the time. In the proposed method genetic algorithm choosing the best number of state and best number of candles from a given set of states and candles. We can apply the genetic algorithm for the second order and third order Markov chain. Here we divided equal states inside each window further we can extend it different number of states in each window. We can also apply the some machine learning approach to optimise the result further.

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References 1. Alizadeh, A., Nomikos, N.: A Markov regime switching approach for hedging stock indices. J. Futures Markets 24(7), 649–674 (2004) 2. Al-Jumeily, D., Hussain, A., Alaskar, H.: Recurrent neural networks inspired by artificial immune algorithm for time series prediction. In: Proceedings of the International Joint Conference on Neural Networks (2013) 3. Andre, D., Teller, A.: Evolving team Darwin United. In: RoboCup-98: Robot Soccer World Cup II 4. Antoni, W.: Time Series Modeling and Forecasting Based on a Markov Chain with Changing Transition Matrices, Expert Systems With Applications (2019) 5. Baboli, M., Abadeh, M.S.: Financial time series prediction by a hybrid memetic computationbased support vector regression (MA-SVR) method. Int. J. Oper. Res. 23(3), 321–339 (2015) 6. Bao, X., Tao, Q.: Dynamic financial distress prediction based on rough set theory and EWMA model. Int. J. Appl. Math. Stat. 48(18), 339–346 (2013) 7. Bingham, N.H.: Modelling and prediction of financial time series. Commun. Stat. Theor. Methods 43(7), 1351–1361 (2014) 8. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Cham, Heidelberg, New York (2006) 9. Brooks, S.: Markov chain Monte Carlo method and its application. J. R. Stat. Soc. Ser. D (The Statistician) 47(1), 69–100 (1998) 10. Brooks, S., Gelman, A., Jones, G., Meng, X.L. (eds.): Handbook of Markov Chain Monte Carlo. CRC Press, Boca Raton, FL (2011) 11. Cheng, C., Xu, W., Wang, J.: A comparison of ensemble methods in financial market prediction. In: Proceedings of the 2012 5th International Joint Conference on Computational Sciences and Optimization, CSO 2012, p. 755 (2012) 12. Chib, S., Greenberg, E.: Markov chain Monte Carlo simulation methods in econometrics. Econ. Theor. 12(3), 409–431 (1996) 13. Yonghui, D., Dongmei, H., Weihui, D.: Modelling mast and computing of stock index forecasting based on neural network and markov chain. Sci. World J. 2014(1), 1–9 (2014). Article ID 124523 14. Longla, M., Peligrad, M.: Some Aspects of Modeling Dependence inCopula-based Markov chains Martial 15. González, A. M., Roque, A.S., García-González, J.: Modeling and forecasting electricity prices with input/output hidden Markov models. IEEE Transact. Power Syst. 20(1), 13–24 (2005) 16. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press (1996)

Chapter 35

Modeling Drivers of Machine Learning in Health care Using Interpretive Structural Modeling Approach Pooja Gupta

and Ritika Mehra

Abstract Ongoing advancing developments and frequent adoption of innovation in health care are driving toward a more intelligent world yet have additionally prompted a huge increment in healthcare data. Artificial intelligence (AI), machine learning (ML), and deep learning (DL) empower healthcare associations to analyze a huge volume and variety of healthcare data. These techniques encourage deeper insights which lead to proactive treatment, minimal future risks, and smooth working processes. This paper is a contribution to develop a structural model of various enablers, fundamental to incorporate machine learning in health care. A variety of ten enablers essential for implementation of machine learning are identified from the extensive literature review. Expert’s opinions were also sought after while identifying these enablers. After several brainstorming meetings contextual relationships among these enablers have been established. Furthermore, a structural model of identified machine learning enablers has built. Enablers have been classified into different clusters based on their driving and dependent powers.

35.1 Introduction With artificial intelligence (AI) shifting through millions of records and profiles to detect links or similarities between biological characteristics and disease, doctors can now detect problems that otherwise remain imperceptible. Future health issues can be highlighted when providers analyze genomes and proteins to identify patients at risk for diabetes, cancer, and other maladies before they strike, this is an example of how digitalization is potentially shifting much of the industry’s focus to preventive care [1]. While anticipating the eventual fate of AI in medication is not a simple assignment, it can be positively said that AI has a task to carry out in medication as P. Gupta (B) · R. Mehra DIT University, Dehradun, Uttarakhand, India e-mail: [email protected] R. Mehra e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_35

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an accomplice. This evaluation depends on specific attributes of AI-oriented frameworks. Frameworks, for example, IBM’s Watson is capable to look through a large number of sheets of information, read endless clinical articles, and far surpass the limit of any human doctor in its broadness and size of information [2]. An exhausted doctor may overlook that a specific patient is vulnerable against a specific medication’s reaction, but an AI-based framework won’t. Artificial intelligence can likewise aid medical surgery procedure in blend with augmented reality programs [3]. Machine learning algorithms support physicians in decision making and can even function independent of them. Healthcare professionals and researchers are foreseeing that machine learning tools and algorithms are future of clinical medicine field [4]. The ever-growing healthcare data and fast development of big data analytic approaches have made possible the present effective applications of AI in health care [2].

35.2 Identification of Enablers of Machine Learning in Health care 35.2.1 Secure Patients Numerous patients are worried about the protection and well-being issues of having AI analyze and treat their wounds and sicknesses. Individuals tend not to confide in machines and would incline toward up close and personal counsels with their primary care physicians [5, 6]. In a study of more than 300 clinical pioneers and healthcare professionals, over 70% of the participants revealed having under half of their patients highly engaged in and 42% of respondents said under 25% of their patients were engaged in when ML was used in healthcare process [6]. Secure patients and patient’s readiness are the key enabler in adoption of machine learning in health care [7, 8].

35.2.2 Shortage of Healthcare Professional The World Health Organization (WHO) appraises about worldwide shortage of 4.3 million doctors, medical attendants, or healthcare experts [9]. This shortage is frequently increasing in developing countries because of the limited numbers of clinical institutes in these nations. As demand for Healthcare professionals outperforms supply, technology comes as savage, machine learning may fill in the gaps. It is anticipated that the demand for doctors in the developed nation like USA will surpass the supply by 46,000–90,000 by the year 2025. Artificial intelligence can support here and help lessen the measure of work and free the clinicians to concentrated on the patients [10].

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35.2.3 Data and Infrastructure The ever-growing medical data is in multiple formats like MRI machines produces data in images, CT scanners, and X-rays can produce enormous complex data that can be stimulating and time-consuming for doctors to examine [11]. An effective, secure, and high-quality data infrastructure would enable to develop and train better ML systems. The volume of healthcare data gathered in recent years, the elevated computing power at low rate now accessible to process enormous datasets, the growing frequency of EMRs, and global advances in computing technologies, all these have completely simulated the AI and machine learning headways in medicine [6].

35.2.4 Data Quality In order to implement machine learning in healthcare, it is beneficial to understand the key enablers required to deploy models. According to leading practitioner’s data and data quality are most important enablers. Machine learning solutions in health care will be effective and safe only if algorithms are trained on high-quality data [12]. Datasets are required to incorporate adequate variability to gratify healthcare professionals, experts, patients and regulators and dodge unintentionally presenting bias or errors into machine learning solutions [7, 13]. Both quantity and quality of the dataset used for training are crucial in developing of state-of-the-art machine learning systems in radiology [14, 15].

35.2.5 Workforce Transformation The global perspective, AI is coming for human jobs, is not true, AI has not arrived to substitute human tasks but to complement them and support people to grow their potential and creativity to the supreme [8, 15]. The greatest fear with respect to the increasing use of technology and machine learning all over the workplace is the risk to jobs. Like workers were replaced from the factory due to automation, similarly healthcare workplace and workforce will be transformed and is sure to look different [6, 10].

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35.2.6 The Regulatory Environment, Ethics, and Confidentiality The regulatory environment is an essential enabler for executing AI and machine learning in health care and stays a broadly discussed theme that is affecting technological advancements over the world [7]. Data Governance has become important due to the rising concerns about the safety of healthcare data, healthcare data should be prevented from unauthorized use and should be well managed, apart from security concern, healthcare data should be complete and accurate [15–17]. The regulatory environment helps to guard the public interest by safeguarding a satisfactory and balanced level of danger for medical expertise that is positioned in the marketplace.

35.2.7 Data Quality Systems and Frameworks There are three chances to improve how all AI items that ingest organized EHR information are converted into clinical consideration [18]. To start with, information quality frameworks and systems should be received to guarantee that AI models have faced legitimacy [19]. Poor quality data from Data Management System (DMS) need to be assessed, analyzed, and cleaned by using data quality frameworks (DQF). These Data Management Systems are applicable as they provide productivity in an organization [13, 20]. DQF’s structure can bypass the distinct essentials of quality assessment [21]. A general plan to analyze and simplify data quality issue must be provided by DQF.

35.2.8 Medical Models New developments in machine learning are empowering new insights for better clinical decision support. Self-explanatory medical model’s development using machine learning is more important. Development, validation, and implementation of machine learning models for health care increase the probabilities of eventually improving patient care [22]. This way, if a doctor disagrees with a model’s prediction, she will at least know why the model said what it did. These models outdone conventional, clinically used predictive models in all cases [23].

35.2.9 Neural Networks Healthcare entities are using machine learning methods, for example, artificial neural networks (ANN) to the maximum in order to optimize delivery of health care at a

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low price [24, 25]. In acute disease diagnosis like cancer or cardiology, artificial neural networks (ANN) are reported as common machine learning technique [26]. Researchers at Google trained a neural network to detect 26 diverse categories of skin conditions, like melanoma, psoriasis, eczema, cysts, and more [27].

35.2.10 Cybersecurity Many medical professionals and healthcare managers are concerned about cybersecurity, and their concerns are not misplaced. Patient records, such as EHRs, are coveted by hackers for their high street value estimated at $50 each on the black market, according to a Harvard University study, because they contain social security numbers and credit card information that hackers can use to drain bank accounts and run up credit card purchases. Some patient diagnoses even have been used by organized crime for blackmail purposes [28, 29].

35.3 Questionnaire Development and Data Collection Ten variables are identified as ML implementation enablers in the field of Healthcare. Seven enablers were recognized during literature review and rest three were involved after brainstorming and discussion with academicians and healthcare professionals. These professionals were the esteemed academicians, healthcare experts and senior administrators of the industry with sound familiarity of ML practices in health care. 60 experts, 30 from academia and 30 from the healthcare field were approached and referred to recognize the contextual relationship between the enablers. Table 35.1 represents all the ten enablers of ML.

35.4 Interpretive Structural Modeling (ISM) ISM is a technique for recognizing and bridging relationships among explicit variables, which characterize an issue or a problem [30]. ISM technique displays the interrelationships of different enablers of machine learning implementation in health care and their levels.

35.4.1 Model Development Phase 1 Table 35.2 shows the contextual relationship obtained between various enablers. A contextual relationship (V, A, X, O) is confirmed among variables if

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Table 35.1 Machine learning in healthcare enablers used in ISM modeling S. No.

ML enablers in health care

Literature references

1

Secure patients

Davenport et al. [7], PwC Report [8], Academy of Medical Royal Colleges Report [6]

2

Shortage of health professionals

Ahuja et al. [34], ESR Report [10]

3

Data and infrastructure

Schmidt-Erfurth et al. [35], Pratt et al. [12], Obermeyer and Lee [36]

4

Data quality

Labuda et al. [14], Sun et al. [15]

5

Workforce transformation

Pwc Report [8], Academy of Medical Royal Colleges Report [6],

6

The regulatory environment, ethics and confidentiality

Belanger et al. [16], Zuboff et al. [22], Elliott et al. [17]

7

Data quality systems and frameworks

Kerr et al. [23], Zappone et al. [13], Chang et al. [24], Bai et al. [25]

8

Medical models

Chen et al. [26], Rajkomar et al. [27], Nguyen et al. [29]

9

Neural network

Shahid et al. [28], Jiang et al. [30]

10

Cybersecurity

Boddy et al. [38], Vargheese et al. [37]

Table 35.2 Structural self-interaction matrix (SSIM) Enabler

10

9

8

7

6

5

4

3

2

1

A

O

O

X

A

V

X

X

A

2

O

O

V

O

O

V

O

V

3

V

X

X

V

V

A

V

4

O

V

X

A

A

A

5

X

V

V

X

V

6

A

O

V

X

7

O

V

V

8

O

X

9

O

1

10

variable 1 is leading variable 2 then SSIM [1, 2] = V and if variable 2 is leading is leading variable 1 then SSIM [1, 2] = A, if variable 2 is leading variable 1 then SSIM [1, 2] = A, if both variable 1 and variable 2 are leading each other than; SSIM [1, 2] = X, if both are independent of each other than; SSIM [1, 2] = O. Phase 2 An initial reachability matrix is developed from structural self-interaction matrix; initial reachability matrix defines pair-wise relationships between variables identified [31]. The SSIM established in last step is changed into a binary matrix, and this binary matrix is identified by name of initial reachability matrix. This binary

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matrix is created by placing 1 and 0 in SSIM. If cell (i, j) contains V in SSIM, then 1 will be placed in cell (i, j); 0 will be placed in cell (j, i) in the reachability matrix. If cell (i, j) contains A in SSIM, then 0 will be placed in cell (i, j); 1 will be placed cell (j, i) in the reachability matrix (Table 35.3). Phase 3 Transitive association is checked in initial reachability matrix derived from the SSIM. The concept of transitivity found in contextual relations is an elementary statement stated in ISM (Table 35.4). Phase 4 By adding the driving power and dependence, we get final reachability matrix, which offers reachability set and antecedent set for individual variable. Common variables appeared in both reachability and antecedent set constructed the intersection set for each variable (Table 35.5).

Table 35.3 Initial reachability matrix Enabler

1

2

3

4

5

6

7

8

9

10

1

1

0

1

1

1

0

1

0

0

0

2

1

1

1

0

1

0

0

1

0

0

3

1

0

1

1

0

1

1

1

1

1

4

1

0

0

1

0

0

0

1

1

0

5

0

0

1

1

1

1

1

1

1

1

6

1

0

0

1

0

1

1

1

0

0

7

1

0

0

1

1

1

1

1

1

0

8

0

0

1

1

0

0

0

1

1

0

9

0

0

1

0

0

0

0

1

1

0

10

1

0

0

0

1

1

0

0

0

1

Table 35.4 Reachability matrix with transitivity Enabler

1

2

3

4

5

6

7

8

9

10

1

1

0

1

1

1

1*

1

1*

1*

1*

2

1

1

1

1*

1

1*

1*

1

1*

1*

3

1

0

1

1

0

1

1

1

1

1

4

1

0

1*

1

1*

0

1*

1

1

0

5

1*

0

1

1

1

1

1

1

1

1

6

1

0

1*

1

1*

1

1

1

1*

0

7

1

0

1*

1

1

1

1

1

1

1*

8

1*

0

1

1

0

1*

1*

1

1

1*

9

1*

0

1

1*

0

1*

*

1

1

1*

10

1

0

1*

1*

1

1

1*

0

0

1

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Table 35.5 Final reachability matrix Enabler

1

2

3

4

5

6

7

8

9

10

Driver power

1

1

0

1

1

1

1*

1

1*

1*

1*

9

2

1

1

1

1*

1

1*

1*

1

1*

1*

10

3

1

0

1

1

0

1

1

1

1

1

8

4

1

0

1*

1

1*

0

1*

1

1

0

7

5

1*

0

1

1

1

1

1

1

1

1

9

6

1

0

1*

1

1*

1

1

1

1*

0

8

7

1

0

1*

1

1

1

1

1

1

1*

9

8

1*

0

1

1

0

1*

1*

1

1

1*

8

9

1*

0

1

1*

0

1*

1*

1

1

1*

8

10

1

0

1*

1*

1

1

1*

0

0

1

7

Dependence

10

1

10

10

7

9

10

9

9

8

35.4.2 Level-Wise Partitioning Final reachability matrix is segregated into different levels. Total six levels are discovered. Enablers with same reachability set and antecedent have been assigned on the same level. Enablers 1, 3, 4, and 7 are on level-I., i.e., the top level in the model. Iterations will be continued until all ten enablers are placed on some level. Total 6 iterations are required to place each enabler at some level (Table 35.6). Table 35.6 Level-wise partition Enabler Reachability set

Antecedent set

Intersection set

1

1, 3, 4, 5, 6, 7, 8, 9, 10

1, 2, 3, 4, 5, 6, 7, 8, 9, 10 1, 3, 4, 5, 6, 7, 8, 9, 10 I

2

1, 2, 3, 4, 5, 6, 7, 8, 9, 10 2

3

1, 3, 4, 6, 7, 8, 9, 10

1, 2, 3, 4, 5, 6, 7, 8, 9, 10 1, 3, 4, 6, 7, 8, 9, 10

I

4

1, 3, 4, 5, 7, 8, 9

1, 2, 3, 4, 5, 6, 7, 8, 9, 10 1, 3, 4, 5, 7, 8, 9

I

5

1, 3, 4, 5, 6, 7, 8, 9, 10

1, 2, 4, 5, 6, 7, 10

1, 4, 5, 6, 7, 10

V

6

1, 3, 4, 5, 6, 7, 8, 9

1, 2, 3, 5, 6, 7, 8, 9, 10

1, 3, 5, 6, 7, 8, 9

II

7

1, 3, 4, 5, 6, 7, 8, 9, 10

1, 2, 3, 4, 5, 6, 7, 8, 9, 10 1, 3, 4, 5, 6, 7, 8, 9, 10 I

8

1, 3, 4, 6, 7, 8, 9, 10

1, 2, 3, 4, 5, 6, 7, 8, 9

1, 3, 4, 6, 7, 8, 9

IV

9

1, 3, 4, 6, 7, 8, 9, 10

1, 2, 3, 4, 5, 6, 7, 8, 9

1, 3, 4, 6, 7, 8, 9

IV

10

1, 3, 4, 5, 6, 7, 10

1, 2, 3, 5, 7, 8, 9, 10

1, 3, 5, 7, 10

III

2

Level VI

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Fig. 35.1 ISM model of machine learning implementation enablers in healthcare

35.4.3 ISM Model of Machine Learning Implementation Enablers in Healthcare Enablers are placed on levels according to partitioning and a directed graph is sketched from the associations specified in final reachability matrix are detached, further the transitive relations are also detached (Fig. 35.1).

35.4.4 MICMAC Analysis MICMAC is abbreviated as Matrice d’Impacts croises-multiplication appliqué and classement (cross-impact matrix multiplication applied to classification). Main objective behind performing MICMAC analysis is examination of the driving and dependence power of each enabler [32]. According to the driving and dependence power of each enablers, they are distributed into four different groups.

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1. Autonomous: These enablers show both weak driving power and weak dependence. These are comparatively isolated from the model. These enablers can have few strong links. In our system no enablers have been found falling in this cluster. 2. Dependent: Enablers with weak drive power but strong dependence power belongs to dependent cluster. None of the enablers have been placed on this cluster. 3. Linkage: Enablers with strong driving power with strong dependence power belong to this cluster. They are placed in cluster–III. Nine enablers have been positioned in this cluster. 4. Independent: Enablers with strong driving power but weak dependence power belongs to this category. They are shown in cluster-IV. Only one enabler is placed in this cluster. Driving power

10

2

Independent IV

9

5

Linkage III

8

6, 8, 9

7

10

1, 7 3 4

6 5 4

Dependent II

3 2

Autonomous I

1

5 1

2

3

4

5

6

7

8

9

10

35.5 Discussions In healthcare field, ML has led to new stimulating developments that are at par and could reinvent cancer diagnosis and treatment. In this effort, we researched for the key enablers that should be considered while implementing machine learning for healthcare tasks, particularly when the performance between trained medical models and human specialists narrows. Four enablers secure patients, data and infrastructure, data quality and data quality system and framework are ranked as most supreme drivers. All four are placed at top level I. Public readiness remains a critical element to the advancement and further adoption of AI throughout health care. Data is also identified as most important enabler that is evident from fact that in both machine learning and health care, data plays a vital role. Dataset as an indispensable part of the system could be used to discover and learn knowledge by learning algorithms. Healthcare is an inherently data-driven field. Fine quality data are vital for fine quality algorithms. We observed many different data quality frameworks used in the

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literature designed largely for healthcare systems. The outcome of this research is a model that is developed using ISM technique which concludes secure patients, data and infrastructure, data quality, health care. Out of ten, nine variables have been recognized as linkage variables and only one variable is acknowledged as an independent variable.

35.6 Limitations The ISM model developed and proposed in this study are mainly built upon experts’ opinions. The views of experts might be biased. The implication of model analysis might differ in actual environment. Ten variables are taken into consideration while developing this model. If some specific industry wants to develop a model, then few variables may be removed and/or added. Hypothesis testing is also suggested for the trial of validity of this hypothetical model.

References 1. https://static.healthcare.siemens.com/siemens_hwem-hwem_ssxa_websites-context-root/ wcm/idc/groups/public/@global/documents/download/mda5/mtmz/~edisp/siemens_healthi neers_paper_embracing_healthcare_4-0-06533719.pdf. Last accessed Feb 2019 2. Sappin, E.: 4 Ways AI Could Help Shape the Future of Medicine (2018). https://venturebeat. com/2018/02/20/4-ways-ai-could-help-shape-the-future-of-medicine/. Last accessed Feb 2019 3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC677911. Last accessed Feb 2019 4. Implementing Machine Learning in Health Care—Addressing Ethical Challenges/Predicting the Future—Big Data, Machine Learning, and Clinical Medicine 5. https://healthitanalytics.com/news/patient-provider-support-key-to-healthcare-artificial-intell igence. Last accessed Feb 2019 6. Academy of Royal Medical Colleges Report (2019), https://www.aomrc.org.uk/wp-content/ uploads/2019/01/Artificial_intelligence_in_healthcare_0119.pdf. Last accessed Jan 2019 7. Davenport, T.H., Hongsermeier, T., McCord, K.A.: Using AI to improve electronic health records. Harvard Bus. Rev. (2018). https://hbr.org/2018/12/using-ai-to-improve–electronic-hea lth-records 8. PwC Report (2019), https://www.pwc.com/m1/en/publications/documents/from-virtual-to-rea lity.pdf. Last accessed Jan 2019 9. The World Health Report 2006: Working Together for Health. WHO, Geneva (2006) 10. ESR Report (2019), https://ai.myesr.org/healthcare/embracing-healthcare-4-0-digitalizing-hea lthcare-as-a-key-enabler-for-high-value-care/. Last accessed Feb 2019 11. Kent, J.: How artificial intelligence is changing radiology, pathology. Health Analytics. Last modified 3 Aug (2018) 12. Pratt, M.K.: Artificial intelligence in primary care. Med. Econ. (2018) 13. Zappone, A., Di Renzo, M., Debbah, M.: Wireless networks design in the era of deep learning: model-based, AI-based, or both? IEEE Trans. Commun. 67(10), 7331–7376 (2019) 14. Labuda, N., Lepa, T., Labuda, M., Kozak, K.: Medical 4.0: medical data ready for deep and machine learning. J. Bioanalysis Biomed. 9(6), 283–287 (2017) 15. Sun, H., Depraetere, K., De Roo, J., Mels, G., De Vloed, B., Twagirumukiza, M., Colaert, D.: Semantic processing of EHR data for clinical research. J. Biomed. Inform. 58, 247–259 (2015)

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16. Belanger, F., Xu, H.: The role of information systems research in shaping the future of information privacy. Inf. Syst. J. 25(6), 573–578 (2015) 17. Elliott, T.E., Holmes, J.H., Davidson, A.J., La Chance, P.A., Nelson, A.F., Steiner, J.F.: Data warehouse governance programs in healthcare settings: a literature review and a call to action. EGEMS 1(1) (2013) 18. Kaushal, R., Hripcsak, G., Ascheim, D.D., Bloom, T., Campion Jr., T.R., Caplan, A.L., et al.: Changing the research landscape: the New York City clinical data research network. J. Am. Med. Inf. Assoc. 21(4), 587–590 (2014) 19. Khatri, V., Brown, C.V.: Designing data governance. Commun. ACM 53(1), 148–152 (2010) 20. Ladley, J.: Data governance: how to design. Deploy and Sustain an Effective Data Governance Program (2012) 21. Rosenbaum, S.: Data governance and stewardship: designing data stewardship entities and advancing data access. Health Serv. Res. 45(5p2), 1442–1455 (2010) 22. Zuboff, S.: Big other: surveillance capitalism and the prospects of an information civilization. J. Inf. Technol. 30(1), 75–89 (2015). Winter, J.S., Davidson, E.: Big data governance of personal health information and challenges to contextual integrity. Inf. Soc. 35(1), 36–51 (2019) 23. Kerr, K.: The development of a data quality framework and strategy for the New Zealand Ministry of Health (2000). Viewed 14 Apr 2009. http://mitiq.mit.edu/Documents/IQ_Projects/ Nov%202003/HINZ%20DQ%20Strategy%20paper.pdf 24. Chang, S.I., Ou, C.S., Ku, C.Y., Yang, M.: A study of RFID application impacts on medical safety. Int. J. Electron. Healthc. 4(1), 1–23 (2008) 25. Bai, L., Meredith, R., Burstein, F.: A data quality framework, method and tools for managing data quality in a health care setting: an action case study. J. Decis. Syst. 27(sup1), 144–154 (2018) 26. Chen, P.H.C., Liu, Y., Peng, L.: How to develop machine learning models for healthcare. Nat. Mater. 18(5), 410 (2019) 27. Rajkomar, A., Oren, E., Chen, K., Dai, A.M., Hajaj, N., Hardt, M., et al.: Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 1(1), 18 (2018) 28. Shahid, N., Rappon, T., Berta, W.: Applications of artificial neural networks in health care organizational decision-making: a scoping review. PloS One 14(2) (2019) 29. Nguyen, O.K., Makam, A.N., Clark, C., Zhang, S., Xie, B., Velasco, F., et al.: Predicting allcause readmissions using electronic health record data from the entire hospitalization: model development and comparison. J. Hosp. Med. 11(7), 473–480 (2016) 30. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., et al.: Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 2(4), 230–243 (2017) 31. Gupta, P., Jain, V.K.: Interpretive structural modeling of GIoT enablers. J. Inf. Technol. Res. (JITR) 13(2), 129–140 (2020) 32. Diabat, A., Govindan, K.: An analysis of the drivers affecting the implementation of green supply chain management. Resour. Conserv. Recycl. 55(6), 659–667 (2011) 33. Davenport, T., Kalakota, R.: The potential for artificial intelligence in healthcare. Future Healthc. J. 6(2), 94 (2019) 34. Ahuja, A.S. (2019). The impact of artificial intelligence in medicine on the future role of the physician. PeerJ. 7, e7702 35. Schmidt-Erfurth, U., Sadeghipour, A., Gerendas, B.S., Waldstein, S.M., & Bogunovi´c, H.: Artificial intelligence in retina. Prog. Retinal Eye Res. 67, 1–29 (2018) 36. Obermeyer, Z., Lee, T.H.: Lost in thought: the limits of the human mind and the future of medicine. New England J. Med. 377(13), 1209 (2017) 37. Lake, D., Milito, R.M.R., Morrow, M., Vargheese, R.: Internet of things: Architectural framework for ehealth security. J. ICT Stand. 1(3), 301–328 (2014) 38. Boddy, A., Hurst, W., Mackay, M., Rhalibi, A.E.: A study into data analysis and visualisation to increase the cyber-resilience of healthcare infrastructures. In Proceedings of the 1st International Conference on Internet of Things and Machine Learning (pp. 1–7) (2017, October)

Chapter 36

Studies on the Optical and Structural Properties of Exfoliated Graphene Oxide Nipom Sekhar Das, Koustav Kashyap Gogoi, and Avijit Chowdhury

Abstract Graphene oxide (GO) has attracted much attention because of its incredible physical, chemical, and electrical characteristics in the field of materials science. Herein, Hummer’s method is followed for synthesizing GO, that is characterized using optical, structural, and morphological techniques like UV–Visible absorption spectroscopy, Photoluminescence spectroscopy (PL), X-ray diffraction (XRD), and scanning electron microscopy (SEM). The UV–visible absorption spectra shows the absorption peak located at 232 nm which originates from π –π * transition in the aromatic C–C bond. Optical energy gaps have been calculated using Tauc’s plot and found to be 3.34, 3.94, and 4.30 eV. The PL spectra shows a broad peak at 445 nm and its corresponding emission energy is found to be 2.77 eV. The interplanar spacing ~0.87 nm of exfoliated GO sheet was calculated from XRD spectra which suggest 2–3 number of sheets in the exfoliated structure. The SEM micrograph shows larger sheet structure with crumpled morphology.

36.1 Introduction Carbon-based nanostructured materials are well known for their exceptional multifunctional properties and widely explored in modern organic electronics especially in energy storage and conversion devices, sensors, transistors, memory devices, and so forth [1–5]. At the moment, graphene oxide has attracted much attention as a very important precursor and functional derivatives of graphene. Various synthesis methods have been followed to obtain few-layered thick exfoliated graphene oxide N. S. Das · K. K. Gogoi · A. Chowdhury (B) Organic Electronics and Sensor Laboratory, Department of Physics, National Institute of Technology Silchar, Silchar, India e-mail: [email protected] N. S. Das e-mail: [email protected] K. K. Gogoi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_36

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(GO) with desired properties in a lucrative and feasible way is still a great challenge. Hummer’s method is one of the oldest techniques and suitable method for having the effective result [6]. Graphene oxide made using modified Hummer’s method is also another suitable method that also provides the desired material property. The modification of Hummer’s method may increase the reaction yield and reduce the harmful gases which are evolved while using the required proportion of KMnO4 and H2 SO4 [7]. The improved synthesis procedure shows the great prospect for developing remunerative and ecofriendly GO [8–12]. Graphene oxide is a few layers thick sheet of graphite containing functional groups such as carbonyl, carboxylic, and epoxy [13–15]. Such functional groups on the GO sheet facilitate nucleation sites where many other semiconducting materials can be covalently anchored, which is practically impossible for other stable carbon-based nanomaterials like graphene, carbon nanotubes (CNTs), etc. [16] Therefore, few layers thick exfoliated GO with large surface area is an excellent template for functionalization to harness the beneficial properties in their nanoregime. The process of synthesizing GO from graphite powder is very simple, and cost-effective techniques in liquid phase can be employed to exfoliate GO sheets by breaking the weak bonds present between the layers [17]. The interlayer separation between the two layers of graphite is very low but can be further increased by controlling the oxidizing agents [18]. GO can easily be dissolved in water and other organic solvents because of the hydrophilic nature which facilitate ease processing of devices based on this material. GOs nanomaterials act as a pivotal role in the electronic device [19]. However, oxygen-containing functional groups are responsible for poor performances that significantly affect the carrier transport properties. Therefore, considerable reduction of functional groups in GO is required which undoubtedly can increase the materials performances and meet all the characteristics identical to graphene. To make GO with significantly lower number of functional groups, it is very important to prepare the exfoliated GO (eGO) first. The functional groups on the plane or between the plane in few layers thick GO can be minimized considerably by following many techniques such as chemical reduction, and thermal reduction. Therefore, keeping all these important factors and possible applications into mind, here in our objective is to synthesize eGO by using costeffective solution processing techniques. GO a very important candidate in memory devices, can be used as an active layer due to its magnificent interest in resistive switching random access memory devices [20–23].

36.2 Experimental Procedure Graphene oxide (GOs) was successfully prepared by using Hummer’s method [24]. Here, 0.22 g of graphite powder and 0.22 g of sodium nitrate were dissolved in 13 mL of sulfuric acid (H2 SO4 ). After 5 h of continuous stirring, 1.2 g of potassium permanganate was summed up to the mixture after maintaining the temperature around 12 °C. After heating the solution at 35 °C, 26 ml of DI H2 O was summed up to the mixture slowly. After overnight stirring, the solution was again heated at 95 °C

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and 56.32 ml of deionized water was summed up dropwise to the prepared solution. At last, 5.6 ml of hydrogen peroxide (H2 O2 ) was summed up to the prepared solution and the solution become yellow and solution was finally cleansed with 10% HCl solution and DI H2 O many times through centrifuged process. The brown precipitate was dried in the oven (hot air) at 65 °C for 12–13 h. GOs powder was thus obtained after grinding the graphene oxide sheet. Liquid phase exfoliation method has been followed for exfoliating the graphene oxide sheets. Powdered graphene oxide is then dissolved in DMF and then placed in a tip sonicator sonication at 40 W for 1 h. In this process, few-layered thick ~2.0 nm (figure not shown) GO can be obtained [25]. The synthesized graphene oxide (GO) powder was characterized through optical, structural, and morphological characterization using the sophisticated instruments such as UV–visible absorption spectroscope (Agilent Technologies, Model: Cary 60), Photoluminescence spectroscope (Fluoromax-4C Spectrofluorometer, HORIBA), X-ray diffractometer (XRD) (Panalytical), scanning electron microscope (Model: ZEISS EVO-MA 10).

36.3 Results and Discussions 36.3.1 Optical Analysis of (eGO) 36.3.1.1

UV–Visible Absorption Spectroscopy of (eGO)

UV–visible absorption spectroscopy of eGO is performed within the wavelength range of 200–800 nm that is shown in (Fig. 36.1a). An intense peak is observed at

Fig. 36.1 a UV–visible absorption spectroscopy, b Tauc’s plot of eGO

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232 nm and it is emanated because of π –π * transition within the sp2 -hybridized carbon atoms that are present in the aromatic C–C ring [26]. Also, a small peak is found at 302 nm and it is mainly because of the transition of n–π * in the energy levels of C=O bonds in carbonyl group [27]. The optical band gaps have been calculated using the Tauc’s plot method (Fig. 36.1b) and the corresponding values are estimated to be 3.34, 3.94, and 4.30 eV [25].

36.3.1.2

Photoluminescence Spectroscopy of eGO

PL spectroscopy of eGO is performed in the aqueous medium (DI water) with the excitation wavelength at λex ~ 232 and 302 nm, respectively. A broad emission peak is observed at around 445 nm for GOs which occur because of the transition from π *–n corresponding to the excitation wavelength at 302 nm [28]. At the excitation wavelength of 232 nm, no emission peaks are found. For locating the peak positions, deconvolution is performed for the PL spectra (Fig. 36.2a) [29]. The emission peaks are well fitted with six band centered at 371,407, 427,445, 446, and 468 nm and the corresponding energy bands are estimated to be 3.34, 3.04, 2.90, 2.79, 2.78, and 2.64 eV, respectively shown in (Fig. 36.2b).

Fig. 36.2 a Deconvoluted PL spectra of eGO b emission energy curve of eGO (inset shows the PL spectra of eGO without deconvolution)

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Graphene Oxide

Intensity (a.u.)

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Fig. 36.3 a X-ray diffraction analysis of GO and b SEM image of graphene oxide powder

36.3.2 Structural and Morphological Studies of GO Powder 36.3.2.1

XRD Studies of Graphene Oxide

XRD spectra (Fig. 36.3a) of the synthesized graphene oxide is carried out in the range 2θ ~ 5°–80°. An intense sharp peak is obtained at 2θ = 10.17°, that reflects from the (001) plane [30] of GO sheet. The (001) plane confirms the hexagonal characteristics of GO. The interplanar spacing has been estimated by the famous Bragg’s law, 2d sin θ = nλ. From the formula, it has been calculated as 0.87 nm [31]. A broad hump is seen at around 2θ = 25.69° which may be due to the disordered states present in graphene oxide. Another sharp peak is obtained at 2θ = 42.62° which assigns the reflection from (101) plane [32]. The thickness of the few-layered thick eGO is ~2.0 nm (figure not shown) which further suggests 2–3 number of sheets in the exfoliated structure.

36.3.2.2

Scanning Electron Microscopy (SEM) of Graphene Oxide

SEM of GO powder has been performed to analyze morphological structure of graphene oxide and the corresponding image is shown in (Fig. 36.3b) which display agglomerated structure covering the whole scanned area and crumpled GO sheets are found with irregular morphology throughout. Such type of irregular structure is associated with the multiple stacked sheets at different places. EDX analysis (figure not shown) confirms that the synthesized product mostly contains carbon and oxygen free from any impurities.

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36.4 Conclusion In summary, we have successfully synthesized exfoliated GO by Hummer’s method and liquid phase exfoliation technique, respectively. Optical energy gaps are estimated to be 3.34, 3.94, and 4.30 eV. Optical energy gap of 3.34 eV is found to be comparable with the emission band at the lower wavelength region (λ ~ 371 nm) obtained from the PL spectra. The interplanar distance between the layers of graphene oxide is estimated to be 0.87 nm. The thickness of the few-layered thick eGO is ~2.0 nm which further suggests 2–3 number of sheets in the exfoliated structure. Acknowledgements The authors are very grateful to CIF, National Institute of Technology, Silchar for giving the opportunity for the characterization of material (XRD analysis).

References 1. Shahriary, L., Athawale, A.A.: Graphene oxide synthesized by using modified Hummers approach. Int. J. Renew. Energy Environ. Eng. 02(01), 58–63 (2014) 2. Saxena, S., Tyson, T.A., Shukla, S., Negusse, E., Chen, H., et al.: Investigation of structural and electronic properties of graphene oxide. Appl. Phys. Lett. 99, 013104 (2011) 3. Mondal, K., Balasubramaniam, B., Gupta, A., Lahcen, A.A., Kwiatkowski M.: Carbon nanostructures for energy and sensing applications. J. Nanotechnol. 1–3 (2019) 4. Messaoud, N.B., Lahcen, A.A., Dridi, C., Amine, A.: Ultrasound assisted magnetic imprinted polymer combined sensor based on carbon black and gold nanoparticles for selective and sensitive electrochemical detection of bisphenol A. Sens. Actuators B: Chem. 276, 304–312 (2018) 5. Sa, K., Mahakul, P.C., Saha, S., Vishwakarma, P.N., Nanda, K.K., Mahanandia, P.: Investigation of electrical, mechanical, and thermal properties of functionalized multiwalled carbon nanotubes-reduced graphene oxide/PMMA hybrid nanocomposites. Polym. Eng. Sci. 59(5), 1075–1083 (2019) 6. Kwon, S., Lee, K.E., Lee, H., Koh, S.J., Ko, J.H., Kim, Y.H., Kim, S.O., Park, J.Y.: The effect of thickness and chemical reduction of graphene oxide on nanoscale friction. J. Phys. Chem. B. 122(2), 543–547 (2018) 7. Biswal, S., Bhaskaram, D.S., Govindaraj, G.: Graphene oxide: structure and temperature dependent magnetic characterization. Mater. Res. Express 5(8), 086104 (2018) 8. Feicht, P., Eigler, S.: Defects in graphene oxide as structural motifs. ChemNanoMat 4(3), 244–252 (2018) 9. Britnell, L., et al.: Field-effect tunneling transistor based on vertical graphene heterostructures. Science 335, 947–950 (2012) 10. Alam, S.N., Sharma, N., Kumar, L.: Synthesis of graphene oxide (GO) by modified Hummers method and its thermal reduction to obtain reduced graphene oxide (rGO). Graphene 6(1), 1–18 (2017) 11. Yu, H., Zhang, B., Bulin, C., Li, R., Xing, R.: High-efficient synthesis of graphene oxide based on improved Hummers method. Sci. Rep. 6(1) (2016) 12. Chen, J., Yao, B., Li, C., Shi, G.: An improved Hummers method for eco-friendly synthesis of graphene oxide. Carbon 64, 225–229 (2013) 13. Katsnelson, M.I.: Graphene: carbon in two dimensions. Mater. Today 10(1–2), 20–27 (2007) 14. Baby, T.T., Ramaprabhu, S.: Investigation of thermal and electrical conductivity of graphene based nanofluids. J. Appl. Phys. 108, 124308950 (2010)

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15. Luo, L., Peng, T., Yuan, M., Sun, H., Dai, S., Wang, L.: Preparation of graphite oxide containing different oxygen-containing functional groups and the study of ammonia gas sensitivity. Sensors 18(11), 3745 (2018) 16. Ma, Q., Zhu, X., Zhanga, D., Liu, S.F.: Graphene oxide—a surprisingly good nucleation seed and adhesion promotion agent for one-step ZnO lithography and optoelectronic applications. J. Mater. Chem. C 2(42), 8956–8961 (2014) 17. Du, D., Song, H., Nie, Y., Sun, X., Chen, L., Ouyang, J.: Blue photoluminescence from chemically derived graphene oxide. Adv. Mater. 22, 505–509 (2010) 18. Wilson, N.R., Pandey, P.A., Beanland, R., Young, R.J., Kinloch, I.A., Gong, L., Liu, Z., Suenaga, K., Rourke, J.P., York, S.J., Sloan, J.: Graphene oxide: structural analysis and application as a highly transparent support for electron microscopy. Am. Chem. Soc. 3(9), 2547–2556 (2009) 19. Blanton, T.N., Majumdar, D.: X-ray diffraction characterization of polymer intercalated graphite oxide. Powder Diffr. 27(2), 104–107 (2012) 20. Yin, Z., Zeng, Z., Liu, J., He, Q., Chen, P., Zhang, H.: Memory devices using a mixture of MoS2 and graphene oxide as the active layer. Small 9(5), 727–731 (2012) 21. Liu, T., Wu, W., Liao, K.-N., Sun, Q., Gong, X., Roy, V.A.L., Yu, Z.Z., Li, R.K.Y.: Fabrication of carboxymethyl cellulose and graphene oxide bio-nanocomposites for flexible nonvolatile resistive switching memory devices. Carbohyd. Polym. 214, 213–220 (2019) 22. Li, L.: Tunable memristic characteristics based on graphene oxide charge-trap memory. Micromachines 10, 151 (2019) 23. Pradhan, S.K., Xiao, B., Mishra, S., Killam, A., Pradhan, A.K.: Resistive switching behaviour of reduced graphene oxide memory cells for low power non-volatile device application. Sci. Rep. 6, 26763 (2016) 24. Gogoi, K.K., Chowdhury, A.: Highly stable write-once-read-many times switching behavior of graphene oxide-polymer nanocomposites. AIP Conf. Proc. 2142, 150028 (2019) 25. Gogoi, K.K., Chowdhury, A.: Electric field induced tunable memristive characteristics of exfoliated graphene oxide embedded polymer nanocomposites. J. Appl. Phys. 126, 025501 (2019) 26. Das, R.C., Gogoi, K.K., Das, N.S., Chowdhury, A.: Optimization of quantum yield of highly luminescent graphene oxide quantum dots and their application in resistive memory devices. Semicond. Sci. Technol. 34(12), 125016 (2019) 27. Lai, Q., Zhu, S., Luo, X., Zou, M., Huang, S.: Ultraviolet-visible spectroscopy of graphene oxides. AIP Adv. 2, 032146 (2012) 28. Gogoi, K.K., Das, N.S., Chowdhury, A.: Tuning of electrical hysteresis in PMMA/GOs/PMMA multi-stacked devices. Mater. Res. Express 6, 085108 (2019) 29. Chien, C.-T., Li, S.-S., Lai, W.-J., Yeh, Y.-C., Chen, H.-A., Chen, I.- S., Chen, L.-C., Chen, K.-H., Nemoto, T., Isoda, S., Chen, M., Fujita, T., Eda, G., Yamaguchi, H., Chhowalla, M., Chen, C.-W.: Tunable photoluminescence from graphene oxide. Angew. Chem. Int. Ed. 51(27), 6662–6666 (2012) 30. Gupta, R.K., Alahmed, Z.A., Yakuphanoglu, F.: Graphene oxide based low cost battery. Mater. Lett. 112, 75–77 (2013) 31. Venugopal, G., Krishnamoorthy, K., Mohan, R., Kim, S.-J.: An investigation of the electrical transport properties of graphene-oxide thin films. Mater. Chem. Phys. 132(1), 29–33 (2012) 32. Yang, H., Jiang, J., Zhou, W., Lai, L., Xi, L., Lam, Z., Shen, Y.M., Khezri, B., Yu, T.: Influences of graphene oxide support on the electrochemical performance of graphene oxide-MnO2 nanocomposites. Nanoscale Res. Lett. 6, 531 (2011)

Chapter 37

Deep Learning for Maize Crop Deficiency Detection Subodh Bansal and Anuj Kumar

Abstract The yield of maize (corn) suffers significant losses due to nutrient deficiencies. Their timely detection is an important task. For this, Machine Learning (ML) models from computer science can be applied. The traditional ML methods involve difficult task of extracting numerous minute features from hundreds of labelled images, by hand. This problem of conventional methods can be solved by the use of ‘transfer learning’ approach. In transfer learning, the learned features from a pre-trained Deep Convolutional Neural Network (CNN) are carried to a new, comparatively small image dataset. The study thus aimed to evaluate and compare three state-of-the-art CNN models for maize deficiency detection, using transfer learning. The pre-training of the CNN models was performed on the Plant Village dataset. Then the models were fine-tuned on a self captured maize deficiency dataset, collected from the fields of S.A.S. Nagar, Punjab (India). Using data augmentation and transfer learning, the experiment shows that DCNNs can be trained, using a few labelled images. The best results were obtained by ZFNet with an accuracy score of 97%, Mean Reciprocal Rank of 99% and Mean Average precision of 98%. The implemented CNNs are reasonable for real-time applications with the classification time less than 1 s per image.

37.1 Introduction Maize (corn) is a widely used crop in many food and non-food industries. Its use ranges from fodder for livestock to the production of bio-fuel, used in many chemical industries. The demand for this crop increases every year. To meet this demand, the farmer tries to increase its production by sowing more seeds every year. Still, farmers are unable to meet the increasing demand. The main reason behind this is S. Bansal (B) University Institute of Engineering and Technology, Panjab University, Chandigarh, India e-mail: [email protected] A. Kumar Department of Computer Science and Applications, Panjab University, Chandigarh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_37

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the damage caused to the crop due to nutrient deficiencies. Farmers provide supplementary nutrients by spraying fertilizers in their fields. The damage caused to the harvest due to deficiencies can only be mitigated by correctly identifying the type of deficiency and applying appropriate fertilizer. A system that can assist the farmers in early nutrient deficiency detection will be highly valuable. So, the present study is an attempt in the same direction [1, 2].

37.1.1 Maize (Corn) Deficiencies The maize crop mostly suffers from the deficiency of Magnesium, Nitrogen, Phosphorous and Potassium. The early and later symptoms of these nutrient deficiencies are described further. Magnesium deficiency (shown in Fig. 37.1a). Symptoms: Early-stage: Interveinal chlorosis on old leaves. Later-stage: Red and purple tints on the chlorotic leaves, development of necrotic edges and Interveinal bleeding [2]. Nitrogen deficiency (shown in Fig. 37.1b). Symptoms: Early-stage: The lower, older leaves develop a v-shaped yellow patch that starts from the tip and advances towards midrib. Later-stage: The patch grows in size and spreads towards the upper younger leaves [2, 3]. Phosphorous deficiency (shown in Fig. 37.1c). Symptoms: Early-stage: Edges of the lower, older leaves become purplish. Later-stage: The purpleness becomes more prominent and advances towards inner portions of the leaf [2]. Potassium deficiency (shown in Fig. 37.1d). Symptoms: Early-stage: Symptoms start with mild chlorosis of the inner veins (not the main veins), leaf margins and tips of older, lower leaves. Later-stage: Necrosis of the chlorotic patches, starting from edges towards the midrib of the leaf. More affected leaves start to curl and crinkle, finally collapsing altogether [2].

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Fig. 37.1 Deficiencies of maize (corn) crop a magnesium deficiency, b nitrogen deficiency, c phosphorous deficiency, d potassium deficiency

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37.1.2 Deep Learning for Image Classification Deep learning is a subset of artificial intelligence and machine learning. It uses multilayered Artificial Neural Networks (ANNs) to achieve a high level of accuracy in tasks like image classification [4]. Deep learning models auto extracts ample features from the sample data and auto shortlist the most effective features for accurate image classification. ANN is an information processing system that simulates the neural connections in the human brain [5]. Convolution Neural Network (CNN) is a type of ANN used for image classification, is discussed further. Convolutional Neural Network (CNN) is a class of deep, feed-forward ANNs, having one or more convolutional layers with fully connected layers on top are called Convolutional Neural Networks [6]. Its main layers are Convolutional layer [7], Pooling layer [8], ReLU layer [9], Fully connected layer [10] and the Loss layer [11]. These layers are customized and stacked, one over the other to maximize the efficiency, throughput and accuracy at the output layer. These models are further optimized by using various control criteria, known as hyper-parameters. The hyperparameters used in the present experimentation are learning rate [12], loss function [13], mini-batch size [14], number of training iterations [15] and momentum [16]. Many state-of-the-art deep learning models are developed using CNNs, for image classification. CNNs used in the present experimentation are AlexNet [17], ZFNet [18] and LeafNet [19]. AlexNet deep learning CNN model consists of five convolutional and three fullyconnected layers. The network is distributed across two Graphical Processing Units (GPUs). Overlapping, pooling and local response were used in this model for the first time. It effectively eliminated the sparse connections between two Convolutional layers. The network has 60 million parameters. The dropout layer is used between fully connected layers to prevent the model from overfitting on the training data. This layer roughly doubles the number of iterations required to converge [17]. ZFNet deep learning CNN model was developed by Matthew D. Zeiler and Rob Fergus. The model consists of five shareable convolutional, max-pooling, dropout, and three fully-connected layers. It uses a 7 × 7 filter and a decreased stride value in the first layer. Softmax is the last layer of this model. Retraining of the softmax layer did wonders for the network and helped the developer in winning the 2nd position at the ILSVRC 2013, for performing better than the previous year’s winner, AlexNet [18]. LeafNet deep learning model uses the concept of dimension reduction modules in its working [20]. In this architecture, these modules are made of 2 convolutional layers followed by a max-pooling layer with a 2 × 2 filter. The architecture starts with five of these modules, followed by a convolution layer with a max-pooling layer

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of filter shape 2 × 2 and ends with three fully-connected layers. The modules make the model deeper, yet computationally feasible [19]. Transfer learning is the reuse of a pre-trained model on a new problem. The technique is gaining popularity in the field of deep learning because it enables to train Deep Neural Networks on a small dataset. This feature makes it useful in solving realworld problems by training the model on limited data. The researcher was greatly benefited by this technique in the present experimentation. In this technique, the weights and biases developed from the previous training are used as the default weights and biases for further training. This pre-optimization of the model (even for a different scenario) brings better and faster convergence in comparison to the same deep learning model trained from scratch [21]. In this work, various Deep Convolutional Neural Network (DCNN) architectures were trained and tested for the detection of deficiencies in the maize crop. In Sect. 37.2, the datasets used for the experiment are illustrated, followed by the methodology used, in Sect. 37.3. The results and conclusions are discussed in Sects. 37.4 and 37.5, respectively.

37.2 Datasets A dataset of maize deficiency is developed by capturing images from the fields of district S.A.S. Nagar of Punjab (India) in August and September 2019. These images captured are cropped and resized to 256 × 256 pixels. The images are filtered and annotated using the expertise of Dr. Harleen Kaur, Assistant Plant Pathologist (Maize), Punjab Agricultural University, Ludhiana. The configuration of the data is illustrated in Table 37.1. To increase the efficiency of the model, the model is trained on a comprehensive dataset, containing images of both healthy and diseased plant leaves. Plant Village [22] dataset best suits this purpose as it consists of 54,305 annotated leaf images, covering both healthy and infected plants (Apple, Blueberry, Cherry, Maize, Grape, Orange, Peach, Bell Pepper, Potato, Raspberry, Soybean, Squash, Strawberry and Tomato). The dataset is divided into 38 classes. Table 37.1 Maize deficiency dataset

Class ID

Deficiency

Condition

No. of images

0

Magnesium

Deficient

5

1

Nitrogen

Deficient

5

2

Phosphorous

Deficient

5

3

Potassium

Deficient

5

4

–

Healthy

5

37 Deep Learning for Maize Crop Deficiency Detection Table 37.2 Neural network input image size

477

Neural network

Input image size

AlexNet [17]

227 × 227 × 3

ZFNet [18]

225 × 225 × 3

LeafNet [19]

256 × 256 × 3

37.3 Experimentation The DCNN learning strategies, learning from scratch and transfer learning are explored to develop a trained and optimised model for image-based maize disease detection, to obtain maximum accuracy. The three state-of-the-art neural networks used in this experiment are AlexNet [17], ZFNet [18] and LeafNet [19]. The DNNs built using Caffe framework are trained using NVIDIA GTX 1080Ti GPU. It is programmed in python language using PyCharm IDE on Ubuntu 18.04 Operating System. The experimentation consists of the steps, described in the following sub-sections.

37.3.1 Normalization Normalization is the first step of the experiment. In this step, the images are resized and cropped according to the input image size required by the neural network. These input sizes are enlisted in Table 37.2.

37.3.2 Augmentation The original images in the dataset often lack variation of scenarios. For example, all the images of the dataset may have leaves facing in one direction, all images may be captured in bright daylight, etc. The classifier trained on such a dataset may over-fit on these scenarios. Such a classifier may fail to recognize images of the same plant with similar conditions, having variation in background, season, time of day or orientation of the leaf or the disease. Augmentation helps to train a DCNN model on multiple scenarios using a limited dataset. This process increases the number and variety of images using image transformation functions. The augmentation strategies used in the experiment are described in Table 37.3. This process increased the size of the dataset from 25 to 500 images.

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Table 37.3 Dataset image augmentation strategy S. No.

Transformation

Description

1

Rotate 0

Original Image (OI)

2

Rotate 90

OI rotated by 90°

3

Rotate 180

OI rotated by 180°

4

Rotate 270

OI rotated by 270°

5

Rotate 0 FH

OI flipped horizontally

6

Rotate 90 FH

OI rotated by 90° and flipped horizontally

7

Rotate 180 FH

OI rotated by 180° and flipped horizontally

8

Rotate 270 FH

OI rotated by 270° and flipped horizontally

9

Rotate 0 FV

OI flipped vertically

10

Rotate 90 FV

OI rotated by 90° and flipped vertically

11

Rotate 180 FV

OI rotated by 180° and flipped vertically

12

Rotate 270 FV

OI rotated by 270° and flipped vertically

13

Rotate 0 D

OI and illuminated to 80% of the original

14

Rotate 90 Dr

OI rotated by 90° and illuminated to 80% of the original

15

Rotate 180 Dr

OI rotated by 180° and illuminated to 80% of the original

16

Rotate 270 Dr

OI rotated by 270° and illuminated to 80% of the original

17

Rotate 0 Br

OI illuminated to 125% of the original

18

Rotate 90 Br

OI rotated by 90° and illuminated to 125% of the original

19

Rotate 180 Br

OI rotated by 180° and illuminated to 125% of the original

20

Rotate 270 Br

OI rotated by 270° and illuminated to 125% of the original

37.3.3 Split The augmented dataset is split using 80:20 strategy for training (400 images) and testing (100 images) purposes, respectively. The images are randomly chosen for training and testing subsets.

37.3.4 Base Learning The DCNN models trained for the experiment are AlexNet, ZFNet and LeafNet. The last layer of the model is replaced with a fully-connected layer having five neurons (equal to the number of classes in the maize deficiency dataset). The hyperparameters given in Table 37.4 are used in the experiment. These hyperparameters are optimized using the random search algorithm, to obtain the best results from the DCNN.

37 Deep Learning for Maize Crop Deficiency Detection Table 37.4 Model hyper parameters

479

Parameter

Value

Epoch size

400

Epochs

20

Batch size

20

Batches/Epoch

20

Total batches

400

Momentum (μ)

5.0 × 10−1

Gamma (ϒ)

5.0 × 10−1

Weight decay

2 × 10−4

Base learning rate (α)

1.0 × 10−2

Learning rate decrease policy

Step

Step size

40

Final learning rate (α)

9.8 × 10−6

37.3.5 Transfer Learning For the transfer learning approach, the pre-training of the model is done on the Plant Village dataset. Then, the trained models are fine-tuned on the actual maize deficiency dataset using the same hyper-parameters as used in the base-learning. The base learning rate (α) was set to 5.0 × 10−3 instead of 1.0 × 10−2 to fully utilize the potential of the pre-trained and to avoid over over-fitting to the maize disease dataset images. The results obtained from the experiment are discussed in the following section.

37.4 Results and Discussion For validation of the results, fivefold cross-validation technique is used. Three performance metrics are used for evaluating the trained models. Average recognition accuracy (ACC) is the number of correctly recognized images divided by the total number of images in the testing dataset. In the Eq. 37.1, m is the total number of images in the test dataset and i is image index. The value of x i = yi is one if the image is correctly recognized, else zero. O1 =

m 1  (xi = y) m i=1

(37.1)

Mean Reciprocal Rank (MRR) is the average score of the classification task. It gives the average of the reciprocals of the ranks achieved by the correct prediction against every image in the dataset. In Eq. 37.2, m is the total number of images in

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the test dataset, i is image index and ranki denotes the rank of the actual prediction class in the neural network output prediction matrix. MRR =

m 1  1 m i=1 ranki

(37.2)

Mean Average Precision (MAP) is the mean of all the average precisions (AP), of all the classes in the test dataset. Its formula used in the experimentation is given in Eq. 37.3 [23]. Avg Pr =

#P 1  Pr(k).I ( f (xi , W ) = yi ) #T P k=1

(37.3)

37.4.1 Results of Learning from Scratch The results obtained from training from scratch and testing DCNN models are shown in Table 37.5. The values of ACC, MRR and MAP are presented in percentage, rounded off to whole numbers. These results are plotted in Fig. 37.2 for visual understanding. From the above results, it is evident that all the used models failed to give good performances without pre-training. The main reason for the poor performance is the lack of training samples. The maximum accuracies achieved by the models are 81% by AlexNet at 14th Epoch, 24% by ZFNet at 13th Epoch, and 58% by LeafNet at 12th Epoch. The results are practically unusable in any real scenarios. Table 37.5 Results of training from scratch Epochs

AlexNet

ZFNet

LeafNet

ACC

MRR

MAP

ACC

MRR

MAP

ACC

MRR

MAP

11

80

90

85

32

56

41

36

57

43

12

80

90

85

32

57

42

37

58

45

13

79

89

84

34

57

43

37

58

45

14

81

90

85

34

57

43

36

57

43

15

81

90

85

34

57

43

36

57

44

16

80

90

85

33

57

43

37

57

44

17

81

90

85

33

57

43

37

58

45

18

80

90

85

33

57

43

37

58

45

19

80

90

85

33

57

43

37

58

45

20

80

90

85

33

57

43

37

58

45

37 Deep Learning for Maize Crop Deficiency Detection

481

Accuracy

90 70 50

AlexNet

30

ZFNet LeafNet

10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Epochs Fig. 37.2 Results of learning from scratch

37.4.2 Results of Transfer Learning Approach The results obtained for the transfer learning approach are displayed in Table 37.6. The values of ACC, MRR and MAP are presented in percentage and are rounded off to whole numbers. The results obtained are plotted in Fig. 37.3. The accuracies achieved by AlexNet, ZFNet and LeafNet models using the transfer learning approach are 89%, 97% and 78% at 3rd, 2nd and 3rd epoch respectively. LeafNet fails to give practically useable results with an accuracy of 78%, whereas ZFNet gives the highest accuracy while detecting the maize deficiencies from the dataset. The MRR and MAP scores of ZFNet model are 99% and 97%, respectively. It shows that, in all the three incorrect classifications, the correct class ranked second, Table 37.6 Results of transfer learning Epochs

AlexNet

ZFNet

LeafNet

MRR

MAP

ACC

MRR

MAP

ACC

MRR

MAP

1

72

82

76

90

95

92

62

77

69

2

85

91

88

97

98

97

75

85

80

3

89

94

91

97

99

98

78

87

82

4

89

94

91

97

99

98

78

87

82

5

89

94

91

97

99

98

77

87

82

Accuracy

ACC

90

AlexNet

70

ZFNet

50

LeafNet 1

2

3 Epochs

Fig. 37.3 Results of transfer learning approach

4

5

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every time in the prediction matrix. Hence, the experimentation concludes that the trained model works very effectively to detect the correct deficiency in the maize crop.

37.5 Conclusion The study produces some significant results. From these results, it can be concluded that pre-training of a model for maize deficiency detection is very important, especially while working on small datasets. It was observed that the maximum accuracy giving model (amongst the three used in the study), ZFNet also failed to produce valid results without pre-training. Hence, the model produced in the experiment works effectively to detect deficiency in the maize crop and can be used in the related field to get reliable and valid results.

References 1. Shukla, G.N., et al.: Maize Vision 2022 A Knowledge Report (2013) 2. Jeffers, D., International Maize and Wheat Improvement Center.: Maize Diseases: A Guide for Field Identification. International Maize and Wheat Improvement Center (CIMMYT) (2004) 3. Sawyer, J.: Nutrient Deficiencies and Application Injuries in Field Crops: Nitrogen Deficiency in Corn (2004) 4. Pan, Y.: Heading toward artificial intelligence 2.0. Engineering 2(4), 409–413 (2016). https:// doi.org/10.1016/J.ENG.2016.04.018 5. Ramachandran, R., Rajeev, D.C., Krishnan, S.G., Subathra, P.: Deep learning in neural networks: an overview. Neural Networks 61, 85–117 (2015). https://doi.org/10.1016/j.neunet. 2014.09.003 6. Steve Lawrence, A.D.B., Lee Giles, C., Tsoi, A.C.: Face recognition: a convolutional neuralnetwork approach. IEEE Trans. Neural Networks 8(1), 98–113 (1997). https://doi.org/10.1016/ j.gene.2017.06.018 7. Zhu, X., Zhu, M., Ren, H.: Method of plant leaf recognition based on improved deep convolutional neural network. Cogn. Syst. Res. 52, 223–233 (2018). https://doi.org/10.1016/j.cogsys. 2018.06.008 8. Paoletti, M.E., Haut, J.M., Plaza, J., Plaza, A.: A new deep convolutional neural network for fast hyperspectral image classification. ISPRS J. Photogramm. Remote Sens. 145, 120–147 (2018). https://doi.org/10.1016/j.isprsjprs.2017.11.021 9. Agarap, A.F.: Deep learning using rectified linear units (ReLU). In: CoRR, vol. abs/1803.0 (2018). https://doi.org/10.1249/01.mss.0000031317.33690.78 10. Jonathan Long, T.D., Shelhamer, E.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015). https://doi.org/10.1109/CVPR.2015.7298965 11. Gao, B., Pavel, L.: On the properties of the Softmax function with application in game theory and reinforcement learning (2017). arXiv Prepr. arXiv1704.00805, [Online]. Available: http:// arxiv.org/abs/1704.00805 12. Nitish Srivastava, R.S., Hinton, G., Krizhevsky, A., Sutskever, I.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014). https:// doi.org/10.1016/0370-2693(93)90272-j

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13. Baldi, P., Sadowski, P., Lu, Z.: Learning in the machine: random backpropagation and the deep learning channel. Artif. Intell. 260(March), 1–35 (2018). https://doi.org/10.1016/j.artint.2018. 03.003 14. Tetko, I.V., Livingstone, D.J., Luik, A.I.: Neural network studies. 1. Comparison of overfitting and overtraining. In: Information Computation Science, pp. 826–833 (1995) 15. Istook, E., Martinez, T.: Improved backpropagation learning in neural networks with. Int. J. Neural Syst. 12(3), 303–318 (2002). https://doi.org/10.1142/S0129065702001114 16. Ilya Sutskever, G.H., Martens, J., Dahl, G.: On the importance of initialization and momentum in deep learning. In: Proceedings of the 30th International Conference on Machine Learning, Atlanta, Georgia, USA, pp. 24–32, May 2013. https://doi.org/10.1017/cbo9781316423936 17. Krizhevsky, B.A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 2017 (2012) 18. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: CoRR, vol. abs/1311.2, p. 2013 (2012) 19. Barré, P., Stöver, B.C., Müller, K.F., Steinhage, V.: LeafNet: a computer vision system for automatic plant species identification. Ecol. Inform. 40(December 2016), 50–56 (2017). https:// doi.org/10.1016/j.ecoinf.2017.05.005 20. Karen Simonyan, A.Z.: Very deep convolutional networks for large-scale image recognition. In: CoRR, vol. abs/1409.1, pp. 1–14 (2015) 21. Donges, N.: Transfer Learning | Experfy Insights (2018). https://www.experfy.com/blog/tra nsfer-learning. Accessed 29 May 2020 22. Hughes, D.P., Salathé, M., Salathe, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics. In: CoRR, vol. abs/1511.0 (2015). https://doi.org/10.1111/1755-0998.12237 23. Goëau, H., et al.: Plant identification in an open-world (LifeCLEF 2016). In: CLEF 2016— Conference and Labs of the Evaluation forum, Sep 2016, no. LifeCLEF, pp. 428–439, [Online]. Available: https://hal.archives-ouvertes.fr/hal-01373780

Chapter 38

Improvement in Fault Clearance Time of the Cascaded H-Bridge Multilevel Inverter Using Novel Technique Based on Frequency Detection Hillol Phukan and Jiwanjot Singh Abstract In this paper, a novel frequency detection technique has been proposed for five-level cascaded H-bridge multilevel inverter (CHBI) to improve the fault clearance time. The open circuit and short circuit fault of semiconductor switches have been diagnosed and cleared by measuring the PWM frequency of different levels of output voltage. The variable frequency sinusoidal frequency pulse width modulation (VFSPWM) has been adopted to generate a different frequency of each level in the output voltage. The simulation of the VFSPWM technique for CHBI, frequency detection technique and the effect of open circuit and short circuit switches has been performed using MATLAB/Simulink. Further, the fault clearance time has been analyzed by incorporating the open circuit and short circuit fault. The fault clearance time has been compared with that of existing results presented in the previous research papers.

38.1 Introduction Multilevel inverters (MLI) have various applications to improve the power quality in renewable energy systems, i.e., solar energy, wind energy and power quality improvement systems used in power system. The power quality is improved by increasing the number of levels or by reducing the total harmonic distortion (THD) in the output voltage of the MLI [1, 2]. Among the classic topologies of MLI, cascaded H-bridge multilevel inverter (CHBI) uses less semiconductor devices, low electromagnetic interferences and modular structure with compared to the other MLI [3]. More-

H. Phukan · J. Singh (B) National Institute of Technology, Silchar, Assam, India e-mail: [email protected] URL: http://www.nits.ac.in/departments/electrical/electrical.php H. Phukan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_38

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over, the semiconductor switches gate driver circuit of MLI also increases with the improvement in power quality; therefore, the probabilities of faults become more [4]. For the reliable operation of multilevel inverter, fast clearance time of the faults of the semiconductor switches is essential. Basically, two types of faults, open circuit and short circuit in semiconductor switches have been analyzed by researchers [5]. To identify the faults, three types of methods in literature have been reported as measurement of output voltage, output current or voltage of each switching device of multilevel inverter [6–8]. Many techniques have been given in literature to diagnosis the faults. Unlike other methods, output voltage measurement method has advantages due to no effect of the load condition. Using this method, there are various fault diagnosis techniques have been implemented. Neural network techniques (NN) have been used for CHBI to clear the faults by measurement of the FFT of the output voltage. The disadvantage of this technique is that the number of layer or structure of NN increases as the number of semiconductor switches increases of CHBI [9–11]. Therefore, to reduce the layers, the principal component analysis (PCA) has been implemented [12]. Further, to use the optimized value for PCA, genetic algorithm has been used [13]. The various NN and PCA-based techniques have been discussed in literature to detect the different types of faults [14, 15]. Based on the measurement of the output voltage, wavelet transform technique and the Park’s vector technique have been reported to clear the different types of faults [16–18]. It has been reported that artificial intelligence (AI) techniques based on neural network (NN) and spectral analysis method requires high computational cost and the fault diagnosis and fault clearance speed is slow [19]. The waveform analysis methods have more speed and the computational cost is less as compared to AI and spectral analysis technique [19]. To overcome these issues, a simple frequency detection technique has been proposed in this paper. The frequency detection technique does not require number of layers as in NN structure to clear the faults of MLI. Therefore, the computational cost decreases and the fault clearance speed become fast. Basically, this proposed technique measures the frequency of the each level of the output voltage and locate the faulty switch. The frequency detection technique has been proposed for five-level CHBI to improve the fault clearance time of the open circuit and short circuit faults in semiconductor switches. In Sect. 38.2, the structure of the proposed frequency detection has been explained for five-level CHBI. In Sect. 38.2.1, VFSPWM scheme has been explained for five-level CHBI. In Sect. 38.2.2, level differentiator of output voltage level with simulation results has been discussed. In Sect. 38.2.3, frequency measurement system has been explained with fault performance index. In Sect. 38.2.4, fault diagnosis system has been explained with the help of lookup table, in Sect. 38.3, simulation results have been depicted. Section 38.4 concludes the paper.

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38.2 Structure of the Proposed Technique for the CHBI Figure 38.1 shows the five-level CHBI with the structure of proposed frequency detection technique. It has been shown that five-level CHBI has open circuit fault in semiconductor switch S11. To clear the open circuit and short circuit fault, the proposed technique has been divided into different parts as following: 1. 2. 3. 4.

Variable frequency SPWM Level differentiator of output voltage of CHBI Frequency measurement system of output voltage level Fault diagnosis system.

Each part of the structure of frequency detection technique has been explained in the next section.

38.2.1 Variable Frequency SPWM In the fault diagnosis system, it is required to have different frequency of each level in the output voltage of CHBI. The VFSPWM technique has been adopted to generate the different frequency voltage level of CHBI. In VFSPWM, the different frequency

Fig. 38.1 Five-level CHBI with structure of proposed frequency detection technique

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Fig. 38.2 Variable frequency SPWM for five-level CHBI obtained by simulation (modulation index = 0.8)

of triangular wave or carrier wave has been compared with sinusoidal reference wave has shown in Fig. 38.2. To generate the five-level voltage, the four carrier waves are required. The modulation index is selected as 0.8. In this paper, the frequency of carrier wave has been selected as C1 = 1000 Hz, C2 = 1500 Hz, C3 = 2500 Hz, C4 = 5000 Hz and the frequency of the reference wave is 50 Hz.

38.2.2 Level Differentiator of Output Voltage Levels In the level differentiator, the input of the level differentiator has been taken as output voltage of five-level CHBI. The output voltage of level differentiator has four outputs f1, f2, f3 and f4; because, the numbers of levels in the output voltage of CHBI are four excluding zero level. The main aim of the level differentiator is to separate the levels of output voltage, and it does not measures the frequency. The number of outputs of level differentiator increases as the number of levels in the output voltage of CHBI increases. The correct action is required for level differentiator; otherwise, correct fault will not be diagnosed. Figure 38.3 shows the five-level input voltage of the differentiator and the output of the level differentiator f1 to f4.

Fig. 38.3 Simulation results of five-level input voltage and the output of level differentiator (f1 to f4) of five levels under normal conditions

38 Improvement in Fault Clearance Time of the Cascaded …

489

Table 38.1 Frequency measurement of five-level CHBI and FPI when open circuit fault occurs label Open switch f1 f2 f3 f4 F of CHBI S11 S12 S13 S14 S21 S22 S23 S24

1500 0 1500 0 1500 0 1500 0

350 1500 350 1500 1500 350 350 1500

700 200 700 200 700 200 200 700

0 700 0 700 0 0 0 700

2550 2400 2550 2400 3700 2050 2050 2900

38.2.3 Frequency Measurement System Frequency measurement system has four inputs and one output. The main aim of frequency system is to convert the frequency into an integer value. Frequency measurement system consists of counter to measure the rising edge and falling edge of the frequency of each level. The rising edge measured for the f1 and f2 inputs and falling edge measured for the f3 and f4. Thus, f1, f2, f3 and f4 are added by summer and the fault performance index is defined in (1). Fault performance index (FPI), F = f1 + f2 + f3 + f4 Tables 38.1 and 38.2 show the measurement of frequency of four levels of the output voltage when open circuit fault and short circuit occurs in each semiconductor switch. Here, open circuit and short circuit are denoted by using subscript of OC and SC.

38.2.4 Fault Diagnosis System To diagnosis the open circuit and short circuit fault occurring in semiconductor switches, the value of FPI is required to be different in each case. Table 38.1 and Table 38.2 show the frequency of f1, f2, f3 and f4 and fault performance index, F when open circuit and short circuit fault occurs in different semiconductor switches. It can be seen from Tables 38.1 and 38.2, F is different for some cases. For such cases, Tables 38.1 and 38.2 have been used as lookup table. When fault occurs, the value of F is compared with lookup table of fault performance index as shown in Fig. 7 and the faulty switch has been diagnosed. Suppose open circuit fault occurs in semiconductor switch S21. The lookup table has fault performance index values, F. The input value of F = 3700 compares with lookup table and the output of lookup table for S11OC will be high. For the other switches, F has different values. According to F, open circuit and short circuit fault in semiconductor switch can be diagnosed.

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Table 38.2 Frequency measurement of five-level CHBI and FPI when short circuit fault occurs Short switch f1 f2 f3 f4 F of CHBI S11 S12 S13 S14 S21 S22 S23 S24

1500 0 0 1500 0 350 0 1500

250 1500 1500 350 350 250 1500 350

700 200 400 700 200 700 400 200

0 700 700 0 700 0 200 0

2450 2400 2600 2550 1250 1300 2100 2050

38.3 Simulation Results The simulation results for five-level cascaded H-bridge have been performed using VFSPWM. The simulation results have been performed using MATLAB/Simulink software. The output frequency of CHBI has been 50 Hz and R-L (R = 100, L = 100 mH) load has been connected. The two DC sources E1 = E2 = 100 V are connected in the input. Further, to check the fault clearance time of the proposed frequency detection technique, two test conditions have been analyzed in the circuit, i.e., open circuit and short circuit. To verify fault clearance time for the open circuit, the fault is assumed in the switch S21 as shown in Fig. 38.4a. A normally closed programmable circuit breaker (CB) is closed and opened calculatedly using simulation connected in series with the S21. In this, a preselected fault has been incorporated by open the CB. After diagnosis the fault, the frequency detection technique gives the step signal to close the circuit breaker to clear the fault. The total time between open and close the circuit breaker decides the fault clearance time of the proposed technique. Accordingly, it is depicted from Fig. 38.4b that from time 0 to 0.04 s, there is no fault; purposely, the fault is included at 0.04 s by open the programmable circuit breaker using simulation is shown in Fig. 38.4b. It is cleared from Fig. 38.4b that the output voltage and current are reduced under the fault condition after 0.04 s. At time 0.05 s, the fault is diagnosed and cleared by the proposed technique, the total fault interval is 0.01 s. Interestingly, the proposed technique takes 0.01 s to clear the open circuit fault. Figure 38.5a shows the circuit required to obtain the short circuit fault clearance time of switch S21, where the occurrence of short circuit fault is implemented by connecting normally open programmable CB parallel to the switch S21. The effect on the load current and voltage of this fault is investigated by Fig. 38.5b. Obviously, to create the fault, CB is opened at 0.04 s, which reduced the output current and voltage. Interestingly, the proposed technique clears the fault at 0.06 s and takes only one cycle.

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Fig. 38.4 Simulation circuit and results of open circuit fault. a Circuit of the faulty CHBI with programmable circuit breaker. b Output voltage and current waveform under different conditions

Fig. 38.5 Simulation circuit and results of open circuit fault. a Circuit of the faulty CHBI with programmable circuit breaker. b Output voltage and current waveform under different conditions

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Table 38.3 Comparison with that of existing techniques to clear the open and short circuit fault Reference Open circuit fault clearance Short circuit fault clearance time time 11 Proposed technique

Six-cycle at 60 hz 0.01 s (half cycle) at 50 Hz

Six-cycle at 60 hz 0.02 s (one cycle) at 50 Hz

It can be depicted from Table 38.3 that existing technique takes six cycles to clear the fault for open circuit and short circuit, whereas the proposed technique clears just in 0.01 s (half cycle) and 0.02 s (one cycle), respectively.

38.4 Conclusion In this paper, a novel frequency detection technique improves the fault clearance time on the CHBI by incorporating open circuit and short circuit faults. The structure of the proposed technique has been explained with suitable circuit diagrams. The simulation results of output voltage using MATLAB/Simulink have been obtained for five-level CHBI when open circuit and short circuit fault occurs. It is observed from the simulation results that the proposed technique clears the open circuit fault in 0.01 s, whereas the total time required to clear the short circuit fault is 0.02 s. From the comparative study of existing fault diagnosis technique, it is observed that the proposed technique has fast speed to clear the open and short circuit fault.

References 1. Colak, I., Kabalci, E., Bayindir, R.: Review of multilevel voltage source inverter topologies and control schemes. Energ. Convers. Manage. 52(2), 1114–1128 (2011) 2. Singh, J., Dahiya, R., Saini, L.M.: Recent research on transformer based single dc source multilevel inverter: a review. Renew. Sustain. Energ. Rev. 82, 3207–3224 (2018) 3. Singh, J., Dahiya, R., Saini, L.M.: Buck converter-based cascaded asymmetrical multilevel inverter with reduced components. Int. Trans. Electr. Energ. Syst. 28(3), e2501 (2018) 4. Anand, A., Raj, N., George, S., Jagadanand, G.: Open switch fault detection in cascaded hbridge multilevel inverter using normalised mean voltages. In: 2016 IEEE 6th International Conference on Power Systems (ICPS), pp. 1–6. IEEE (2016) 5. Lu, B., Sharma, S.K.: A literature review of igbt fault diagnostic and protection methods for power inverters. IEEE Trans. Ind. Appl. 45(5), 1770–1777 (2009) 6. Kim, S.M., Lee, J.S., Lee, K.B.: A modified level-shifted pwm strategy for fault-tolerant cascaded multilevel inverters with improved power distribution. IEEE Trans. Ind. Electron. 63(11), 7264–7274 (2016) 7. Raj, N., George, S., Jagadanand, G.: Open transistor fault detection in asymmetric multilevel inverter. In: 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), pp. 1–5. IEEE (2015)

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8. Manjunath, T., Kusagur, A.: Performance evaluation of modified genetic algorithm over genetic algorithm implementation on fault diagnosis of cascaded multilevel inverter. In: 2015 International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), pp. 51–56. IEEE (2015) 9. Khomfoi, S., Tolbert, L.M.: Fault diagnosis system for a multilevel inverter using a principal component neural network. In: 2006 37th IEEE Power Electronics Specialists Conference, pp. 1–7. IEEE (2006) 10. Xu, B., Yang, D., Wang, X.: Neural network based fault diagnosis and reconfiguration method for multilevel inverter. In: 2008 Chinese Control and Decision Conference, pp. 564–568. IEEE (2008) 11. Khomfoi, S., Tolbert, L.M.: Fault diagnosis and reconfiguration for multilevel inverter drive using ai-based techniques. IEEE Trans. Ind. Electron. 54(6), 2954–2968 (2007) 12. Khomfoi, S., Tolbert, L.M.: A diagnostic technique for multilevel inverters based on a geneticalgorithm to select a principal component neural network. In: APEC 07-Twenty-Second Annual IEEE Applied Power Electronics Conference and Exposition, pp. 1497–1503. IEEE (2007) 13. Hao, X., Jian, Z., Jie, Q., Tianzhen, W., Jingang, H.: Rpca-svm fault diagnosis strategy of cascaded h-bridge multilevel inverters. In: 2014 First International Conference on Green Energy ICGE 2014, pp. 164–169. IEEE (2014) 14. Hao, X., Tianzhen, W., Tianhao, T., Benbouzid, M.E.H.: A pca-mrvm fault diagnosis strategy and its application in chmlis. In: IECON 2014-40th Annual Conference of the IEEE Industrial Electronics Society, pp. 1124–1130. IEEE (2014) 15. Wang, T., Xu, H., Han, J., Elbouchikhi, E., Benbouzid, M.E.H.: Cascaded h-bridge multilevel inverter system fault diagnosis using a pca and multiclass relevance vector machine approach. IEEE Trans. Power Electron. 30(12), 7006–7018 (2015) 16. Babu, B.P., Srinivas, J., Vikranth, B., Premchnad, P.: Fault diagnosis in multi-level inverter system using adaptive back propagation neural network. In: 2008 Annual IEEE India Conference, vol. 2, pp. 494–498. IEEE (2008) 17. Vinothkumar, V., Muniraj, C.: Fault diagnosis in diode clamped multilevel inverter drive using wavelet transforms. In: 2013 International Conference on Green High Performance Computing (ICGHPC), pp. 1–6. IEEE (2013) 18. Ventura, R.P., Mendes, A.M., Cardoso, A.M.: Fault detection in multilevel cascaded inverter using park’s vector approach with balanced battery power usage. In: Proceedings of the 2011 14th European Conference on Power Electronics and Applications, pp. 1–10. IEEE (2011) 19. Lezana, P., Pou, J., Meynard, T.A., Rodriguez, J., Ceballos, S., Richardeau, F.: Survey on fault operation on multilevel inverters. IEEE Trans. Ind. Electron. 57(7), 2207–2218 (2009)

Chapter 39

Impact and Scope of Electric Power Generation Demand Using Renewable Energy Resources Due to COVID-19 Manish Kumar, Muralidhar Nayak Bhukya, Anshuman, and Sachin

Abstract This paper highlights the drop in demand of electricity all over the globe during and after COVID-19. The effect of COVID-19 pandemic on electric power generation was hugely unpredictable. The Government of India (GOI) has planned to launch a few projects and schemes regarding the usage of renewable energy in the early quarter of 2020, which is not succeeded due to the pandemic situation. Therefore, this paper presents a review on global energy and fuel usage by the country in the first quarter of the year.

39.1 Introduction Latest data represents that, in the First Quarter (Q1 ) of 2020 the global energy demand is decreased by 3.8% compared to the Q1 of 2019. If the lockdown is extended for few more months the demand for energy will be approximately decreased by 6% in the Second Quarter (Q2 ) of 2020. In Q1 2020, there are different restrictions on economic activities, which reduced the coal demand by 8% as compared to Q1 2019 [1]. The thermal power generation sector is affected abruptly across the world due to crisis and dropped to −2.5% which is numerically equal to power generated by natural gas [2]. Apart from this, there is decrease in oil demand around the world by 5%. Due to COVID-19 crisis, there are several restrictions on travelling and transportation across the borders [3]. The demand for natural gas is decreased by 2%, which affects the price value in the market. In the period of covid-19 the use of renewable energy is increased by 1.5% in first quarter of the year, as wind power plants and solar panels are installed across the world to produce huge amount of electrical energy [4]. Due to lockdown in India, the generation of electricity is reduced by 30% per week and the annual energy demand is reduce by 0.6% [5]. If the situation continuous, M. Kumar (B) · M. N. Bhukya · Anshuman · Sachin Department of Electrical Engineering, School of Engineering & Technology, Central University of Haryana, Mahendragarh, Haryana 123031, India e-mail: [email protected] M. N. Bhukya e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_39

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the annual energy demand will be reduced by 1.5% [6]. China is the first nation to implement lockdown and the energy demand is collapsed by around 15%. They had the most significant drop in energy demand which decreased by 7% compared to the earlier year. Similarly, in the United States the demand for energy is decreased by 6% [7].

39.2 Decline in Energy Demand The projected 6% decline in energy demand would be more than seven times the impact of 2008 financial crisis on global energy demand. All fuels except the renewable sources are going to set demand for the next decades. • The demand of oil could be dropped by 9% across the year. • The demand of coal could be dropped by 8%, due to fall in electricity the average drop is 5% across the year. • The demand of gas reduced further in first quarter of the year due to less demand in industries and workplaces. • Less demand of nuclear power due to lower electricity demand. • Renewable sources are expected to launch some new projects online in coming 2020, they are expected to boost the output and raise the demand of electricity across world. • Renewable sources projects are the future in these kinds of situations, they are cheaper at operating and installment and provide a sufficient amount of energy required. Due to crisis, the demand for energy across all the regions has gave the impression to be declined gradually. In China, there is a decline of 4% and the annual growth demand is nearly 3% in between 2010 and 2019. If the pandemic is completed and the lockdown is not further extended without any restrictions by the government there is a chance of speedy recovery in global demand of electricity across the world [8]. In this recovery, the renewable energy is the only source to reach the demand of electricity. Figure 39.1 shows the impacts of energy demand with different resources for power generation and it is clear that the overall energy demand is in the negative side [9].

39.3 Demand for Electricity The electricity demand is decreased by 2.5% in the first three months of the year 2020, as the lockdown occurred. Similarly, there was a huge drop of electricity demand in several other countries across the world, numerically global energy demand is depreciated by 2.5–4.5% in Europe, Japan and Korea [10].

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2.00% 0.00% -2.00% -4.00% -6.00% -8.00% -10.00%

Fig. 39.1 Change in primary energy demand

The most affected sector by the lockdown is the service sector which includes the retails shops, education institutes, hospitality, offices and the tourism activities all are restricted in the lockdown and it causes a major economic crisis among the country [11]. Some of the industries and the factories are resumed to work under the precautionary measures to protect workers. Country-wise reduction of electricity demand after lockdown in a day-wise pattern is shown in Fig. 39.2. It is observed that the depreciation of global electricity demand is 5%. Every country has their own crisis due to covid-19 affect. China and India are not similar in terms of electricity

Fig. 39.2 Reduction in electricity demand after lockdown

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energy demand, they both are dealing with the crisis on their own and the electricity demand is reduced for both the countries.

39.4 Renewable Energy Renewable energy has been the most strong and easiest source for the covid-19 lockdown, as it was unaffected by the global energy demand and produce energy being unaffected and make proper uses of renewable energy sources. By doing so, there is a major growth in using renewable energy by 1.5% in Q1 2020 as related to Q1 2019. This will increase the global rate of using renewable energy around the world. By the collected data, we came to know that there is an increase in use of renewable energy by 1% in 2020 [12]. There is an expansion of renewable sources like solar, wind, and hydro plants to increase the use of renewable energy generation by 5% in coming years.

39.4.1 Q1 of 2020 The use of renewable energy in Q1 was 1.5% times more than the Q1 of 2019. This was done by the completion of solar PV projects and wind projects respectively of 100 GW and 60 GW, these projects were completed in 2019, by the help of these projects there is an increment of 3% in generation of electric power through renewable energy [13]. During lockdown, there is a major lifting in the share of renewable energy sources which was now around 28%.

39.4.2 2020 Projection According to estimate data, the renewable energy demand increased by 5% in the crisis of covid-19. This actually affects in increase of demand in power generation through renewable energy sources by 30% around the world. The hydro power plants share the highest power generation as it holds more than the 60%. It is dependent on rainfall and there are no any such crucial conditions for generating power. The Photovoltaic system is the fastest and widest among renewable source of energy around the world [14]. This can be installed in the large, small, and even on rooftops. It can also be used by the individuals at their houses for generating sufficient amount of energy. Wind power plants are growing in the field of power generating sources steadily and smoothly. There are several wind projects launched around the world which generate a huge amount of power. The weather is expected to be windy in the first quarter of year and help the plants to generate the essential amount of energy and

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boost the global energy demand [15]. The annual growth for renewable electricity generation for the year 2108–2020 has been shown in Fig. 39.3. It is observed that the impact of the annual growth of the current year is less as compared to the other years [16].

39.5 Impact of Renewable Energy Projects There was a major impact on the renewable projects due to covid-19 outbreak. Countries like China, Vietnam and Thailand are interdependent on the sources of renewable energy, more than 40% supply reliant on global sector in China. Many countries are dependent on raw wind material imports around the world [17]. China and Europe hold more than 60%. The global wind industry imports the wind project equipments from the different countries around the world. Global report says that the major concern was all about the renewable sources revolve around the demand of energy. As the renewable sources solar and wind having the issues of delaying their projects to be launched. On April 17, 2020, Ministry of New Renewable Energy (MNRE) declared that the projects which are ongoing under renewable energy sources are extended until the period of lockdown and some more time to develop the project [18].

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39.6 Impacts of Power Sector in India The global economy has been hit hard by the crisis of the covid-19. The renewable sector was already dealing some difficulties with the geographical and multiple sectors in the country and here come the new problem of covid-19 outbreak. Solar projects are largely affected by covid-19 as the solar modules are imported from China but since the lockdown was from December 2019 [19]. The implementation of new projects was delayed because of lockdown as the developers could miss the deadlines. Similarly the wind turbines manufacturer in India were also got suspended. This would cause a delay in the wind and solar projects till the lockdown goes on, after the lockdown things get back to normal. Rooftop solar projects are most affected projects by the outbreak of covid-19 in the renewable energy generating sources [20]. These rooftop projects are affected more than the grid-connected which was under essential services. The individual dealing with the problems of financial crisis have the best option rooftop solar projects installation on their list leading to a good source of power generation. MNRE stated that the status of projects going on under the RE projects remain unchanged during the lockdown period. India announced a target energy generation of 175 GW by 2022 of renewable energy, coal which share more the half of nation’s power generation would be replaced by the renewable energy sources.

39.7 Impacts of Renewable Energy in India The solar plants which are planned to be operated in this period of time, now have the shortage of modules [21]. The prices of modules are expected to be high due to this covid-19 outbreak, because there is shortage of module glass and wafers to create these systems. These plants are expected to generate 2–4 GW amounts of power. If the things are going on it is very difficult to develop these projects in the future. In the time of crisis, wind energy industry in India has the expected to raise the prices of wind turbines, according to the global report [22]. The prices are expected to be increased by 10% in the second quarter of 2020. The wind energy industry is expected for the recovery at the end of second quarter of 2020. The lack of production and importation of wind turbine equipment into India is expected to drive the costs for turbines to $864/KW to $904/KW in the first quarter of 2020 and $943/KW in the second quarter of 2020. The impact on renewable energy pre-virus and post virus has been shown in Fig. 39.4. It is clear that wind energy reduces in −11.4% and the solar energy reduce the -24.8% in India due to the COVID-19 [23].

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39.8 Conclusions This paper presents impact and scope of electric power generation demand using renewable energy during COVID-19. During the crisis many plants had the shortage of fuel and hence result in less production of electricity. On the other hand, renewable energy has a gradual increase in producing electricity during pandemic. The Covid-19 crisis is also influencing the path for clean energy transitions. Global CO2 emissions are set for the largest year-to-year reduction on record. The least emission of carbon recorded all over the world in time of pandemic. It has been clear that wind energy reduces in −11.4% and the solar energy reduce the −24.8% in India due to the COVID-19

References 1. Central Collection and Publication of Electricity Generation, Transportation and Consumption Data and Information for the Pan-European Market (2020). https://transparecy.entsoe.eu 2. Bhukya, M.N., Kota, V.R., Depuru, S.R.: A simple, efficient and novel standalone photovoltaic inverter configuration with reduced harmonic distortion. IEEE Access 7, 43831–43845 (2019) 3. Electric Power Statistics Information System, Average Electric Power by Month (2020). http:// epsis.kpx.or.kr/epsisnew/selectEkgeEpsAepChart.do?menuld=040103&locale=eng 4. IEA, Monthly OECD Electricity Statistics: Data up to Jan 2020 (Statistics Report—Apr 2020) (2020). https://www.iea.org/reports/monthly-oecd-electricity-statistics 5. Kota, V.R., Bhukya, M.N.: A simple and efficient MPPT scheme for PV module using 2dimensional lookup table. In: IEEE Power and Energy Conference at Illinois, Feb 2016. https:// doi.org/10.1109/peci.2016.7459226 6. International Energy Agency, World Energy Balances (2019). https://www.iea.org/reports/ world-energy-balances-2019

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7. Kota, V.R., Bhukya, M.N.: A novel global MPP tracking scheme based on shading pattern identification using artificial neural network for photovoltaic power generation during partial shaded condition. IET Renew. Power Gener. 13, 647–659 (2019) 8. National Bureau of Statistics of China, Energy Production in the First Two Months of 2020 (2020). https://www.stats.gov.cn/english/PressRelease/202003/20200317_1732703.html 9. Bhukya, M.N., Kota, V.R.: A quick and effective MPPT scheme for solar power generation during dynamic weather & partial shaded conditions. Eng. Sci. Technol. Int. J. 22, 869–884 (2019) 10. National Energy Administration Releases. http://www.nea.gov.cn/2020-03/23/c_13890839. html 11. Réseau de Transport d’Électricité, Electricity Demand (2020). Available: https://www.rte-fra nce.com/en/eco2mix/eco2mix-consommation-en 12. IEA based on U.S. EIA, POSOCO (India), RTE (France), TERNA (Italy), ELEXON (UK), China NBS, Red Electrica (Spain) and ENTSO-E 13. Kota, V.R., Bhukya, M.N.: A novel linear tangents based P&O scheme for MPPT of a PV system. Renew. Sustain. Energy Rev. 71, 257–267 (2017) 14. Global Energy Review 2020 “The Impacts of the Covid 19 Crisis on Global Energy Demand and CO2 Emissions” (2020) Available: www.iea.org/corrigenda 15. Bhukya, M.N., Kota, V.R.: DCA-TR based MPP tracking scheme for photovoltaic power enhancement under dynamic weather conditions. Electr. Eng. 100, 2383–2396 (2018) 16. OCCTO (Organization for Cross-Regional Co-ordination of Transmission Operators, Japan): Demand Data Provided by Regional TSOs and Sources Provide Therein (2020). Available: https://www.occto.or.jp/index.html 17. Bhukya, M., Kota, V.: A novel P&OT —Neville’s interpolation MPPT scheme for maximum PV system energy extraction. Int. J Renew. Energy Dev. 7, 251–260 (2018) 18. Choudhary, S.: GAIL expects gas demand to pick up soon. The Economic Times (2020). Available: https://economictimes.indiatimes.com/industry/energy/oil-gas/gail-expects-gasdemand-to-pick-up-soon/articleshow/75145258.cms?from=mdr 19. EIA (Energy Information Administration): Natural Gas Weekly Update, US Department of Energy (2020). Available: https://www.eia.gov/naturalgas/weekly 20. Enagás, Demanda de gas natural - Histórico de demanda [Natural gas demand – demand history] (database) (2020). Available: https://www.enagas.es/enagas/es/Gestion_Tecnica_Sis tema/DemandaGas/SeguimientoDemanda 21. ENTSOG (European Network of Transmission System Operators for Gas): Transparency Platform (database) (2020). Available: https://transparency.entsog.eu 22. COVID-19 Impact: Indian Renewable Sector (2020). Available: https://www.saurenergy.com/ research/care-ratings-report-on-covid-19-impact-indian-renewable-sector 23. National Grid, Transmission Operational Data (database) (2020). Available: https://www.nat ionalgridgas.com/data-and-operations/transmission-operational-data

Chapter 40

Demand Side Management-Based Load Frequency Control of Islanded Microgrid Using Direct Load Control Subash Chandra Sahoo, Abdul Latif, Satyajeet Naidu, Shruti Patel, Ranjan Kumar, and Dulal Chandra Das Abstract The increase in energy demand toward modern society makes the power system to include non-conventional energy sources along with conventional ones, however, the most significant challenges we are facing in the power system toward maintaining the frequency deviation. The more and more penetration of renewable energy sources (RES) will affect the system frequency. So we need to think about such a system that will sustain these intermittencies of the RES with minimal frequency deviation. In this paper, the direct load control method of demand side management (DSM) has been used for control of frequency variation. This paper proposes a model of hybrid microgrid (hμG) system where the traditional RES like solar PV (SPV) and wind turbine generator (WTG) have been included. We have also included biodiesel engine generator (BDEG), refrigerator (REFG), heat pump (HP), and plugged-in hybrid electric vehicle (PHEV) to study the system performance toward frequency variation. Without any storage device, a demand response (DR) controller helps in monitoring and managing the controllable loads to adjust the system frequency. We have considered the PID controller the hμG, whose parameters are optimized by selfish herd optimization (SHO). Various case studies have been considered for observing system behavior. The simulated results were found satisfactory and confirm that the system frequency varies within the allowable limit, which enhances the system robustness.

40.1 Introduction The energy we are using is mostly coming from fossil fuels, and as we all know that fossil fuels are limited. If we continue using fossil fuel at the same rate we are using, it would not be available to us for a long time. Also, because of technological development and an increase in population, our society is becoming more dependent on energy. This change has brought everyone’s attention to renewable energy sources. S. C. Sahoo (B) · A. Latif · S. Naidu · S. Patel · R. Kumar · D. C. Das National Institute of Technology, Silchar, Assam, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_40

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Table 40.1 Parameters and values used for designing the microgrid Symbols Nomenclature Values PWTG , K WTG , TWTG PSPV , K SPV , TSPV PBDEG , K VA , TVA

K BE , TBE PPHEV , K PHEV , TPHEV PHP , K HP , THP PREFG , K REFG , TREFG PL R D M PID

Power output (p.u), gain and time constant of WTG unit Power output (p.u), gain and time constant of SPV unit Power output (p.u), valve gain and valve actuator delay of BDEG unit The engine gain and time constants of BDEG unit Power output (p.u), gain and time constant of PHEV unit Power output (p.u), gain and time constant of HP unit Power output (p.u), gain and time constant of REFG unit Net critical load demand for hμG (p.u) Droop constant of hμG Load damping factor of hμG Equivalent inertial coefficient of hμG Proportional integral derivative controller

–, 1, 1.5 s –, 1, 1.8 s –, 1, 0.05 s

–, 1.0, 0.5 s –, −1, 0.2 s –, −1, 0.1s –, −1, 0.265s

2 0.012 0.2 –

The major players in the area of renewable energy sources include SPV, WTG, biomass, ocean wave, geothermal energy sources, etc. SPV and WTG are nature dependent. Geothermal energy, sea wave energy, biodiesel, and biomass also have the potential to be considered as sources of energy. RES like SPV and WTG has excellent potential for contributing energy and provides clean energy [13], and it does not increase global warming, unlike fossil fuels (Table 40.1). The biggest drawback of RES is that we cannot be entirely dependent on it because of its intermittent nature. RES can provide balanced and more diversified energy while operated with other energy sources [8]. But the electricity from these resources cannot be relied on to be the only source of energy without having any backup sources [15] as it leads to voltage and frequency perturbation. To overcome these issues, diesel engine generators (DEG) [17] may help compensate for the deficiency. Again fossil fuel-based generators produce more carbon dioxide and carbon monoxides like gases that are harmful to livelihood and environment. Instead of using traditional DEGs, we can use the biodiesel generator (BDEG) [7], which uses biodiesel as a fuel. Energy storage might be a possible solution that may provide additional power to the hybrid systems and can store during excess generation. The most commonly used backup source is the battery energy storage system(BESS) [26], and several authors

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have hinted that battery storage is the solution to this problem that we face [5, 10, 14]. But the battery energy storage system has some severe drawbacks too, like higher cost, shorter lifespan, and higher maintenance. It emits some poisonous gases like hydrogen sulfide, which may have serious health effects, and disposal of batteries is harmful to the environment when disposed of in landfills. These problems of BESS have brought everyone’s attention toward demand side management (DSM). As we know, renewable resources are not predictable, so wind or solar energy cannot always be relied on, so we used BDEG to provide the necessary backup when needed. And this is how the power continuity is maintained. In controllable loading PHEV, HP and REFG are considered. PHEV is a special kind of vehicle, its uniqueness owing to its battery device [7]. The battery charges itself when pluggedin, and when there is a deficit of power, it can start to supply. HP is a machine that works with the help of a material refrigerant [22]. The substance then mixes with heat, and the compressor pumps this mixture to the heat exchanger, which consists of coils and where the temperature is drawn up by surroundings. In a REFG unit, there is a control center that will send a signal to the refrigerator, and on receiving it, the REFG will keep a part of the compressor shut down, and this helps reduce the load, and hence frequency resumes to its normal value [21]. Here, load is further divided into two parts. The first one, which can only consume energy and another one is which can take power, and it can also supply whenever it is needed. Here, REFG and HP can only intake power, while in the case of more power generation than demand, PHEV stores energy. In the case of deficiency, PHEV supplies the power to the μG. One controller is used to control the non-critical loads, and another controller is used to manage the BDEG to control the generation. In light of the above, the major contributions of the work are: • Implementation demand response control scheme for load frequency control (LFC). • Applying SHO for tuning controllers for bio-renewable-based generator and controllable loading of isolated hμG.

40.2 Demand Side Management The department of energy defines DSM programs as: “The planning, implementation, and monitoring of utility activities designed to encourage consumers to modify patterns of electricity usage, including the timing and level of electricity demand. It refers to energy and load-shape modifying activities that are undertaken in response to utility-administered programs” [19]. DSM can be implemented by various methods like converting the consumer’s demand for energy as financial incentives and behavioral changes through education to save energy. Generally, through DSM, consumers can be encouraged to use less energy during peak hours or to shift the uses of energy into the off-peak hours [21].

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Fig. 40.1 a Schematic diagram, b block diagram of proposed hμG

The DSM can be categorized into two programs: (a) Energy efficiency programs and (b) demand response program. The demand response (DR) program is of more significance due to its contribution to smart grids [9]. The demand response has been well defined in [16]. It regulates the loads on the demand side in response to voltage and frequency fluctuations while satisfying technical and economic concerns of the grid operators. The programs under demand response are of the following two types : (a) Pricebased program and (b) incentive-based program. In a DR program that is incentivebased, the utilities producing power shut down users’ appliances directly, and the users, in return, get incentives for them [1]. An unexpected variation in the system provides generating services with a chance to implement this program. This program further includes DLC, demand bidding, emergency demand reduction, and interruptible load [16].

40.3 Hybrid Microgrid System Components The proposed system consists of different energy sources. It includes generators as an energy source, non-critical load, and critical loads. The energy sources which have been considered here are SPV, WTG, and BDEG. Here, we have considered PHEV, HP, and REFG as non-critical loads that can be controllable. Modeling and description of each component have been described in the following sub-sections.  KI1 + s K D1  f s

(40.1)

 KI2 + s K D2  f K P2 + s

(40.2)

 C1 =

K P1 + 

C2 =

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40.3.1 System Components Description The WTG could produce power if it is installed near to the seashore. The generation depends upon the wind speed and varies accordingly. The cut-in and cut-off are the two boundary speed conditions to be applied for all WTG. For the WTG array, the linear transfer function model can be expressed as (40.3) [2]. The SPV converts the photon energy into electrical energy. SPV provides DC power, and that power is converted to AC through an inverter and synchronized with the grid. For the SPV array the linear transfer function model can be expressed as (40.4) [13, 23]. Biodiesel is a fuel that can be obtained from plants, animal fats, and residue of edible oils. Generally, it produced from non-edible energy crops, the residue of edible oils, and others have the same characteristics as diesel by the transesterification process. For the BDEG unit, the linear transfer function model can be expressed as (40.5) [4, 6]. Plug-in hybrid electric vehicle (PHEV) is an electric vehicle that can run from battery power charged from supply mains. The advantage of this type of car is that it can store energy during excess generation from the grid and can deliver power to the μG if there is any shortfall. For the PHEV unit, the linear transfer function model can be expressed as (40.6) [4]. An HP unit works with the help of a material refrigerant. The substance then mixes with heat, and the compressor pumps this mixture to the heat exchanger, which consists of coils and where the temperature is drawn up by surroundings and can be used as a controllable load. For the HP unit, the linear transfer function model can be expressed as (40.7) [22]. A REFG unit helps to keep a part of the compressor on or off as per the controller. This process helps maintain the load as per the generation and can be used as a controllable load. For the REFG unit, the linear transfer function model can be expressed as (40.8) [21]  PWTG =  PSPV =  PBDEG =

K WTG 1 + sTWTG K SPV 1 + sTSPV

K VA 1 + sTVA 

PPHEV =  PHP =



 PW

(40.3)

PPV

(40.4)



K BE 1 + sTBE

K PHEV 1 + sTPHEV K Hp 1 + sTHP

 C1

(40.5)

 C2

(40.6)

 C2

(40.7)

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 PREFG =

K REFG 1 + sTREFG

 C2

(40.8)

40.3.2 Modeling of System Dynamics The designed hμG is having various types of sources, basically RES (SPV & WTG), BDEG unit instead of traditional diesel engine generator, controllable loads (PHEV, HP & REFG), and some critical loads. The difference between the power produced by the sources to the consumption by the load can be expressed as (40.9). It is the overall change in the generated electricity at any moment of the proposed hμG. The linearized system-generator model can be shown as (40.10). PEG = PWTG + PSPV + PBDEG ± PPHEV − PHP − PREFG − PL (40.9)   f 1 (40.10) = G SYS (s) = PEG D + sM

40.4 Methods and Data Selection Here, we have tried to formulate the objective function along with the selection of efficient algorithms by comparing different algorithms to be used for tuning the PID controllers gains.

40.4.1 Objective Function The objection function minimizes the frequency deviation occurs due to load and source variation in the hμG and settles faster. Different techniques are available for reducing the error value. But, here, we have considered the integral squared error (ISE) for this study toward change in frequency deviation of the system. By using this technique, the objective function of the hμG can be developed as (40.11). It will help to observe change in frequency  f and calculating the controller gains [20]. tsim Minimize, J =

( f )2 dt 0

subject to : K min ≤ K c ≤ K max

(40.11)

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Table 40.2 Parameters obtained from different algorithms for PID controllers Algorithms\ gains

K P1

KI1

K D1

K P2

KI2

K D2

Jmin

GA

71.877672

4.869879

22.284547

78.733547

66.335139

80.622666

0.000303

PSO

29.407168

78.332981

71.562717

2.849586

66.559194

0.056120

0.002686

GOA

30.852638

59.887625

13.609695

0.109227

0.005432

0.004469

0.000214

GWO

32.165675

100.000000 13.644864

0.091286

0.000000

0.000000

0.000198

SSA

39.546275

92.942994

13.763237

0.004439

0.000054

0.001836

0.000141

MBA

39.753162

73.442413

16.872489

0.002739

0.000000

0.001388

0.000123

SHO

0.863972

99.872791

22.894951

0.011781

0.000082

0.005302

0.000095

where K C is the controller gains, i.e., K P , K I , K D for PID controllers and K Cmax & K Cmin are the boundary value, i.e., lower bound lb and upper bound ub for the controllers gain during the optimization.

40.4.2 Optimization Algorithm The optimization technique helps to tune the parameters of the controller in consideration of the objective function in an efficient manner. The designed μG has been optimized with some recent optimization technique like genetic algorithm (GA) [12], particle swarm optimization (PSO) [11], grasshopper optimization algorithm (GOA) [4], gray wolf optimization (GWO) [24], slap swarm algorithm (SSA) [3], mine blast algorithm (MBA) [25], and selfish herd optimization (SHO) [18]. By comparing these parameters for the gains, it can be stated that SHO has a faster settlement with minimum objective function value as 0.000095. The common parameters considered for each optimization algorithm for PID tuning are as follows: Max.Itr = 200, ub = 100 & lb = 0 ,and simulated time (tsim ) = 120 s. The controller gains obtained from these algorithms have been listed in Table 40.2. The different algorithm comparison has been shown in Fig. 40.2.

40.5 System Robustness The schematic diagram for hμG as shown in Fig. 40.1a has been modeled with MATLAB /Simulink application and Fig. 40.1b represents the block diagram of the proposed hμG. The parameters which have been considered for designing the hμG has shown in Table 40.1. Different case studies have been performed to verify the system’s robustness.

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Fig. 40.2 Algorithm comparison

40.5.1 Case-I: Normal Weather Condition Here, we considered that for normal weather conditions, all the energy sources are operational. The frequency response plot is shown in Fig. 40.3a deviates within the allowable limit. The generation and load demand response graph has been shown in Fig. 40.3b, c. The change in load occurs at t = 20 s and t = 60 s. In this case, all the RES units, along with the BDEG unit, contributes to the load demand. The PHEV, HP, and REFG units contribute during the change in load and source.

40.5.2 Case-II:Only SPV Available Here, we have considered that all the energy sources are operational except SPV. The frequency response has been shown in Fig. 40.3d, which is within the allowable limit. The generation and load demand response graph has been shown in Fig. 40.3e, f. In this case, the WTG unit, along with the BDEG unit contributes to the load demand. The PHEV, HP, and REFG units contribute during the change in load and source.

40.5.3 Case-III: Only WTG Available Here, we have considered that all the energy sources are operational except WTG. The frequency response has been shown in Fig. 40.4a, which is within the allowable

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Fig. 40.3 a Case:1-frequency response. b Case: 1-power shared by WTG, SPV, BDEG units with respect to load. c Case: 1-power shared by PHEV, HP, REFG units with respect to load. d Case: 2-frequency response. e Case: 2-power shared by WTG, SPV, BDEG units with respect to load. f Case: 2-power shared by PHEV, HP, REFG units with respect to load

Fig. 40.4 a Case:3-frequency response. b Case:3-power shared by WTG, SPV, BDEG units with respect to load. c Case:3-power shared by PHEV, HP, REFG units with respect to load. d Case:4frequency response. e Case:4-power shared by WTG, SPV, BDEG units with respect to load. f Case:4-power shared by PHEV, HP, REFG units with respect to load

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limit. The generation and load demand response graph has been shown in Fig. 40.4b, c. In this case, the SPV unit, along with the BDEG unit contributes to the load demand. The PHEV, HP, and REFG units contribute during the change in load and source.

40.5.4 Case-IV: Source and Load Variation in Real Time Here, the real-time load and RES data have been considered for Bhubaneswar city [6]. The frequency response has been shown in Fig. 40.4d, which is within the allowable limit. The generation and load demand response graph has been shown in Fig. 40.4e, f. In this case, all the RES units, along with the BDEG unit, contributes to the load demand. The PHEV, HP, and REFG units contribute during the change in load and source. The supplied power quality can be monitored and improved by adopting suitable techniques.

40.6 Conclusion The performance of demand side management for minimizing the frequency fluctuation of solar and wind-based isolated μG has been investigated in this study. The system consists of RES like SSPV and WTG, and the controllable loads like PHEV, REFG, HP, and critical loads. A demand response (DR) controller is considered for reducing the frequency deviation in the proposed system. The controller gains are optimized with different algorithms and found SHO gives better performance than others. It has been found that the simulation results that the SHO gives a better result with a faster settlement. This work could be further extended to multi-area-based hμG.

References 1. Babahajiani, P., Bevrani, H., Shafiee, Q.: Intelligent coordination of demand response and secondary frequency control in multi-area power systems. In: 1st IEEE Conference on New Research Achievements in Electrical and Computer Engineering (CBCONF), Tehran, Iran (2016) 2. Barik, A.K., Das, D.: Coordinated regulation of voltage and load frequency in demand response supported bio-renewable cogeneration-based isolated hybrid microgrid with quasi-oppositional selfish herd optimisation. Int. Trans. Electr. Energ. Syst. (2019) 3. Barik, A.K., Das, D.C.: Active power management of isolated renewable microgrid generating power from rooftop solar arrays, sewage waters and solid urban wastes of a smart city using salp swarm algorithm. In: 2018 Technologies for Smart-City Energy Security and Power (ICSESP), pp. 1–6. IEEE (2018)

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4. Barik, A.K., Das, D.C.: Expeditious frequency control of solar photovoltaic/biogas/biodiesel generator based isolated renewable microgrid using grasshopper optimisation algorithm. IET Renew. Power Gener. 12(14), 1659–1667 (2018) 5. Barik, A.K., Das, D.C.: Optimal load-frequency regulation of bio-renewable cogeneration based interconnected hybrid microgrids with demand response support. In: 15th IEEE India Council International Conferences, pp. 1–6 (2018) 6. Barik, A.K., Das, D.C.: Proficient load-frequency regulation of demand response supported bio-renewable cogeneration based hybrid microgrids with quasi-oppositional selfish-herd optimisation. IET Gener. Trans. Distrib. 13(13), 2889–2898 (2019) 7. Barik, A.K., Das, D.C., Muduli, R.: Demand response supported optimal load-frequency regulation of sustainable energy based four-interconnected unequal hybrid microgrids. In: 2019 IEEE International Conference on Sustainable Energy Technologies (ICSET), pp. 273–278. IEEE (2019) 8. Behl, R., Chhibar, R., Jain, S., Bahl, V., El Bassam, N.: Renewable Energy Sourcesand Their Applications. Agrobios (International), Jodhpur (2013) 9. Behrangrad, M.: A review of demand side management business models in the electricity market. Renew. Sustain. Energ. Rev. 47, 270–283 (2015) 10. Bhuyan, M., Das, D.C., Barik, A.K.: A comparative analysis of DSM based autonomous hybrid microgrid using PSO and SCA. In: 2019 IEEE Region 10 Symposium (TENSYMP), pp. 765– 770. IEEE (2019) 11. Das, D.C., Roy, A., Sinha, N.: PSO optimized frequency controller for wind-solar thermaldiesel hybrid energy generation system: a study. Int. J. Wisdom Based Comput. 1(3), 128 (2011) 12. Das, D.C., Roy, A., Sinha, N.: Ga based frequency controller for solar thermal-diesel-wind hybrid energy generation/energy storage system. Int. J. Electr. Power Energ. Syst. 43(1), 262– 279 (2012) 13. Das, D.C., Roy, A., Sinha, N., et al.: Genetic algorithm based pi controller for frequency control of an autonomous hybrid generation system. In: World Congress on Engineering 2012. July 4-6, 2012. London, UK. vol. 2189, pp. 953–958. Citeseer (2010) 14. Das, G., Das, D.: Demand side management for active power control of autonomous hybrid power system. In: 2018 2nd International Conference on Power, Energy and Environment: Towards Smart Technology (ICEPE), pp. 1–6. IEEE (2018) 15. Delavari, A., Kamwa, I.: Demand-side contribution to power system frequency regulation:-a critical review on decentralized strategies. Int. J. Emerg. Electric Power Syst. 18(3), (2017) 16. Deng, R., Yang, Z., Chow, M.Y., Chen, J.: A survey on demand response in smart grids: mathematical models and approaches. IEEE Trans. Ind. Inform. 11(3), 570–582 (2015) 17. El-Fergany, A.A., El-Hameed, M.A.: Efficient frequency controllers for autonomous two-area hybrid microgrid system using social-spider optimiser. IET Gener. Trans. Distrib. 11(3), 637– 648 (2017) 18. Fausto, F., Cuevas, E., Valdivia, A., González, A.: A global optimization algorithm inspired in the behavior of selfish herds. Biosystems 160, 39–55 (2017) 19. Gupta, P.: Demand Side Management: an approach to peak load smoothing (2012) 20. Kundur, P., Balu, N.J., Lauby, M.G.: Power System Stability and Control. McGraw-hill New York (reprint 2009) 21. Latif, A., Das, D.C., Ranjan, S., Hussain, I.: Integrated demand side management and generation control for frequency control of a microgrid using PSO and FA based controller. Int. J. Renew. Energ. Res. (IJRER) 8(1), 188–199 (2018) 22. Latif, A., Pramanik, A., Das, D.C., Hussain, I., Ranjan, S.: Plug in hybrid vehicle-wind-diesel autonomous hybrid power system: frequency control using FA and CSA optimized controller. Int. J. Syst. Assur. Eng. Manage. 9(5), 1147–1158 (2018) 23. Lee, D.J., Wang, L.: Small-signal stability analysis of an autonomous hybrid renewable energy power generation/energy storage system part i: Time-domain simulations. IEEE Trans. Energ. Convers. 23(1), 311–320 (2008) 24. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

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25. Ranjan, S., Das, D.C., Latif, A., Sinha, N.: LFC for autonomous hybrid micro grid system of 3 unequal renewable areas using mine blast algorithm. Int. J. Renew. Energ. Res. (IJRER) 8(3), 1297–1308 (2018) 26. Sahoo, S.C., Barik, A.K., Das, D.C.: Selfish-herd optimisation based frequency regulation in combined solar-thermal and biogas generator based hybrid microgrid. In: 2020 International Conference on Contemporary Computing and Applications (IC3A), pp. 330–335. IEEE (2020)

Chapter 41

Dynamics of a Class of Modified Leslie-Gower Predator-Prey Model with Strong Allee Effect on Prey and Non-monotonic Rational Functional Response Udai Kumar and Partha Sarathi Mandal Abstract In this paper, we have modified Leslie-Gower prey-predator model with strong Allee effect on prey and non-monotonic rational functional response. We have demonstrated the existence of axial equilibria and its stability. Extinction of the all species is possible in our considered model as extinction equilibrium point is always stable. Our considered model exhibited atmost two coexistence equilibria. We have provided the stability properties of interior equilibrium points with the help of graphical Jacobian method. Our proposed model reported bi-stability behavior between trivial and coexistence equilibrium points, and trivial equilibrium point is the global attractor under some parameter choice. We have reported a comprehensive study of the global dynamics of our proposed model. In the course of bifurcation analysis, we have provided the co-dimension two bifurcation diagram by considering Allee as a bifurcation parameter. On co-dimension two bifurcation plane, our proposed model reported rich dynamics in small parametric range. We have reported all possible local and global bifurcations, namely saddle-node bifurcation, Hopf bifurcation, Bogdanov-Takens bifurcation and Homoclinic bifurcation, respectively. Numerical examples are performed to validate the analytical findings.

41.1 Introduction In recent years, modified Leslie-Gower type prey-predator models are existing in many fields of the Mathematical Ecology, which have been analyzed comprehensively due to their increasing significant importance [1, 2]. They are derived from the seminal model proposed by Leslie in 1948 [15], as an alternative to the LotkaVolterra model. Main feature is the equation for predators is a logistic-type growth function [1, 18, 23]. In this paper, we analyze the dynamics of prey-predator model characterized by the following features: (i) Functional response or predator conU. Kumar (B) · P. Sarathi Mandal Department of Mathematics, NIT Patna, Patna, Bihar 800005, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_41

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sumption rate is non-monotonic or Holling type II [17, 23], (ii) Strong Allee effect is incorporated in prey growth function. [8, 9, 20, 25]. So, it is assumed that carrying capacity for predators Ky is proportional to the prey sufficiency [23], as in the paper by Leslie [15]. Then, if x = x (t) represents the prey abundance, Ky = K(x) = nx. Implicitly, this statement presupposes that the predator is specialist. Denoting by y = y (t) the predator population in the logistic predator model, the Leslie-Gower term nxy represents the loss in the predator’s mortality (per capita yx ) due to insufficient availability of suited food [3]. In ecology, some mathematical models have been developed in which local stability becomes global stable. So happen in the LeslieGower model, which is demonstrated constructing a suitable Lyapunov function [14]. Nevertheless, it can be proven that phenomena are not equivalent in general, as it has been shown in [1, 2, 13, 19] considering Leslie-Gower type models and other mathematical forms to describe the consumption function. The important element of the prey-predator relationship is the predator functional response which measures intake rate of prey density per unit of time per predator [17]. In many prey-predator models, the functional response is the monotonic and being the inherent assumption the more prey in the environment, which is suitable for the predator [23]. We will consider that the predator consumption function is prey-dependent and represented α ˜ by the function h(x) = xβq+x aβ , with [4, 22] of Holling type IV [17] form. Allee effect is the mechanism which builds a positive relationship between individual fitness and density of species [20, 21]. It describes a biological scenario characterized by a positive correlation between the population sizes and per capita growth rate [8]. It has been denominated in different ways in Population Dynamics [16] and depensation in Fisheries Sciences [7, 12, 16]. The main characterization of Allee effect is that the per capita growth rate is positive for low population sizes. This is a common phenomenon in some animal populations and populations and various mechanisms have been proposed as potential sources of this biological phenomena [9]. The mathematical expression of the Allee effect is given by the equation [4, 5] dx dt

  = r 1 − Kx (x − L) x.

(41.1)

where L is the Allee effect. There are two types of Allee effect, first strong Allee effect [24, 25] and second weak Allee effect [10, 11, 16]. Strong Allee effect takes place if L > 0 and for L < 0 weak Allee effect appear. In the strong Allee effect, there always exist threshold below which, per capita growth rate of population is negative. In this case, all the species settle to extinction. In the weak Allee effect, no population threshold appear. In the strong Allee effect, per capita growth rate of population is always positive. The division of the paper is as follows: In the next section, we present modified Leslie-Gower model. In Sect. 41.3, we derive particular model. Section 41.4 describes stability properties of equilibrium. We discuss bifurcation analysis in Sect. 41.5. Section 41.6 reports global feature. Finally, we provide brief conclusion in Sect. 41.7.

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41.2 The Model Leslie-Gower type model with inclusion of strong Allee effect takes the following form:    dx α y = r 1 − Kx (x − m) x − xqx β +a β , dt   (41.2) Xμ : dy y = s 1 − nx y. dt where x is the prey and y is the predator population sizes. All parameters are positives, i.e., μ = (r, K, q, a, s, n, m, α, β) ∈ R6+ × ]−K, K[ × N2 . Furthermore, 1 ≤ α < β and for ecological purpose a < K and m 0, y ≥ 0 = R+ 0 × R0 . Adopting the procedure given in [10, 13, 18, 19], we transform system (41.2) to qn , a normal form. Let with x = Ku, y = nKv, A = Ka , τ =  β rK a β  t, Q = K β+1−α u u +( K ) S = rKs and M = Km . After rescaling variable and a time [6], we have the following: Yη (u, v) :

 du dτ dv dτ

    = (1 − u) (u M ) uβ + Aβ − Quα−1 v u2 , − = S (u − v) uβ + Aβ v.

(41.3)

41.3 A Particular Model For α = 1 and β = 2 the system (41.3) is now described by Yν (u, v) : with A =

a , K

Q=

 du dτ dv dτ

qn , rK 2

  2   2 2 − Qv u , = (1 − u) (u − M u + A )   2 2 = S (u − v) u + A v.

S=

s rK

and M =

v = u, v =

m . K

(41.4)

Solving the prey nullcline, we get

  1 (1 − u)(u − M ) u2 + A2 . Q

(41.5)

For M < u < 1, predator nullcline is the continues curve and attains maximum in the interval (M , 1). The first component, u, ofthe interior equilibrium point fulfills  the equation Qu − (1 − u) (u − M ) u2 + A2 = 0, which can be written as     u4 − (1 + M ) u3 + A2 + M u2 + Q − (1 + M ) A2 u + A2 M = 0.

(41.6)

Using Descarte’s rule of sign, if Q ≥ (1 + M ) A2 , the above equation can have maximum two positive real roots and if Q < (1 + M ) A2 , it has maximum 4 positive real roots. Here, we only consider the case Q ≥ (1 + M ) A2 . It is hard to find the feasible equilibrium points analytically, however, using numerical simulation technique,

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1

v→

0.8 0.6 0.4 0.2 0

0

0.2

0.4

0.6

u→

0.8

1

1.2

Fig. 41.1 Intersection of non-trivial nullcline for different value of Q which gives the interior equilibrium point. Non-trivia prey and predator nullclines do not intersect at Q = 0.25. Therefore, no interior equilibrium point appears. Two-interior equilibrium coincides at Q = 0.13726074722, and two-interior equilibrium appear at Q = 0.125. We fixed other parameters value at M = 0.1, S = 0.12, A = 0.2

we can explain the possible number of feasible equilibrium points. We choose the parameter values M = 0.1, A = 0.2, S = 0.12. The number of interior equilibrium points can varies from zero to two with decreasing the value of Q from Q = 0.25 to Q = 0.125 (see Fig. 41.1). Let E1∗ (u1∗ , v1∗ ) and E2∗ (u2∗ , v2∗ ) be the two interior equilibrium points, and at threshold magnitude QSN = 0.13726074722, both equilibrium points coincide which causes the appearance of saddle-node bifurcation. Therefore, two interior equilibria generate for Q < QSN , and no interior equilibria appear for Q > QSN . We derive the transversality condition in bifurcations section.

41.4 Local Stability Analysis of the Equilibria This section investigate stability of axial equilibrium points by finding the eigenvalues of the Jacobian matrix at concern equilibrium points. Lemma 41.1 For all η = (A, S, C, Q) ∈]0, 1[×R3+ (1) The axial equilibrium point (M , 0) is unstable. (2) The axial equilibrium point (1, 0) is saddle.

    Proof (1) The eigenvalues at (M , 0) are M 2 (1 − M ) A2 + 1 and SM A2 + M 2 . Since M < 1, (M , 0) is unstable point.     (2) The eigenvalues at (1, 0) are − (1 − M ) 1 + A2 and S 1 + A2 . Therefore, (1, 0) is unstable point.

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41.4.1 Stability Analysis of Interior Equilibrium Points Jacobian matrix at any point (u, u) is expressed by DYν (u, u) =

2 −Qu DYν 11 (u, u)   . Su A2 + u2 −Su A2 + u2

(41.7)

    where DYν 11 (u, u) = u2  −4u3 +3(M + 1) u2 − 2 M + A2 u + A2 (M + 1) . Then, det DYν (u, u) = Su A2 + u2 Qu2 − DYν 11 (u, v) and tr[DYν (u, u)] = DYν 11 (u, v) − Su A2 + u2 . Since we could not provide the analytic expression of the interior equilibrium point. Therefore, analytically, we cannot derive stability properties of interior equilibria. We describe the stability properties of interior equilibrium point by the following graphical Jacobian procedure (Hastings 1997) (see Fig. 41.2). Let F(u, v) = ((1 − u)(u − M )(u2 + A2 ) − Qv)u2 = u2 f (u, v) and G(u, v) = S(u − v)(u2 + A2 )v = S(u2 + A2 )vg(u, v). Therefore, we get the Jacobian matrix at any point (u∗ , v∗ ) as follows: DYν (u∗ , v∗ ) =

∂F ∂u ∂G ∂u

∂F ∂v ∂G ∂v

.

(41.8)

(u∗ ,v∗ )

    | = u∗2 −4u∗3 + 3 (M + 1) u∗2 − 2 M + A2 u∗ + A2 (M + 1) , ∂F where ∂F ∂u (u∗ ,v∗ )     ∂v |(u∗ ,v∗ ) = −Qu∗2 , ∂G | = Su∗ A2 + u∗2 and ∂G | = −Su∗ A2 + u∗2 . To ∂u (u∗ ,v∗ ) ∂v (u∗ ,v∗ ) determine the stability of (u∗ , v∗ ), it is sufficient to determine the sign of each component of DYν (u∗ , v∗ ). Then clearly,

predator (v) →

1 F positive

0.8 F negative

0.6

E2*

F negative

E1* F positive

0.4 0.2 0 0

0.2

0.4

0.6

0.8

1

prey(u)→ Fig. 41.2 Blue curve is predator nullcline. Green curve is prey nullcline. E1∗ and E2∗ are two interior equilibrium points. Nature of stability of interior equilibrium point is depicted in this figure

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Sign(DYν (u∗ , v∗ )) =

? − . +−

(41.9)

| , we use graphical concept. ∂F | means the To determine the sign of ∂F ∂u (u∗ ,v∗ ) ∂u (u∗ ,v∗ ) effect of changing u on F at equilibrium (u∗ , v∗ ), while holding v constant. In Fig. 41.2, follow the boldfaced arrow of magenta color and it goes from a region of evaluated phase space where F is negative to one where F is positive. Therefore, ∂F ∂u at (u2∗ , v2∗ ) is positive. Hence, Sign(DYν (u2∗ , v2∗ )) =

+− . +−

(41.10)

the curve Let S1 and S2 be slopes of tangents

f (u, v) = 0 and g(u, v) = 0 at to ∂f ∂f ∂g ∂g and S2 = . Also, (u2∗ , v2∗ ) respectively. Then S1 = ∂u ∂v E2∗ ∂u ∂v E2∗ 

   ∂f ∂f ∂F ∂F ∂g ∂g ∂G ∂G = and = . From ∂u ∂v E2∗ ∂u ∂v E2∗ ∂u ∂v E2∗ ∂u ∂v E2∗ 

 ∂F ∂F ∂G ∂G Fig. 41.2, it is clear that S1 > S2 . Hence, > , ∂u ∂v E1∗ ∂u ∂v E1∗ which implies det DYν (u2∗ , v2∗ ) > 0. Therefore, if tr(DYν (u2∗ , v2∗ )) < 0, then E2∗ is locally asymptotically stable, otherwise unstable. Now, follow the boldfaced arrow of red color in Fig. 41.2 and it goes from a region evaluated of phase space where F is positive to one where F is negative. Hence, ∂F ∂u at (u1∗ , v1∗ ) is negative and the Jacobian matrix becomes Sign(DYν (u1∗ , v1∗ )) =

−− . +−

(41.11)

Therefore, tr(DYν (u1∗ , v1∗ )) < 0 and det DYν (u1∗ , v1∗ ) > 0. Hence, E1∗ = (u1∗ , v1∗ ) is always locally asymptotically stable.

41.5 Local Bifurcations This section investigates the different type of local bifurcations produced by model (41.4). In the local bifurcation, either number of equilibrium point changes or equilibrium switches its stability. Here, we derive the threshold and corresponding transversality condition of co-dimension one bifurcation, namely saddle-node bifurcation and Hopf bifurcation. Model (41.4) also generates co-dimension two bifurcations, namely Bogdanov-Taken bifurcation.

41 Dynamics of a Class of Modified Leslie-Gower Predator-Prey …

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41.5.1 Saddle-Node Bifurcation In Sect. 41.3, we have observed that the number of interior equilibrium point changes fro zero to two w.r.t the parameter Q. When equilibria E1∗ (u1∗ , v1∗ ) and E2∗ (u2∗ , v2∗ ) of model (41.4) emerges at a single equilibrium point ESN (usn∗ , vsn∗ ), i.e., two nonsaddle-node bifurtrivial nullclines of (41.4) touch each other ESN (u sn∗ , vsn∗ ). The at 

∂f ∂g ∂g ∂f = , which cation threshold for Q is QSN . Clearly, ∂u ∂v ESN ∂u ∂v ESN 

 ∂G ∂G ∂F ∂F = . Therefore, det DYν (usn∗ , vsn∗ ) = 0. implies ∂u ∂v ESN ∂u ∂v ESN Hence, one of the eigenvalues of DYν (usn∗ , vsn∗ ) is equal to zero with multiplicity one. The saddle-node bifurcation threshold QSN is obtained by making 3 2 + 3(1 + M )usn∗ − 2(M + det DYν (u, v)|(ESN ;QSN ) = 0, which gives QSN = −4usn∗ 2 2 the transversality conditions of saddle-node bifurA )usn∗ + A (1 + M ). We derive



 1 1 cation (Perko 2000). Let v˜ = , w˜ = be the eigenvectors of DYν (usn∗ , usn∗ ) 1 1 and [DYν (usn∗ , usn∗ )]T corresponding to the eigenvalue zero, respectively. Let F1 (u, v) = (F(u, v), G(u, v))T . Now, we have F1Q (u, v) = (FQ (u, v), G Q (u, v))T . dF 3 |(ESN ;QSN ) = −usn∗ and G Q |(ESN ;QSN ) We can get from Eq. (41.4), FQ |(ESN ;QSN ) = dQ dG = dQ |(ESN ;QSN ) = 0, and the first transversality condition for saddle-node bifurcation takes the form

3    −usn∗ TF 3 (u, v) = 11 = 0. = −usn∗ w˜ 1Q 0 [ESN ;QSN ] Now, we have, D2 F1 (ESN ; QSN ) (V, V ) = Fuu (usn∗ , usn∗ )v˜1 v˜1 +

Fuv (usn∗ , usn∗ )v˜1 v˜2 + Fvu (usn∗ , usn∗ )v˜2 v˜1 + Fvv (usn∗ , usn∗ )v˜2 v˜2

G uu (usn∗ , usn∗ )v˜1 v˜1 +

G uv (usn∗ , usn∗ )v˜1 v˜2 + G vu (usn∗ , usn∗ )v˜2 v˜1 + G vv (usn∗ , usn∗ )v˜2 v˜2

(41.12)

Using the equilibrium relation, we get D2 F1 (ESN ; QSN ) (V, V ) =

4 3 2 + 18(1 + M )usn∗ − 10(M + A2 )usn∗ + 4A2 (1 + M )usn∗ − 4QSN usn∗ −28usn∗ . 0 (41.13)

2 We have w˜ T D2 F1 (ESN ; QSN ) (V, V ) = −6usn∗ + 3(1 + M )usn∗ − (M + A2 ) = 0. Here, we check the saddle-node bifurcation for the choice of parameters value. W fixed the parameters value A = 0.2, S = 0.12, M = 0.1. For Q = 0.125, sys-

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tem (41.4) has two interior equilibrium points E1∗ (0.5438495290, 0.5438495290), E2∗ (0.7773245906, 0.7773245906). Further, increasing Q and at threshold value Qsn = 0.13726074722, E1∗ (usn∗ , usn∗ ) and E2∗ (usn∗ , usn∗ ) coincide, i.e., E1∗ (usn∗ , usn∗ ) = E2∗ (usn∗ , usn∗ ) = (0.66794, 0.66794). From Eq. (41.7), we get det DYν ((usn∗ , usn∗ ))Qsn = 0.

41.5.2 Hopf Bifurcation We demonstrate the system dynamics when E2∗ switches its stability. We have examined that E1∗ is always stable when it exists and stability of E2∗ can get changed through Hopf bifurcation. Further, we investigate the magnitude of threshold and corresponding transversality of Hopf bifurcation. At the threshold value M = MH , the transversality condition is given by as follows: d u2 (3u2 − 2u2∗ + A2 ) {Re(λ)}|M =MH = − 2∗ 2∗ dM 2



  2 Su2∗ A2 + u2∗  2  = 0. Qu2∗ − DYν 11 (u2∗ , u2∗ ) (41.14)

2 if u2∗ + A2 = 2u2∗ and Qu22∗ = DYν 11 (u2∗ , u2∗ ). Since, the components of equilibrium points cannot be expressed in explicit, hence, we cannot determine the analytical expressions of threshold magnitude of Hopf bifurcation and the magnitude of Lyapunov exponents explicitly. Hence, we use numerical simulation technique to investigate the Hopf bifurcation thoroughly. Here, we are verified the Hopf bifurcation condition for the choice of parameters value numerically. We choose the parameters value A = 0.2, Q = 0.125, S = 0.12 and MH = 0.1487781608791. Further, we get the value of first Lyapunov coefficient (l1 ) 4.2567 > 0. Since, l1 is positive, hence, system undergoes a subcritical Hopf bifurcation. Now, we get the value 2 2 + A2 = 2.1544, 2u2∗ = 1.4096 and Qu2∗ − DYν 11 (u2∗ , u2∗ ) = of expression, 3u2∗ 1.0248(= 0). The value of the expression of transversality condition in the Eq. (41.14) is −0.0063(= 0). Also, we find from Eq. (41.7) trDYν ((u2∗ , u2∗ ))MH = 0 and det DYν ((u2∗ , u2∗ ))MH = 0.000757962503 is positive.

41.5.3 BT Bifurcation BT bifurcation curve arises when Hopf and saddle-node bifurcation curves coincide in co-dimension two bifurcation planes. In this subsection, we show that the system (41.4) undergoes a Bogdanov-Takens bifurcation of co-dimension two. For this bifurcation, the Jacobian matrix evaluated at the unique interior equilibrium point ESN = (usn∗ , vsn∗ ) appearing through the saddle-node bifurcation, when E1∗ and E2∗ touch, has a zero eigenvalue of algebraic multiplicity two. We cannot deter-

41 Dynamics of a Class of Modified Leslie-Gower Predator-Prey …

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mine the analytic expression for the thresholds of the Bogdano-Takens bifurcation explicitly due to rigorous expression of the component of interior equilibrium point. The parametric condition for this is given by det DYν (u, v)|(ESN ;MBT ;QBT ) = 0 and trDYν (u, v)|(ESN ;MBT ;QBT ) = 0 where (MBT , QBT ) is the BT bifurcation point. For M = MH , trDYν (u, v)|ESN = 0. Hence we choose MBT = MH . Putting M = MBT in det DYν (u, v)|ESN = 0, QBT is obtained as 3 2 + 3(MBT + 1)usn∗ − 2(MBT + A2 )usn∗ + A2 (MBT + 1). QBT = −4usn∗

(41.15)

For the set of parameters value A = 0.2, S = 0.12 and threshold value of MBT = 0.2925, QBT = 0.092825707, E(usn∗ , usn∗ ) = (0.7177925570, 0.7177925570), we found from Eq. (41.7) trDYν ((usn∗ , usn∗ ))(MBT ,QBT ) = 0 and det DYν ((usn∗ , usn∗ ))(MBT ,QBT ) = 0.

41.6 Global Dynamics Properties We have reported various local and global bifurcation in Sect. (41.5). We have analyzed that the parameters M and Q play significance role in the investigation of bifurcation theory for our proposed model. In this section, we study the local and global bifurcation on positive M − Q plane. The whole M − Q plane is divided into different subregions by the bifurcations curve. The model (41.4) exhibits local bifurcation (saddle-node bifurcation, Hopf bifurcation and B−T bifurcation) and homoclinic as global bifurcation for parametric restriction Q ≥ (1 + M )A2 . The whole M − Q plane is divided into four subregions B, C, D and E by bifurcation curves. System produces no interior equilibrium points in domain B, and two interior equilibria appear in C ∪ D ∪ E. Axial equilibria appear in each sub-regions independent of parametric condition. In Fig. 41.5, we have reported the schematic bifurcation diagram of model (41.4). Model (41.4) exhibits no interior equilibrium point when (M,Q) ∈ B. Decreasing the parameter Q such that (M , Q) inter in region C. At threshold value of Qsn , a saddle-node bifurcation curve appears which is illustrated by blue color in Fig. 41.5. Moving in region D from C, a Hopf bifurcation curve appears which is represented by green color in Fig. 41.5. Further, the parameter set move in the domain D through the homoclinic bifurcation denoted by the red colored curve in Fig. 41.5. Next, we report the phase portraits for fixed parameter value A, S and varying M and Q to describe the dynamical properties. We have illustrated the phase portraits of each regions of Fig. 41.5 by Figs. 41.6 and 41.7. We fixed the parameters value A = 0.2, S = 0.12. The parameter value of M and Q will be taken from each of the five domains of Fig. 41.5. The parameter values for M and Q for various domains are given in the caption of Figs. 41.6 and 41.7. In Figs. 41.6 and 41.7, stable and unstable equilibrium points are marked with red colored solid and black colored open circles. Unstable limit cycle is boundary of two attractors marked

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Table 41.1 Table illustrate the stability nature of the equilibrium points belonging to the each domain of Fig. 41.5 Domain Number of interior equilibria Nature of equilibrium points B

Nil

C

E1∗ , E2∗ exist

D

E1∗ , E2∗ exist

E

E1∗ , E2∗ exist

E0 is stable, (M , 0) is repellor and (1, 0) is saddle E2∗ is unstable and E1∗ is saddle E0 is stable, (M, 0) is repellor and (1, 0) is saddle E1∗ is saddle and E2∗ is stable surrounded by unstable limit cycle E0 is stable, (M, 0) is repellor and (1, 0) is saddle E2∗ is stable and E1∗ is saddle E0 is stable, (M, 0) is repellor and (1, 0) is saddle

with large black open circle, and magenta colored curve is the separtrix by which two attractors are separated. All three axial equilibria E0 , (M , 0) and (1, 0) appear in each subregions. We choose parameter values (M ; Q) from the region B. In this domain, system (41.4) does not exhibit interior equilibrium point and three axial equilibrium points (0, 0), (0.1, 0) and (1, 0) appear. The trivial equilibrium point E0 is stable and equilibrium points (0.1, 0), (1, 0) are unstable and saddle points, respectively. Here, E0 is global attractor all trajectories starting from any initial condition will be slatted to the origin which is shown in Fig. 41.6a. In region C, two interior equilibria E1∗ (saddle) and E2∗ (unstable) appear. In domain D, E2∗ is surrounded by an unstable limit cycle. The solution trajectory started from the inside the limit cycle will converge to the stable equilibrium point E2∗ , and other side, it will be settled to origin (See Fig. 41.7a). Enlarge version of limit cycle is depicted in (see Fig. 41.7c). Unstable limit cycle disappear when the parameter set move from region D to region E. Here, the model reported bi-stability between coexistence and trivial equilibrium points. The attractors are separated by the magenta colored curve. The trajectories starting right side of the separtrix will be settled toward the stable coexistence and other side it will be attracted to origin (see Fig. 41.7b). The existence and stability properties of equilibria are summarized in Table 41.1. System (41.4) exhibits subcritical Hopf bifurcation i.e. unstable limit cycle emerges around stable equilibrium point at the threshold value M = MH = 0.148778 16087959194, Q = 0.125 see Fig. 41.3. We examine the variation of the prey level of both interior equilibrium points with respect to change in parameter Q. We conclude that the prey level of stable equilibrium point decreases and prey level of unstable equilibrium point increase with increment in parameter Q. At M = 0.1 and

41 Dynamics of a Class of Modified Leslie-Gower Predator-Prey … 0.706

525

(a)

Predator

0.7055 0.705 0.7045 0.704 0.7035 0.7035

0.704

0.7045

0.705

0.7055

0.706

600

800

1000

Prey Prey, Predator

0.707 0.706

(b)

0.705 0.704 0.703 0

200

400

Time(t) Fig. 41.3 Right panel: Time series plot of model (41.4) for the parameters value A = 0.2, S = 0.12, MH = 0.14877816087959194, Q = 0.125. Left panel: Graph of limit cycle for the same parameters value

Prey population

1 0.8 0.6 0.4 0.2 0 0

0.02

0.04

0.08

0.06

0.1

0.12

0.14

Q Fig. 41.4 Saddle-node bifurcation of model (41.4) for the parameters value A = 0.2, S = 0.12, Qsn = 0.13726074722, M = 0.1

Qsn = 0.13726074722, the prey levels of two interior equilibrium points coincide which is reported by (see Fig. 41.4) where solid blue curve indicates the prey level of stable equilibrium point and dotted red curve indicates prey level of unstable equilibrium point.

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Homoclinic Hopf Saddle

B

Q

BT

C

E

D

M Fig. 41.5 Schematic bifurcation diagram in M − Q plane for fixed parameters value A = 0.2, S = 0.12, Q = 0.25. Red colored curve is the Hopf bifurcation curve; green colored curve is the homoclinic bifurcation curve; blue colored curves represents saddle-node bifurcation curve. Hopf bifurcation and saddle-node bifurcation curve coincide at the point BT point, as bifurcation point of co-dimension two, marked by black colored solid circle

41.7 Conclusion In our considered model, we are not provided the explicit expression of the interior equilibria. Our investigation assures that the existence of interior equilibria are possible under the parametric restriction Q ≥ (1 + M )A2 . In section (41.5), we have demonstrated the appearance of saddle-node bifurcation w.r.t the parameter Q and Hopf bifurcation w.r.t the parameter M . Saddle-node bifurcation brings a boundary between coexistence and extinction equilibrium points. From Fig. 41.5, it is clear that above the saddle-node bifurcation curve no coexistence appears and below it two interior equilibria appear. It is very rigorous to derive the explicit expressions of the coexistence state of prey and predator populations but with the help of bifurcation analysis, both coexistence and stability properties are verified under implicit parametric condition. The summarized result is reported in Table 41.1. In our proposed

41 Dynamics of a Class of Modified Leslie-Gower Predator-Prey …

Predator

1.5

527

(a)

1

0.5

0

1.5

1

0.5

0

Prey

Predator

0.8

(b)

0.6 0.4 0.2 0

0

0.2

0.4

Prey

0.6

0.8

1

Fig. 41.6 Left panel: Phase portraits for the parameter value A = 0.2, S = 0.12, M = 0.1, Q = 0.25 chosen from region B in M − Q plane. Right panel: Phase portraits for the parameter value A = 0.2, S = 0.12, M = 0.15, Q = 0.125 chosen from region C in M − Q plane

model, the total extinction of population is always possible as trivial equilibrium point is always stable. Initially, for low prey concentration, system always settles to total extinction due to admission of strong Allee effect in the prey growth. The magnitude of parameters is responsible for the basin of attraction of the stable equilibria which are shown in Figs. 41.6 and 41.7. Unstable limit cycle serves as the boundary of the basin of attraction of the stable equilibria. In bifurcation diagram, local bifurcation curves demonstrate the variation of equilibrium points, while global bifurcation curves report the drastic change in the system dynamics. The basin of attraction of E2∗ changes significantly through homoclinic bifurcation curve for the migration of parameter set from D to E. The complete bifurcation analysis is demonstrated at global dynamics section. In the inclusion of strong Allee effect, system parameters and initial population concentration are responsible for the survival of the prey-predator population.

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Predator

(a) 0.6 0.4 0.2 0

0

0.2

0.4

0.6

0.8

1

Prey

Predator

1.5

(b)

1

0.5

0

0.2

0

0.4

1

0.8

0.6

1.2

Prey

Predator

0.8

(c)

0.7 0.6 0.5

0.5

0.6

0.7

0.8

0.9

Prey Fig. 41.7 Left panel: Phase portraits for the parameter value A = 0.2, S = 0.12, M = 0.148, Q = 0.125 chosen from region D in M − Q plane. Right panel: Phase portraits for the parameter value A = 0.2, S = 0.12, M = 0.1, Q = 0.09 chosen from region E in M − Q plane

Acknowledgements Udai Kumar’s research is supported by research fellowship from MHRD, Government of India. Partha Sarathi Mandal’s research is supported by SERB, DST project [grant: YSS/2015/001548].

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References 1. Aguirre, P., González-Olivares, E., Sáez, E.: Two limit cycles in a Leslie-Gower predator-prey model with additive Allee effect. Nonlinear Analys. Real World Appl. 10, 1401–1416 (2009) 2. Aguirre, P., González-Olivares, E., Sáez, E.: Three limit cycles in a Leslie-Gower predator-prey model with additive Allee effect. SIAM J. Appl. Math. 69(5), 1244–1269 (2009) 3. Aziz-Alaoui, M.A., Daher Okiye, M.: Boundedness and global stability for a predator-prey model with modified Leslie-Gower and Holling-type II schemes, Appl. Math. Lett. 16, 1069– 1075 (2003) 4. Bazykin, A.D., Berezovskaya, F.S., Isaev, A.S., Khlebopros, R.G.: Dynamics of forest insect density: bifurcation approach. J. Theoret. Biol. 186, 267–278 (1997) 5. Bazykin, A.D.: Nonlinear Dynamics of Interacting Populations. World Scientific (1998) 6. Chicone, C.: Ordinary Differential Equations with Applications (2nd edn.), Texts in Applied Mathematics, vol. 34, Springer (2006) 7. Clark, C.W.: Mathematical Bioeconomics: The Mathematics of Conservation, 3nd ed. John Wiley and Sons Inc. (2010) 8. Courchamp, F., Clutton-Brock, T., Grenfell, B.: Inverse dependence and the Allee effect. Trends Ecol. Evol. 14, 405–410 (1999) 9. Courchamp, F., Berec, L., Gascoigne, J.: Allee Effects in Ecology and Conservation. Oxford University Press (2008) 10. González-Olivares, E., Mena-Lorca, J., Rojas-Palma, A., Flores, J.D.: Dynamical complexities in the Leslie-Gower predator-prey model as consequences of the Allee effect on prey. Appl. Math. Modell. 35, 366–381 (2011) 11. González-Olivares, E., Gallego-Berrío, L.M., González-Yañez, B., Rojas-Palma, A.: Consequences of weak Allee effect on prey in the may-holling-tanner predator-prey model. Math. Methods Appl. Sci. 38, 5183–5196 (2015) 12. González-Olivares, E., Flores, J.D.: Consequences in an open access fishery model considering multiple Allee effects. J. Bio. Syst. 23(01), 101–121 (2015) 13. González-Yañez, B., González-Olivares, E., Mena-Lorca, J.: Multistability on a leslie-gower type predator-prey model with nonmonotonic functional response. In: Mondaini, R., Dilao, R. (eds.) BIOMAT 2006—International Symposium on Mathematical and Computational Biology. World Scientific Co. Pte. Ltd., pp. 359–384 (2007) 14. Korobeinikov, A.: A Lyapunov function for Leslie-Gower predator-prey models. Appl. Math. Lett. 14, 697–699 (2001) 15. Leslie, P.H.: Some further notes on the use of matrices in Population Mathematics. Biometrika 35, 213–245 (1948) 16. Liermann, M., Hilborn, R.: Depensation: evidence, models and implications. Fish Fish. 2, 33–58 (2001) 17. May, R.M.: Stability and Complexity in Model Ecosystems, 2nd edn. Princeton University Press (2001) 18. Mena-Lorca, J., González-Olivares, E., González-Ya, B.: The leslie-gower predator-prey model with Allee effct on prey: A simple model with a rich and interesting dynamics. In: Mondaini R. (ed.), Proceedings of the 2006 International Symposium on Mathematical and Computational Biology BIOMAT 2006, E-papers Servicios Editoriales Ltda., Rio de Janeiro, pp. 105–132 (2007) 19. Sáez, E., González-Olivares, E.: Dynamics on a predator-prey model. SIAM J. Appl. Math. 59, 1867–1878 (1999) 20. Stephens, P.A., Sutherland, W.J.: Consequences of the Allee effect for behaviour, ecology and conservation. Trends Ecolog. Evol. 14, 401–405 (1999) 21. Stephens, P.A., Sutherland, W.J., Freckleton, R.P.: What is the Allee effect? Oikos 87, 185–190 (1999) 22. Taylor, R.J.: Predation. Chapman and Hall (1984) 23. Turchin, P.: Complex Population Dynamics. A Theoretical/Empirical Synthesis, Monographs in Population Biology 35, Princeton University Press (2003)

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24. van Voorn, G.A.K., Hemerik, L., Boer, M.P., Kooi, B.W.: Heteroclinic orbits indicate overexploitation in predator-prey systems with a strong Allee. Math. Biosci. 209, 451–469 (2007) 25. Wang, J., Shi, J., Wei, J.: Predator-prey system with strong Allee effect in prey. J. Mathe. Biol. 62, 291–331 (2011)

Chapter 42

Mathematical Modelling, Design and Simulation of a Bipedal Walker Randheer Singh, Vikas Kukshal, and Vinod Singh Yadav

Abstract Designing a dynamically stable biped robot is the most sought-after problem in robotics in the current era of automation. A bipedal model can walk only if there is a prescribed motion provided to the biped’s processor or the model’s neural network is trained beforehand to handle the real-time information. In both cases, the mathematical model for the trajectory and gait generation is developed initially so that the model can follow the instructions. The present paper includes a mathematical model for a bipedal walker. Furthermore, based upon this mathematical model simulation of the designed CAD model is performed and the results are compared with the available literature. The stability in the mathematical model is ensured by the Zero Moment Point method (ZMP). The torque requirements are also calculated from the simulation and an approximate method (Neglecting dynamics) is used to determine ZMP. A MATLAB SimScape simulation of a walking biped is created as a proof of concept for the design.

42.1 Introduction Humans have been very keen to imitate their motions in machines since the eighteenth century. In the last two decades, humans have produced a number of breakthroughs in the field of robotics such as industrial automation, humanoid robots, painting robots, dancing robots, firefighting robots, underwater robots, robot astronauts, surgical robots to name a few. But humanoid robots are still the most soughtafter problem in this field, because of its complex interdisciplinary nature. Designing a full-scale humanoid robot that can walk on uneven surfaces with the same efficiency as humans is still the most challenging task in humanoid robotics. Every human can walk easily but comprehending the complete biomechanics responsible for making a human walk is somewhat difficult. The decoupling of the human bio-mechanics reveals that trajectory generation for the gait synthesis is an essential tool to design a R. Singh (B) · V. Kukshal · V. S. Yadav Department of Mechanical Engineering, National Institute of Technology Uttarakhand, Srinagar, Uttarakhand, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_42

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simple biped walking robot. Understating the bio-mechanics is very essential while designing a new humanoid robot, the state-of-the-art is required to gain the basic insights of trajectory planning [1–3] and straight walking gait synthesis [4–6]. The present work focuses on the mathematical modelling, design and simulation a biped walking robot. The mathematical formulation is done to develop the trajectory generation using a cubic spline and subsequently synthesizing the required gait for the designed biped. The swing and stable leg’s trajectories are determined and results are plotted against time. The geometric method is used to determine inverse kinematics solutions for the biped’s legs. These solutions are used to find the joint angles necessary for making the biped walk. The stability of the biped robot is ensured using the Zero Moment Point (ZMP) [7–9] method. A MATLAB SimScape simulation of a walking biped is created as a proof of concept for the design.

42.2 Designing a Biped Model The designed model has a total of 6 degrees of freedom. Each leg has three revolute joints, one at the hip, one at the knee and one at each ankle of the leg. So, each leg has 3 degrees of freedom. The proposed biped walker has two chains as depicted in Fig. 42.1. Initially, a CAD model was developed for the kinematics feasibility and subsequently a simpler model is developed for simulations. This model consists of a trunk mass, symbolic electric motors, U-clamps and feet sole plates. However, motors are used only for their symbolic mass representations. The next joint is used at the knee location for the pitching movement of the bipedal. The last two joints are at the ankle position for pitch and roll movements of the feet and these joints are also mutually perpendicular. Since the bipedal is symmetric about the sagittal plane, therefore the same configuration of the joints is used for the right leg of the bipedal. The torque requirements are to be determined through simulation for all the actuators of the model. The dimensions are decided based upon the available biomechanical data [10]. The different lengths of the links and other dimensions are shown in Table 42.1. The dimensions are used for

Fig. 42.1 SimScape model for simulation

42 Mathematical Modelling, Design and Simulation of a Bipedal Walker Table 42.1 Dimensions of the model

533

S. No.

Part name

Dimensions (mm)

1.

Trunk height

140

2.

Trunk width

140

3.

Trunk depth

60

4.

Distance between both hip joints

80

5.

Hip to knee joint distance

74

6.

Knee to ankle joint distance

101

7.

Foot length

120

8.

Foot breadth

60

9.

Foot height

10

Table 42.2 Mass properties of the biped walker Links description

Location

Value (kg)

m4 (mass of the thigh link)

At the distal end of thigh

0.20064

m1 (mass of the shin link)

At the distal end of shank

0.1074

m3 (mass of the foot link)

At the ankle joint

0.20304

m2 (mass of the trunk portion link)

At the geometric centre of trunk

0.5442

designing a biped walker. The dimension selection affects the trajectory generation and hence the maximum height of the swing leg, the step length, the step duration is decided based on the dimensions of the model. The masses of the different links are determined using the SimScape model based on the density. These mass properties are used for the zero-moment point (ZMP) calculation which is subsequently used for determining the dynamic stability of the biped walker. The mass properties are shown in Table 42.2. Link mass affects the dynamic stability due to the frontal shift of the trunk mass towards the stable leg during the single support phase.

42.3 Kinematics 42.3.1 Forward Kinematics The forward kinematics of each leg of the robot is developed using the classical D-H model assuming the trunk as reference. Each leg chain has a total of 4 reference frames attached one for each revolute joint as shown in Fig. 42.2. For the left leg, one revolute joint is at the hip location for rolling and the other revolute joint is at the knee joint for pitching moment. Similarly, one revolute joint is at the ankle joint. The general transformation matrix for the classical convention is mentioned in Eq. 42.1.

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Fig. 42.2 Kinematic diagram of the biped walker

The global transformation matrix defines the relation between any two consecutive frames in space. The joint angle is the variable quantity in the transformation matrix for the revolute joint and di is the variable quantity for the prismatic joint. The D-H (DenavitHartenberg) parameters are shown in Table 42.3. The revolute joint has θ as the parameter and hence, θ 1 , θ 2 , θ 3 are the joint parameters for the hip, knee and ankle joints of the swing leg respectively. Since we have only prismatic joints in the bipedal model so the only variable in the transformation matrix is the joint variable θ i . ⎤ cos θi − sin θi ∗ cos αi − sin θi ∗ sin αi ai ∗ cos θi {i−1} ⎢ sin θi cos θi ∗ cos αi − cos θi ∗ sin αi ai ∗ sin θi ⎥ ⎥ Ti= ⎢ ⎦ ⎣ 0 sin αi cos αi di 0 0 0 1 ⎡

(42.1)

Table 42.3 D-H parameters of the kinematic diagram S. No.

θ i (joint angle)

α i (link twist)

ai (link length)

d i (link offset)

1

θ1

−90

L 12

0

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θ2

0

L 23

0

3

θ3

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0

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{i−1}

Ti defines the relationship between link ith and the (i − 1)th frames. This transformations matrix is a 4 × 4 square matrix, in which the first 3 × 3 matrix gives the information about the angle of rotation between the ith and (i − 1)th frames respectively. Whereas the last column of the 4 × 4 transformation matrix gives information about the translational offset between the ith and (i − 1)th frames respectively. The transformation between any two frames can be found by multiplying all the consecutive individual transformation matrices together. The final matrix obtained is called the arm matrix of the biped. {0}

i.e., T3

{0}

{1}

{2}

= T1 ∗ T2 ∗ T3

The individual transformation matrices of each joint can be obtained from the general transformation matrices by substituting the value of corresponding parameters from Table 42.3 into Eq. 42.1.

42.3.2 Inverse Kinematics The inverse kinematics of this model is required to map the Cartesian co-ordinates into joint co-ordinates for the trajectory synthesis of the designed model. The co-ordinates are to be converted into the respective joint co-ordinates of all the joint of the biped so that the motors can supply these inputs to the joints to perform the required trajectory by the biped. The geometrical method is used for determining inverse kinematics in this work due to its simplicity and accuracy (Fig. 42.3). In the geometrical method, we identify the geometry and then use trigonometric expressions to find the specific joint angles. In Fig. 42.3a, b the sagittal plane view is used to find

Fig. 42.3 a, b Inverse kinematic parameters of the biped walker

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the angles A, B, C and D. For straight walking, the hip roll angles are assumed to be zero. This has been adapted from the literature. Hence, θ4 = 0, θ9 = 0

42.4 Trajectory Generation and Synthesis for Straight Walking There are two phases during the trajectory generation of a bipedal robot. The first phase is the single support phase in which one leg of the robot is completely off the ground while the other foot is fully in contact with the ground. While during the second phase both the legs are on the ground. In the current work, only a single support phase is considered for the trajectory synthesis purpose of the biped. The leg which is off the ground is called the swing leg and the leg which is on the ground is called the stable leg.

42.4.1 Swing Leg Trajectory In the case of the swing leg’s trajectory synthesis, the hip is assumed to be stationary while the ankle is considered to have a curvilinear motion. Since the x position x A (t) of the ankle is a function of time, x A (t) is the x position of the ankle of the swing leg at any time instant ‘t’. 

xf x A (t) = xi + 3 2 tf





xf t −2 3 tf 2

t3

(42.2)

Similarly, we can synthesize the trajectory for the motion of the ankle of the swing leg in the z-direction using a simple cubic polynomial. However, the z position zA {x A (t)}, of the ankle is the function of, x position of the ankle x A (t). Since the ankle position is already a function of time. Eventually, the z position zA , of the ankle is also a function of time. The terms x 0 , x f , x m denotes the value of x position of the ankle at the times t = 0, t = tf /2 and t = tf respectively.

 2   h − x f + xi ∗ xi h xi + x f xi + 3x f x A (t) z A (t) = 2 + 2   (xm − xi ) xm − x f − xi (xm − xi ) xm − x f − xi

42 Mathematical Modelling, Design and Simulation of a Bipedal Walker

 h 3xi + 2x f (x A (t))2 + (h(x A (t))3 − 2  (xm − xi ) xm − x f − xi

537

(42.3)

42.4.2 Stable Leg Trajectory This approach of finding a suitable trajectory for the hip is similar to the approach followed for ankle trajectory generation in the previous section. xf + Vs t + x H (t) = 4



   xf 3 (Ve − Vs ) (Vs + Ve ) 3 2 t −2 − r4 t f − t 2t f 2 2t 3f 2t 3f (42.4)



x e) ; where r4 = −2 2tf3 − (Vs2t+V 2 f f Similarly, we can synthesis the trajectory for the motion of the hip of the stable leg in the z-direction using a simple cubic polynomial. This polynomial is also a third-degree polynomial which is also a function of time. However, the z position zH {x H (t)}, of the ankle is the function of, x position of the ankle x H (t). Since the hip position is already a function of time.  z H (t) =



x f

2 (d1 + d2 )2 − x H (t) − xi + 2

(42.5)

42.5 Results 42.5.1 Joint Trajectory Plots The interdependence of the ‘x’ coordinate and ‘z’ coordinates of the swing leg’s ankle [5] is shown in Fig. 42.4. The results are validated with the available literature and are found to be in agreement. However, there is a slight variation in both results due to the selection of different maximum height and step length. Figure 42.5 is more circular in nature but this curvature is highly dependent upon the degree of spline chosen and the initial and final conditions defined. In the present work, the maximum height achieved is 2.5 cm where in [5] the maximum height is 5 cm and also the step length is 72 cm whereas in the current study the step length is merely 18 cm. The difference in input parameters results in the difference between both the trajectories.

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Fig. 42.4 Variation of ‘z-position’ of swing feet with the ‘x’ (SSP)

Fig. 42.5 Variation of ‘z-position’ of swing feet with the ‘x’ (SSP) [5]

The trajectory traced by the stable leg’s hip is plotted against the swing leg’s ankle as shown in Fig. 42.6. However, Fig. 42.7 shows the same variation as per the literature [1]. This is taken for the reference and validation of the results. The similar shape of both the graphs validates the current work because they are in line with already published literature.

Fig. 42.6 Variation of ‘z versus x-position’ of stable leg’s hip and swing leg’s ankle (SSP)

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Fig. 42.7 Variation of ‘z versus x-position’ of stable leg’s hip and swing leg’s ankle (SSP) [1]

42.5.2 Stability of the Bipedal Robot The stability of a biped is ensured through the zero-moment point (ZMP) [4]. According to this concept, the ZMP of the biped should lie within the support polygon of the biped. This support polygon is constructed by connecting the most outer points of both the feet of the biped on the ground during the double support phase. However, during a single support phase, the stability is confirmed if the ZMP lies within the supporting or stable foot’s polygon on the ground. In the current work, the ZMP is calculated by approximation methods by neglecting the acceleration terms of the body. The velocity of the biped is assumed to be very small and the inertia values of the links of the biped are also neglected. This approximation makes the ZMP calculation easy and possible without even calculating the dynamics of the designed bipedal robot. The X ZMP values are plotted against time in Fig. 42.9. This shows the values within the bounds for the 3 s time duration. The flowchart for the stability algorithm is depicted in Fig. 42.10. The following conditions help to monitor the ZMP values and their location within the bounds is ensured by the walking stability of the designed bipedal robot. Foot position at start(at t0 ) : Swing foot 0 ≤ x ≤ 12 and 4 ≤ y ≤ 4; Stable foot 9 ≤ x ≤ 21 and 4 ≤ y ≤ 12; The x and y values of the ZMP of the designed biped are plotted in Fig. 42.8. The Y ZMP values are plotted against X ZMP values for the duration of 3 s (Fig. 42.9). The fact that when x increases, the y distance increases at a faster rate is due to the transfer of weight of the trunk mass towards the stable leg’s foot quickly. The transfer of weight is due to the prescribed upper body motion (Fig. 42.10).

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Fig. 42.8 The X ZMP and Y ZMP values (SSP)

Fig. 42.9 The X ZMP between the required limits values (b1 and b2 ) (SSP)

The x and y Co-ordinates of the zero moment points lie within the supporting leg’s polygon during the time duration of 0.8–2.8 s. This ensures that the designed biped is table during this time span. However, during the remaining time (i.e., before 0.8 s and beyond 2.8 s) the stability can be taken care of by the double support phase calculations. This work only discusses the single support phase walking stability of the designed biped. Finally, the zero-moment point (ZMP) is calculated and checked whether it lies in the supported polygon or not. If the ZMP lies outside the supported polygon then the initial conditions are to be revised such that the ZMP lies within the support polygon. If now the ZMP lies within the support polygon then the dynamic balance margin (DBM) [11] is defined and it is obtained such that there is a maximum margin of stability [4] for the robot. Figure 42.11 shows the different phases of the designed walking biped.

42.5.3 Torque Requirement Figure 42.12 shows the variation of torque (N m) requirement with respect to time (s) for ankle, knee and hip joints of the designed robot. It is observed that the maximum torque of 13.9 N m is required for the hip joint and 4.8 N m for the knee joint and 4.4 N m for ankle joints. This is attributed to the maximum load carried by the hip joint as compared to the knee and ankle joints. It is also found that the minimum torque is required for the ankle joint because it carries less load.

42 Mathematical Modelling, Design and Simulation of a Bipedal Walker Fig. 42.10 Trajectory generation flowchart

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Fig. 42.11 Walking sequence during the simulation in MATLAB SimScape

Fig. 42.12 Torque (N m) requirements of ankle, knee and hip joints respectively with time (s)

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42.5.4 Nature of Motion in the Upper Body The upper body distance increases but the pattern in which it is depicted in Fig. 42.13 is an interesting fact. This trend is followed due to the fact that the upper body is oscillating back and forth due to the intermittent motion of the trunk and the switching of feet during the single support phase. The trunk mass height also fluctuates as shown in Fig. 42.14. It is clear from the joint angles that the body bends to take the next step during the single support phase. The sequence of the joint angles affects the upper body height. The nature of the variation is also due to the cyclic nature of the joint angle. Fig. 42.13 The upper body motions

Fig. 42.14 The variation of torso height in sagittal plane

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42.6 Conclusion The mathematical model is formulated based on the designed CAD model. The dimensioned are referred from the full-scale biped model. A simple biped is also designed to perform the simulations. The simulation algorithm uses the mathematical model formulated in the kinematics section. Physical contact modelling is also done to ensure the contact is as close as possible to the contact in the real world. The trajectory generation algorithm calculates the ZMP of the biped. This ZMP lies within the support polygon of the stable leg for the duration of 0.8–2.8 s. During this timeframe, the biped is stable dynamically and can walk without falling down. The simulation results are aimed to determine the torque required for each joint, the joint velocity trajectories and the variation of center of mass of the torso.

References 1. Panwar, R., Sukavanam, N.: Effect of Upper Body Motion on Biped Robot Stability. Springer Singapore 2. Panwar, R.: Stable polynomial gait of a biped robot with toe joint, pp. 382–387 (2017) 3. Huang, Q., Yokoi, K., Kajita, S., Kaneko, K.: Planning walking patterns for a biped robot 17(3), 280–289 (2001) 4. Dip, G., Prahlad, V., Kien, P.D.: Genetic algorithm-based optimal bipedal walking gait synthesis considering tradeoff between stability margin and speed 27, 355–365 (2009) 5. Mu, X., Wu, Q.: Synthesis of a complete sagittal gait cycle for a five-link biped robot, Oct 2003, 581–587 (2015) 6. Chevallereau, C., Bessonnet, G., Abba, G., Aoustin, Y.: Bipedal Robots: Modeling, Design and Walking Synthesis. Wiley-ISTE, Hoboken (2010) 7. Bajrami, X., Shala, A., Hoxha, G., Likaj, R.: Dynamic modelling and analyzing of a walking of humanoid robot. Stroj. Cas. 68(3), 59–76 (2018) 8. Takanishi, A., Lim, H.-O., Tsuda, M., Kato, I.: Realization of dynamic biped walking stabilized by trunk motion on a sagittally uneven surface, pp. 323–330 (2002) 9. Lee, D.W., Lee, M.J., Kim, M.S.: Whole body imitation of human motion with humanoid robot via ZMP stability criterion. In: IEEE-RAS International Conference on Humanoid Robots, vol. 2015-Dec, pp. 1003–1006 (2015) 10. Winter, D.A.: Biomechanics and Motor Control of Human Movement, 4th edn. Wiley, Hoboken (2009) 11. Vundavilli, P.R., Sahu, S.K., Pratihar, D.K.: Dynamically balanced ascending and descending gaits of a two-legged robot 4(4), 717–751 (2007)

Chapter 43

A Simple Approach to Enhance the Performance of Traditional P&O Scheme Under Partial Shaded Condition by Employing Second Stage to the Existing Algorithm Muralidhar Nayak Bhukya, Manish Kumar, and Shobha Rani Depuru Abstract During partial shaded conditions, the traditional Perturb & Observe (P&O) tracking scheme fail to track global power point of the photovoltaic (PV) system. This constraint gives a scope to replace traditional algorithms with metaheuristic schemes to track global maximum power point (GMPP) effectively. At the same time, metaheuristic algorithms employed are complex and expensive. Hence, in this paper, a second stage is introduced to the traditional algorithm, such that they perform efficiently during partial shaded condition. The proposed second stage is experimentally validated by integrating to the P&O controller.

43.1 Introduction Solar power generation mainly depends on available irradiance, temperature, and PV panel conversion efficiency [1]. Solar irradiance and temperature are inconsistence; hence, the change in irradiance and temperature effects the power generation. In view of the above limitation, it is necessary to transmit maximum solar power to the load in any weather condition [2]. To transmit maximum solar power, MPP has to be tracked constantly with the help of MPPT algorithms. As discussed the intensity of G and T changes for every clock second [3, 4]. Hence, every step change in G and T has its own MPP and operated at every change in MPP using MPPT scheme [5]. From the M. N. Bhukya (B) · M. Kumar Department of Electrical Engineering, School of Engineering & Technology, Central University of Haryana, Mahendergarh, Haryana 123031, India e-mail: [email protected] M. Kumar e-mail: [email protected] S. R. Depuru Department of Electrical and Electronics Engineering, Institute of Aeronautical Engineering, Hyderabad 500043, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_43

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literature, based on their execution methodology the existing MPPT algorithms are classified as (a) online and (b) offline methods. In terms of implementation, offline methods are simple and can be easily implemented in microcontroller. In economy point of view, these methods are not recommended for long lasting power generating systems. While online methods such as derivatives of hill climbing are commonly used MPPT schemes, the selection of MPPT controller for any particular application is mainly based on implementation complexity, efficiency, accuracy, dynamic tracking ability, tracking speed and cost. Zhang et al. [6] stated a new approach to attain the MPP of the PV system based on the inverse relation between change in inductance range with current characteristics and achieved dynamic operating control. Kermadi et al. [7] proposed an effective MPPT scheme based on adaptive P&O and particle swarm optimization algorithm incorporates an effective search skip judge mechanism, which helps to eliminate the constant region in the P–V characteristics. The proposed hybrid scheme has satisfactory performance using buck–boost converter but fails to reproduce the same with the rest of the power converters. Power quality issues in grid-connected PV systems are addressed by Sangwongwanich and Blaabjers [8], by improving sampling rate efficiency of MPPT scheme. The proposed method selects values randomly in between fast and slow sampling values and reduces inter-harmonics frequency spectrum effectively. Carrasco et al. [9] designed and developed an analog multiplier circuit to harvest maximum solar power from low-rated panels, i.e., is used in satellite applications. Putri et al. [10] simulated PV system with INC-based MPPT scheme using buck– boost converter as an intermediate between source and load. The results are evident to show the better performance of INC compared with P&O, but still it fails to track the power path accurately and efficiently during dynamic weather conditions [11, 12]. In [13], a new MPPT scheme based on back-stepping sliding mode technique is presented and the controller has acceptable dynamic response time during fast changing weather conditions. At the same time, there exists a major percentage of tracking error, which forces the system to operate at a duty cycle nearer to the MPP value [14]. Lashen and Adbel-Salam [15] enhanced the performance of conventional HC method by combing with ANFIS controller. The hybrid algorithm is tested using Ropp and sinusoidal irradiance profiles. Lag communication between HC and ANFIS may misjudge duty ratio of the converter [16]. Al-Majidi et al. [17] integrated a new scheme to the grid using fuzzy logic. Efficiency of the proposed algorithm is evaluated using the standard EN50530 test. Under steady-state conditions, the proposed tracker had achieved efficiency greater than 90%, while under variable conditions efficiency is limited to less than 90%. In [18] and [19], intelligent MPPT controllers are presented to mitigate the drawbacks associated with conventional schemes. Applications of these intelligent controllers are limited to small-scale PV systems. Boukenoui et al. [20] assessed the performance of conventional, improved and soft computing-based MPPT techniques using d-Space controller. During low and high irradiance periods, fuzzy-based algorithm has better performance over conventional and improved schemes.

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INC controller is succeeded in extracting maximum power from PV panel compared to P&O during fast changing weather conditions [21] and but requires expensive components for hardware implementation. At the same time, indirect methods such as lookup table, β-method and curve fitting methods are simple to implement. MPPT schemes based on derivation, estimation, numeric analysis and bisection are complex in implementation and requires high-speed microcontrollers. Offline data schemes are simple to implement but require huge and exact pre-calculated or pre-estimated data for reliable operation. Array reconfiguration and sun tracking systems require manual supervision. Ripple correlation control, slide mode control, incremental conductance, and gradient descent methods fail to operate effectively during dynamic weather conditions. Intelligent controllers such as fuzzy logic, neural network, and metaheuristic algorithms are having advantages such as response time, transient response, and reliable working in any weather conditions. Apart from MPPT schemes, DC–DC converter efficiency and the number of intermediate conversion stages play an important role in solar power generation [22].

43.2 Effect on P–V and I–V Curve Due to T and G Thermal stress during photovoltaic effect raise in solar irradiance and particularly solar irradiance within the band of infrared wavelength are the three main reasons for the rise of PV cell temperature [23]. The mathematical equation governing V OC and I SC of the PV system is   VOC = VOC,STC log(e + 0.5G)(1 − 0.0028T )  ISC = ISC,STC

G (1 + 0.025T ) G STC

(43.1)

 (43.2)

where G = (G/GSTC ) − 1 and T = T − T STC . The nonlinear characteristics of a 100 W solar module are depicted in Fig. 43.1a–d [24].

43.3 Proposed Two-Stage MPPT Scheme P&O scheme fails to achieve GMPP during partial shaded condition. In order to eliminate the contradictions associated with traditional scheme, a new two-stage MPPT scheme is proposed. Figure 43.2 depicts P–V curve of three series and three parallel (3S3P) configuration. During uniform irradiance, P&O tracks MPP of the PV system as depicted in Fig. 43.2a. At the same time under partial shaded condition, P&O stucks at first peak as shown in Fig. 43.2b. Hence, from that operating point,

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(a)

(c)

(b)

(d)

Fig. 43.1 a–d Solar module parameters under variable temperature and irradiance

the rest of the P–V curve has to be sampled, such that the exact global operating point can be achieved. Stage I: PV system is operated with traditional P&O scheme. P&O perturbs in the positive direction until MPP is reached. As the direction of perturbation reverses, the second stage of the proposed MPPT is activated. Let the MPP obtained by the conventional controller be denoted by MPP1 . Stage II: From MPP1 , the P–V curve is sampled with a fixed scale as shown in Fig. 43.2b, up to V OC . Using interpolation, maximum point in the sampled interval is determined. Figure 43.3 depicts flowchart of the proposed MPPT scheme, and Fig. 43.4 shows convergence direction of the sampled points. Intermediate polynomial equations are expressed as a function of power as P(Vi, j ) =

(V − V j )P(Vi ) − (V − Vi )P(V j ) Vi − V j

(43.3)

where i and j are respective sampled points on the P–V curve. From Fig. 43.4, it is clear that all the sampled points are computed to obtain the final maximum point, and hence the final, Neville polynomial expression in this paper is mathematically written as P(V1,19 ) =

(V − V19 )P(V1 ) + (V − V1 )P(V19 ) V1 − V19

(43.4)

PV System Power "W"

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800

600

MPP of the PV system under uniform irradiance

400

200

0

0

10

20

30

40

50

60

PV System Voltage "V"

(a) PV System Power "W"

350 MPP Obtained from P&O

300

V6

V12

V5

V13

V11 V7

250

V10 V9

V1

V15

V8

V4

200 V3

V16

150 V2

V17

100

V18 50 0

V19 = Voc 0

10

20

30

40

50

60

70

PV System Voltage "V"

(b) Fig. 43.2 MPP of the PV system under a uniform weather conditions and b partial shaded phenomenon for 3S3P configuration

It is well known that P–V curve possess constant current and voltage regions in any weather conditions such that derivation of Eq. 43.4 with respect to voltage gives exact maximum point in the region of sampled points. Let the voltage obtained from interpolation is termed as VMPP2 . At last, both the MPP voltages, i.e., VMPP1 and VMPP2 , are compared. Under uniform irradiance, VMPP2 will be less than VMPP1 . Whereas during partial shaded conditions both the voltages are compared and highest magnitude point is treated as GMPP. Entire P–V axes are scanned by the two stages, and the proposed MPPT scheme gives exact MPP in any weather condition. The appreciable feature associated with the proposed two-stage MPPT scheme is, it treats both the uniform irradiation and partial shading condition alike. VMPP

   d P V1,19 = P I (V1,19 ) = dV

550 Fig. 43.3 Flowchart of the proposed MPPT scheme

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43 A Simple Approach to Enhance the Performance of Traditional P&O … Fig. 43.4 Convergence of the sampled points using Interpolation

551

V1 V1,2 V2

V1,j

Vj-1 Vj, j-1 Vj

=

(V19 − V )P I (V1,18 ) − P(V1,18 ) + (V − V1 )P I (V2,19 ) + P(V2,19 ) V19 − V1 (43.5)

43.4 Simulation Test The proposed two-stage MPPT scheme is simulated using three series and threeparallel (3S3P) configuration as shown in Fig. 43.5. Irradiance on each individual module is depicted in Table 43.1, and their corresponding P–V curves are plotted in Fig. 43.6. The developed characteristics of PV configuration are similar to RNG 100D 100 W solar panel, and numeric values of filter inductor and capacitor of the boost converter are 2 mH and 2 µF. The proposed two-stage MPPT controller is tested the usage of a 3S3P. Figures 43.7 and 43.8 depict PV system output parameters. Case A and B represents uniform irradiance, whereas Case C and D indicates PSC on PV system. Initially, PV system is simulated with case A; i.e., uniform irradiance after 0.2 s the irradiance pattern is shifted to case C. Figure 43.7 depicts PV system output parameters during testing from case A to C. Under uniform irradiance, the conventional P&O controller operates and extracts a maximum power of 900 W. After 0.2 s, PSC is introduced and the second stage is succeeded in scaling the P–V characteristics and obtaining GMPP of the PV system with respect to case C. Similarly, Fig. 43.8 shows the PV system output parameters from case B to case D. The output voltage, current, and power obtained at case D are 58.3 V, 8.2 A, and 480 W, respectively. From the simulation result, it is evident that the proposed two-stage MPPT scheme tracks the GMPP of the PV system during PSC.

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Fig. 43.5 3S3P configuration of PV system

Table 43.1 Irradiance on PV module Pattern

Case A

Case B

Case C

Case D

G1

1000

800

550

775

G2

1000

800

550

900

G3

1000

800

155

650

G4

1000

800

635

500

G5

1000

800

315

220

G6

1000

800

225

275

G7

1000

800

150

650

G8

1000

800

365

820

G9

1000

800

1000

715

(a) Fig. 43.6 Characteristics of 3S3P configuration

(b)

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900 800

900W 700

Oscillations untill Neville Interpolation tracks GMPP of the PV System

Case A

600 500 400

Case C 230W

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0.05

0.1

0.15

0.2

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0.3

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0.4

0.35

0.4

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0.4

Time (Sec)

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60 50 56.7V

40

Case A

30 38V

Case C

20 10 0 0

0.05

0.1

0.15

0.2

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0.3

Time (Sec)

(b) PV System Current "A"

18 16 14

15.8A

12

Case A

10 8 Case C

6 4

5.95A

2 0 0

0.05

0.1

0.15

0.2

0.25

0.3

Time (Sec)

(c) Fig. 43.7 PV system output parameters from case A to case C a power, b voltage and c current

43.5 Conclusion A new two-stage tracking scheme based on traditional P&O and interpolation is presented for solar power generation. The two-stage scheme tracks the power path accurately during partial shaded conditions. Apart from this, the proposed scheme achieves GMPP quickly under partial shaded condition and it is very simple to

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700 720W

600

Case B

500 400 Case D

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300 200

480W

100 0 0

0.05

0.1

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Time (Sec)

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60 50.3V

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Case B

40 Case D

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20 10 0 0

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Time (Sec)

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10

13.5A

8

Case D

6

8.2A

4 2 0

0.05

0.1

0.15

0.2

0.25

0.3

Time (Sec)

(c) Fig. 43.8 PV system output parameters from case B to case D a power, b voltage and c current

implement. Another advantage of the proposed scheme is that the second stage of the proposed scheme can be combined with other conventional schemes, such that existing conventional schemes can track global point during partial shaded condition.

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References 1. Salem, A., et al.: Stand alone three phase symmetrical multi level inverter. In: IEEE Transportation Electrification Conference and Expo, pp.1–6 (2015). https://doi.org/10.1109/INTEC. 2015.7572459 2. Pai, F.-S., Chao, R.-M.: A new algorithm to photovoltaic power point tracking problems with quadratic maximization. IEEE Trans. Energy Convers. 25(1), 262–264 (2010) 3. Kota, V.R., Bhukya, M.N.: A simple and efficient MPPT scheme for PV module using 2dimensional lookup table. In: IEEE Power and Energy Conference at Illinois (PECI), pp.1–7 (2016). https://doi.org/10.1109/PECI.2016.7459226 4. Kjaer, S.B., Pedersen, J.K., Blaabjerg, F.: A review of single-phase grid-connected inverters for photovoltaic modules. IEEE Trans. Ind. Appl. 41(5), 1292–1306 (2005) 5. Hamed, T., Quaicoe, J., Benjamin, J.: Power and frequency controllable multi level MHz inverter with soft switching. In: IEEE APE Conference & Expo, pp. 2576–2581 (2017). https:// doi.org/10.1109/APEC.2017.7931061 6. Zhang, L., Hurley, W.G., Wolfle, W.H.: A new approach to achieve maximum power point tracking for PV system with a variable inductor. IEEE Trans. Power Electron. 26(4) (2011) 7. Kermadi, M., Salam, Z., Ahmed, J., Berkouk, E.M.: An effective hybrid maximum power point tracker of photovoltaic arrays for complex partial shading conditions. IEEE Trans. Ind. Electron. 66(9), 6990–7000 (2019) 8. Sangwongwanich, A., Blaabjers, F.: Mitigation of interharmonics in PV systems with maximum power point tracking modification. IEEE Trans. Power Electron. 34, 8279–8282 (2019) 9. Carrasco, J.A., de Quiros, F.G., Alares, H., Navalen. M.: An analog maximum power point tracker with pulse-width modulation multiplication for a solar array regulation. IEEE Trans. Power Electron. 34, 8808–8815 (2019) 10. Putri, R.I., Wibowo, S., Rafi, M.: Maximum power point tracking for photovoltaic using incremental conductance method. Energy Procedia 68, 22–30 (2015) 11. Ali, A.I.M., Sayed, M.A., Mohamed, E.E.M.: Modified efficient perturb and observe maximum power point tracking technique for grid-tied PV system. Int. J. Electr. Power Energy Syst. 99, 192–202 (2018) 12. Bhukya, M.N., Kota, V.R., Rani, D.S.: A simple, efficient and novel standalone photovoltaic inverter configuration with reduced harmonic distortion. IEEE Access, Feb 2019. https://doi. org/10.1109/ACCESS.2019.2902979 13. Vimalarani, C., Kamaraj, N., Chitti, B.B.: Improved method of maximum power point tracking of photovoltaic (PV) array using hybrid intelligent controller. Optik 168, 403–415 (2018) 14. Bhukya, M.N., Kota, V.R.: A new MPPT scheme based on trifurcation of PV characteristic for photovoltaic power generation. Int. J. Pure Appl. Math. 114(10), 439–447 (2017) 15. Lashen, M., Adbel-Salam, M.: Maximum power point tracking using hill-climbing and ANFIS technique for PV application. A review and a novel hybrid approach. Energy Convers. Manage. 171, 1002–1019 (2018) 16. Kota, V.R., Bhukya, M.N.: A novel global MPP tracking scheme based on shading pattern identification using artificial neural networks for photovoltaic (PV) power generation during partial shaded condition. IET Renew. Power Gener. (2019). https://doi.org/10.1049/iet-rpg. 2018.5142 17. Al-Majidi, S.D., Abbod, M.F., Al-Raweshidy, H.S.: A novel maximum power point tracking technique based on fuzzy logic for photovoltaic system. Int. J. Hydrogen Energy 43, 14158– 14171 (2018) 18. Sundareswaran, K., Sankar, P., Nayak, P.S.R., Simon, S.P., Palani, S.: Enhanced energy output from a PV system under partial shaded conditions through artificial bee colony. IEEE Trans. Sustain. Energy 1–12 (2014). https://doi.org/10.1109/TSTE.2014.2363521 19. Blaifi, S.-A., Maulahoum, S., Benkercha, R., Taghezouit, B., Saim, A.: M5P model tree based fast fuzzy maximum power point tracker. Solar Energy 163, 405–424 (2018)

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20. Boukenoui, R., Ghanes, M., Barbot, J.-P., Bradai, R., Melit, A., Salhi, H.: Experimental assessment of maximum power point tracking methods for photovoltaic systems. Energy 132, 324–340 (2017) 21. Bhukya, M.N., Kota, V.R.: A quick and effective MPPT scheme for solar power generation during dynamic weather and partial shaded conditions. Eng. Sci. Technol. Int. J. (2019). https:// doi.org/10.1016/j.jestch.2019.01.015 22. Bhukya, M.N., Kota, V.R.: A novel P&OT-Neville’s interpolation MPPT scheme for maximum PV system energy extraction. Int. J. Renew. Energy Dev. 7(3), 251–260 (2018). https://doi.org/ 10.14710/ijred.7.3.251-260 23. Bhukya, M.N., Kota, V.R.: DCA-TR-based MPP tracking scheme for photovoltaic power enhancement under dynamic weather conditions. Electr. Eng. 100(4), 2383–2396 (2018) 24. Kota, V.R., Bhukya, M.N.: A novel linear tangents based P&O scheme for MPPT of a PV system. Renew. Sustain. Energy Rev. 71, 257–267 (2017). https://doi.org/10.1016/j.rser.2016. 12.054

Chapter 44

One-Dimensional Model for Removal of Volatile Organic Compound Propane in a Catalytic Monolith Umang Bedi

and Sanchita Chauhan

Abstract During the past years, due to industrial development and rapid urbanization, air pollution has become a primary environmental concern. In urban areas, automobiles are one of the most significant contributors to air pollution due to their sheer number. The monolithic catalytic converters recognized as an essential afterburn treatment device that reduces emissions such as volatile organic compounds (VOCs) and other pollutants. Mathematical modeling for VOC propane reduction was performed using Pt/δ-Al2 O3 catalyst to control the emissions to the atmosphere. A transient one-dimensional model for predicting the complete combustion of propane emissions in the monolithic catalytic converter has been formed during the warm-up period to simulate the thermal and conversion characteristics using a combination of catalytic reactions, the mass and heat transfer between the catalytic surface, and the exhaust gas. The modeling equations consist of a system of coupled partial differential equations (PDEs), which are analyzed and solved by the implicit scheme with MATLAB software.

Nomenclature A Cg t L K cat k 0cat kg v Cp S

Catalytic surface area, cm2 /cm3 Gas concentration, mol/cm3 Time, s Monolith length, cm Rate constant, mol/cm2 h KPa5 Pre-exponential factor for reactions, mol/cm2 s Pa5 Mass transfer coefficient, cm/s Gas velocity, cm/s Specific heat, J/g K Geometric surface area, cm2 /cm3

U. Bedi (B) · S. Chauhan Dr. S. S. Bhatnagar University Institute of Chemical Engineering and Technology, Panjab University, Chandigarh 160014, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_44

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λ Ts x Tg −H ρ R E cat h C T g T s t z

U. Bedi and S. Chauhan

Conductivity, J/cm K s Solid temperature, K Axial coordinate, cm Gas temperature, K Heat of reaction, cal/mol Density, g/cm3 Gas constant, J/mol K Activation energy, cal/mol Heat transfer coefficient, J/cm2 s K Gas concentration in dimensionless form Gas temperature in dimensionless form Solid temperature in dimensionless form Time in dimensionless form Axial coordinates in dimensionless form

44.1 Introduction Pollution is one of the biggest environmental health concerns in both developing and developed countries [1]. Air pollution from vehicle emissions has become an issue of great concern over the past few decades. “The World Health Organization (WHO) has recognized climate change as one of the main health coercions of the twenty-first century, and air pollution as the single biggest environmental health risk” [2]. “Environmental legislation has imposed progressively stringent targets for permitted levels of atmospheric emissions.” For example, “the Gothenburg Protocol sets maximum permitted levels of emissions (emission ceilings) for each signatory for different classes of pollutants, which are sulfur, oxides of nitrogen, volatile organic compounds (VOCs), and ammonia” [3]. VOCs are an important part of these emissions. Catalytic oxidation of VOCs is becoming a progressively feasible technology in heating appliances, chemical processed heaters, and power generation [4]. Catalytic converters are widely used for the decrease of vehicular exhaust emissions [5]. The oxidation reactions for VOCs on the solid catalyst surface have lower activation energies as a comparison of the gas phase, and therefore, the reactions get started at a low temperature. Therefore, these catalytic reactions are more appropriate for the oxidation of VOCs [6]. To accomplish these catalytic reactions, monolithic vessels are favored over packed vessels because of high surface area and better structural integrity [6]. The operating temperature is reached faster in these monolithic structures, as they show better axial thermal conductivity [7]. Also, monolith has other advantages like a simple construction, low-pressure drop, good mechanical strength, and thermal stability [8]. The present work focused on propane. Propane exposure causes adverse effects on living organisms which depends upon the duration of exposure and its concentration. Upon extreme exposure, it may cause oxygen loss

44 One-Dimensional Model for Removal of Volatile Organic Compound …

559

and asphyxia, ultimately unconsciousness and even death [9]. For catalytic oxidation, transition metal oxides and noble metals are the most widely used catalysts [10]. Metal oxides are less active than Pt-based catalysts for the exclusion of even nominal amounts of VOCs [11]. Noble metals like Pd and Pt distributed over Al2 O3 are used over metal oxides because of their superior resistance to inactivation and activity [12]. Pd catalysts are prone to lead poisoning, sulfur poisoning, and thermal inactivation as compared to other metals [13]. A one-dimensional model is used effectively to predict the converter behavior throughout the warm-up period [14– 16]. In this study, a mathematical model of VOC propane is formed by using the mass and energy balance equations of gas phase and solid phase. The equations are a system of PDEs which are strongly coupled and solved by using an implicit scheme.

44.2 Model Kinetics The propane oxidation is given by: C3 H8 + 5O2 → 3CO2 + 4H2 O

(44.1)

The rate expression [17] is given by: (−r )propane = kcat PC1.1 P −0.6 Here kcat = k0cat e 3 H8 O2

−E cat RTs

(44.2)

The reaction is exothermic, and values for the heat of reactions (−H) is = − 2043 kJ/mol. Parameter values: Pre-exponential factor: k 0cat 3.1010 × 1011 mol/Pa0.5 s cm2 . 104.7 kJ/mol. Activation energy: E cat

44.3 Assumptions for Model Assumptions for modeling include [18, 19]: 1. 2. 3. 4. 5. 6. 7.

No deactivation of catalyst. No gas-phase radiation. Axial diffusion in the gas phase is negligible. Gas uniform properties are supposed to each channel. The monoliths have a cylindrical shape and circular cross-section channel. Flow over a single channel is considered. Heat exchange among the substrate and the surroundings at both entering and exit points is neglected. 8. Diffusion inside the wash-coat is neglected.

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9. The heat released because of the reactions is completely transferred to the gas phase through convection inside the wash-coat.

44.4 Modeling Equations 44.4.1 Mass Balance Gas-Phase Equation {Convective mass transportation of gas along axial axis (x)} + {Reactant Mass transfer from gas to solid phase} = {Mass accumulation} 

∂Cg ∂t



 =v

∂Cg ∂x

 + kg S(Cg − Cs )

(44.3)

44.4.2 Mass Balance Solid-Phase Equation {Rate of reactant consumption due to catalytic reaction} = {Mass transportation of the reactant} a(−rpropane )cat = kg S(Cg − Cs )

(44.4)

44.4.3 Energy Balance Gas-Phase Equation {Convective heat transportation of gas along axial axis (x)} − {Heat transport from the gas to the solid wall} = {Net heat accumulation}  ρg C pg

∂ Tg ∂t



 = −vρg C pg

∂ Tg ∂x

 − h S(Tg − Ts )

(44.5)

44.4.4 Energy Balance Solid-Phase Equation {Conduction to wall along axial axis (x)} + {Heat transport to the wall} + {Heat released by chemical reaction} = {Heat accumulation in the solid wall}

44 One-Dimensional Model for Removal of Volatile Organic Compound …

 ρs C ps

∂ Ts ∂t



 = h S(Tg − Ts ) + a(−H )(−rpropane )cat + λs

561

∂ 2 Ts ∂x2

 (44.6)

44.4.5 Initial Conditions and Boundary Conditions Initial conditions Tg (0, t) = Tg0 , Cg (0, t) = Cg0 , Ts (x, 0) = Ts0

(44.7)

Boundary condition x = 0,

∂Cg ∂ Tg ∂ Ts ∂ Ts = 0, x = L , = 0, = 0, =0 δx ∂x δx ∂x

(44.8)

Using following dimensionless expressions, all equations from (44.3) to (44.8) are changed to dimensionless form Cg =

Cg Tg Ts x t ,T = ,T = , z = , t = Cg0 g Tg0 s Ts0 L t0

(44.9)

44.4.6 Gas-Phase Mass Balance Dimensionless Equation The mass balance dimensionless equation by combining Eqs. (44.3) and (44.4) 

∂Cg ∂z



  − A2 −0.0265 + 1.7256Cg ∂t   Ecat  −0.1581Cg2 + 0.0037Cg3 + 0.0156Cg4 e RTs

= −A1

∂Cg

cat

(44.10)

44.4.7 Gas-Phase Energy Balance Dimensionless Equation Energy balance Eq. (44.5) for gas phase becomes: 

∂ Tg ∂z

 = −B1

∂ Tg ∂t 

− B2 (Tg − Ts )

(44.11)

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44.4.8 Solid-Phase Energy Balance Dimensionless Equation Energy balance Eq. (44.6) for solid phase becomes: 

∂ 2 Ts ∂z 2



    ∂ Ts   T − D2 −0.0265 + 1.7256Cg − 0.1581Cg2 + D − T 1 g s ∂t   Ecat  (44.12) +0.0037Cg3 + 0.0156Cg4 e RTs

= D3

cat

A1 , A2 , B1 , B2 , D1 , D2, and D3 are dimensionless number. Values are: A1 =

Tg0.5 θ B−0.6 a L K cat R 0.5 Cg−0.5 L L Sh L 0 0 , B1 = , A2 = , B2 = , vt0 v vt0 vCpg ρg

D1 =

Tg0.5 (−H ) θ B−0.6 a L 2 K cat R 0.5 Cg0.9 L 2 Cps ρs Sh L 2 0 0 , D2 = , D3 = λs λs Tg0 λ s t0

(44.13)

44.4.9 Dimensionless Form of Initial and Boundary Conditions Boundary and initial conditions in dimensionless form Tg (0, t  ) = z = 0,

Tg Ts , C  (0, t  ) = 1.0000, Ts (0, t  ) = 0 , Tg0 g0 Ts

∂ Tg ∂Cg ∂T  ∂ Ts = 0, z = L , s = 0, = 0, =0 δx δx ∂x ∂x

(44.14)

44.5 The Methodology for the Solution of Dimensionless Equations Dimensionless Eqs. (44.10), (44.11), and (44.12) are coupled PDEs which are solved along with the boundary and initial conditions (44.14) by using implicit schemes with MATLAB using data cited in literature in Table 44.1 [20, 21]. The numerical method of lines (NMOLs) is used here, which involves reducing the PDEs to a system of ordinary differential equations (ODEs) in time by the use of a space discretization [21–23]. Spatial derivatives in equations are replaced with central finite-difference estimates and boundary conditions with backward finite-difference estimates. Now

44 One-Dimensional Model for Removal of Volatile Organic Compound … Table 44.1 Parameters used in the simulation [18, 19]

563

Parameters

Values

v λ h S ρg Cpg

1800 cm/s 0.01675 J/cm K s 0.0169 J/cm2 K s 23.02 cm2 /cm3 1.1125 × 10−3 (J/K cm3 ) 1.678 (J/K cm3 ) 0.1 10 cm 268–2144 cm2 /cm3

ρs C p s to L a

set of ordinary differentials equations (ODEs) are solved with MATLAB by calling ode23tb solver which is an execution of the TR-BDF2 method [20, 21, 24, 25].

44.5.1 Discretization of the Dimensionless Equations Discretized solid-phase mass balance equation  A1

dCi dt



     Ci+1 − Ci−1 =− − A2 −0.0265 + 1.7256Ci − 0.1581Ci2 2h  Ecat  RT  3 4 +0.0037Ci + 0.0156Ci e si

At i = 1, dimensionless concentration = 1.0000, i = p, Discretized gas-phase energy balance equation  B1

dTgi dt





=−

Tgi+1 − Tgi−1



2h

 Ci −Ci−1 h

= 0.



− B2 Tgi − Tsi

At i = 1, inlet gas temperature = 450 K. Tg −Tg

At exit i = p, i h i−1 = 0. Discretized solid-phase energy balance equation  D3

dTsi dt



 =

Tsi+1 − 2Tsi + Tsi−1





+ D1 Tgi − Tsi

h2  + D2 −0.0265 + 1.7256Ci − 0.1581Ci2  Ecat  RT  3 4 +0.0037Ci + 0.0156Ci e si

At t = 0, converter is at 298 K temperature along the reactor length.

564

At i = 1,

U. Bedi and S. Chauhan Tsi+1 −Tsi h

= 0, At i = p,

Tsi −Tsi−1 h

= 0.

44.6 Result and Discussions In Fig. 44.1, propane gas at concentration 16 ppm [25] and temperature 450 K enters the catalytic converter which is initially at a temperature of 298 K, and it heats the solid catalyst to its operational temperature. Figure 44.1 shows the comparison of the modeled and experimental results of propane conversion using Pt/δ-Al2 O3 catalyst [17]. It is observed that there is hardly any conversion for experimental and modeled results at a temperature below 414 K, and conversion values are 2.00% for experimental and 8.50% for modeled outcomes at temperature 414 K. As soon as the reaction initiates, the reaction rate rises because of heat released by the exothermic reaction. About 55.00% conversion is obtained for experimental results, and 59.50% conversion is obtained for modeled results at a temperature of 439 K. About 78.40% VOC propane conversion is obtained for the modeled outcomes and 80.00% conversion for experimental outcomes at a temperature of 443 K, and the two outcomes are found in agreement. Figure 44.2 shows the propane concentration variation with the converter axial distance with dimensionless time. The inlet dimensionless value of propane concentration is 1.0000. There is barely any variation in the propane concentration up to dimensionless time 40.00 as the values of the dimensionless concentrations are 0.9945, 0.9919, 0.9901, 0.9895, and 0.9894 at axial length of 0.10, 0.20, 0.40, 0.80, and 1.00, respectively. The dimensionless concentrations are 0.9895, 0.7551, 0.5030, 0.3042, and 0.1019 for the dimensionless time 40.00, 84.00, 108.00, 129.00, and 169.00 at axial length of 1.00, respectively. At the start, there is barely any change as the hot arriving gas warms the converter to its operational temperature. The catalytic conversion increases as the converter temperature rise because of the exothermic catalytic reaction. As the propane travels with the axial distance of the catalytic

100

Simulation

Experimental

80 Conversion (-)

Fig. 44.1 Comparison of modeled and experimental results for propane conversion with reaction temperature

60 40 20 0 200

300 400 Temperature (K)

500

44 One-Dimensional Model for Removal of Volatile Organic Compound … Fig. 44.2 Variation of gas concentration with dimensionless time along the axial length

565

t (-) 40

t (-) 84

t (-) 108

t (-) 129

time (-)

t (-) 169 1 Concentration (-)

0.8 0.6 0.4 0.2 0 0 0.2 0.4 0.6 0.8 1 Dimensionless Axial Length (-)

converter, extra surface area of the solid catalyst is available and as a result further reduction in its concentration. Figure 44.3 shows the gas temperature variation with dimensionless time with the axial distance of the catalytic converter. Hot gas at a temperature of 450 K arrives the catalytic converter at a temperature 298 K. The gas temperature declines as gas moves with the converter axial distance because of the transporting of heat from the hot gas to the converter through the convention. The values of gas temperatures are, 439.33 K, 429.15 K, 409.71 K, 376.58 K, and 363.10 K at axial length 0.10, 0.20, 0.40, 0.80, and 1.00 at dimensionless time 40.00, respectively. As time progresses, heat is released as a result of exothermic reactions on the solid catalyst and transported to the gas, thus increasing the temperature of the exit gas. Values of gas temperatures are 449.30 K, 448.34 K, 445.55 K, 436.66 K, and 430.82 K, at axial length 0.10, 0.20, 0.40, 0.80, and 1.00, respectively at dimensionless time 169.00. Figure 44.4 shows the solid temperature variation with the axial length with time. Firstly, the converter was at 298 K, as time changes, the catalyst temperature rises, primarily because of heat provided by the gas and afterward because of the beginning of catalytic reactions. The values of solid temperatures at axial distances 0.10, 0.20, 0.40, 0.80, and 1.00 are 380.47 K, 371.92 K, 357 K, 335.49 K, and 328.18 K at Fig. 44.3 Gas temperature variation with the dimensionless time along the axial length Gas Temperature (K)

t (-) 40 t (-) 108 t (-) 169

t (-) 84 t (-) 129

time (-)

455 440 425 410 395 380 365 350 0

0.2

0.4

0.6

0.8

Dimensionless Axial Length (-)

1

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Solid Temperature (K)

Fig. 44.4 Solid temperature variation with dimensionless time along the axial length

450 425 400 375 350 325 300

t (-) 40 t (-) 108 t (-) 169

0

0.2

t (-) 84 t (-) 129

0.4

0.6

0.8

time (-)

1

Dimensionless Axial Length (-)

dimensionless time 40.00, respectively. While at dimensionless time 169.00, the solid temperatures at axial distances 0.10, 0.20, 0.40, 0.80, and 1.00 are 444.67 K, 442.08 K, 436.01 K, 421.21 K, and 413.95, respectively. Figure 44.5 shows the exit concentration of propane at different values of cell density. At dimensionless time 90.00, the propane concentrations are 0.9382, 0.8112, 0.6794, 0.4751, and 0.2684 for cell density are 100 CPSI, 200 CPSI, 300 CPSI, 600 CPSI, and 900 CPSI, respectively. The increase of cell density has a favorable effect on catalyst conversion performance. Figure 44.6 signifies the exit concentration of propane at different values of surface area per unit reactor volume. At dimensionless time 100.00, the concentrations of propane are 0.7270, 0.5870, 0.4602, 0.3627, and 0.1869 for surface areas per unit reactor volume are 18.98 cm2 /cm3 , 23.02 cm2 /cm3 , 27.09 cm2 /cm3 , 31.01 cm2 /cm3 , Fig. 44.5 Exit concentration of propane with time for different values of cell density

Concentration (-)

100 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

200

600

0

900

50

300 Cell density (CPSI)

100 150 200 250

Dimensionless Time (-)

44 One-Dimensional Model for Removal of Volatile Organic Compound … Fig. 44.6 Exit concentration of propane at different values of surface area

18.98

567

23.02 surface area 31.01 cm2/cm3

27.09

Concentration (-)

43.11 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

50

100

150

200

Dimensionless Time (-)

and 43.1 cm2 /cm3 , respectively. The propane conversion rises with increase in surface area. The more surface area available, faster reaction rate will be and therefore higher the conversion.

44.7 Conclusion A model formed for propane catalytic combustion was analyzed to estimate the solid temperature, gas temperature, and gas concentration variations with the axial length with varying time. Initially, when the catalytic converter was started, it was observed that the solid temperature did not help the initiation of catalytic reactions due to low temperature. VOC propane conversion begins only after heating the catalyst to its operational temperature by incoming gas. The effect of changing the cell density and surface area on conversion was also analyzed. The increase in cell density and surface area helps in bringing a quicker conversion. Acknowledgements The author Umang Bedi was thankful to Technical Education Quality Improvement Program (TEQIP-III) at Dr. SSBUICET Panjab University, Chandigarh, for providing PhD assistantship.

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References 1. Dong, X., Zhao, X., Peng, F., Wang, D.: population-based air pollution exposure and its influence factors by integrating air dispersion modeling with GiS spatial analysis. Sci. Rep. 10(1), 1–12 (2020) 2. Campbell-Lendrum, D., Prüss-Ustün, A.: Climate change, air pollution and noncommunicable diseases. Bull. World Health Organ. 97(2), 160 (2019) 3. Solsona, B., Garcia, T., Agouram, S., Hutchings, G.J., Taylor, S.H.: The effect of gold addition on the catalytic performance of copper manganese oxide catalysts for the total oxidation of propane. Appl. Catal. B 101(3–4), 388–396 (2011) 4. Dupont, V., Zhang, S.H., Bentley, R., Williams, A.: Experimental and modeling studies of the catalytic combustion of methane. Fuel 81, 799–810 (2002) 5. Poulopoulos, S.G., Philippopoulos, C.J.: MTBE, methane, ethylene and regulated exhaust emissions from vehicles with deactivated catalytic converters. Atmos. Environ. 38(27), 4495– 4500 (2004) 6. Hayes, R.E., Kolaczkowski, S.T., Thomas, W.J.: Finite-element model for a catalytic monolith reactor. Comput. Chem. Eng. 16, 645–657 (1992) 7. Votruba, J., Sinkule, J., Hlavacek, V., Skrivanek, J.: Heat and mass transfer in monolithic honeycomb catalysts-I. Chem. Eng. Sci. 30, 117–123 (1975) 8. Chen, J., Ring, Z.: Monolith catalysts/reactors and their industrial applications. Hydrocarbon World, pp. 56–58 (2007) 9. Propane: Your Environment, Your Health | National Library of Medicine: Tox Town (2020). Retrieved 20 Jan 2020, from https://toxtown.nlm.nih.gov/chemicals-and-contaminants/pro pane 10. Xiao, Y., Logan, J.A., Jacob, D.J., Hudman, R.C., Yantosca, R., Blake, D.R.: Global budget of ethane and regional constraints on US sources. J. Geophys. Res. Atmos. 113(D21) (2008) 11. Garetto, T.F., Apesteguıa, C.R.: Oxidative catalytic removal of hydrocarbons over Pt/Al2 O3 catalysts. Catal. Today 62(2–3), 189–199 (2000) 12. Liotta, L.F.: Catalytic oxidation of volatile organic compounds on supported noble metals. Appl. Catal. B 100(3–4), 403–412 (2010) 13. Honkanen, M., Wang, J., Kärkkäinen, M., Huuhtanen, M., Jiang, H., Kallinen, K., Vippola, M.: Regeneration of sulfur-poisoned Pd-based catalyst for natural gas oxidation. J. Catal. 358, 253–265 (2018) 14. Heck, R.H., Wei, J., Katzer, J.R.: Mathematical modeling of monolithic catalysts. AIChE J. 22(3), 477–484 (1976) 15. Mohapatra, P., Mittal, M.: A simplified one-dimensional mathematical model to study the transient thermal behavior of an oxidation catalyst with both low and high levels of CO concentration at the inlet. Chem. Prod. Process Model. 14(3) (2019) 16. Nair, S., Mohapatra, P., Mittal, M.: Mathematical modeling of electrical heater assisted CO conversion in an oxidation catalyst at low loads and cold start conditions. In: ASME 2019 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers Digital Collection (2019) 17. Ma, L., Trimm, D.L., Jiang, C.: The design and testing of an autothermal reactor for the conversion of light hydrocarbons to hydrogen I. The kinetics of the catalytic oxidation of light hydrocarbons. Appl. Catal. A Gen. 138(2), 275–283 (1996) 18. Bedi, U., Chauhan, S.: Modeling for catalytic oxidation of volatile organic compound (VOC) in a catalytic converter. Mater. Today Proc. (2019) 19. Chauhan, S., Sharma, L., Srivastava, V.K.: A theoretical study for one-dimensional modeling for VOC in a catalytic converter. Combust. Theor. Model. 14(3), 367–379 (2010) 20. Schiesser, W.E., Griffiths, G.W.: A Compendium of Partial Differential Equation Models: Method of Lines Analysis with Matlab. Cambridge University Press, Cambridge (2009) 21. Choose an ODE Solver—MATLAB & Simulink—MathWorks India: In.mathworks.com (2020). Retrieved 10 March 2020, from https://in.mathworks.com/help/matlab/math/choosean-ode-solver.html

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22. Shakeri, F., Dehghan, M.: The method of lines for solution of the one-dimensional wave equation subject to an integral conservation condition. Comput. Math. Appl. 56(9), 2175–2188 (2008) 23. Schiesser, W.E.: The Numerical Method of Lines: Integration of Partial Differential Equations. Elsevier, Amsterdam (2012) 24. Hosea, M.E., Shampine, L.F.: Analysis and implementation of TR-BDF2. Appl. Numer. Math. 20(1–2), 21–37 (1996) 25. Takigawa, A., Matsunami, A., Arai, N.: Methane emission from automobile equipped with three-way catalytic converter while driving. Energy 30(2–4), 461–473 (2005)

Chapter 45

Neural Machine Translation: Assamese–Bengali Sahinur Rahman Laskar, Partha Pakray, and Sivaji Bandyopadhyay

Abstract Neural machine translation (NMT) is a state-of-the-art technique in the task of machine translation (MT), where a source-language text is converted into a target language text while preserving its meaning. NMT attracts attention because it handles sequence to sequence learning problems for variable-length source and target sentences. With the attention mechanism, the NMT system performs well in the context-analyzing ability. But it needs sufficient parallel training corpus, which is a challenge in low resource language scenario. To overcome the bar of a handy parallel corpus, there is an increase in demand for direct translation among similar language pairs. This paper investigates the NMT system for direct translation of low resource similar language pair: Assamese–Bengali. The main contribution of this work is Assamese–Bengali parallel corpus. The NMT system has achieved a bilingual evaluation understudy (BLEU) score of 7.20 for Assamese to Bengali translation and BLEU score 10.10 for Bengali to Assamese translation, respectively.

45.1 Introduction MT is a well-defined task of Natural language processing (NLP), in which automatic translation is made between two languages while retaining its meaning. The main challenge behind MT is to minimize language incomprehensibility issues among people from diverse linguistic backgrounds. MT systems gradually switched from a rule-based (knowledge-based) approach to a corpus-based (data-driven) approach, which eliminated the urgency of linguistic prowess. Corpus-based MT systems are S. R. Laskar (B) · P. Pakray · S. Bandyopadhyay Department of Computer Science and Engineering, National Institute of Technology Silchar, Assam, India e-mail: [email protected] P. Pakray e-mail: [email protected] S. Bandyopadhyay e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_45

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categorized into example-based machine translation (EBMT), statistical machine translation (SMT) and NMT. The scope of EBMT is limited because everything that one wants to translate cannot be covered by examples only. Hence, SMT has been introduced [7, 8], which depends on Bayesian inference to predict translation probabilities of phrase pairs corresponding to source-target languages. The main drawbacks of SMT are the impotence of context analysis, system complexity, different trained units, long-term dependency problem and issue of accuracy. To handle such issues, NMT system has been introduced [2, 3, 16]. Despite the advanced architecture of the NMT system, there is a need for enough parallel training corpora to provide promising results. For this reason, there is a growing interest in similar language translation to take benefit of the similarity between languages to provide an accurate output. Our work investigates a similar language translation for a low resource language scenario, namely, the Assamese–Bengali language pair. The same has been investigating in both forward and backward directions using the NMT system with an attention mechanism. The automatic BLEU score is used to evaluate predicted translations of the NMT system [12]. The Assamese and Bengali languages belong to the eastern zone of Indo-Aryan languages. These two languages are known as a sister language of each other. Assamese is the official language of the state Assam of India and is respected as the most widely used language of entire North-East India (also known as seven sister states). It is actively spoken by Assamese (also known as “Asamiya”) people as a first language, they are known as native speakers of the language and additionally, non-Assamese individuals who use it as a second language mainly in the Brahmaputra Valley region of the Assam state of India. On the other hand, Bengali (also known as “Bangla”) is a stand out among the most prevalent talked languages in eastern South Asia. It is the official language of Bangladesh, the Indian state of West Bengal, Tripura, and moreover additional official language of Barak Valley region of the Assam state of India. The native speakers of Bengali language are known as “Bangali.” In terms of linguistic aspect, both are very much similar since they have originated from the same root language “Sanskrit” and follow the same subject-object-verb (SOV) word order. The rest of the paper is organized as follows: Sect. 45.2, briefly outlines related works. Section 45.3, details of corpus preparation. System description is described in Sect. 45.4. Section 45.5, presents results and discussion and lastly, Sect. 45.6, concludes the paper with future work.

45.2 Related Works NMT has achieved state-of-the-art results since it has overcome different issues of SMT and attained good accuracy in various languages [2, 3, 13, 15, 16]. In the context of low resource scenarios like the English to Mizo translation, the NMT system outperforms the conventional SMT system on grounds of BLEU score [14]. Encoder

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and decoder constitute key elements of the NMT system architecture. The basic encoder-decoder recurrent neural network (RNN) assists encoding of variable-length source sentence into the fixed-length context vector. It then decodes the fixed-length context vectors to predict the target sentence. Moreover, the basic encoder-decoder model adapts long short term memory (LSTM) to handle the translation quality of longer sentences. But such a basic encoder-decoder model fails to encode entire essential information onto the context vector when the sentence is too long. Hence, to overcome this issue, an attention-based encoder-decoder model is introduced, which facilitates the decoder to focus on different parts of the source sequence at different decoding steps [1]. The work [11], improved the attention mechanism which merges global by associating all source words and local which only pays attention to a part of source words. Apart from the modification of NMT system architecture, translation accuracy can be improved by increasing the size of the parallel training corpus. But it is a challenge in the low resource language scenario. Hence, a direct translation approach, especially for the Assamese–Bengali pair, is incorporated in this paper. Here, the main goal is to explore the NMT system for a similar language translation task. The Assamese ⇔ Bengali translation lack background work. However, the literature survey finds similar work at WMT19.1 for an Indian language pair, HindiNepali [10]. And, Punjabi to Hindi translation [5], which exploits similarity between the two languages.

45.3 Corpus Preparation The parallel source–target sentence pairs have been prepared in this work through manual back-translation. The same has been cross-verified by linguistic experts, who possess linguistic knowledge of Assamese and Bengali languages. In our experiments, the linguistic expert is an undergraduate and a post-graduate student, who are native speakers of Assamese and Bengali. The source and target language are the Assamese and Bengali and vice-versa. The data is collected from different online sources such as the Bible,2 Wikipedia,34 multilingual question paper5 and other websites.67 In the parallel corpus building, parallel sentences are not directly available in the Wikipedia source. Therefore, we have manually translated into corresponding Assamese, Bengali sentences. We have considered three varieties of structures like short, medium, and long in the parallel corpus building. The parallel corpus is partitioned into train, validation and test set. The corpus statistics are summarized 1 http://www.statmt.org/wmt19/similar.html 2 https://www.bible.com 3 https://en.wikipedia.org/wiki/History_of_Assam 4 https://en.wikipedia.org/wiki/History_of_India 5 https://sebaonline.org/ 6 http://learn101.org/assamese_grammar.php 7 http://learn101.org/bengali_grammar.php

574 Table 45.1 Corpus statistics Corpus nature Train Validation Test

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Corpus name

Total instances

Train set Validation set Test set

1,48,650 500 100

in Table 45.1. This paper will be considered as a baseline paper of the Assamese– Bengali pair. Therefore, we have considered only 100 test sentences. In the future, we shall increase the size of the parallel corpus.

45.4 System Description The major steps of system operations are data preprocessing, training, and testing, which are explained in the successive subsections. The OpenNMT [6] toolkit is employed to build the NMT system for preprocessing, training, and testing/translating processes.

45.4.1 Data Preprocessing The primary function of the preprocessing step is the tokenization of source and target sentences to make a dictionary, which indexes words during the training process. The vocabulary size has a dimension of 50,002 and 50,004 for source and target sentences, respectively and the validation data set is used to verify the convergence of the trained system.

45.4.2 System Training Data preprocessing is followed by the system training process. In the training process, parallel source–target sentences of Assamese to Bengali and vice-versa are fed into the sequence-to-sequence RNN with an attention mechanism. In the NMT system architecture, encoder and decoder consist of a double layer network of LSTM nodes, which contains 500 units in each layer. Our system is trained up to 100,000 epochs in both the directions, and checkpoint is saved at an interval of 1000 on a single NVIDIA Quadro P2000 GPU. We have used the stochastic gradient descent (SGD) for optimization and the learning rate set to 1. We have trained the model using the

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default global attention mechanism [11] through softmax. The default dropouts are 0.3 and layer normalization is enabled for a stable training. NMT System with Attention Mechanism In the basic encoder–decoder model, the role of an encoder is to compress the entire source sequence into a context vector through input time steps. The decoder is accountable for trampling from the context vector through output time steps. Nevertheless, if the input sequence is too long, then the encoder omits all-important facts in the corresponding context vector. To solve this issue, attention-based encoder–decoder is introduced [11]. The key idea behind the attention mechanism is to represent each word with a fixed-length vector called annotation vector unlike the fixed sentence vector in the primary model. The context vector is calculated by the convex combination of the annotation vectors or convex coefficients of the source sequence, which are called as attention weights. The attention weights are computed each time when each target word is yielded using an alignment model, as shown in Eq. 45.1. It represents the probabilistic alignment of words while generating target words. ei, j = align(z i−1 , h j ), ∀ j ∈ 1, 2, . . . T, ∀i ∈ 1, 2, . . . T 

(45.1)

where ei, j is the alignment score of j-th source word corresponding to i-th target word, h j denotes annotation vector of j-th source word, z i−1 denotes decoders last state and T and T  are the lengths of source and target sentences. Then, alignment scores are transformed into probabilistic measures, are called attention weights αi, j using Eq. 45.2. exp(ei, j ) (45.2) αi, j =  n  exp(en  ,m ) Lastly, the context vector is computed using the annotation vector (ci ) as shown in Eq. 45.3. T  αi, j × h j (45.3) ci = j=1

After context vector is computed then calculate decoders next state as a non-linear function (in case of LSTM) of context vector ci , last target word μi−1 and decoders previous state using Eq. 45.4. z i = f (ci , μi−1 , z i−1 )

(45.4)

The NMT system architecture is shown in Fig. 45.1, where the source sentence “

” (Moi apnunak buju) is translated into the target sentence

” (Ami apnake bujhi). Its equivalent sentence in English is “I “ understand you". Here, < eos > denotes the termination of a sentence.

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Fig. 45.1 NMT system architecture

45.4.3 System Testing System training is followed by the system testing process. It is required for predicting translations of test sentences. An optimized heuristic best first search technique, a beam search is used to search the best translations.

45.5 Results and Discussion The system results obtained from the NMT system have been evaluated based on unigram (BLEU-1), bi-gram (BLEU-2) and tri-gram (BLEU-3) using multi-bleu.perl.8 The reason behind to compute BLEU score up to tri-gram (BLEU-3), is that the BLEU score decreases to very low when it crosses BLEU-3. Table 45.2 presents results for both the directions, which show the highest average BLEU score of 7.20 in Assamese to Bengali translation and BLEU score 10.10 in Bengali to Assamese translation. To analyze different BLEU scores on predicted sentences during the training process, Figs. 45.2 and 45.3 are presented. In Figs. 45.2 and 45.3, comparison of BLEU scores with respect to the number of epochs, where X -axis represents 1000 epochs at each point from 0 to 100 i.e. up to 100,000 epochs and Y -axis represents corresponding BLEU scores. From the graphs and Table 45.2, it is observed that the average BLEU score up to BLEU-3 is close to the BLEU-2 score (as shown bold in Table 45.2). It visualizes the effect of the bi-gram model in the Indian language pair Assamese– Bengali [4, 9]. From the predicted sentences, it is observed that translations are not appropriate in both directions. The NMT system suffers the problem of the wrong translation of named entities, numbers, under-translation, and over-translation of source words.

8 https://github.com/OpenNMT/OpenNMT/blob/master/benchmark/3rdParty/multi-bleu.perl

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Table 45.2 BLEU scores on Assamese–Bengali pair translation Translation BLEU-1 BLEU-2 BLEU-3 Assamese to Bengali Bengali to Assamese

AVG BLEU

17.3

3.6

0.7

7.20

23.9

5.4

1.0

10.10

Fig. 45.2 Number of epochs versus BLEU scores for Assamese to Bengali language translation

Fig. 45.3 Number of epochs versus BLEU scores for Bengali to Assamese language translation

45.6 Conclusion and Future Work The current work is a similar language translation in the context of Assamese to Bengali and Bengali to Assamese translation using the sequence-to-sequence RNN with an attention mechanism. In this investigation, we have acquired BLEU scores for low resource language scenarios, which is optimum so far due to a lack of work on the Assamese–Bengali pair. However, close observation of predicted sentences

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points out that the NMT system needs improvement for precise translation. Hence, the conventional NMT system requires modification to address the specific challenges of low resource similar language scenarios. In the future, we will perform the comparative analysis of our present NMT system with the advance NMT techniques. Acknowledgements We would like to thank Department of Computer Science and Engineering and Center for Natural Language Processing (CNLP) at National Institute of Technology Silchar for providing the requisite support and infrastructure to execute this work.

References 1. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015). http://arxiv.org/abs/1409.0473 2. Cho, K., van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder–decoder approaches. In: Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 103–111. Association for Computational Linguistics, Doha, Qatar (2014). https://doi.org/10.3115/v1/W14-4012 3. Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734. Association for Computational Linguistics, Doha, Qatar (2014). https://doi.org/10.3115/v1/D14-1179 4. Ghosh, S., Girish, K.V.V., Sreenivas, T.: Relationship between indian languages using long distance bi-gram language models. In: Proceedings of ICON 2011, 9th International Conference on Natural Language Processing, pp. 104–113. Macmillan (2011) 5. Josan, G., Lehal, G.: A Punjabi to Hindi machine translation system, pp. 157–160 (2008) 6. Klein, G., Kim, Y., Deng, Y., Senellart, J., Rush, A.: OpenNMT: Open-source toolkit for neural machine translation. In: Proceedings of ACL 2017, System Demonstrations, pp. 67–72. Association for Computational Linguistics, Vancouver, Canada (2017). https://www.aclweb. org/anthology/P17-4012 7. Koehn, P.: Statistical Machine Translation. Cambridge University Press (2009). https://doi.org/ 10.1017/CBO9780511815829 8. Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E.: Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, pp. 177–180. Association for Computational Linguistics, Prague, Czech Republic (2007). https://www.aclweb.org/anthology/P07-2045 9. Laskar, S.R., Dutta, A., Pakray, P., Bandyopadhyay, S.: Neural machine translation: English to Hindi. In: 2019 IEEE Conference on Information and Communication Technology, pp. 1–6 (2019) 10. Laskar, S.R., Pakray, P., Bandyopadhyay, S.: Neural machine translation: Hindi-Nepali. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp. 202–207. Association for Computational Linguistics, Florence, Italy (2019). https:// doi.org/10.18653/v1/W19-5427 11. Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1412–1421. Association for Computational Linguistics, Lisbon, Portugal (2015). https://doi.org/10.18653/v1/D15-1166

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12. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: A method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL ’02, pp. 311–318. Association for Computational Linguistics, Stroudsburg, PA, USA (2002). https://doi.org/10.3115/1073083.1073135 13. Pathak, A., Pakray, P.: Neural machine translation for Indian languages. J. Intell. Syst. pp. 1–13 (2018). https://doi.org/10.1515/jisys-2018-0065 14. Pathak, A., Pakray, P., Bentham, J.: English–Mizo machine translation using neural and statistical approaches. Neur. Comput. Appl. 30, 1–17 (2018). https://doi.org/10.1007/s00521-0183601-3 15. Saini, S., Sahula, V.: Neural machine translation for English to Hindi, pp. 1–6 (2018). https:// doi.org/10.1109/INFRKM.2018.8464781 16. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 3104–3112. Curran Associates, Inc. (2014). http://papers.nips.cc/paper/5346-sequence-to-sequence-learningwith-neural-networks.pdf

Chapter 46

An Overview of Crossover Techniques in Genetic Algorithm Joseph L. Pachuau, Arnab Roy, and Anish Kumar Saha

Abstract Artificial Genetic Algorithm is proposed to mimic the natural selection process. It provides an elegant and relatively simple way to solve non-polynomial problems. The crossover, one of the basic step of GA, is an imitation of reproduction in biological beings. Crossover exchanges information between different individuals to generate offspring with the hope of obtaining better genes. The basic aim of crossover is the same, but various approaches are proposed depending on the problem. This paper presents a review on the crossover, basics and some problem-specific techniques.

46.1 Introduction Optimization aims to obtain the sub-optimal or optimal results for constrained or unconstrained based problem. Genetic Algorithm (GA) is one of the techniques of optimization. It is a subset of Evolutionary algorithm. Evolutionary Algorithms are modelled after the biological process of evolution for optimization. GA does not guarantee an exact solution but provides the result with lesser computational time. It generally performs better for problems with Non-deterministic Polynomial time (NP) complexity. The Travelling Salesman Problem (TSP) and Job Shop Problem (JSP) are some examples of NP-hard problems where GA can be applied to obtain a result in polynomial time [1]. TSP is a classic optimization problem to describes a salesman, who has to travel a set of cities while covering minimum distances. For a large number of cities, conventional optimization would take years to get the optimal solution. An extension of the TSP known as the multiple travelling salesman, which is more difficult than the TSP is possible to be solved using GA [2]. JSP is another commonly known NP-hard problem. In generic, n number of jobs are determined to process by m machines. Each job has a set of operations to be J. L. Pachuau · A. Roy · A. Kumar Saha (B) Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar788010, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_46

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processed by a single machine at a time. The goal of JSP is to obtain a schedule with minimum processing time. TSP is a special case of the JSP, which is possible to be solved using conventional GA [3]. Conventional GA provides a solution with the same efficiency as solutions in JSP. The performance is possible to be improved by designing a GA more suited for the JSP. The simplicity and flexibility of GA make it popular in various fields. In Economics, Cobweb model used GA for finding various features. Cobweb model describes the market behaviour of goods, so firms can have more profits. The set of solutions in GA is known as Population. In the Cobweb model, multiple populations give better results than the normal single population GA. Multiple populations allow for multiple rules in decision making. GA decision making does not require the user to have good knowledge of the problem, while in other learning algorithms, the user is required to know how to maximize profit [4]. In image processing, edges are sudden changes in pixels, and the change of value will have a threshold for various edge detection. GA is also applied to determine the threshold value for obtaining proper edges [5]. In optimal structure design, GA is also applied for optimizing truss size [6]. Different crossover techniques like single, 2-point, multi-point etc were compared. GA has been applied effectively in different scheduling problems like time table generation and project scheduling [7, 8] In steam run power plants, scheduling power distribution between different components is necessary for reducing coal consumption. This was solved using GA to find optimal distribution of power for lowest coal consumption, while maintaining power requirements [9]. In cloud based manufacturing system, complex scheduling is maintained for resource allocation and sharing. In [10] GA approach for optimal schedule is applied. Results shows that GA rapidly provide an optimized schedule. Vehicle routing is another scheduling problem that can be optimized with the help of GA. In [11], GA was applied for vehicle routing while minimizing the CO2 emission. GA works find the optimal or sub-optimal solution by taking a set of solutions and exchanging information between them to find a better solution. Among the different operators in GA, the crossover is where most of the exploration of solution space takes place. So in this paper, we focus on the different crossover that has been implemented. In the following section, we explain the structure of a generic GA, followed by different crossover techniques. The different crossover techniques are discussed based on the chromosome types. These crossovers are general purpose and can work on almost all problem as long as it fits the chromosome type. Besides these general purpose crossover, several crossovers specific to particular problems are discussed.

46.2 GA Methodology As GA is modelled after biological evolution there are certain terms borrowed from it. Some of these are genes, chromosomes and individual. Each solution is known as an Individual. Each individual is formed with the chromosome, which is a data structure to represent the solution. Each single data in the chromosome are known as

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gene. Individuals are measured in terms of fitness, the fitter individual means closer to the optimal solution. Although GA has been modified and implemented in different ways the basic structure remains the same. The first step in GA is the initialization of the population. The population is a set of individuals whereupon the algorithm runs to produce the best solution. The initial population consist of individuals that are randomly generated. The population size is an important parameter in GA [12]. It size determines the performance of the algorithm. Smaller population size does not explore the search space sufficiently while a larger population size increases the computational complexity of the GA. Once we have a population the following operators are applied. The next operation, selection, selects the fittest individuals of the current generation to pass move on to the next step of GA evolution. These selected individuals are then used for the remaining operations to generate the next population. Selection selects the best individuals, but when only the best are passed on to the next generation the search proceeds in a single direction or get stuck at local minima. Thus for a better exploration of the search space, the Selection must consider a certain amount of diversity. Several techniques such as Roulette Wheel Selection, Rank Selection, Steady State Selection, Tournament Selection etc. [13] are available. The next operation, crossover, also known as recombination produces new individuals from the existing individual in the current population. The individuals participating in the crossover are called parents and the individuals produces are called offspring. The parents are first selected from the population, which may be completely random or based on some criteria. The selected parents then exchange information (genes) to form new individuals known as Offspring. The Offspring is a recombination of different genes from the parents. This gives the chance for coming up with a fitter individual. Next operation, Mutation makes a random change in the gene of the individuals. Mutation provides random changes in chromosomes and maintains diversity in the population. This prevents from premature convergence and getting stuck in local minima. The chance of mutation known as mutation rate is generally kept very low( f (P 2 ).

46.3.2.5

Blend Crossover

The Blend crossover also known as the BLX-α produce one offspring from two parents. It calculates an upper and lower bounds according to parent value then decides the child value at random within this range [19]. Let the offspring be given as, C = {C1 , C2 , C3 , . . . , Cn }. Here, Ci is selected at random uniformly within the range [Li − I .α, Ui + I .α], where, Li = min(Pi1 , Pi2 ), Ui = max(Pi1 , Pi2 ) and I = Ui − Li . Li is the larger value between the parents and Ui is the smaller value of the two parents. This provides an adaptive search mechanism. If I value is higher the range of the Ci value is wider and smaller if I is small. Therefore at the earlier population, the search for a new solution takes a larger step. As the population converges, the difference between the parents starts decreasing and the search will take smaller steps. This makes the crossover precise in finding the optimal or suboptimal solution. At α = 0, i,e. BLX-0, the offspring is selected between the range of the two parents’ value. This is also known as the flat crossover. As the value of α increases, the range also increases.

46.3.2.6

Laplace Crossover

The Laplace crossover (LX) is similar to the heuristic crossover, but the random, β multiplier follows a laplace distribution [20]. The offspring are generated as: Y1 = X1 + β | X1 − X2 |

and

Y2 = Y2 + β | X1 − X2 | .

A uniformly distributed random number u ∈ [0, 1] is generated. Then another random number β, which follows Laplace distribution is generated as follows:  a − b loge (u) if u ≤ 21 , β= a + b loge (u) if u > 21 . 46.3.2.7

Parabolic Crossover

Parabolic crossover uses the arithmetic crossover to first calculate a trail chromosome. Using the parabola that passes through this trial chromosome and the two parents the offspring is generated [21]. Consider two parent chromosomes X = {xi | 1 < i < n} and Y = {yi | 1 < i < n}, where n is the length of the chromosome. The genes of trial chromosomes Y can

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be calculated using the arithmetic crossover as, zi = αxi + (1 − α)yi . α is a random value such that, 0 ≤ α ≤ 1 and satisfy the relation, F(Z) < {F(Y ) − F(X )} + F(X ), where F(X ) represents the fitness of X . The offspring is calculated by the vertex of the parabola that passes through X , Y and Z, which is: f (α) = aα 2 + bα + c, where, a=

(1 − α)F(X ) + α − F(Z) F(Z) − F(X ) + α 2 {F(X ) − F(Y )} ,b= , c = F(X ). α(1 − α) α(1 − α)

b The offspring O can be obtained as: oi = txi + (i − t)yi , t = − 2a .

46.3.3 Permutation Coded Several optimization problems have ordered solutions. The solution, in this case, is a set of values or symbols that have a certain order of arrangement. For these chromosomes the previously discussed crossover are not compatible. Permutation chromosome are used in problems like the travelling salesman, scheduling and network optimization. The genes of the permutation coded chromosomes have a specific set of values they can take from. The crossovers are mainly about re-ordering of the genes. Some crossover techniques available for permutation coded GA are discussed below.

46.3.3.1

Order Crossover

The Ordered Crossover creates offspring by using the order of genes from both parents [22]. The processes are discussed in the following steps: Step 1 The parent chromosome is divided into 3 parts at random point. Step 2 The child first take the value of Section 2 from Parent 1 as it is. Step 3 Section 3 of the child is filled first by taking genes from Parent 2. Only the gene values that are not inserted in step 2 are considered. The genes are inserted according to the order in Parent2. Step 4 Similar to Step 3 the Section 1 of the child gene is filled up. Step 1: 1 2 3 Parent 1: 7 6 4 3 2 8 1 5 Parent 2: 5 2 7 1 8 4 6 3 Offspring: _ _ _ _ _ _ _ _

Step 2: 1 7 6 4 5 2 7 _ _ 4

2 3 3 2 8 1 5 1 8 4 6 3 3 2 _ _ _

Step 3: 1 7 6 4 5 2 7 _ _ 4

2 3 3 2 8 1 5 1 8 4 6 3 3 2 5 7 1

Step 4: 1 7 6 4 5 2 7 8 6 4

2 3 3 2 8 1 5 1 8 4 6 3 3 2 5 7 1

For the second offspring the same steps are followed with the role of the parent interchanged.

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Partially Mapped Crossover

Partially mapped crossover also divides the chromosomes into 3 sections. The sections from different parents are combined to form the child chromosome. Then a partial mapping of the chromosome is done to get rid of all the duplicate genes [23]. The following are the steps of the crossover: Step 1 The parent chromosome are randomly divided into 3 sections. Section1 Section2

Section3

Parent 1: 7 6 4 3 2 8 1 5 Parent 2: 5 2 7 1 8 4 6 3 Step 2 For the child chromosome, Section 1 and Section 3 of Parent 1 are copied and Section 2 of Parent 2 is copied. Child 1: 7 6 7 1 8 8 1 5 Step 3 The child chromosome now have duplicate genes. All the duplicate genes in Section 2 are mapped to the same section of Parent 1. The mapping here are: 7 → 4, 1 → 3, 8 → 2. Step 4 Now the duplicate genes in Section 1 and 3 of the child are replaced according to the mapping. Child 1: 4 6 7 1 8 2 3 5

46.3.3.3

Cycle Crossover

In cycle crossover, the position of each of the genes in the child chromosome is taken from either one of the parent chromosome [24]. The cycle crossover is carried out in the following steps, and corresponding example shown below. Take parents Parent 2: 34576128. chromosomes: Parent 1: 87264351 Step 1 Select the first gene of Parent 1 and place it in the same location in Offspring. The gene value selected here is 8. Offspring: 8_______ Step 2 Now we look at the gene value corresponding to the position of previously inserted value. This value is inserted to the offspring in the same location of that value in Parent 1. In this case the gene values is 5 and its location in Parent 1 is 8th. Offspring: 8____3__ Step 3 Repeat step 3 until a gene that is already inserted is encountered. Here, the next number to be inserted is 8, Which is already inserted so proceed to step 4. Offspring: 8____3_1 Step 4 Now the rest of the genes will be filled up by the genes of Parent 2 in their corresponding location. Offspring: 84576321

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46.3.3.4

Edge Recombination

The edge recombination was introduced to solve the TSP [25]. The chromosome contains all the cities in the order of traversal. The crossover is carried out in the following steps: Step 1 Create a list of neighbours for each point from the parent. The list of neighbour for a particular point consist of the point’s neighbour in both the parent chromosome. Step 2 Take the first gene from first parent. Insert it into the child chromosome at the first location. Then remove that gene value from all edge list. Step 3 From the points in the edge list of the previously inserted point, select the one which has the shortest edge list. In case of a tie select randomly. Step 4 Insert this selected point into the first empty location in child chromosome. Delete this point from all the edge list. Step 5 Repeat Step 3 and 4 till the child chromosome is full. For example consider two parent chromosomes for crossover. Parent 1: 18265473 Parent 2: 16352487 From the parents we can get the neighbour list as: 1 → 8, 3, 6, 7. 2 → 6, 8, 4, 5. 3 → 1, 7, 5, 6 4 → 7, 5, 8, 2. 5 → 4, 6, 2, 3. 6 → 5, 2, 3, 1. 7 → 3, 4, 1, 8. 8 → 2, 1, 4, 7. Starting from 1 the child can be generated as: Child: 1 _ _ _ _ _ _ _ → 1 7 _ _ _ _ _ _ → 1 7 8 _ _ _ _ _ → 1 7 8 4 _ _ _ _ → 1 7 8 4 2 _ _ _ → 1 7 8 4 2 6 _ _ → 1 7 8 4 2 6 5 _ → 1 7 8 4 2 6 5 3 ⇒ Child: 1 7 8 4 2 6 5 3

46.3.3.5

Sequential Constructive Crossover

The edge recombination does not take into account the cost of traversal from one city to another. Sequential Constructive Crossover considers the sequence of traversal from both parent and choosing one with the lower cost [26]. The cost of traversal from city to city is represented as a matrix, Cij = cost of getting from node i to node j. The process of this crossover is explained in the following steps: Step 1 Start construction the child chromosome by taking the first point i.e, point 1 as P. Step 2 Search for the next gene value in both parents. Take the gene values as A and B. If the next gene values are already present in the child take the first value from the sequential list of nodes. Step 3 Now compare the cost of getting from P to A, CPA and the cost of getting from P to B, CPB . If CPA < CPB , then select A as the new P. Step 4 Add the new P to the child chromosome. Step 5 Repeat Steps 2, 3 and 4 till the child chromosome contains all nodes or the length of the child chromosome is equal to the parent chromosome.

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46.3.3.6

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MPOX Crossover for Combinatorial Crossover

The multi-parent order crossover is a modification of the order crossover for more than 2 parents[27] . It takes N number of parents and produces 1 offspring. The MPOX crossover takes place in the following steps: Step 1 Select N number of parents for crossover. Step 2 Generate (N − 1) number of crossover points randomly. Step 3 From crosspoint(1) to crosspoint(2) take all elements and insert in the same location in offspring. Step 4 While i < N repeat step 5. Step 5 While crosspoint(i) < j < crossopoint(i + 1). Starting from crosspoint(i) in Parent(i) select the next element (If the end of Parent(i) is reached continue gene 1). If the element is not present in the offspring insert to offspring(j). Increment j. Step 6 For Parent(N ). Follow Step 5 by taking 0 < j < crosspoint(1) The more number of parents allow for more diversity in the crossover and thus producing better offspring. The solution in MPOX crossover increases performance when the number of parents increases up to 6, then gets worse as the number of parents further increases.

46.3.4 Problem Specific Crossover In the above section the more commonly used representations have been discussed. In this section we discuss crossover that are designed for specific problems.

46.3.4.1

Matrix Crossover

The generalized transportation problem (GTP) is an optimization problem that deals with a matrix. The solution matrix has a constraint that the sum of all the rows and columns are fixed. A crossover operator has been proposed for this particular problem [28].This crossover maintains the problem’s constraints by producing only feasible solutions. The crossover steps for the GTP problem are as follows: Step 1 Select two parent chromosome for crossover, P1 = (pij1 ) and P2 = (pij2 ). Step 2 Create two matrices M = (mij ) and N = (nij ) where, mij = (pij1 + pij2 )/2 and rij = [(pij1 + pij2 ) mod 2]. Step 3 Split N into two matrix. Such that N = N1 + N2 and the sum of the corresponding rows and column of N1 and N2 are equal, which is equal to half the sum of corresponding rows and columns of N. Step 4 Calculate the two offsping as:O1 = M + N1 and O2 = M + N2 .

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Crossover for Variable Length Chromosomes

Variable-length chromosomes (VLC) as the name suggest the chromosomes are not of the same length as seen in the other examples. VLC implementation in Network Intrusion Detection was proposed with the use of a single point crossover [29]. With an additional rule that the crossover point must be within the length of the shorter parent. Inspired by the single point crossover the same adjacency (SA) was introduced [30] for use in path optimization for networks. In the case of network path optimization, the chromosome represents a path in the network, i.e, set of nodes in order of traversal. Since the paths cannot be broken and joined at any arbitrary point, therefore the normal single point crossover doesn’t work. The steps for the SA crossover are given below. Step 1 Select two chromosomes as parents for the crossover. P = {p1 , p2 , . . . , pm }, G = {g1 , g2 , . . . , pn } Step 2 Start with i = 0, and j = 0. Step 3 If gj ∈ E(pi ), add index i to crosspointlist and add j to matchpointlist(i). E(pi ) represnts all the notes that are connected to pi . Step 4 While j ≤ n increment value of j by 1 and goto Step 2. Step 5 While i ≤ m increment value of i by 1 and goto Step 2. Step 6 Select the a random index a from crosspointlist and then selecr random index b from matchpointlist(i). Step 7 Take (pa , gb ) as a crossover point and perform a single point crossover.

46.3.4.3

Three Chromosomes Juggling Crossover

The three chromosome juggling crossover (TCJC) was proposed for the open shop scheduling problem (OSSP) [31]. It takes three parents and produces two offspring. This method is designed specifically for the open shop scheduling problem. The open shop problem is consist of a set of machine and a set of jobs. Each job has different operations. One operation of a job can be processed at only one machine at a time and one machine can process one operation at a time. Consider an OSSP problem with m = 3 machines and n = 2 jobs. Each operation of the Jobs is identified with two indexes for the job number and machine number as Oij . For example, O12 denotes an operation of job 1 in machine 2. The TCJC takes place with the following steps: Step 1 For offspring 1 take the first chromosome of Parent 3. Add the index values. Step 2 If the sum of the index is even select the first gene from the left of Parent 2 and copy to the child chromosome. Step 3 If the sum of the index is odd elect the first gene from the left of Parent 1 and copy to the child chromosome. Step 4 After adding a chromosome in the child, cancel that chromosome in both parent 1 and parent 2. These chromosomes are no longer taken into consideration.

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Step 5 Take the next gene of Parent 3 and repeat the process till the child chromosome is full. Consider the following three chromosomes as parents as an example. Parent 1: O22 O13 O11 O12 O21 O23 Parent 2: O12 O22 O21 O13 O23 O11 Parent 3: O11 O23 O13 O21 O22 O12 Parent 1: Parent 2: Parent 3: Child 1:

O22 O12 O11 O12

O13 O11 O12 O21 O23 O22 O21 O13 O23 O11 O23 O13 O21 O22 O12 O22

Parent 1: O22 O13 O11 O12 O21 O23 Parent 2: O12 O22 O21 O13 O23 O11 → Parent 3: O11 O23 O13 O21 O22 O12 Child 1: O12 Parent 1: O22 O13 O11 O12 O21 O23 Parent 2: O12 O22 O21 O13 O23 O11 → ... → Parent 3: O11 O23 O13 O21 O22 O12 Child 1: O12 O22 O21 O13 O11 O23

Taking the first chromosome of parent 3, Sum of the index = 1+ 1 = 2, which is even. Therefore copy O12 from parent 2, and cancel from both Parent 1 and Parent 2. Taking the next gene in parent 3, Sum of index = 2 + 3 = 5, which is odd. Therefore the first available gene from parent 1 is taken. Which is O22 . This process is continued till the child chromosome is full. The backward TCJC crossover may also be applied to get another offspring. In the backward TCJC, the genes to be inserted are taken from the first available gene from the right.

46.3.4.4

New Crossover for TSP

A new crossover technique for TSP was proposed in [32]. In this process, we divide the parent chromosomes into 3 parts. And the parts are recombined to form the offspring by comparing their cost from the cost table. The steps by step procedure of this crossover are as follows: Step 1 The two parents for crossover are selected at random. Then the cost of these parents are selected from the cost table. Step 2 Divide the chromosomes into 3 section: first,middle and last. Step 3 Select the first part of offspring 1 from parent 1 and for offspring 2 select from parent 2. Step 4 The middle section of both parents are selected and compared for the cost. The lower cost is selected for both offspring. Step 5 For the last section select the last part of parent2 for offspring 1 and for offspring 2 select from parent1. Step 6 Now remove all the duplicate genes. Step 7 Now the missing gene values are inserted either at the front or the end, whichever position has less cost.

46.3.4.5

Aircraft Arrival Sequencing and Scheduling

Arrival Sequencing Scheduling (ASS) is an important task for Aircraft arrivals in Airports. Air-crafts allows lower airborne delay and efficient use of airport space.

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This is an NP-hard problem suitable for optimization with GA. A binary matrix chromosomes and crossover designed to fit this problem was implemented in [33], which performs better over the permutation crossover. The chromosomes are taken as a square matrix, C of size (n × n) where n is the number of expected aircraft. If an aircraft i is the first expected, then C(i, i) = 1. If an aircraft j is to land after i then C(i, j) = 1. The remaining entries in C are 0. The Crossover process takes place as follows: Step 1 Select two parents C1 and C2 for crossover. Then create C3 with AND operation on all corresponding elements of C1 and C2. Step 2 If C3(i, j) = 1 & i = j, set C4(i, m) = 1 & C4(m, j) = 1, where m = 1, 2, . . . , n. Step 3 If C3(i, i) = 1, set C4(m, m) = 1, where m = 1, 2, . . . , n. Step 4 Set C5 = C3. If C3(i, i) = 0 for all 1 ≤ i ≤ n. Choose random m and set C5(m, m) = 1. Step 5 Select the i where C5(i, i) = 1. While all entries of C4 are not zero repeat step 6 and 7. Step 6 Select a random j such that C5(i, j) = 0. Set C5(i, j) = 1 and C4(m, j) = 1, C4(i, j) = 1 for 1 ≥ m ≥ n. Step 7 Take j as new i value (i = j). Step 8 G5 is the new offspring.

46.3.4.6

Hierarchical Crossover

The organization of IoT devices is an important feature for the performance of the network. Such organisation are difficult to manually configure for optimum performance. The GA implementation of this problem was proposed with an array representation of a tree with a hierarchical crossover operator [34]. The chromosome used in this method is an array of integers representing the hierarchical tree structure of the IoT organization. Here the number of a leaf node is considered as a fixed number, say N and the maximum number of levels are also fixed, say M. The tree structure is represented as an array of length (N − 1). The elements li represents the level number on which the ith leaf node separates from the (i + 1)th. For example the tree in Fig. 46.1 can represented as [3, 2, 3, 3, 3]. This representation allows the basic crossovers like a single point, multi-point, etc. to operate on a tree structure. The hierarchical crossover process is as follows: Step 1 Select two random parents P1 and P2. If max(P2) > max(P1), exchange P1 and P2. If max(P2) = max(P1), select one randomly. T = number of levels in P1. Step 2 Select random node from P1 with level between 1 and (T − 1). Let the level of this selected node be S. Select a random node from P2 in level S or the max level (whichever is smaller).

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Fig. 46.1 Hierarchical tree structure

Step 3 Now exchange the sub-structure from the selected nodes of the two trees. The two resulting trees are the offspring.

46.4 Conclusion This paper provides a basic case study of different types of crossover in GA. In this paper, we have discussed the use of some general purposed crossover based on the type of chromosome, which is listed in Table 46.1. Table 46.2 list the different problem specific crossover discussed in this paper. We have seen that the Three Chromosome Juggling, New Crossover for travelling salesman can be solved using permutation crossovers but the specially designed crossover performs better. GTP, and Aircraft sequencing have constraints that are handled by the crossover. In network intrusion detection and Sentiment analysis, we see a unique chromosome structure where the chromosome does not have a specific length. With all the different crossover technique discussed here, we see that GA does not have a specific implementation. Instead, it may be customized and tailored to fit the specific need of the problem.

Table 46.1 General-purpose Crossover Crossover Single point, Multipoint, Uniform, Shuffle, Ring Average, Heuristic, Parabolic Arithmetic, Geometric, Blend, Laplace Ordered Partially Mapped, Cycle, Edge recombination, Sequential Multi-parent ordered

Chromosome coding, No. of parents , offspring Binary, Value Coded, 2, 2 Value, 2, 1 Value, 2, 2 Permutation, 2, 2 Permutation, 2, 1 Permutation, multiple, 1

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Table 46.2 Problem specific crossover Purpose Chromosome coding, No. of parents, offspring Generalised transportation problem

Matrix, 2, 2

Network intrusion detection

Variable length, 2, 2

Network path optimization

Variable length chromosome, 2, 2

Three chromosomes juggling

Permutation, 3, 4

New crossover for TSP

Permutation, 2, 2

Aircraft sequencing and scheduling

Binary Matrix, 2, 2

IoT device organization

Tree structure, 2, 2

Remarks This crossover handles constrains by producing only feasible solutions This crossover works for genes that are not of the same length which is crucial to the implementation This is a single point crossover which can work on different lengths of chromosome This crossover was proposed for OJSP. The forward process produce 2 offspring and the reverse can produce 2 more offspirng This was proposed for the TSP and therefore will work for most permutation coded chromosome It performs better than a permutation chromosome for this particular problem. It also handles the problem constraints This crossover allows single point, multi-point etc. to be performed on tree chromosomes

References 1. Kumbharana, N., Pandey, G.M.: A comparative study of aco, ga and sa for solving travelling salesman problem. Int. J. Soc. Appl. Comput. Sci. 2(2), 224–228 (2013) 2. Yuan, S., Skinner, B., Huang, S., Liu, D.: A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms. Eur. J. Oper. Res. 228(1), 72–82 (2013) 3. Nakano, R., Yamada, T.: Conventional genetic algorithm for job shop problems. ICGA 91, 474–479 (1991) 4. Arifovic, J.: Genetic algorithm learning and the cobweb model. J. Econom. Dynam. Control 18(1), 3–28 (1994) 5. Jin-Yu, Z., Yan, C., Xian-Xiang, H.: Edge detection of images based on improved sobel operator and genetic algorithms. In: 2009 International Conference on Image Analysis and Signal Processing, pp. 31–35. IEEE (2009) 6. Hasançebi, O., Erbatur, F.: Evaluation of crossover techniques in genetic algorithm based optimum structural design. Comput. Struct. 78(1–3), 435–448 (2000)

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7. Sutar, S.R., Bichkar, R.S.: University timetabling based on hard constraints using genetic algorithm. Int. J. Comput. Appl. 42(15), 3–5 (2012) 8. Kadri, R.L., Boctor, F.F.: An efficient genetic algorithm to solve the resource-constrained project scheduling problem with transfer times: the single mode case. Eur. J. Oper. Res. 265(2), 454–462 (2018) 9. Rajesh, K., Visali, N., Sreenivasulu, N.: Optimal load scheduling of thermal power plants by genetic algorithm. In: Emerging Trends in Electrical, Communications, and Information Technologies, pp. 397–409, Springer (2020) 10. Lin, Y.-K., Chong, C.S.: Fast ga-based project scheduling for computing resources allocation in a cloud manufacturing system. J. Intell. Manufact. 28(5), 1189–1201 (2017) 11. Xiao, Y., Konak, A.: A genetic algorithm with exact dynamic programming for the green vehicle routing and scheduling problem. Journal of Cleaner Production 167, 1450–1463 (2017) 12. Roeva, O., Fidanova, S., Paprzycki, M.: Population size influence on the genetic and ant algorithms performance in case of cultivation process modeling. In: Recent Advances in Computational Optimization, pp. 107–120, Springer (2015) 13. Shukla, A., Pandey, H.M., Mehrotra, D.: Comparative review of selection techniques in genetic algorithm. In: 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), pp. 515–519, IEEE (2015) 14. Haupt, R.L.: Optimum population size and mutation rate for a simple real genetic algorithm that optimizes array factors. In: IEEE Antennas and Propagation Society International Symposium. Transmitting Waves of Progress to the Next Millennium. 2000 Digest. Held in conjunction with: USNC/URSI National Radio Science Meeting (C), vol. 2, pp. 1034–1037, IEEE (2000) 15. Safe, M., Carballido, J., Ponzoni, I., Brignole, N.: On stopping criteria for genetic algorithms. In: Brazilian Symposium on Artificial Intelligence, pp. 405–413, Springer (2004) 16. Murata, T., Ishibuchi, H.: Performance evaluation of genetic algorithms for flowshop scheduling problems. In: Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, pp. 812–817, IEEE (1994) 17. Michalewicz, Z., Nazhiyath, G., Michalewicz, M.: A note on usefulness of geometrical crossover for numerical optimization problems. Evol. Program. 5(1), 305–312 (1996) 18. Wright, A.H.: Genetic algorithms for real parameter optimization. In: Foundations of Genetic Algorithms, vol. 1, pp. 205–218, Elsevier (1991) 19. Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In: Foundations of Genetic Algorithms, vol. 2, pp. 187–202, Elsevier (1993) 20. Deep, K., Thakur, M.: A new crossover operator for real coded genetic algorithms. Appl. Math. Comput. 188(1), 895–911 (2007) 21. Bort, E., Franceschini, G., Massa, A., Rocca, P.: Improving the effectiveness of ga-based approaches to microwave imaging through an innovative parabolic crossover. IEEE Antennas Wireless Propag. Lett. 4, 138–142 (2005) 22. Moscato, P., et al.: On genetic crossover operators for relative order preservation. C3P Report, vol. 778 (1989) 23. Goldberg, D.E., Lingle, R., et al.: Alleles, loci, and the traveling salesman problem. In: Proceedings of an International Conference on Genetic Algorithms and their Applications, vol. 154, pp. 154–159, Lawrence Erlbaum, Hillsdale, NJ (1985) 24. Oliver, I.M., Smith, D.J., Holland, J.R.C.: A study of permutation crossover operators on the traveling salesman problem. In: ICGA (1987) 25. Whitley, L.D., Starkweather, T., Fuquay, D.: Scheduling problems and traveling salesmen: the genetic edge recombination operator. ICGA 89, 133–40 (1989) 26. Ahmed, Z.H.: Genetic algorithm for the traveling salesman problem using sequential constructive crossover operator. Int. J. Biometrics Bioinform. (IJBB) 3(6), 96 (2010) 27. Arram, A., Ayob, M.: A novel multi-parent order crossover in genetic algorithm for combinatorial optimization problems. Comput. Ind. Eng. 133, 267–274 (2019) 28. Gen, M., Choi, J., Ida, K.: Improved genetic algorithm for generalized transportation problem. Artif. Life Rob. 4(2), 96–102 (2000)

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Chapter 47

Escalating Demand, Present and Future Status on Hybrid Electric Vehicles Manish Kumar, Muralidhar Nayak Bhukya, Anshuman, and Sachin

Abstract This paper presents a comprehensive review about different aspects of hybrid electrical vehicles (HEV). In a country like India, most of the transportation vehicles use petrol, diesel, and natural gas as fuel, which is identified as major source of pollution. Therefore, HEV uses electric energy as a primary source which is a clean and produces less amount of green house gases and carbon dioxide (CO2 ). It can be benchmarked as EHV helps us to reduce pollution. Hence, this paper helps us to understand about working, importance, present and future status of EHV along with advantages and challenges. As the demand of vehicles and transportation is increasing day by day, the need to manufacture EHV vehicles is also escalating which works on clean energy with maximum efficiency to fulfill the demand.

Nomenclature HEV Hybrid Electric Vechiles

47.1 Introduction India has less number of petroleum reserves. So, India is completely dependent on import. The rank of India in oil imports is third. India imports 82% of crude oil and 45% of natural gas. Because of this, large import pollution is increasing, and also large import is not in favor of India. Large import produces a loss in the Indian economy so we need to shift clean and new technology. Most of the petroleum products are consumed by vehicles. Exhaust emission from automobiles cannot be ignored. Vehicle emissions, which basically assume greenhouse emission, CO, CO2 , M. Kumar (B) · M. N. Bhukya · Anshuman · Sachin Department of Electrical Engineering, School of Engineering & Technology, Central University of Haryana, Mahendragarh, Haryana 123031, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_47

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particulate matter (PM2.5 and PM10), and nix, are due to fact that the fundamental individual for the effects of greenhouse gases is the increase in many varieties of cancer and special extreme diseases. The constantly speedy developing transportation zone user concerning around 49% oil sources. Following the existing developments in oil utilization and crude resources, the world’s oil assets are predicted to be drained through 2039. Therefore, exchange unsustainable electricity sources with sustainable power resources and the help of appropriate electricity-saving automation appears to be necessary. The electric automobile as a probable resolution for allay the site visitors-associated surrounding troubles is inspected and studied seriously [1]. On the basics of the U.S. Department of Energy and Resources, around 14% of the whole fuel power is exhaust to drive a vehicle and its special attachments. The maximum of the strength is remodeled into heat all through ignition that consequently and immediately supplies closer to heating. The standard power float for Associate in Nursing ICE car. In widespread, ICE automobiles wasted their power through resistance on a transferring half of and warmth loss from total energy in gasoline. Therefore, frequent preservation is required for Associate in Nursing ICE automobiles. After all-electric vehicles consume over 75th of electricity solely to run the auto. These days electric vehicles unit has an average of 3–9 miles vary per kWh of hold on electricity. The aim of this paper is to study the innovation of the market strength deliver, electricity generator for electric automobile, strength device, lowstage control electricity management strategy, and excessive supervisor management algorithmic rule use in cars [2].

47.2 Need of Hybrid Electric Vehicle We know that demand of the vehicles is increases day by day. For the fulfill, those demand lot of auto company developed the new feature of the vehicles. Last previous years (2005–2012) data collection of energy consumption different sectors has been shown in Fig. 47.1. In this shown the four sectors residential, commercial, industrial, and transportation sectors energy consumption. We can observe that transportation sector is the second largest sector of energy consumption. So with the help of HEV, we can target a large sector and work on its improvement [3]. Figure 47.2 shows the yearly carbon dioxide emissions in million metric tons. It can observe that transportation sector is top in carbon emission so transportation sector is the major region of pollution. With the help of HEVs, a major change will occur in pollution level. It also observed that we cannot control pollution level without working on transportation sector. Consideration of energy consumption and reduced the pollution by transportation sector, the new technology is required in the auto markets. So that, the all company increases the interest and invest the money of the HEV area [4].

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2005 Y 250

Residentail sector

200 2012 Y

2006 Y

150 100

Commerical sector Industrial sector

50 0 2011 Y

2007 Y

2010 Y

TransportaƟon sector

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Fig. 47.1 Yearly energy consumption Residentail sector Industrial sector

Commerical sector TransportaƟon sector

2000 1800 1600 1400 1200 1000 800 600 400 200 0 2009

2010

2011

2012

Fig. 47.2 Yearly CO2 emissions (million metric tons)

47.3 Classification of Vehicles in Market According to the fuel consumption and the technology, the vehicles are classified in the three categories. It is given in below: i. Hybrid electric vehicles (HEV) ii. Internal combustion engine vehicles (ICEV) iii. All-electric vehicles (AEV).

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47.3.1 Hybrid Electric Vehicle (HEV) The hybrid electric car or HEV is an automobile that is the combination or use of both ICE and electric-powered motor as strength assets to shift the automobile. Now, at present times, on the basics of the driven train’s architectures six types of HEVs. The mild-HEV has the similar benefit with the micro-HEV but the electric motor in the moderate-HEV has an electric-powered power of 7–12 kW with 150 V operational supply and may move the car collectively along ICE. It commonly profits gas efficiency as much as 30% and might lessen the dimensions of ICE. The Honda (civic, accord), Saturn Vue, and GMC Sierra pickup are examples for slight-HEV [5]. Nowadays, the maximum of the automobile makers has a similar tempo for supply complete HVEs because of its operation of cutup electricity course either jogging on simply ICE or the EM. After compromising the using work, a complete HVE can keep as a lot as 38–42% of fuel. Commonly, this sort of HEV having a high bulk energy storage device (ESD) with working voltage 330 V (288 V). A full hybrid electric vehicle can be split into a prolonged range electric car or series complete hybrid electric vehicle, hybrid electric-powered automobile or parallel, series–parallel full, complex full hybrid electric vehicle, and plug-in a hybrid electric-powered automobile [6]. EREV uses EM as the sole impulse of electricity into a battery electric automobile. However, the distinction is they still have a better capability ICE generator integrated to charging the battery is little. Chevrolet ability unit as one amongst the EREVs presently accessible inside the marketplace. Such a car is diagnosed as a series of complete hybrid electric vehicle or collection connection hybrid electric vehicle. The benefit of this composition is the car’s battery which can be decreased relying on the generator electricity and the fuel functionality system. This cut down the general car ability or efficiency to around 25.8% this is the minimum amongst all entire full HEVs. However, it is appropriate in a forestall and run driving pattern that is metropolis using pattern. It stocks and shops maximum of the power from the regeneration braking system to the ESS. Bearing on typical configuration, the parallel complete hybrid electric vehicle has two propulsion powers (ICE and EM) in the mechanical coupling and is able of raising the overall hybrid electric vehicle potency to 44.5%. Parallel complete HIEV, on the alternative hand, contains powerless battery functionality. One amongst the benefits of the parallel full hybrid electric vehicles is that the EM and ICE addition one another for the duration of driving. Allows this makes the parallel complete hybrid electric vehicle a number of the captivating cars under every road driving and metropolis using conditions. As in comparison to collection fully hybrid electric vehicle, the parallel complete hybrid electric vehicle has bigger potency due to shorter EM and the length of the battery [7]. The series–parallel full-HEV power train employs two electricity couplers which might be robotically supercharged and electrically supercharged. Though it possesses the gain of collection complete hybrid electric vehicle and parallel full hybrid electric vehicle, it is relatively a whole lot of sophisticated and expensive. Complicated hybrids look to be the identical composition with a collection-parallel hybrid. The

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important thing difference is that the ability converter is an aspect to the motor or generator and motor. This makes the complicated complete hybrid electric vehicle a variety of workable and dependable than series–parallel complete hybrid electric vehicles. For series–parallel full hybrid electric vehicle and complicated complete hybrid electric vehicle, they are a whole lot of flexible on their management methods than the opposite two configurations. Having said that the most undertaking is that they have a specific management approach. Furthermore, full hybrid electric vehicle configuration gives rock bottom charge and also the possibility of mistreatment existing producer methods for engine, batteries, and vehicles. Lexus CT200, Toyota Prius, Lexus LS 600th, and Toyota Auris are commercially handy series– parallel complete HEV while ford Escape, Honda Insight, and Honda Civic Hybrid are commercially handy parallel complete hybrid electric vehicle [8]. However, the connection in hybrid electrical automobiles has similarities to full the hybrid electric vehicles but the battery is often connection into the grid. Indeed, the connection in the hybrid electric automobiles is directly made over from any form of a hybrid electric vehicle. For detail, series–parallel HEV reworking into PHEV by means of sum charger beside the battery. Consequently, for the duration of running, the using force will set the capacity to draw from battery percent quite a few in place of ICE anywhere it is one among the techniques to better the car work. For example, in city force or a short distance force, the using pressure would possibly pick out the electric motor mode so as to obtain fuel efficiency correlated to the employment of the ICE engine. This method makes PHEV convenient each in town riding and street using patterns [9].

47.3.2 Internal Combustion Engine Vehicle (ICEV) An internal combustion engine vehicle has a combustion chamber whose main purpose is to convert chemical reaction energy into heat by combustion, heat energy to electrical, and kinetic electricity to an automobile. Following varieties of automobiles: 1. Conventional ICEVs with minimal fuel economy and no EM to help and accumulate micro-hybrid electric vehicles (micro-hybrid electric vehicle) with EM with low running voltage 14 V (12 V) and energy only recirculation, to begin with, does not exceed 5 kW. ICE from the off state without contributing any electricity to thrust the car. While coasting, braking, or stopping the ICE stops for you to increase the fuel economy by 6–17% (depending on the urban and city environment). 2. The Citroen C3, a reality currently offered in countries in Europe, is a microhybrid electric vehicle [10]. Figure 47.3 shows the energy flow diagram of conventional vehicle. In the above diagram, we can see that how 30–60% energy is just use for vehicle operations. If we work on new technology and reduce the energy consumed in operation, then the

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Fig. 47.3 Energy flow of conventional internal combustion engine vehicle

efficiency of the system will increase. HEVs use this type of technology to increase the efficiency.

47.3.3 All-Electric Vehicle (AEV) All electric-powered automobiles or all-electric vehicles are a car that accepts electric; electricity is the only resource to move the car. Currently, all-electric vehicles have six forms of energy transfer structure, but the best three variants are well-known for use by car makers. The configuration of the drive educates the design in BEV and FCEV. Fuel is one of the major energy suppliers or secondary electricity providers based on cellular demand and modern technology. The gear container and seize still continue in the car, using an unmarried tool transmission without a clutch to reduce the amplitude and load of the mechanical transmission [11]. The performance is lower in both structures than the other four compositions. Also to illustrate driveline design, one is using continuous gearing and differential individual automobiles and stuck gearing with their drive shafts to perform at particular speeds at some point of cornering. The range of the BEV can be reduced to be the Mitsubishi’s Colt EV. This type of size is most convenient in the metropolis due to its general low weight. This deigns calls for an improved torque traction motor to initiate acceleration. Therefore, the capacity is low due to larger losses in the structure of joule heating through large current days in motor windings. Nevertheless, this composition has the lowest mechanical strength that minimizes the loss of power transfer between mechanical and electrical. When assembled, the primary drawback of the BEV is that it is capable of traveling only short distances. The BEV is convenient in accessing the city or city as well as

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stopping and running the use. By increasing the differential variation of the BEV so that it is suitable in metropolitan and dual carriageway demand figures, the gearbox is installed within the vehicle. This increases the efficiency of the traction gadget over the entire range. When BEV shifting without gears can change or increase performance due to a cut in the volume of the component. Therefore, there is no hidden power in the equipment and differential system. This structure reduces the car’s central gravitational force. The housing of the motor inside the wheel increases the uncertain weight of the wheel, which has a detrimental effort on the handling of the automobile [12].

47.4 Classification of Electrical Automobile Taking the ability to supplement and propulsion devices below attention, electron volt can be labeled into three absolutely different sorts: i. Pure electric automobile (PEA) ii. Hybrid electric automobile(HEA) iii. Fuel mobile electrical automobile (FMEA). The pure electric automobile is only fed through an electricity garage unit, whereas the propulsion of pure electric automobiles is on my own add by an electrical motor. The riding system of hybrid electric vehicle links the electric motor and also the engine, while the capacity assets associate each energy and gas or diesel. Fuel mobile electrical automobiles are driven via an electric motor and could be immediately or discursively powered mistreatment atomic number 1, methanol, ethyl alcohol, or gas [13]. In pure electric automobiles, loosely designated as battery electrical automobiles (BEA), the energy garage capacity actually depends on battery generation. Zero discharge should be the main advantage of pure electric automobiles because the energy itself is provided from the car’s hookup battery. On the other hand, the ban on this state of on-board battery production of pure electric automobiles is much more modestly with similar monetary and ride needs than ICEVs. The high power density battery also has high charging time with quick charging due to low power density; one hour too much time is important for full charging. Thus, the fundamental challenges are limited practice limits, high initial cost, and lack of charging base. For realistic application, the size and location of the battery inside a pure electric automobile must also be stranded. FEAs are entangled as a result of zero margin discharge. Even taking into account the normal emissions, to get rid of chemical flora and road reformers, the fuel mobile electric automobile still looks aggressive. Cell (FC) is the principal energy issuer, and furthermore, the critical era for fuel mobile electric automobiles is a chemical technical knowledge that generates DC electricity through a chemical reaction system [14]. There are mainly five predominant parts in fuel cell: i.

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Anode An anode layer A cathode catalyst layer and Electrolyte.

With a suitable parallel or series association of fuel cell assets, the specified quantity of energy may be created to drive the automobile. In phrases of exercise variety, it is adored ICEV, therefore main to a large variety of software of farm credit system from little scale flora of the order of two hundred watts to little electricity plant life of the order of five hundred kilowatts. However, the larger initial value and the absence of fueling satiation location unit still taken into consideration important challenges for the success of fuel mobile electric automobiles. Also, the provision power continuity of the farm credit system is a smaller quantity dependable than a typical battery applied in EVs. The important benefit of BEV and FMEA is that the “0 ejaculations” and thence decreased pollutants. However, the “0 ejaculation” of BEV and FMEA is not completely thinking about the ejaculation for the duration of the complete method. However, “what is important due to the fact the main pollutants contributor and how” region unit the topics that vicinity unit rarely referred to. As an example, the pollutants members encompass chemical infection as soon as production the electric cellular and therefore the battery (or the chemistry plant for fuel cells), discharge for the duration of the automobile manufacturers, the pollution from discard battery procedure, and so on. The hybrid electric vehicle joints the homes of ICEV and BEV. Driving energy origin of hybrid electric vehicle embodies each gas or diesel and strength; the propulsion relies upon on the motor and engine [15]. With regular fully one-to-one refueling measures, hybrid electric vehicles can be labeled as both normal hybrid vehicles and grid-capable hybrid electric vehicles. The supported degree of merger may be more advanced for traditional HEV3 variants: smaller, sensitive, and full HEV. The grid is able hybrid electric vehicle may be a connection in hybrid electric vehicle or variety extended electric automobile. The monetary software of HEV seems to require a variety of blessings than plug electric vehicle way to the status of gift battery technology. The requirement for petrol and engine isn’t removed in hybrid electric vehicles; hence, there is emission in a small amounts. The merge of the electrical generator and engine will increase the complexness of the producing approach and consequently the basic fee. The main objection for hybrid electric vehicles is area unit specializing in regulating these two propulsion gadgets to get a pleasant potency while less the appearance complexness at equivalent. Researching the general improvement of EVs and thinking about each the economic system and consequently the generation, the hybrid electric vehicle has the foremost capability to progress and dominate resulting few many years. The energy sources into attention, EVs area unit totally or partly energized from the batteries, that themselves place unit without delay or indirectly charged from both an influence station and chemical reactions. Therefore, varied sustainable energy assets have to be accustomed to enhance the general emission of EVs. Figure 47.4 offers the electricity diversification supported definitely exceptional feeding measures for

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Fig. 47.4 Energy diversification of EV

the work unit [16]. It is shown the different types of energy resources use for different types of vehicles. We use different types of combination to increase the efficiency of the vehicles.

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47.5 Technologies of Hybrid Electric Car or Vehicle 47.5.1 Regular Hybrid Electric Vehicle 47.5.1.1

Small and Gentle Electric Vehicle

Hybrid electric vehicles are small, soft, and in full HEV mode. When we compare with ICEV, the working of micro-hybrid electric vehicle based on an engine motor work smoothly. We cannot strictly label micro-hybrid electric vehicles as hybrid electric cars because the electric motor provides never-ending energy. In light HEVs, the change is through a starter generator that includes a simple start motor; it is actually set between the engines and is low on the basis that the ISG controls or controls the engine to drive the vehicle. One of the outstanding samples of lightweight HEV is Buick Field Entertainment. The running rule of a lightweight hybrid electric vehicle is summarized as follows: Once the vehicle is stopped, the electric generator functions while the fuel engine is smoothed. Later, all working equipment can be considered true on the electric motor on its own. Once the foot pedal is freed, and consequently, the car is multiplied, the fuel can start the engine and affect the entire propulsion below a constant small speed. These techniques produce a major feature: The engine of automobile stalls must be cleared once; it is conceptualized as an idol forest start function. As the automobile recharges the battery is often recharged to either extend or apply brakes. The vision of the ISG is for each engine and motor that gives it enough speed to run the motor simultaneously. The Honda CR-Z is one of the most common representatives for a lightweight hybrid electric vehicle [17].

47.5.1.2

Full- and Dual-Mode Hybrid Electric Vehicle

For a fully hybrid electric vehicle, the critical generation is an electrical actuation transmission (EVT) that is equally managed as an impact splitter. The power cacophonic provided by the EVT receives an electric projection that refers to the initial acceleration below full wattage. This continues to the general public of the benefits of various varieties of common HEVs such as idle stop-start, regenerative braking, short-length engines, and electric launches. The Toyota Prius followed the entire HEV mode in production in 1997 and advanced more including a planetary wheel to help the capacity cacophonous. Once an excellent fashion in the automobile marketplace and the actual acceptance of the hybrid energy grid, an excellent variety of motor groups dedicate it to an extended degree of rectangular degree of extra gasmonetary and ambient–pleasant condition. The Lexus LS600HI has upgraded the entire hybrid mode and introduction “true 0 emissions.” To deal with the case of gasoline intake at a populated location during start, forest, and restart, dual hybrid supported fully hybrid electric automobile gadgets are added to boost general efficiency. The dual-mode ‘way that hybrid gadgets and

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motors are effectively part of the forces to realize a top rate performance under quick acceleration and full speed conditions. Lexus ct200h and BMW × 6 rectangular new technology motor known through the majority and regularly measuring high quantity examples. It is important to mention that dual mode has no longer contribution to traditional hybrid electric vehicle technologies, although some in relation to hybrid electric vehicle technology. The simple hybrid electric vehicle technology has been adequately explored and has advanced much further. As regular hybrid electric vehicles have changed. Smaller and lighter common hybrid electric vehicles prefer gas or diesel systems while electric generators or batteries serve as accessories. In distinction, the fact that whole or twin-mode HEV uses strength which is the main energy to propel the car. Currently, HEV has played a major role. Although common HEVs can be adapted to dual-mode or full HEVs to enhance the golf field and gas financial system, the dangers of flammable fuel or diesel, large-scale battery P.C., and the high initial rate cannot be unheard. Furthermore, the complexity of the production method is probably any other assignment. Thus, conventional HEV transmission becomes at once irrelevant, taking into account troubles such as loss, equipment noise, and smoothness. However, it can be pointed out that HEV can be seen by researchers as a “high initial cost” tool. However, the research that indexed the “excessive initial cost” was due to the fact that the error of the theory was very little about the price of maintenance and the value of the building or there was no record. Refueling or recharging facilities [18].

47.5.2 Grid-Able HEV (PHEV) In regular HEVs, the grid-in state HEV capacity can be delayed without being connected to the grid, relative to the amount of energy covered by the battery%. Researchers found that grid-enabled HEVs, also known as PHEVs, for decades. Typically, a creative change in PHEV is to switch hook battery% (used in specific HEVs) with reversible batteries. This conclusion allows the battery to be recharged from the external power supply as well as an increase in power capacity. PHEV will supply a pure electric golf range for almost as long as every PEV and ICEV. Although it is advanced than traditional HEV, the operating mode of PHEV differs properly from that of normal HEV. The same old HEV fuel is structured, suggesting strength from the battery and generator support parts for the engine. In contrast, energy from reversible batteries may play a primary role in PHEV while the fuel engine is maintained because of the auxiliary propulsion unit [19].

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47.6 Challenges in Hybrid Electrical Vehicles We face some challenges when we work with non-hybrid electrical vehicles. The challenges are: i.

LOW EFFICIENCY: In hybrid electrical vehicles, we use two and more types of output sources but we use one at a time, so the total efficiency of the system decreases. ii. HIGH INITIAL COST: The initial cost of the hybrid electrical vehicles is high, so these vehicles are not in the reach of all the people. iii. HIGH VOLTAGE BATTERY: The high output batteries are not safe in unfavorable conditions and at the time of the accident. iv. HIGH MAINTENANCE COST: Maintenance or a hybrid electric vehicle is very high, and the maintenance sources are not available everywhere.

47.7 Advantage of Hybrid Electrical Vehicle The advantages of hybrid electric vehicles are: 1. ENVIRONMENT FRIENDLY: Hybrid vehicles produce less pollution than other vehicles. 2. ECONOMICAL: The per kilometer cost of HEV is very less than other types of vehicles. 3. LESS FOSSIL FUEL DEPENDENT: These vehicles are more dependent on electrical energy. 4. REGENERATIVE BREAKING SYSTEM: When we apply break, we get a small amount of electrical energy. 5. LIGHT BUILD: These vehicles are made with lightweight material so the total weight of the body is decreased, and we need less power to move the vehicles. 6. HIGHER RESALE COST: If we want to resale the vehicle, then we get a good amount. [20]

47.8 Conclusion This paper presents a technical report on HEV and benchmarks the necessity in the modern world. In future, HEV is the best mode of transport with low CO2 emission, economical, and environment friendly. The nations have to encourage the usage of electric vehicles for public and private transportation, and an adequate budget has to be allotted for R and D on electric vehicles. Acknowledgements I owe my special thanks to Dr. Manish Kumar and Dr. Muralidhar Nayak Bhukya for all his support and guidance without which all this research was totally impossible. I

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also thanks my family and colleagues for their best wishes. I would also owe special thanks to all my friends for their moral support.

References 1. Akhavan-Rezai, E., Shaaban, M.F., El-Saadany, E.F., Karray, F.: Demand response through interactive incorporation of plug-in electric vehicles. In: Power & Energy Society General Meeting, IEEE, July, pp. 1–5 (2015) 2. Al Baghdadi, M.A.R.: Modeling of proton exchange member of fuel cell performance based on semi-empirical equations. J. Renew. Energy 30, 1587–1599 (2005) 3. Amjad, S., Neelakrishna, S., Rudramoorthy, R.: Review of design considerations and technological challenges for successful development and deployment of plug-in hybrid electric vehicles. Renew. Sustain. Energy Rev. 14, 1104–1110 (2010) 4. Arora, S., Shen, W., Kapoor, A.: Review of mechanical design and strategic placement technique of a robust battery pack for electric vehicle. Renew. Sustain. Energy Rev. 60, 1319–1331 (2016) 5. Chakraborty, A.: Advancement in power electronics drives in interface with growing renewable energy resources. Renew. Sustain. Energy Rev. 15(4), 1816–1827 (2011) 6. Schmidt, R.: Information technology energy usage and our planet. In: 11th Intersociety Conference on Thermal and Thermo Mechanical Phenomena in Electronic Systems, 2008. ITHERM 2008 (2008) 7. Chau, K.T., Chan, C.C.: Emerging energy-efficient technologies for hybrid electric vehicles. Proc. IEEE 95(4), 821–835 (2007) 8. Momoh, O.D., Omoigui., M.O.: An overview of hybrid electric vehicle technology. In: Vehicle Power and Propulsion Conference, 2009.VPPC’09. IEEE (2009) 9. Sudworth, J.: The sodium/nickel hloride (ZEBRA) battery. Power Sources 100(1–2), 149–163 (2001) 10. Torotrak: 2011 [cited 2011 5 December 2011]; Available from: www.torotrak.comS. 11. Mehrdad Ehsani, Y.G., Ali, E.: Modern Electric, Hybrid Electric, and Fuel Cell Vehicles, 2nd edn, p. 534. CRC Press (2010) 12. Bjornar Kruse, S.G., Cato, B.: Hydrogen Status of Muligheter. In: Kruse, B. (ed.), p. 53 13. Serrano, E., Rus, G., Garcia-Martinez, J.: Nanotechnology for sustainable energy. Renew. Sustain. Energy Rev. 13(9), 2373–2384 (2009) 14. Beretta, G.P.: World energy consumption and resources: an outlook for the rest of the century. In: Advanced Energy System Division. ASME Congress (2008) 15. Bormaghim, G., K.G., Serfass, J., Serfass, P., Wagner, E.: Hydrogen and fuel cells: the U.S. market report (2010) 16. Aragno, F.V.M.: 8, Collegno, I-10093,IT) Piritore, Giuseppe (Via Guarini, 48, Venaria, I-10078, IT), Vehicle Featuring an Auxiliary Solar Cell Electrical system, Particularly for Powering the Air Conditioning System of a Stationary Vehicle.1994, (Corso Giovanni Agnelli 200, Torino, I-10135, It): FIATR AUTO S.P.A. 17. Garner, I.F.: Vehicle auxiliary power applications for solar cells. In: Eighth International Conference on Automotive Electronics (1991) 18. Connors, J.: On the subject of solar vehicles and the benefits of the technology. In: ICCEP’07. International Conference on Clean Electrical Power (2007) 19. Young, W.R., Jr.: Photovoltaics and the automobile, In Southcon/94, Conference record (1994) 20. Market, T., et al.: Energy storage system requirement for hybrid fuel cell vehicles. In: Advanced Automotive Battery Conference. National Renewable Energy Laboratory Nice, France, p. 12 (2003)

Chapter 48

Analysis and Control of Civilian Aircraft Model Using Simulink [PECS] 2020 Utkarsh Sharma and Sudhir Nadda

Abstract According to the study carried out in the year 2018 by BOEING on October 24, a Lion Air Flight 610 which was a BOEING 737 MAX Aircraft was crashed into Java Sea after 13 min after Take-Off killing all 189 passengers including crew members with 1 Rescue Diver. Afterwards in 2019 on March 29, a Ethiopian Air Flight 302 which was also a BOEING 737 MAX Aircraft was also crashed near the Town of Bishoftu after 6 min of Take-Off killing all 157 passengers aboard. There were many conclusions that were drawn by analyzing both the crashes that were there was faults in stability system as it does not amended the incorrect reading from Faulty Angle Of Attack Sensors from which it was taking the information. Also there were some faults in Angle of Attack sensors also as they did not provide the correct reading to the Stability Systems Lastly as it was a newly upgraded Aircraft so its engine was made a little larger for increasing the fuel capacity but that in turn shifted the Center Of Gravity as its placement was also on the Aft side of the wing, hence the Plane crashed. This study provides analysis of the Aircraft Model that maneuvers in Unstable Environment like Gust Winds and controls its altitude using SIMULINK in MATLAB.

48.1 Introduction The Boeing 737 MAX is the fourth generation of the Boeing 737, a narrow body airliner manufactured by Boeing Commercial Airplanes (BCA). It succeeds the Boeing 737 Next include its distinctive split-tip winglets and airframe modifications. [1, 2]. The 737 MAX series has been offered in four variants, offering 138–204 seats in typical two-class configuration and a 3,215–3,825 Boeing 737 MAX had received 4,932 firm orders and delivered 387 aircraft. The first flight took place on January 29, U. Sharma (B) · S. Nadda Amity Institute of Space Science and Technology, Amity University, Noida 201313, India e-mail: [email protected] S. Nadda e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_48

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2016, at Renton Municipal Airport, nearly 49 years after the maiden flight of the original 737–100 and delivered. 1A002 s used for performance and engine testing: climb and landing performance, crosswind, noise, cold weather, high altitude, fuel burn and water-ingestion. Aircraft systems including autoland were tested with 1A003. 1A004, with an airliner layout, flew function-and-reliability certification for 300 h with a light flight-test instrumentation.

48.1.1 Core Aspects The aircraft length is 129 feet, inches in length, with a wingspan of 117 feet, 10 inches. It stands upto 40 feet, 4 inches tall. The aircraft’s maximum takeoff weight is 181,200 lb (82,191 kg) and it carries 6853 gallons of jet fuel in it [3–5]. It has a maximum range of 3550 nautical miles, speed s Mach 0.79. dBa of noise on takeoff, it 40 percent quieter than BOEING 737–800 series. The 737 MAX series has been offered four variants, offering 138 to 204 seats in typical two-class configuration and a 3,215–3,825 nautical miles (5,954–7,084 km) range (Fig. 48.1).

Fig. 48.1 Top view, front view, and side view of aircraft (see Ref. [4])

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48.1.2 Stability Problem For providing more stability in the Boeing 737 Max aircraft, a new stability augmented system was installed on board. It was known as the MCAS or maneuvering characteristics augmentation system that will ensure that aircraft would not stall in the conditions of high gust winds or inadequate angle of attack. But in both Lion Flight 610 and Ethiopian Air Flight 302, this stability system fails to prevent stalling of these aircrafts and hence they both got. In both these aircrafts, MCAS got faulty readings from angle Of attack sensors that are used as an input for this system, and then due to error in software code of stability system did not check the readings from the angle of attack (AoA) sensors and directly used them for producing the output. The aircraft was flying at 0 degrees but the system showed +15° due to error in data. So as the system should work, it decreased the angle of attack from +15° to 0° but it was actually doing from 0° to −15° that is complete Nose down. And the pilot cannot control this system so he tried to pull the aircraft up but could not help it and both of them got crashed. That was the main issue with these aircrafts.

48.2 Flight Equations and Derivatives Required for Analysis 48.2.1 Control Limits and Saturation There are basically four control units in the aircraft like ailerons, rudders, elevators, throttle for engine, but as I am considering a two engine aircraft, there is Throttle-1 for engine 1 and Throttle-2 for engine 2.

48.2.2 Intermediate Variables There are few intermediate variables that are always required for the analysis of model of aircraft like calculating airspeed, calculating angle of attack (alpha), calculate the dynamic pressure, etc.

48.2.3 Aerodynamic Forces and Moments There are some aerodynamic forces also with the help of which the aircraft flies like Total lift Force, Total Drag force, Total Side force, etc.

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48.2.4 Dimensional Aerodynamic Forces These are some of the forces that are required for the stability of the aircraft like body forces, stability axis, stability forces, rotational forces, etc.

48.2.5 Aerodynamic Moment Coefficient About Aircraft Now, we define some important aerodynamic moment coefficient like dCM/dx and dCM/du, etc., and arrange them all in the matrix form.

48.2.6 Aerodynamic Moment About Aircraft Now, we have to normalize the aerodynamic moment about the aircraft like pitching moment, rolling moment, etc.

48.2.7 Aerodynamic Moments About Center of Gravity Now, it is the time for converting the moments to CoG for the whole aircraft and analyze them serially.

48.2.8 Engine Forces and Moments in Engines Effect of both engines and calculating the thrust provided by each engine, each engine thrust is to be aligned with fuselage of the aircraft, Calculating the engine moment due to offset of engine thrust from CoG.

48.2.9 Gravity Effects To calculate all the gravitational forces in the body frame of the aircraft. This causes no moment about the center of gravity of the aircraft.

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48.3 Non Linear 6 DOF Civil Aircraft Model See Fig. 48.2.

48.3.1 About the Model This is the SIMULINK Model of the civil aircraft where the full function and code for calculating the X Dot (State Derivatives Dot) is written and saved inside RCAM_Model. It takes two input X and U where X Dot State derivative matrix (9*1). U Dot Control vector matrix (5*1). Where the control vectors are δ a , δ t , δ r , δ en1 , δ en2 and they stands for δa δt δr δ en1 δ en2

Aileron control. Tail/elevator control. Rudder Control. Engine control coefficient for engine 1. Engine control coefficient for engine 2.

and the state derivatives are as follows: u

Velocity in x-direction.

Fig. 48.2 Aircraft model flowchart

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Fig. 48.3 Block diagram representation

ϕ v Θ w Ψ p q r

Roll angle. Velocity in y-direction. Pitch angle. Velocity in z-direction. Yaw angle. Angular rate/roll rate about X-axis. Angular rate/pitch rate about Y-axis. Angular rate/yaw rate about Z-xis.

48.3.1.1

Simulating Block Diagram

In this model, there are two sinks, with one interpreted MATLAB function that is getting the output from a multiplexer and the multiplexer is connected with the state derivatives and control vector matrix both. Interpreted MATLAB function—It is a user defined function in SIMULINK Library. It applies the specific MATLAB function or expression to the input. Sinks—It is just used to display or export the signal data blocks such as scope to the main workspace (Fig. 48.3).

48.4 Plotting the Results By this plot that at the time of running the model, we have no aileron, no rudder movement, no elevator or tail movement is shown and also there is constant thrust provided by engine 1 and engine 2, respectively (Figs. 48.4 and 48.5). Here, the first column of graphs is showing plots of u, v and w, respectively. So the first graph indicates that we started off at 85 m/s and then accelerated to 135 m/s and then it lowered the velocity and stabilized it.

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Fig. 48.4 State derivatives plots for civil aircraft model

There is longitudinal motion so we can see there is v component as well as no p and no r component also is shown even no ϕ and no Ψ component is present here in the plots.

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Fig. 48.5 Control vector plots for the aircraft

48.4.1 Plotting the Final Results See Figs. 48.6 and 48.7.

48.5 Conclusion Various sets of information were made into this study and the results were obtained and plotted likewise here on MATLAB and then I analyzed all the various plots for the code that has helped us to construct a nonlinear 6 DOF aircraft model as well as plotting all the results thereby. As discussed before this conclusion that all the results have been analyzed & all the data came from the code and from the SIMULINK combined everything to produce a fine output and also the output shows at last, the aircraft is finally flying in a steady state or it is flying steadily. Due to unavailability of sufficient data, parameters, such as the wind effects, gust winds parameters, aerodynamic features such as drag, downwash, etc., more analysis of this model was not possible. These were the outcomes of the processing the model:

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Fig. 48.6 Final plotted results

1. Here, the aileron is deflected at 30 s for 2 s time period and then it gets back and the fourth graph says engine 1 is switched off and the force produced by the engine 1 is completely zero in this case. 2. In these results as we can see by all 9 graphs as that we are flying the aircraft stably and we flying the aircraft in the steady state as well, but as we can see by the last graph that indicates the aircraft is turning into circular motion and that because one engine is shut off but all the other graphs indicate that the aircraft is flying stably after we revised the saturation and control limits.

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Fig. 48.7 Final plotted results

References 1. Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column aircrafts manual for classification. In: 2012 IEEE Conference on Computer Vision and Recognition (CVR), pp. 3642–3649. IEEE (2012) 2. Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Classification of high speed aircrafts. In: Document Analysis and Recognition (2011) 3. Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Framework of aircraft. In: Document Analysis and Recognition (2011) 4. Farabet, C., Martini, B., Akselrod, P., Talay, S., LeCun, Y., Culurciello, E.: Aircraft aerodynamic electronic systems. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS) (2010) 5. Hinton, G.: A practical guide to aircraft landing and cruising with momentum (2010)

Chapter 49

Factors Affecting the Efficiency of Solar Cell and Technical Possible Solutions to Improve the Performance Muralidhar Nayak Bhukya, Manish Kumar, Vipin, and Chandervanshi

Abstract Around the globe, the demand for energy is increased abruptly to fulfill the demand all nations are looking after alternate energy resource. From the last decade, solar energy is more popular among other resources. In this paper, a review is presented in the view of material used for the design and fabrication of solar cell along with existing technologies. Therefore, this review is important, as the review suggest material and available technologies to improve overall efficiency of solar cell as well as performance in theoretical and practical manner considering cost and life of solar cell. Apart from this, the study also focuses on cooling factor and solar tracking system for the better efficiency and performance of solar cell.

49.1 Introduction In the current scenarios, solar energy plays a key role in the power generation sector. According to REN21, renewable global status reports solar energy share 2.4% of world power generation [1]. Many countries are making polices on the utilization of solar energy and also investing money. Because lot of benefits of solar energy like “never end” energy source in the universe, eco-friendly energy “no pollution, no produce harmful radiation and no noise”, and many other benefits of solar energy as compared to other energy sources [2]. Solar energy basically produced by solar panel and combination of solar cell makes solar panel. So that the main role of solar energy production is by solar cell. We know many researchers work on the design of solar cell with different materials for give better performance and produce more energy of the solar cell [3]. In this review paper, we discuss about the how many materials are used and each materials efficiency also discussed. Lot of many factors affects the efficiency of the solar cell. M. N. Bhukya · M. Kumar (B) · Vipin · Chandervanshi Department of Electrical Engineering, School of Engineering & Technology, Central University of Haryana, Mahendragarh, Haryana 123031, India e-mail: [email protected] M. N. Bhukya e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_49

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For improving the efficiency and performance of the cell, many methods have been proposed by researchers. In this paper, study the different type of solar cell generation and to find which materials have better efficiency. To find the different method for improving the efficiency and performance of solar cell. This paper mainly focus on two factor for improving the efficiency and performance of solar cell. Different types of the cooling method for reducing the temperature of the cell and other one is solar tracking system for improvement of efficiency and performance of solar system.

49.2 Classification of Solar Cell Material In 1958, the first solar cell participated in practical application on satellite. Semiconductor materials (mono-crystalline silicon and poly-crystalline silicon) are used for deigning the first basic silicon solar cell. Because during those time, researchers know only these particular materials and semiconductor have property to absorb the sunlight. Recently, a lot of research has been done regarding the solar cell material for considering the some factor like solar cell performance, cost of cell, etc. Basically, it is classified as four generation groups of 1st, 2nd, 3rd and 4th generation solar cell. Figure 49.1 has been shown the classification of solar cell according to the materials. In first generation, generally, materials used are poly-crystalline silicon and monocrystalline silicon. These are also known as conventional and traditional solar cells. As the title/name suggest (Mono-crystalline-silicon) means made from single crystal st

Monocrystalline solar cell

rd

1 generation

3 generation Solar cell

wafer based silicon

technology

nd

2 generation Polycrystalline

new emerging

Nanocrystal based solar cell

thin flim solar

solar cell

Cdte thin flim solar cell

cell

Polymer based solar cell

Amorphous Si

Concentrated solar cell

thin flim solar cell

Dye sensitized solar cell

Pervoskite based solar cell

Fig. 49.1 Classification of solar cell according to generation

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silicon. There is a big-sized cylindrical ingot from which the crystal of silicon is cut. The process used for manufacturing the cells is known as Czohralski process [2–6]. In 1958 to 2020, global researchers are interested to research of solar energy production. They are deigning the solar cell with different material and developed different technology according to required market application has been shown in Fig. 49.2. It is observed that researches on nanoparticles with developed different required technology are decreased agglomeration, electrical contact, charge separation, photo management &optical enhancement, no vacuum processing and low temperature fabrication to achieve the deign thin film cell and dye sensitized solar cell [7]. In dye sensitized solar cell deign include the new dyes, polymer, electrolytes and nano-crystalline material with considered different technology (technique for ultra thin film, structure thin film pn junction, solid state, sol–gel, improved stability, photovoltaic efficiency (less than 10% for module) and encapsulation). Demand of the market flexible solar cell developed researcher with polymers, electrolyte and nano-crystalline material by different technology like low temperature fabrication, techniques of ulta thin film and structure thin film pn junction etc. [8, 9].

49.3 Efficiency The demand of any products will be increased depending upon the efficiency (practical and theoretical), cost, and life span. Similarly, solar cell also depends upon this parameters and different types of solar cell are discussed according to those parameters [10–12]. The maximum and minimum efficiencies (%) of theoretical as well as practical in different solar cell have been shown in Figs. 49.3 and 49.4, respectively. It is observed that Gallium arsenide solar cell is more efficient as compared to others solar cell material. But the life span of this solar cell is less as compared to others. The life span and cost per watt (INR) of different solar cell have been shown in Figs. 49.5 and 49.6, respectively. According to the life span, Bifacial solar cell having highest and the second one is mono-crystalline silicon solar cell to other solar cell. If we consider the factors of cost, lowest cost per watt (INR) is perovskite solar cell and the highest is monocrystalline silicon solar cell. It is observed that according to above-mentioned three factor (efficiency %, life span and cost per watt), mono-crystalline silicon solar cells have better efficiency, long life span, and average cost. So that it has made highly marketable.

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Decreased agglomeration

Electrical contact Nanoparticles Charge separation

Photo manegement & optical enhacement

Thin film cells

New dyes No vaccum processing

Polymers

Low temperature fabrication Dye sensitized solar cell Encapsulation

Electrolytes

Photovoltaic efficiency (less than 10% for module

Improved stability Nanocrystalline material

Sol-gel Flexible solar cell Solid state

Structure thin flim pn junction

Technique for ultra thin flim

Market applicationd

Basic research Required technology developments

Fig. 49.2 Basic research underway with the technology developments required to achieve the desired application

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Monocryst alline silicon

40 Perovskite

30 20

Copper Indium Gallium selenide

10 ,Bifacial

Pra cal Max. Efficiency %

Gallium arsenide thin film

0

Gallium arsenide

Theore cal Max. Efficiency %

Amorphou s silicon Cadmium telluride

Fig. 49.3 Comparison of maximum range of theoretical and practical efficiency of solar cell material Monocryst alline silicon

30 Perovskite

20

Copper Indium Gallium selenide

10 ,Bifacial

0

Gallium arsenide

Theore cal Min. Efficiency % Pra cal Min. Efficiency %

Gallium arsenide thin film

Amorphou s silicon Cadmium telluride

Fig. 49.4 Comparison of minimum range of theoretical and practical efficiency of solar cell material

49.4 Performance of Solar Cell Improved by Material Structure The performance of solar cell depends upon the quality of the material. Many parameters considered in the high quality of the material like large carrier diffusion lengths,

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Life Span

30 25 20 15 10 5 0

Fig. 49.5 Life span of all solar cells

Fig. 49.6 Cost per watt (INR) of solar cell

conductivity of materials, cost, availability, etc. Figure 49.7 has been shown the some of the key requirements for the development of performance and efficient solar cell material system with some form of summary [13]. Therefore, lot of future and current investigations have been required to find new material. Recently, thin films and crystalline materials are common materials used in design of PV semiconductors. Their performance and efficiency are calculated based on their energy conversion efficiencies and light absorption as well as their production costs and manufacturing technology [14, 15].

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Fig. 49.7 Identification of improved structures and base materials for solar cells

Ongoing research on solar can be divided into three main areas, (a) Cost-effective technology developed, (b) solar cell architectural designs and (c) developed the new technology for better absorber or carrier change energy. The overall motive of research is to improved efficiency as well as performance of the solar cell. Some new materials are investigated novel dyes, multilayers of ultrathin nano-crystalline and quantum dots (QDs) materials. A report of the capacity of the solar cell lifter version from 1975 to 2020 is shown in Fig. 49.8 [16].

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Fig. 49.8 Timeline of solar cell energy conversion efficiency

49.5 Parameters Effects the PV System Performance and Efficiency The basic concept of power produced by sun energy has been shown in Fig. 49.9. It required the PV module, charge controller, battery and AC/DC converter. Many factors effect the solar cell efficiency [17].

Fig. 49.9 Basic diagram of electricity produced by sunlight

49 Factors Affecting the Efficiency of Solar Cell …

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631

Effects of cable resistance Temperature effect on PV system Shading effects on solar panels Controller panel of charging which effects the I-V characteristics Efficiency effect by inverter on PV system (a) (b) (c) (d) (e)

Communication low-loss conversion Temperature management Power optimization Monitoring and securing.

Figure 49.10 has been shown the behavior of electrical efficiency and cell temperature for a typical summer day. It is observed that temperature of the cell increased vice versa efficiency of the electricity will decreased. So, the overall performance of the system shall also reduce. The position of the sun moves hourly so the sun light not comes properly to solar plate thus the efficiency and performance of solar cell reduce [18].

Cell Temperature (°C)

(a) 60

40

20

0 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00

Electrical Eefficiency (%)

(b)

Time ( hours)

17 16 15 14 13 12 11

Fig. 49.10 Effects of efficiency with change in cell temperature in a summer day

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49.6 Different Solar Tracking Technology Solar tracking is the very good phenomena to improve the efficiency and overall performance of the solar system. Recently, lot of solar tracking methods are classified in some criteria, for example, basis on the tracking strategy, basis of driving system, basis of control strategies and based on degree of freedom [20]. Figure 49.11 has been shown the different solar tracking method with different technology. Basis on drive is divided into two categories: passive and active solar tracking system. In passive solar tracking system, no driving instrument like motors is used to change the position of solar panel. In this method, a low boiling point compressed gas is used for change of orientation of solar cell. When heat is more at one side due to direct radiation than gas expands move toward other and change the orientation of solar cell. It is an effective idea but fails to improve efficiency and performance at low temperature. Passive solar tracking is different because it uses electrical drives to change the position of solar panel. It uses motors, sensors, micro-controller to track the solar radiations. It is more accurate and efficient because of sensors and micro-controller. The tracker based on active solar tracking system shows an overall gain of 40% in stored thermal energy.

SOLAR TRACKING SYSTEM

Based on control strategies Closed loop solar tracker

Passive solar tracker

Active solar tracker

Open loop solar tracker

Microprocessor drive system

Sensor drive system

Intelilgent drive system

Neutral network

Combination

Single axis solar tracker

Bimetallic thermal actuator mechanism

Hybrid sytem

Fuzzy logic

Sensor based microprocessor drive system

Based on tracking strategies

Based on degree of freedom

Based on drives

Thermo hydraulic actuator mechanism

Shape memory allow thermal actuator mechanism

Fig. 49.11 Classification of solar tracking method

Vertical axis/ azimuth axis ST surface inclined

Horizontal axis single axis solar tracker

Inclined axis single axis solar tracker

Dual axis solar tracker

Date and time Electro optical sensors Sensors, date & time

Tip and Tit

Azimuth Altitude

Other combination

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Based on control strategies, tracking system are classified in closed- and openloop systems. In case of closed-loop system used sensors to detect the position of sun and which is feedback to the system because of closed-loop system then micro-controller rectifies the signal and provides required signal to driving system. In open-loop tracking system, the input is given to the micro-processer and the controller directly gives it the motor. Based on degree of freedom, it is classified in single- and dual-axis solar tracking system. In single-axis system, it can only rotate around single axis to get a position of perpendicular to the sun. The position along the north meridian axis is preferred the most. In case of dual-axis system, it rotates along two axes or involves rotation of two axes. It has a complex control system and needs of maintenance but it is more efficient than single-axis solar tracking system. Based on the tracking strategy, it is divided in three categories: data & time, microprocessors and electro optical sensor, and sensor & data time. In data & time tracking system, we uses predefined calculation about the sun position at a particular date or time. So used these calculations to orient the photovoltaic cell and there is no use of feedback system or sensors and micro-controllers. Basically, it involves the usage of sensors for detection of sun orientation. Sensors works as input to microcontrollers and then micro-controller further feeds to driving system [21]. In case of data & time and sensors, its working is predefined but sensors are used to check the orientation of sun and drive the motors accordingly to orient the PV panels. As we have discussed many type advancement on solar tracking system. The dual-axis solar tracking system is proved to be more efficient than single-axis solar tracking system after system is equipped with different design and technique [22].

49.7 Conclusion According to materials explained the theoretical and practical maximum/minimum efficiency also discussed the life span and cost per watt of the solar cell materials. Solar cell deigns according to market application with different technology with different materials. In this paper has been observed that the efficiency of the solar cell material will be improved followed by some criteria of material property. It also improved the quality of the structure of material. Many cooling methods have been proposed by researcher for the better efficiency of solar system. It observed that all methods performed improve the efficiency as well as performance according to infrastructure/facility available. Different types of solar tracking method also identify its works based on control techniques, based on strategy, based on tracking strategy, etc. It is observed that all solar tracking system improved the overall performance and efficiency of solar system. Acknowledgements I owe my special thanks to Dr. Manish Kumar and Dr. Muralidhar Nayak Bhukya for all his support and guidance without which all this research was totally impossible. I also thanks my family and colleagues for their best wishes. I would also owe special thanks to all my friends for their moral support.

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References 1. REN21 (Renewable Energy Network for the 21st Century) (2019) Renewable Global Status Report Paris, Available: www.ren21.net/gsr 2. Solar auction companies seeking lowest state support to win. Retrieved 2020 3. Choubey, P.C., Oudhia, A., Dewangan, R.: A review: solar cell current scenario and future trends. Recent Res. Sci. Technol. 4, 99–101 (2012) 4. Chu, Y., Meisen, P.: Review and Comparison of Different Solar Energy Technologies. Report of Global Energy Network Institute (GENI), Diego (2011) 5. McEvoy, A., Castaner, L., Markvart, T.: Solar Cells: Materials, Manufacture and Operation, 2nd edn., pp. 3–25. Elsevier Ltd., Oxford (2012) 6. Fahrenbruch, A.L., Bube, R.H.: Fundamentals of Solar Cells. Academic Press Inc., New York (1983) 7. Chandra Sekaran, J., Nithyaprakash, D., Ajjan, K.B., Maruthamuthu, S., Manoharan, D., Kumar, S.: Hybrid solar cell based on blending of organic a din organic materials—an overview. Renew. Sustain. Energy Rev. 15, 1228–1238 (2011) 8. Asim, N., Sopian, K. et al.: A review on the role of materials science in solar cells. Renew. Sustain. Energy Rev. 16, 5834–5847 (2012) 9. Narayan, M.R.: Review: dye sensitized solar cells based on natural photo-sensitizers. Renew. Sustain. Energy Rev. (2011) 10. Martin, C.:Solar Panels Now So Cheap Manufacturers Probably Selling at Loss. Bloomberg View (2016). Bloomberg LP. Retrieved 3 January 2017. 11. Angadi, R.V., et al.: A review on different types of materials employed in solar photovoltaic panel. Int. J. Eng. Res. Technol., 1–5 (2019) 12. INDIA MART: Available: https://m.indiamart.com/proddetail.php?i=9241798312 13. Farrenbruch, A.L., Bube, R.H.: Fundamentals of solar cells: photovoltaic solar energy conversion. Academic Press, New York (1983) 14. Shevaleevskiy, O.: The future of solar photovoltaics: a new challenge for chemical physics. Pure Appl. Chem. 80, 2079–2089 (2008) 15. Mah, O.: Fundamentals of photovoltaic materials. NSPRI (National Solar Power Research Institute, Inc.) (1998) 16. Kazmerski, L., Gwinner, D., Hicks, A.: Reported Timeline of Solar Cell Energy Conversion Efficiencies. National Renewable Energy Laboratory, USA (2007) 17. Swar, A., Mohammed, H.A., Ilkan, M.: A review of photo cells cooling techniques. In: EWA Web Conference 2017. https://doi.org/10.1051/E3SCONF/20172200205 18. Dewi, T., Risma, P., Oktarina, Y.: A Review of factors affecting the efficiency and output of a PV system applied in tropical climate. IOP Conf. Ser.: Earth Environ. Sci. 258, 012039 (012039) 19. Habiballahi, M., Ameri, M., Mansouri, S.: Efficiency improvement of photovoltaic water pumping systems by means of water flow beneath photovoltaic cells surface. J. Sol. Energy Eng. 137(4), 044501 (2015) 20. Mousazadeh, H., Keyhani, A., Javadi, A., Mobli, H., Abrinia, K., Sharifi, A.: A review of principle and sun tracking methods for maximizing solar systems output. Renew. Sustain. Energy Rev. 13(8), 1800–1818 (2009). https://doi.org/10.1016/j.rser.2009.01.022 21. Parmar, N.J., Parmar, A.N., Gautam, V.S.: Passive solar tracking system. Int. J. Emerg. Technol. Adv. Eng. 5, 138–145 (2015) 22. Poulek, V.: New low cost solar tracker. Sol. Energy Mater. Sol. Cells 33, 287–291 (1994). https://doi.org/10.1016/0927-0248(94)90231-3

Chapter 50

Order Reduction of Linear Time Invariant Large-Scale System by Improved Mixed Approximation Method Pragati Shrivastava Deb and G. Leena Abstract Model order reduction (MOR) has been proved robust and widely applicable to simulating large mathematical models in engineering and sciences. Such reduced order models (ROM) are useful not only for system analysis and simulation but also for reduced complex controller design. Wide application of MOR has been also found in optimizing and control. There are many reduction methods present for reducing the order of large-scale linear SISO and MIMO systems in time domain and frequency domain. Some mixed methods have been developed by considering the algorithm of two different reduction methods. The aim of the paper would be to develop the strong reduction method to reduce the large-scale linear system into reduced model, which approximates the original system effectively and some important properties of the original system are also preserved.

50.1 Introduction Systems with higher orders or higher processing rates need to be controlled for engineering problems. The dynamics of these physical systems are represented by higher-order simultaneous linear differential equations. It has been found that the order of the matrix A of the state space model of such systems is very large. It will not be possible to work with the original form of complex systems and also the analysis, design, and synthesis of such higher-order systems are difficult, expensive, and time consuming on computational and economic attentions. Therefore, in such cases, it is required to study the process of approximation to get a simpler model. Several methods exist which estimates the ‘dominant’ part of the system in higher P. S. Deb (B) · G. Leena Manav Rachna International Institute of Research and Studies, Faridabad, India e-mail: [email protected] G. Leena e-mail: [email protected] P. S. Deb ABES Engineering College, Ghaziabad, Uttar Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_50

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order and helps to find a reduced system with similar characteristics same as the original system. Balanced truncation method is the popular one in time domain. The other methods used for model order reduction are singular perturbation, aggregation, Krylov subspace, and Hankel norm. In frequency domain, Pade approximation is most popular. Routh approximation, Routh stability equation, and pole clustering methods are also used for reduction. Denominator and numerator polynomial of transfer function model have been approximated by using these different approaches. Mostly these reduction methods depend on the preservation of dominant poles of original higher-order systems in reduction model, because dominant poles of dynamical systems are used to control their performance and non-dominant poles are used to ensure that the transfer function of controller can be realized by physical components. Several mixed reduction methods were presented in frequency and in time domain. When these methods were applied on a system, both advantages and disadvantages have been shown.

50.2 Approaches of Order Reduction In two ways order reduction is possible such as A time domain: In this approach, convert the system dynamics into first-order linear differential equation and consider an nth order LTI system given as Eqs. (50.a) and (50.b): .

v(t) = av(t) + br (t)

(50.a)

y(t) = cv(t) + dr (t)

(50.b)

where v(t)—state vector having n dimension r(t)—input vector having dimension m y(t)—output vector having dimension q an×n , bn×m , cq×n and d q×m are matrices. B Frequency Domain: In this approach, first it is required to obtain Laplace transform of differential equation. Let the higher-order system, transfer function is expressed as: G(s) =

b1 s n−1 + · · · + bn a0 s n + · · · + an

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Suppose that a ROM Rr (s) of order ‘r’ < ‘z’ which approximate the system G(s) is given by Rr (s) =

b1 s r −1 + · · · + br a0 s r + · · · + ar

50.3 Model Order Reduction Methods Various techniques were presented for order reduction to examine higher-order LTI systems in time and also in frequency domain.

50.3.1 Pade Approximation In frequency domain, the well-known methodology of MOR for linear systems is created on Krylov subspaces also referred as Pade approximation or moment matching method. These methods efficiently work on circuit simulation, simulation of machine tools, and many engineering applications. The survey paper [1] discussed the issues related to moment matching methods. Initially asymptotic waveform evaluation (AWE) method has been developed which is numerically not stable because the moments in this method are explicitly computed. To overcome this problem, numerically more strong methods were introduced such as Pade via Lanczos and PRIMA. In general, it was observed from the literature that the Pade approximation method does not give the guarantee of producing reduced stable system even if the original model is stable.

50.3.2 Balanced Truncation Method Balanced truncation is the most accepted and effective method for the MOR in time domain. In order to solve the problems related to linear systems, having ordinary or partial differential equations, balance truncation and related methods were used. These methods have the desirable property of preserving stability. The survey paper [1] has been reviewed based on few of the admired MOR methods with theoretic background for linear as well as nonlinear large-scale dynamical systems. For dealing with linear problems, balanced truncation (BT) is the best-chosen method as it preserves the stability and provides a bounded global computable error. The standard balanced truncation does not preserve the passivity of the system. Therefore, for preserving passivity, many balancing related model reduction techniques were implemented.

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50.3.3 Mixed Combination of Two Methods Further numerous approaches have been introduced, created by the structures of two different techniques. Several mixed reduction techniques based on preservation of poles nearer to the origin have been presented in frequency domain and in time domain [14–17]. When these methods applied on a particular system, the system has shown sometimes good results or vice versa. This paper has proposed mixed method which reduces the large-scale of higher-order system into reduced order and also gives the best results in terms of stability, error analysis, and structure preservation.

50.4 Problem Statement Let the higher-order original system of order ‘z’ transfer function is given as G(s) =

m 0 + m 1 s + m 2 s 2 + · · · + m z−1 s z−1 N (s) = D(s) f0 + f1 s + f2 s 2 + · · · + f z s z

(50.1)

where m0 , m1 … mz-1 and f 0 , f 1 … f z-1 are the original system known constants. Let the transfer function of the reduced system of order ‘r’ is given as G r (s) =

g0 + g1 s + g2 s 2 + · · · + gr −1 s r −1 Yr (s) = Ur (s) h 0 + h 1 s + h 2 s 2 + · · · + hr sr

(50.2)

Where g0 , g1 … gr−1 and h0 , h1 … hr are the unknown constants of reduced order model.

50.5 Proposed Method The method is applied in two steps in order to get the ROM.

50.5.1 Determine Denominator of Gr (s) Using Improved Routh Approximation Method [2] Step 1 Denominator polynomial of the original systems should be reciprocated. D(s) = s z D

  1 s

(50.3)

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Table 50.1 Alpha parameters e00 = e0 e01

= e1 =

− α1 e21

e21

= e3

e22

=

e40

e40 = e4 e41

= e5

− α1 e41

e42

= e60 − α1 e61

…

α1 =

e00 e01

e02

α2 =

e01 e02

e03 = e21 − α2 e22

e23 = e41 − α2 e42

α3 =

e02 e03

e04 = e23 − α3 e23

…

…

e20

e20 = e2

e60 = e6

…

…

…

Step II Determine the alpha table by using coefficient of D(s), and then find the alpha parameters α 1 , α 2 , α 3 ….. from Table 50.1 Step III By using below equation, obtain the rth order denominator. Ur (s) = αr asU r −1 (s) + Ur −2 (s), r = 1, 2, 3 . . . with U 0 (s) = U−1 (s) = 1

(50.4)

Step IV The desired denominator polynomial is found out by taking reciprocal transformation of the U r (s).

50.5.2 Determine Numerator Polynomial of the Reduced Model by Using Improved Pade Approximation Method [3] A large-scale original system of high order is expressed in the series expansion as G(s) =

∞ 

Mi s −i−1 (abouts = ∞)

i=0

=−

∞ 

Ti s i (abouts = 0)

(50.5)

i=0

where T i —Time moment & M i —Markov parameter of G(s), respectively. The rth lower order model is considered as r −1 i gi s Yr (s) = i=0 G r (s) = (50.6) r i Ur (s) i=0 h i s

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The coefficients of numerator Yr (s) is obtained from the below given set of Eqs. (50.7). g0 = h 0 T0 g1 = h 0 T1 + h 1 T0 g2 = h 0 T2 + h 1 T1 + h 2 T0 .. . gα−1 = h 0 Tα−1 + h 1 Tα−2 + · · · + h α−2 T1 + dα−1 T0 gr −β = h r Mβ−1 + h r −1 Mβ−2 + · · · + h r −β+2 M1 + h r −β+1 M0 gr −β+1 = h r Mβ−2 + h r −1 Mβ−3 + · · · + h r −β+3 M1 + h r −β+2 M0 .. . gr −2 = h r M1 + h r −1 M0 gr −1 = h r M0

(50.7)

By solving the above linear Eqs. (50.7). The numerator coefficients gj where j = 0, 1, 2 … (r − 1) can be obtained. Therefore, the numerator Yr (s) is given as Yr (s) = g0 + g1 s + g2 s 2 + · · · + gr −1 s r −1

(50.8)

50.6 Numerical Example The integral square error (ISE) and integral absolute error (IAE) are obtained using the below given Eqs. (50.9) and are defined as [4, 5]. ∞ [y(ti ) − yr (ti )]2 dt

ISE = 0

∞ |y(ti ) − yr (ti )|dt

IAE = 0

Example 1 Consider the fifth-order single-input single-output LTI System

G(s) =

s5

s 4 + 7s 3 + 42s 2 + 142s + 156 + 25s 4 + 248s 3 + 930s 2 + 1441s + 745

(50.9)

50 Order Reduction of Linear Time Invariant Large-Scale System … Table 50.2 Alpha parameter table

1441

641 258

796.61

24.48

α 1 = 0.517

213.71

1

α 2 = 1.8089

745

930

α 3 = 3.727

20.752

α 4 = 10.298

1

1

25

Step 1: Reciprocate the original system denominator polynomial as D(s) = s n D

  1 s

We get, D(s) = 1 + 25s + 258s 2 + 930s 3 + 1441s 4 + 745s 5

(50.10)

Step 2: With the help of Eq. (50.10), the alpha array is obtain as. Step 3: By substituting values of α1 and α2 from Table 50.2 in Eq. (50.11), the second-order denominator polynomial of reduced model is obtained as D(s) = 1 + α2 s + α1 α2 s 2

(50.11)

D(s) = 1 + 1.8089s + 0.9352s 2

(50.12)

Step 4: Take the reciprocal transformation of Eq. (50.12) in order to compute the denominator of the desired lower order model Dr (s) Dr (s) = s 2 + 1.8089s + 0.9352

(50.13)

Step 5: The denominator polynomial obtained in Eq. (50.13) is used to find out the numerator coefficient of the reduced order system. As per the method described in Sect. 5.2, the Nr (s) is obtained as Nr (s) = 0.17826s + 0.1958

(50.14)

Therefore, the desired transfer function of lower order is given as G(s) =

0.17826s + 0.1958 Nr (s) = 2 Dr (s) s + 1.8089s + 0.9352

(50.15)

In Fig. 50.1, unit step response of original model has been compared with the model obtained from the proposed method and also from the other methods given in

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Fig. 50.1 Step response

Table 50.3 With respect to ISE and IAE, comparison has been done of proposed model with other reduction techniques Model order reduction technique

Reduced model

ISE

Proposed method

0.17826s+0.1958 s 2 +1.8089s+0.9352 142.0174s+156 909.5238s 2 +1441s+745

0.0001032

0.01488

0.0017

0.2679

2130.041s+9360.18 5580s 2 +34584s+44700 0.007s+156 909.5238s 2 +1441s+745

0.0381

1.0222

0.10534

1.9481

0.1961s+0.0019 s 2 +0.9295s+0.0114 1142s+156 930s 2 +1441s+745 91.1332s+156 770.2174s 2 +1197.6291s+745

0.30663

15.1664

0.0021

0.2926

0.0049

0.4844

Stability equation and Pade approximation methods [6, 7] Differentiation method [8] Stability equation and factor division method [9] Balanced truncation method [10] Truncation method [11] Routh stability and Pade approximation [12, 13]

IAE

literature. The error indices of different models also been compared with the proposed method are presented in Table 50.3.

50.7 Conclusion A new mixed approximation method has been proposed. Improved Routh approximation and improved Pade approximation method are used to find the lower-order model, compared to the step responses with different reduction techniques as shown

50 Order Reduction of Linear Time Invariant Large-Scale System …

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Table 50.4 Comparison with respect to rise time, settling time and overshoot Model order reduction technique

Rise time Settling time Overshoot (%)

Original system

2.2231

3.8179

0

Proposed method

2.1933

3.5469

0.1445

Stability equation and Pade approximation methods 2.1685 [6, 7]

3.2351

0.9381

Differentiation method [8]

1.1985

2.1327

0

Stability equation and factor division method [9]

3.0653

4.9048

Balanced truncation method [10]

1.2355

214.2534

26.1314

Truncation method [11]

0.156

6.2167

198.087

Routh stability and Pade approximation [12, 13]

2.0578

4.9321

2.3576

0.3392

in Fig. 50.1. It specifies that the reduced system found by proposed method almost retains fundamental characteristics of the original system. Error indices have been found and compared. It has been estimated that the accuracy, efficiency, and performance of the proposed method are much better than the other methods in the literature shown in Table 50.3. From Table 50.4, it has been found that the transient and steadystate response of the suggested reduced model is superior and very much closer to the original system performance.

References 1. Baur, U., Benner, P., Feng, L.: Model order reduction for linear and nonlinear systems: a system-theoretic perspective. Arch. Comput. Methods Eng. 21(4), 331–358 (2014) 2. Hutton, M.F., Friedland, B.: Routh approximations for reducing order of linear, time-invariant systems. IEEE Trans. Autom. Control 20(3), 329–337 (1975) 3. Pal, J.: Improved Pade approximants using stability equation methods. Electronics Lett. 19(11), 426–427 (1983) 4. Narwal, A., Prasad, R.: A novel order reduction approach for LTI systems using cuckoo search optimization and stability equation. IETE J. Res. 62(2), 154–163 (2015). https://doi.org/10. 1007/s10845-017-1309-3 5. Sikander, A., Prasad, R.: Reduced order modelling based control of two wheeled mobile robot. J. Intell. Manuf., 1–11 (2017) 6. Chen, T.C., Chang, C.Y., Han, K.W.: Stable reduced-order Pade approximants using stabilityequation method. Electr. Lett. 16(9), 345–346 (1980) 7. Chen, T.C., Chang, C.Y., Han, K.W.: Model reduction using the stability-equation method and the Pade approximation method. J. Franklin Inst. 309(6), 473–490 (1980) 8. Gutman, P., Mannerfelt, C., Molander, P.: Contributions to the model reduction problem. IEEE Trans. Autom. Control 27(2), 454–455 (1982) 9. Sikander, A., Prasad, R.: Linear time-invariant system reduction using a mixed methods approach. Appl. Math. Model. 39, 4848–4858 (2015) 10. Moore, B.C.: Principal component analysis in control system: controllability, observability, and model reduction. IEEE Trans. Autom. Control 26(1), 17–36 (1981) 11. Shamash, Y.: Truncation method of reduction: A viable alternative. Electron. Lett. 17, 79–98 (1981)

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12. Shamash, Y.: Model reduction using the Routh stability criterion and the Pade approximation technique. Int. J. Control 21(3), 475–484 (1975) 13. Pal, J.: Stable reduced-order Pade approximants using the Routh-Hurwitz array. Electr. Lett. 15(8), 225–226 (1979) 14. Taylor, P., Vishwakarma, C.B., Prasad, R.: Clustering method for reducing order of linear system using Pade approximation clustering method for reducing order of linear system using Pade approximation, no. October, pp. 37–41 (2014) 15. Singh, J.: MIMO system using Eigen algorithm and improved Pade approximations. SOP Trans. Appl. Math. 1(1), 60–70 (2014) 16. Singh, J., Chatterjee, K., Vishwakarma, C.: System reduction by Eigen permutation algorithm and improved Pade approximations. Waset. Org 1, 124–128 (2014) 17. Singh, J., Vishwakarma, C.B., Chattterjee, K.: Biased reduction method by combining improved modified pole clustering and improved Pade approximations. Appl. Math. Model. 40(2), 1418– 1426 (2016)

Chapter 51

Seven Level Enhanced Modified T-type Multilevel Inverter (MLI) with Reduce Part Count Hillol Phukan, Tamiru Debela, and Jiwanjot Singh

Abstract Multilevel inverters (MLIs) is finding wide range of application in low, medium and high voltage system because of its capability to generate high-quality output voltage. However, this comes with the fact that number of components used in the converter is still high. As the number of components increases, the probability of fault occurring across any of this component increases, complexity of the circuit increases along with cost and increase gate driver circuit. This paper proposes the topology as symmetric single-phase enhanced T-type 7-level multilevel inverter. The proposed MLI has the capacity to self-balance, the capacitor voltage along with quality output. The proposed topology is verified in Matlab/Simulink and a comparative analysis is done with its previous models. The output across the load is verified by resistive, inductive resistive and asynchronous load.

51.1 Introduction Multilevel inverter is power electronics converter that produces alternating voltage across the output using dc source/s and power electronics switches in steeped form such that the harmonic content, dv/dt across the switch, filter requirement, electronic interference (EMI) are reduced. As the number of dc sources and power electronics switches are increased the output performance of the MLI is improve. That is the output voltage and current becomes more sinusoidal. But it comes with additional problems like increase cost and reliability of the device decreases. The solution of this problem comes with fault tolerant topology and reduce part count multilevel inverter. Fault tolerant topology is classified as switch level, leg level, module level and system levels [1]. Fault tolerant is out of scope in this paper. RPC-MLI is classified as Hybrid MLIs and Non-Hybrid MLIs [2]. In [2] Hybrid MLIs are subdivided into H. Phukan · T. Debela · J. Singh (B) Department of Electrical Engineering, NIT Silchar, Silchar, Assam, India e-mail: [email protected] H. Phukan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_51

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R1, R2 and R3 and Non-Hybrid are subdivided as R4, R5 and R6. In [3, 4] R1RPC-MLI utilizes two dc source and 8 power electronic switches to produce 7 level output voltage. In [5] R2-RPC-MLI uses two dc sources and 9 power electronic switches and produces 7 level across the output. Similarly R3-RPC-MLI [6], R4RPC-MLI [7], R5-RPC [8] and R6-RPC-MLI [9, 10] produces 7 level output voltage with reduce device count. In [11–13], 5 level single phase inverter based on T-type topology is discusses. Further in [14] modified 5 level t-type inverter which is having fault tolerant capacity is discussed. The proposed topology is the modified version of [11–14] which produces 7-level across the output instead of 5 level with RPC. Characteristic of the enhanced improved T-type MLIs are as follows 1. 2. 3. 4. 5. 6.

Device count of the MLI is reduced. Cost, complexity, gate driver circuit are reduced. Self-balance of the capacitor. Number of clamping diodes required is zero. Conduction losses are reduced since only two switches conducts at a time. The output voltage is not affected by dynamic load change.

MLIs are finding applications for low-voltage application like Power Factor correction rectifier, automotive inverter system [15], Uninterruptable power supplies (UPS), servo drives, electric vehicles [16], centralized AC Microgrid [17], Renewable energy application, distributed generation [18], STATCOM, Unified Power Flow Controller (UPFC), Static Synchronous Series Compensator (SSSC) [19], military, aerospace [20], production industry [21], V /f control application of induction motor like rolling mills [22], telecommunication system [23] etc. Since the application of MLIs is increasing day by day so the reliability of the converter is of utmost importance. Power electronics switches are more prone to fault then the magnetic component. To increase the reliability of the whole system the proposed topology is developed with reduced power electronic switches.

51.2 Proposed Topology Figure 51.1 shows five-level single-phase multilevel inverter based on T-type topology [11–13]. It utilizes four two quadrant switch and one four quadrant switch. The peak voltage is twice of the supply voltage for symmetrical supply. It has two redundant zero state. Two switches conducts at a time due to which the conduction losses are reduced. But it only produces five level across the output due to which the total harmonic distortion (THD) is high and the four quadrant switch has standing voltage of twice of supply voltage. Figure 51.2 shows modified T-type multilevel inverter which is having fault tolerant capability [14]. Fault tolerant is produced by redundant switching combination which is because of the extra bidirectional switch which is connected between the neutral point of the two voltage source and the negative terminal of the load. When fault occurs across the power switches the output voltage reduces in some cases but the system need not be shut down. Figure 51.3

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Fig. 51.1 Five-level single T-type MLI

Fig. 51.2 Modified Five-level T-type MLI with fault tolerant capability

shows the enhanced proposed topology which utilizes six power electronics switches, two dc source and one capacitor. The capacitor is connected in series with the four quadrant switch. The advantage of the enhanced proposed topology is that instead of five level it produces seven level across the output due to which the THD is low and the standing voltage of the bidirectional switch is reduced since it gets connected

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S6

S3

S1

VS

IO S5

Load VO

CV VS S2

S4

Fig. 51.3 Enhanced modified T-type MLI for single phase load

between the positive terminal of the load the ground, this happen because of the use of capacitor which is connected in series with one of the bidirectional switch. Voltage across the capacitor is self-balanced and it discharges exponentially from V S and settles at 0.5V S . The current across the capacitor is charged from −5 A and settles at 2 A. The output across the load is verified by resistive, inductive resistive and asynchronous machine load. The output voltage is not changed by the load change but the current across the load changes.

51.2.1 Modulation Scheme Multi carrier LS-PD-PWM scheme is used to generate the switching pulses to switches. UCS is used to controlling the gate pulses across the power electronic switch [24]. Figure 51.4 shows LS-PD-PWM modulation scheme. One reference sinusoidal signal of 50 Hz is taken and six carrier signals of frequency 1 kHz is used and is compared with the reference signal. The magnitude of the carrier signal is one and they are in phase disposition i.e. there is no phase difference between consecutive carrier signal Fig. 51.5 shows UCS control strategy. LS-PD-PWM is feed to a relational operator from where pulses are generated. These pulses are added and compared with constant. From the look up table required pulse are given to the switches. This whole process is known as UCS. LS-PD-PWM is a subset of UCS. The output of the scheme is given the switches.

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Fig. 51.4 Multi carrier LS-PD-PWM scheme

Reference signal

>=

==3

C1(t)

>=

==2

C2(t)

>=

==0

+ _

3

==1

Witching Signal to Nswitch

+ _

C3(t)

>=

+ _

>=

-1

>=

Lool-up table

==-1 ==-2 ==-3

Fig. 51.5 Universal control scheme (UCS)

51.2.2 Proposed Symmetrical Enhanced Modified T-type MLI Topology Figure 51.6 shows the working mode for the proposed topology where only two switches conducts at a time. Table 51.1 shows the switching table of enhanced improve T-type MLI. Table 51.2 compares seven level multilevel inverter with respect to device count. The output voltage is twice of the supply voltage. The topology which are having the same magnitude of input source voltage are known as symmetrical topology. The proposed topology is having two input source voltage of 100 V each.

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S6

S6

VS

S1 S5

VS

VC

S2

IO

VS

S3

S1

VO S2 Load S4

VC

VS

(a)

(b)

S6

S6

S1

IO

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S1

S5 VC

VS

S2

IO

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VO Load S4

VO S2 Load S4

VC

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(c)

(d)

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S6 VS

S

IO

S3

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S

S5 VC S2 (e)

VO Load S4

IO S3

1

1

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S3

S5

VO Load S4

VS

IO

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V C

S2

VO Load S4

(f)

Fig. 51.6 Current path for output voltage of a V O = 2V S , b V O = V S , c V O = V C (Voltage across the capacitor), d VO = 0, e V O = −V S + V C , f V O = −V S , g V O = −2V S

51 Seven Level Enhanced Modified T-type …

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Fig. 51.6 (continued)

S6 VS

S1 S5

VS

VC S2

IO

S3

VO Load

S4

(g)

Table 51.1 Switching combination of the proposed topology

State

Switching combination

Output voltage (V)

1

S1 , S4

2V S

2

S1 , S6

VS

3

S4 , S5

VC

4

S2 , S4

0

5

S5 , S6

−V S + V C

6

S2 , S6

−V S

7

S2 , S3

−2V S

Table 51.2 Comparison analysis of device count for 7 level inverter Components

[3, 4]

[5]

[6]

[7]

[8]

[9, 10]

Proposed topology

Dc sources

2

2

2

2

2

2

2

Power switches

8

8

10

6

6

6

6

Capacitor

0

0

0

0

0

0

1

Clamping diodes

0

0

0

0

8

0

0

51.2.3 Simulation and Results Input dc voltage is taken as 100 V. Total six switches are used out of which two are four quadrant switch and the remaining four switches are two quadrant switch. Capacitor which is connected in series with the four quadrant switch is taken as 10 mF. Figure 51.7 shows the output voltage and current waveform with respect to time. Both Output voltage and current are found to be symmetrical. Figure 51.8 shows that the capacitor voltage starts discharging exponentially from 100 V and finally reaches 50 V in steady state condition. Figure 51.9 shows capacitor current starts to rise exponentially from −5 A and finally reaches 2 A in steady state condition.

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Fig. 51.7 Output voltage and current of the proposed topology

Fig. 51.8 Capacitor voltage with respect to time

Fig. 51.9 Capacitor current with respect to time

Figure 51.10 shows the dynamic load change. Load change doesn’t affect the output voltage performance whereas the output current do gets affected. When load is changed to RL the output current magnitude increases during a short transient period and then its magnitude settles to a constant value as in r load. The current waveform becomes sinusoidal during rl condition whereas when the load is changed to asynchronous machine the output current is sinusoidal but peak to peak amplitude ´ 20 mH respectively. increases. Output resistance, inductance are taken to be 20 ,

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Fig. 51.10 Dynamic load change effect on output performance

Fig. 51.11 Output current during dynamic load change

Figure 51.11 shows the exact current waveform during dynamic load change. When the load is changed to induction machine there is a huge rise in current amplitude. The peak current becomes more than twice of the current during r load.

51.3 Conclusion The proposed topology is enhanced improved T-type MLI for single phase load which produces seven levels across the output. In the proposed topology the capacitor voltage is self-balanced. The conduction losses are low since only two devices conducts at a time. The voltage across the output is not affected by the load change and hence its stability is high. The output voltage produces twice of the source voltage and hence it would be suitable for photovoltaic application where input voltage is low. The proposed topology is the modification of five level T-type MLI. The enhance topology is able to generate seven level by using six switches and one capacitor. The standing voltage across one of the bidirectional switch is reduced. The result of the proposed topology is validated in matlab Simulink. Further study on fault tolerant of the proposed topology using redundant leg or redundant switch can be done.

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References 1. Zhang, W., Xu, D., Enjeti, P.N., Li, H., Hawke, J.T., Krishnamoorthy, H.S.: Survey on faulttolerant techniques for power electronic converters. IEEE Trans. Power Electron. 29(12), 6319– 6331 (2014) 2. Agarwal, R., Jain, S.: Comparison of reduced part count multilevel inverters (RPC-MLIs) for integration to the grid. Int. J. Electr. Power Energy Syst. 84, 214–224 (2017) 3. Su, G.J.: Multilevel DC-link inverter. IEEE Trans. Ind. Appl. 41(3), 848–854 (2005) 4. Ridge, O: (12) United States Patent. Patent No.: US 6,577,087 B2 (45) Date of Patent. 2(12) (2003) 5. Nilkar, M., Babaei, Sabahi, M.: A new single-phase cascade multilevel inverter topology using four-level cells. In: 201220th Iranian Conference on Electrical Engineering, pp. 348–353. IEEE (2012) 6. Toupchi, M., Khosroshahi, Toupchi.: Crisscross cascade multilevel inverter with reduction in number of components. IET Power Electron. 7(12), 2914–2924 (2014) 7. Gupta, K.K., Jain, S.: A novel multilevel inverter based on switched dc sources. IEEE Trans. Ind. Electron. 61(7), 3269–3278 (2014) 8. Mokhberdoran, A., Ajami, A.: Symmetric and asymmetric design and implementation of new cascaded multilevel inverter topology. IEEE Trans. Power Electron. 29(12), 6712–6724 (2014) 9. Babaei, E., Alilu, S., Laali, S.: A new general topology for cascaded multilevel inverters with reduced number of components based on developed H-bridge. IEEE Trans. Industr. Electron. 61(8), 3932–3939 (2014) 10. Babaei, E., Laali, S., Alilu, S.: Cascaded multilevel inverter with series connection of novel H-bridge basic units. IEEE Trans. Ind. Electron. 61(12), 6664–6671 (2014) 11. Ceglia, G., Guzman, V., Sanchez, C., Ibanez, F., Walter, J., Gimenez, M.I.: A new simplified multilevel inverter topology for DC-AC conversion. IEEE Trans. Power Electron. 21(5), 1311– 1319 (2006) 12. Rahim, N.A., Chaniago, K., Selvaraj, J.: Single-phase seven-level grid-connected inverter for photovoltaic system. IEEE Trans. Ind. Electron. 58(6), 2435–2443 (2011) 13. Martins, G.M., Pomilio, J.A., Buso, S., Spiazzi, G.: Three-phase low-frequency commutation inverter for renewable energy systems. IEEE Trans. Ind. Electrons 53(5), 1522–1528 (2006) 14. Dewangan, N.K., Gupta, S., Gupta, K.K.: An approach to synthesis of fault tolerant reduced device count multilevel inverters (FT RDC MLIs). IET Power Electron. 12(3), 476–482 (2019) 15. Choi, U.M., Blaabjerg, F., Lee, K.B.: Reliability improvement of a T-type three-level inverter with fault-tolerant control strategy. IEEE Trans. Power Electron. 30(5), 2660–2673 (2015) 16. Katebi, R., He, J., Weise, N.: Investigation of fault-tolerant capabilities in an advanced threelevel active t-type converter. IEEE J. Emerg. Select. Topics in Power Electron. 7(1), 446–457 (2019) 17. Antunes, H.M.A., Silva, S.M., Brandao, D.I., Machado, A.A.P., Ferreira, R.V.: A fault-tolerant grid-forming converter applied to AC microgrids. Int. J. Electr. Power Energy Syst. 121 (2020) 18. Vosoughi, N., Hosseini, S.H., Sabahi, M.: A New transformer-less five-level grid-tied inverter for photovoltaic applications. IEEE Trans. Energy Convers. 35(1), 106–118 (2020) 19. Song, W., Huang, A.Q.: Fault-tolerant design and control strategy for cascaded H-bridge multilevel converter-based STATCOM. IEEE Trans. Ind. Electrons 57(8), 2700–2708 (2010) 20. Aly, M., Ahmed, E.M., Shoyama, M.: A new single-phase five-level inverter topology for single and multiple switches fault tolerance. IEEE Trans. Power Electron. 33(11), 9198–9208 (2018) 21. Xu, S.Z., Wang, C.J., Wang, Y.: An improved fault-tolerant control strategy for high-power ANPC three-level inverter under short-circuit fault of power devices. IEEE Access 7, 55443– 55457 (2019) 22. Reddy, K.N., Pradabane, S.: Modified H-bridge inverter based fault-tolerant multilevel topology for open-end winding induction motor drive. IET Power Electron. 12(11), 2810–2820 (2019)

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23. Maddugari, S.K., Borghate, V.B., Sabyasachi, S.: A reliable and efficient single-phase modular multilevel inverter topology. Int. J. Circuit Theory Appl. 47(5), 718–737 (2019) 24. Gupta, K.K., Bhatnagar, P.: Multilevel Inverters: Conventional and Emerging Topologies and Their Control, pp. 1–209 (2017)

Chapter 52

Controller Design for Dynamic Stability and Performance Enhancement of Renewable Energy Systems Isha Rajput, Jyoti Verma, and Hemant Ahuja

Abstract Renewable energy technique is getting more interest because of extending demand and threat zero carbon foot prints. The increasing demand of wind energy tends to generate a quality output power in terms of grid integration. This paper focuses on various methods and technologies used to analyze the new dynamic stability concerns and control performance of a power system in the context of increased MG penetration. The increased penetration of renewable energy sources (RES) based microgrid (MG) to the power system can affect the system stability because there is difference in dynamic characteristics of this MG from the conventional generators. This paper gives a review of wind energy conversion systems (WECS) and Power System interaction, with an emphasis on the impact of RES based MG penetration on the dynamic stability and control of a multi-machine multi-area system using AI Techniques under varying operating conditions.

52.1 Introduction Energy resources Conservation, sustainable development and protection of environment are the three of the major challenges at present that the world faces. One important concern is to make certain the requirement of energy for people does not cause rapid reduction of the natural energy resources and indignity of the environment. A general consent between the countries is that greater attention should be given on using the renewable energy resources for generation of electric power. Renewable energy resources are the real alternatives to the conventional forms of energy and I. Rajput (B) · J. Verma Department of Electrical and Electronics Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, India e-mail: [email protected] J. Verma e-mail: [email protected] I. Rajput · H. Ahuja ABES Engineering College Ghaziabad, Uttar Pradesh, Ghaziabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_52

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play an important role in reducing energy-related emissions. Renewable energy is clean and contaminate and does not produce any green house gases or any wastes in the task of production of electricity. It is a feasible source that is depended on for long time duration. Renewable energy is efficient and cost effective. Increasingly, Governments around the world are turning to renewable energy to reduce their dependence on fossil fuels. Solar energy, biomass, Wind, geothermal, and hydro energy are in the forefront among the various sustainable energy forms, all of which are abundantly available on our planet for most of which the fundamental resource is the sun. Among these, wind energy is the most promising and famous efficient power vitality source all over the world. Despite the fact that the possibility of mechanical power from wind energy is hundreds of years old, wind energy of commercial-scale has been generated most effective for the last 2–3 decades and has become economically possible just over the most recent couple of years [1]. Electricity generation from wind makes economic in addition to environmental experience. Wind is free and it is a clean energy resource; it will never run out. A wind turbine (WT) creates reliable, cost-effective, pollution free energy in mechanical form that can be converted into electricity with ease. The industry of wind energy making and designing WTs, raising and running them is developing quick and is set to grow as the world searches for cleaner and sustainable ways to produce power. WTs are turning out to be less expensive and powerful, with bigger sharp edge lengths which can use more wind and in this manner produce greater power, cutting down the cost of generation of renewable. Although wind energy is considered to be a promising resource by most of the power and energy engineers, it is generally acknowledged that its variable nature prohibits it from being used as the single major energy resource in any system.

52.2 WECS and Associated Control Aspects There is an entire wide range of distinct design philosophies for rectifiers, frequency converters and inverters for the variable speed WECS [2]. During ongoing years diverse converter topologies have been explored for their applicability in WECS. They are mainly multi-level converters, back-to-back converters, matrix converters, tandem converters and resonant converters. The back-to-back converter is highly relevant to WTs today and they may be utilized for benchmarking the other topologies of converter. Matrix converter and multi level converters are the most genuine contenders to the back-to-back converter [2]. Typical self-commutated converters are dominating the market as they consist large switching frequencies and harmonics can be separated out without any problem. Today, the most commonly used device is an IGBT having a typical switching frequency of the order of 2–20 kHz. In contrary, GTO converters cannot arrive at switching frequencies which are greater than around 1 kHz; thus, they probably are not possible for what’s to come at medium power levels. Self-commutated converters are either current source converters (CSCs) or voltage source converters (VSCs) and can control both frequency and the voltage. VSCs and

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CSCs provide a relatively clear-cut switched voltage and current waveform, individually, at the terminals of the generator and the grid. It must be focused on that voltage source transformation and current source transformation are various concepts. They are executed in different manners: pulse width modulation (PWM), pulse amplitude modulation (PAM) and six step. By utilizing the PWM method, lower frequency harmonics can be removed; the frequencies of the first higher order harmonic exist at around the switching frequency for the rectifier or inverter [3]. The most popular WECS concept i.e. Type C, using DFIG, depends upon a similar hardware as the notable static Scherbius drive. The stator of DFIG based WECS is directly connected to the grid, but in order to control the rotor frequency and thus the rotor speed, its rotor is connected to grid through a power converter cascade. Contingent upon the area of the frequency converter (normally rated at around 25– 30% of titular power of generator) this WT topology can operate in a vast speed span. Ordinarily, the variable speed rage is ±30% about the synchronous speed, that forms this idea appealing and well known according to monetary perspective [4]. The fundamental limitation of DFIG is that it is so sensitive to grid disturbance, particularly for the voltage plunge, because its stator is straightly associated to the grid. Due to voltage plunge there is cause of over voltage and over current in the rotor windings and in this manner harm the rotor side converter. To give a DFIG with great FRT, the WT and the power converter ought to have the option to make sure about itself, without separating during faults. So as for satisfying the necessity, a crowbar is required to provide additional security [5, 6]. One normal methodology in effective modeling of DFIGs for WTs is to use a model based on the space vector theory of a slip-ring induction machine [7]. This method provides sufficient accuracy also in cases where the voltage dips due to single or two phase faults in the network occur. A vector control approach is generally used for GSC with the reference outline situated along the grid voltage vector, where an autonomous control of active and reactive power is accomplished. Hysteresis current control (HCC) and synchronous voltage oriented control (VOC) are the state of the art control techniques being used presently for the inner current control loop. In synchronous reference outline control, all the voltage and current variables are transformed to a synchronously rotating reference frame so that the control variables become DC quantities and hence one can use traditional PI controllers to control the same. But in instance of Stationary reference outline control the control factors are time varying quantities and hence one cannot use normal PI controllers. To control the system on stationary reference frame, one needs to use Proportional Resonant (PR) Controllers [8], which makes the control complex. Phase angle detection has a significant role in control of GSC. Number of research papers are revealing few algorithms fit for identifying the grid voltage phase angle are detailing a few algorithms that are efficient of determining the phase angle of voltage of grid like zero intersection detection, utility of arc tangent function or Phase locked loop (PLL). For smaller systems, a diode rectifier– chopper–inverter configuration is selected as this is simple and cost-effective [9, 10]. To join grid the inverter is utilized while the pair rectifier-chopper is utilized for output current/torque control of PMSG. For medium and high power WECS using PMSG, completely controlled rectifier–inverter pair is typically utilized, along with

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a vector torque control design for changing speed operation [11–13]. The generator is operated to acquire most extreme power from the incident wind with greatest efficiency under various load conditions. The power factor regulation of the wind generator is achieved through vector control of GSC. A PWM CSC for GSC is also an option for PMSG based WECS [14–16].

52.2.1 WECS and Power System Interaction The impact of a projected total wind penetration of 3000 MW in the Greek power system is investigated. System is modeled to compare LVRT capabilities of a fixed and variable speed WECS and it is realized that variable speed WECS have a better LVRT capability which reduces generation losses and limits outages. A case study for transient stability of wind turbine and the impacts of a three short circuit fault on the power grid is presented [17]. It is observed [18] that voltage stability isn’t just correlated to the qualities of wind turbine yet in addition has cozy relationship with the power grid. If the grid is sufficiently strong and can give enough reactive power, it can ensure voltage stability. It is analysed [19] that to which extent the LVRT capability of an SCIG based WECS can be intensified by the utilization of a STATCOM; this is compared with a thyristor based SVC. A simple analytical methodology that is dependent on torque slip characteristics is first proposed to evaluate the impact of STATCOM and SVC on the transient stability margin. A technique for assessing the critical speed and critical clearing time for various ratings of a STATCOM or an SVC is presented. It is observed that performance of STATCOM is better than the SVC in terms of LVRT ability if similar rating is expected for the devices. Estanqueiro [20] highlighted the existing technical barriers that prevent accomplishing a high wind penetration in a power system. The problems related to limited transmission capacity, supply security, power unit scheduling, variability of wind, reliability and operational energy congestion are reported. Technical solutions for LVRT, wind power control and curtailment, remote reactive power control are also discussed. Muljadi [21] endeavors to examine the impact of penetration of wind power on the transient stability of the grid and the results are proposed as basic critical clearing time (CCT). He has considered a 3-generator, 9-bus power system network with total load of 315 MW. With 10% increase in load, a wind power plant (WPP) is added and the system is investigated for its transient stability. It is found that the position of the fault influences the voltage plunges at the various generators. The synchronous generator is more touchy to the voltage dip than a WPP. It is also found that the capacity of the WPP to reduce the oscillation is credited to the wind generator utilized with the controllability to change real and reactive power separately in a fast way. Muljadi [22] described the method to build up an equivalence of a collector system in a enormous wind power plant, by making the following assumptions: (1) The current infusion from all the wind turbines is thought to be indistinguishable in angle and magnitude. (2) Reactive power that is produced by the

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shunt capacitors are calculated with the voltage of the line set at 1 pu. The equivalent system is represented for daisy chain connection of wind turbines and two representations are suggested, one for Equivalence of major lines (EOML) and other for equivalence of complete circuit (EOCC). The method described has given a brisk and straightforward estimation for foreseeing the behavior and losses of the arranged wind power plant with a superior degree of confidence. Rodriguez [23] presented the fundamental examination of the effect of increased penetration of wind power in the arranging and operation of the Spanish power system for the next six years, through PSS/E simulations. In this paper, dynamic models for wind farms (equivalent) based on variable speed DFIG and SCIG (fixed speed) are developed. Tripping of wind generators due to short circuit fault is reduced in case of DFIG and also voltage recovery is very fast whereas SCIG is sluggish. For the same fault, voltage in DFIG recovers in 0.4 s whereas in fixed speed SCIG it occurs 0.5 s. It is concluded that a huge amount of uncontrolled wind production connected in little territories could have negative effect on system dynamics; so a variable speed system or carefully designed dynamic voltage support system is recommended.

52.2.2 Impact of RES Based Micro Grid Penetration on Power System Performance Power System integrated with renewable energy resources are becoming very popular and penetration level of RES is increasing because of decrease in reserves and increase in price of fossil fuels. Intrinsic nature and variable generation from RES poses challenges to integrate them with the power system and maintain stable electrical power system. An important issue when integrating RES to power system is the effect on the stability of the system and transient behavior. System stability is to a great extent connected with power system faults in a system for example, loss of production capacity (generator unit failure), short circuits and tripping of transmission lines. These non performances disturb the level of active and reactive power, and change the flow of power. Huge voltage drops suddenly may occur, despite the fact that the capacity of the working generators might be satisfactory. The re-distribution and unbalance of real and reactive power in the system may drive the voltage to change after the limit of stability. A period of low voltage (brownout) may occur and maybe be trailed by an all out loss of power (blackout) [24–27].

52.3 Comparative Control Strategies Various techniques are presented for Performance Enhancement of Renewable Energy Systems (Fig. 52.1).

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Fig. 52.1 Different Control Strategy of WECS [28]

52.3.1 To Improve Pitch Control of a Wind Turbine For pitch control of wind turbines there is a Proportional-integral-derivative (PID) controller that is the most normally utilized controller. Yet, because of unpredictability in wind speed profiles and wind turbine modeling, there is requirement of increasingly successful controller. In this paper [29] an advanced controller called fractional-order fuzzy PID (FOFPID) has presented to enhance the performance of pitch control of a 5-MW wind turbine. In the mean time, the chaotic evolutionary optimization techniques are utilized, to determine the parameters of the controller. Different wind speed profiles and divergence has been applied to ideal controllers and their performances have been compared to 4 appropriate criteria, so as to exhibit that the controllers are competent to work without disappointment in wind speed and working conditions, that are not optimized. The outcomes of simulations demonstrate that the fractional- order fuzzy controller has greater robustness and performance in reducing the error (IAPE- integral absolute power error, IAE- integral absolute error, IALSSTE- integral absolute low-speed shaft torque error) furthermore, then again, in limiting the fatigue damages over FPID (Fuzzy PID), GSPID(Gain-scheduling PID) and FOPID (Fractional-order PID) controllers. Meaning of Less IAE is less generator speed error. Meaning of Less IAPE is better regulation and power quality. Furthermore, IALSSTE alludes to the deviation of low-speed shaft torque. Meaning of Less IALSSTE implies less torque changes and so as results fewer fatigue damage to the drive train (Figs. 52.2 and 52.3).

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Fig. 52.2 FOFPID/FPID control structure

Fig. 52.3 a Wind speed changes. b Step response of FOPID, GSPID, FPID and FOFPID in three wind speeds

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52.3.2 To Reduce Chattering Phenomenon This paper [30] presented a control strategy (Super-Twisting sliding mode control) for Wind Energy Conversion System (WECS) having Doubly-Fed Induction Generator (DFIG). The STW control methodology is more influential in the matter of power extraction maximization, energy quality, chattering reduction, finite convergence time, higher accuracy and robustness against system disturbances and parameters changesas compared to Proportional-Integral (PI) and conventional Sliding Mode (SM) controllers. To minimize the chattering phenomena in active and reactive powers and RSC current harmonic distortion in a DFIG based wind turbine, [31] presented a control methodology which is dependent on the root tree optimization (RTO). To modify the parameters (Kp, Ki) of PI controller (RTO-PI) the root tree optimization is utilized. The obtained simulations results indicate that the presented controller adequately improve the DFIG based wind turbine control performances in terms of steady-state perfor- mances, greater dynamics and chattering phenomena limiting, switching frequency decrease.

52.3.3 To Damp the Oscillations In another paper [32] the static var compensator (SVC) incorporated with Proportional Integral Derivative (PID) controller is integrated with the PV based power system in single machine infinite bus (SMIB) for reducing low frequency oscillations created because of disturbances in the system for improvement of transient stability. To differential search algorithm (DSA) quasi oppositional learning was integrated for determining the optimized parameters of SVC-PID controller. The presented strategy QDSA dependent PID is reducing the oscillations most effectively in comparison to conventional PID, PSO optimized PID, DSA optimized PID which proves its capability in improving the stability of the system most efficiently. The paper [33] focused on variable-rotor-speed and variable-blade-pitch wind turbines which operate in the high wind speed region, where generator torque and blade pitch controllers are used to limit the turbine’s capturing energy to the rated power value. Orlando [34] investigated the blade pitch controller (BPC) incorporated with Proportional-Integral–Differential (PID) for the wind energy conversion system (WECS) to upgrade the damping of oscillations in the voltage and output power. Single wind turbine system is connected with BPC-PID (MFO). MFO is used to search for optimized parameters of the controller. Thusly, the proposed design can ensure stability of the system under the expanded mechanical torque disturbance and extreme wind speed with vulnerabilities of parameters of controller. Results of simulation accentuation on the better execution of the presented BPC-PID (MFO) in comparison to the conventional BPC-PID based on Zeigler Nichols, BPC-PID based on simplex algorithm, and genetic algorithms-based PID controllers in capturing

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system nonlinearities above a extensive span of working conditions and vulnerabilities of control system parameters. To achieve a constant regulated output voltage in wind energy conversion (WEC) system Robust control technique is required. So a Fractional order PI controller is designed that uses Genetic algorithm for tunning and implementation using MATLAB/ SIMULINK. The robustness characteristic of FOPI controller is that the proposed controller is less Oscillation and regulated output voltage, overshoot problem is not there [35]. A control strategy which is dependent on Type 2 Fuzzy Logic Controller (T2FLC) to control the active and reactive powers of the Doubly Fed Induction Generator (DFIG) of Wind Energy Conversion System (WECS) was implemented as well as tested using MATLAB/Simulink. The outcomes manifest that the controller which is dependent on Type 2 Fuzzy Logic Controller (T2FLC) is having good performance, very good convergence, does not consist any oscillation or overshoot and it is fast as compared to the Type 1 Fuzzy Logic Controller (T1FLC) method [36]. The effect of MG penetration which is based on RES on the control and dynamic stability of a Two-area four generators system having RES based microgrid under differing working conditions is investigated. Power system stabilizer (PSS) which is based on a novel type-2 fuzzy fractional order PID is designed to improve the electromechanical oscillation damping execution of the power system for upgrading the dynamic stability. Optimized Parameters for the type-2 fuzzy FoPID controller are determined by an effective hybrid algorithm in which dynamic genetic algorithm is integrated to bac- teria foraging algorithm. The hybrid control technique provides the benefit of lustiness against all vulnerabilities that are available in the system. The complete analysis manifests that, when tunning of the type-2 fuzzy FoPIDPSS is done for the ostensible system and executed, it does not need the tuning again for any annoyed conditions in the dynamic power system and in this manner guarantees the system reliability. The results of nonlinear simulation exhibit the impact of the fractional parameter impact on the optimal dynamic performance of the system with different MG penetration ratios under various operating disturbance vulnerabilities and conditions. To represent the predominance for the type-2 fuzzy FoPIDPSS, its performance is compared to the other three kinds of PID based PSSs [37] (Figs. 52.4 and 52.5).

52.4 Conclusion Control techniques are increasing more significance in collecting energy proficiently from wind. They assume a vital role in energy transformation process. The discontinuous nature of the wind speed tends a provoking errand to control procedure to get high reliable and quality power supply. Because of expanding penetration of wind turbine in electrical framework it is important to acquire power productively as per the grid code. In this paper a through survey of various techniques is provided and analysed implementation of different controllers based on certain parameters. Use of

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Fig. 52.4 Type-2 Fuzzy logic control scheme

Fig. 52.5 Structure of the fractional order type-2 fuzzy PID controller [37]

these techniques in future can prompt to reliable generation of power and decreased cost and computational speed of the overall framework.

52.5 Future Scope of Work Wind energy is one of the most driving sustainable power source. They are utilized to defeat the vitality request and add to safe eco-accommodating framework. However,a suitable con-troller is required to tap the most elevated potential from the accessible energy and to produce a perfect energy for grid integration. Various MPPT control strategies are used to track optimum power point of variable speed wind turbine. The future research on wind system mainly depends upon the fault ride through (FRT) of the grid connected system. The assortment system of seaward wind energy framework is additionally developing region in the field of WECS. Multi objective controller for pitch angle and MPPT controller can minimise the input parameters for each converter and also provide efficient system at ic approach for future controller. Multi objective controller for pitch angle and MPPT controller can limit the input

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parameters for every converter and furthermore give effective approach for future controller.

References 1. Rahman, S.: Green power: what is it and where can we find it? IEEE Power Energ. Mag. 1(1), 30–37 (2003) 2. Novotny, D.V., Lipo, T.A.: Vector Control and Dynamics of A.C Drives. Clarendon Press, Oxford, UK (1996) 3. Mohan, N., Undeland, T.M., Robbins, W.P.: Power Electronics, 3rd edn. Wiley, New Delhi (2003) 4. Muller, S., Deicke, M., De Doncker, R.W.: Doubly fed induction generator systems for wind turbines. IEEE Ind. Appl. Mag. 8(3), 26–33 (2002) 5. Aoyang, H., et al.:Study of the factors affected the rotor over-current of dfig during the threephase voltage dip. In:Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies—DRPT 2008, pp. 2647–2652 (2008) 6. Wang, Y., Li, J., Hu, S., Xu, H.: Analysis on DFIG wind power system low-voltage ride through. In: International Joint Conference on Artificial Intelligence, pp. 676–679 (2009) 7. Tapia, A., Tapia, G., Ostolaza, J.X., Saenz, J.R.: Modeling and control of a wind turbine driven doubly fed induction generator. IEEE Trans. Energy Convers. 18(2), 194–204 (2003) 8. Li, H., Chen, Z., Pedersen, J.K.: Optimal power control strategy of maximizing wind energy tracking and conversion for VSCF doubly fed induction generator system. In: IEEE 5th International Conference on Power Electronics and Motion Control—IPEMC 2006, vol. 3, no. 14–16, pp. 1–6 (2006) 9. Higuchi, Y., Yamamura, N., Ishida, M., Hori, T.: An improvement of performance for small scale wind power generating system with permanent magnet type synchronous generator. In: 26th Annual Conference of IEEE Industrial Electronics Society—IECON 2000, vol. 2, pp. 1037– 1043 (2000) 10. Knight, A.M., Peters, G.E.: Simple wind energy controller for an expanded operating range. IEEE Trans. Energy Convers. 20(2), 459–466 (2005) 11. Chen, Z., Spooner, E.: Grid interface options for variable speed, permanent magnet generators. IEE Electric Power Applications 145(4), 273–283 (1998) 12. Schiemenz, I., Stiebler, M.: Control of a permanent magnet synchronous generator used in a variable speed wind energy system. In: IEEE International Electric Machines and Drives Conference—IEDMC 2001, pp. 872–877 (2001) 13. Akhmatov, V.: Analysis of Dynamic Behavior of Electric Power System with Large Amount of Wind Power. Ph.D. Thesis, Technical University of Denmark, Denmark (2003) 14. Lang, Y., Wu, B., Zargari, N.: Reactive power control of current source converter based wind energy system. IEEE Electrical Power Conference—EPC 2007, Canada, pp. 479–483 (2007) 15. Bose, B.K.: Modern Power Electronics and AC Drives. Prentice Hall, Englewood Cliffs, NJ, USA (2002) 16. Pena, R.S., Cardenas, R.G., Asher, G.M., Clare, J.C.: Vector controlled induction machines for stand alone wind energy applications. In: IEEE Industry Applications Annual Meeting—IAS 2000, pp. 1409–1415 (2000) 17. Voumvoulakis, E.M., Markou, G.S., Hatziargyriou, N.D.: Large scale integration of wind power in the greek interconnected power system. In: IEEE Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century, pp. 1–4 (2008) 18. Yang, Q., Zhang, J., Wu, Z., Li, W., Yang, J.: Analysis on stability of integration of wind farms into power systems. In: IEEE Power and Energy Engineering Conference, 2009. Asia-Pacific, pp. 1–4 (2009)

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19. Tore, U., Suul, J.A., Monilas, M.: Low voltage ride through of wind farms with cage generators: STATCOM versus SVC. IEEE Trans. Power Electron. 23(3) (2008) 20. Estanqueiro, A.I., Ferreira, J.M., Ricardo, J., Pecas, J.A.: Barriers (and solutions….) to very High wind penetration in power systems. In: IEEE Power Engineering Society General Meeting, pp. 1–7 (2007) 21. Muljadi, E., Nguyen, T.B., Pai, M.A.: Transient stability of the grid with a wind power plant. In: IEEE Power Systems Conference and Exposition PSCE 2009, pp. 1–7 (2009) 22. Muljadi, E., Butterfield, C.P., Ellis, A., Mechenbier, J., Hochheimer, J., Young, R., Miller, N., Delmerico, R., Zavadil, R., Smith, J.C.: Equivalencing the collector system of a large wind power plant. In: IEEE Power Engineering Society General Meeting (2006) 23. Rodriguez, J.M., Fernandez, J.L., Beato, D., Iturbe, R., Usaola, J., Ledesma, P., Wilhelmi, J.R.: Incidence on power system dynamics of high penetration of fixed speed and doubly fed wind energy systems: study of Spanish case. IEEE Trans. Power Syst. 17(4), 1089–1095 (2002) 24. Karapidakis, E.S.: Transient analysis of Crete’s power system with increased wind power penetration. In: IEEE Power Engineering Energy and Electric Drives POWERENG 2007, Setubal, Portugal, pp. 18–22 (2007) 25. Milano, F.: Assessing adequate voltage stability tools for networks with high wind power penetration. In: IEEE Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies DRPT 2008, Nanjing, China, pp. 2492–2497 (2008) 26. Vittal, E., O’Malley, M., Keane, A.: A steady-state voltage stability analysis of power systems with high penetration of wind. IEEE Trans. Power Syst. 25(1), 433–442 (2010) 27. Shi, L., Dai, S., Ni, Y., Yao, L., Bazargan, M.: Transient stability of power systems with high penetration of DFIG based wind farms. In: IEEE Power & Energy Society General Meeting, Calgary, pp. 1–6 (2009) 28. Belabbas, B., et al.: Higher performance of the Type 2 fuzzy logic controller for direct power control of wind generator based on a doubly fed induction generator in dynamic regime, 2 (no date) 29. Muljadi, E., Nguyen, T.B., Pai, M.A.: Impact of wind power plants on voltage and transient stability of power systems. IEEE Energy 2030, Atlanta, Georgia, USA (2008) 30. Asgharnia, A., Shahnazi, R., Jamali, A.: Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms. ISA Trans. 79, 27–44 (2018). https://doi.org/10.1016/j.isatra.2018.04.016 31. Benamor, A., et al.: A new rooted tree optimization algorithm for indirect power control of wind turbine based on a doubly-fed induction generator. ISA Trans 88, 296–306 (2019). https:// doi.org/10.1016/j.isatra.2018.11.023 32. Boubzizi, S., et al.: Comparative study of three types of controllers for DFIG in wind energy conversion system. Protection and Control of Modern Power Systems, pp. 1–12 (2018) 33. Ray, P.K., et al.: Improvement of stability in solar energy based power system using hybrid PSOGS based optimal SVC damping controller. Energy Procedia109(November 2016), 130–137. https://doi.org/10.1016/j.egypro.2017.03.078 34. Orlando, G.: starting observer based blade-pitch control of observer based control of observer based control of wind turbines operating above a wind turbines operating above a wind turbines operating above a. IFAC-PapersOnLine 50(1), 9914–9919. https://doi.org/10.1016/ j.ifacol.2017.08.1631 35. Ebrahim, M.A., Becherif, M., Abdelaziz, A.Y.: Dynamic performance enhancement for wind energy conversion system using Moth-Flame Optimization based blade pitch controller. Sustain. Energy Technol. and Assess. 27(March), 206–212 (2018). https://doi.org/10.1016/ j.seta.2018.04.012 36. Jainl, R. V., Aware, M.V., Junghare, A.S.: Tuning of fractional order PID controller using particle swarm optimization Technique for DC Motor Speed Control. IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (2016). https://doi. org/10.1109/ICPEICES.2016.7853070 37. Abdulkhader, H.K., Jacob, J., Mathew, A.T.: Electrical power and energy systems robust type2 fuzzy fractional order PID controller for dynamic stability enhancement of power system

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Chapter 53

A Note on Lyapunov Krasvoskii Funtional for Discrete Time Delayed Systems Vipin Chandra Pal, Sudipta Chakraborty, Avadh Pati, and Gurpreet Singh

Abstract The modern era is becoming smarter from the technology of digitalization which are used in computers and industrial developments. The basis of processing the information in above devices is in the form of discrete signal. The computational process of information transfer among different parts of system takes some time which is known as delay. Therefore, the stability of discrete time delayed system is very prominent area of research. In this paper different types of Lyapuonv Krasvoskii functional (LKF), which are used for stabilization of discrete time delayed systems, are discussed.

53.1 Introduction Time-delays come usually from controllers, filter actuators or the contact problem of pure mechanical systems because of constraint in rate of information processing, output data transmission between other unit of practical systems [1, 2]. Time delays may be classified as [3, 4] (i) constant or time varying, (ii) known or unknown, (iii) deterministic or stochastic. It is well known fact that the presence of delays might be always significant cause of instability or unbounded oscillations [5–9]. This common characteristic V. C. Pal (B) · S. Chakraborty · A. Pati National Institute of Technology Silchar, Silchar, Assam 788010, India e-mail: [email protected]; [email protected] S. Chakraborty e-mail: [email protected] A. Pati e-mail: [email protected] G. Singh ABES Engineering College, Ghaziabad 201009, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_53

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particularly critical for real time application since, it may degrade the control action in real time system as a result the behavior or output result will deviate. Therefore, the stability analysis of time delayed systems has been investigated in literature [11, 12]. On the basis of using information of delay in formation of LMI for stability, time delayed systems are classified into. (i) Delay dependent (ii) Delay independent. For limited range of delay delay-dependent criterion gives better result [7, 9, 10] whereas for infinite delay later method is applicable for measuring the stability. For deriving LMI based stability conditions, mainly two method of Lyapunov functional are used i.e. (i) Krasovskii method of Lyapuonv functionals and (ii) Razumikhin method of Lyapuonov functions [9–11]. It has been observed that the Krasvoskii functional is mostly applied for deriving the stability conditions for time delayed systems due to following advantages: (i) The LMI condition obtained by using Krasvoskii method is less restrictive. (ii) LMI is linear in terms of decision variables by using above method. (iii) By using Razumikhin method, LMI are computational difficult in comparison to Krasovskii method. In this paper, different type of Lyapunov Krasvoskii functional, used for stability of discrete systems, are discussed. The recent developments for reducing the conservatism by applying the modified Lyapunov Krasvoskii functional are represented. The above conditions will be based on LMI technique and delay-dependent. This paper is organized as follows. In Sect. 53.2, mathematical model of discrete delayed system is represented. In Sect. 53.3, different delay-dependent criterion is discussed.

53.2 Time Delayed System 53.2.1 Mathematical Modeling of Time-Delayed Systems Time-Delayed Systems of a discrete time-delay system results in a system of difference equations. Consider a discrete system in presence of actuator saturation and delay 





x(r + 1) = A x(r ) + Ad x(r − m(r )) + Bu(r ) 

 

 

y(r ) = C x(r ) + D u(r )

where

(53.1a) (53.1b)

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r ∈ z + and z + indents the set of positive integers. The x(r ) ∈ n is the state   vector, u(r ) ∈  p is input vector while u(r ) ∈  p is controlled output vector. 



Matrices A ∈ n×n , Ad ∈ n×n , B ∈ n× p , C ∈ t×n , D ∈ t×n are known constant matrices representing a nominal plant. The time varying delay satisfies 0 ≤ m 1 ≤ m(r ) ≤ m 2

(53.2)

where lower bound on delay m 1 and upper bound m 2 are positive integers. The initial condition is 

x 0 = φx (r )



− m 2 ≤ r ≤ 0 be φ(r, x 0 )

(53.3)

For equilibrium state (i.e. origin), the expression of domain of attraction may be deduced as      φx (r ), −m 2 ≤ r ≤ 0 : lim φx (r, x 0 ) = 0 r →∞

53.2.2 Basic Definition [9, 10] For time delayed systems characterized by (53.1), with state feedback controller   u(r ) = K x(r ) is asymptotically stable if there exist continuous positive functional Vl : R × C[−m, 0] → R such that its difference along (53.1) is negative as 





Vl (x(r )) = Vl (x(r + 1)) − Vl (x(r )) = − w(|φ(0)|)

(53.4)

where w(r ) > 0 for r > 0.

53.3 Lyapunov Krasvoskii Functional The stability condition of time-delayed systems can be derived by using information of delay in Lyapunov Krasvoskii functional. The advancement in this field is going on for deriving less conservative result by modifying the Lyapunov functional. The advancement in the technique of modification in Lypaunov functional can be viewed as follows:

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53.3.1 Standard Lyapunov Functional It has been observed that the simples form of Lyapunov functional which is used in [5, 6] as given in Theorem 53.1. Theorem 53.1: [4] If there exists positive symmetric definite matrices P, Q and a   state feedback law u(r ) = K x(r ) such that the following LMI satisfies.

−P + A T P A + A T P Ad (Q − AdT P Ad )−1 AdT P A + d2 Q < 0

(53.5)

where Q − AdT P Ad > 0 Then the closed loop system will be asymptotically stable for time varying delay 0 < m(r ) ≤ m 2. In this above approach system is transformed into augmented systems without delay. Fridman and Shaked have proposed descriptor model transformation approach in [5] to remove the two difficulties (i) curse of dimension and (ii) unknown delay and time varying delay. Following Lyapunov functional is considered 

T



Vl (x(r )) = x (r )P x(r ) +

−1 r −1  

yiT [R + Q]yi +

n=−m 2 i=r +n

r −1 

T



x (r )S x(r ) (53.6)

i=r −m 2

where P, Q, R, S are symmetric positive definite matrices. The new stability criteria is developed by bounding the cross between rproduct −1 −1 two vector and finding the difference terms rm=r ˜ −m r (·) and m=r ˜ −r M (·) which was largely ignored previously [6]. Further, Shao and Han [7] has modified the    stability condition by considering forward difference x(i) = x(i + 1) − x(i) in Lyapunov functional 

T



Vl (x(r )) = x (r )P x(r ) +

2 r −1  

T



x (i)Q j x(i)

n=1 i=r −h n r −1 

+

T



x (i)Q 3 x(i) +

+

n=−m 1 l=r +n

r −1 

T



x (l)Q 3 x(l)

n=−m 2 +1 l=r +n

i=r −m(r ) −1 r −1  

−m 1 

T



m 1 x (l)W1 x(l) +

−m 1 −1 m−1  

T



d12 x (l)W2 x(l)

n=m 2 l=m+n

(53.7)

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and the difference of upper and lower bound is considered as m 2 − m 1 = m 2 − m(k) + m(k) − m 1 .

53.3.2 Reciprocal Convex Approach [8, 9] For discrete time delayed autonomous system (53.1), following reciprocally convex technique is used. Lemma 53.1 [8, 9]. For any vectors 1 ,2 matrices F, X and real numbers ς1 ≥ 0, ς2 ≥ 0 satisfying. 

F X ∗ F

 ≥ 0, ς1 + ς2 = 1

(53.8)

e = 0 if ςe = 0 (e = 1, 2) then T     1 T 1 T F X 1 1 − 1 F1 − 2 F2 ≤ − 2 ∗ F 2 ς1 ς2

(53.9)

is utilized for improving the existing criteria in terms of smaller computational burden and less conservative results. Theorem 53.2 [8] An autonomous system given by (1) is asymptotically stable if there exists positive definite matrices P, Qi (i = 1,2,3), R1 , R2 , and appropriate dimension matrix S such that.  =

R2 S S T R2

 >0

E 1 = E 0 − Y T OY < 0 where ⎛ ⎞ 3  E 0 = [Ae1 + Ad e2 ]T P[Ae1 + Ad e2 ] + e1T ⎝ Q j − P + d12 Q 3 ⎠e1 − e3T Q 1 e3 j=1

−

e4T

Q 2 e4 −

e2T

Q 3 e2 − (e1 − e3 ) R1 (e1 − e3 ) + [(A − I )e1 + Ad e2 ]T T

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2 × d12 R1 + d12 R2 [(A − I )e1 + Ad e2 ]  e3 − e2 ; Y = e2 − e4 

m 12 = m 2 − m 1 The Lyapuonov functional is chosen as T





Vl (x(r )) = x (r )P x(r ) +

2 r −1  

T



x (i)Q j x(i) +

n=1 i=r −h n

+

−m 1 

r −1 

T

+

T



x (i)Q 3 x(i)

i=r −d(k) 0 



x (l)Q 3 x(l) +

n=−m 2 +1 l=r +n −m 1 

r −1 

r −1 

m 1 η T (l)W1 η(l)

n=−m 1 +1 l=r −1+n

r −1 

m 12 η T (l)W2 η(l)

n=−m 2 +1 l=r −1+n

53.3.3 Triple Lyapunov Functional [12–15] Recently, researcher have find a noble way to reduce the conservatism by introducing  −1 r −1 T  triple Lyappunov Functional as −1 s=−h m u=s v=r +u  x Q 3  x. In this approach [12–16], it has been claimed that the triple LKF utilizes more information of delay. The Lyapunov functional is chosen as [12, 16] 

Vl (x(r ) =

4 



Vla (x(r ))

a=1

where T



Vl1 (x(k)) = x (r )Px(r ) r −1 



Vl2 (x(r )) =

T



x (a)P1 x(a) +

a=r −m 1

+

r −1  a=r −m(k)

r −m 1 −1 

T



x (a)P2 x(a)

a=r −m 2 T



x (a)P3 x(a) +

r −m 1

r −1  T  x (b)P3 x(b)

a=r −m 2 +1 b=a

53 A Note on Lyapunov Krasvoskii Funtional …

Vl3 (x(k)) =

677

r −m 1 −1 r −m −1 0 r −1 1 −1 r −m 1 −1   m 21    T 1  η (c)Qη(c) + ηT (c)Rη(c) 2 a=−d b=a c=r +b 2 a=r −m b=a c=b 2

1

+

−m r −1 0  1 −1  

(m 22 − m 21 ) 2 a=−m



Vl4 (x(r )) = m 1

−1 

r −1 

2

+(m 2 − m 1 )

(53.10)

b=a c=r +b

T



x (b)Sx(b) + m 1

a=−m 1 b=r +a −m r −1 1 −1  

ηT (c)R1 η(c)

T

r −1 −1   a=−m 1 b=r +a



x (b)R2 x(b)+(m 2 − m 1 )

a=−m 2 b=r +a

ηT (b)Tη(b)

−m r −1 1 −1  

ηT (b)R3 η(b)

a=−m 2 b=r +a

(53.11)

53.3.4 Wirtinger Inequality Based LKF [17–21] In the most of all the above discussed technique the bound on cross terms have been done by using Jensen’s inequality. In the direction of advancement a new summation based Wirtinger inequality is used for reducing the conservatism in results. Lemma 53.2: [20] “For a given symmetric positive definite matrix W ∈ m×m ,   any sequence of discrete-time variable x(q) in −m 0 ∩ Z → m , the following inequality holds:

0 

ωT (i)W ω(i) ≥

i=−m+1



 T    1 θ0 W 0 θ0 W 0 3 m+1 θ1 m θ1 m−1

(53.12)



where m ≥ 1, ω(i) = x(i) − x(i − 1) and   θ 0 = x(0) − x(−h), 0    2 x(i)”. θ 1 = x(0) + x(−m) − m+1 m+1 i=−m “Since, the factor m−1 is difficult to handle in some practical systems with time-varying delay. So, the following Lemma 53.3 is given to make disappear from inequality”. Lemma 53.3: [20, 21] “For a given symmetric positive definite matrix W ∈ m×m ,    any sequence of discrete-time variable x in −m 0 ∩ Z → m , where m ≥ 1, then the following inequality holds:

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0 

ωT (i)W ω(i) ≥

i=−m+1 



 T    1 θ0 W 0 θ0 , 0 3W θ 1 m θ1 

(53.13)



where ω(i) = x(i) − x(i − 1) and θ 0 = x(0) − x(−h),    2 0 θ 1 = x(0) + x(−m) − m+1 i=−m x(i)”.

53.4 Conclusion In this paper, the Lyapunov Karasvoskii functional is enlightened in respect of discrete time delayed systems. Tremendous development in reducing the conservatism have been presented by modifying the functional with different technique. Further, research is going for obtaining more and more less conservative result with different applications and modification in Lyapunov Karasvoskii functional.

References 1. Lin, W., He, Y., Zhang, C., Wu, M.: Stochastic finite-time H∞ state estimation for discretetime semi-Markovian jump neural networks with time-varying delays. IEEE Trans. Neur. Netw. Learn. Syst., 1–12 (2020). https://doi.org/10.1109/TNNLS.2020.2968074 2. Roy, R., Kapat, S.:Discrete-time framework for analysis and design of digitally current mode controlled intermediate bus architectures for fast transient and stability. IEEE J. Emerg. Selected Topics Power Electron. https://doi.org/10.1109/JESTPE.2020.2971513 3. Chen, K.F., Fong, I.K.: Stability analysis and output-feedback stabilization of discrete time systems with an interval time-varying state delay. IET Control Theory Appl. 4(4), 563–572 (2010) 4. Song, S.-H., Kim, J.-K., Yim, C.-H., Kim, H.-C.: H∞ control of discrete-time linear systems with time-varying delays in state. Automatica 35, 1587–1591 (1999) 5. Fridman, E., Pila, A., Shaked, U.: Regional stablization and H∞ control of time-delay systems with saturating actuators. Int. J. Robust Nonlinear Control 13, 885–907 (2003) 6. Gao, H., Chen, T.: New results on stability of discrete-time systems with time-varying state delay. IEEE Trans. Autom. Control 52(2), 328–334 (2007) 7. Shao, H., Han, Q.-L.: New stability criteria for linear discrete-time systems with interval-like time-varying delays. IEEE Trans. Autom. Control 56(3), 619–625 (2011) 8. Liu, J., Zhang, J.:Note on stability of discrete-time time-varying delay systems. IET Control Theory Appl. 6(2), 335–339 (2012) 9. Park, P., Ko, J.W., Jeong, C.: Reciprocally convex approach to stability of systems with timevarying delays. Automatica 47(1), 235–238 (2011) 10. Vladimir, L.: Kharitonov: robust stability analysis of time delay systems: a survey. Ann. Rev. Control 23, 185–196 (1999) 11. Fridman, E.: Tutorial on Lyapunov-based methods for time-delay systems. Eur. J. Control 20, 271–283 (2014) 12. Wang, T., Xue, M., Fei, S., Li, T.: Triple Lyapunov functional technique on delay-dependent stability for discrete-time dynamical networks. Neurocomputing 122, 221–228 (2013)

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13. Kim, S.H.: Further results on stability analysis of discrete-time systems with time-varying delays via the use of novel convex combination coefficients. Appl. Math. Comput. 261, 104–113 (2015) 14. Shi, K., Zhu, H., Zhong, S., Zeng, Y., Zhang, Y., Wang, W.: Stability analysis of neutral type neural networks with mixed time-varying delays using triple-integral and delay-partitioning methods. ISA Trans. 58, 85–95 (2015) 15. Liu, Z., Yu, J., Xu, D., Peng, D.: Triple-integral method for the stability analysis of delayed neural networks. Neurocomputing 99(1), 283–289 (2013) 16. Pal, V.C., Negi, R.: Delay-dependent stability criterion for uncertain discrete time systems in presence of actuator saturation. Trans. Inst. Meas. Control 40(6) 1873–1891 (2018) 17. Sun, Y., Li, N., Shen, M., Wei, Z., Sun, G.: Robust H∞ control of uncertain linear system with interval time-varying delays by using Wirtinger inequality. Appl. Math. Comput. 335, 1–11 (2018) 18. Zhang, L., He, L., Song, Y.: New results on stability analysis of delayed systems derived from extended wirtinger’s integral inequality. Neurocomputing 283, 98–106 (2018) 19. Seuret, A., Gouaisbaut, F.: On the use of the Wirtinger inequalities for time-delay systems. In: Proceedings of the 10th IFAC Workshop on Time Delay Systems The International Federation of Automatic Control Northeastern University, Boston, USA, June 22–24, pp. 260–265 (2012) 20. Seuret, A., Gouaisbaut, F., Fridman, E.: Stability of discrete-time systems with time-varying delays via a novel summation inequality. IEEE Trans. Autom. Control 60(10), 2740–2745 (2015) 21. Pal, V.C., Negi, R., Zhu, Q.: Stabilization of discrete-time delayed systems in presence of actuator saturation based on Wirtinger inequality. Math. Probl. Eng. Article ID 5954642, 1–14 (2019)

Chapter 54

Bidirectional DC-DC Buck-Boost Converter for Battery Energy Storage System and PV Panel Krishna Kumar Pandey, Mahesh Kumar, Amita Kumari, and Jagdish Kumar Abstract This paper presents modeling and analysis of bidirectional DC-DC buckboost converter for battery energy storage system and PV panel. PV panel works in accordance with irradiance available. When the irradiance to PV array is capable to produce the sufficient voltage then PV array will charge the battery through bidirectional DC-DC converter and also supplies power to load during that time. When the irradiance to PV array is unable to produce the sufficient voltage then the battery will supply the load through same bidirectional DC-DC converter and at this time the battery discharges through load. Conventional buck or boost converter does not have the capability of bidirectional power flow; therefore, a bidirectional DC-DC power flow converter is obtained by connecting buck and boost converter in anti-parallel with each other. According to the irradiance availability, charging and discharging behavior of the battery and voltage across the load is shown in this paper. To achieve the adequate results, different proportional integral controllers are modeled and designed to produce the desired duty cycle for MOSFET/IGBT switches.

K. K. Pandey · M. Kumar (B) · A. Kumari · J. Kumar Punjab Engineering College, Sector 12, Chandigarh 160012, India e-mail: [email protected] K. K. Pandey e-mail: [email protected] A. Kumari e-mail: [email protected] J. Kumar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_54

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54.1 Introduction Photovoltaic cell is also called as a solar cell. The phenomenon of the conversion of light energy into electrical using the solar cell is achieved by utilizing the photovoltaic effect. A Photovoltaic (PV) array is combination of Photovoltaic (PV) modules, which is obtained by interconnecting PV cells in series and parallel depending on the power requirement. The voltage produced by the PV array mainly depends on the light irradiance, wavelength of light, angle of incidence of irradiance on PV array, PV array material and area of PV array. The main challenges to photovoltaic array are photovoltaic material, cost, installation, maintenance and its efficiency. As the operation of PV array mainly depends on irradiance availability, hence in the absence of irradiance it becomes inoperable and the efficiency of PV array reduces considerably [1]. The PV array output voltage is variable in nature due to continuous variation in irradiance. A DC link capacitor is used as a filter and also as a coupling element. It improves the quality of voltage produced by PV array [2]. A system that contains energy storing element(s), filtering element(s), high frequency switch(s) and produces DC voltage after applying DC input DC voltage is called a DC-DC converter. Isolated and non-isolated are mainly two types of DC-DC converter based on the presence of an electrical isolating element e.g. transformer [3]. A usual DC-DC buck or boost converter does not possess the bidirectional power flow capability which is an important requirement for a battery charging and discharging purpose with a common DC-DC converter [4]. A DC-DC bidirectional power flow converter is obtained by interconnecting buck and boost converter in anti-parallel with each other. This configuration has a bidirectional power flow capability along with stepping up or stepping down to applied voltage. A bidirectional DC-DC buckboost converter has two switches one is responsible for buck operation and other is for boost operation [5]. When one switch operates in conduction mode then other remains in off mode. An arrangement to store the energy in some intermediate form like thermal, compressed air, electro-mechanical or any other form is known as Energy Storage System (ESS). BESS is a technology that stores energy in the form of electric charge. The BESS is superior to other ESS technologies because it does not contain any geographical constraints. Round trip efficiency, response time, ramp rate, capacity and power rating and depth of discharge are the main characteristics of a BESS. The BESSs are applicable for both commercial as well as residential purpose. We use BESS as commercial applications for load shifting, emergency backup and in renewable integration. Whereas in case of residential purpose, we use BESS for solar self-consumption, off grid applications and emergency backup. In both the cases the BESS improves the efficiency of PV array and reduces the cost of electric energy. The main types of BESS are lithium ion, lead acid, nickel based and flow batteries. Lithium ion batteries are more preferable over others due to their better characteristics. In this paper lithium ion battery is used for simulation purpose [6].

54 Bidirectional DC-DC Buck-Boost Converter …

683

A controller is a rule of procedure or mechanism that reduces the difference between desired value and actual value. Linear and non-linear controllers are two main types of controller. Proportional (P) controller, Proportional Integral (PI) controller and Proportional Integral Derivative (PID) controllers come in the category of linear controllers. Fuzzy Logic Controller, Sliding Mode Controller, and Passivity Based Controllers fall in the category of nonlinear controllers. The regulation of the bidirectional DC-DC converter is accomplished by using different voltage and current PI controllers. MOSFET/IGBT is preferred over other switches owing to high switching frequency and low switching losses. MOSFET consists of an anti-parallel diode and this diode can be used as a freewheeling diode when switch is off, hence MOSFET is used as switch in this paper.

54.2 System Modeling The model and layout of the proposed DC-DC buck boost converter with battery energy storage system and PV array is designed in MATLAB/Simulink as shown in Fig. 54.1.

54.2.1 PV System A photovoltaic array is created by joining many solar cells in series or parallel as per required voltage and current rating. Figure 54.2 shows the single diode model of the solar cell. The fundamental unit of a solar panel is a solar cell. Despite all the benefits conferred by the generation of energy through the utilization of PVs, the efficiency of energy conversion is presently low and also the initial price for its implementation is high. Hence it becomes necessary to operate PV module at maximum power point applying the suitable technique and achieving maximum efficiency in operation. It should be noted that there is a particular point pertaining to maximum power called the Maximum Power Point (MPP). PV cell parameters are described by the equation

Fig. 54.1 Block diagram of proposed system

684

K. K. Pandey et al.

Fig. 54.2 Single diode model of solar cell

[7]: 



V + I Rs I = IL − I0 exp nVT



 V + I Rs −1 − Rsh

(54.1)

where, V I0 I Rs IL Rsh ID n VT

Output voltage of PV array. Reverse saturation current of diode. Output current of PV array. Series resistance. Photo current, function of irradiation level. Shunt resistance. Diode current. Ideality factor of diode. Thermal voltage.

As the irradiance change, the MPP point shifts to new location. The VMP and IMP correspond to voltage andcurrent at maximum power point respectively [8].

54.2.2 Buck Converter The output electrical quantity of the PV array varies with change in irradiance and temperature. PV array output is regulated using buck converter at required voltage for load and at the same time directly applied for charging of BESS [9]. Value of parameters of buck converter is given in Table 54.1. The schematic connection of buck converter for regulation of PV array output voltage is shown in Fig. 54.3.

54 Bidirectional DC-DC Buck-Boost Converter … Table 54.1 Parameters of Buck converter

685

Parameter

Value

DC link capacitor

200 µF

Inductor, L

10mH

RC filter, R

6

RC filter, C

200 µF

Load resistor

6

Fig. 54.3 PV array regulated voltage using Buck converter

54.2.3 Bidirectional DC-DC Buck-Boost Converter The bidirectional DC-DC converter consists of two diodes; D1 and D2 connected in anti-parallel with two switches S1 and S2 respectively. It operates in two modes; buck and boost [10–12]. The circuit diagram of bidirectional DC-DC converter is shown in Fig. 54.4. The value of parameters of bidirectional DC-DC converter is given in Table 54.2. Buck mode: When switch S1 and diode D2are on and switch S2 and diode D2 are off, the bidirectional converter operates in buck mode.

Fig. 54.4 Bidirectional DC-DC converter

686 Table 54.2 Data of bidirectional DC-DC converter

K. K. Pandey et al. Parameter

Value

Load Side Resistor

0.001 

Load Side Capacitor

1000 µF

Battery Side Resistor

0.05 

Battery Side Capacitor

0.576 µF

Boost mode: When switch S2 and diode D1 are on and switch S1 and diode D2 are off, it operates in boost mode. The bidirectional converter is an interlink between PV array and battery. The power can flow in both directions i.e. from load to BESS and vice versa through bidirectional converter. When the irradiance available is sufficient to produce the required voltage for load then power flows from PV array to BESS and BESS charges simultaneously [13]. At this time, the bidirectional converter will operate in buck mode. When the irradiance available is unable to produce sufficient voltage required for load then the power flows from BESS to load and BESS discharges subsequently. At this state of time bidirectional converter operates in boost mode.

54.2.4 Battery Energy Storage System (BESS) BESSs store the energy in the form of electric charge. When battery will charge by the PV array then Percentage State of Charge (% SOC) of battery increases. When battery supplies power to load then it discharges. At this time %SOC of battery decreases.

54.3 Control Techniques Different PI controllers are used in this paper for switching control of switches with desired duty ratio. The output of the different PI controllers is applied to corresponding PWM generators, which will produce the required duty cycle or the corresponding switches. A separate PI controller is used for the switching of buck converter, which will regulate the output voltage of PV array at desired value. For the control purpose of bidirectional DC-DC buck-boost converter, three PI controllers are utilized namely voltage controller charge, voltage controller discharge and current controller respectively [14, 15].

54 Bidirectional DC-DC Buck-Boost Converter … Table 54.3 Data of PV Array connected Buck converter

687

Parameter

Value

DC link capacitor

200 µF

Inductor

10 mH

Load resistor

6

Load capacitor

200 µF

Proportional constant [P]

100

Integral constant [I]

1000

PWM generator switching frequency

5000 Hz

Sample time

5 µs

54.3.1 PV Array Buck Converter PI Controller As the irradiance varies with time, PV array output voltage fluctuates continuously. This fluctuation is removed by connecting a buck converter directly with PV array output voltage using a DC link capacitor. For this purpose, closed loop control of buck converter is achieved using a PI controller. PV array actual voltage and PV array required regulated voltage is taken as the inputs for the comparator and the error produced by the comparator is given to the PI controller. Further, the PI controller output is provided to PWM generator which produces the desired duty ratio and in this way the PV array regulated voltage is maintained at required value with minimum fluctuations. The different parameters and their values used for the combination of PV Array and buck converter are given in Table 54.3.

54.3.2 Voltage Control Charge PI Controller A voltage control charge PI controller is used to produce reference battery charging current. Reference charging voltage of battery is taken as 25.98 V (approx. 90% of fully charged voltage 27.93 V) and actual battery voltage is taken as comparator’s feedback. The error signal generated by the comparator is the input for the voltage control charge PI controller. The output of the voltage control charge PI controller is battery reference charging current. The battery charging current will be negative. This generated battery reference charging current is used to produce required pulses for different switches by using current control PI controller. The parameters of voltage control charge PI Controller are given in Table 54.4.

688 Table 54.4 Voltage control charge PI controller

Table 54.5 Voltage control discharge PI controller

K. K. Pandey et al. Parameter

Value

Proportional constant [P]

40

Integral constant [I]

2000

Output saturation upper limit

22

Parameter

Value

Proportional constant [P]

0.25

Integral constant [I]

50

Output saturation upper limit

22

Output saturation lower limit

0

54.3.3 Voltage Control Discharge PI Controller Voltage control discharge PI controller is used to produce discharging reference current of the battery (nearly about 16 A). The input to voltage control PI controller is given by the comparator. Required load voltage (48 V) and actual load voltage is taken as the two inputs of the comparator, where actual load voltage is taken as feedback voltage. The error generated by the comparator is input to voltage control PI controller. The parameters and their values used in voltage control discharge PI controller is given in Table 54.5.

54.3.4 Current Control PI Controller The battery reference charging current is nearly about −16 A and battery charging current is taken as feedback for the comparator of current controller. The error produced by the comparator will act as input for current control PI controller. The output produced by the current control PI controller is the input for DC-DC PWM generator. This will produce the required duty ratio for the switches of bidirectional DC-DC buck-boost converter. The voltage control discharge PI controller provides the battery discharging reference current (near about 16 A). This reference discharging current is compared with actual battery discharging current by comparator and error is produced as output. The produced error is given to current control PI controller which will produce the required duty ratio for the switches of bidirectional converter using DC-DC PWM generator. The parameters and their values used in current control PI controller are given in Table 54.6.

54 Bidirectional DC-DC Buck-Boost Converter … Table 54.6 Current control PI controller

689

Parameter

Value

Proportional constant

0.005

Integral constant

10

Output saturation upper limit

0.95

Output saturation lower limit

0

Initialization integrator

0.54

54.3.5 Logic Switch Control The two inputs of the logic switch are reference battery charging current and reference battery discharging current. The output of the logic switch is reference battery current. Threshold voltage of the logic switch is defined as 46 V. When the regulated output voltage of the combination of photovoltaic array and buck converter is greater than threshold voltage (46 V), the output of the logic switch is reference battery charging current and battery will be in charging mode. In this case, the load will be supplied by the combination of photovoltaic array and buck converter. When the output regulated voltage of the combination of photovoltaic array and buck converter is less than the threshold voltage (46 V), the output of the logic switch is reference battery discharging current and the battery will be in discharging mode and will supply the load [16].

54.4 Simulation and Results The main subsystems of this MATLAB/Simulink are PV Array connected buck converter, connection switch, bidirectional DC-DC converter, voltage control charge and discharge PI Controllers, current control PI Controller and logic switch. The PV Array used utilizes three parallel and three series connected modules per string. Irradiation of PV Array used is 1000 W/m2 for time 0–5 s and 200 W/m2 for 5–10 s. Temperature is kept constant at 25 ºC throughout the Simulation. The connections between different subsystems are shown in Fig. 54.5. In MATLAB/Simulink model, a logic switch is introduced, which will make the automatic control for charging mode and discharging mode of battery according to defined threshold voltage of the logic switch. During the day time (0–5 s) irradiance is 1000 W/m2 . This time the photovoltaic array is capable to supply the load. This time the reference battery current is reference battery charging current. This time the battery will be in charging mode. The battery will charge with a constant current (nearly about −16 A) and at a constant voltage (nearly about 25.98 V). This time the voltage across load is 48volts. During this time the battery percentage State of Charge increases continuously.

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Discrete 5e-06s.

gate

gate

+ve

Vout

Vout

Buck Pulse

+ve

Vload

-ve

Vload

SOC

m Ir +

25

Buck Converter

-ve

-ve

+

+ Connection Switch

DC link Capacitor

+

Group1 Irr

Bidirectional Converter

m _

IB

load T VB

Im

Scope PV

Voltage Control PI

Voltage Control PI 1

Logic Switch

Current Control PI

Scope

Fig. 54.5 MATLAB/Simulink model of proposed system

During (5–10 s) irradiance is 200 W/m2 , so, the photovoltaic array is not capable to supply the load. During this period the reference battery current is reference battery discharging current. The battery will discharge through load with a constant current (nearly 22 A) and at constant voltage (nearly about 25.91 V) maintaining constant load voltage of 48 V. During this time the battery percentage State of Charge (%SOC) decreases continuously. Figure 54.6 shows the variation of Irradiance with time. Figure 54.7 shows the variation of battery state of charge with time. Figure 54.8 shows the variation of battery current with time. Figure 54.9 shows the variation of battery voltage with time. Figure 54.10 shows the variation of load voltage with time.

Fig. 54.6 Irradiance versus time Curve

54 Bidirectional DC-DC Buck-Boost Converter … Fig. 54.7 State of charge versus time curve

Fig. 54.8 Battery current versus time curve

Fig. 54.9 Battery voltage versus time curve

Fig. 54.10 Load voltage versus time curve

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54.5 Conclusions The bidirectional power flow has been attained by the designed bidirectional DCDC buck-boost converter. PV Array power at maximum power point is nearly about 1500 W, which is achieved in MATLAB/Simulink. Maximum power point condition is also accomplished in MATLAB/Simulink; consequently, the efficiency of the PV Array has been increased. When there is a transient in irradiance at t = 5 s, transient in load voltage occurs only for few seconds and again the load voltage becomes 48 V as per requirement within the safe time. The produced load voltage is 47.5 to 48.5 V throughout the simulation, which is much closer to the required load voltage. Ripple produced in load voltage is nearly 2% which is very low.

References 1. Jain, M., Daniele, M., Jain, P.K.: A bidirectional DC-DC converter topology for low power application. IEEE Trans. Power Electron. 15(4), 595–606 (2000) 2. Kiran, B.R., Ezhilarasi, G.A.: Design and analysis of soft-switched Buck-Boost Converter for PV applications. In: Annual IEEE India Conference (INDICON) (2015) 3. Kumar, B.V., Member, S., Singh, R.K., Mahanty, R.: A modified non-isolated bidirectional DCDC converter for EV/HEV’s traction drive systems. In: IEEE Conference on Power Electronics, Drives and Energy Systems (2016) 4. Majumder, R., Ghosh, A., Ledwich, G., Zare, F.: Power management and power flow control with back-to-back converters in a utility connected microgrid. IEEE Trans. Power Syst. 25, 821–834 (2010) 5. Inoue, S., Akagi, H.: A bidirectional DC–DC converter for an energy storage system with galvanic isolation. IEEE Trans. Power Electron. 22(6), 2299–2306 (2007) 6. Levron, Y., Guerrero, J.M., Beck, Y.: Optimal power flow in microgrids with energy storage. IEEE Trans. Power Syst. 28(3), 3226–3234 (2013) 7. Tripathi, R.N., Singh, A., Badoni, M.: A MATLAB-simulink-based solar photovoltaic array (SPVA) module with MPPT. In: Proceedings—2013 International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, IEEEC2SPCA, vol. 1, pp. 1–6. IEEE (2013) 8. Choudhary, D., Saxena, A.R.: DC-DC Buck-converter for MPPT of PV system. Int. J. Emerg. Technol. Adv. Eng. 4(7) (2014) 9. Kumar, S., Sydulu, M.: Bidirectional DC-DC converter for integration of battery energy storage system with DC grid. Int. J. Ind. Electron. Electr. Eng. 2(3) (2014) 10. Arthika, E., Priya, G.S.: Modeling and simulation of interleaved Buck Boost Converter with PID Controller. In: IEEE International Conference on Intelligent System & Control (2015) 11. Abbas, G., Samad, M.A., Gu, J., Asad, M.U., Farooq, U.: Set-point tracking of a DC-DC boost converter through optimized PID controllers. In: IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (2016) 12. Elserougi, A.A., Massoud, A.M., Ahmed, S.: A unipolar/bipolar high-voltage pulse generator based on positive and negative Buck-Boost DC-DC Converters operating in discontinuous conduction mode. IEEE Trans. Ind. Electron. 64(7), 5368–5379 (2017) 13. Chen, J., Maksimovi´c, D., Erickson, R.W.: Analysis and design of a low-stress buck-boost converter in universal-input PFC applications. IEEE Trans. Power Electron. 21(2), 320–329 (2006)

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14. Pavlovic, T., Bjazi, T., Ban, Z.: Simplified averaged models of DC-DC power converters suit able for controller design and microgrid simulation. IEEE Trans. Power Electron. 28(7), 3266– 3275 (2013) 15. Zhang, G.Q., Dai, Y.J., Cui, J.M.: Design and realization of a bi-directional DC-DC converter in photovoltaic power system. In: International Forum on Energy, Environment and Sustainable Development (IFEESD) (2016) 16. Saravanan, S., Vidhya, R., Thangavel, S.: Online SOC estimation and Intelligent Battery Charger for solar PV System. Int. J. Eng. Adv. Technol. 2 (2013)

Chapter 55

Performance Comparison of DSTATCOM and PV Fed DSTATCOM for Mitigation of Power Quality Problems Gurpreet Singh, Yash Pal, and Anil Kumar Dahiya

Abstract In this paper design and performance comparison of two systems one is three phase DSTATCOM and the other is three phase PV fed DSTATCOM both feeding non linear load has been presented. It shows the benefits of using PV-DSTATCOM instead of DSTATCOM for mitigation of power quality problems. In addition to maintaining IEEE-519 standards for power quality PV-DSTATCOM also share the load with grid so that the energy can be save. PV-DSTATCOM performance has been analyzed for two different conditions one is during day time and other is during night time. Maximum power is also extracted from the PV using Maximum Power Point Technique (MPPT) in case when PV-DSTATCOM is subjected to variable irradiation. Both systems has been simulated in MATLAB Simulink environment for non linear load and their results are also compared.

55.1 Introduction As the area of power electronics technology is developing day by day, application of power electronic based devices are increasing exponentially at consumer level of power system. These power electronic based devices being energy saver and compact makes system more efficient but on the other hand they draw non sinusoidal current from the source. This non sinusoidal current injects harmonics in the distribution system which further effects on the performance of the other sensitive loads connected in the vicinity. In addition to this, non-linear behaviour of the load causes other power quality related problems such as poor power factor, reactive power burden, unbalanced currents and an excessive neutral current in polyphase systems due to unbalancing. To get over these problems, initially LC filters (passive filters) are used but due the bulkiness, resonance with system impedance and fixed compensation of G. Singh (B) · Y. Pal · A. K. Dahiya NIT Kurukshetra, Haryana, India e-mail: [email protected] G. Singh ABES Engineering College, Ghaziabad, Uttar Pradesh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_55

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passive filters, Custom Power Devices (CPDs) were designed. Various CPDs such as Distribution Static Compensator (DSTATCOM) [9], Dynamic Voltage Restorer (DVR) [14] and Unified Power Quality Conditioner (UPQC) [2] were used in literature for mitigating the PQ related issues caused by non linear loads in addition to that protect the sensitive loads from grid side PQ related problems. Shunt connected compensator i.e. DSTATCOM serves in current harmonics compensation and unbalanced currents, in addition with power factor improvement, which better addresses the PQ related problems than passive filters [7]. With the growing popularity of power generation from non-conventional energy resources, Photovoltaic (PV) is emerging as a subsitute to conventional energy source, there area of application is growing wider and wider with the time. PQ related problems mitigation through a PV-DSTATCOM is one such area of application [13]. A PV fed DSTATCOM can fed active and reactive power to the load when solar irradiations are available i.e. during day time or control the flow of reactive power in the duration of low or no irradiation i.e. during night time. Considering this advantage of PV fed DSTATCOM, this paper compare the performance of PV fed DSTATCOM with DSTATCOM in mitigating PQ issues related to current. This paper present the design of 3-phase DSTATCOM and 3-phase PV-DSTATCOM and their performance for mitigation of PQ related issues has been compared. Further the performance of PV-DSTATCOM has been analyzed for two different operating conditions one is, during day time and other is during night time. The load used here is a non-linear load. The paper is organized as: Sect. 55.2 explain the structure of the DSTATCOM and PV fed DSTATCOM. Section 55.3 discuss the control structure for both the systems. Section 55.4 analyzes and compare the performance of both the systems. Section 55.4 includes conclusion.

55.2 System Designing and Configuration The structure of three phase DSTATCOM and three phase PV-DSTATCOM are shown in Figs. 55.1 and 55.2 respectively. DSTATCOM is connected on load side in both the cases. In case of PV-DSTATCOM, PV array has been integrated with DC link of the DSTATCOM through boost converter. Integration of shunt compensator i.e. DSTATCOM with grid is done through the interfacing inductors. Ripple filters has been employed for filtering the harmonics induced due to converters switching.

55.2.1 Designing of DSTATCOM DSTATCOM design involves the design of the Voltage Source Converter (VSC) and its other peripheral components such as, interfacing inductors (L f ), ripple filter (Rf ), (Cf ), DC bus voltage level (Vdc ), and the DC capacitance (Cdc ) [15]. 3-phase, 415

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Fig. 55.1 System configuration of DSTATCOM

Fig. 55.2 System configuration of PV-DSTATCOM

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V power supply at 50 Hz, with a source impedance consisting of (Rs ) = 0.04  and (L s ) = 1 mH, has been considered. The parameters can be calculated as follows: Calculation of V dc The voltage of the DC Link can be calculated using (55.1) √ 2 2VLL Vdc = √ 3m

(55.1)

where, VLL and m indicates grid line voltage and modulation index respectively. Here grid line voltage is taken 415 V and the value of m is taken as unity. The minimum value of DC bus voltage so obtained as 677.7 V, which is adopted as 700 V. Calculation of C dc Value of Cdc can be obtained by Eq. (55.2) Cdc =

3KaVph Ish t 2 0.5 × (Vdc2 − Vdcm )

(55.2)

where, Vdcm is the minimum level of Vdc , a is overloading factor, Vph is the per phase voltage, t is the minimum time required by which DC bus voltage recovered, Ish is phase current of DSTATCOM and K is factor considering variation in energy during dynamics. The value so obtained of Cdc is 8.51 mF and it is adopted as 10 mF. Calculation of Lf Selection of L f depends on current ripple (Icrpp ), with switching frequency ( f sw ), the interfacing inductance can be calculated using (55.3) √

Lf =

3mVdc 12a f sw Icrpp

The calculated value of L f is 1.39 mH whic is selected as 1.5 mH.

Fig. 55.3 Control strategy for DSTATCOM

(55.3)

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55.2.2 Designing of PV-DSTATCOM The designing of PV-DSTATCOM is similar to DSTATCOM on its three-phase side, DC side design includes proper selection of PV array, selection of DC-DC converter, that may be boost, buck or buck-boostr, here boost converter is used and tracking of Maximum Power Point operation of solar PV array by using MPPT techniques. The rating of PV array is selected as half of the rated value of load, that means at full radiation the load capacity of PV is half of the rating of the full load. PV Array Selection The specifications of PV array model, Topsun TS-S257TA1, choosen from MATLAB Simulink Library are shown in Table 55.1. Design of Boost Converter Parameters of boost converter are calculated by considering output voltage of boost converter 700 V same as required for dc link of DSTATCOM [6]. The parameter values are obtained as Vo =

VMPPT 1− D

(55.4)

where, VMPPT is Voltage of PV array at MPP and Vo is output voltage. The value of duty cycle (D) obtained as 0.25. Output current of boost converter can be obtained by Eq. (55.5) (55.5) Io = IMPPT (1 − D) the value so obtained here is 25.29 A. The boost inductor (L b ) is selected by using (55.6): VMPPT × D (55.6) Lb ≥ r × IMPPT × f SW where, r is inductor current ripple ratio, which lies in the range of 0.3–0.5, Value of L b so calculated as 0.96 mH, and selected as 1 mH. The selection of PV capacitor (Cpv ) can be done as per (55.7) [8]:

Table 55.1 PV array specifications PV array specifications Maximum power Open circuit voltage Short circuit current Voltage at maximum power point Current at maximum power point Number of parallel strings Number of series connected modules per string

17.47 kW 642.6 V 35.8 A 518.33 V 33.72 A 4 17

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Cpv ≥

8×

2 f SW

D × L b × VMPPT

(55.7)

where, VMPPT is equals to 1% of input voltage to boost converter. The value of Cpv is calculated as 31.25 µF, and the value to be selected as 35 µF.

55.2.3 Control of DSTATCOM The selection of control strategy is very much essential for achieving desired compensation objective. Control strategy is the core of any Custom Power Device. It can be implemented in three steps; first one is Signal conditioning in which certain currents and voltages are sensed for accumulate accurate system information, second step is to estimate compensating signals in which compensating signals are induced on the basis on various control methods and third step is to generate firing signals for switching devices [3]. The various techniques for generation of compensating signals [12] which are used in literature are summarised in the Table 55.2 with a brief comparison of their performance which are implemented to Custom Power Devices [5, 11]. In this paper controlling of DSTATCOM and PV-DSTATCOM is done by same technique i.e. Synchronous Reference Frame (SRF) control technique [4] and the performance of both the systems has been compared for same technique. Block diagram for SRF control technique has been shown in Fig. 55.3. It consist of Phase Locked Loop (PLL), for extracting the phase angle of supply voltage which is further used to convert sensed source current into dq domain. The dq components of source current are filtered using low pass filters (LPFs). The output of these LPFs are used to obtain the reference value of current in abc frame after conversion from dq to abc. The DC link voltage is to be maintained at reference value for which proper setting of PI Controller parameters are required. The reference value of current is compared with sensed source current in hysteresis controller, output of which generates gate pulses for the DSTATCOM.

55.2.4 Control of PV-DSTATCOM Control strategy for PV-DSTATCOM consists of two stages, such as tracking of maximum power point (MPPT) of PV array and controlling of DSTATCOM. Out of these DSTATCOM controlling will remain same as in in previous case other stage MPPT is described here:

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Table 55.2 Comparison of different compensating signal generation techniques Compensation Level of Transient Settling time Conduct Applications scheme difficulty response time during during dynamically unbalanced Load load variations P-Q theory Synchronous reference frame (SRF) theory DC link voltage regulation method Fast fourier transform (FFT) Real discrete fourier transform (RDFT) Wavelet

Higher Higher

Poor Fair

High Medium

Poor Fair

3-Phase 3-Phase

Higher

Poor

High

Fair

3-Phase

Higher

Poor

High

Fair

Both single and 3-Phase

Higher

Poor

High

Good

Both single and 3-Phase

Higher

Poor

High

Good

Fuzzy logic controller (FLC) Artificial neural network (ANN) Adaptive neuro-fuzzy interface system (ANFIS) Genetic algorithm (GA)

Lower

Good

Low

Good

Both single and 3-Phase Both single and 3-Phase

Lower

Good

Low

Good

Both Single and 3-Phase

Lower

Good

Low

Good

Both single and 3-Phase

Lower

Fair

Low

Fair

Both single and 3-Phase

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Table 55.3 Comparison of MPPT techniques MPPT technique Convergence speed Hill-climbing/P and O IncCond Fuzzy logic control Neural network RCC Current sweep dP/dV or dP/dI feedback control IMPP and VMPP computation State-based MPPT Slide control

Implementation, complexity

Sensed parameters

Varies Varies Fast Fast Fast Slow Fast

Low Medium High High Low High Medium

Voltage, current Voltage, current Varies Varies Voltage, current Voltage, current Voltage, current

N/A

Medium

Fast Fast

High Medium

Irradiance, temperature Voltage, current Voltage, current

Tracking of MPPT of PV Array In the past many years, many techniques for achieving the Maximum Power Point have been designed [10]. A brief comparison of some of the techniques are summarised in Table 55.3 [1]. Among the various techniques listed in Table 55.3, the Perturb and Observe (P and O) method and the Incremental Conductance (InCond) algorithms are most commonly implemented techniques because of their simplicity, in this paper Perturb and Observe technique has been used.

55.3 Results and Discussion The performance of DSTATCOM and PV-DSTATCOM has been analyzed in MATLAB Simulink environment. Both systems are connected to same type of non linear load. Source resistance and inductance of grid is taken as 0.04  and 1 mH respectively. The performance of DSTATCOM is analyzed under steady state conditions, whereas the performance of PV fed DSTATCOM is analyzed under two conditions one is linearly varying solar irradiations from its maximum value of 1000 W/m2 to the zero taking the consideration of variable sunlight throughout the day and the other is of zero irradiation taking consideration of night time situation. The system performances are illustrate as three phase grid voltages (Vs ), three phase load voltages (VL ), DC link voltage (Vdc ), three phase source currents (Is ), three phase load currents (ILa , ILb , ILc ) and three phase shunt connected DSTATCOM currents (Isha , Ishb , Ishc ). In case of PV-DSTATCOM, compensator currents is shown as Ic other than these parameters PV voltgae (Vpv ), PV current (Ipv ) are illustrated during all operating conditions.

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Fig. 55.4 Performance of DSTATCOM under steady state

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Fig. 55.5 Power drawn from grid when only DSTATCOM is used

55.3.1 Performance of DSTATCOM The waveforms of different sensed signals of DSTATCOM when subjected to Non Linear Load is shown in Fig. 55.4. The DC link voltage is retained at reference value by PI controller and source voltage, load voltage and source current are sinusoidal and balanced. Power drawn from the grid and load demand are also shown in Fig. 55.5, which shows the value of power drawn from grid when only DSTATCOM is used. Also The harmonics in load current are compensated by DSTATCOM which maintaining the source current sinusoidal. The Total Harmonic Distortion (THD) of source current has been analyzed and it is seen that it is below 5%, meeting the requirements of IEEE-519 standards.

Fig. 55.6 Power flow during day time

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Fig. 55.7 Performance of PV-DSTATCOM during day time

55.3.2 Performance of PV-DSTATCOM Performance of PV-DSTATCOM has been analyzed for two conditions one is when PV is subjected to varying solar irradiation (day time) and other is when it is subjected to zero irradiation (night time).

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Fig. 55.8 Power flow during night time

During Day Time During this case PV is subjected to linearly varying irradiations varying from maximum value of 1000 W/m2 to minimum value of zero. The duration for which solar irradiation are available PV will share the load demand with the grid therefore decreasing the value of power drawn from the grid. It is seen that, for the same load which was used for DSTATCOM, also in PV-DSTATCOM DC link voltage is maintained at reference value. The source voltage, load voltage and source current are remained sinusoidal and balanced as shown in Fig. 55.7. In addition to this, power drawn from grid is reduced as compare to only DSTATCOM operation, because PV is also feeding the load which is shown in Fig. 55.6. It is seen that power required by the load is approximately 3 kW which is shared by grid and PV and as the irradiation decreases the power shared by PV also get decreasing meanwhile maintaining the power quality of grid current are maintained by maintaining THD below 5%. During Night Time During this case PV is considered at night time when there is zero irradiation. As the irradiation is zero the output power of PV is zero, hence the power drawn from the grid is equal to the power required by the load. Therefore under this mode the power demand of the load will only be supplied by grid as shown in Fig. 55.8. The waveforms of different sensed signals under this condition of PVDSTATCOM when subjected to non linear load is shown in Fig. 55.9. It is seen that, DC link voltage is also maintained at reference value. The source voltage, load voltage and source current are remained sinusoidal and balanced, whereas current drawn from the PV is zero. during this duration PV-DSTATCOM act as a DSTATCOM.

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Fig. 55.9 Performance of PV-DSTATCOM during night time

55.4 Conclusion Both systems has been developed in MATLAB simulink environment and their performance was compared for same load with same control strategy. It is seen that both the systems mitigates current harmonics induced by non linear load and maintains the THD level of source current well with in the limits of IEEE-519 standards as shown in Table 55.4. In case of PV-DSTATCOM, in addition to mitigation of harmonics, it

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Table 55.4 THD of the systems during different modes of operation Mode of operation Parameter Fundamental value (A) THD (%) Only DSTATCOM

Source current Load Current PV-DSTATCOM (day Source current time) Load current PV-DSTATCOM Source current (night time) Load current

61.39 61.2 46.72

2.76 29.24 4.02

61.43 62.38

29.17 2.86

61.28

29.21

is also sharing the load of grid during day time, thus saving the power at consumer end. The controlling of both the system has found to be simple and PV-DSTATCOM makes system a better solution for renewable energy integration.

References 1. Ashok Kumar, L., Sumathi, S., Surekha, P.: Solar PV and Wind Energy Conversion Systems: An Introduction to Theory, Modeling with MATLAB/SIMULINK, and the Role of Soft Computing Techniques. Springer (2015) 2. Devassy, S., Singh, B.: Design and performance analysis of three-phase solar pv integrated upqc. IEEE Trans. Ind. Appl. 54(1), 73–81 (2017) 3. Jain, S.: Control strategies of shunt active power filter. In: Modeling and Control of Power Electronics Converter System for Power Quality Improvements, pp. 31–84. Elsevier (2018) 4. Kesler, M., Ozdemir, E.: Synchronous-reference-frame-based control method for UPQC under unbalanced and distorted load conditions. IEEE Trans. Ind. Electron. 58(9), 3967–3975 (2010) 5. Kumar, R., Bansal, H.O.: Shunt active power filter: current status of control techniques and its integration to renewable energy sources. Sustain. Cities Soc. 42, 574–592 (2018) 6. Kumar, S., Verma, A.K., Hussain, I., Singh, B.: Performance of grid interfaced solar pv system under variable solar intensity. In: 2014 IEEE 6th India International Conference on Power Electronics (IICPE), pp. 1–6. IEEE (2014) 7. Mishra, S., Ray, P.K.: Improvement of power quality using photovoltaic fed shunt power quality conditioner. Int. J. Power Electron. 7(3–4), 261–275 (2015) 8. Motahhir, S., El Ghzizal, A., Sebti, S., Derouich, A.: Modeling of photovoltaic system with modified incremental conductance algorithm for fast changes of irradiance. Int. J. Photoenergy 2018, (2018) 9. Myneni, H., Ganjikunta, S.K., Dharmavarapu, S.: Power quality enhancement by hybrid dstatcom with improved performance in distribution system. Int. Trans. Electr. Energ. Syst. 30(1), e12,153 (2020) 10. Nahak, T., Pal, Y.: Comparison between conventional, and advance maximum power point tracking techniques for photovoltaic power system. In: 2016 IEEE 7th Power India International Conference (PIICON), pp. 1–5 (2016) 11. Pal, Y., Swarup, A., Singh, B.: A review of compensating type custom power devices for power quality improvement. In: 2008 Joint International Conference on Power System Technology and IEEE Power India Conference, pp. 1–8 (2008)

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12. Pal, Y., Swarup, A., Singh, B.: Comparison of three control algorithms for single-phase upqc. In: 2011 International Conference on Energy, Automation and Signal, pp. 1–5. IEEE (2011) 13. Patel, H., Agarwal, V.: Investigations into the performance of photovoltaics-based active filter configurations and their control schemes under uniform and non-uniform radiation conditions. IET Renew. Power Gener. 4(1), 12–22 (2010) 14. Rauf, A.M., Khadkikar, V.: An enhanced voltage sag compensation scheme for dynamic voltage restorer. IEEE Trans. Ind. Electron. 62(5), 2683–2692 (2014) 15. Singh, B., Chandra, A., Al-Haddad, K.: Power Quality: Problems and Mitigation Techniques. Wiley (2014)

Chapter 56

Numerical Simulation of Blocked Blood Vessel for Early Diagnosis of Coronary Artery Disease Sandip Saha, Pankaj Biswas, and Sujit Nath

Abstract In this work, we present a numerical simulation of blocked blood vessel in a two-dimensional rectangular channel along with two different sizes of blood vessel diameters (4 and 1 mm). It is assumed that the blood flow is laminar, compressible, viscous, unsteady, non-newtonian, and the arterial wall be elastic. Simple algorithm was employed to solve the governing momentum and mass equations. More precisely, the present study analyzed the blood flow in presence of blocked blood vessel which caused by plaque deposited by fat and cholesterol accumulation on the side walls of the blood vessels. For different size of blockages, the current work investigates the blood velocity profile and pressure profile at eight different locations. Furthermore, it also presents the plaque’s deposition from the initial stage to complete arterial blockage [6–88%] and its impact on blood flow. It is found that, the pressure is more noticeable at 4 mm artery rather than 1 mm artery. The numerical simulations could be used to better understand the blockages and to detect coronary artery disease at a very early stage, which ignores the angiography.

56.1 Introduction Cardiovascular system is a complex network which allows blood to circulate and transport nutrients, oxygen, etc., to the body and more precisely it helps in fighting diseases. Flow conditions of the blood vessel are closely related to many cardiovascular diseases, such as stenosis, aneurysm, occlusion, and atherosclerosis and may affect the heart and its various organs [1, 2]. The third leading cause of death in the S. Saha (B) · P. Biswas Department of Mathematics, NIT Silchar, Silchar 788010, India e-mail: [email protected] P. Biswas e-mail: [email protected] S. Nath Department of Mechanical Engineering, NIT Silchar, Silchar 788010, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_56

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Western world is heart disease [3]. Coronary arterial disease is one of the major and common types of arterial disease [4, 5], which is characterized by plaques [6–8]. To understand the vascular function, at first it is necessary to know the behavior and material properties of the blood vessels [9–11]. Without a sufficient amount of blood, heart is thirsty for oxygen, which causes chest pain. When the artery is fully blocked, it leads to a heart attack [12, 13]. To resolve this issue, detailed analysis of disordered flow pat patterns are essential for the diagnosis of localized cardiovascular disease in its premature stages which ignores surgery. Fluid dynamic characteristics of blood flow in arteries with variable cross sections have been played a major role to diagnose many cardiovascular disorders [14– 16]. Mathematically it is very helpful to analyze blood flow in confined arteries. In arteries with large diameter blood is treated as a Newtonian fluid with high shear rates but in small arteries blood behaves like a non-Newtonian fluid with low shear rates [17–19]. Numerical modeling is used for a wide range of phenomena and has been demonstrated to know the fact of cardiac fluid circulation to diagnose arterial diseases [20, 21], evaluate healthy and diseased blood vessels hemodynamic [22], and optimize various vascular medical devices [23, 24]. Numerical simulations and computer modeling provide quick, easy, and reliable ways of investigating cardiovascular phenomena. During the last few decades, a wide number of researchers have been used computational technique to simulate and analyze blood flow and to understand the relationship between hemodynamic and vascular diseases [25]. To interact the relation between blood flow and arterial walls, Torii et al. [26] numerically studied the stabilized space-time method and stated that numerical simulation is useful to know the hemodynamic of cardiovascular diseases. Using finite element method, Crosetto et al. [27] numerically studied the temporal evolution and mean velocity distribution of wall shear stress. Moreover, they present 3D simulations on several heartbeats using a physiological geometry. In this work, coronary artery is treated as two-dimensional rectangular channel. The current research analyzes the velocity magnitude and contour pressure on artery. Fluent software has been employed to model the geometry of the artery and the desired simulations. This present work is to carry out the scenarios between a healthy artery (6%) as well as disordered artery (88%) due to plaque at eight different locations (inlet, center, outlet, [T1–T5]) for the blood vessels with diameter 4 and 1 mm. Stress profile was also analyzed to validate the model against literature. This numerical simulation model is helpful to analyze the potential of coronary artery disease. Moreover, it simulates the growth of plaque and investigates the blood flow patterns which are simpler and faster for the detection of blockage. Consequently, heart disorders can be solved easily. Paper is organized in following sections: Sect. 56.2 presents the flow geometry in three different locations, while the governing equations are highlighted in Sect. 56.3, grid test on Sect. 56.4, results, and discussions are described in Sect. 56.5.

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Fig. 56.1 50% blockage at center (a), inlet (b), outlet (c), and T5 (g) for vessel diameter 4 mm and 50% blockage at center (d), inlet (e), outlet (f) for vessel diameter 1 mm

56.2 Flow Geometry Flow geometry in a rectangular channel along with different size of blockages at four different locations (center, inlet, outlet, and T5) of blood vessel diameters 4 and 1 mm which are depicted in Fig. 56.1a–g. Flow geometry is divided into three domains, such as inlet section where the blood flows, elastic artery wall, and outlet. Computational domain is divided into eight sections, such as inlet, T1, T2, T3, center, T4, T5, and outlet. Variations of blockages are 6, 12, 24, 36, 48, 72, 84, and 88% of arteries with diameters 4 mm and 1 mm with length 20 mm. Simulations in arteries with diameters 4 mm and 1 mm and without any plaque have been performed and compared the same with natural arteries. T1, T2, T3 have been taken in between inlet and center and T4, T5 lies in between center and outlet which are measured form inlet section as shown in Table 56.1.

56.3 Mathematical Formulation A two-dimensional laminar flow is considered for simulation purpose in a domain  over a time interval of interest (0, T ). Blood flow dynamics was described in a microchannel which is governed by the following momentum and mass equations:

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Table 56.1 Different position measured from inlet section Different position

Distance from inlet (mm)

T1

5

T2

7

T3

9.5

T4

13

T5

16

Table 56.2 Material characteristics for simulation Parameters Density

Blood

(kg/m3 )

Dynamic viscocity

Artery

Fat/cholesterol deposit

1060

1060

1050

0.005 Pa-s

–

–

Poissons ratio

–

0.49

0.11

Young’s modulus (MPa)

–

2

20

ρ

∂v + (v.∇)v = ∇.σ in  × (0, T ) ∂t ∇.v = 0 in  × (0, T )

(56.1) (56.2)

where ρ, v and σ denoted as the density of blood, velocity, and stress tensor, respectively. In 2007, Torii et al. [26] mathematically present the motion of the vessel wall which is governed by Eq. 56.3. ρw

∂ 2 vw − f w = ∇.σw in  × (0, T ) ∂t 2

(56.3)

whereas ρ w , vw , f w and σ w noted as arterial wall density, displacement vector, body force acting on arterial wall, and Cauchy stress tensor, respectively. The behavior of arterial wall is nonlinear but in this study we assume linear elastic material. Material characteristics used for numerical simulations are shown in Table 56.2.

56.3.1 Boundary Conditions To solve the governing equations, the following initial and boundary conditions are required. • Inlet boundary condition: at inlet section, nominal velocity of blood flow is set to 0.169 m/s. • Outlet boundary condition: ∂∂vx = 0 on ∂  × (0, T ).

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Fig. 56.2 Number of elements versus average pressure coefficient along upper wall

• Wall boundary conditions: No slip condition: vx = 0, no penetration boundary condition: vy = 0. • Initial condition: v = v0 on  × 0.

56.4 Grid Test Computational domain is discretized using uniform mesh with element size 30 µm and approximately 78,041 no. of cells. Grid test has been performed to know the effect of mesh size taking the number of elements along x axis and average velocity along y axis which is shown in Fig. 56.2.

56.5 Results and Discussions Due to the blockage, heart encounters pressure. Pressure increases as the size of blockage increases. Figure 56.3a–i shows the variations of velocity streamlines and contour pressure of blocked blood vessels with diameter 4 mm along with the variation of blockages [6–88%] at center position. At two different positions (inlet, and outlet), Fig. 56.3d, h presents the velocity streamlines along with 88% blockage of vessel diameter 4 mm. Moreover, in center portion, Fig. 56.3i shows the pressure contour along with 88% blockage of vessel diameter 1 mm. It is observed that as the size of blockage increases the stress and velocity also increase. This shows a variation which occurs very early due to vascular wall deposition. Consequently, a significant backpressure on the heart can be observed which is shown in Fig. 56.3d, h. Backpressure is more pronounced when the blockage present in inlet portion as compared to the outlet as shown in Fig. 56.3d, h. Backpressure is a major factor in the occurrence of any abrupt vascular disease. Figures 56.4a–h and 59.5a–h state a

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Fig. 56.3 Velocity streamlines and pressure contour at center of 6% (a, e), 48% (b, f), and 88% (c) blockage, 88% blockage at inlet (d) for blood vessel diameter 4 mm, and blockage at outlet (h) at center for blood vessel diameter 1 mm

clear relationship between velocity profile and stress profile for 4 and 1 mm artery with various sizes of blockages at different locations. In case of 4 mm artery along with 6, 12, 48, and 84% blockages, it is observed that the velocity magnitude is more pronounced at outlet section as compared to center and inlet section which are shown in Fig. 56.4a–d, h. However, more stress produced in center portion for the blockage size 6 and 84% (Fig. 56.4e–f) as compared to inlet and outlet. But for 12% blockage (Fig. 56.4g) stress become more pronounced on outlet section while the stress become more pronounced at center for blockage size 6 24, 48% (Fig. 56.5e–g) of blood vessel diameter 1 mm. Moreover, flow becomes unstable and reverse flow arises in inlet section as shown in Fig. 56.5d. Further analyzing the T1, T2, and T3 sections, flow becomes asymmetric at T3 section for 88% (Fig. 56.6a) blockage along with 4 mm artery. It is also observed that pressure rises on T1 rather than T2, T3 sections for the blockage size 72% for vessel diameter 4 mm as shown in Fig. 56.5h

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Fig. 56.4 Velocity profiles of 6% (a), 84% (b), 88% (c), 48% (d), 12% (h) and stress profiles for 6% (e), 84% (f), 88% (g) blockage at different location

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Fig. 56.5 Stress profiles at different locations with 6% (a), 12% (b), 24% (c), 36% (d) and 72% (h) blockage of vessel diameter 4 mm and 6% (e), 24% (f), 48% (g) blockage of vessel diameter 1 mm

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Fig. 56.6 Velocity profile with 88% blockage (a), stress on artery wall versus blood flow velocity (b), pressure drop versus various blockage size for vessel diameter = 4 mm (c) and 1 mm (d)

but more stress appeared on T3 section for 6, 12, 24, 36% blockage size rather than the T1 and T2 which are shown in Fig. 56.5a–d. Figure 6b shows the increase in pressure on artery wall (vessel diameter = 4 mm) as the blockage size increases from (25–85%) and for 85% blockage the pressure reaches to 2.53 MPa which is more than normal Young’s modulus of artery (Table 56.2). This situation/condition is called ischemic situation/condition. It can be observed from Fig. 56.6c that for flow velocity v ≥ 24.9 cm/s the wall pressure exceeds 2 Mpa, which is the elastic limit of artery (Table 56.2). However, in 1 mm artery diameter, center position versus pressure on artery wall has been drawn in Fig. 56.6d. For 85% blockage, the pressure has been reached at 0.020 MPa which is less than 2 MPa as compared to 4 mm artery.

56.6 Conclusion In this study, we have investigated different sizes of blockages (6–88%) for 4 mm and 1 mm arteries to predict early diagnosis of coronary artery disease. In this work, different size of blockages has been taken at eight distinct locations to analyze the flow patterns, velocity, and stress profile. The concluding remarks are prescribed as below:

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• It is observed that as blockage size increases the pressure on arterial wall also increases for both 4 and 1 mm arteries. It is found that the pressure is more noticeable at 4 mm artery rather than 1 mm artery. • In case of 4 mm artery along with 85% blockage, pressure reaches to 2.53 MPa which is more than normal Young’s modulus of artery. • Moreover in 50% blockage along with 4 mm artery it can be observed that for flow velocity v ≥ 24.9 cm/s the wall pressure exceeds 2 Mpa, which is the elastic limit of artery. • However in 1 mm artery at 85% blockage pressure has been reached at 0.020 MPa which is less than 2 MPa as compared to 4 mm artery. • Furthermore, it is also concluded that when the blockage formed at inlet section backpressure is generated on cardiac muscle but it decreases as the distance increases from the inlet section. Thus, the present work can help to predict early diagnosis of heart malfunctioning and thus surgery or angiography test can be avoided.

References 1. Mendis, S., Puska, P., Norrving, B.: Global atlas on cardiovascular disease prevention and control, 1st ed. World Health Organization in collaboration with the World Heart Federation and the World Stroke Organization, Geneva (2011) 2. Mancini, B.G., Dahlof, B., Diez, J.: Surrogate markers for cardiovascular disease: structural markers. Circulation 109, 22–30 (2004) 3. Kroon, M., Holzapfel, G.A.: Modeling of secular aneurysm growth in a human middle cerebral artery. J. Biomech. Eng. 130(5), 1–10 (2008) 4. Chakrabarty, S., Mandal, K.P.: A non-linear two- dimensional model of blood flow in an overlapping arterial stenosis subjected to body acceleration. Math. Comp. Model 24, 43–58 (1996) 5. Salonen, T.J., Salonen, R.: Ultrasonographically assessed carotid morphology and the risk of coronary heart disease. Arterioscler. Thromb. 11, 1245–1249 (1991) 6. Johnson, A.G., Borovetz, S.H., Anderson, L.J.: A model of pulsatile flow in uniform deformable vessel. J. Bio mech 25, 91–100 (1992) 7. Nwoye, E.O., Arogundade, C.T., Fidelis, P.O.: Modelling and simulation of myocardial infarction in the human cardiovascular system. Niger. J. Technol. 38, 258–266 (2019) 8. Mirzaee, R.M., Ghasemalizadeh, O., Firoozabadi, B.: Simulating of human cardiovascular system and blood vessel obstruction using lumped method. World Acad. Sci. Eng. Technol. 41, 366–374 (2008) 9. Kamalanand, K., Srinivasan, S.: Modelling and analysis of normal and atherosclerotic blood vessel mechanics using 3d finite element models. Intact J. Soft Comput 2, 261–264 (2011) 10. Vito, R.P., Dixon, S.A.: Blood vessel constitutive models-1995-2002. Annu. Rev. Biomed. Eng. 5, 413–439 (2013) 11. Taylor, C.A., Figueroa, C.A.: Patient-specific model of cardiovascular mechanics. Annu. Rev. Biomed. Eng. 11, 109–134 (2009) 12. Bastien, M., Poirier, P., Lemieux, I., Desprs, J.: Overview of epidemiology and contribution of obesity to cardiovascular disease. Prog. Cardiovasc. Dis. 56, 369–381 (2014) 13. Midya, C., Layek, C.G., Gupta, S.A., Roy, T.M.: Mageneto hydrodynamics viscous flow separation in a channel with constrictions. AMSE J. Fluid Eng 125, 952–962 (2009)

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14. Sankar, S.D.: Two-fluid nonlinear mathematical model for pulsatile blood flow through stenosed arteries. Bull. Malays. Math. Sci. Soc 35(2), 487–495 (2012) 15. Shukla, J.B., Parihar, S.R., Rao, P.B.: Effects of stenosis on non-Newtonian flow of the blood in an artery. Bull. Math. Biol. 42, 283–294 (1980) 16. Chen, J., Lu, Y.X.: Numerical investigation of the Non-newtonian pulsatile blood flow in a bifurcation model with a non-planar branch. J. Bio. Mech. 39, 818–832 (2006) 17. Chuchard, P., Puapansawat, T., Siriapisith, T., Wu, H.Y., Wiwatanapataphee, B.: Curtin University Numerical simulation of blood flow through the system of coronary arteries with diseased left anterior descending. Int. J. Math. Comput. Simul. 5, 334–341 (2011) 18. Kokalari, I., Karaja, T., Guerrisi, M.: Review on lumped parameter method for modeling the blood flow in systemic arteries. J. Bio. Sci. Eng. 6(1), 92–99 (2013) 19. Haldar, K.: Oscillatory flow of blood in a stenosis artery. Bull. Math. Biol. 49, 279–287 (1987) 20. Mekheimer, K.S., Kot, E.M.A.: The micro polar fluid model for blood flow through a tapered artery with a stenosis. Acta. Mech. Sin. 24, 637–644 (2008) 21. Maasrani, M.: Patients specific simulations of coronary fluxes in case of three-vessel disease. J. Biomed. Sci. Eng. 4(1), 34–45 (2011) 22. Sankar, D.S., Lee, U., Ismail, A.I.M.: Mathematical analysis for MHD flow of blood in constricted arteries. Int. J. Nonlinear Sci. Numer. Simul. 14, 195–204 (2013) 23. Leo, M.C., Jayashree, S., Darren, R.G., Marsel, M.M., Todd, B.P.: Hemodynamic response changes in cerebrovascular disease: Implications for functional MR imaging. AJNR Am. J. Neuroradiol. 23, 1222–1228 (2002) 24. Dirk, J.D., Akos, K., Daphne, M., John, M.C.J.: Regulation of coronary blood flow in health and ischemic heart disease. Prog. Cardiovasc. Dis. 57, 409–422 (2015) 25. Xenosa, M., Girdhara, G., Alemua, Y., Jesty, J., Slepian, M., Einava, S., Bluestein, D.: Device thrombogenicity emulator (DTE)-design optimization methodology for cardiovascular devices: a study in two bileaflet MHV designs. J. Biomech. 43, 2400–2409 (2010) 26. Torii, R., Oshima, M., Kobayashi, T., Takagi, K., Tezduyard, T.E.: Computer modeling of cardiovascular fluid structure interactions with the deforming-spatialdomain/stabilized spacetime formulation. Comput. Methods Appl. Mech. Eng. 195, 1885–1895 (2006) 27. Crosettoa, P., Reymond, P., Deparis, S., Kontaxakis, D., Stergiopulos, N., Quarteroni, A.: Fluid structure interaction simulation of aortic blood flow. Comput. Fluids 43, 46–57 (2011)

Chapter 57

Green Supplier Selection: An Empirical Investigation Sudipta Ghosh , Chiranjib Bhowmik , Madhab Chandra Mandal , and Amitava Ray

Abstract The prime objective of this research is to evaluate green supply chain management (GSCM) performance of green suppliers in eastern India-based manufacturing sector. Fifteen influential parameters that contribute significantly in GSCM, have been identified and three distinct types of manufacturing industries have been considered for performance analysis. this research proposes an integrated multivariate-multi-criteria decision-making (MCDM) approach where item analysis is used to check the consistency and interrelation of the data, principal component analysis (PCA) is used to assess the importance weights of criteria and simple additive weighting (SAW) method is used to prioritize, rank and benchmark the supplier organizations accordingly. Results show that ‘Collaboration with suppliers for green purchasing’ is the most influential parameter. Also, PCA shows that ‘Total CO2 emission’ and ‘Investment in CSR’ have large positive association with eigen values. Study also finds that constraints like ‘Lack of knowledge and awareness among workers’, ‘Lack of top management commitment’, ‘Reluctance of suppliers towards green innovation’ etc. are responsible for poor performance of organizations. Strategies of benchmarked industry and improvement measures have also been explored. This research portrays a unique roadmap which could help decision-makers to select optimal supplier strategically in manufacturing enterprises.

57.1 Introduction Manufacturing organizations play a vital role in the economic development and industrial progress of a nation [1]. Basically, manufacturing industries generate and discharge a huge amount of offensive pollutants and potentially hazardous waste, which have detrimental impact on both ecology and public health [2]. Due to S. Ghosh · M. C. Mandal · A. Ray Jalpaiguri Government Engineering College, Jalpaiguri, West Bengal 735102, India C. Bhowmik (B) Faculty of Engineering and Technology, PIET, Parul University, Vadodara 391760, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_57

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augmenting cognizance over global environmental issues (like: greenhouse effect, climate change, global warming, ozone-layer deterioration, rapid depletion of natural resources, etc.), it becomes imperative for manufacturing enterprises to adopt green practices throughout their supply chain and business activities [3]. Manufacturing organizations are confronting dual pressure at a time, i.e., to reduce the adverse impact on environment and to sustain its market with long-term financial benefit [4]. Due to several factors like; government legislation for environmental protection, pressures from both market and stakeholders, growing mass awareness about sustainable development, etc., manufacturing companies become bound to implement green principles into their supply chain policies [5]. For this purpose, sustainability-focused and environmentally-conscious sourcing is very essential for manufacturing companies [6, 7]. Selection of supplier is considered as one of the most crucial factors for organization’s strategy and policy makers, because selecting inappropriate supplier may lead to severe loss in organization’s overall performance. On the other hand, selecting green suppliers results in both performance improvement and competitive advantage [8]. Green supplier selection has a significant influence on organization’s overall sustainability performance [9–11]. Thus, sustainable sourcing becomes an indisputable variable in corporate success, and green supplier selection has become a unique option for decision-making in various industrial segments [12–14]. Due to escalating degradation of the environment, green supply chain management (GSCM) is acquiring research interest among researchers, scientists, academician, and industry practitioners [15]. GSCM integrates green and sustainable thinking into each stages of supply network (i.e., procurement, design, manufacturing, distribution, reverse logistics, etc.) ranging from suppliers to producer to consumer [16, 17]. GSCM enables enterprises to enhance financial performance whilst lowering impact on global environment [18]. GSCM is extensively uttered by several researchers during last few decades and a rich literature is available. The literature review reveals that (i) several researches have addressed issues related to GSCM and green supplier selection in developed countries [19–21], (ii) there is still a lack of study related to GSCM in the context of developing countries [22], (iii) most of the researchers deployed various method to select suppliers under sustainability perspective by using various decision-making approaches among which multi-criteria decision-making (MCDM) is popular (iv) most of the researchers have examined the effect of GSCM practices on environmental performance, economic performance and operational performance individually but very little studies are there which put emphasis on all the three dimensions of sustainability (environmental, operational and economic) simultaneously. (v) whatever GSCM performance frameworks have been employed, are bit difficult to understand and adopt, hence, a comprehensive and relatively less complicated method needs to be developed for GSCM performance measurement. The accost the aforesaid research gaps, this paper aims in evaluating green suppliers in manufacturing units and benchmark in the order of their GSCM performance. This research takes into consideration, three leading manufacturing companies in India which are striving for achieving sustainability in business by incorporating GSCM practices into their product, process, and services. The rest of the paper is depicted as follows: Sect. 2 narrates research design and describes the proposed

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research methodology, Sect. 3 explicates the expediency of the proposed research framework through a real-life supplier selection problem, Sect. 4 analyzes the results and discusses the outcomes. Section 5 draws conclusion by highlighting limitations and delineating directions for future research.

57.2 Research Design This research aims to the evaluation of green suppliers in Indian manufacturing sectors based on an integrated statistical-MCDM methodology. The flowchart of the research framework is shown in Fig. 57.1. First, literature review has been carried out and commonly used attributes for GSCM performance measurement have been identified. Then, few influential parameters are selected that have never been exercised by any researcher. Total 15 criteria (Table 57.1) are selected finally which seem to be influential and contributing to GSCM performance measurement. Three prominent organizations from three different manufacturing segments in eastern India have been considered for performance evaluation. The reason behind selecting these organizations is the distinction in the nature of supply chain characteristics. They are termed as ‘Supplier A’, ‘Supplier B’, and ‘Supplier C’, respectively. Supplier A is a reputed steel industry and it supplies quality steel for construction, furniture making, etc. Supplier B is a frontier and leading car manufacturing company and Supplier C is a food processing company which produces mainly high-quality, healthy, and refreshing energy beverages. All the three organizations are esteemed manufacturers in various regions in India, having world-wide market. They are striving to achieve sustainability goals by implementing GSCM practices. All of them are accredited to ISO 14001 certification. They are investing a big amount in corporate social responsibility (CSR), R&D and environmental protection. Usually, they collaborate with supplier and cooperate with customer for green innovation and fruitful adoption of GSCM. The companies utilize a large portion of energy form alternative sources like renewable energy. Three organizations have started several initiatives to recycle wastes and utilize resources effectively. They are committed to reduce harmful impact of industrial emissions on nature, by applying preventive measures. A questionnaire has been formed to collect relevant data and information from the selected organizations. The questionnaire comprises various greenery issues. The questionnaire contains three sections where the first section includes the queries regarding general profile of the respondents and organizations. The second section carries questions regarding important information and particulars of the corresponding organizations. In the last section, respondent requires to rate his/her preference over criteria. Surveys and industry visits have been conducted with prior permissions from each organization. The industry experts are selected from strategic, tactical, and operational level stakeholders of each organization. The selected employees of each organization are highly skilled personnel in respective fields having more than 20 years of corporate experience. 5 members form strategic level, 5 members form tactical level

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Table 57.1 Criteria considered for GSCM performance evaluation Parameters

Notations

Nature

Dimension

Use of energy-efficient technologies

c1

Benefit

Operational

Total energy consumption

c2

Cost

Economical

Effective waste management

c3

Benefit

Environmental

Frequency of environmental accidents

c4

Cost

Environmental

Quality

c5

Benefit

Operational

Reuse of material

c5

Benefit

Operational

Design for proper utilization of resources

c7

Benefit

Operational

Total CO2 emission

c8

Cost

Environmental

Reduce scrap material

c9

Benefit

Operational

Internal investment recovery

c10

Benefit

Economical

Generation of hazardous waste

c11

Cost

Environmental

Use of eco-friendly packaging material

c12

Benefit

Environmental

Top management commitment towards GSCM

c13

Benefit

Environmental

Collaboration with supplier for green purchasing

c14

Benefit

Environmental

Investment in CSR

c15

Benefit

Economical

and 10 members form operational level have been considered for this case. Communication with concerned experts was made through e-mails, telephones, and site visits. During survey, the questionnaire was placed to various managerial representatives to collect their feedback, opinion, and suggestions regarding various points mentioned in the questionnaire. To make the survey free form personal bias, interview with different persons are arranged separately and responses received form each respondent are checked very carefully. Preference ratings for each criterion are measured against a 9-point linguistic scale where numerical rating of 1 means less significant and 9 means extremely significant. As data are collected from 20 members of each members of each supplier organization separately, therefore, for a single criterion, there are 20 responses having variations within it. Therefore, the mean values of those responses are taken for final assessment. Next, a multivariate analysis coupled with MCDM has been used to simulate the data. Item analysis is used to assess the internal reliability and consistency of the data statistically by finding Cronbach’s alpha value. According to the theory of item analysis, if Cronbach’s alpha value is less than 0.75, then the dataset requires further revision or it may be assumed that discrepancy or vagueness is present within the data. Once, the data is found to be consistent, then it fits for further simulation. Then principal component analysis (PCA) is used to identify most influential criteria having the maximum variation in the data. PCA is also used to determine the criteria weights. If the cumulative proportion of the variables is greater than 0.50, it can be concluded that the first principal components are responsible for the maximum proportion of  the total variance. Data is normalized ri j or converted into dimensionless numbers using the following two equations

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For non-beneficial or cost criteria : ri j = For beneficial criteria: ri j =

di j dimax j

dimin j di j

(1) (2)

For, i = 1, 2, 3, . . . , m& j = 1, 2, 3, . . . , n, where, di j implies the performance measure of ith alternative on jth criteria. If the criteria weights (Wi j ) are previously known, then we can directly go for simple additive weighting (SAW) method otherwise, weights need to be determined by PCA method. Then, weighted nnormalized Wi = 1. decision matrix (Ci j ) is obtained using formula:Ci j = Wi j × Ri j , i=1 The performancescore (Pi ) for each alternative is calculated using the following m formula: Pi = j=1 j=1 C i j , i = 1, 2, 3, . . . , n. Hence, prioritizing, ranking, and benchmarking can be done according to the performance scores.  the most  And n . optimal alternative can be chosen using formula: O ASAW = max Pi |i=1

57.3 Application The proposed research model is validated through a real-life application of green supplier selection in manufacturing organizations of eastern-India. The data are simulated using TIBCO STATISTICA software. The obtained results and their interpretations are expressed in the following tables and stanzas.

57.3.1 Item Analysis Table 57.2 shows the omitted item statistics which can be used to examine the consistency of the data by comparing alpha values and its variability when items are omitted sequentially. In this item analysis, the alpha value is 0.8549 which is greater than 0.75. This implies the data are highly interrelated. Therefore, according to the condition, the dataset can be considered to be consistent for making strong decision. Omitted item statistics is also used to determine whether removing an item from the dataset improves the consistency. The ‘Adjusted total mean’ is used to observe how the magnitude of the total mean changes when an item is removed from the statistics. In the result (Table 57.2) the adjusted total mean for first item is 92.250 which implies that when first item is removed from the analysis then the sum of the mean value for rest item is 92.250. Similarly, ‘Adjusted total standard deviation’ and ‘Adjusted total correlation’ represent adjusted total SD and adjusted total correlation, respectively, when one of the items is eliminated. From the above table, it can be seen that removing an item results in no significant change in the alpha value. For example, if the first item is omitted then the alpha value becomes 0.8572 which is very close to the current alpha value (i.e., 0.8549). Similarly, if other items are

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Table 57.2 Omitted item statistics Omitted variable

Adjusted total mean

Adjusted total standard deviation

Item-adjusted total correlation

Cronbach’s alpha

C1

92.250

10.404

0.1942

0.8572

C2

93.750

9.708

0.5341

0.8436

C3

92.500

9.539

0.7637

0.8304

C4

94.250

9.979

0.9546

0.8381

C5

93.250

9.535

0.5147

0.8469

C6

92.500

10.116

0.3958

0.8507

C7

92.500

10.247

0.2548

0.8564

C8

92.750

10.079

0.2572

0.8605

C9

92.250

9.979

0.9546

0.8381

C10

93.750

9.912

0.7414

0.8385

C11

92.500

9.747

0.8037

0.8332

C12

93.000

10.424

0.0254

0.8705

C13

94.750

9.845

0.4310

0.8501

C14

93.000

9.129

0.6199

0.8422

C15

93.500

9.469

0.8253

0.8267

omitted, then the altered value of alpha remain almost same as current alpha value. Therefore, there is no need to eliminate or add more items in the analysis. As the alpha value for all omitted items is similar, the result suggests that all items measure the same characteristics and the dataset is reliable.

57.3.2 Principal Component Analysis (PCA) Table 57.3 shows the eigen analysis of the components using PCA. Among the thirty criteria, only five eigen values are shown in Table, because, except first two eigenvalues, the rest eigenvalues are zero or have no contribution over the variation. Eigenvalue of the first principal component is 8.2728 and eigenvalue of second principal component is 4.8576 and eigenvalue for rest 13 items are zero. The proportion in variance of first principal component is 0.552 or 55.2% while the proportion for second principal component is 0.324 or 32.4%. Figure 57.2 represents the Table 57.3 Eigenvalue table of components Eigenvalue

8.2728

4.8576

1.8696

0.0000

0.0000

Proportion

0.552

0.324

0.125

0.000

0.000

Cumulative

0.552

0.875

1.000

1.000

1.000

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Eigenvalue

6 5 4 3 2 1 0 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

Component Number

Fig. 57.2 Scree plot of variables

eigenvalues in an order (larger to smaller). The standardized pattern of scree plot is basically a steep curve (straight line followed by a bend). In the present result, the first two principal components have eigenvalues greater than unity. Hence these two components explain the entire variation in the dataset. From Fig. 57.2 it can be noted that eigenvalues tend to follow a straight line from third principal component. The first principal component occupies approximately 55% of the total variation of data whereas, first and second principal component together occupy approximate 87% of total variation; hence, it is quite adequate amount in variation. Therefore, only first principal component is considered in the study. Now, weights of the criteria are determined by simply squaring the corresponding eigen values of the first principal component. The weights of criteria obtained by PCA method is shown in Table 57.4. From the following table, it can be said that principal component has large positive association with C14. Therefore, C14 is the most influential criteria. Table 57.4 PCA weights of criteria Criteria

Weights

Criteria

Weights

Criteria

Weights

C1

0.024636

C6

0.012286

C11

0.028561

C2

0.021609

C7

0.001225

C12

0.024649

C3

0.014541

C8

0.207849

C13

0.045369

C4

0.003844

C9

0.001225

C14

0.564001

C5

0.055433

C10

0.020449

C15

0.086547

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Table 57.5 Rank of suppliers Suppliers

Performance score yielded from SAW method

Rank of suppliers

Supplier A

0.886502

2

Supplier B

0.658338

3

Supplier C

0.956889

1

These weights are employed in SAW method to rank the alternatives (supplier organizations) respectively. The ranking of supplier obtained by SAW method is shown in Table 57.5.

57.4 Results and Discussions The PCA weight-based SAW method yields the priority ranking of the suppliers which is in the order of Supplier C > Supplier A > Supplier B. The result also reveals the benchmarking of the supplier organizations in the order of adopting GSCM efficiently in their supply chains. From the result, it can be concluded that ‘Supplier C’ is the benchmarked supplier. From Table 57.4 it can be seen that C14 bears maximum weight i.e. Collaboration with supplier for green purchasing is the most influential attribute as it gains maximum weight. Therefore, C14 can be considered as a critical criterion. Supplier 3 or organization 3 adheres a strong internal environmental management policy in which top management professional is highly committed to attain sustainability in its products, process, and services. The organization has collaboration with various national and multi-national organizations for policy making and engraving corporate excellence. It has successfully implemented GSCM principles to eliminate adverse impact on environment, caused by its operations and processes. The GSCM policy of the organization provokes several sustainable practices like minimizing energy consumption, reducing scrap and waste, recycling, reducing hazardous emissions, investing a huge amount in CSR and R&D, etc. It has made some innovative steps and challenges to make its supply chain greener and sustainable. The company has installed few renewable power generating grids, waste water treatment plants. Till now, it has been able to reduce total energy consumption by 43% through the use of renewable energy sources. Also, the company becomes successful to reduce its carbon footprint to great extent, by using anti-pollution measures and replacing conventional machineries with latest energy-efficient technologies. Supplier C has put emphasis on the use of eco-friendly labeling/packaging and 3R policies, i.e., reduce, reuse and recycle. The company provides training to its workers, collaborate with vendors and cooperate with customers for development of green products and services. The organization becomes successful to reduce emission due to logistics activities on a collective basis, by collaborating with all business partners in a concurrent manner. Considering ‘Supplier C’ as a pioneer organization, rest companies can aim in few improvement measures like:

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1. Optimizing process and operations for optimal utilization of resources and reducing waste generation. 2. Encourage the deployment and dissemination of eco-friendly technologies for minimizing ecological-footprint and carbon emission. 3. Collaborate with suppliers and cooperate with customers for green innovation. 4. Creating awareness about sustainability issues among stakeholders from all levels. 5. Establishing a future society and supply network in harmony with the nature.

57.5 Conclusion This research can be entitled as an empirical study carried out with the aim of selecting best supplier and benchmark the supplier organizations in the order of GSCM implementation. Also, this research aims in exploring the benchmarked organization’s strategy of how it has implemented GSCM principles and how much contributory outcome, it received as a benefit. Empirical method implies the research procedure of carrying out a literature study on a specified target sample, development of a questionnaire, acquisition of relevant data and information, simulation of the data and at last making the proper decision. Therefore, this study fulfills all the essential exigencies of empirical research. The literature reveals a deficiency of field-based study which underpins the applicability of the proposed methodology. This research has made an attempt to evaluate the environmentally-conscious supplier using integrated multivariate-MCDM approach where item analysis is used to check the reliability of data, PCA is used to assess the criteria weights, and SAW method is used to prioritize and rank the alternatives. As the study considers a real-world supplier selection problem therefore relatively less vagueness is present in data. So far to our knowledge, there is no such supplier evaluation model in the context of developing countries. This research considers three distinct types of supplier organizations for performance evaluation. This is for the first time; item analysis coupled with PCA and SAW method is being used to evaluate the GSCM performance. The selected criteria encompass both cost factors (C2, C4, C8 & C11) and benefit factors (C1, C3, C5, C6, C7, C9, C10, C12, C13, C14 & C15). Also, the criteria considered in this study comprises both objective (quantitative) and subjective (qualitative) factors. Since this research considers all the aspects of sustainability, therefore, the benchmarked organization’s strategy will have indirect impact on development of cleaner production system and overall sustainable development. Supplier C has taken various innovative and prudent steps towards successful implementation of GSCM. The company invested around 316 million towards environmental protection, 125 million in R&D and 95 million in CSR activities. Also, the benchmarked organization put more stress on collaboration with supplier for green innovation. From Table 57.5 it can be seen that performance of ‘Supplier A’ is satisfactory but performance of ‘Supplier B’ is not up to the mark. Supplier C has been able to implement sustainable policies effectively in its business activities. Other organizations should follow the steps taken by

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‘Supplier C’ in order to enhance both their sustainability performance and competitive advantage. Also, organizations need to put more stress on collaboration with suppliers for green procurement. This research examines the reliability and consistency of data before simulation. Item analysis reveals that components are highly interrelated and the selected criteria suits best for evaluating GSCM performance and making a sound and precise decision. PCA determines the relative importance of the considered criteria. Thus, PCA contributes in identifying critical criteria. Thus, using statistical method can help to identify influential factors for successful implementation of GSCM. Other organizations have faced more hurdles in the way of implementing GSCM effectively as compared to benchmarked organization. Among the barriers, lack of knowledge, communication, and awareness among workers about environmental issues and lack of organizational encouragement is most significant. Except this, resistance to adoption of advanced technology becomes an obstacle to obtain sustainability and sometimes, top management remains unconcerned about the investment for practicing GSCM. While in the case of benchmarked industry, it provides training to workers at regular intervals and top management is highly committed to attain sustainability in business, that’s why it has invested a huge amount behind GSCM. On the other side, strengthen relationship with supply results in lower inventory levels, reduced cost, and risk. Suppliers’ reluctance to change towards GSCM is one of the most critical constraints. The benchmarked industries have more than 1500 suppliers worldwide, having a strong collaboration with suppliers. Due to this fact it has already attained several competitive advantages like (i) it offers green products to customer at relatively low price than other competitive products in the market (ii) it has acquired a highly skilled staff & labor (iii) it become able to gain customer satisfaction and improve reputation in market through providing sustainable products and services. Therefore, it can be said that benchmarked industry put more emphasis on critical criteria. Few innovative strategies have enabled the benchmarked company to outperform its competitors.

57.5.1 Theoretical Contribution and Practical Implications There is shortage of empirical studies in the field of green supplier evaluation in developing countries [23, 24]. This study has made an attempt to empirically investigate the GSCM performance of manufacturing industries of developing countries like India. The GSCM implementation in Indian manufacturing organizations are still in its rudimentary stage and it requires additional research interest [1]. In such case, the proposed research framework and outcome obtained from a real-life case of green supplier selection can enable policy makers to strategically select suppliers for effective GSCM implementation. The proposed method is comprehensive, less complicated, and relatively easy to understand and implement. This study may contribute to identifying influential criteria that should predominantly be taken into consideration while measuring the performance of green suppliers. The proposed model can be

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used in various industrial decision-making perspectives, involving any number of criteria and alternatives.

57.5.2 Limitations and Scope for Future Research There are few shortcomings of field-based study. It is a very tedious and timeconsuming process. The research mainly put focus on manufacturing organizations in developing countries. Therefore, results and recommendation may vary across countries. Obviously, error due to human bias is present. This research can be extended in many avenues. Further studies can be carried out in the context of other developing countries and the outcome can be compared with current study findings. Further research can be done by introducing other GSCM attributes and survey can be conducted with a greater number of supplier organizations. The proposed method can be coupled with other soft-computing approaches and the results can be compared. The proposed research model can be applied to other industrial sectors apart from manufacturing like service sector, construction sector, pharmaceutical or chemical sectors, etc. Acknowledgements The authors are thankful to the Department of Higher Education, Science & Technology and Biotechnology (DST), Govt. of West Bengal, India, for providing financial support to carry out this research.

References 1. Digalwar, A.K., Dambhare, S., Saraswat, S.: Social sustainability assessment framework for indian manufacturing industry. Mater. Today: Proceed. (2020) 2. Mathivathanan, D., Kannan, D., Haq, A.N.: Sustainable supply chain management practices in Indian automotive industry: a multi-stakeholder view. Resour. Conserv. Recycl. 128, 284–305 (2018) 3. Mitra, S., Datta, P.P.: Adoption of green supply chain management practices and their impact on performance: an exploratory study of Indian manufacturing firms. Int. J. Prod. Res. 52(7), 2085–2107 (2013) 4. Kushwaha, G.S., Sharma, N.K.: Green initiatives: a step towards sustainable development and firm’s performance in the automobile industry. J. Clean. Prod. 121, 116–129 (2016) 5. Kumar, A., Jain, V., Kumar, S., Chandra, C.: Green supplier selection: a new genetic/immune strategy with industrial application. Enterprise Inf. Syst. 10(8), 911–943 (2015) 6. Luthra, S., Govindan, K., Kannan, D., Mangla, S.K., Garg, C.P.: An integrated framework for sustainable supplier selection and evaluation in supply chains. J. Clean. Prod. 140, 1686–1698 (2017) 7. Liu, Y., Blome, C., Sanderson, J., Paulraj, A.: Supply chain integration capabilities, green design strategy and performance: a comparative study in the auto industry. Suppl. Chain Manage. Int. J. (2018) 8. Kafa, N., Hani, Y., El Mhamedi, A.: Sustainability performance measurement for green supply chain management. IFAC Proc. 46(24), 71–78 (2013)

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9. Banaeian, N., Mobli, H., Nielsen, I.E., Omid, M.: Criteria definition and approaches in green supplier selection—a case study for raw material and packaging of food industry. Prod. Manuf. Res. 3(1), 149–168 (2015) 10. Björklund, M., Martinsen, U., Abrahamsson, M.: Performance measurements in the greening of supply chains. Supply Chain Manage. Int. J. 17(1), 29–39 (2012) 11. Hussain, M., Al-Aomar, R.: A model for assessing the impact of sustainable supplier selection on the performance of service supply chains. Int. J. Sustain. Eng. 1–16 (2017) 12. Orji, I., Wei, S.: A decision support tool for sustainable supplier selection in manufacturing firms. J. Ind. Eng. Manage. 7(5) (2014) 13. Namagembe, S., Ryan, S., Sridharan, R.: Green supply chain practice adoption and firm performance: manufacturing SMEs in Uganda. Manage. Environ. Qual.: An Int. J. (2018) 14. Sangwan, K.S., Choudhary, K.: Benchmarking manufacturing industries based on green practices. Benchmarking: Int. J. 25(6), 1746–1761 (2018) 15. Srivastava, S.K.: Green supply-chain management: a state-of-the-art literature review. Int. J. Manage. Rev. 9(1), 53–80 (2007) 16. Chin, T.A., Tat, H.H., Sulaiman, Z.: Green supply chain management, environmental collaboration and sustainability performance. Procedia CIRP 26, 695–699 (2015) 17. Zhu, Q., Sarkis, J., Lai, K.: Examining the effects of green supply chain management practices and their mediations on performance improvements. Int. J. Prod. Res. 50(5), 1377–1394 (2012) 18. Freeman, J., Chen, T.: Green supplier selection using an AHP-Entropy-TOPSIS framework. Supply Chain Manage. Int. J. 20(3), 327–340 (2015) 19. Hong, P., Jungbae Roh, J., Rawski, G.: Benchmarking sustainability practices: evidence from manufacturing firms. Benchmarking Int. J. 19(4/5), 634–648 (2012) 20. Shaw, S., Grant, D. B., Mangan, J.: Developing environmental supply chain performance measures. Benchmarking Int. J. 17(3), 320–339 (2010) 21. Tuni, A., Rentizelas, A., Duffy, A.: Environmental performance measurement for green supply chains. Int. J. Phys. Distrib. Logistics Manage. (2018) 22. Luthra, S., Luthra, S., Haleem, A.: Hurdles in implementing sustainable supply chain management: an analysis of indian automobile sector. Proc. Soc. Behav. Sci. 189, 175–183 (2015) 23. Govindan, K., Kaliyan, M., Kannan, D., Haq, A.N.: Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. Int. J. Prod. Econ. 147, 555–568 (2014) 24. Govindan, K., Azevedo, S.G., Carvalho, H., Cruz-Machado, V.: Impact of supply chain management practices on sustainability. J. Clean. Prod. 85, 212–225 (2014)

Chapter 58

Solar-Driven Potassium Formate Liquid Desiccant Dehumidification System with Thermal Energy Storage A. Sai Kaushik and Satya Sekhar Bhogilla

Abstract With a conscious effort of shifting toward cleaner sources of energy to reduce the carbon footprint and emissions around the world, the current technological advancements for the production of energy are primarily focused on harnessing the renewable sources of energy for efficient use. Solar photovoltaic energy is one of the most abundant and promising sources of renewable energy with a lot of scope and potential for development in generation of power for useful work. Since the efficiencies of solar cells are very low, coupling the solar panel system with a thermal extraction cycle for supplying energy to various applications can improve the overall efficiency of the combined system. One such application is the usage of the thermal energy obtained from the photovoltaic (PV) panels as input to the liquid desiccant air-conditioning system primarily for dehumidification of air. Considered as the alternative to traditional vapor absorption/compression air conditioning systems, desiccant air conditioning systems are becoming increasingly popular for cooling and dehumidification of air because of their higher efficiencies and ability to use low-grade sources of energy to produce the required output. A comprehensive analysis of the working characteristics of the liquid desiccant dehumidification system using potassium formate as the liquid desiccant solution has been performed in this study. The liquid desiccant dehumidification system has been mathematically modeled using MATLAB R2018b; primarily to study the amount of dehumidification, the system is able to produce using the energy supplied to it from the photovoltaic module. Different configurations of the system have been modeled to identify the best possible system for maximum performance. The motive of implementing the proposed system is to improve the performance and efficiency of the renewable energy systems and to enhance the utilization of eco-friendly technologies in everyday life. As The future heads toward a greener and cleaner path, focusing on harnessing eco-friendly technologies with the maximum efficiency, can be regarded as the need of the hour. A. Sai Kaushik · S. S. Bhogilla Indian Institute of Information Technology Design and Manufacturing Kurnool, Kurnool, India S. S. Bhogilla (B) Indian Institute of Technology Jammu, Jammu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_58

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Nomenclature 

m2 m3



as

Specific surface area per unit volume

cp

Specific heat capacity at constant pressure   Mass flow rate of the coolant fluid kgs  2 Area of the collector m  kg Mass flux m2 . s Temperature  (°C)  kJ Enthalpy kg Height of the packed tower (m)

m˙ cf A G T h z



kJ kg .K



Greek Letters a l αh αm βl δ m t γ ϕm ϕt ρ μ ω

Air Liquid   Heat transfer coefficient mW  2. K  Mass transfer coefficient mkg 2. K Desiccant solution concentration (%)  kJ Latent heat of vaporization of water kg Moisture effectiveness of the packed tower Thermal effectiveness of the packed tower Mass flux ratio Logarithmic function of moisture effectiveness Logarithmic function ofthermal effectiveness  kg Density of the medium m3   Condensation/evaporation rate m2g. s   kgv Humidity ratio kg da

58.1 Introduction The recent developments and advancements in technology have increased the amount of energy consumed across various sectors and industries of the world. Traditional sources of energy production use a large amount of fuel as input to produce energy but also release a lot of harmful residues as a by-product. The huge energy demand has led to heavy usage of fossil fuels and other non-renewable sources for meeting

58 Solar-Driven Potassium Formate Liquid Desiccant …

739

the required demand of energy, thereby increasing the amount of carbon footprint and emissions around the world. In order to meet the surging demand for energy without causing damage to the eco-system, the need to utilize alternative cleaner sources of energy has become an issue of prime importance. Renewable sources of energy have gained lot of attention and focus in the past decade. A lot of research and analysis has been put into harness the energy provided by sources such as solar, wind and geothermal. Harnessing solar energy has found widespread application across the world because of its abundance availability, ease of implementation and the amount of flexibility it offers to be integrated with other power-driven systems. A comprehensive study and analysis of dehumidification of air through a liquid desiccant system has been carried out in this study with potassium formate chosen as the primary desiccant solution. Several studies have been conducted on analyzing various operational parameters of the photovoltaic/thermal (PV/T) systems to observe their influence on the performance of the total system [1]. The PV/T system has been utilized for various applications depending on the capacity of the panels and the amount of heat absorbed by the coolant fluid. Detailed analysis has been performed on water PV/T collectors [2] for calculating its performance characteristics and also on energy performance of hybrid PVT-PCM collectors [3]. Numerous studies have also been done to model the working of the liquid desiccant-type dehumidification method based on the effectiveness of NTU model [4]. Comprehensive experimental study of dehumidification system using suitable desiccant liquids has been carried out [5]. Various studies have also been performed on various configurations of the liquid desiccant system for dehumidification using an analytical and simulation-based approach [6]. The study and analysis of liquid desiccant systems in cross-flow arrangement and its feasibility to be utilized in coupled systems have also been performed [7–10]. Comprehensive studies on the liquid desiccant regenerator system have also been carried out to analyze its performance [11]. Numerous studies related to the liquid desiccant dehumidification system or for air-conditioning have primarily focused lithium chloride or lithium bromide as the desiccant liquids [13]. Little research has been conducted to the numerical investigation of energy performance of a potassium formate-based counter-flow liquid desiccant system [12]. The scope of using liquid desiccant liquids such as potassium formate that offer a lot of benefits and advantages is yet to be explored and has tremendous potential for improvement and research. A similar type of analytical (finite) model-based method study of the dehumidification system using potassium formate has been carried out in this study.

58.2 Liquid Desiccant Dehumidification System DAC systems are classified into solid DAC systems and liquid DAC systems. The primary focus in this research work is based on the liquid DAC systems due to the following reasons:

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Fig. 58.1 Liquid desiccant dehumidification cycle

• Liquid DAC systems have a small pressure drop at the air flow, lower maintenance that is required, and offer a great range of flexibility for recovering low-grade waste heat energy obtained from the solar panel for the regeneration of the liquid desiccant. • They have higher air humidification capacity when compared to other working medium at the same driving temperature. • High energy storage can be obtained by utilizing hygroscopic solutions. • Liquid desiccant systems also perform an additional function of absorbing pollutants and bacteria in the air mixture. The principle of operation of the desiccant to absorb moisture in the air is through the lower vapor pressure offered by the desiccant liquid as compared to that of water that enables the moisture present in the air to be transferred to the liquid as the air passes through it. Some of the common working fluids used are lithium bromide, potassium formate, calcium chloride, lithium chloride, etc (Fig. 58.1).

58.2.1 Model Description The liquid desiccant system primarily consists of the dehumidifier unit, regeneration unit, PV/T module, recuperative heat exchanger and auxiliary power equipment for the circulation of the coolant fluid. Thermal energy storage in the form of PCM can be utilized in the analysis for supplementary power supply. As shown in Fig. 58.2, the liquid desiccant first enters the dehumidifier (58.2.1) wherein it comes into contact with moisture filled air flowing through it. Due to the vapor pressure difference between the desiccant liquid and the water present in the air, the water is transferred and absorbed by the desiccant solution before exiting the dehumidifier. The diluted desiccant solution from the dehumidifier next undergoes regenerative heating (58.2.2) through a recuperative heat exchanger. The preheated desiccant solution

58 Solar-Driven Potassium Formate Liquid Desiccant …

741

Fig. 58.2 Solar-driven liquid desiccant system with TES schematic

then enters the regenerator (58.2.3) after getting heated by the energy supply source and comes into contact with dry air for the desorption process. The amount of heat required for the system for desorption of the desiccant solution is provided by the PV/T module (primary source) through the thermal extraction cycle. The TES system acts as a supplementary source to supply energy when the PV/T module cannot produce the required amount of energy during the night hours or during bad weather. The desiccant solution on being heated by the heat exchanger from the PV/T (or) TES module now releases the moisture present in the desiccant solution to the air before leaving the regenerator as a hot concentrated desiccant liquid. The heated desiccant liquid now passes through the recuperative heat exchanger (58.2.4) to exchange heat with the incoming diluted desiccant solution (58.2.2 ) before entering the dehumidifier for the cycle to begin all over again.

58.2.2 Mathematical Model There are various models of heat and mass exchangers that are available such as the packed tower, falling film type and spray time towers. The falling film type is selected for the liquid desiccant system analysis. A counter-flow arrangement of the falling film type tower for the dehumidification of air has been considered in the analysis. The schematic representations of the dehumidifier and the regenerator in the counter-flow arrangement are shown in Fig. 58.3.

58.2.3 Assumptions and Generalizations to Be Considered • Adiabatic condensation and evaporation process. • Variation of potassium formate and air properties along the width of the tower is negligible.

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Fig. 58.3 Schematic representation of the dehumidifier

• Thermo-physical properties of potassium formate are constant. • Mass flux along the packed tower for the air and potassium formate is constant. The considerations and equations to examine the liquid desiccant system along with the simulation procedure of the modeled system using recursive techniques have been considered from Kiran Naik et al. [6]. The reference data for modeling the liquid desiccant system using potassium formate has been considered from Jradi et al. [12]. MATLAB R2018b software has been utilized to run the simulations of the dehumidifier for various operating conditions. Potassium formate has been selected as the desiccant liquid for the analysis. The changes in the parameters are observed, and the performance of the system is validated.

58.3 System Modeling 58.3.1 Properties of Air The heat and mass balance across the air side interface is given by the following equation:

58 Solar-Driven Potassium Formate Liquid Desiccant …

  G a ∂h a = G a cp,v Ta + δ ∂ωa + αh as (Tl − Ta )∂z

743

(58.1)

The output temperature of air across the packed tower is given by:   Tao = Tai − t Tai − Tli

(58.2)

where t is the thermal effectiveness of the dehumidification tower. The output humidity of the air across the packed tower is given by:   ωao = ωai − m ωai − ωe

(58.3)

where m is the moisture effectiveness of the packed tower and ωe is the specific humidity at equilibrium. The heat transfer coefficient of the packed tower is represented by:   G a ϕt cp,a + ωavg cp,v αh = as z

(58.4)

  1 is the logarithmic function of thermal effectiveness, and ωavg where ϕt = ln 1−ε t is the average humidity of the packed tower. The mass transfer coefficient of the packed tower is given as: αm =

G a ϕm as z

(58.5)

  1 is the logarthmic function of the moisture effectiveness and where ϕm = ln 1−ε m αm is the mass transfer coefficient.

58.3.2 Properties of Desiccant Liquid The heat and mass balance across the air side interface is given by the following equation:   G l ∂h l = αm as (Ta − Tl )∂z − G a cp,v Ta + δ ∂ωa

(58.6)

where G a is the air mass flux and h l is the desiccant solution enthalpy. The outlet desiccant concentration for the packed tower is expressed as, βlo = βli eγ (ωa −ωa ) i

where γ =

Ga Gl

o

is the mass flux ratio of the air and the desiccant fluid.

(58.7)

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Condensation rate for the dehumidifier is given by:   μ = −G a ωao − ωai

(58.8)

58.3.3 Numerical Simulation of the Dehumidifier The mathematical analysis of the liquid desiccant system is performed using backtracking algorithm in MATLAB R2018b software. This algorithm is a recursive technique of evaluation of a system that performs numerous iterations of mathematical equations to calculate the output parameters. It progresses on a specific set of conditions and runs the iterative loop of equations until the desired set of values is obtained in accordance with the conditions set for the system. In the current analysis, the mathematical model based on Eqs. (58.1–58.8) is utilized to calculate the change in various parameters of the liquid desiccant system. The experimental data of a similar system from Jradi et al. [12] is taken as reference to set the conditions and clauses to run the iterative loop using the algorithm. As shown in Fig. 58.4, the first phase of modeling the liquid desiccant system is to determine the thermal and moisture effectiveness of the packed tower so that the outlet air temperature, outlet air humidity, mass transfer coefficient, outlet desiccant solution concentration and the condensation rate (dehumidifier) can be calculated. The recursive backtracking algorithm is employed by using the experimental data of the system available [12] to determine the parameter ‘ϑ.’ The parameter, namely referred to as the proximity constant, ensures that the resultant values obtained from the simulations for various input conditions are in close

Fig. 58.4 Flowchart—I—Training the model

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745

agreement with the experimental values of the same. In the flowchart mentioned in Fig. 58.4, the thermal and moisture effectivenesses of the packed tower are determined for the input conditions through the recursive loop until the percentage error between the calculated output temperate and humidity values with the experimental output temperature and humidity values is less than ϑ. The thermal effectiveness and the moisture effectiveness of the packed tower for various input conditions are calculated to determine their average values. Hence, the model is trained through this process to calculate the effectiveness of the heat exchanger to produce validated results for a range of operating conditions. Using the data and results obtained through the first phase of the training model, the analysis is carried on further to determine the remaining output parameters of the packed apart from the ones obtained in Fig. 58.4. The phase 2 of the model is utilized to determine the average humidity and average temperature values of the packed tower. Assuming the packed tower to be divided into ‘n’ no. of intervals and ‘n + 1’ nodal points for analysis as shown in Fig. 58.5, the expression for calculating the humidity of air at every nodal point ‘k’ (2 ≤ k ≤ n + 1) is given by:    (1−k)z ωak = ωai + ωe − ωai 1 − eτm n

Fig. 58.5 Nodal representation of the packed tower

(58.9)

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Fig. 58.6 Flowchart—II—Average humidity

where τm = αGm aa s is the function of the mass transfer coefficient of the packed tower. The average humidity of the packed tower is given by: ωaavg =

ωasum n+1

(58.10)

Based on the flowchart as shown in Fig. 58.6, the average humidity value of the packed tower and the heat transfer coefficient are calculated. The expression for calculating the humidity of air at every nodal point ‘k’ (2 ≤ k ≤ n + 1) is given by: Tak

=

Tai

+



Tli

−

Tai

 

τh n

1−e

(1−k)z

(cp,a +ωavg cp,v )

(58.11)

where τh = αGh aa s is the function of the heat transfer coefficient of the packed tower. The average temperature of the packed tower is given by: Taavg =

Tasum n+1

(58.12)

Based on the flowchart as shown in Fig. 58.7, the average temperature value of the packed tower is calculated.

58 Solar-Driven Potassium Formate Liquid Desiccant …

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Fig. 58.7 Flowchart—III—Average temperature

58.4 Dehumidifier Analysis and Discussion 58.4.1 Variation of Parameters Through the Length of the Dehumidifier The properties and the initial operating parameters of the dehumidifier are given in Table 58.1. As per Fig. 58.8, the temperature of air reduces gradually as it passes over the dehumidifier. The air outlet temperature for the dehumidifier decreases at the outlet when compared to the inlet condition because the moisture present in the water vapor condenses to water to get absorbed by the desiccant solution. As a result, energy and Table 58.1 Dehumidifier properties and initial operating conditions Height of the exchanger (z)

0.5

Specific surface area (as )

200

Surface area (A)

25

Air flow rate (G a )

1.288

Desiccant flow rate (G l )

2.067

Density of moist air (ρa )

1.1605

Density of desiccant liquid (ρl ) Desiccant inlet concentration

(βli )

1497.3 74%

Input air humidity (ωai )   Input air temperature Tai

0.023

Equilibrium humidity (ωe )

0.0043

Desiccant inlet temperature (Tli )

20

30

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Fig. 58.8 Air temperature variation across the dehumidifier

mass transfer occur from the air side to the desiccant solution, thereby decreasing the temperature of the air. As per Fig. 58.9, the humidity of air decreases as it passes over the dehumidifier. The outlet humidity of air decreases when compared to the inlet values because of the transfer of moisture from air to the desiccant solution because of the vapor pressure difference between the two fluids. As shown in Figs. 58.8, 58.9, the predicted outlet temperatures and humidity of air along the length of the dehumidifier through the

Fig. 58.9 Air humidity variation across the dehumidifier

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Table 58.2 Simulated dehumidifier parameters Outlet desiccant concentration (βlo )

73.49%

Mass transfer coefficient (αm )

0.0135

Heat transfer coefficient (αh )

0.0109

Condensation rate of the dehumidifier (μ)  avg  Average air humidity ωa

15.65

Average air temperature

0.0152

avg (Ta )

25.62

simulations have been compared with the experimental data from Jradi et al. [12] and seem to be in good agreement within the desired range of accuracy. The results of other parameters of this dehumidifier configuration are as follows (Table 58.2).

58.5 Conclusion The counter-flow arrangement of the packed tower is selected as the primary configuration for heat and mass exchange in the dehumidification model. Potassium formate is chosen as the primary desiccant fluid in the analysis. The output values obtained in the simulations have been compared with that of the experimental results performed by Jradi et al. [12] and are in good agreement within the desired range of accuracy. Based on the analysis and observations of various simulations of the dehumidifier that have been carried out, it can be concluded that: • The temperature and humidity of air decrease along the length of the dehumidifier. • The temperature of air reduces to 24.4 °C across the dehumidifier with a temperature difference (cooling) of 5.6 °C obtained at an input supply temperature of air at 30 °C. kgv • The humidity of air reduces to 0.0108 kg across the dehumidifier with a humidity da

difference (moisture absorption) of 0.0122 humidity of 0.0230

kgv . kgda

kgv kgda

obtained at an input supply

References 1. Riffat, S.B., Cuce, E.: A review on photovoltaic/thermal collectors and systems. Int. J. LowCarbon Technol. 6, 212–241 (2011) 2. Astea, N., Del Peroa, C., Leonfortea, F.: Water PVT collectors performance comparison. In: The 8th International Conference on Applied Energy—ICAE2016 3. Shouquat, H.M.D., Bin, Abd R.N., Jeyraj, A.L., Adarsh Kumar, P.: Experimental investigation on energy performance of hybrid PV/T –PCM System. In: Fifth Conference on Electrical Energy Systems—ICEES 2019

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4. Langroudi, L.O., Pahlavanzadeh, H.: Statistical evaluation of a liquid desiccant dehumidification using RSM and theoretical study based on the effectiveness NTU model. J. Ind. Eng. Chem. (2013) 5. Fumo, N., Goswami, D.Y.: Study of an aqueous lithium chloride desiccant system: air dehumidification and desiccant regeneration. Solar Energ. 62(4), 351–361 6. Kiran Naik, B., Muthukumar, P., Bhattacharyya, C.: Thermal modelling and parametric investigations on coupled heat and mass transfer processes occurred in a packed tower. Heat Mass Transfer. (2018). (Springer) 7. Liu, X.H., Jiang, Y.: Heat and mass transfer model of crossflow liquid air dehumidifier/regenerator. Energy Conserv. and Manage. 48, 546–554 (2007) 8. Subhra Das, R., Jain, S.: Performance characteristics of cross flow membrane contractors for liquid desiccant systems. Appl. Energ. 141, 1–11 (2015) 9. Koranaki, I.P., Christodoulaki, R.I., Papaefthimiou, V.D., Rogdakis, E.D.: Thermodynamic analysis of a counter flow adiabatic dehumidifier with different liquid desiccant materials. Appl. Therm. Eng. 50, 361–373 (2013) 10. Kiran Naik, B., Muthukumar, P., Sunil Kumar, P.: A novel finite difference model coupled with recursive algorithm for analyzing heat and mass transfer process in a cross flow dehumidifier/regenerator. Int. J. Therm. Sci. 131, 1–13 (2018) 11. Gandhidasan, P.: Quick performance prediction of liquid desiccant regeneration in a packed bed. Sol. Energ. 79, 47–55 (2005) 12. Jradi, M., Riffat, S.: Energy performance of an innovative liquid desiccant dehumidification system with a counter-flow heat and mass exchanger using potassium formate. Renew. Bioresources 2 (2014). ISSN: 2052-6237 13. Conde, M.R.: Properties of aqueous solutions of lithium and calcium chlorides: formulation for use in air-conditioning equipment design. Int. J. Therm. Sci. 43, 367–382 (2004)

Chapter 59

Performance Studies with Trapezoidal, Sinusoidal and Square Corrugated Aluminium Alloy (AlMn1Cu) Plate Ducts Partha Pratim Dutta, Hirakjyoti Kakati, Monoj Bardalai, and Polash P. Dutta Abstract Superior heat transfer is possible in corrugated aluminium alloy plate heat transferring devices due to high turbulence generated by fluid flowing over it. Therefore, a standard k-E turbulence model coupled with heat transfer in fluid model was employed to study performance of three corrugated channels. This study covers an investigation of 2-D heat transfer and fluid flow through sinusoidal, square, and trapezoidal corrugated ducts. The fluid used in the analysis was air with a density of 1.225 kg/m3 . The air with an inlet temperature of 380 K was passed through the channel (between the corrugated plates) and in the process, air transfers heat to the plates. On outside of the channel made of corrugated plates was maintained at room temperature. Variations of Nusselt number (Nu), change of velocity and pressure drop with the arc length of the corrugated channel have been computed for three geometries. It was observed that Nu variation from the inlet to outlet for trapezoidal, sinusoidal and square ducts were 52–71, 49–65 and 46–61 respectively. Velocity variations were found to be of 2–2.58, 2–2.35 and 2–2.30 m/s in the channels respectively. The pressure drops from the entry to exit were found 1.33 Pa, 1.7 Pa and 2.28 Pa for trapezoidal, square and sinusoidal channels respectively.

Nomenclature Gk K k kg/s kW/m2 mm Nu

The conversion of turbulence kinetic energy for mean velocity gradients (m2 /s2 ) Kelvin Turbulent kinetic energy (m2 /s2 ) Kilogram/second Kilowatt/metre2 Millimetre Nusselt number

P. P. Dutta · H. Kakati · M. Bardalai (B) · P. P. Dutta Department of Mechanical Engineering, Tezpur University, Assam, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 B. Das et al. (eds.), Modeling, Simulation and Optimization, Smart Innovation, Systems and Technologies 206, https://doi.org/10.1007/978-981-15-9829-6_59

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P Dh A f q/Qu Re T u/V u´ v v´ x y

P. P. Dutta et al.

Pressure (Pa) Hydraulic diameter Duct cross sectional area (m2 ) Friction factor Heat flux/useful heat gain (W/m2 /W) Reynolds number Absolute temperature X-coordinate velocity Streamwise velocity fluctuation Y-coordinate velocity Transverse velocity fluctuation Streamwise direction Transverse direction

Greek symbols σ ρ E μ 

Diffusion Prandtl number Density (kg/m3 ) Dissipation kinetic energy (m2 /s3 ) Dynamic viscosity of the fluid (kg/m s) Thermal diffusivity (m2 /s)

Subscripts in t

Inlet Turbulent

59.1 Introduction A heat exchanger is a mechanical unit used for transmission of heat energy from higher to lower temperature fluid. In recent years, the importance and development of efficient heat transferring equipment has grown from the aspects of energy management, transformation, retrieval, and fruitful execution of innovative energy sources. Compact heat exchangers are characterized by better heat transfer coefficients relating to its counterparts. Research has been reported to obtain heat exchanger with high effectiveness, low-pressure losses, low weight, and volume, better trustworthiness at justifiable cost. To obtain these requirements, heat exchanger contains special geometries like corrugation, wavy and curved flow channels to augment its

59 Performance Studies with Trapezoidal, Sinusoidal …

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thermal performance. Corrugated plates are used as instability initiators to enhance heat transfer rate. Turbulence is produced by corrugation since the fluid transports in constricted streams with numerous sudden alterations in path and velocities. When the fluid flows through the corrugated channels, it undergoes breaking and the de-establishment of the thermal boundary layer. Abdel-Aziz et al. [1] carried out a study on liquid-solid mass transfer behaviour of V-corrugated plate with two-phase dynamics. The mass transfer coefficient was reported enhancement with the increasing groove angle and it decreased with increment of groove’s peak to valley height for defined superficial gas velocities. Akdag et al. [2] numerically investigated the enhancement of heat transfer using nanofluids in a trapezoidal channel with corrugation when the flow is laminar. It was reported that application of nanoparticles with pulsating flow conditions increased the heat transfer rate in relation to the steady flow. Farhangmehr et al. [3] performed an investigation on heat transfer and turbulent fluid flow in a channel of corrugated walls. Optimum heat transfer was achieved in a corrugation height and angle of 15 mm and 60° with Reynolds number of 4000. Gao et al. [4] made a numerical investigation on turbulent flow through the channel of triangular geometry with baffles of profile in delta shaped to study the heat transfer characteristics in the range of Re from 1000 to 6000. It was reported that 60° apex angle performed best for heat transfer when corrugation height was equal to baffle height. Islamoglu et al. [5] made a study on influence of height of the channel for heat transfer improvement in heat exchanger with corrugation. Two different values of channel heights (5 and 10 mm) were considered for a single corrugation angle of 20° at Reynolds number 1200– 4000. It was found that the Nusselt number and friction factor augmented with the channel elevation. Dutta et al. [6] estimated the heat exchanger performance in terms of thermal and hydraulic aspects which are influenced by the heights of different corrugated channels. Karami et al. [7] analysed heat transfer and pressure drop behaviours of nanofluids flows inside corrugated tubes. Investigations were carried out at Reynolds number 100–4000 in carbon nanotubes of different hydraulic diameter and different mass flow rate. Reynolds numbers above 500 was favourable group for the use of corrugated tubes. Khan et al. [8] performed the analysis of impact on miscible oil in a mixed arrangement chevron plate heat exchanger. The study concluded that coefficient of heat transfer increased by increasing concentration of oil within 0–3%. For a given aspect ratio and area, sinusoidal duct was more efficient than the triangular duct. Kilic et al. [9] experimented heat transfer performance in corrugated plate heat exchangers with varied chevron angles 30° and 60°. They observed that at 60° chevron angle, heat transfer rate and effectiveness were higher. A study was done by Mohammed et al. [10] to investigate the influence of geometry of corrugated channel in different arrangement of phases at Re and heat flux 8000–20,000 and 0.4–6 kW/m2 respectively. The best values of tilt angles of corrugation, channel and height of waves were 60°, 17.5 mm and 2.5 mm accordingly. The Nu is increased by augmenting the heights of waves. Naphon et al. [11] studied the heat transfer and pressure drop in the corrugated channels with fixed heat flux. The corrugated surfaces of three different tilt angles in the range of 20°–60° and channel height was considered to be 12.5 mm.

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It was reported that the pressure drop along the test unit increased with increment of air Reynolds number. The Nusselt number increased with augmentation of wavy angle. Panday et al. [12] made a performance study experimentally to investigate the thermo-hydraulic characteristics using a corrugated plate with nanofluid as coolant. The report mentioned that effectiveness of heat transfer enhanced by increasing Re and Pe and while by decreasing the mass fraction of nanofluid. Pehlivan et al. [13] performed an experiment for investigation of rate of heat transfer in sinusoidal section. The Nu for H = 5 mm was approximately 80% more than for H = 10 mm at Re = 4000. Sakr et al. [14] made a numerical analysis on convective heat transfer in a V corrugated channel with different phase shift. At uniform wall heat flux 290 W/m2 , they performed simulation by taking air as a fluid. Reynolds number and channel heights varied in the range 500–2000 (channel height 12.5–15.0, 17.5–20 mm). It was reported that with an increase of phase shifts, the pressure declination in the corrugated channels enhanced. Tokzok et al. [15] performed the study on the characteristic of flow and enhancement of heat transfer. They reported that for enhanced aspect ratio, heat transfer rate was augumented. Yang et al. [16] performed an experiment on heat transition correlations in case of one phase flow of heat in a unit of multiple plate heat transfer. It was reported that specification of the geometry can be controlled to determine the thermal properties. Yang et al. [17] studied on thermo-hydraulic performance in a V channel with corrugation on the top and bottom surfaces. Re = 2000–5500, angles of V plates (20°, 40° and 60°), and different heat fluxes (q = 580, 830, 1090 W/m2 ) were taken in their study. Beigzadeh and Ozairy [18] observed that ANN model yielded better thermo-hydraulic performance in wavy channel. Numerical results were validated with experimental data obtained by Naphon, and a good agreement was reported. Thermohydraulic performance dependence on corrugated/serpentine channels, geometrical parameters and application of aluminum foam are also found in many previous publications [19–22]. Spizzichino et al. [23] experimented on micro heat exchanger and observed that serpentine cell gave the best efficiency irrespective of flow regime. Ziaei et al. [25] performed fluid flow simulation in a rectangular channel. They observed that r.m.s. error of simulated water depth profile by k − ε model was