Innovations in Electrical and Electronics Engineering: Proceedings of the 4th ICIEEE 2019 (Lecture Notes in Electrical Engineering, 626) 9811522553, 9789811522550

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Lecture Notes in Electrical Engineering 626

H. S. Saini T. Srinivas D. M. Vinod Kumar K. S. Chandragupta Mauryan   Editors

Innovations in Electrical and Electronics Engineering Proceedings of the 4th ICIEEE 2019

Lecture Notes in Electrical Engineering Volume 626

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, Humanoids and Intelligent Systems Laboratory, Karlsruhe Institute for Technology, Karlsruhe, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, Munich, Germany Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Sebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering & Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Junjie James Zhang, Charlotte, NC, USA

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H. S. Saini T. Srinivas D. M. Vinod Kumar K. S. Chandragupta Mauryan •

•

•

Editors

Innovations in Electrical and Electronics Engineering Proceedings of the 4th ICIEEE 2019

123

Editors H. S. Saini Guru Nanak Institutions Hyderabad, India D. M. Vinod Kumar Department of Electrical Engineering National Institute of Technology Warangal, India

T. Srinivas Department of Electronics and Communication Engineering Kakatiya University Warangal, India K. S. Chandragupta Mauryan Department of Electrical and Electronics Engineering Guru Nanak Institutions Hyderabad, India

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-15-2255-0 ISBN 978-981-15-2256-7 (eBook) https://doi.org/10.1007/978-981-15-2256-7 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Committees

Editorial Board Members Chief Patrons Sardar Tavinder Singh Kohli, Chairman, Guru Nanak Institutions Sardar Gagandeep Singh Kohli, Vice-Chairman, Guru Nanak Institutions Patrons Dr. H. S. Saini, Managing Director, Guru Nanak Institutions Dr. M. Ramalinga Reddy, Director, Guru Nanak Institutions Technical Campus Dr. S. Sreenatha Reddy, Principal, Guru Nanak Institute of Technology Conference Chair Prof. P. Parthasaradhy, Associate Director, GNITC Conference Co-chair Dr. S. V. Ranganayakulu, Dean—R&D, GNITC Steering Committee Dr. Dr. Dr. Dr.

R. K. Singh, Associate Director, GNITC Rishi Sayal, Associate Director, GNITC K. Chanthirasekaran, Dean Academics, GNITC Anmol Kumar Goyal, Dean Academics, GNIT

Conveners Dr. K. Santhi, Professor and HOD, GNITC Dr. K. S. Chandragupta Mauryan, Professor and Assistant Dean R&D, GNITC

v

vi

Committees

Co-conveners Dr. P. V. Kishore, Professor, GNITC Dr. Mruthyunjay Das, Professor and HOD, GNIT Committee Members Mr. Mr. Mr. Mr. Mr. Mr. Mr. Mr. Mr. Mr.

J. Mahesh Yadav, GNITC T. Manidhar, GNIT D. Naveen Kumar, GNITC D. Krishna Chaitanya, GNIT G. Ranga Purushotham, GNITC P. Parthasaradhy Reddy, GNITC K. Rajasekhar, GNITC S. Rajender Reddy, GNITC B. Sravan Kumar, GNITC Ch. Srinivas Reddy, GNITC

Coordinators Mr. K. Girinath Babu, GNITC Mr. R. Santhosh Kumar, GNITC International Advisory Board Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr.

Bimal K. Bose, University of Tennessee, USA Muhammad H. Rashid, University of West Florida, USA Victor C. M. Leung, University of British Columbia, Canada Ali O. Abid Noor, University of Technology, Iraq Atsede Gualu Endegnanew, SINTEF Energy, Norway Wan Zuha Bin Wan Hasan, Universiti Putra Malaysia Nurul Amziah Md Yunus, Universiti Putra Malaysia P. N. Suganthan, Nanyang Technological University, Singapore Ateequr Rahaman Mohammed, Information Technology, Saudi Arabia Ir. Leake Enquary Weldemariam, Mekelle Institute of Technology, Ethiopia Akhtar Kalam, Victoria University, Melbourne, Australia Neeraj Magotra, Western New England University, USA P. Kishore, Stevens University, New Jersey, USA Rajasekhar, Mekelle Institute of Technology, Ethiopia Surender Reddy Salkuti, Woosong University, South Korea

National Advisory Board Dr. Dr. Dr. Dr.

Uday Desai, Indian Institute of Technology Hyderabad Debapriya Das, Indian Institute of Technology Kharagpur T. Srinivasulu, Kakatiya University, Warangal M. Vinod Kumar, National Institute of Technology, Warangal

Committees

Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr. Dr.

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K. Manjunathachari, GITAM University, Hyderabad Girish Kumar, Indian Institute of Technology Mumbai B. V. Sankar Ram, JNTU Hyderabad M. Surya Kalavathi, JNTU Hyderabad E. Vidyasagar, Osmania University, Hyderabad P. V. N. Prasad, Osmania University, Hyderabad S. Moorthi, National Institute of Technology, Trichy Mohammed Zafar Ali Khan, Indian Institute of Technology Hyderabad Sanjay Mandal, Central University, Dibrugarh Pradeep M. Nirgude, CPRI, Hyderabad P. Lakshmi, Anna University, Chennai L. Ashok Kumar, PSG College of Technology, Coimbatore Josephine R. L., National Institute of Technology Trichy A. Srinivasula Reddy, CMR College of Engineering, Hyderabad

Conference Committee Members

S. No.

Name of Committee

Coordinator

Co-coordinator

1. 2.

Budget Conference Team

Mr. Mr. Mr. Mr.

3. 4.

Website Physical Campaigning Team Pre-conference Tutorials

Dr. K. Santhi Prof. P. Parthasaradhy Dr. K. Santhi Dr. K. S. Chandragupta Mauryan Mr. S. Rajender Reddy Mr. D. Naveen Kumar Dr. P. V. Kishore Dr. Mrutyanjay Das

Mr. Ch. Srisailam Mr. Rajashekar Mr. Sivabalaji Mrs. Lizi Joseph Mr. R. Santosh Kumar Mr. K. Girinath Babu Mr. D. K. Chaitanya

5.

6.

Dr. K. S. Chandragupta Mauryan

7.

Receiving Paper, Acknowledgement Review and Sending Report, Acceptance Letters to Authors Conversion of Papers

8. 9.

VIP Committee Invitation

Dr. K. Santhi Mr. G. Ranga Purushotham

Dr. K. S. Chandragupta Mauryan

Srinivas Reddy J. Mahesh Yadav D. Naveen Kumar T. Manidhar

Mr. G. Indrareddy Mr. B. Sravan Kumar Mr. T. L. V. Nagalathish Ms. L. Vandana Mr. S. Rajender Reddy Mr. G. Indra Reddy (continued)

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Committees

(continued) S. No.

Name of Committee

Coordinator

Co-coordinator

10.

Mr. D. Naveen Kumar

Mr. B. Seshidher Ms. P. Harika Reddy Ms. Swarnalatha

16. 17.

Registration and Conference Kits Bag Reception VC, MD, JNTUH Messages Banners Proceedings Conference Office and Certificates Transport Committee Program Committee

18.

Purchase Committee

Mr. B. Sravan Kumar

19.

Food Committee

Mr. A. Radha Krishna

20. 21.

Accommodations Inauguration

Mr. Ch. Srisailam Dr. K. Santhi

22. 23.

Photos and Video Press Release and Media

Mr. V. Chandrashekar Dr. K. Santhi

24.

Conference Report

Dr. K. Santhi

25.

After Conference Attending Queries After Conference Contacts Keynote Speech Arrangements

Dr. K. S. Chandragupta Mauryan

11. 12. 13. 14. 15.

26. 27.

Ms. Anitha Mr. J. Mahesh Yadav Mr. B. Sravan Kumar Mr. G. Ranga Purushotham Mr. D. Naveen Kumar Mr. G. Indra Reddy Mr. J. Mahesh Yadav

Mr. B. Seshidher Mr. Ch. Sriram Mr. T. L. V. Nagalathish Mr. R. Jagan Mr. M. Suryakanth Mr. A. Ranganadh Mr. Ashok Mr. N. Praneeth Mr. K. Subhash Dr. K. S. Chandragupta Mauryan Mr. K. Subhash Dr. K. S. Chandragupta Mauryan Dr. K. S. Chandragupta Mauryan Mr. K. Girinath Babu Mr. R. Santosh Kumar

Mr. S. Rajender Reddy

External Reviewers Dr. R. Maheswar, Professor, VIT Bhopal University, Madhya Pradesh Dr. K. Srinivasan, Professor and Head, Sri Ramakrishna Engineering College, Coimbatore Dr. Mohanasundaram, Professor, Vel Tech Multi Technology, Chennai Dr. S. Muthu Vijaya Pandian, Professor and Head, SNS College of Engineering, Coimbatore Dr. A. Prakash, Dean R&D, QIS College of Engineering and Technology, Andhra Pradesh Dr. K. Ilango, Associate Professor, Amrita Vishwa Vidyapeetham, Kerala

Committees

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Dr. R. Anand, Assistant Professor, Amrita Vishwa Vidyapeetham, Bengaluru Dr. R. Vijay, Associate Professor, CVR College of Engineering, Hyderabad Dr. G. Balaji, Associate Professor, CVR College of Engineering, Hyderabad Dr. Joseph Prabhakar, Professor and Head, Sri Indu College of Engineering, Hyderabad Dr. Veeramani, Associate Professor, Sri Indu College of Engineering, Hyderabad Dr. G. Sivagnanam, Professor, Sri Krishna College of Technology, Coimbatore Dr. P. Ponmurugan, Associate Professor, Sri Krishna College of Technology, Coimbatore Dr. M. Mohanraj, Associate Professor, Kumaraguru College of Technology, Coimbatore Dr. Sivam, Professor, Amrita Vishwa Vidyapeetham, Bengaluru Dr. S. Gunasekar, Assistant Professor, Coimbatore Institute of Technology, Coimbatore Dr. Siva Prasad, Professor, Vidya Jyothi Institute of Technology, Hyderabad Dr. E. Vidya Sagar, Principal, Osmania University, Hyderabad Dr. K. Manjunathachari, Professor and HOD, GITAM University, Hyderabad Dr. K. Madhu Sudan Rao, Professor, Vidya Jyothi Institute of Technology, Hyderabad Dr. K. Rajender Reddy, Professor, Nalla Narasimha Reddy Engineering College, Hyderabad Dr. K. Sreelatha, Professor, St. Peter’s Engineering College, Hyderabad Dr. Rajasekhar, Professor, Malla Reddy Engineering College, Hyderabad Dr. K. Vijay Bhaskar Reddy, Professor, BVRIT Hyderabad Dr. A. Sivaprakasam, Associate Professor, Anna University, Chennai Dr. Vijay Kumar, Professor, JNTU Anantapur Dr. Ganesh, Professor, JNTU Pulivendula Dr. L. Ramesh, Dean, Dr. MGR Educational and Research Institute, Chennai Dr. S. Rama Reddy, Professor, Jerusalem Engineering College, Chennai Dr. P. Srinivas Varma, Dean R&D, KL University, Vijayawada

Internal Reviewers Prof. P. Parthasaradhy, Associate Director, GNITC Dr. S. V. Ranganayakulu, Dean R&D, GNITC Dr. K. Chanthirasekaran, Dean Academics, GNITC Dr. K. Santhi, Professor and HOD, GNITC Dr. K. S. Chandragupta Mauryan, Professor and Assistant Dean R&D, GNITC Dr. P. V. Kishore, Professor, GNITC Dr. Mruthyunjay Das, Professor and HOD, GNIT Dr. S. J. Sugumar, Professor, GNITC Dr. T. Vijayakumar, Professor, GNIT Dr. Ch. Subbalakshmi, Professor, GNITC

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Dr. Jeyanthi, Professor, GNIT Dr. Thirumalai, Professor, GNITC Mr. J. Mahesh Yadav, Associate Professor, GNITC Mr. G. Ranga Purushotham, Associate Professor, GNITC Mr. Parthasaradhy Reddy, Associate Professor, GNITC Mr. D. Naveen Kumar, Associate Professor, GNITC Mr. K. Rajasekhar, Associate Professor, GNITC Mr. S. Rajender Reddy, Associate Professor, GNITC Mr. D. Krishna Chaitanya, Associate Professor, GNIT Mr. T. Manidhar, Associate Professor, GNIT

Committees

Preface

The Fourth International Conference on Innovation in Electrical and Electronics Engineering (ICIEEE 2019) was organized on July 26 and 27, 2019, by the Department of Electrical and Electronics Engineering at Guru Nanak Institutions Technical Campus, Hyderabad. This conference has provided an interactive platform for researchers, scientists, technocrats, and academicians to exchange their innovative ideas and research findings in the field of electrical and electronics engineering. Our earlier conferences got a huge success and acknowledged by national and international eminent scholars with their active participation and contribution. The distinguished speakers from India and abroad have shared their innovative ideas and technologies on this topic. Over 256 papers were received in which 79 high-quality papers have been selected for Springer Conference Proceedings publication. The authors of these particular papers presented them remarkably. Parallel sessions were also conducted to accommodate all the authors, and ample time was allotted to discuss their ideas. We, the Department of Electrical and Electronics Engineering, made all arrangements for smooth conduct of this conference and received positive feedback that gives good encouragement for us to conduct such conferences in the future. We would like to thank all the keynote speakers, participants, speakers of the pre-conference tutorial sessions, session chairs, committee members, reviewers, international and national board members, Guru Nanak Institutions’ management and all the people who have directly or indirectly contributed to the success of ICIEEE 2019. The institutions’ editorial board members expressed their sincere thanks to Springer Editorial Team for their kind support in publishing the papers as a part of the “Lecture Notes in Electrical Engineering” Series. Hyderabad, India Warangal, India Warangal, India Hyderabad, India

Dr. H. S. Saini Dr. T. Srinivas Dr. D. M. Vinod Kumar Dr. K. S. Chandragupta Mauryan

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Contents

Power Electronics Power System Security Analysis Using FACTS Devices by Means of Intelligent and Hybrid Techniques Under Different Loading Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Venkata Padmavathi, A. Jayalaxmi and Sarat Kumar Sahu Implementation of Three-Phase Shunt Active Filter Using Instantaneous Real Power Calculation and Triangular Carrier Current Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Athira Ajith, P. V. Manitha and K. Ilango A Small DC-Link Capacitor Inverter Fed by Front-End Three-Phase Diode Rectifiers Used to Control Induction Motor . . . . . . . . . . . . . . . . . Ashwini V. Potdar and Ch. Mallareddy Different Topologies of Inverter: A Literature Survey . . . . . . . . . . . . . . Kalagotla Chenchireddy, V. Jegathesan and L. Ashok Kumar Analysis and Design of Extended Range Zero Voltage Switching (ZVS) Active-Clamping Current-Fed Push–Pull Converter . . . . . . . . . . Koyelia Khatun and Akshay Kumar Rathore

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13

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45

Switched Reluctance Motor Converter Topologies: A Review . . . . . . . . Velakurthi Mahesh Kumar, K. Vinoth Kumar and R. Saravanakumar

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Design of Five-Level Cascaded H-Bridge Multilevel Inverter . . . . . . . . . S. Swathy, N. Niveditha and K. S. Chandragupta Mauryan

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Performance Analysis of Asymmetrical Cascaded H-Bridge Multilevel Inverter Using Multicarrier Pulse-Width Modulation Techniques . . . . . D. Naveen Kumar and P. V. Kishore Interoperable Wireless Charging for Electric Vehicles . . . . . . . . . . . . . . A. Maideen Abdhulkader Jeylani, J. Kanakaraj and A. Mahaboob Subhani

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Power Quality Enhancement Using DSTATCOM with Reduced Switch-Based Multilevel Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Sudheer Vinnakoti, Anusha Palisetti and Venkata Reddy Kota Dual-Input Multioutput Using Non-Cloistered DC–DC Boost Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 K. Sakthidhasan and K. Mohana Sundaram Application of Nonlinear and Optimal Control Techniques to High Gain DC–DC Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Nibedita Swain An Innovative Multi-input Boost Chopper for HEV . . . . . . . . . . . . . . . 145 A. Ranganadh and M. Chiranjeevi Performance Evaluation of Transistor Clamped H-Bridge (TCHB)-Based Five-Level Inverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 V. Kiranmayee and A. Sharath Kumar Enhancement of Power Quality by the Combination of D-STATCOM and UPQC in Grid Connected to Wind Turbine System . . . . . . . . . . . . 173 M. Sumithra and B. C. Sujatha Transient Steadiness and Dynamic Response in Transmission Lines by SVC with TID and MPPT Controller . . . . . . . . . . . . . . . . . . . . . . . . 181 Ajay Kumar and T. S. Prasanna Three-Level DCMLI-Based Grid-Connected DSTATCOM . . . . . . . . . . 195 D. Suresh and R. Chander Reducing Number of Switches in Multilevel Inverter Using Diode Clamped and H-Bridge Inverters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Karanam Deepak, M. Rama Prasad Reddy, K. Jaya Sree and P. Partha Saradhi Reddy Harmonic and Reactive Power Compensation with IRP Controlled DSTATCOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Haresh Nanda and Srinivas Reddy Chalamalla Performance of Static VAR Compensator for Changes in Voltage Due to Sag and Swell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 M. S. Priyadarshini and M. Sushama A New Efficient Z-H Boost Converter for DC Microgrids . . . . . . . . . . . 235 Ch. Sajan, T. Praveen Kumar and P. Balakishan A Hybrid Power Conversion System Using Three-Phase Single-Stage DC–AC Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Shaik Rafi, Simhadri Lakshmi Sirisha and Ravipati Srikanth

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Enhanced Optimal Control Scheme for Attaining Improved Efficiency and Dynamic Response of WECS Using SVC . . . . . . . . . . . . . . . . . . . . 255 C. Veeramani, A. N. Malleswa Rao, K. V. G. Aravind, M. Likhitha Reddy, M. Vipin Krishna and K. Maheswari Design and Modelling of L-type Bi-directional Roller Conveyers for Glass Hauling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 S. Madhankumar, T. Vignesh, P. Anand Raj, Anirudh Varadarajan, T. Arul Praveen and S. Rajesh Gyro-stabilized Platform in Ambulance . . . . . . . . . . . . . . . . . . . . . . . . . 281 T. Vignesh, S. Madhankumar, P. Anand Raj, Anirudh Varadarajan and T. Arul Praveen Power Systems Performance and Comparison of Harmonics Using Active Power Filters and DVR in Low-Voltage Distributed Networks . . . . . . . . . . . . . 291 P. V. Kishore and D. Naveen Kumar Overview of Restructured Power System . . . . . . . . . . . . . . . . . . . . . . . . 305 Prakash Vodapalli and Ramaiah Veerlapati Single-Phase PV System with Continuous H-Bridge Inverter . . . . . . . . . 311 Vodapalli Prakash, Mucherla Narasimha Rao and Chillappagiri Pavan Kumar Comparison of Renewable Energy Generation in an Electrical Network with Energy Storage System . . . . . . . . . . . . . . . . . . . . . . . . . . 321 N. Loganathan, A. Arvin Tony, T. Malini and S. Gobhinath Performance Analysis of Single-Phase Shunt Active Filter using Conventional PI Control Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 Rameshkumar Kanagavel and V. Indragandhi Calculation of VFTO and VFTC in the 550 kV GIS with Mitigation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 M. Naga Jyothi, C. V. K. Bhanu and CH. Ramya Energy Efficiency and Conservation Schemes Proposed for an Educational Building in Oman . . . . . . . . . . . . . . . . . . . . . . . . . . 349 Ch. Ramya, Ch. Venkateswara Rao, Nurul Hasan Shaikh, Mohammed Kashoob, Syed Aqeel Ashraf and C. H. V. Suryanarayana Design and Analysis of PV-Based DSTATCOM with LCL Filter for Localized Distribution System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Pratap Ranjan Mohanty and C. V. Harshavardhan Reddy

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Optimal Scheme and Power Controlling aspects in Shipboard System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Vijay Raviprabhakaran and Teja Sree Mummadi Global Optimization Algorithm to Solve Economic Load Dispatch Problem Considering Equality and Inequality Constraints . . . . . . . . . . 381 Prakash Arumugam and Anand Rajendran Implementation of Conventional Controllers in HVDC Links for Improvement of the Power System Stability . . . . . . . . . . . . . . . . . . . 395 G. Ranga Purushotham, S. Satyanarayana and Ch. Saibabu Low Voltage Ride Through (LVRT) Capability Enhancement of Axial Flux Induction Generator-Based Wind Energy Conversion System . . . . 405 V. Ramesh Babu and A. Ganapathi The Application of Genetic Algorithm with Multi-parent Crossover to Optimal Power Flow Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 T. Srihari, Madhu Boppa, S. Anil Kumar and Harish Pulluri Genetic Algorithm with Multi-Parent Crossover Solution for Economic Dispatch with Valve Point Loading Effects . . . . . . . . . . . . . . 429 Harish Pulluri, M. Vyshnavi, Patange Shraddha, B. Sai Priya, T. Sri Hari and Preeti Teaching Distance Relay Protection and Circuit Breaker Co-ordination of an IEEE 9 Bus System Using MATLAB/SIMULINK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 Cholleti Sriram and Muppalla N. R. Kishore Necessity of Power System State Estimation: A Generalized Linear State Estimation Solution with Application of PMU Measurements . . . . 449 M. Ravindra, R. Srinivasa Rao, V. Srinivasa Rao, N. Praneeth and Vasimalla Ashok Sensorless Operation of PMBLDC Motor Drive Using Neural Network Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463 Poonam M. Yadav and S. Y. Gadgune Comparative Performance Analysis of Active- and Resistive-Type SFCL in Reducing the Fault Current . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 G. Ganesh, Ravilla Madhusudan, L. Vamsi Narasimha and B. Sambasiva Rao Squirrel Search Optimizer for Solving Economic Load Dispatch Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 V. P. Sakthivel, M. Suman and P. D. Sathya

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Voltage Sag Mitigation for PMSG System Using DVR Based Hybrid Fuzzy Logic Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503 Basagonda Chandrika and B. C. Sujatha Different Types of Energy Storage Systems: A Literature Survey . . . . . 515 Rama Rao Bomma, J. Jayakumar and T. Bogaraj Renewable Energy Design and Implementation of Efficient Energy Management System in Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 R. Gauthami, Vineeth V. Nair, Aswin Sathish, K. Vishnu Soureesh, K. Ilango, R. S. Sreelekshmi, S. A. Ilangovan and S. Sujatha A Cost-Effective PV-Based Single-Stage Conversion System for Power Backup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561 B. Kavya Santhoshi and K. Mohana Sundaram Solar Tracking System Using IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 Krishna Chaitanya Diggavi, Manidhar Thula and B. Pakkiraiah Demand Management System for OFF-Grid PV System . . . . . . . . . . . . 579 Mrutyunjay Das, Kuldip Singh and Ch. Laxmi PV-Wind-Integrated Hybrid Grid with P&O Optimization Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 R. Rekha, B. Srikanth Goud, Ch. Rami Reddy and B. Nagi Reddy A Practical Approach in Design and Fabrication of Solar-Powered Four-Wheeled Electric Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601 S. Gobhinath, S. Boobalan, R. Ashwin, Jan Meshach and K. Rajkumar Survey on Security Aspects in Smart Grid: Performance and Parametric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 V. V. Vineeth, S. Sophia and S. Jayanthy A Literature Survey on Renewable Energy Sources in India . . . . . . . . . 617 Praveen Mannam and R. P. Singh Designing of Solar Hybrid Electric Vehicle from Source to Load . . . . . 625 P. Ajay Sai Kiran and B. Loveswara Rao Design and Development of Solar Photovoltaic System Using Single-Phase MLI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639 J. Prakash, K. Gunalan and K. Mohana Sundaram A Novel Technique to Observe the Performance of Virtual Solar PV Module System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 G. Suresh Babu and N. R. Sai Varun

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Contents

Power Quality Analysis for Brushless DC Motor Drive Fed by a Photovoltaic System Using SRF Theory . . . . . . . . . . . . . . . . . . . . . 661 S. R. Rajasree, V. Ravikumar Pandi and K. Ilango Energy Management Scheme for Green Homes Using Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673 A. Naresh Kumar, P. Shiva Kumar and Thati Mahesh Control Systems Comparative Analysis Between Conventional and Neuro-Fuzzy Control Schemes for Speed Control of Induction Motor Drive . . . . . . . 683 Shubhangi Kangale, B. Sampathkumar and N. Raut Mrunmayi Estimation of Nonlinear Hybrid Systems Using Second-Order Q-Adaptive Self-switched Derivative-Free Estimators . . . . . . . . . . . . . . 693 Sayanti Chatterjee Control Quality Enhancement of Inverted Pendulum Using Fractional Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705 K. Muralidhar Goud and C. Srisailam A Review on Interference Management in Millimeter-Wave MIMO Systems for Future 5G Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715 E. Udayakumar and V. Krishnaveni Preventing the Vehicle Accidents on Highways and Implementing Safety and Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723 P. Parthasaradhy and K. Manjunathachari MRI-Based Medical Image Enhancement Technique Using Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729 S. Sakthivel, V. Prabhu and R. Punidha Health Monitoring System Using IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . 739 A. Selvanayakam, A. C. Varishnee, M. Kalaivani and G. Ranjithkumar Measurement of Fuel Level in Tank Using IR Sensors and Reporting Over IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751 M. L. S. N. S. Lakshmi and Chandrasekhar Reddy Miscellaneous Image Denoising Using Spatial and Frequency Domain Filters: A Tool for Image Quality Enhancement . . . . . . . . . . . . . . . . . . . . . . . . 759 Santhi Krishnamoorthi, Nirmala Madian and Dhanasekaran Rajagopal Comparative Study and Analysis of Human Knee Angle Measurement System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769 S. Boobalan, K. Lakshmi and K. N. Thirukkuralkani

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Voice and Image BER Analysis of the OFDM System with MECCT and MLNST Companding Techniques Over Mobile Radio Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 777 B. Sarala, M. Zaheer Ahamed, S. Sree Hari and V. Bhagya sree Enhancement of Performance and PAPR Reduction Using Combination of PTS and SLM Scheme with Opposition-Based GWO in MIMO–OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 787 K. Aruna Kumari and K. Sri Rama Krishna Women Safety Device with GPS Tracking and Alerts . . . . . . . . . . . . . . 797 A. Ranganadh A Smart Machine for Fitness Care Scrutinizing Technique—A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 807 N. Pooranam, M. Diwakaran, A. Archana, S. Agalya, A. Anindhitha and E. GokulaPriya Configuring MPLS Cloud Providers with Virtual Private Network . . . . 817 M. L. S. N. S. Lakshmi and Naga Venkata Sai Sudheer Bandaru A Prototype Development of Digirail-Ticket Verification and Seat Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 827 S. Gobhinath, S. Karthikeyan, A. Guru Prakash, B. Balamurugan and N. Gokul Performance Analysis of Thyroid Tumor Detection and Segmentation Using PCA-Based Random Classification Method . . . . . . . . . . . . . . . . . 833 B. Shankarlal and P. D. Sathya Factors Influencing the Success of Recommendations in E-Commerce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 843 K. Srihari, K. Moorthi and S. Karthik Implementation of Alexa-Based Intelligent Voice Response System for Smart Campus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849 K. Srihari, V. Sakthivel, G. Venkata Koti Reddy, S. Subhasree, P. Sankavi and E. Udayakumar Synthesis of Visual Attention-Based Robotic System and Its Present Utilization in Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 857 Sridhar Prattipati, Vasimalla Ashok and N. Praneeth Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 863

About the Editors

Dr. H. S. Saini is a Managing Director for Guru Nanak Institutions and obtained his Ph.D. in the field of Computer Science. He has over 22 years of experience at university/college level in teaching UG/PG students and has guided several B.Tech., M.Tech., and Ph.D. projects. He has published/presented more than 30 highquality research papers in international and national journals and proceedings of international conferences. He is the editor for Journal of Innovations in Electrical and Electronics Engineering (JIEEE) published by Guru Nanak Publishers. He has two books to his credit. Dr. H. S. Saini is a lover of innovation and is an advisor for NBA/NAAC accreditation process to many institutions in India and abroad. Dr. T. Srinivas, Dean, Department of Electronics & Communication Engineering, Kakatiya University, Warangal, obtained M.Tech. from IIT Dhanbad, and Ph.D. degree from Kyushu University, Japan. He was completed proficiency courses and diplomas from IISc Bangalore, IIT Kharagpur, CEDT Gorakhpur, and NRDC New Delhi. He worked as Scientist at NIRM, Government of India Research Institute, around two decades. He published over 142 research papers in various international and national journals and conferences. He is reviewer for International Journal of Measurements, Elsevier Publisher, and ISOI Journal. He obtained product patent of MBSC system and commercialized. He is regular reviewer to Indian and

xxi

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About the Editors

abroad IEEE conference papers as TPC member. He has successfully completed 15 industry-, DST-, and AICTE-sponsored research projects. He is author to a book entitled Real Time Systems for Coal Mine Applications. He has received several awards, honors and distinctions, International Bridge Fellowship, International JSPS Fellowship, International JSPS Medals, best paper awards, Gold Card NSL USA. He is Fellow of Institution of Electronics and Telecommunication Engineers and Fellow of Telangana Academy of Sciences. He is the advisory board member of IC, Cambridge University, UK. He was secretary to Instrument Society of India, Hyderabad, and EC member of IJAA. He is the life member of ISTE, BCSI, ISCA, and ISOI. His areas of research include real-time systems, wireless sensor networks/IOT, and cognitive radios. Dr. D. M. Vinod Kumar joined at National Institute of Technology Warangal in 1981 as a faculty member. He was Head, Department of Electrical Engineering, during 2007–2009, and Dean (Academic) during 2009–2011. He was Chairman, Institution of Engineers (India) Warangal Local Chapter during 2012–2014. At present, he is a Professor of Electrical Engineering at National Institute of Technology, Warangal. He obtained his B.E. and M.Tech. degrees from Osmania University, Hyderabad, during 1979 and 1981, respectively. He earned his Ph.D. degree from IIT Kanpur in the year 1996. During 2002–2003, he was Post-Doctoral Fellow at Howard University, Washington, D.C., USA. He has published more than 100 papers in international journals and conferences. He is Fellow of Institution of Engineers (India) Kolkata and the life member of Systems Society of India (SSI). He is the member of Board of Studies of the Department of Electrical Engineering, Osmania University, Hyderabad, and JNTU, Hyderabad, Kakinada, and Anantapur. He delivered expert lectures on neural networks and fuzzy logic at various institutions. His areas of interest are power systems operation and control, power system stability and security, neural networks and fuzzy logic applications, flexible AC transmission systems, power system deregulation and

About the Editors

xxiii

restructuring and smart grid technologies, multiobjective evolutionary algorithm applications, and renewable energy systems. Dr. K. S. Chandragupta Mauryan is working as a Professor in EEE and Assistant Dean R&D, Guru Nanak Institutions Technical Campus, Hyderabad. He has completed Ph.D. in Electrical Engineering from Anna University, Chennai, in the year 2015, M.E. in Power Systems Engineering from PSG College of Technology, Coimbatore, in the year 2004, and B.E. in Electrical and Electronics Engineering from Bharathiar University, Coimbatore, in the year 2001. He has also qualified the GATE 2001 Examination in Electrical and Electronics. He has published more than 30 technical papers in international and national journals. He has published 6 patents and filed 1 patent in Patent Office, India. He has presented more than 25 research papers in international and national conferences. He is the member of professional societies like IEEE, ISTE, ISRD, IRED, and IAENG. He is having 15 years of teaching and 11 years of research experience. His research interests are power systems optimization, smart grid technologies, soft computing applications, renewable energy sources, and electric vehicle.

Power Electronics

Power System Security Analysis Using FACTS Devices by Means of Intelligent and Hybrid Techniques Under Different Loading Conditions S. Venkata Padmavathi, A. Jayalaxmi and Sarat Kumar Sahu

Abstract Power system security issue is a severe concern in restructured power market. In order to conserve the security of a system, flexible alternating current transmission system (FACTS) apparatus are one of the options. In this work, node voltage deviations and line apparent power flow factors are taken as the security indices and these are considered as objectives for security problems. The devices considered are thyristor-controlled series capacitors (TCSCs), static VAR compensators (SVCs), and unified power flow controllers (UPFCs). The main idea of this work is to compare distinct algorithms such as hybrid differential evolution (DEPSO) and fuzzy adaptive gravitational search algorithm (FAGSA) to attain the good location of the devices on IEEE 30 bus network with loading conditions. Keywords DEPSO


FAGSA
FACTS
TCSC
SVC
UPFC

1 Introduction Today’s power network has become tortuous and less secure with increase of power demand. FACTS apparatus can augment power system transfer capacity and flexible line flow control [1]. These devices play a major task in power system security and can control the network parameters to influence the line power flows and voltages [2–4]. There are various types of FACTS controllers: SVC [5, 6], TCSC [7], UPFC [8], etc.

S. V. Padmavathi (&) EEE Department, GITAM (Deemed to Be University), Hyderabad, Telangana, India e-mail: [email protected] A. Jayalaxmi EEE Department, Jawaharlal Nehru Technological University, College of Engineering, Kukatpally, Hyderabad, TS, India S. K. Sahu EEE Department, M.V.G.R Engineering College, Vizianagaram, Andhra Pradesh, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_1

3

4

S. V. Padmavathi et al.

Evolutionary and fuzzy adaptive methodologies are well-liked in current years. Some reputable techniques like DE were utilized to allocate the FACTS and improve the security [9], and PSO was introduced by ‘John Kennedy and Eberhart’ [10]. It is important to know, the better location for FACTS since their cost and to evade needless transmission loss, in [11] GA-based optimization technique was implemented to get the fine placements and sizing of the FACTS to augment the network loadability, in [12] multiobjective optimization process was utilized to get the better placement of FACTS to optimize the cost, line losses and loadability. In [13], GA to ask for the good placement of multi-type FACTS in a network; in [14], genetic algorithm is exercised to advance power system security; in [15, 16], hybrid differential evolution is presented to resolve the power flow trouble and system security; in [17, 18], GSA technique is implemented; in [19], FAGSA is applied to resolve bidding problem; in [20], reactive power planning is presented. In this work, the main intention is to examine the various algorithms such as DEPSO and FAGSA to set the good location of FACTS and to get the lowest cost of FACTS apparatus, minimum loss and to improve the electrical power system security, which is obtained by bringing down the security index. These algorithms are tested using the standard IEEE 30 bus system. It is noticed that power system security is augmented by minimizing the system loss and security index.

2 FACTS Device Modelling The three FACTS utilized in this work are TCSC, SVC, and UPFC models [1, 5, 6, 12], and constraints are considered as ðiÞ  0:8XL  XTCSC  0:2XL P:u ðiiÞ  100 MVAR  QSVC  100MVAR ðiiiÞ both ð1Þ and ð2Þfor UPFC

ð1Þ ð2Þ

where XTCSC is reactance [7] added to the transmission line by employing TCSC, XL is the reactance of line, and QSVC is the reactive power interjected [5, 6] at the node. The UPFC is used to control both parameters [8, 11].

3 Power System Security The main intention of the security [2–4], [9] is to conserve the profile of voltage and line power flow within the limits. These are modelled as voltage and line apparent power security indices ‘Jv’ and ‘Js’ [9]

Power System Security Analysis Using FACTS Devices by Means …

JS ¼

n X n X i

JV ¼

i;j¼1

n X

Wi

Sij Smax ij

5

!2

 2 Wi Vi  Vref;i 

ð3Þ

ð4Þ

i

where i, j: node numbers Wi Sij Smax ij Vref;i

Weighing factor and taken as 1 Apparent power in the i − j line Apparent power limit in line i − j Nominal voltage.

4 Problem Formulation The proposed work is to diminish the installation cost of FACTS, loss, and security indices. By combining all, objective (Objfn) or fitness function is created. Objfn ¼ F ¼ a1 ðJS Þ þ a2 ðJv Þ þ a3 ðTotal Investment CostÞ þ a4 ðLossesÞ

ð5Þ

The cost functions in (US$/KVAR) of devices are expressed in Eqs. (6)–(8). For TCSC CTCSC ¼ 0:0015S2  0:713S þ 153:75

ð6Þ

CSVC ¼ 0:0003S2  0:3051S þ 127:38

ð7Þ

CUPFC ¼ 0:0003S2  0:2691S þ 188:

ð8Þ

For SVC

For UPFC

where S is the operating range of the FACTS in MVAR [20, 21]. The coefficients a1–a4 will be equal to 0.25.

6

S. V. Padmavathi et al.

5 Overview of Algorithms and Its Implementation 5.1

Hybrid Differential Evolution (DEPSO)

In the DEPSO, one-to-one competition is initiated which will provide rapid convergence swiftness towards optimum. It uses fewer populations in the evolutionary procedure to get the global result [15, 16]. To get rid of the problems in DE and PSO technique [22, 23] and to get the advantages of both, the DEPSO method is developed. The procedure is as follows: • First produce random values of population (N).This is taken as parent vector. • Determine the fitness function F1 (i) for each of the particles in the parent vector, for i = 1, 2, 3, …, N. • Now, do the operations like selection, crossover, and mutation. The consequent vector is the target vector. • Find the fitness value F2 (i) for each agent in the target vector. • Obtain the Gbest up to this iteration. • Evaluate each particle or agent velocity in the parent vector using these Pbest and Gbest values. • By using the PSO algorithm, update the positions the particles. • By using these values, evaluate the fitness value F3 (i) and compare the three fitness values. • Now, these selected set of particles become parent vector for subsequent iteration.

5.2

Fuzzy Adaptive Gravitational Search Algorithm (FAGSA)

It is a good method for controlling the parameter and to overcome the problems of GSA [19, 24], which is used to tune the ‘gravitational constant (G)’ using ‘IF/ THEN’ rules of fuzzy. Proper selection of ‘G’ provides a brace between the global and local exploration and exploitation [8, 19]. The inputs for FIS are the current best performance evaluation as ‘normalized fitness value (NFV)’ and the recent ‘G’. The outputs are ‘DG’. The membership functions are considered as triangular. NFV =

objfn  objfnmin objfnmax  objfnmin

ð9Þ

Power System Security Analysis Using FACTS Devices by Means …

7

Here, the poorer value of NFV gives the superior result. Objfn is calculated from Eq. (5). The limit of ‘G’ is considered between 0.4 and 1.0, and NFV is considered between 0 and 1.0 and ‘DG’ range in between −0.1 and +0.1. Gt þ 1 ¼ Gt þ DG

5.3

ð10Þ

Initialization

Using the algorithms, the primary particles’ population is produced haphazardly between the prearranged limits and calculated the fitness function. The FACTS variables are their placement and setting. By using these values, the objective function shown in Eq. (5) is calculated.

6 Results and Discussion The functioning of these algorithms is examined on the IEEE-30 [25] bus, and the solutions are obtained. The FACTS apparatus setting, cost, security indices, loss were found by means of these algorithms. The FACTS are installed in a particular location to lessen the loadings of active and reactive powers by regulating the powers in other directions, and the better locations are obtained by these algorithms. This is observed from security indices Js, Jv which are reduced by using these optimization techniques with loading conditions. Fuzzy rules, PSO, DE, and GSA parameters are shown in Tables 1, 2, 3, and 4. The security objectives for 40% light load, 60% over load and device location, and ratings are given in Tables 5, 6, 7, and 8 and observed that the security indices and loss are lessened, and hence, security has been progressed.

Table 1 Fuzzy rules Rule no.

NFV

G

ΔG

1 2 3 4 5 6 7 8 9

S S S M M M L L L

S M L S M L S M L

ZE NE NE PE ZE NE PE ZE NE

8

S. V. Padmavathi et al.

Table 2 Parameters of PSO C1, C2 Wmax Wmin No. of swarm being No. of iterations

1.5 0.9 0.4 50 100

Table 3 DE parameters NP

D

F

CR

Iterations

30

2

1.2

0.5

100

Table 4 GSA parameters NP

Go

Iterations

30

100

100

Table 5 Security objectives under 40% light load at bus 7 JS

JV

Cost ($) * 106

Loss

Techniques

Base case

DEPSO

FA GSA

Base case

DEPSO

FA GSA

Base case

DEPSO

FA GSA

DEPSO

FA GSA

Without FACTS

9.7

–

–

0.0263

–

–

15.7

–

–

–

–

TCSC

–

9.28

9.23

–

0.0215

0.0205

–

15.21

15.14

2.66613

2.9784

SVC

–

9.46

9.42

–

0.0154

0.0148

–

15.24

15.19

1.9376

1.5212

UPFC

–

8.98

8.94

–

0.0143

0.0140

–

15.09

15.02

4.3664

2.7637

Table 6 FACTS placement and the ratings Techniques

Line/bus DEPSO

TCSC (Xtcsc) SVC (Qsvc) UPFC (Xtcsc & Qsvc)

2–4 21 2–4

FA GSA 3–4 8 6–8

Rating DEPSO 0.0421 15.8 0.0236 13

FA GSA 0.0210 12.3 −0.054 9.8

Base case

11.3 – – –

Techniques

Without FACTS TCSC SVC UPFC

– 10.65 10.79 10.54

JS DEPSO – 10.56 10.74 10.48

FA GSA 0.0259 – – –

Base case

Table 7 Security objectives under 60% over load at bus 7

– 0.0192 0.0191 0.0181

JV DEPSO – 0.0186 0.0169 0.0172

FA GSA 19.7 – – –

Loss Base Case – 19.18 19.24 18.76

DEPSO

– 19.14 19.20 18.69

FA GSA

– 4.2953 2.6350 6.9996

– 3.9305 2.3701 7.5196

Cost($) * 106 DEPSO FA GSA

Power System Security Analysis Using FACTS Devices by Means … 9

10

S. V. Padmavathi et al.

Table 8 FACTS placement and the ratings Techniques

TCSC (Xtcsc) SVC (Qsvc) UPFC (Xtcsc &Qsvc)

Line/bus DEPSO 3–4 3 4–6

FA GSA 12–16 21 2–4

Rating DEPSO 0.2441 21.8 0.0209 24.5

FA GSA −0.0107 19.5 0.0810 21.2

7 Conclusions In this work, placement of FACTS apparatus is inexorable, because the utmost capacity of the system is utilized by means of establishing FACTS. Here, the problem of device placement is analysed using DEPSO and FAGSA methodology, and the gained results are compared. The effectiveness of the installation of these devices in advancement the security is measured in terms of diminishing the indices, loss, and cost. The study shows after the proper positioning of devices, security indices are lessened, thus progressing the system security. Further, analysis discloses that FAGSA shows better performance. Henceforth, the FAGSA yields a competent result which considerably diminishes security indices. The acquired results clearly depict that 1. The appropriate installation TCSC successfully lessens the loading of line when contrasted to SVC. 2. The fixing of SVC in good location raises the profile of voltage as contrasted to TCSC. 3. Appropriate UPFC incorporation furnishes better presentation in reducing both loading of line and voltage difference when contrasted to other FACTS controllers. Another significant practical problem considered for installing FACTS is the cost. UPFC is a costly device when contrasted with TCSC and SVC devices.

References 1. N.G. Hingorani, L. Gyugyi, Understanding FACTS: Concepts & Technology of Flexible AC Transmission Systems (Wiley-IEEE Press, New York, 2000) 2. A. Berizzi, M. Delfanti, P. Marannino, M. Savino, M. Pasquadibisceglie, A. Silvestri, Enhanced security-constrained OPF with FACTS devices. IEEE Trans. Power Syst. 20(3), 1597–1605 (2005) 3. R. Zarate-Minano, A.J. Conejo, F. Milano, OPF-based security redispatching including FACTS devices. IET Gener. Transm. Distrib. 2(6), F821–F833 (2008)

Power System Security Analysis Using FACTS Devices by Means …

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4. N. Yorino, E. El-Araby, H. Sasaki, S. Harada, A new formulation for FACTS allocation for security enhancement against voltage collapse. IEEE Trans. Power Syst. 18(1), 3–10 (2003) 5. S. Venkata Padmavathi, S.K. Sahu, A. Jayalaxmi, Modeling and simulation of static var compensator to enhance the power system security, in IEEE International Conference (Asia Pacific Conference), IEEE Proceedings, pp. 52–55 (2013) 6. H. Ambriz-perez, E. Acha, C.R. Fuerte-Esquivel, Advanced SVC models for Newton-Raphson load flow and Newton optimal power flow studies. IEEE Trans. Power Syst. 15(1), 129–136 (2000) 7. S. Venkata Padmavathi, S.K. Sahu, A. Jayalaxmi, Power system security analysis using firing angle control model of FACTS devices. Int. J. Darshan Inst. Eng. Res. Emerg. Technol. 3(2), 37–42 (2014) 8. S. Venkata Padmavathi, S.K. Sahu, A. Jayalaxmi, Power system security improvement by using fuzzy adaptive gravitational search algorithm based FACTS devices under fault condition. International Springer Conference on Soft Computing in Data Analytics, Published in—Advances in Intelligent Systems and Computing (AISC), Springer Series (2018), pp. 95– 106 9. H.R. Baghaee, B. Vahidi, S. Jazebi, G.B. Gharehpetian, A. Kashefi, Power system security improvement by using differential evolution algorithm based FACTS allocation. IEEE Power India Conference Proceedings, New Delhi, pp. 1–6 (2008) 10. J. Kennedy. R. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural networks (Perth, 1995), pp. 1942–1948 11. E. Ghahremani, I. Kamwa, Optimal placement of multiple-type FACTS devices to maximize power system loadability using a generic graphical user interface. IEEE Trans. Power Syst. 28 (2), 1–15 (2012) 12. A.L. Ara, A. Kazemi, S.A.N. Niaki, Multiobjective optimal location of FACTS shunt-series controllers for power system operation planning. IEEE Trans. Power Syst. 27(2), 481–490 (2012) 13. S. Gerbex, R. Cherkaoui, A.J. Germond, Optimal location of multi-type FACTS devices in a power system by means of genetic algorithms. IEEE Trans. Power Syst. 16(3), 537–544 (2001) 14. S. Venkata Padmavathi, S.K. Sahu, A. Jayalaxmi, Comparison of hybrid differential evolution algorithm with genetic algorithm based power system security analysis using FACTS. J. Electr. Syst. 11(2), 189–202 (2015) 15. K. Gnanambal, N.S. Marimuthu, C.K. Babulal, A hybrid differential evolution to solve power flow problem in rectangular coordinate. J. Electr. Syst. 395–406 (2010) 16. S. Venkata Padmavathi, S.K. Sahu, A. Jayalaxmi, Hybrid differential evolution algorithm based power system security analysis using FACTS. J. Electr. Eng. 15(1), 1–10 (2015) 17. E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, GSA: A gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009) 18. A. Bhattacharya, P.K. Roy, Solution of multi-objective optimal power flow using gravitational search algorithm. IET Gener. Transm. Distrib. 6(8), 751–763 (2012) 19. J. Vijay Kumar, D.M.V. Kumar, K. Edukondalu, Strategic bidding using fuzzy adaptive gravitational search algorithm in a pool based electricity market. Appl. Soft Comput. 13(5), 2445–2455 (2013) 20. P. Preedavichit, S.C. Srivastava, Optimal reactive power dispatch considering FACTS devices. Electr. Power Syst. Res. 251–257 (1998) 21. M. Saravanan, S.M.R. Slochanal, P. Venkatesh, J.P.S. Abraham, Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability. Electr. Power Syst. Res. 77(9), 276–283 (2007) 22. S. Venkata Padmavathi, S.K. Sahu, A. Jayalaxmi, Adaptive fuzzy particle swarm optimization coordination of FACTS devices to enhance the power system security. J. Electr. Eng. 15(3), 1–12 (2015)

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23. S. Venkata Padmavathi, S.K. Sahu, A. Jayalaxmi, Particle swarm optimization based control setting of TCSC for improving reliability of composite power system. Int. J. Comput. Appl. 55(14), 36–39 (2012) 24. S. Venkata Padmavathi, S.K. Sahu, A. Jayalaxmi, Application of gravitational search algorithm to improve power system security by optimal placement of FACTS devices. J. Electr. Syst. 11(3), 326–342 (2015) 25. H. Saadat, Power System Analysis (WCB/McGraw-Hill, New Delhi, 1999)

Implementation of Three-Phase Shunt Active Filter Using Instantaneous Real Power Calculation and Triangular Carrier Current Control Athira Ajith, P. V. Manitha and K. Ilango

Abstract The power quality improvement has been done with a three-phase shunt active filter (ShAF) using instantaneous real power calculation (IRPC) and triangular carrier current controller. Instantaneous real power calculation method uses the instantaneous real power (p) for reference current generation along with the triangular carrier current controller (TCCC) to produce ShAF switching pulses. The harmonics produced by the nonlinear load can be filtered out using the ShAF to a great extent using the proposed control method. The effectiveness of the algorithm is verified using PSIM simulation software for a nonlinear load of 5 kW power fed by distribution of 400 V, 50 Hz system, and then its performance is compared with that of the conventional IRPT algorithm. Keywords Shunt active filter VSI Control strategy





Real power calculation
Harmonics elimination


1 Introduction The present-day industries including transmission/distribution sectors uses solid-state power converters to a great extent. These solid-state power converters constitutes nonlinear load which can lead to serious power quality issues. The power quality issues can lead to poor power factor which can decrease the system efficiency. High-quality power has to be ensured by the utility side in case of sensitive loads. The presence of harmonic content in voltages and currents, one of the main A. Ajith
P. V. Manitha (&) Department of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, Bengaluru, India e-mail: [email protected] K. Ilango Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_2

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power quality issues, can be overcome by the use of the harmonic filters which can filter out non-fundamental components of current and voltage thereby ensuring good quality power. Power quality problems are normally solved by basic and active filters. Basic filters are less preferred due to their large size, resonance with line and their ability to provide only fixed compensation. Recent developments in active power filter [1–3] design have solved many problems related to current harmonics and compensation of reactive power. Active power filters can be either VSI or CSI that can provide necessary compensating current and voltages. A ShAF produces compensating current sufficient to satisfy the load reactive power and harmonic requirements. Therefore, harmonics caused at the source side is cancelled, and hence, we get a sinusoidal source current waveform. Even hybrid filters are gaining popularity today due to their cost-effectiveness. They retain both active and passive filter advantages.

2 System Configuration with Shunt Active Filter The test system configuration is shown in Fig. 1. The system consists of a three-phase distribution supply grid of 400 V, 50 Hz rating feeding a nonlinear load rated for 5 kW power. Most of the literature surveys focuses on q compensation for

Fig. 1 System configuration

Implementation of Three-Phase Shunt Active Filter Using …

15

reconstructing the source current waveform free of harmonics. Even the active power loss can be compensated by the system to achieve the same.

3 Control Strategy of Shunt Active Filter There are a variety of algorithms typically for three-phase ShAFs discussed in the reference papers. The IRPT, SD, DC bus voltage algorithm and Icosø are some among that [3–8]. Here, an ShAF based on instantaneous real power calculation is discussed. To demonstrate its performance, the instantaneous reactive power theory is compared with the algorithm. Also, a ShAF prototype based on this algorithm is developed and tested. (a) Instantaneous Reactive Power Theory This algorithm is based on Park’s transformation; it transfers the three-phase main voltage and load currents into dq axis as per following equations:





v/ vb IL/ ILb





#2 3 qffiffiffiffiffiffi" vsa 1= 1= 1 pffiffiffi2 pffiffiffi2 4 vsb 5 ¼ 2=3 3  3 0 2 2 vsc

ð1Þ

#2 3 ILa 1= 2 pffiffiffi 4 ILb 5  3 2 ILc

ð2Þ

qffiffiffiffiffiffi" 1 ¼ 2=3 0

1 p=ffiffiffi2 3 2

The instantaneous p and the q that the load has consumed can be obtained and represented as p ¼ v/ iL/ þ vb iLb

ð3Þ

q ¼ vb iL/ þ va iLb

ð4Þ

The ac term of the instantaneous p and the entire instantaneous q has to be mitigated by the active power filter for suppressing the current harmonics and for compensating the q. The reference compensating current can be calculated as "

Ic/ Icb

# ¼


1 v/ v2/ þ v2b vb

vb v/


  p q

ð5Þ

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The reference current can be obtained by transferring the compensating current in the dq axis back to three-phase system as shown below 2 3  1 qffiffiffiffiffiffi Ica 1= 4 I  5 ¼ 2= 6 2 cb 34  1= Icc 2 2

3
 p0ffiffiffi 3 7 Ic/ 2 5 I pffiffiffi cb  3 2

ð6Þ

From the literature reviews, it is clear that it is very difficult to implement the algorithm as an analog circuit because it requires large number of components. In this paper, real power injection theory is simulated which is compared with the simulation results of basic pq theory. Real power injection theory algorithm operates in steady state or transient state which can control the SAF in real time. (b) Instantaneous Real Power Calculation Method Choosing an efficient control scheme enables the three-phase inverter to inject sufficient and proper compensating current to the grid leading to harmonics and q compensation. Instantaneous real power calculation method is actually derived from the conventional pq theory. Instantaneous real power calculation method consists of (1) extraction of reference current to remove the reference current from the line current which is distorted. (2) PWM-VSI current control technique for switching pulse generation to drive the shunt active filter. This strategy mitigates the harmonics in the source current making them sinusoidal as well as balanced. The three-phase mains voltage and source currents are transferred to dq axis using Clarke’s transformation as shown in Eqs. 1 and 2, and instantaneous p can be calculated as given by Eq. 3. For the purpose of suppression of current harmonics and for compensating the q, the total power loss which is calculated as in Eq. 4 is considered. The instantaneous q is taken equal to 0. p ¼ pac þ pdc;losses

ð7Þ

q¼0

ð8Þ

Here, the instantaneous compensating source currents isa and isb are calculated from the instantaneous voltages va, vb with instantaneous real power p, assuming q as zero.


Is/ Isb

 ¼


1 v/ v2/ þ v2b vb

vb v/


 p 0

ð9Þ

The reference compensating source currents in turn can be calculated using inverse Clarke’s transformation as given in Eq. 6. It is obvious that there are reductions in calculations.

Implementation of Three-Phase Shunt Active Filter Using …

17

Fig. 2 Block diagram of triangular carrier current controller

Next section in implementing the instantaneous real power calculation method is the PWM-VSI current control technique for switching pulse generation to drive the ShAF In this paper, an indirect current control technique named ‘triangular carrier current controller’ is discussed. The block diagram of the triangular carrier current controller is shown in Fig. 2. For implementing this technique, the three-phase actual currents (isa, isb, isc) are compared with their reference counterparts (i*sa, i*sb, i*sc) which are generated by reference current extraction method. The resultant signal is then passed through a PI controller which is then again compared with a triangular carrier wave of frequency equal to the switching frequency of the PWM inverter. The pulses are then used to trigger the switches of the ShAF. The advantage of using the above-mentioned control technique is that it offers instantaneous response without any delay. Moreover, indirect current control technique has been adopted owing to reasons namely less percentage of THD in source current, less computation time and low power consumption.

4 Design of Shunt Active Filter Parameters The three-phase inverter which does the function of active filtering has an energy storage capacitor (dc link capacitor) at the DC side. The inverter is then connected to the filter via coupling inductance. (a) Coupling/Interfacing Inductance Coupling or interfacing inductor is essential for isolation of the filter from the power system and also for protection from transient disturbances. The value of coupling inductance should be between Lmin and Lmax. Lmin ¼

DVmin DV  max  5 mH ðfor 5% drop), Lmax ¼  8 mH xg  Imax 4  ddti  fs max

where DVmin is the IXL drop at fundamental. The value of inductance should be between Lmin and Lmax which can be taken as 6 mH.

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Fig. 3 Source voltage, load current, filter current and source current of ‘a’ phase of three-phase shunt active filter based on basic PQ theory for thyristor bridge rectifier load

Fig. 4 PSIM model of the three-phase system with active filter

(b) DC Link Capacitance DC link capacitance value is measured based on the energy change which occurs at the DC link capacitance.   1 2 2 E ¼  Cdc  Vdc  Vdc min 2

ð10Þ

Implementation of Three-Phase Shunt Active Filter Using …

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Fig. 5 Source voltage, load current, filter current and source current of ‘a’ three-phase shunt active filter based on instantaneous real power calculation method for thyristor bridge rectifier load

Consider variation of dc link voltage for T = 0.5 s, apparent power rating of the filter P = 3 kVA and Vdc = 680 V, we get Cdc ¼ 0:0065  6 mF.

5 Simulation Results and Discussions A 5 kW load consisting of a three-phase thyristor bridge rectifier is connected to a 3 ø, 400 V, 50 Hz supply. A 3 kVA filter prototype has been developed. The PSIM model of the system is shown in Fig. 6. (a) Without Filter: Thyristor bridge rectifier feeding a resistive load was studied without using a filter and its performance is observed. The load draws a fundamental current value of 10.03 A from the source. The system is simulated under balanced source and load conditions. The source current was found to be highly distorted.

Fig. 6 Adder circuit using 741 IC

20 Table 1 Simulation parameters

A. Ajith et al. Simulation parameters

Values

Supply voltage DC link capacitance Coupling Inductance DC link voltage

3 ø, 400 V, 50 Hz 6 mF 6 mH 680 V

(b) With Instantaneous Reactive Power Theory-based Shunt Active Filter: The simulation is then performed with basic IRPT-based shunt active filter in the system. Under balanced source and load conditions, the system is simulated successfully. The simulation results with the addition of active filter which includes load current, source current and actual filter current are shown in Fig. 3. The measured fundamental value of current drawn from the source is 9.548 A. (c) With Instantaneous Real Power Calculation-based TCC controlled Shunt Active Filter: Using shunt active filter, the simulation is done in the system. The circuit for instantaneous real power calculation method was simulated in PSIM. The PSIM model of the simulated system is shown in Fig. 4. Simulation parameter values are the same as given in Table 1. The simulation results with the addition of active filter which includes load current, source current and actual filter current are shown in Fig. 5. The THD is maintained within the prescribed limits. Moreover, after interfacing the filter, comparatively smaller value of fundamental value of current, which is 8.172 A, is absorbed from the source. From the results, it is observed that, it is easier to implement IRPC theory due to its simplicity. Secondly, it is clear that the harmonic content in the source current waveform produced by IRPC theory is less than that by IRPT theory. Moreover, from the results obtained from the simulation, it has been identified that the system based on IRPC theory draws relatively smaller fundamental current value when compared to that of the IRPT-based system.

6 Analog Computation of Instantaneous Real Power Calculation Method The most application circuits using 741 IC are inverting and non-inverting amplifier, integrator, differentiator, low-pass filter, high-pass filter and band-reject filter. Some such circuits which are used in the implementation of the controllers are adder, subtractor, comparator and low-pass filter. The circuit diagrams of those blocks are given below in Figs. 6, 7, 8 and 9, respectively.

Implementation of Three-Phase Shunt Active Filter Using …

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Fig. 7 Subtractor circuit using 741 IC

Fig. 8 Comparator circuit using 741 IC

Fig. 9 Low-pass filter circuit using 741 IC

7 Hardware Implementation of the Controller To test the effectiveness of the controller, it is required to complete the hardware implementation of the system. It can be implemented either in analog or digital format. Analog circuits are well known for their accurate response with some propagation delay. Analog circuits are susceptible to malfunctioning due to offsets in the electronic circuits and tolerance of the components caused by heating. Texas Instruments facilitates development of software for TI DSPs by offering Code Composer Studio (CCS) Integrated Development Environment (IDE). Used in

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Fig. 10 Digital set-up in MATLAB

combination with a Target Support Package software and Real Time Workshop software, CCS provides an integrated environment that once installed requires no coding. The IRPC algorithm can be completely implemented digitally, by replacing all the analog circuit by the digital controller. The digital implementation of this algorithm will allow adjustment of the gain in the amplifiers thereby permitting the algorithm to be implemented without any modification, for a wide range of ratings of three-phase system. (a) Digital Set-up: The digital controller used for the IRPC algorithm is TMS320f28335. The set-up consists of a F28335 ADC, the transition block, the IRPC algorithm subsystem, digital output and target preference. Figure 10 shows the digital set-up of the algorithm in MATLAB. (b) Analog-to-Digital Converter (ADC): The ADC block gives the digital values representing the analog input signal and stores the converted values in the result register of the digital signal processor. Since the C280x/C28x3x ADC is a 12 bit converter, the output values are in the range 0–4095. A and B DSP module is used (ADCINA0 through ADCINA7 and ADCINB0 through ADCINB7). Six input channels are used for the IRPC algorithm In the above figure, rate transition block, when inserted between two blocks of differing sample rates, automatically configures its input and output sample rates. The algorithm is implemented in MATLAB and is connected to the TMS kit. (c) Hardware Set-up: The inputs are fed from voltage and current sensors. Voltage sensors are prepared with transformers of ratings 6 V–0–6 V and 500 mA. It will make the voltage to a very low range. Again voltage divider circuit is made to reduce the voltage a still smaller range of 1 V which is applied safely for the operation of the TMS kit. The current sensors used are from EL100P2 series manufactured by ABB. It works on the principle of closed-loop Hall effect technology.

Implementation of Three-Phase Shunt Active Filter Using …

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Fig. 11 Sensed three-phase voltage waveforms

Fig. 12 Sensed three-phase current waveforms

The input voltage and current-sensed waveforms are shown as below. The load used is nonlinear which is an AC voltage controller with a firing angle of 60 degree connected to a resistive load. It can be seen that the resultant current waveform, shown in Fig. 12, is distorted (Fig. 11). The sensed source voltage and current waveforms are offsetted using an offset circuit. The ADC module of the TMS kit will function only with unipolar signals. The offsetted current and voltage signals are fed to the ADC pins of the processor and the outputs are taken from GPIO pins. The output is observed in digital oscilloscope. Figure 13 shows the laboratory hardware set-up. Output rms line voltage waveforms of the Semikron IGBT inverter are observed with observations taken at a particular firing angle of 60°. Test conditions involve a supply line voltage of 210 V. AC voltage controller circuit with the firing angle of 30° connected to a resistive load bank. (d) Hardware Results: A DC link voltage of 680 V as per design is applied across the inverter. Line voltages Vab, Vbc, Vca were observed as in Fig. 14.

24

Fig. 13 Laboratory hardware set-up

Fig. 14 Output line voltages of the inverter

A. Ajith et al.

Implementation of Three-Phase Shunt Active Filter Using …

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Fig. 15 Compensating currents

The inverter functioning as a shunt active filter injects compensating currents to the grid which eliminates the current harmonics. The resultant compensating current waveform obtained across an inductor before interfacing the inductor to the grid is shown in Fig. 15. The inverter is integrated to the grid after synchronisation. The harmonics are eliminated; then, the source current becomes purely sinusoidal. Thus, the power quality of the system which has deteriorated to a great extent has been restored thereby protecting the other loads connected to the same system.

8 Conclusion The past few years saw an increasing importance for improving the supply power quality for assuring good quality power been supplied to the loads connected to the grid. Various filtering techniques have been developed which includes passive, active and hybrid filters. Active filters are based on certain algorithms which ensure generation of proper triggering pulses to the filter. There exist a number of algorithms among which a simple one has been formulated and analysed. The analysis shows that the algorithm ensures better performance with less complexity in formulation. The filter developed is found to work efficiently at nonlinear load conditions.

References 1. M. Rashid, Power Electronics Handbook (University of West Florida, Copyright @, Academic Press, 2001) 2. B. Singh, K. Al-Haddad, C. Ambrish, A review of active filter for active filters for power quality ımprovement. IEEE Trans. Industral Electron. 46(5) (1999) 3. J.H. Akagi, Y. Kanazaw, A. Nabel, Instantaneous reactive power compensators comprising switching devices without energy storage components. IEEE Trans. Ind. Appl. 20(3) (1984)

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4. C.L. Chen, C. Lin, C. Huang, Reactive and harmonic current compensation for unbalanced three phase systems using synchronous detection method. Elect. Power sys. Res. 26(6), 163–170 (1993) 5. S. Rahmani, K. Al-Haddad, F. Fnaiech, P. Agarwal, Modified PWM with indirect current control technique applied to a single phase shunt active power filter, in IEEE Conference on Power and Energy (2003) 6. H.L. Jou, Performance comparison of three phase active power filter algorithmns. IEEE Proc. 142(6) (1995) 7. G. Bhuvaneswari, M.G. Nair, Design, simulation, and analog circuit ımplementation of a three phase shunt active filter using the ıcosø algorithm. IEEE Trans. 23(2) (2008) 8. P. Karuppanan, Design and Implementation of Shunt Active Power Line Conditioner using Novel Control Strategies. Doctor of Philosophy Thesis, Department of Electronics &Communication, NIT Rourkela 9. K. Ilango, P.V. Manitha, M.G. Nair, Modified IcosU controller for shunt active filter interfacing renewable energy sources and grid, in AASRI Conference on Power and energy Systems (2012) 10. S. Sindhu, M.R. Sindhu, M.G. Nair, G.K. John, Implementation of three phase shunt hybrid filter using ıcos U algorithm. Asian Power Electron. J. 5(1) (2011)

A Small DC-Link Capacitor Inverter Fed by Front-End Three-Phase Diode Rectifiers Used to Control Induction Motor Ashwini V. Potdar and Ch. Mallareddy

Abstract This paper contains an approach to applications of a three-phase variable speed drive to improve power density as well as reliability by using small film capacitor inverter-based induction motor control. A performance by the electrolytic capacitor is very poor for the inverter fed by front-end diode rectifiers intercepted by motor controller which is compounded and strong in nature. The controller is designed with a hexagon voltage manipulating controller (HVC) and with composition of a model-based controller (MBC). The work of MBC and HVC together gives an output. MBC gives the command output voltage with losses of rotor flux with bisection of the torque. The enjoin voltage vector is set on simply by the requirement of torque command and the inverter which has a hexagon-shaped voltage boundary in the HVC mode. Prosperous utilization of the control proceed towards is supported by a Mathematically and graphically measure that normally head to a single voltage selection rule. The paper discovers the performance reactivity is very sensible to accumulation of motor parameter to sort out how to remove unwanted AC distortion or oscillations with state filter design.




Keywords Hexagon voltage manipulating controller (HVC) Model-based controller (MBC) Small film capacitor inverter Front-end diode rectifiers







1 Introduction The HVAC system is low-cost application of speed drive which is a variable; in this diode, rectifiers are generally used as the front-end circuit for higher reliability [1]. As shown in Fig. 1, three-phase diode rectifier and PWM inverter for IM drive electrolytic capacitors are generally used to balance time-to-time base i/p and o/p power and also voltage spike suppression occurred by operation of switching and A. V. Potdar (&) Punysholk Ahilydevi Holkar Solapur University, Solapur, India Ch. Mallareddy FTCOER, Sangola, Solapur, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_3

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A. V. Potdar and Ch. Mallareddy

leakage of inductance [2, 3]. And general study power electronics devices failed because of voltage spikes caused by parasitic lead of inductance. Electrolytic capacitors are responsible for most of the breakdown according to the survey [4]. Therefore, for long time closed-loop current controller is used to regulating the air-gap torque and flux linkages in AC motors according to the studies [4–6].

2 Problem Statement As shown in Fig. 1 as per given construction, DC-link voltage and o/p power to the motor decrease with the time because of the absence of energy storage device. So, the speed is decreased below base level and this leads to the problem of field weakening, anti-windup control and over-modulation. On the other hand, there is problem regarding utilization of voltage at extinct level because of circular voltage boundary.

3 Methodology This paper gives the new idea about the position sensor-less vector controlled IM drive system integrated in HVAC system a Small DC link film fed by capacitor inverter a three-phase diode front-end diode rectifier is feeds power supply to motor. Above-mentioned problems overcome by PI motor current regulator-free control strategy with combination of hexagon voltage manipulating controller (HVC). The MBC deals with the command output voltage with the intersection of torque and rotor flux linkage. Figure 2 shows the idea about the various facts related to vectors. The MBC controls action at low speed, and HVC operates at high speed. ve dsHVC

¼

Bn þ

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi B2n  4Mnc 2Mn

Fig. 1 Three-phase diode rectifier and PWM inverter for IM drive

ð1Þ

A Small DC-Link Capacitor Inverter Fed by Front-End Three-Phase …

29

Fig. 2 Proposed IM control strategy for small capacitor inverters e ve qsHVC ¼ Mn
vdsHVC þ Bn

ð2Þ

where c¼

ve ds

Te 3 P L2m 1 2 2 Lr w2e LS rLs

MBC ¼ we rLs

ve dsMBC ¼ we Ls

! Te þ Rs ie qs 3 P Lm e k 2 2 Lr dr

ð3Þ

ke dr þ Rs ie qs Lm

ð4Þ

Equations 1, 2 deal with the hexagonal voltage boundary, and Eqs. 3, 4 deal with model-based controller which is shown in Figs. 3 and 4. The motor torque is regulated around a desired torque line in the presence of rapid voltage variations. Figure 4 shows the idea about the representations of stator voltage solutions between the torque curves and rotating hexagon, which bus shrinks the inverter DC bus voltage.

Fig. 3 Voltage command selection in the MBC mode

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A. V. Potdar and Ch. Mallareddy

Fig. 4 Voltage command selection in the HVC mode

In this paper, we are replacing the electrolytic capacitor with the DC-link film capacitor, as well as design the state filter as shown in Fig. 5. So, from the following simulation result we can compare between systems with different capacitors. From the following figures, we can calculate THD also, so the results are improved in terms of DC-link film capacitor.

Fig. 5 Overall control block diagram

A Small DC-Link Capacitor Inverter Fed by Front-End Three-Phase …

31

4 Result 4.1

Output Graph for Electrolytic Capacitor

See Fig. 6.

(a)

(b)

Fig. 6 a Simulink result with parameter 1. DC-link voltage, 2. flag signal, 3. air-gap torque, 4. s rotor flux linkage; b X–Y-axis graph of hexagonal voltage boundary X-axis vs ds and Y-axis vqs

32

4.2

A. V. Potdar and Ch. Mallareddy

Output Graph for DC-Link Film Capacitor

Figure 6a, b shows the result of the system with the electrolytic capacitor. Figure 7a, b shows the proposed system with DC-link film capacitor, and 6.4 shows

(a)

(b)

Fig. 7 a Simulink result with parameter 1. DC-link voltage, 2. flag signal, 3. air-gap torque, 4. s rotor flux linkage b X–Y-axis graph of hexagonal voltage boundary X-axis vs ds and Y-axis vqs

A Small DC-Link Capacitor Inverter Fed by Front-End Three-Phase …

33

the result of the same. The resulting figure clearly shows the difference between the results by using the electrolytic capacitor.

5 Conclusion This paper gives analytical solution leading to the dynamic voltage modification at each time step with respect to the available DC bus voltage from the results of both methods. We can say that DC-link film capacitor has advantages than electrolytic capacitor. By means, hexagonal boundary losses are reduced.

References 1. K.W. Lee, M. Kim, J. Yoon, S.B. Lee, J.Y. Yoo, Condition monitoring of DC-link electrolytic capacitors in adjustable-speed drives. IEEE Trans. Ind. Appl. 44(5), 1606–1613 (2008) 2. M.L. Gasperi, Life prediction modeling of bus capacitors in AC variable frequency drives. IEEE Trans. Ind. Appl. 41(6), 1430–1435 (2005) 3. A. Layhani, P. Venet, G. Grellet, P.J. Viverge, Failure prediction of electrolytic capacitors during operation of a switchmode power supply. IEEE Trans. Power Electron. 13(6), 1199– 1207 (1998) 4. A.M. Imam, T.G. Habetler, R.G. Harley, D.M. Divan, Real-time condition monitoring of the electrolytic capacitors for power electronics applications. in Proceedings IEEE Applied Power Electronics Conference (2007), pp. 1057–1061 5. A. Yoo, S.K. Sul, H. Kim, K.S. Kim, Flux-weakening strategy of an induction machine driven by an electrolytic-capacitor-less inverter. IEEE Trans. Ind. Appl. 47(3), 1328–1336 (2011) 6. L. Malesani, L. Rossetto, P. Tenti, P. Tomasin, AC/DC/AC PWM converter with reduced energy storage in the DC link. IEEE Trans. Ind. Appl. 31(2), 287–292 (1995)

Different Topologies of Inverter: A Literature Survey Kalagotla Chenchireddy, V. Jegathesan and L. Ashok Kumar

Abstract DC to AC control change is a key job in the cutting edge set up of age, transmission, appropriation, and use. DC to AC control converters assume key job in variable recurrence drives, uninterruptible power supplies, cooling, and high-voltage DC control transmission, electric vehicle drives, and static VAR compensators. This paper exhibits a survey on most significant topologies and strategies of control of inverters. Keywords Inverter topologies


Modulation techniques
Reduce device count

1 Introduction DC to AC control change is a key activity in the bleeding edge set up of age, transmission, transport, and use. DC to AC control converters accept key employment in Variable Recurrence Drives (VRD), uninterruptible power supplies (UPS), cooling (AC) and high-voltage DC control transmission (HVDC), electric vehicle drives, static VAR compensators. In light of the possibility of the yield voltage waveforms, inverter can be named: single-stage, three-phase, two-measurement inverters and stunned inverters. In [1], surveyed nine reduce contraption count stunned inverters. Stunned inverters continue grabbing hugeness for high power and medium voltage applications. The upside of reduce device stunned measurement inverters, direct structure, low conduction and trading setbacks, diminished parts, less cost. In [2], studied single-stage transformer less inverters. These inverters planned for photovoltaic applications. Transformerless inverters are growing unmistakable quality in K. Chenchireddy (&)
V. Jegathesan Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India L. Ashok Kumar Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, India © Springer Nature Singapore Pte Ltd. 2020 H. S Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_4

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European and Australian markets. The advantages of transformerless inverter are lightweight, high change profitability, lightweight, minimal size, low spillage current, and high constancy. In [3], surveyed Z-source inverter topology enhancements and talked about favorable circumstances and impediments of Z-source inverter. In [4], evaluated SiC MOSFET-based three-stage inverter lifetime expectation. In [5], looked into module inverter topologies. There are two noteworthy viewpoints survey in this paper: (1) different inverter topologies (2) audit different inverter control strategies.

2 Introduction to Inverter Topologies This section reviews the different inverter topologies presented in literature.

2.1

Nine-Level-Reduced Device Count Active Neutral-Point-Clamped Inverter

Figure 1 indicates nine-level-diminished gadget tally [6] dynamic nonpartisan point braced inverter (9L RDC ANPC Inverter). This inverter defeats the issues of 5L ANPC which are improved yield waveform quality, diminish number of gadgets, and lessen control misfortune.

2.2

Multi-input Zero Current Switched DC/DC Front-End-Converter-Based Multi-level Inverter

Figure 2 indicates Multi-Input Zero Current Switched DC/DC [7] Front-EndConverter-Based Multi-level Inverter. The proposed inverter coordinates two diverse sustainable power sources. Notwithstanding for inconsistent info voltages at the information side, the converter moves about equivalent flows, which lessens transformer immersion related issues.

Fig. 1 9L RDC ANPC Inverter

Different Topologies of Inverter: A Literature Survey

37

Fig. 2 Multi-input zero current Switched DC/DC front-end-converter-based multi-level inverter

2.3

Cross-Connected Source-Base Multi-level Inverter

Figure 3 shows cross-related [8] source-base stunned inverter (CCS-MLI). The proposed CCS-MLI vanquishes the issues differentiated and the set up fell H-interface inverter which are DC voltage sources, diodes, driver circuits, decrease device numbers, the unpredictability size, cost and backing.

2.4

Four-Switch-Based Three-Phase Inverter

Figure 4 demonstrates four-switch based [9] three-phase inverter. The proposed inverter reduced two switches differentiated and old-style three-phase inverter. The four-switch-based three-organize inverter expected for maintainable power source mix.

2.5

Three-Phase Voltage Source Grid-Connected Interleaved Inverter

Figure 5 three-stage voltage source [10] lattice associated interleaved inverter. The upsides of interleaved inverter diminished channel size, and high-lattice aggravation dismissal contrasted with other ordinary two-level voltage source inverter with LCL yield channel.

2.6

A New Single-Phase Cascaded Multi-level Inverter

Figure 6 shows another single-phase [11] cascaded multi-level inverter. The principle focal points of new single-stage fell staggered inverter was expanding the quantity of yield levels by diminishing the quantity of IGBTs, control diodes, door drive circuits, and dc voltage sources.

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Fig. 3 CCS-MLI

Fig. 4 Four-switch-based three-phase inverter

2.7

Single-Phase Multi-level Inverter

In Fig. 7 a staggered inverter utilizing [12] arrangement/parallel transformation of dc voltage source was proposed. The proposed inverter decreased the quantity of exchanging segments contrasted and ordinary staggered in a similar number of yield voltage levels.

2.8

Seven-Level Inverter

Figure 8 demonstrates seven-level [13] inverter. The proposed inverter utilized low-pass channel and decreased absolute symphonious contortion. The exchanging misfortune and voltage worry over the influence gadgets diminished the proposed inverter. Figure 9 demonstrates single-stage [14] six-level inverter. This inverter intended for medium-power and high-voltage applications. The benefits of these inverter diminished number of segments, control misfortune, and the expense additionally diminished.

Different Topologies of Inverter: A Literature Survey

39

Fig. 5 Three-phase voltage source grid-connected interleaved inverter

2.9

Single-Phase Six-Level Inverter

In [15], fell sub-staggered inverter. This inverter advantages decreased number of switches, number of DC sources, and expanded number of yield voltage level. This inverter worked both symmetric and hilter kilter conditions. In [16], highproficiency two-organize three-level matrix associated PV inverter. This inverter conquers the low effectiveness issue of old-style two-organize inverter. In [17], high recurrence attractive connection-based fell staggered inverter. The principle highlight of this inverter is adaptability, least number of intensity electronic parts without changing execution and increasingly number of yield levels. In [18, 19], single-stage transformer less inverters structured and looked into. The transformer less inverters for lattice associated applications. The benefits of transformer less

40

Fig. 6 A new single-phase cascaded multi-level inverter

Fig. 7 Single-phase multi-level inverter

K. Chenchireddy et al.

Different Topologies of Inverter: A Literature Survey

41

Fig. 8 Seven-level inverter

Fig. 9 Single-phase six-level inverter

inverters higher proficiency, high unwavering quality, no spillage reactance and lower explicit cost, low air conditioning yield mutilation was accomplished, higher exchanging recurrence activity was permitted to lessen yield current swell. In [20], Improved Cascaded Multi-Level Inverter (CMLI). The improved CMLI numerous favorable circumstances contrasted with traditional CMLI which are decreased switch check, minimization of the spillage current, low exchanging and conduction misfortunes.

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3 Conclusion This paper has been tended to a review of prevalent inverter topologies that are most regular in research and mechanical applications. These topologies are single-stage transformerless inverter and fell inverter. Other than that, it appears just as the prominent methods of control of staggered that are SPWM, SVM, space vector control and specific symphonious end PWM. It is a survey that presents a general thought with respect to inverters topologies and their control techniques and presents a significant theoretical for research and perception.

References 1. K.K. Gupta, A. Ranjan, P. Bhatnagar, L.K. Sahu, S. Jain, Multilevel inverter topologies with reduce device count: a review. IEEE Trans. Power Electron. 31(1) (2016) 2. R.C. Variath, M.A Andersen, O.N. Nielsen, A. Hyldgard, A review of module inverter topologies suitable for photovoltaic systems. IEEE (2010) 3. O. Ellabban, H. Abu-Run, Z-source inverter topology improvements review. IEEE Ind. Electron. Mag. (2016) 4. Z. Ni, X. Lyu, O.P. Yadv, D. Cao, Review of SiC MOSFET based three-phase inverter lifetime prediction. IEEE (2017) 5. R.C. Variath, M.A.E. Andersen, A review of module inverter topologies suitable for photovoltaic systems. IEEE (2010) 6. N. Sandeep, U.R, Yaragattin, Designand implementation of active neutral-point nine level reduce device count inverter. IET power electron 11 (2018) 7. N.K. Redid, M.R. Ramteke, H.M. Suryawanshi, An isolated multi-input ZCS DC-DC front-end-converter based multilevel inverter for the integration of renewable energy Sources. IEEE Trans. Ind. Appl. (2017) 8. R. Agrawal, S. Jain, Multilevel inverter for interfacing renewable energy sources with low/ medium- and high-voltage grids. IET Renew Power Gener 11 (2017) 9. S. Dasgupta, S.K. Sahoo, Application of four-switch based three-phase grid connected inverter to connected renewable energy source to a generalized unbalanced micro-grid system. IEEE (2011) 10. M.A. Abusara, S.M. Sharkh, Design and control of a grid-connected interleaved inverter. IEEE Trans. Power Electron. 28(2) (2013) 11. E. Babaei, S. Laali, S. Alilu, Cascaded multilevel Inverter with series connection of novel H-bridge basic units. IEEE Trans. Ind. Electron. (2013) 12. Y. Hinago, H. Koizumi, A single-phase multilevel inverter using switched series/papallel DC voltage sources. IEEE Trans. Ind. Electron. 57(8) (2010) 13. C.-H. Hsich, T.-J. Liang, S.-M. Chen, S.-W. Tsai, Design and implementation of a novel multilevel DC-AC inverter. IEEE Trans. Ind. Appl. (2016) 14. Q.A. Le, D.-C. Lee, A novel six-level inverter topology for medium-voltage applications. IEEE Trans. Ind. Electron. (2016) 15. M.F. kangarlu, E. Babaei, A generalized cascaded multilevel inverter using series connection of sub-multilevel inverters. IEEE (2011) 16. J.-S. Kim, J.-M. Kwon, B.-H. Kwon, High-efficiency two-stage three-level grid-connected photovoltaic inverter. IEEE Trans. Ind. Electron. (2017) 17. M.M. Hasan, A. Abu-Siada, S.M. Islam, A new cascaded multilevel inverter topology with galvanic isolation. IEEE Trans. Ind. Appl. (2018)

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18. S.V. Araujo, P. Zacharias, R. Mallwitz, Highly efficient single-phase transformer less inverters for grid-connected photovoltaic systems. IEEE Trans. Ind. Electron. 57(9) (2010) 19. B. Gu, J. Dominic, J.-S. Lai, C.-L. Chen, T. Labella, B. Chen, High reliability and efficiency single-phase transformer less inverter for grid-connected photovoltaic systems. IEEE Trans. Power Electron. 28(5) (2013) 20. S. Jain, V. Sonti, A highly efficient and reliable inverter configuration based cascaded multi-level inverter for PV systems. IEEE (2016)

Analysis and Design of Extended Range Zero Voltage Switching (ZVS) Active-Clamping Current-Fed Push–Pull Converter Koyelia Khatun and Akshay Kumar Rathore

Abstract This paper proposes extended soft-switched active-clamped type current-fed isolated push–pull voltage DC–DC converter. Proposed topology retains soft-switching for extended operating range. Steady-state analysis and simulation results are demonstrated. The turn-off voltage spike is eliminated. The higher load voltage is achieved with the help of voltage doubler on the load side. To validate the proposed analysis, design, and performance evaluation, simulation results are presented. Keywords Fuel cells


DC–DC power conversion
Soft-switching

1 Introduction In recent years, developing low-cost, high-efficient and small-size power conversion systems for renewable energy sources is getting more attention, due to environmental aspects and limitation of global energy sources. Fuel cells are popular alternative energy resources as they provide continuous power in all seasons and are not dependent on weather condition unlike solar and wind energy sources. The proposed paper introduces an extended range soft-switched small-size, lightweight and low-cost converter. The main concern is to maintain soft-switching over the wide operating range of source voltage and load current owing to fuel flow and fuel cell stack temperature.

K. Khatun
A. K. Rathore (&) Electrical and Computer Engineering, Gina Cody School of Engineering and Computer Science, Montreal, QC, Canada e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_5

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K. Khatun and A. K. Rathore iin L Sa1

vin

Cin

Da1

Da2

Sa2

Lk1 D1

C1 Co

Lp

CC Ds1

SM1

Ds2

1:n

SM2 Lk2

RL C2

D2

Fig. 1 Active-clamped L–L type pulse width modulated current-fed push-pull DC–DC voltage doubler

Many DC–DC circuits are presented for this application [1–15], but hardly, any of them were able to maintain soft-switching over the complete load and source variation. Converter proposed in [1] is voltage-fed secondary controlled voltage doubler with additional devices and control requirements with high circulating current. Voltage-fed converters with inductive output filter come with variety of problems such as duty cycle loss, secondary resonance, snubber across secondary and limited soft-switching capability. The ZVS is achieved by using many extra components including resonant tank or auxiliary transition circuits making the circuit complex and less efficient. A detailed study of ZVS DC/DC converters is reported in [5]. Majority of the converters lose soft-switching with supply voltage variation. Current-fed half-bridge DC/DC converter topology [4, 6, 7] was justified for such applications requiring high voltage gain. However, hard-switching and switch turn-off voltage spike are the major limitations. An active-clamping [8, 12– 15] based solution was proposed, analyzed and designed for device voltage clamping and soft-switching. Auxiliary active-clamping circuit limits the voltage overshoot effectively along with achieving soft-switching but fails to maintain ZVS for the extended operating range of load current and source voltage. In order to achieve extended range soft-switching operation, variable frequency switching approach is usually adapted and that is complex. Maintaining soft-switching over wide operating range of source voltage and load current, while maintaining high efficiency, notably for high voltage gain applications is a challenge. This article avails magnetizing inductance energy of the transformer to elevate the soft-switching range of the semiconductor devices and introduces a new design. An active-clamped current-fed push–pull voltage doubler is proposed for higher voltage gain is illustrated in Fig. 1. Steady-state operation, mathematical analysis, circuit design and simulation results of this converter are illustrated.

Analysis and Design of Extended Range Zero Voltage Switching …

47

2 Operation and Analysis of the Converter The proposed configuration as illustrated in Fig. 1 is obtained from the hard-switched push–pull converter by adding two auxiliary switches Sa1, Sa2 and one high-frequency capacitor Cc. For simplicity, transformer with single winding on the secondary side is used. The transformer is used to provide isolation and voltage matching. Converter consists of two main switches SM1 and SM2, two anti-parallel diodes DS1 and DS2, two auxiliary clamping switches Sal and Sa2, two clamping diodes Da1 and Da2 and a clamping capacitor CC. Besides, the current feeding is provided by the constant voltage source Vin in series with the input inductor L. The push–pull transformer is represented by center-taped primary windings LP1 and LP2 and the secondary windings Ls. The leakage inductances are reflected on the primary side by LK1 and LK2. Finally, the output is constituted by the voltage doubler diodes D1 and D2, voltage doubler capacitors C1, C2, output filter capacitor Co and the output resistance Ro. Cs1, Cs2, Ca1 and Ca2 are being the snubber capacitors of their respective switches. The purpose of using voltage doubler on the secondary side is to increase the voltage at the output with less components count. Voltage doubler is electrically controlled circuit which charges the capacitor from input voltage through switches and develops 2  the voltage across the load as its input (voltage on secondary side of transformer). Figure 2 indicates the gate pulses VgM1, Vga1 for switches SM1 and Sa1, respectively. The two main switches SM1 and SM2 are operated with gating pulses delayed by half switching cycle with an overlap. Complimentary gating pulses control the auxiliary switches. Operational waveforms are illustrated in Fig. 3.

Fig. 2 Gating signals for the devices

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K. Khatun and A. K. Rathore

Vgm2 Vgm1 Vga2 Vga1 Iin+I’Lp, peak

I(Ls)

-(Iin+I’Lp, peak) I’Lp, peak

I(Lp) -I’Lp, peak

3Iin/2+I’Lp,peak

-I’Lp, peak

I(M1)

Iin/2+I’Lp, peak

Iin/2-I’Lp, peak

I(M2) 3Iin/2+I’Lp,peak

I(a1)

Iin/2+I’Lp, peak Iin/2-I’Lp, peak

I(a2) I(D1)

I(D2) Vab

to t1

t3

t2

t4

t5t6 t7

t8

t9

t11 t10 t12

t13 t14 t 15

Fig. 3 Operational waveforms of proposed converter configuration

t18 t16 t17

Analysis and Design of Extended Range Zero Voltage Switching …

49

3 Design In this section, converter design is explained for the following: Rated load power Po (Full load) = 1kW, input voltage Vin = 22 to 41 V, output voltage Vo = 400 V, minimum load Ro = 160 X, output power Po (10% load) = 100 W, minimum load Ro = 1600 X, switching frequency fs = 40 kHz, converter’s efficiency η = 95%. 1. Average input current: Input current Iin ¼ D¼1

Po ¼ 47:8 A g:vin

ð1Þ

n:Vin V0

2. Dmax is selected at minimum input voltage, i.e., Vin = 22 V and full load based on maximum switch voltage rating VSW(max) using Vmax ¼ 1 

VinðmaxÞ VSWðmaxÞ

ð2Þ

For VSW(max) = 140 V, Dmax = 0.85 Transformer turns ratio: 0.5 < D < Dmax 0:5\1 

n:Vin \0:85 V0

2.7 < n < 9.1 Lower turns ratio reduces the range of ZVS. Higher high turns ratio increases the conduction loss. n = 4.5 for D = 0.77 is chosen. 3. Inductor values L and Lp

L:

DIin ¼ Vin DT

ð3Þ

DIin ¼ 2:5% of Iin ¼ 1:25 A L ¼ 125 lH

ð4Þ

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K. Khatun and A. K. Rathore

Lp ¼ K:4:52 :1:34 lH

ð5Þ

4. Values of leakage inductances: LLK : LLK

  Iin 1 Vin Vo  ¼ ð1  DÞTs 2 1  D n   1 V in Vo 1  D  ¼ : 2 1D I in :f s n

ð6Þ

LLK1 ¼ LLK2 ¼ 1:3 lH 5. Clamping capacitor:

C¼

Iin :ð1  DÞ2 :Ts 0:02ðVin Þ

ð7Þ

Cc ¼ 60 lF 6. Voltage doubler diodes: Diodes voltage rating VD(max) = V0 = 400 V Average voltage doubler current: IDðavgÞ ¼

Po 2:Vo

ð8Þ

IDðavgÞ ¼ 1:25 A 7. Output capacitor: Co ¼ Co = 22 lF; C1 = C2 = 44 lF

Io :ð0:5  DÞ:Ts DVo

ð9Þ

Analysis and Design of Extended Range Zero Voltage Switching …

51

Fig. 4 Simulation waveform at Vin = 22 V and full load: main switch current I(M1) and I(M2), auxiliary switch current I(a1) and I(a2), diode current I(D1), output voltage Vo, voltage Vab, inductor current I(L), parallel inductor current I(Lp), output voltage (Vo) and diode current I(D1)

Fig. 5 Simulation waveform at Vin = 22 V and 10% full load: main switch current I(M1) and I (M2), auxiliary switch current I(a1) and I(a2), current across inductor I(L), voltage Vab, current across parallel inductor I(Lp), output voltage Vo and current across diode I(D1)

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Fig. 6 Simulation waveform at Vin = 41 V and full load: current for two main switches I(SM1) and I(SM2) and current for two auxiliary switches I(Sa1) and I(Sa2), parallel inductor current I(Lp), voltage Vab, inductor current I(L), output voltage Vo and diode current I(D1)

Fig. 7 Simulation waveform at Vin = 41 V and 10% load: current for two main switches I(SM1) and I(SM2) and current for two auxiliary switches I(Sa1) and I(Sa2). Parallel inductor current I(Lp), voltage Vab, inductor current I(L), output voltage Vo and diode current I(D1)

4 Simulation Results The converter is designed and simulated for 1 kW using PSIM 11. Simulation results for four operating conditions of Vin = 22 V, rated power and 10% of rated power, Vin = 41 V, full load and 10% load are presented in Figs. 4, 5 6 and 7, respectively. At higher voltage and light-load condition, the duty cycle is low to maintain the same output voltage, and therefore, VAB appears for longer time. It makes the currents ILs and ILp to be constant for a very small duration, and their

Analysis and Design of Extended Range Zero Voltage Switching …

53

appearance looks like triangular. To achieve zero voltage switching, the body diodes (main and auxiliary) should conduct prior to the conduction of corresponding switches causing zero voltage turn-on. Cording to simulation results, turn-on ZVS is achieved. It should be observed that the duty cycle is reduced with increase in input voltage and/or reduction in load current. Therefore, it causes increase in peak value of parallel inductor current (magnetizing), which adds to series inductor current and helps extended ZVS operation of the converter.

5 Summary and Conclusion To achieve ZVS over wide source voltage variation and varying output power/load while maintaining high efficiency has been a challenge, particularly for low voltage high current input specifications. Simulation results using PSIM 11 have been presented. Because of high Lp⊲Ls ratio, the circulating current is very low compared to voltage-fed converters. Traditional and even advanced converters lose soft-switching at partial load current and higher supply voltage resulting in reduced partial load efficiency. Proposed current-fed push–pull converter offers wide range ZVS, high voltage gain and better light-load efficiency resulting in less fuel (hydrogen) demand or better fuel utilization, which further reduces the cost of energy due to fuel savings. Detailed study on steady-state operation and design is reported. Simulation results are presented to evaluate converter performance for extended operating range.

References 1. J. Wang, F.Z. Peng, J. Anderson, A. Joseph, R. Buffenbarger, Low cost fuel cell converter system for residential power generation. IEEE Trans. Power Electron. 19(5), 1315–1322 (2004) 2. N. Mynand, M.A.E. Andersen, High-efficiency isolated boost dc-dc converter for high-power low-voltage fuel cell applications. IEEE Trans. Ind. Electron. 57(2), 505–514 (2010) 3. R.J. Wai, High-efficiency power conversion for low power fuel cell generation system. IEEE Trans. Power Electron. 20(4), 847–856 (2005) 4. S. Han, H. Yoon, G. Moon, M. Youn, Y. Kim, K. Lee, A new active clamping zero-voltage switching PWM current-fed half bridge converter. IEEE Trans. Power Electron. 20(6), 1271– 1279 (2005) 5. A.K. Rathore, A.K.S. Bhat, and R. Oruganti, A comparison of soft switched dc-dc converters for fuel-cell to utility-interface application, in Proceedings of Power Conversion Conference, Nagoya, Japan, pp. 588–594, April 2007 6. S.J. Jang, C.Y. Won, B.K. Lee, J. Hur, Fuel cell generation system with a new active clamping current-fed half-bridge converter. IEEE Trans. Energy Convers. 22(2), 332–340 (2007) 7. A.K. Rathore, A.K.S. Bhat, and R. Oruganti, Analysis and design of active-clamped ZVS current-fed dc-dc converter for fuel cells to utility interface application, in Proceedings IEEE International Conference on Industrial and Information Systems, Sri Lanka, 2007, pp. 503–508

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8. J.-M. Kwon, B.-H. Kwon, High step-up active-clamp converter with input-current doubler and output-voltage doubler for fuel cell power systems. IEEE Trans. Power Electron. 24(1), 108–115 (2009) 9. W.C.P. de Aragao Filho, I. Barbi, A comparison between two current-fed push-pull dc-dc converters—Analysis, design and experimentation, in Proceedings of IEEE International Telecommunications Energy Conference INTELEC, 1996, pp. 313–320 10. Q. Li, P. Wolfs, A leakage-inductance-based ZVS two-inductor boost converter with integrated magnetics. IEEE Power Electron. Lett. 3(2), 67–71 (2005) 11. P. Mantovanelli, I. Barbi, A new current-fed, isolated PWM dc-dc converter. IEEE Trans. Power Electron. 11(3), 431–438 (1996) 12. F.J. Nome and I. Barbi, A ZVS clamping mode-current-fed push pull dc-dc converter, in Proceedings IEEE ISIE (1998), pp. 617–621 13. K. Harada and H. Sakamoto, Switched snubber for high frequency switching, in Proceedings IEEE Power Electronics Specialists Conference (1990), pp. 181–188 14. R. Watson, F.C. Lee, A soft-switched, full-bridge boost converter employing an active-clamp circuit, in Proceedings of Power Electronics Specialists, IEEE Conference (1996), pp. 1948–1954 15. J.-C. Hung, T.-F. Wu, J.-Z. Tsai, C.-T. Tsai, and Y.-M. Chen, An active-clamp push-pull converter for battery sourcing applications, in Proceedings on IEEE APEC (2005), pp. 1186–1192

Switched Reluctance Motor Converter Topologies: A Review Velakurthi Mahesh Kumar, K. Vinoth Kumar and R. Saravanakumar

Abstract Many reserachers focuses on the special machine like Switched reluctance motor (SRM) because of peculiar performance compared to various standard motors. This paper reviews the various power convertor topologies developed for the SRM. Switched reluctance motor (SRM) is gaining abundant interest in industrial applications like wind energy systems and electrical vehicles—thanks to its straightforward and rugged construction, high‐speed operation ability, inability to warm temperature, and its options of fault tolerance. This paper provides indepth analysis with completely different topologies have been emerged and presented less torsion ripple, high potency, high power issue, and high power density. However, there has forever been a trade‐off between gaining a number of the advantageous and losing some with every new technology. During this chapter, numerous SRM topologies, design, principle of operation, and individual section change schemes are extensively reviewed, and their blessings and downsides are mentioned. Keywords Switched reluctance motor


Torque ripple
Harmonics

1 Introduction The first thought of exchanged hesitance engines goes back to 1814; in any case, these engines were reexamined and came into commonsense that used in ongoing decades in accordance with the advancement of intensity electronic gadgets. Exchanged hesitance engines have remarkable shafts in both the rotor and the stator and go as a single‐excited setup with inert (coil-free) rotors. The stator has a concentrated twisting framework with numerous stages. The loops are bolstered routinely and successively from a DC control supply, and accordingly, they create electromagnetic torque. In light of their straightforwardness and auxiliary quality, V. M. Kumar (&)
K. Vinoth Kumar Department of EEE, Karunya Institute of Technology and Sciences, Coimbatore, India R. Saravanakumar Department of EEE, Vellore Institute of Technology, Chennai, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_6

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SRMs have been of extraordinary enthusiasm for as long as two decades, and they are relied upon to discover more extensive applications regarding the cost and quality contrasted with different engines. What is more, numerous examinations have been completed to improve the execution of these engines as potential option in contrast to AC (nonconcurrent and synchronous) engines. At present, exchanged hesitance engines are in their outset in business terms; however, it is normal that they will be utilized all the more broadly sooner rather than later (Fig. 1).

2 Switched Reluctance Motor Drives In [1], digital PWM current controller is utilized to accomplish quick reaction, exact following, invulnerability clamor, model confuse, and solidness. This controller can either control the current straightforwardly or by the implication of controlling transition linkage. SRM drive is constrained by advanced PWM current controller, and this controller is constrained by a control board with a DSP. SRM drive is constrained by SRM control calculation, and this is actualized in TI’S DSP TMS320F28335. In [2] this paper, FEA is utilized to set up engine model of three stages of 6/4 shafts SRM. CCC, DITC, and TSF are utilized as control techniques for SRM, and these strategies are contemplated. Among these techniques, TSF and Fig. 1 SRM drive and attraction force. a three-phase SRM drive. b Attraction force produced by stator and rotor poles

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DITC demonstrate the preferable torque-swell minimization over the CCC. Among TSF and DITC, TSF has increasingly basic structure and unrivaled execution. By TSF control method, we can acquire better torque ripple minimization. In [3] this paper, transition swell is limited by fluffy controller rather than hysteresis controller alongside DTC consolidated known as DTFC. DTC comprises of voltage vector so as to control the adequacy of stator motion linkage and electromagnetic torque. DTFC thinks about by taking reference as electromagnetic torque and wanted stator motion is contrasted and the evaluated qualities; at that point, we get the motion and torque blunders, and these mistakes are fuzzily into fluffy sets. In [4] this, FLC with PI, FLC, with PID controller is utilized to show the signs of improvement results. In this, FLC gives the repaying current to remunerate torque swells; FLC has two information sources—reference current and rotor position—and the yield is a remunerating current. By utilizing ANN, DITC, torque-sharing capacity techniques, we can acquire better results. In [5] this, artificial information solidifies with fuzzy and neuro method and makes them tune with AI. Compensator produces the yield banner which is added to the PI and gives the reference flag to the present controller. The expansion of PI controller [6] is adjusted by using FLC scheme. Here, we use standard-based FLC, and these standards are used to revive the expansion of the normal PI controller. In [7] this paper, torque-swell minimization is done using PI, FLC, and ANFIS controller. FLC gives favored results over PI controller. ANFIS gives favored results over FLC. In [8] this paper, another position sensorless control methodology for the SRM is proposed, and this procedure is a blend of direct estimation system and change observer technique. In this system, the first rotor position is dictated by numerical procedure by using relationship among position, arrange current, and change linkage; phase-locked loop is arranged reliant on the above data. This PLL lessen the uproar. In [9] this paper, isolated rotor advancement for concentrated SRM with FEA-based semi-numerical assessment is used for finding torque and estimation of torque-swell sources. On segmental dive at the point of convergence of rotor section for incredible torque-swell minimization, multi-dimensional and multi-target improvements were used to differentiate the rotor execution and parameter assortments in rotor. The last rotor is arranged with torque swell of 6.95% which is vital improvement. In [10] this paper, Advancements in the control frameworks of exchanged hesitance machines (SRM) for parity and vehicle applications. The unparalleled of the SRM drive structure is exemplified in the smooth torque yield and high benefit control procedures in the motoring and making methods for development. The control strategies of SRM’s are regularly nonlinear, mirroring that they are fixing up on a machine with spatial and appealing nonlinearities. In [11], instantaneous torque control and common torque control (ATC) systems of exchanged hesitance machine (SRM) are nearly broke down to pick suitable control mode for the use of SRM in electric vehicles (EVS). Three epic procedures are advanced to streamline ATC, meeting the execution basics of EVS. The ordinary torque shut float control of SRM acknowledges a central occupation in the EV structure such that it can reduce the impact of battery voltage minor departure from

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working shows. In [12], axial movement separated rotor-traded reluctance motor (SSRM) topology could be a potential plausibility for in-wheel electric vehicle application. This topology has the upside of the extended unique surface zone for the torque creation when diverged from the winding movement SSRM for a given volume. The distinctive structure systems to improve the general execution of the AFSSRM are discussed. First, the number of openings/rotor sections and effect of bending polarities on the execution of the AFSSRM are examined. Second, to lessen the torque swell, the stator post and rotor area roundabout portion focuses are updated. In [13], a comprehensive speed with low-swell torque control of traded aversion motor (SRM) that drives using torque-sharing limit (TSF) is proposed. Two operational modes are described for the online TSF in the midst of correspondence: In mode 1, out and out estimation of pace of the advancement of progress linkage (ARCFL) of moving toward stage is higher than dynamic stage; in mode 2, ARCFL of dynamic stage is higher than moving toward stage. The most outrageous TRFS of the proposed online TSF is extended to about 4000 rpm, which is in the abundance of different occasions as high as the best case in these standard TSFS. In [14], three sorts of bearing less traded reluctance motors (BLSRMs) which have decoupled movement characteristics between the torque and suspending current are shown in detail. The separated BLSRMs are a 8/10 crossbreed BLSRM, a 12/14 cream BLSRM, and a twofold stator BLSRM. The 8/10 crossbreed BLSRM has a low electrical repeat with decoupling characteristics. Three BLSRMs with decoupled suspending force control are proposed. Characteristics of the three sorts of BLSRM are analyzed. In [15], a control strategy for torque-swell minimization in the traded aversion motor (SRM) drives the subject to a torque-sharing limit (TSF) thought. In the proposed procedure, the reference torque is explicitly changed over into the reference current waveform using the logical explanation. The procedure for upgrade of TSFs, for instance, customary immediate or sinusoidal TSFs has been portrayed. The essential SRM show is used to perceive the perfect parameters of TSF to outfit the torque-swell minimization with maximal SRM drives capability and holding commendable torque speed capacity. In [16] this paper, A story Lyapunov work based direct torque controller for minimization of torque expands in a traded reluctance motor (SRM) drive structure is represented. SRM polarization qualities are outstandingly nonlinear, where torque controller is a flighty and coupled limit of the stage streams and rotor position. The quick torque control (DTC) plot keeps up a vital good ways from the multifaceted methodology of torque-to-current change as required in indirect torque control plan. In [17] this paper, system for torque-swell lessening in traded reluctance motors set out represented. The online simplex streamlining, used to set the implanted current music for least torque swell at low speed, is uncovered and contacted higher speeds, at which control of the trading edges is abused. The undertaking of the picked, simplex, online minimization strategy has been abused. Its undertaking has been shown by propagation at low speed and insisted by the estimation on a comparative drive that used to offer data to the reenactments. In [18], the appraisal

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at low speeds of a methodology for torque-swell minimization of a traded aversion motor by the implantation of a movement of current music is delineated. Changes of enormity and time of a mixed consonant is seemed to incite a looking at least of torque swell. A technique has been portrayed for the minimization of torque swell in a SR motor by successively adding different music to the standard current intrigue banner and propelling the size and time of every along these lines. In [19], switched aversion motor is featured with more focal points like strong errand and essential improvement. In any case, it shows especially significant torque swell while running, which limits its huge use. As drive game plan of traded aversion motor, it is erratic time-changing and non-direct system, and this makes it difficult to apply standard controls to traded reluctance motor. In any case, direct torque control advancement as communicated in this article will clearly consider the torque controlled objective. In [20], the particular audit for low disturbance traded aversion motor (SRM) drives in electric vehicle (EV) applications. There is a particular example to utilize SRM in some enormous scale-assembling markets. For predominant vehicle applications, it is indispensable and desperate to streamline the SRM system to beat the drawbacks of the upheaval and vibration. SRMs are expanding much excitement for EVs in light of the ground-breaking structure. In [21], an improved constrained-state perceptive torque controls (FS-PTC) to limit the torque swell of traded reluctance motor (SRM) drive. The proposed FS-PTC procedure not solely can confine the torque swell yet also can diminish reasonably the copper adversities and ordinary trading frequency by the division portion framework, the proposed FS-PTC count simply needs to Fig. 1 or 9 voltage vectors for each time step, keeping away from finding out every one of the 27 voltage vectors. In [22], the efficiency execution of a vehicle gauges quick traded aversion drive. It investigates the impact of a smooth torque control count on the adequacy and mishaps in the drive. The present wave structure enormously influences the capability of the drive. The total drive capability is diminished by 4–9% for this particular motor. A couple of countermeasures have been proposed to lessen the capability degradation. In [23], switched aversion motor (SRM) drives and generally uses the methods at low speed and voltage control systems at quick. A steady quick torque is obtained by controlling the rotational speed of the stator movement linkage. Six phase SRMs have lower torque swell differentiated and other normal SRMs. Torque control procedure is used for a decrease in the proportion of torque swell with both standard and proposed converter. In [24], proper pay is the key-affecting element for setting up a higher demonstration of SRM drive. It address the headway of a multi stage bridgeless SMR drive with dynamic substitution move reliant on the recognized dc interface current. The aided and particularly oversaw DC interface voltage is set up from the mains to improve the SRM drive execution under high speeds. In [25], switched aversion motor is controlled by current profiling under run of the mill and open-stage working condition. The new current profiling method is associated and went after for ordinary and damaged movement of a certified SRM. The torque swell was assessed at a 10 kHz trading repeat. In [26], a traded

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reluctance motor with 12/10 posts is investigated. Differentiated and the standard SRMs with single mode, the machine not solely could be filled in as a six phase motor yet what’s more could be used as a three phase motor, which is thusly named multimode SRM (MMSRM). In solicitation to procure least torque-swell bends of the stator and rotor post are picked as the improvement objects. In [27], the selection of the correct electric footing drive was an essential advance in plan and execution enhancement of jolted power trains. Exchanged hesitance engine drives begin to locate their legitimate spot in the developing electric impetus advertise. Regular SRMs are notable for their minimal effort straightforward setup. In [28], due to extremely high torque/weight proportion, the hub motion exchanged hesitance engine (AFSRM) can be properly utilized in numerous applications, particularly electric vehicles and aviation framework. Since the torque swell is commonly the disadvantage of SRMs, a new structure is proposed for the twofold AFSRM in which the torque swell is fundamentally decreased. In [29], the hypothetical system and trial results for clamor decrease of an exchanged hesitance engine with a high number of shafts are displayed utilizing a novel streamlined current profile at low-speed and low torque locale. The disentangled current profile is proposed to take out the third symphonious segment in the total of outspread power.

3 Control Technology SRM In [30], it introduces the control strategy of the edge position for the switched reluctance motor drive reliant on feathery method of reasoning. The hardware of the model of Switched Reluctance motor structure and the principal circuit of the four-arrange lopsided augmentation control converter are introduced. The control plan and the decision kind of the cushy control are in like manner displayed. The model got the control methodology and is attempted likely. The intentional systematical capability, the conscious stage current zenith regard, and the purposeful rotor speed twist, while the store is emptied or included, are moreover given. The Switched Reluctance motor drive with the point position shut circle speed control reliant on soft reason has the good condition, this method provides the high systematical capability. This [31] paper shows a novel method to manage learning control in traded aversion motors (SRMs) for torque-swell lessening using a cerebellar model articulation controller (CMAC) neural framework. In particular, current profiles can be expected to have charming characteristics by the assurance of learning rate work with reasonable trading focuses in the midst of the readiness of the framework. This paper has shown a novel method to manage learning control of SRMs using CMAC neural frameworks. A balanced LMS adaptable computation has been proposed subject to the use of a variable LRF. This paper has given a record of the repercussions of the assortment of a LRF in setting up the CMAC upon the execution of academic current profiles.

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In [32], the purpose of this paper is to unite the perfect control of a traded reluctance machine in a four-quadrant drive with smooth advancement between the control-mode assignments. The smooth change is accomplished since the ending point conditions of one working mode are gotten from the conditions of the other working mode. The proposed control plan is viably completed since the data of the machine charge curves isn’t required. In this paper, another four-quadrant multimode perfect control plot for SRM drives was proposed. It was seemed smooth change between PWM/single-beat modes and motoring/braking assignments which is accomplished. This is cultivated since the ending edge conditions are consistent limits at the centers where SRM action is changed. The suitability of the proposed control plot is appeared on a model test structure. In [33], another sensorless control plot for the traded reluctance motor (SRM) drive at low speed is displayed in this paper. The consistent inductance of each unique stage is assessed using the terminal estimation of this stage. The assessed stage relentless inductance is stood out from an insightful model, which addresses the utilitarian associations between the stage slow inductance, arrange current, and rotor position, to evaluate the rotor position. By invigorating the insightful mode when the SRM is latent, the showed rotor position estimation plan can give exact rotor position information even as the appealing properties of the SRM change in the view of developing. In [34], a cushioned method of reasoning-based mood killer edge compensator for torque-swell lessening in a traded reluctance motor which is proposed. The mood killer edge, as an amazing limit of motor speed and current, is normally changed for a wide motor speed range to lessen torque swell. Preliminary outcomes are shown that show swell lessening when the mood killer point compensator is used. The proposed compensator offers an essential reduction in torque swell for a wide extent of motor speed movement. No torque banner was used, which extends the compensator straightforwardness and relentless quality. In [35], an inductance surface estimation and learning for the utilization with a stochastic model predictive control (MPC) plot for the present control of switched reluctance motors (SRM) are introduced. This MPC is outfitted with state estimators and is executed as a recursive straight quadratic controller for logical associations in cream vehicle applications. The displayed control plan can adjust to noise similarly as vulnerabilities inside the machine nonlinear inductance surface. This paper was revolved around an instrument to learn and acclimate to the inductance surface of a changed reluctance motor to play out a model judicious current control of this machine. This inductance surface was taken care of as a table to be used with a model farsighted current controller with Kalman state estimator. This [36] present a novel electromagnetic actuator having 2-degrees-of-chance controllability for rotational and straight sanctioning. It depends upon an exchanged abhorrence engine. Torque and push can be controlled straightforwardly. The model actuator is proposed to perform 2-measurement of chance inception with the most essential rotational speed of 1000 min 1 and most exceptional push power of 30 N. In the engine execution testing, it has been attested that uninhibitedly control of rotational speed and direct position understands it. The SRM with the

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2-degrees-of-chance controllability for rotational and direct advancement has been made. Its yield torque and push are self-governing controlled. As the execution testing, the joined advancement control for rotational speed and straight organizing has been appeared.

4 Conclusion This paper has presented an up–to-date review of the most power converter designed for the switched reluctance motor. Each topology has its own advantages and drawbacks. The selection of a converter depends upon the application and the required performance specifications.

References 1. F. Peng, A. Emadi, A digital PWM current controller for switched reluctance motor drives 2. Y. Guo, Q. Ma, Relative study on torque ripple suppression method of three-phase 6/4 switched reluctance motor 3. F. Sofiane, H. Le-Huy, I. Kamwua, Fluffy direct adaptive direct torque control of switched reluctance motor 4. Dr. E.V.C.S. Rao, Torque ripple minimization of switched reluctance motor by using fuzzy logic controller 5. L.O.P. Henriques, L.G.B. Rolim, W.I. Suemitsu, P.C. Branco, J.A. Dente, Torque ripple minimization of switched reluctance drive using a neuro-fuzzy control technique 6. P. Ramesh, P. Subbaiahissn, Speed control of SR drive using FLC. Int. J. Grid Distributed comput. 8(6), 185–192 (2015) 7. L. Kalaivani, P. Subburaj, W.I. Mariasiluvairaj, Man-made reasoning based control for torque ripple minimization in switched reluctance motor drives 8. F. Peng, J. Ye, A. Emadi, Y. Huangieee, Position sensor less control of switched reluctance motor drives based on numerical method 9. A. Kabir, I. Husain, Fragmented rotor design of concentrated wound Switched Reluctance Motor (SRM) for torque ripple minimization 10. Y. Sozer, I. Husain, Direction in choosing propelled control procedure for exchanged hesitance machine drives in developing applications. IEEE Exchange Ind. Appl. https://doi. org/10.1109/tia.2015.2444357 11. H. Cheng, H. Chen, Z. Yang, Normal torque control of exchanged hesitance machine drives for electric vehicles. https://doi.org/10.1049/iet-epa.2014.0424. ISSN 1751-8660 12. R. Madhavan, B.G. Fernndes, Execution improvement in the pivotal transition fragmented rotor-exchanged hesitance engine. IEEE Exchanges Vitality Transform. 13. J. Ye, B. Berkerbilgin, A. Emadi, An broadened speed low-swell torque control of exchanged hesitance engine drived. IEEE Exchange Power Electron. https://doi.org/10.1109/TPEL.2014. 2316272 14. Z. Xu, D-H. Lee, Comparative examination of bearingless exchanged hesitance engines with decoupled suspending power control. IEEE Exchange Ind. Appl. https://doi.org/10.1109/TIA. 2014.2331422 15. V. P. Vujicic, Minimization of torque swell and copper misfortunes in exchanged hesitance drive. IEEE Exchange Power Hardware. 27(1) (2012)

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16. S. Kumarsahoo, S. Dasgupta, A Lyapunov work based vigorous direct torque controller for an exchanged hesitance engine drive framework. IEEE Exchange Power Gadgets. 27(2) (2012) 17. J.M. Stephenson, A. Hughes and R. Mann, Torque swell minimization in an exchanged hesitance engine by ideal symphonious current infusion. IEEE procedures online no. 20010480. https://doi.org/10.1049/ip-epa:20010480 18. J. Keyan, Study on direct torque control arrangement of exchanged hesitance engine. IEEE 978-1-4244-97’8/11/2011 19. C. Gan, J. Wu, A audit on machine topologies and control systems for low – clamor exchanged hesitance engines in electric vehicle applications. IEEE Access 16, 31430–31443 (2018) 20. C. Li, G. Wang, An improved finite– state prescient torque control for exchanged hesitance engine drive. IET Electric Power Appl. 21. I. Relev, F. Qi, B. Burkhart, Imapct of smooth torque control on the effectiveness of a rapid car exchanged hesitance drive. IEE Trans. Ind. Appl. https://doi.org/10.1109/tia.2017.2743680 22. X. Deng, B.C. Mecrow, R. Marin, Design and advancement of low torque swell variable-speed drive framework with six-stage exchanged hesitance motors. IEE Trans. Vitality Convers. https://doi.org/10.1109/tec.2017.2753286 23. H.N. Huang, K. Hu, Switch-mode rectifier sustained exchanged hesitance engine drive with dynamic compensation moving utilizing dc connect current. IET Electric Power Appl. 24. D. Peter, P. Rafajdus, Control of exchanged hesitance engine by flow profiling under ordinary and open stage working condition. IET Electric Power Appl. 25. Y. Hu, W. Ding, T. Wang, Investigation on a multi-mode exchanged hesitance motor: design, optimization, electromagnetic investigation and experiment. IEE Trans. Modern Gadgets 26. E. Bostanci, M. Moallem, Opportunities and difficulties of exchanged hesitance engine drive for electric propulsion: a comparative study. IEEE Trans. Transp. Zap 27. M. Jafari, H. Kermanipour, B. Ganji, Modification in geometric structure of twofold – sided pivotal transition exchanged hesitance engine for relieving torque swell adjustment dela structure geometrique dun motion hub a hesitance drive AFSRM a twofold face pour uneondulation de couple attenuee. 38(4) (2015) 28. N. Kurihara, J. Bayless, Noise decrease of exchanged hesitance engine with high number of posts by novel streamlined current waveform at low speed and low torque region. IEE Trans. Ind. Appl. 29. H. Chen, D. Zhang, ZY. Cong, ZF. Zhang, Fluffy logic control for switched hesitance engine drive, in Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, 45 November 2002 30. C. Shang, D. Reay, B. Williams, Adjusting CMAC neural networks with constrained LMS algorithm for efficient torque ripple decrease in switched reluctance motors. IEEE Trans. Control Syst. Technol. 7(4), 401–413 (1999) 31. I. Kioskeridis, C. Mademlis, A unified approach for four-quadrant optimal controlled switched reluctance machine drives with smooth transition between control operations. IEEE Trans. Power Electron. 24(1), 301–306 (2009) 32. H. Gao, F.R. Salmasi, M. Ehsani, Inductance model-based sensor less control of the switched reluctance motor drive at low speed. IEEE Trans. Power Electron. 19(6), 1568–1573 (2004) 33. M. Rodrigues, P.J. Costa Branco, W. Suemitsu, Fluffy logic torque ripple reduction by turn-off angle compensation for switched reluctance motors. IEEE Trans. Ind. Electron. 48(3), 711–715 (2001) 34. X. Li, P. Shamsi, Inductance surface learning for model predictive current control of switched reluctance motors. https://doi.org/10.1109/tte.2015.2468178 35. Y. Sato, Improvement of a 2-degree-of-freedom rotational/linear switched reluctance motor. IEEE Trans. Magn. 43(6), 2564–2566 (2007) 36. S.M. Lukic, A. Emadi, State-switching control technique for switched reluctance motor drives: theory and implementation. IEEE Trans. Ind. Electron. 57(9), 2932–2938 (2010)

Design of Five-Level Cascaded H-Bridge Multilevel Inverter S. Swathy, N. Niveditha and K. S. Chandragupta Mauryan

Abstract The abstract of this paper is to design a five-level cascaded H-bridge multilevel inverter using phase disposition pulse width modulation (PD-PWM) technique, to obtain optimal switching angles for harmonic reduction and to compare the THD content of the output waveform of the five-level cascaded H-bridge multilevel inverter for different modulation index by using mathematical approach and MATLAB/SIMULINK.




Keywords Multilevel inverter Modulation techniques elimination Total harmonic distortion FPGA








Selective harmonic

1 Introduction Demand for energy is getting increased day by day. The key source of energy available now is from non-renewable sources like fossil fuels. The over utilization of these sources to meet our daily requirements have put it in a degradation state [1, 2]. Hence, there is a rapid development in the research to produce energy from alternate sources, such as wind, solar, tidal, etc. Among them, energy taken from photovoltaic systems plays an important role. The energy which is taken from a photovoltaic system (PV system) is DC in nature. Most of the equipments are used for domestic and industrial purposes which work on an AC source, the DC output from a PV system is converted into an AC and for this purpose, power inverters play a major role.

S. Swathy (&)
N. Niveditha Department of Electrical and Electronics Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India K. S. Chandragupta Mauryan Department of Electrical and Electronics Engineering, Guru Nanak Institution Technical Campus, Hyderabad, Telangana, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_7

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2 Multilevel Inverter Concepts Multilevel inverters concept attracts academia as well as industry over wide range. They combine switched waveforms with lower levels of harmonic distortion than an equivalently rated two-level converter [1–3]. It found that with the increase in level, the steps increases and the output waveform approaches to a near sinusoidal waveform. Thus, it reduces the THD with a disadvantage of complex control and voltage imbalance problem. They are employed mainly for high-power, high-voltage/medium-power applications. They create more switching states, thereby stepping up output inverter voltages in small increments. These smaller voltage steps help in creating high-quality waveforms, lower dv/dt and reduced electromagnetic compatibility. But in order to increase the number of levels, more number of components are required and same will make the circuit complex [2]. High switching frequency employed in multilevel inverters helps in minimizing the output harmonics and reducing the passive component size in the power circuit. Figure 1 shows different number of voltage-level output waveform of MLI. There are also different topologies of multilevel inverters that generate a stepped output voltage waveform and that are suitable for different applications. By designing multilevel circuits in different ways, many topologies with properties have been developed. The basic multilevel inverter topologies include: Diode-clamped multilevel inverter, capacitor-clamped multilevel inverter, cascaded H-bridge (CHB) multilevel inverter.

2.1

Cascaded H-Bridge Multilevel Inverter (CHB-MLI)

The concept of multilevel inverter is based on connecting H-bridge inverters in series to get a sinusoidal voltage output. Figure 2 shows a full-bridge inverter. One full-bridge is itself a three-level cascaded H-bridge multilevel inverter and every module added in cascade which extends the inverter with two voltage levels. Each full-bridge inverter can create three voltages VDC, 0 and −VDC. To change one level of voltage cascaded H-bridge multilevel inverter turns one switch ON and other switch OFF in one full-bridge inverter. For example, to achieve voltage +Vdc,

Fig. 1 3, 5, 7 level output waveform of multilevel inverter at fundamental frequency

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switches S1 and S2 are turned ON, for −Vdc, the switches S3 and S4 are turned OFF. When there is no current following through the full-bridge, then 0 voltage level is achieved [3, 4]. The output voltage in each bridge is the summation of the voltage that is generated by each cell. The number of output voltage levels are 2n + 1, where n is the number of cells. The cascaded H-bridge multilevel inverter is capable of producing the total voltage source magnitude in both positive and negative half cycles, while many other topologies can only produce half the total DC-bus voltage source magnitude. Full-bridge inverter that is connected in series can contribute with the same voltage, thus meets topology. There is possibility to charge every module in a cascaded H-bridge multilevel inverter with different voltages. In Fig. 3, there are two full-bridge inverters connected in series for obtaining five different output voltage levels, −2VDC, VDC, 0 −VDC and +2Vdc. The advantages of this type of multilevel inverter are that it needs less number of components comparative to the diode clamped or the flying capacitor. However, the number of sources is higher, for the phase-leg to be able to create a number if m voltage level and switches 2 * (m − 1) [1, 2, 4].

3 Modulation Techniques for Multilevel Inverter Multilevel inverters have different modulation techniques for obtaining a better output voltage response with minimum harmonic distortions. There are basically two groups of methods: modulation with fundamental switching frequency or high switching frequency pulse width modulation (PWM) [5–11].

3.1

Pulse Width Modulation Techniques

A multilevel pulse width modulation method uses high switching frequency carrier waves in comparison to the reference waves to generate a sinusoidal output wave as

Fig. 2 Single-phase full-bridge inverter

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Fig. 3 Single-phase five-level multilevel inverter

such in the two-level PWM case. To reduce harmonic distortions in the output voltage waveform, phase-shifting techniques are used [12–20]. The carrier-based pulse width modulation techniques can be broadly classified into: • Phase-shifted modulation • Level-shifted modulation. In both modulation techniques, for an m-level inverter, (m-1) triangular carrier waves are required and all the carrier waves should have the same frequency and same peak-to-peak magnitude. Phase Disposition Pulse Width Modulation: In phase disposition modulation technique, all the triangular carriers are in phase and are arranged one over the other as shown in Fig. 4. These arranged triangular carriers are compared with reference wave to obtain the pulses for the multilevel inverter switches. This technique is generally accepted as the method that creates the lowest harmonic distortion in line-to-line voltage.

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Fig. 4 Reference and carrier wave for a five-level cascaded H-bridge multilevel inverter with PD-PWM

4 Operating Modes of Five-Level Cascaded H-Bridge Multilevel Inverter Mode1: +2Vdc: Figure 5 shows the operating mode for getting output voltage of +2Vdc. In this mode, switches SW1, SW2, SW5 and SW6 are ON and all the other switches SW3, SW4, SW7 and SW8 are OFF. Mode2: +Vdc: Figure 6 shows the operating mode for getting output voltage of +Vdc. In this mode, switches SW1, SW2, SW8 and SW6 are ON and all the other switches SW3, SW4, SW7 and SW5 are OFF. Mode3: 0: Figure 7 shows the operating mode for getting output voltage of zero. The lower-leg switches are triggered; hence, there will no flow of current in the power circuit. Fig. 5 Operating mode for getting output voltage of +2Vdc

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Fig. 6 Operating mode for getting output voltage of +Vdc

Fig. 7 Operating mode for getting output voltage of zero

Mode4: −Vdc: Figure 8 shows the operating mode for getting output voltage of −Vdc. In this mode, switches SW3, SW4, SW8 and SW6 are ON and all the other switches SW1, SW2, SW7 and SW5 are OFF. The flow of current is opposite to the load current.

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Fig. 8 Operating mode for getting output voltage of −Vdc

Mode5: -2Vdc: Figure 9 shows the operating mode for getting output voltage of −2Vdc. In this mode, switches SW3, SW4, SW8 and SW7 are ON and all the other switches SW1, SW2, SW6 and SW5 are OFF. The flow of current is opposite to the load current.

Fig. 9 Operating mode for getting output voltage of −2Vdc

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5 Fourier Analysis of PD-PWM Technique The concept of a two-level pulse width modulated converter system is that a low-frequency reference waveform is compared against a high-frequency carrier waveform and the compared output is used to control the switches. The consequence of switching process has fundamental component, the reference waveform and also incorporates a series unwanted harmonics. Determination of harmonic frequency components is complex and it is often done by fast Fourier transform analysis of a simulated time-varying waveform. This approach also reduces mathematical effort but uncertainly, it leaves error. In contrast, an analytical solution which exactly identifies the harmonic component of a PWM waveform ensures that precisely the harmonics are being considered when various PWM strategies are compared against each other [21–27]. Table 1 gives the switching function condition of a five-level multilevel inverter. f ðt Þ ¼

1 Aoo X þ ½Aon cosð½nxo tÞ þ Bon sinð½nxo tÞ 2 n¼1 1 X þ ½Amo cosð½nxc tÞ þ Bmo sinð½mxc tÞ m¼1

þ

1 X 1 X

½Amn cosð½mxc t þ nxo tÞ

m¼1 n¼1 n6¼0

þ Bmn sinð½nxc t þ nxo tÞ m carrier index variable n base-band index variable. The final expression for harmonic components can be obtained by on substituting the equation

Table 1 Switching function condition of a five-level multilevel inverter F(x, y)

When p  x  0

When 0\x  p

+2VDC

p M cos y [ 12  2p

+VDC

p p  2p \M cos y\ 12  2p

0

p p  12  2p \M cos y\  2p

−VDC

p p 1  2p \M cos y\  12  2p

p 2p p 1 p 2p \M cos y\ 2 þ 2p 1 p p  2 þ 2p \M cos y\ 2p p 1 þ 2p \M cos y\  12 p M cos y\  1 þ 2p

−2VDC

M cos y\  1 

M cos y [

p 2p

1 2

þ

þ

p 2p

Design of Five-Level Cascaded H-Bridge Multilevel Inverter

Vaz ðtÞ ¼ 2MVdc cos x0 t þ

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1 1 8Vdc X 1 X 1 J2k1 ð½2m  12pM Þ 2 p m¼1 2m  1 k¼1 2k  1

 f1 þ 2 sinð½2k  1u cos kpg  cosð½2m  1xc tÞ 1 1 2Vdc X 1 X J2n þ 1 ð4mpM Þ cos np cosð2mxc t þ ½2n þ 1x0 tÞ þ p m¼1 2m n¼1 þ 

1 1 X 1 X 4Vdc X 1 ½J2k1 ð½2m  12pM Þ cos kp p2 m¼1 2m  1 n¼1 k¼1 n6¼0

cosð½n  k pÞ þ 2 sinð½2k  1  2nuÞ cosð½n  k pÞ þ 2 sinð½2k  1 þ 2nuÞ þ  ½2k  1  2n ½2k  1 þ 2n



 cosð½2m  1xc t þ 2nx0 tÞ

6 Simulation Results of Cascaded H-Bridge Multilevel Inverter Using PD-PWM Technique The simulation is carried out using MATLAB/SIMULINK software. The simulation diagram is shown in Fig. 10. Table 2 gives the design parameters for cascaded H-bridge multilevel inverter. Figure 11 shows the output voltage and output current of five-level cascaded H-bridge multilevel inverter for switching frequency of 2 kHz M = 1 and Fig. 12

Fig. 10 Simulation diagram of cascaded H-bridge multilevel inverter using PD-PWM technique

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Table 2 Design parameters for cascaded H-bridge multilevel inverter S. No.

Parameter

Five-level cascaded H-bridge inverter

1 2 3 4

Input voltage Load Switching frequency Modulation index

130 V R = 50 Ω 1 and 2 kHz 0.8 and 1

Fig. 11 a Output voltage and b Output current of five-level cascaded H-bridge multilevel inverter for switching frequency of 2 kHz M = 1

shows the harmonic spectrum of output current of five-level cascaded H-bridge multilevel inverter for the switching frequency of 2kH and modulation index, M = 0.8 and M = 1. Comparison of THD values for modulation index 0.8 and 1 and switching frequencies of 1 kHz and 2 kHz are given in Table 3. Also the Comparison of THD for PDPWM and SHE technique is for 0.8 and 1 modulation index and 1 and 2 kHz switching frequencies is given in Table 4.

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Fig. 12 Harmonic spectrum of output current of five-level cascaded H-bridge multilevel inverter (2 kHz) a M = 0.8 b M = 1

Table 3 Comparison of THD for various modulation index and switching frequencies Parameter

Switching frequency (1 kHz) Modulation index 0.8 1

Switching frequency (2 kHz) Modulation Index 0.8 1

Voltage (%THD) Current (%THD)

38.07 38.07

36.74 36.74

26.64 26.64

23.02 23.02

Table 4 Comparison of THD for PD-PWM and SHE techniques Total harmonic elimination

PD-PWM

SHE Third harmonic elimination

Fifth harmonic elimination

Seventh harmonic elimination

%THD for voltage %THD for current

26.64

18.54

23.38

22.59

26.64

18.54

23.38

22.59

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7 Hardware Implementation FPGA Kit The control signal for the power switches of a five-level cascaded H-bridge multilevel inverter is developed with the help of SPARTAN 6-XC6SLX25 trainer kit. Figure 13 shows the schematic of SPARTAN 6 FPGA kit. Design specification for hardware implementation of a five-level cascaded H-bridge multilevel inverter is given in Table 5. Output voltage and output current for switching frequency of 1 kHz and modulation index of 0.8 are shown in Fig. 14a and b. Also the FFT of output voltage and current for switching frequency of 1 kHz and modulation index of 0.8 is shown in Fig. 15a, b. The harmonic spectrum with R-Load for switching frequency of 1 kHz and modulation index of 0.8 is shown in Fig. 16 for output voltage, current and power. The comparison of different modulation indices and switching frequency is given in Table 6.

Fig. 13 SPARTAN 6-FPGA kit

Table 5 Design specification for hardware implementation of a five-level cascaded H-bridge multilevel inverter S. No.

Parameters

Specification

1 2 3 4 5

Input voltage Load Switching frequency (fs) Voltage and current measurement Harmonic measurement

130 V 50 Ω 1 and 2 kHz Digital storage oscilloscope Fluke 43B

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Fig. 14 a Output voltage and b Output current of five-level cascaded H-bridge multilevel inverter

8 Conclusion In this work, a single-phase five-level cascaded H-bridge multilevel inverter is studied and analyzed in terms of output voltage, output current and harmonic spectrum. Phase disposition PWM modulation technique is used to generate

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Fig. 15 FFT of a Output voltage and b Output current

switching pulses for the inverter. Further, optimal switching angles for the inverter are calculated for harmonic reduction (third harmonic, fifth harmonic and seventh harmonic). Results are verified using simulation done in MATLAB/SIMULINK. The five-level cascaded H-bridge multilevel inverter is implemented as a hardware prototype. The pulses for cascaded H-bridge multilevel inverter are generated using Spartan-6 XC6SLX25 FPGA kit. A comparison of the output THD with

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Fig. 16 Harmonic spectrum of a Output voltage, b Output current and c Power

Table 6 Comparison of different modulation indices and switching frequency Parameters

Switching frequency (1 kHz) Modulation index 0.8 1

Switching frequency (2 kHz) Modulation index 0.8 1

Voltage (%THD) Current (%THD) Power (W)

36.5 36.5 89

33.2 32.9 93

26.5 26.5 144

21.1 21.2 134

modulation index of 0.8 and 1, and switching frequencies 1 and 2 kHz is carried out. As a future scope, multilevel inverter can be analyzed for different output levels by changing modulation index and switching frequency. By designing suitable filters, the total harmonic distortion can further be reduced on the output, to meet IEEE harmonic standards.

References 1. A.R. Beig, R.Y. Udaya Kumar, V.T. Ranganathan, A novel fifteen level inverter for photovoltaic power supply system, in Industry Applications Conference, 39th IAS Annual Meeting, vol. 2. (2004), pp. 1165–1171 2. J. Rodriguez, J.-S. Lai, Multilevel inverters: a survey of topologies, controls, and applications. Ind. Electron. IEEE Trans. 49(4), 724–738 (2002) 3. M.H. Rashid, Power Electronics Circuits, Devices, and Applications, 3rd edn. (Pearson Prentice Hall, 2004) 4. P. Palanivel, S.S. Dash, Analysis of THD and output voltage performance for cascaded multilevel inverter using carrier pulse width modulation techniques. IET Power Electron. 4(8), 951–958 (2011) 5. A.K. Panda, Y. Suresh, Research on cascaded multilevel inverter with single DC source by using three-phase transformers. Electr. Power Energy Syst. 409–420 (2012) 6. Y. Suresh, A.K. Panda, Research on a cascaded multilevel inverter by employing three-phase transformers. IET Power Electron. 5(5), 561–570 (2012)

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7. J. Wang, D. Ahmadi, A precise and practical harmonic elimination method for multilevel inverters. IEEE Trans. Ind. Appl. 46(2) (2010) 8. Y. Suresh, A.K. Panda, Research on cascaded multilevel inverter with reduced dc sources. Renew. Sustain. Energy Rev. 2649–2659 (2013) 9. R. González, J. López, L. Marroyo, Transformerless single-phase multilevel-based photovoltaic inverter. IEEE Trans. Ind. Electron. 55(7) (2008) 10. E. Villanueva, P. Correa, Control of a single phase cascade H-Bridge multilevel inverter for grid-connected photovoltaic systems. IEEE Trans. Ind. Electron. 56(11) (2009) 11. S. Daher, J. Schmid, F.L. Antunes, Multilevel inverter topologies for stand-alone PV systems. IEEE Trans. Ind. Electron. 55(7) (2008) 12. P. Roshankumar, L.G. Franquelo, A five level inverter topology with single-DC supply by cascading a flying capacitor inverter and an H-Bridge. IEEE Trans. Power Electron. 27(8) (2012) 13. N. Bodo, E. Levi, M. Jones, Investigation of carrier-based, pwm techniques for a five-phase open-end winding drive topology. IEEE Trans. Ind. Electron. 60(5) (2013) 14. S. Mariethoz, Systematic design of high-performance hybrid cascaded multilevel inverters with active voltage balance and minimum switching losses. IEEE Trans. Power Electron. 28 (7) (2013) 15. Z. Pan, F.Z. Peng, A sinusoidal PWM method with voltage balancing capability for diode-clamped five-level converters. IEEE Trans. Ind. Appl. 45(3) (2009) 16. A. Nasrudin Rahim, K. Chaniago, J. Selvaraj, Single-phase seven-level grid-connected inverter for photovoltaic system. IEEE Trans. Ind. Electron. 58(6) (2011) 17. W. Bin, High Power Converters and AC Drives. IEEE Press, Wiley-Interscience, A John Wiley and Sons Inc., Publications (2006) 18. T.A. Meynard, H. Foch, Multilevel conversion: high voltage choppers and voltage source inverters, in Proceeding IEEE PESC (1992), pp. 397–403 19. S.H. Hosseini, M. Ahmadi, S. Ghassem Zadeh, Reducing the output harmonics of cascaded H-bridge multilevel inverter for electric vehicle applications, in 8th International Conference on Electrical Engineering/ Electronics, Computer Telecommunications and Information Technology (ECTI-CON 2011) (Thailand, 2011), pp. 752–755 20. Y. Huang, J. Wang, A universal selective harmonic elimination method for high-power inverters. IEEE Trans. Power Electron. 26(10), 2743–2752 (2011) 21. G. Holmes, T.A. Lipo, Pulse width modulation for power converters (2003) 22. A.R. Beig, A. Dekka, Experimental verification of multilevel inverter-based standalone power supply for low-voltage and low-power applications. IET Power Electron. 5(6), 635–643 (2012) 23. D.W. Kang, S. Hyun, Simple harmonic analysis method for multi-carrier PWM techniques using output phase voltage in multi-level inverter. IEEE Proc. Electron. Power Appl. 152(2) (2005) 24. K.K. Gupta, S. Jain, Topology for multilevel inverters to attain maximum number of levels from given DC sources. IET Power Electron. 15(4), 435–446 (2012) 25. D.W. Kang, J. Rodríguez, Direct torque control with imposed switching frequency in an 11-level cascaded inverter. IEEE Trans. Ind. Electron. 51(4) (2004) 26. F. Wanmin, W. Bin, A Generalized and formulation of quarter wave symmetry SHE-PWM problems for multilevel inverters. IEEE Trans. Power Electron. 24(7) (2009) 27. R. Gupta, A. Joshi, Multiband hysteresis modulation and switching characterization for sliding-mode-controlled cascaded multilevel inverter. IEEE Trans. Ind. Electron. 57(7) (2010)

Performance Analysis of Asymmetrical Cascaded H-Bridge Multilevel Inverter Using Multicarrier Pulse-Width Modulation Techniques D. Naveen Kumar and P. V. Kishore

Abstract Multilevel inverters are desirable in the earlier years because of their ability to produce waveforms with enhanced harmonic spectrum and realize necessary voltage. The significant benefits of multilevel inverters are reasonable cost, good performance, lower EMI and less harmonic content. The most prevalent multilevel inverter topologies are diode-clamped, flying capacitor and cascaded H-bridge inverter. Among the multilevel inverters, cascaded H-bridge multilevel inverter has been appealed for middle-level and high-voltage renewable-energygenerating systems such as PV system because of its modular nature. Performance analysis of cascaded multilevel inverter topologies with different DC sources and different carrier-based PWM techniques is presented in this paper. Keywords Cascaded H-bridge


Level shift
Phase shift and phase disposition

1 Introduction Multilevel power converters condense more advantages compared to a conventional two-level converter. Multilevel inverters have engaged a foremost role in most systems such as high-rated motor drives, FACTS and renewable energy systems [1–3]. They offer output voltage with low distortion, reduced dv/dt stress, reduced switching frequency and lower peak inverse voltage (PIV) on switches [4–7]. There are chiefly three multilevel converter topologies so-called diode-clamped, flying capacitor [8] and cascaded H-bridge [9]. Cascaded MLIs are customarily used for medium-voltage and larger power requirement applications because of reliability and nature of modularity. Pulse-width modulation techniques are applied to control the gating signals of multilevel inverters so that desired voltage is obtained. The performance analysis of cascaded H-bridge multilevel inverter using carrier-based

D. Naveen Kumar (&)
P. V. Kishore Guru Nanak Institutions Technical Campus, Ibrahimpatnam, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_8

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pulse-width modulation is presented in this paper. Without escalating the amount of level, high power can be transferred by using asymmetrical cascaded multilevel inverters [10].

2 Multilevel Inverter The three most widespread multilevel inverters are as follows: 1. diode-clamped multilevel inverter, 2. flying capacitor multilevel inverter and 3. cascaded H-bridge multilevel inverter. 2.1 Diode-clamped multilevel inverter: The diode-clamped multilevel inverter was projected by Nabae et al. in 1981. This is the extensively used multilevel inverter. If n is the quantity of levels of the output, then the (n − 1) number of capacitors and the (n − 1)(n − 2) quantity of clamping diodes are required. The drawback of diode-clamped multilevel inverter is when the ‘n’ level increases, then the number of clamping diodes also increases. 2.2 Flying capacitor multilevel inverter: Flying capacitor multilevel inverter is analogous to the diode-clamped, but instead of using diodes for clamping, capacitors are used. For n levels, (n − 1) number of DC-side capacitors and (n − 1) (n − 2)/2 number of auxiliary capacitors are used. Here also as the ‘n’ level increases, the amount of clamping capacitors also increases. 2.3 Cascaded H-bridge multilevel inverter: CHB-MLI consists of series of H-bridges which are supplied by isolated DC sources. For ‘n’ number of levels, (n − 1)/2 numbers of bridges are required. Cascaded H-bridge structure, due to its modularity, can be simply made extensive to higher number of levels. However, this requires isolated DC sources. The conduction losses are more in this inverter.

3 Multicarrier-Based PWM Techniques Sinusoidal pulse-width modulation is the most popular method of switching the power converters. In this, a reference signal is related to carrier signal to produce the gating signals. To increase the performance of the multilevel inverters, multicarrier pulse-width modulation is implemented. Basically, the vertical shift in the carrier is called as level-shifted PWM, and horizontal shift is called as phase-shift PWM. The proposed model is presented in Fig. 1, and the corresponding switching states of the inverter are presented in Table 1.

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Fig. 1 Proposed Simulink model for cascaded seven-level H-bridge inverter

Table 1 Switching states of the proposed model Module A output

Module B output

MLI output

Switching states S11 S12 S13

S14

Vdc −Vdc 0 Vdc −Vdc −Vdc 0 0

0 2Vdc 2Vdc 2Vdc 0 −2Vdc −2Vdc 0

Vdc

1 0 0 1 0 0 0 0

0 1 0 0 1 1 0 0

3.1

2Vdc 3Vdc −Vdc −3Vdc −2Vdc 0

1 0 0 1 0 0 0 0

0 1 0 0 1 1 0 0

Level-Shifted PWM Technique

In level-shifted PWM, the gating signals are created by the comparison of (n − 1) number of carrier signals with the sinusoidal reference signal. The gating signals are given to the IGBTs in sequence, so that multilevel output is obtained. Level-shifted PWM techniques are classified as follows: 1. PD technique, 2. POD technique and 3. APOD technique.

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Phase-Shifting PWM Technique

In phase-shifting PWM, the carrier signals are having equal peak-to-peak amplitude with same frequency but are phase-shifted by an angle. Simulink model of seven-level cascaded H-bridge inverter is shown in Fig. 1 which needs six carrier signals, and the frequency of output waveform is determined by the reference signal waveform. The output voltage and the FFT analysis for different modulation strategies are given below.

3.3

Phase Disposition-PWM

In phase disposition technique, the six carrier signals are in phase, whereas in level, they are shifted with same peak-to-peak amplitude. The PWM signals are shown in Fig. 2. The output waveform of the load voltage is shown in Fig. 3. The FFT analysis of the output waveform shown in Fig. 4 indicates the complete harmonic distortion of the output waveform.

3.4

Phase Opposition Disposition-PWM

In phase opposition disposition technique, the carrier signals below zero are 180° in phase opposite to the carrier signals above zero maintaining the same peak-to-peak amplitude as shown in Fig. 5.

Fig. 2 Gating signals using PD-PWM

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Fig. 3 Load-voltage waveform using PD-PWM

Fig. 4 PD-PWM load-voltage FFT

The output waveform of the load voltage using POD is shown in Fig. 6. The FFT analysis of the output waveform indicates the total harmonic distortion of the output waveform as shown in Fig. 7 which displays the harmonic content in the load voltage.

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Fig. 5 Gating signals using POD-PWM

Fig. 6 Load-voltage waveform using POD-PWM

3.5

Alternate Phase Opposition Disposition-PWM

In alternate phase opposition disposition technique, among the six carrier signals, three are adjacent to each other 180° apart (Fig. 8). The output waveform of the load voltage is shown in Fig. 9. The FFT analysis of the output waveform as shown in Fig. 10 indicates the total harmonic distortion of the output waveform.

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Fig. 7 POD-PWM load-voltage FFT

Fig. 8 Gating signals using APOD-PWM

4 Conclusion In this paper, a seven-level cascaded H-bridge inverter supplied by asymmetrical DC sources is simulated through advanced multicarrier PWM techniques. Comparison has been performed with regard to the total harmonic distortion in the

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Fig. 9 Load-voltage waveform using APOD-PWM

Fig. 10 APOD-PWM load-voltage FFT

output voltage waveform. Among all the multicarrier PWM techniques, phase opposition disposition technique is superior in terms of less harmonic content and smoother load voltage.

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References 1. J.S. Lai, F.Z. Peng, Multilevel converters—a new breed of power converters. IEEE Trans. Ind. 32(3), 509–517 (1996) 2. P. Lezana, J. Rodriguez, D.A. Oyarzun, Cascaded multilevel inverter with regeneration capability and reduced number of switches. IEEE Trans. Ind. Electron. 55(3), 1059–1066 (2008) 3. P. Panagis, F. Stergiopolos, P. Marabeas, S. Manios, Comparison of state of the art multilevel inverters, in Proceeding power electronics specialist’s Conference PESC 2008. (IEEE, 2008), pp. 4296–301 4. T.A. Meynard, H. Foch, F. Forest, et al. Multicell converters: derived topologies. IEEE Trans. Ind. Electron. 49(5), 978–87 (2002) 5. Z. Du, L.M. Tolbert, B. Ozpineci, J.N. Chiasson, Fundamental frequency switching strategies of a seven-level hybrid cascaded H-bridge multilevel inverter. IEEE Trans. Power Electron. 24(1), 25–33 (2009) 6. K.A. Corzine, Y.L. Familiant, A new cascaded multilevel H-bridge drive. IEEE Trans. Power Electron. 17, 125–131 (2002) 7. E. Babaei, S.H. Hosseini, G.B. Gharehpetian, M. Tarafdar Haque, M. Sabahi, Reduction of dc voltage sources and switches in asymmetrical multilevel converters using a novel topology. Electr. Power Syst. Res. 77, 1073–1085 (2007) 8. J. Rodriguez, J.S. Lai, F.Z. Peng, Multilevel inverter: a survey of topologies, controls, and applications. IEEE Trans. Ind. Electron. 49(4), 724–738 (2002) 9. E. Barcenas, S. Ramirez, V. Cardenas, Echavarria, Cascaded multilevel inverter with only one dc source. in Proceeding VIII IEEE Inttech Proceeding CIEP (2002), pp. 171–176 10. K.S. Chandra Gupta Mauryan, G. Sivagnanam, R. Purrnimaa Siva Sakthi, Design of nine level diode clamped multilevel inverter. J. Emerg. Technol. Innov. Res. 6(1), 513–518 (2019)

Interoperable Wireless Charging for Electric Vehicles A. Maideen Abdhulkader Jeylani, J. Kanakaraj and A. Mahaboob Subhani

Abstract The world is modernizing with each second passing by. World is ushering toward an era of environmental consciousness. So, green technologies and sustainable solutions vandalize previous technologies, the same is with the automobile industry. Although man learned and started to fly, roadways are the most preferred mode of transport. Usage of conventional fuel vehicles contributes a cumbersome share to the pollution caused. Obvious solution which tackles above problem is electric vehicle. Even though electric vehicles were widely commercialized in the market a decade or two ago, it was not able to reach common masses and impact their life. As rightly said, there is no 100% efficient perpetual machine in the universe, electric vehicle too have a bunch of problems associated with it. The major problem which hindered the substantial success of electric vehicle is charging. Existing vehicles are obscure in case of long-distance travel and require very frequent charging. To tackle the so arisen problem, charging stations need to be established which requires extensive changes in infrastructure and will be lucrative to respective governments. Our paper involves methods to wirelessly charge an electric vehicle. The paper emphasizes on the usage of multiple coils which develops the energy that transmits about the capability and specially designed three-phase coil which improves efficiency along with inductive power transfer mechanism. A method for measuring the energy consumed by the vehicle is also discussed. Keywords Electric vehicle Energy Efficiency





Inductive power transfer
Charging
Coils


A. M. A. Jeylani (&) Sri Krishna College of Engineering and Technology, Coimbatore, India e-mail: [email protected] J. Kanakaraj
A. Mahaboob Subhani PSG College of Technology, Coimbatore, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_10

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1 Introduction Charging [1, 2] can also be called non-wired energy transmitter. This technology is the one which enables current that transfers from magnetic energy to electric energy which is loaded over different gaps, exclusive from cord connection. The technology also used in many low- to high-end applications even in hybrid E-vehicles due to user experience is good compared with other features like climatic condition or any change in public health which will reduce the damages. Our main dependencies are on fuel like petroleum which is so susceptible to provide interrupt. This also can be reduced by E-vehicles through plug-in and it reduces the fuel cost abundantly. There are various ways to charge the vehicle because it has rechargeable batteries like our gadgets which help us in many ways in our daily life. By charging often, there is no need to go to a gas station or any other fuel station. Electric vehicles also react very quickly. Moreover, they are extremely approachable, especially they also good torque. Most charging can be done in many ways; nowadays, charging a vehicle in a public place is customary; the simple way is that a plug-in charge station is enough to make the vehicle a better movable one. They raise the series of all E-vehicles which can also improve the quantity of every electric mile travelled using chargeable hybrid E-vehicle system which provides usefulness to the people who are all in need for a long trip. Mainly in hybrid vehicle, the public charger uses only the DC so the transmission to it is equipment, otherwise known as EVSE. DC fast-charge which delivers the energy more than 65 miles which reach so faster when compared to others. Public charging is located in spots where vehicles are highly concentrated, such as shopping centers, city parking lots and garages, airports, hotels, government offices and other crowded areas. The existence of electric vehicles has overcome many disadvantages over conventional vehicles. The drawback we may face in this technology is that it consumes more time for plug-in charging. So, the concept of wireless charging of electric vehicles has been discussed in this paper.

2 Wireless Power Transfer—The Future Though wired charging points are gaining popularity in parking lots and roadside parking bays, there will be soon more electric vehicles on the road that fixed charging point wired stations would not and can not suffice to the need. Wireless overcomes the need to stop at charging stations. Most of the major automakers are launching or planning wireless charging vehicles built on a global standard. The market for wireless charging is increasing and holding a major chunk of global share day by day. Pike research estimates wireless products to triple by 2020 [3]. According to another research, global wireless power revenue is expected to grow from $8.5 billion in 2017 to $17.9 billion in 2024 [4]. The graph below depicts the growth of wireless market (Fig. 1).

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Fig. 1 Depicts the growth of wireless market

3 Challenges Following are the challenges which hinder the phenomenal and substantial implementation of wireless charging: 1. The real, use of wireless control, even in devices that are readily available with it, has been determinedly low. Customer responsiveness remains a challenge and present wireless power technology does not provide users with a true wireless experience. Moreover, opposing hard work principles comprises of not permitted which produces the wireless power entirely. However, the greater than before dependencies on electronics and the stable need to control them will continue to drive the adoption of wireless power [5]. Battery removal brings in a great deal of parts leading to crunch in currency reserves [6] (Fig. 2). 2. When electric vehicles are commercialized in a large scale, the load on the power grid increases multiple times. As of now, there is no mechanism to measure the power consumed by wirelessly charging devices. 3. Nevertheless, normally wireless charging incurs higher implementation cost compared to plug-in charging. Moreover, as a wireless charging system creates additional high temperatures than that of hyperchargers, extra charge on crafting materials may be incurred. Fig. 2 Electric vehicles are commercialized in a large scale

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4 Literature Survey There are many methods proposed by scientists across the globe to wirelessly charge any equipment/device. The broad classification is shown in the chart below (Fig. 3). As illustrated, wireless incriminated technologies are classified into nonradioactive coupling-based arraign and radioactive RF-based indicting. The previous study consists of different method: Inductive coupling method [7], magnetic resonance coupling [8] and capacitive coupling [9], while the latter is supplementary of sorting the directive RF power beam into structures and non-directive RF power transfer [10]. In electrical capacitive union process, the attainable quantity of combination of the capacitance is reliant on the accessible region of the mechanism [11]. However, intended for a characteristic-volume a transferable gadget, it is tough to produce enough energy thickness for indict, which imposes a demanding the plan constraints. As for directive RF power, ray appearance is the limitation lies in that the stallion needs to know an exact location of the Due to the obvious limitation of above two techniques, wireless charging is usually realized through other three techniques.

5 Inductive Power Transfer This paper emphasizes the usage of inductive coupling and inductive control transmits energy to transfer the power wirelessly. The advantages of using this technique are that it is safe for humans as it is nonradioactive. It also scores over others in terms of having simple implementation. The effective charging distance using this method ranges beginning with a small millimeter to a small centimeter. The drawbacks of using this technology include short charging distance, heating effect and need tight alignment between charging devices used (Fig. 4). The energy efficient of a coil is measured by the induction and the dependency between the different coils, based on nonradioactive substances and the two

Fig. 3 Broad classification

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Fig. 4 A block diagram of nonradioactive wireless charging system

different factors which mainly focus on the proportionality of each coefficient is measured in which it creates the induction coil rotates propositional to another and produce coupling [12]. The factor Q is going to define the power or capacity which stores the energy in a generator [13] that provides a very small range of energy loss through energy transmission. Therefore, in a higher Q control structure is the alternation/ significance refuses gradually. The inherent feature of fabricated substance is based on some issues which will lead to change in the quality of the affected effects. The feature affects most on turning point from the distances. Hence, frequency of the load matching process is having various different gaps [14], the main focus is on the matched frequency. To refrain a consignment that identical the feature which is used to continue the quality incidence identical at different expanse which can be accessible, by writing different resolutions such as combination treatment, frequency matching and resonant parameter tuning. From some of recently developed hardware implementation of IPT systems, it is inferred that 50–80% charging effectiveness of several centimeters charging distance for IPT systems (Fig. 5). The advantages of IPT system are listed below 1. The system is safe. 2. Reliable.

Fig. 5 Inductive power transfer illustration

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The system has low maintenance. It has long product life. Energy wastage is overcome. Magnetic field radiation problem is overcome.

6 Proposed Method The figure shown represents the chunk illustration of proposed wireless power supply system for charging battery of electric vehicle. It consists of three parts, a transmitter to generate analog signal to be transmitted by the coil is so powerful that is, both for sender and receive the energy with non-wired and receiver to convert received AC signal into DC voltage for charging the battery of electric vehicle [13]. The aim of implemented system is to design a prototype of wireless power supply system that refreshes the sequence of an electric vehicle and avoids wastage of power (Fig. 6). Coil design is given utmost importance as it determines the quantity and quality of power transmitted and received. To keep the losses, at bay and as minimum as possible, both the transmitter and receiver end coil are tuned to have the same frequency. A specially designed three-phase coil structure is discussed in the subsequent section. The type of material used in both source plus recipient part is ferrite core.

Fig. 6 Block diagram for non-wired authority transmitter

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Rectifier is used to convert AC to DC, as battery used in electric vehicle charges only with constant dc supply. The components used in rectifiers are IGBT, IGCT. Using these components rather than diode or SCR reduces the conversion losses by 3%. In the filtering block, LC strain is used on the way to eliminate any harmonics if any to provide regulated ripple-free DC supply to charge the battery. Voltage regulator is also employed before charging phase on the way to decrease the magnitude of the DC supply. As inductive power transfer is used, the distance connecting main and minor coil is kept at 6–8 cm to achieve high efficiency.

6.1

Coil Design

Coil design is of great importance since the coil determines both the power and efficiency of a wireless charging system. In this paper, a three-phase coil structure, which consists of three transmitters and receivers as shown in figure, can be used to improve the efficiency of the power transferred to a larger extent (Fig. 7). 3D FEA simulations were performed to show the purpose of the attractive ground produced through future spiral arrangement be determined in the coil structure [12]. As this coil consists of three transmitters and three receivers, its efficiency is very high because the attractive fluctuation which is produced through one transmitter passes through its two adjacent receivers and then goes back to that transmitter. The major dissimilarity is among coaxial coil and this one is in the former coil the magnetic field is more concentrated in the coil structure compared to the latter. By using ANSYS MAXWELL as the 3D FEA analysis tool, the parameters of two coils viz., three-phase coil and coaxial coil are tabulated below [12] (Tables 1 and 2). After simulation, the following conclusions can be drawn from the magnetic field in YZ-plane and ZX-plane: 1. Both the three-phase coil structure and the coaxial coil structure give good performance in YZ-plane, where the attractive pasture that the three-part spiral arrangement which is stronger around receiver’s coil and weaker in the center

Fig. 7 Three-phase coil structure

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Table 1 Simulation results Parameters

Proposed coil

Coaxial coil

Transmitters self-inductance Receivers self-inductance Coupling coefficient between Lf and Lr Mutual inductance between Lf and Lr

131.69 uH 33.88 uH 0.138 9.22 uH

17.94 uH 15 uH 0.582 9.55 uH

Table 2 Excitation in simulation Parameters

Proposed coil

Coaxial coil

Transmitters current Receivers self-current Turn number of transmitters in coil Turn number of receivers in coil

3.71 A 5A 30 18

3.58 A 15 A 6 6

while the attractive pasture that the coaxial coil structure is weaker around the receiver’s coil and stronger in the center. 2. For the magnetic fields in ZX-plane, the three-phase coil structure performs better as the generated magnetic fields are more concentrated in the coil structure,m thereby affecting existing electronic devices less [12].

6.1.1

Microcontroller—PIC16F877

PIC16F877 is 40 pin IC and 8-bit microcontroller. Because of its high quality, low price and simplicity to access, the most used data is experimental and modern applications. PIC16F877 microcontroller gives the subsequent skin texture [14]: 1. 2. 3. 4. 5. 6. 7. 8.

14 K bytes of flash memory 368 bytes of RAM 3.256 bytes of EEPROM data memory Two 8-bit and one 16-bit timer Five input–output ports Two serial communication ports (MSSP, USART) 8-channel 10-bit ADC 2 CCP modules. PIC16F877 scores over other microcontrollers in the following aspects:

1. It requires less power supply comparatively. 2. It has onboard analog to digital converter (ADC) to sense voltage and display it in digital format. 3. It is cost-effective and trustworthy in case of large applications.

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The microcontroller used here is programmed in a method to give pulse width modulated output to the driving circuit. In the second case, it is programmed to display the status of charging the battery used here.

6.2

Measuring Energy—An Experimental Setup

When electrical vehicles are wirelessly charged on a large scale, it creates a lot of load on the grid. Power cannot be given free of cost to all the users of electric vehicles as it will have a negative impact on the economy of respective government. So, energy consumption monitoring is very much essential to understand the trends over a period of time and meet the demands in a smooth manner. The data acquired during monitoring will help to take necessary steps for saving the energy. This paper discusses design of the power measuring system which uses some technology from GSM mobile technique will deliver some data between the power and the consumables which calculate some data within some time period. The so designed energy meter can be placed on the receiver side of the not wired systems transmit energy, i.e., electric vehicle. Following are the major equipments used to design the energy measuring system: A. B. C. D. E.

Energy meter Arduino Uno board GSM 900 module Electrical load Other miscellaneous equipments (12 V adapter, Cables) (Fig. 8).

Fig. 8 Experimental setup

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In this system, Arduino Uno is used as microcontroller. It serves as a communication medium/channel between GSM module and energy meter. Arduino is coded with a simple programming language with the aim of measuring the energy consumed by the electrical load connected across it by using a predefined formula. Ordinary energy meter indicates power consumption by indicator LEDs. When the LED blinks 3200 times, one kilowatt-hour (kWh) energies are extremely used by consignment. So by basic mathematical analysis, it is safe to conclude that each blink/pulse indicates consumption of 0.0003125 kWh energy. Arduino counts/captures the number of blinks or pulses to measure the energy consumed. Arduino and GSM 900 module are interfaced with each other using connector wires. Arduino is programmed using open-source Arduino software. As an additional feature, the messages sent by GSM module are linked with software called If This Then That (IFTTT). This a free open-source service provided with a very small uncertain announcement known as applet (Fig. 9). In this setup, an applet is created to draft the messages received in an android phone to a Google sheet in Google drive. When the component inspired through a shipment and the cost is drafted in a Google sheet, it is easy to sort, pictorially represent the data (Fig. 10). The Arduino board used in this experimental setup uses ATmega 328 with 16 MHz onboard crystal oscillator. When this system is used in electric vehicle, it provides real-time monitoring of the energy consumption to both the customer and producer of power. This method can be efficiently installed in all-electric vehicles irrespective of the size of the vehicle and the magnitude of energy consumed by the vehicle. This solves single data which is most important confront of electric vehicle as discussed in the previous section titled challenges.

Fig. 9 Setup an applet

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Fig. 10 Messages received in Google sheets

7 Conclusion Wireless power transfer recommends the options of eliminating frequent option which is held with the cord connection which will be mandatory for the electronic gadgets. This promising technology when implemented in electric vehicles can reduce the negative impacts of conventional fuel vehicles, thereby improving the ecological balance. This paper substantiated one such method of wireless power transfer–inductive power transfer. This paper also focused on new innovative coil design and a novel method to measure the wireless energy consumed. The methods discussed in this paper are reliable and safe to use in a macroscale.

References 1. A. Costanzo et al., Electromagnetic energy harvesting and wireless power transmission: a unified approach. Proc. IEEE 102(11), 1692–1711 (2014) 2. J. Garnica, R.A. Chinga, J. Lin, Wireless power transmission: From far field to near field. Proc. IEEE 101(6), 1321–1331 (2013) 3. S.L. Ho, J. Wang, W.N. Fu, M. Sun, A comparative study between novel witricity and traditional inductive magnetic coupling in wireless charging. IEEE Trans. Magn. 47(5), 1522– 1525 (2011) 4. A. Kurs, A. Karalis, R. Moffatt, J.D. Joannopoulos, P. Fisher, M. Soljacic, Wireless power transfer via strongly coupled magnetic resonances. Science 317(5834), 83–86 (2007) 5. M. Kline, I. Izyumin, B. Boser, S. Sanders, Capacitive power transfer for contactless charging. in Proceedings IEEE Applied Power Electronics Conference Exposition, (Fort Worth, TX, USA, 2011), pp. 1398–1404

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6. S.Y. Hui, Planar wireless charging technology for portable electronic products and Qi. Proc. IEEE 101(6), 1290–1301 (2013) 7. J.W. Nilsson, S.A. Riedel, Electric Circuits, 7th edn. (Pearson/Prentice-Hall, EnglewoodCliffs, NJ, USA, 2005), pp. 243–244 8. T. Imura, Y. Hori, Wireless power transfer using electromagnetic resonant coupling. J. Inst. Elect. Eng. Japan 129(7), 414–417 (2009) 9. T. Imura, Y. Hori, Maximizing air gap and efficiency of magnetic resonant coupling for wireless power transfer using equivalent circuit and Neumann formula. IEEE Trans. Ind. Electron. 58(10), 4746–4752 (2011) 10. T.P. Duong, J.W. Lee, Experimental results of high-efficiency resonant coupling wireless power transfer using a variable coupling method. IEEE Microw. Wireless Compon. Lett. 21 (8), 442–444 (2011) 11. T.C. Beh, M. Kato, T. Imura, Y. Hori, Wireless power transfer system via magnetic resonant coupling at fixed resonance frequency—power transfer system based on impedance matching. in Proceedings World Battery Hybrid Fuel Cell, (Shenzhen, China, 2010) 12. I. Awai, T. Komori, A simple and versatile design method of resonator-coupled wireless power transfer system. in Proceedings of the International Conference Communication Circuits System (ICCCAS), (Chengdu, China, 2010) 13. A three phase wireless charging system for lightweight autonomous underwater vehicles, Published in Applied Power Electronics Conference and Exposition (APEC), IEEE (2017) 14. W.-T. Chen, R.A. Chinga, S. Yoshida, A 36 W wireless power transfer system with 82 percent efficiency for LED lighting application. Trans. Japan Inst. Electron. 6(1), 32 (2013)

Power Quality Enhancement Using DSTATCOM with Reduced Switch-Based Multilevel Converter Sudheer Vinnakoti, Anusha Palisetti and Venkata Reddy Kota

Abstract From the past few decades, the increased usage of non-conventional energy sources and nonlinear loads alarmed the researchers more concern about the power quality (PQ). Distribution static compensator (DSTATCOM) is voltage source inverter (VSI)-based shunt compensating custom power devices (CPD) used for current harmonic mitigation and also for reactive power compensation. The features of traditional multilevel inverters at high levels motivated the researchers to implement reduced switch topologies (RST) as they aim for reduction in cost, volume and to improve reliability of the system. This paper proposes a five-level RST-based DSTATCOM, which reduces the switch count to 33% compared to conventional multilevel converters. %THDs of five-level diode-clamped converter (DCC) and the proposed RST-based DSTATCOMs under same loading conditions are compared to show the potency of the converter. All the simulations will be carried out in MATLAB/Simulink software.







Keywords Power quality (PQ) Custom power devices (CPD) Distribution static compensator (DSTATCOM) Reduced switch topology (RST) Synchronous reference frame (SRF) Total harmonic distortion (THD)










1 Introduction From past few decades, the increased automobile and large-scale industries are demanding for high quality power. The growing power demand and degradation of conventional fossil fuels made the researchers to focus on alternate powergenerating sources. Natural sources (such as solar, wind and biomass) are found to S. Vinnakoti
A. Palisetti (&) Department of E.E.E, Raghu Engineering College (Autonomous), Dakamarri, Visakhapatnam, Andhra Pradesh, India V. R. Kota Department of E.E.E, University College of Engineering, JNTUK, Kakinada, Visakhapatnam, Andhra Pradesh, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_11

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be the best alternate sources due to their continuous availability with time and less environmental effects. Renewable energy sources [1, 2] united to the grid through electronic converters are causing some power quality issues. With the advent of semi-conductor-based sensitive loads, the concern for power quality increased from industries to consumers as the distorted supply results in malfunction of the equipment and reduces its efficiency. The passive filters used to suppress the harmonics have limited applications in high power due to their bulkiness in size, tuning problems and fixed range of reactive power compensation [3, 4]. Flexible AC transmission system [FACTS] [5] and custom power devices [CPD] introduced later gave better performance than passive filters and help in maintaining the desired power quality. FACTS devices help in improving the reliability and stability of the transmission system whereas compensating-type CDP is widely used in distribution networks for harmonic mitigation, VAR compensation, power quality and power factor improvement. It is researched and proven that (DSTATCOM) [6, 7] effectively mitigates all the current-related PQ issues when connected in shunt. It generally injects compensated currents 180° phase out with the harmonics in load current and makes the supply current harmonic free. In addition to the harmonic mitigation, DSTATCOM compensates reactive power, thereby aids in improving the power factor. The traditional two-level inverter-based DSTATCOMs are predominantly suitable for small scale and utility grid due to their low output levels and high harmonics. The multilevel inverters [MLI] [8, 9] introduced later overcome the drawbacks of two-level as they produce high multistepped output with low THDs and are appropriate for coupling the RES to grid. Baker in his patent on MLI explained the generation of multistepped waveform, by cascading the single-phase H-bridge inverter fed with distinct DC sources. Later, several new topologies such as diode clamped or neutral point, flying capacitor were configured. These conventional MLIs have improved power factor, high-voltage levels with low device ratings, reduced stress and switching losses, lower THD, hence offering wide range of applications. But at very high level, the MLIs require more switching components, and the gate control circuitry associated with it becomes complex and effects the reliability of the system. The improved features of conventional MLI motivated researchers to configure new topologies with reduced switch count as they aim for improving the structure, reliability and efficiency with reduced size, cost and volume. THD is the other important factor which the researchers need to focus along with reduction in switch count and can be maintained low by proper control techniques. This paper proposes a new reduced switch topology [RST] which generates a five-level output. The control techniques enhances the performance of the CPD [10], that they generate the reference current signals. The switching pulses to voltage source inverter (VSI) are generated by the controller based on error between the reference currents generated by the control algorithms and actual currents. The time-domain controls are commonly used due to its less computational time. Prominent control schemes are instantaneous reactive power theory (IRP), synchronous reference frame theory (SRF) [10–12], artificial neural network (ANN) [13], fuzzy logic controller (FLC) [14, 15], etc.

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In this paper, the performance of RST-based DSTATCOM is analyzed in three-phase distribution system with nonlinear loads using SRF control. The THDs of source current of proposed RST-based DSTATCOM are measured and compared with traditional five-level DCC-based DSTATCOM using MATLAB/SIMULINK software. In this paper, Sect. 2 deals with the basic operating principle of DSTATCOM, and Sect. 3 explains multilevel concept with reduced switch count. SRF control scheme for DSTATCOM is discussed in Sect. 4. All the simulation results are discussed in Sect. 5. Work concludes with Sect. 6.

2 Distribution Static Compensator Distribution static compensator [6] is a VSI-based shunt compensating device whose configuration is similar to the STATCOM but used in distribution systems. It is generally connected at point of common coupling (PCC) to reduce the current harmonics injected in systems due to nonlinear loads. It can be operated in both voltage and current control modes. In the former case, it balances the load voltage and maintains a constant value in order to protect the equipment from the voltage fluctuations. In the later case, it nullifies the harmonics in the supply current by injecting the distorted currents at PCC but in phase opposition to the harmonics introduced in load current. In addition to the harmonic filtering, it compensates reactive power, corrects power factor and enhances the power quality. The size selection and optimal location of the DSTATCOM play a pivotal role in reducing the cost and providing an effective compensation. The basic configuration of DSTATCOM involves (i) three-phase VSI which converts DC capacitor voltage into three-phase AC required by the system (ii) a coupling inductor which smoothens the output from ripples and (iii) DC bus capacitor. Figure 1 shows the block diagram representation of two-level inverter-based DSTATCOM with nonlinear load.

Fig. 1 Block diagram of two-level inverter-based DSTATCOM

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In the equivalent model for DSTATCOM in Fig. 2a, Vs represents supply voltage, Vsh represents shunt compensated voltage of DSTATCOM, and Ish represents the current injected at PCC. The basic operating principal of DSTATCOM can be explained in three modes based on the supply voltage and shunt voltage generated by DSTATCOM. (i) If the output voltage Vsh of DSTATCOM and the supply voltage are of equal magnitudes and are in phase, then DSTATCOM neither injects nor absorbs the reactive power. (ii) If the injected voltage Vsh is less than the Vs, then the shunt current Ish lags the supply voltage VS by certain angle as shown in Fig 2b. In this case, the DSTATCOM acts in inductive mode, i.e., it absorbs the reactive power. (iii) In Fig. 2c, Ish leads the supply voltage VS by certain angle as Vsh generated by DSTATCOM is more than the supply voltage Vs. Here, the DSTATCOM acts in capacitive mode and supplies all the reactive power required by the

Fig. 2 a Equivalent model of DSTATCOM b Inductive mode c Capacitive mode

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load. Thus, from the above discussion, we can summarize that DSTATCOM can either inject or absorb the reactive power, and its magnitude depends on the angle between Vsh and VS. The design of DC bus capacitor plays a prominent role and should be maintained at least 0.9 p.u. The DC bus capacitance (CDC) value depends on the DC bus voltage (VDC) which should be maintained twice the peak value of phase voltage to reduce voltage ripples and avoid resonance problems. The minimal values of VDC and CDC to be maintained for effective compensation are given by Eqs. (1, 2), respectively. pffiffiffi pffiffiffi 2 2VLL 2 6Vph ¼ pffiffiffi ¼ pffiffiffi 3m 3m

ð1Þ

3Vph Is ta CDC i ¼h 2 ðVDC Þ2 ðVDC1 Þ2

ð2Þ

VDC

where VLL is the line voltage; Vph is the phase voltage: m is the modulation index whose value is between 0 < m < 1; t is the response time, a is the overloading factor, VDC is the DC bus capacitor voltage, and VDC1 is the dip in voltage.

3 Reduced Switch Multilevel Inverter The output generated with conventional two-level inverters is low with more harmonics, and hence, its application to high power is limited. In high-power applications, these conventional two-level inverters need to operate at high frequency, which increases voltage stress and temperature, reducing the efficiency of the system. Multilevel inverters (MLI) introduced by Baker in 1975 are found to be well suited for interfacing the RES with grid. As they generate high-level multistepped output by using different pulse width modulation (PWM) techniques, their application extended to medium and large-scale industries. Baker explained the synthesis of staircase waveform by adding of single-phase full bridges each fed with distinct DC sources hence named cascaded MLI, and later, he proposed another new topology by using clamping diodes and named as diode-clamped multilevel inverter (DCMLI). In 1992, Foch and Meynard replaced all the clamping diodes with clamping capacitors and named it as flying capacitor (FC) MLI. The advantages of MLI over two-level are high efficiency, reduced switch stress, reduced switching loss, low harmonic content which reduces the filtering cost. Table 1 compares the components of conventional topologies for m-levels. From the table, it is clear that with the increase in levels, the components required increase, and the control of gate circuitry associated with it becomes complex affecting the systems reliability. The above drawbacks and features of MLI made

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Table 1 Comparison of components among the conventional MLI Converter type

Diode-clamped

Main switching devices Main diodes Clamping diodes DC bus capacitors Balancing capacitors

(m (m (m (m 0

− − − −

1)  2 1)  2 1)  (m − 2) 1)

Flying capacitors (m (m 0 (m (m

− 1)  2 − 1)  2 − 1) − 1)  (m − 2)/2

Cascaded H-Bridge (m − 1)  2 (m − 1)  2 0 (m − 1)/2 0

the researchers to focus on new inverter topologies generating high output levels with reduced switch count as they reduce cost, size and volume. THD is the other important factor that can be minimized by implementing proper control technique. This paper uses a well-known three-phase VSI. The basic configuration of proposed RST using three groups of conventional three-phase two-level VSI [16] generating a five-level output is shown in Fig 3b. The two inverter groups in RST are cascaded in such a way that a five-level voltage appears across open-ended primary winding of transformer. As the two ends of the open-ended primary are fed with two separate VSIs, zero sequence currents circulation is avoided, and the maximum possible voltage that can be generated by combination of inverter switches in proposed RST is 2VDC with an input of VDC. The voltages V1 V2 and V3 are five levels (+2VDC, +VDC, 0, −VDC, −2VDC) generated by making the following connections: the first leg of inverter 1 is connected to the second leg of inverter 3

Fig. 3 Block diagram of RST-based DSTATCOM

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Table 2 Comparison between conventional MLI and proposed RST Comparison

Conventional cascaded MLI

Proposed RST

Maximum voltage No. of switches No. of switches

2VDC 6 single phase 24

2VDC 3 three phase 18

for generating V1. Similarly, the voltage V2 is generated by connecting the first leg of inverter 2 to the second leg of inverter 1, and the voltage V3 is measured by connecting the first leg of inverter 3 to the second leg of inverter 2. The triggering pulses to the inverter switches are generated by PWM current controller based on error between the reference current signals extracted by control schemes and actual current signals. Table 2 compares between conventional cascaded MLI and proposed RST. The conventional cascaded H-bridge multilevel inverter uses six single-phase bridges, whereas the proposed RST uses exactly half the number but in three-phase module for generating same voltage levels. Table 2 shows that proposed RST uses lesser number of switches compared to conventional MLI and reduces the cost and switching losses associated with switches. The block diagram of RST-based DSTATCOM is shown in Fig. 3a.

4 Synchronous Reference Frame Theory Control techniques play a prominent role in deciding the performance of DSTATCOM, and they generate the reference current signals based on which the pulses are given to the switches. The control algorithms are available in both time and frequency domains. Nowadays, some soft computing techniques are also been used for extraction of reference signals. In this paper, the time-domain control is being implemented as they involve mathematical calculations and require less time. SRF theory is commonly used traditional method to mitigate all the current and voltage-related harmonics and can operate in transient and steadystate mode to control active power filters. In this theory, the sensed harmonic load currents in three phase (ila, ilb, ilc) are first converted into the stationary two-phase frame (a-b-0 frame) and later into the synchronously rotating d-q-0 frame by using different transformation techniques as depicted in Fig. 4. The input voltage signals (Va, Vb, Vc) are passed through the phase-locked loop (PLL) to generate the signals in terms of sine and cosine functions. The currents id and iq in d-q plane involve average (
id and
ip ) and oscillatory components (~id and ~ip ). The oscillatory components are undesirable as they cause harmonic currents and are filtered by using the low-pass filters (generally a second-order Butterworth LPF) so that only DC components are extracted. The average DC components are transferred back to (a-b-c) by reverse transformation techniques. The transformation and reverse transformation

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Fig. 4 Block diagram representation for reference currents generation by using SRF control

techniques used for converting the three-phase quantities into synchronously rotating two phase are given by Eqs. 3 and 4, respectively. A comparison between reference source current and the source currents generates an error signal, based on which hysteresis current controller generates the triggering pulses to VSI given in Fig. 4.  1 pffiffiffi 2 qffiffiffiffiffiffi6 IS0 4 ISd 5 ¼ 2= 6 sin ð wt Þ 36 4 ISq cosðwtÞ

 1 pffiffiffi 2   sin wt  2p=3   cos wt  2p=3

3  1 pffiffiffi 2 3 2   7 ILa 7 sin wt þ 2p=3 7 4 ILb 5  5 ILc cos wt þ 2p=3

2 pffiffiffi 1 qffiffiffiffiffiffi6  2 ISaref 1 pffiffiffi 4 ISbref 5 ¼ 2= 6 2 36 4 pffiffiffi IScref 1 2

sinðwtÞ   sin wt  2p=3   sin wt þ 2p=3

3 2 3 cosðwtÞ   7 IS0 cos wt  2p=3 7 7 4I 5   5 Sd ISq cos wt þ 2p=3

2

2

2

3

3

ð3Þ

ð4Þ

In Fig. 5, the reference currents generation by using SRF control under nonlinear loads is shown. The DC link capacitor voltage must be maintained constant for effective compensation. This can be attained by proper tuning of the by proper tuning of the gains of the PI controllers. These controllers are generally used for estimating the losses in DC bus. For SRF theory, the output of PI controller is added to d-axis component. The PLL used in SRF works effectively under low distortion, as its performance is poor in case of high distortions. To overcome this, modified PLL is used to improve the system performance under unbalance and high distortion conditions.

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Vdc* 300

PI Vdc1 Add1

PI Vdc2 Add3

Add2

PI Vdc3 0

Add5

0 ILabc

abc

0 d

sin_cos

vsab

vscb

vsab

vscb

wt

Sin_Cos

a

ISaref

b

ISbref

C

IScref

d

Add4

q

Discrete Butterworth Filter

0 q

sin_cos

dq0_to_abc Transformation2

Fig. 5 Reference currents generation by using SRF control under nonlinear load in Simulink model

5 Simulation Results The proposed RST-based DSTATCOM with SRF control in three-phase distribution system with nonlinear loads is designed in MATLAB/SIMULINK software. Figure 6 depicts the supply voltage (VSabc), load current (ILabc) and supply current (ISabc) before and after connecting RST-based DSTATCOM. The proposed RST-based DSTATCOM is connected at 0.2 s. Compensating currents are injected by the RST-based DSTATCOM at PCC in phase opposition to the harmonics generated by nonlinear loads, thereby making the source current (in Fig. 6c) harmonic free. For current harmonic mitigation, a constant voltage of 700 V needs to be maintained across each DC bus capacitors which can be maintained by proper tunning of PI controllers. Extension to above, analysis also concentrated on the reactive and active powers. It can be noticed that before connecting the proposed RST-based DSTATCOM, i.e., from 0 to 0.2 s, the source caters entire active power requirement of load, i.e., 8 KW. Figure 7a, b and c gives the complete active power profiles of supply, load and RST-based DSTATCOM, respectively, before and after compensation. After connecting the RST-based DSTATCOM, it mitigates the supply current harmonics by consuming 0.997 KW active power from the supply. It can be clearly observed

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(a) Voltage (Volts)

400 200 0

-200 -400 0.15

Current (Amps)

(b)

0.2

0.3

0.35

0.3

0.35

Load Current (ILabc )

20 10 0 -10 -20 0.15

0.2

0.25 Supply Currents (ISabc )

(c) 100 Current (Amps)

0.25

50 0 -50 -100 0.15

0.2

0.25

0.3

0.35

Time (sec) Fig. 6 Effect of compensation on a supply voltage b load current and c supply current

from Fig. 7a that the supply active power is increased from 8 to 10 KW during the time period 0.25–0.4 s. The dynamic state of system can be observed during the interval 0.2–0.25 s. Similarly, Fig. 8 shows the complete analysis of reactive power at load and supply before and after connecting the RST-based DSTATCOM. From Fig 8a, it is observed that source supplies all the 2.2 kVAR reactive power needed by the load during the period 0–0.2 s, whereas the proposed RST-based DSTATCOM remains at 0 KAVR as it is not connected to system. After connecting proposed RST-based DSTATCOM at 0.2 s, the DSTATCOM itself delivers all the reactive power needed by the load making the source to deliver only the active power needed by load. The reactive power at supply and load before and after connecting proposed

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10000

Active Power (Watt)

8000 6000 4000 2000 0 0.15

Active Power (Watt)

(c)

15000 10000 5000 0

0

Time (sec) Fig. 7 Active power at a supply b load c DSTATCOM

RST-based DSTATCOM is shown in Fig 8a, b, respectively. The proposed RST-based DSTATCOM alone delivering all the reactive power required by load is shown in Fig. 8c. It can be noticed from Table 3 that %THD of supply currents in respective phases before compensation are 20.81% with their fundamental components as 16.61%. After connecting the RST-based DSTATCOM, it is clearly observed that the % THD of phase A, phase B and phase C of supply current are lowered to 3.00, 3.19 and 3.10% with an increase in their fundamental component as shown in

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2000 1500 1000 500 0

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0.2

0.4

0.6

0.8

1

Time (sec) Fig. 8 Reactive power at a supply b load c DSTATCOM

Table 3. In addition to the above, the study is also conducted on conventional five-level diode-clamped MLI, and the observed results are compared with proposed RST to prove the effectiveness of the topology.

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Table 3 Comparison between conventional MLI and proposed RST-DSTATCOMs using SRF Nonlinear load

Five-level DCC DSTATCOM

Proposed RST-DSTATCOM

SRF controller Fundamental component (A) Before After ISa ISb ISc ISa ISb Isc

16.61 16.61 16.61 16.61 16.61 16.61

18.72 18.52 17.83 18.74 18.31 17.78

% THD Before

After

20.81 20.81 20.81 20.81 20.81 20.81

3.47 3.46 3.23 3.00 3.19 3.10

6 Conclusion The performance of the proposed RST-based DSTATCOM is examined under nonlinear load with SRF control technique. Then, the potential of the proposed RST-based DSTATCOM is compared with that of conventional multilevel inverters by using MATLAB/SIMULINK software. From the results, it is clearly noticed that the proposed RST-based DSTATCOM mitigates all the supply current harmonics effectively compared to the traditional multilevel inverters (DCC). Further, it can be noted that the %THD of the supply currents are minimized after the compensation. Hence, from the above discussion, it can be concluded that the proposed RST-based DSTATCOM has improved performance over the conventional MLI with reduced switching cost and losses.

References 1. J. Benedek, T.T. Sebestyen, B. Bartok, Evaluation of renewable energy sources in peripheral areas and renewable energy-based rural development. Renew. Sustain. Energy Rev. 90, 516– 535 (J. Elsevier, Romania, 2018) 2. S. Guo, Q. Liu, J. Sun, H. Jin, A review on the utilization of hybrid renewable energy. Renew. Sustain. Energy Rev. 91, 1121–1147 (J. Elsevier, China, 2018) 3. O. Prakash Mahela, A. Gafoor Shaik, Topological aspects of power quality improvement techniques: a comprehensive overview. Renew. Sustain. Energy Rev. 58, 1129–1142 (J. Elsevier, India, 2016) 4. W.U. Tareen, S. Mekhilef, M. Seyedmahmoudian, B. Horan, Active power filter (APF) for mitigation of power quality issues in grid integration of wind and photovoltaic energy conversion system. Renew. Sustain. Energy Rev. 70, 635–655 (J. Elsevier, Australia, 2017) 5. F.H. Gandoman, A. Ahmadi, A.M. Sharaf, P. Siano, J. Pou, B. Hredzak, V.G. Agelidis, Review of FACTS technologies and applications for power quality in smart grids with renewable energy systems. Renew. Sustain. Energy Rev. 82, 502–514 (J. Elsevier, Australia, 2018)

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6. O. Prakash Mahela, A. Gafoor Shaik, A review of distribution static compensator, renewable and sustainable energy reviews. Renew. Sustain. Energy Rev. 50, 531–546 (J. Elsevier, Jodhpur, India, 2015) 7. B. Singh, P. Jayaprakash, D.P. Kothari, A. Chandra, K. Al Haddad, Comprehensive study of DSTATCOM configurations. IEEE Trans. 10, 854–870 (India, 2014) 8. A. Sinha, K.C. Jana, M.K. Das, An inclusive review on different multi-level inverter topologies, their modulation and control strategies for a grid connected photo-voltaic system. Solar Energy 170, 633–657 (J. Elsevier, Dhanbad, India, 2018) 9. N. Prabaharan, K. Palanisamy, A comprehensive review on reduced switch multilevel inverter topologies, modulation techniques and applications. Renew. Sustain. Energy Rev. 76, 1248– 1282 (J. Elsevier, Tamil Nadu, India, 2017) 10. R. Kumar, H.O. Bansal, Shunt active power filter: current status of control techniques and its integration to renewable energy sources. Sustain. Cities Soc. (J. Elsevier, Rajasthan, India, 2018) 11. R.S. Herrera, P. Salmeron, H. Kim, Instantaneous reactive power theory applied to active power filter compensation: different approaches, assessment, and experimental results. IEEE Trans. 55(1), 184–196 (Chungnam, Korea, 2008) 12. B. Singh, J. Solanki, A comparison of control algorithms for DSTATCOM. IEEE Trans. 56 (7), 2738–2745 (New Delhi, India, 2009) 13. M. Qasim, P. Kanjiya, V. Khadkikar, Artificial-neural-network-based phase-locking scheme for active power filters. IEEE Trans. 61(8), 3857–3866 (Abu Dhabi, UAE, 2014) 14. D. Amoozegar, DSTATCOM modelling for voltage stability with fuzzy logic PI current controller. Electr. Power Energy Syst. 76, 129–135 (J. Elsevier, Shiraz, Iran, 2016) 15. J. Fattahi, H. Schriemer, B. Bacque, R. Orr, K. Hinzer, J.E. Haysom, High stability adaptive microgrid control method using fuzzy logic. Sustainable Cities Soc 25, 57–64 (J. Elsevier, Ontario, Canada, 2016) 16. V.F. Pires, A. Cordeiro, D. Foito, J.F. Silva, Three-phase multilevel inverter for grid-connected distributed photovoltaic systems based in three three-phase two-level inverters. Solar Energy 174, 1026–1034 (J. Elsevier, Setubal, Portugal, 2018)

Dual-Input Multioutput Using Non-Cloistered DC–DC Boost Converter K. Sakthidhasan and K. Mohana Sundaram

Abstract This paper proposes a new non-cloistered dual-input multioutput DC– DC boost converter using a single magnetic field storage component (L). The proposed DC–DC boost converter can be used for conveying energy between different energy resources such as battery, FC, PV and ESS. In this paper, solar cell (PV) and battery are considered as a generating power source and an energy storage system (ESS) to produce two different voltage magnitudes in its output. Two different power operations like charging and discharging are defined. The main advantage of the converter module is to use least/minimum number of power electronic components. This converter is suitable for electric vehicle applications. The results of the proposed DIMO converter were verified with the help of the software (MATLAB/Simulink) and laboratory-based prototype.




Keywords DIMO DC–DC converter Boost converter Energy storage system (ESS) Electric vehicle





Solar cell (PV)


1 Introduction In this paper, based on the combination of two converters a new non-cloistered dual-input multioutput DC–DC boost converter is proposed. The proposed converter is presented in Fig. 1. In that Fig. 1 the converter interfaces two input power sources Vin1 and Vin2 . A multilevel inverter can be made possible with the load resistance R1 and R2 which can represent the equivalent power feeding. The control of power flow between input sources in addition to boost up the input sources voltage is possible by proper switching of switches. The outputs from this proposed

K. Sakthidhasan (&)
K. Mohana Sundaram Department of EEE, Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_12

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Fig. 1 Equivalent circuit for switching state 1

converter are capable to have different or equal output voltage magnitudes. This output voltage magnitudes can also be used for connecting to a multilevel inverter. The proposed converter has two input sources solar cell (PV) and energy storage system (such as battery) combination. The input source Vin1 can deliver power to the source Vin2 but not vice versa. In this system, solar cell (PV) which cannot be charged is located in Vin1 and also in Vin2 such as ESS (battery) can be charged is placed. Charging and discharging modes are used separately and it is working in single mode, i.e., DC–DC.

2 Different Mode of Operation 2.1

Battery Charging Mode

In this mode of operation, input source Vin1 not only supplies to the load but also delivers power to Vin2 (battery). This condition occurs when load power is low and battery requires to be charged. For each switch, a specific duty is considered.S1 is switched to regulate the total output voltage Vt ¼ Vout1 þ Vout2 to a desired value. The output voltage Vt is controlled by switch S2 . It is clear that by regulation of Vt and Vout1 , the output voltage Vout2 is regulated too. In Fig. 4, the gate signals of switches and voltage and current waveforms of inductor are shown. According to different switching states, there are three different operation modes in one switching period is given as follows;

Dual-Input Multioutput Using Non-Cloistered DC–DC …

2.1.1

119

Switching State 1 (0 < T < DT1)

In this state, switches S1 and S2 are turned ON, switch S3 is OFF and cannot be turned ON. The diode D2 is reverse biased and does not conduct. In this state Vin1 charges inductor L, and it increases the proposed converter for this state is shown in Fig. 1. Also in this mode, capacitors C1 and C2 are discharged and deliver their stored energy to load resistance R1 and R2 . The L and C equations are as follows; 9 diL > ¼ Vin1 > > > dt > > dvout1 Vout1 = ¼ C1 : dt R1 > > > > dvout2 Vout2 > > ; ¼ C1 : dt R1 L

2.1.2

ð1Þ

Switching State 2 (DT1 < T < DT2)

In this state, switch S1 is OFF, and switch S2 and S3 switch is turned ON. The diode D2 is reverse biased and does not conduct. In this state, inductor L is discharged and delivers its stored energy to C1 and R1 , so inductor current decreases. Figure 2 shows the equitant circuit of the proposed system. C1 is charged and capacitor C2 is discharged and deliver their stored energy to load resistance R2 . The L and C equations in this mode are as follows;

Fig. 2 Equivalent circuit for switching state 2

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9 diL > ¼ Vin1  Vout1 > L > > dt > > dvout1 Vout1 = ¼ iL  C1 : dt R1 > > > > dvout1 Vout2 > > ; ¼ C2 : dt R2

2.1.3

ð2Þ

Switching State 3 (DT2, DT3 < T < T)

In this state, switch S1 is OFF and S2 is turned ON. The diode D2 is forward biased and the diode D1 is reverse biased and does not conduct, consequently S3 is OFF. Equivalent circuit of proposed converter for this state is shown in Fig. 3. In this state, inductor L is discharged and delivers its stored energy to capacitors C1 and C2 and load resistance R1 and R2 and thus capacitors C1 and C2 are charged. The inductor and capacitors equations in this mode are as follows (Fig. 4); 9 diL > ¼ Vin1  Vt > > > dt > > dvout1 Vout1 = ¼ iL  C1 : dt R1 > > > > dvout1 Vout2 > > ; ¼ iL  C2 : dt R2 L

Fig. 3 Equivalent circuit for switching state 3

ð3Þ

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Fig. 4 Steady state waveform for battery charging mode

2.2

Battery Discharging Mode

In this mode, two input power sources Vin1 and Vin2 are responsible for supplying the loads. For each switch, a specific duty is considered. Here, S1 is active to regulate the battery current to a desired value by controlling inductor current. Output voltage Vout1 is controlled by switch S2 . It is obvious that by regulation of Vt and Vout1 , the output voltage Vout2 is regulated too. Gate signals of switches and also voltage and current waveforms of L are shown in Fig. 8. The total output voltage Vt is regulated as Vt ¼ Vout1 þ Vout2 .

2.2.1

Switching State 1 (0 < T < DT1)

S1 is ON, and switch S2 is OFF. The diode D1 , D2 are reverse biased and does not conduct, so switch S3 is turned OFF. In this state, the Vin2 charges inductor L charge is increases. Equivalent circuit of proposed converter for this state is shown in Fig. 5. Also in this mode capacitors C1 and C2 will get discharge and deliver its stored energy to load resistance R1 and R2 . The inductor and capacitors equations in this mode are as follows;

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Fig. 5 Equivalent circuit for switching state 1

9 diL > ¼ Vin2 > L > > dt > > dvout1 Vout1 = ¼ C1 : dt R1 > > > > dvout2 Vout2 > > ; ¼ C1 : dt R2

2.2.2

ð4Þ

Switching State 2 (DT1 < T < DT2)

In this state, switch S1 is OFF and S2 is turned ON. The diode D2 is forward biased and the diode D1 is reverse biased and does not conduct, consequently S3 is OFF. Equivalent circuit of proposed converter for this state is shown in Fig. 6. In this state, inductor L is discharged and delivers its stored energy to capacitors C1 and C2 and load resistance R1 and R2 and thus capacitors C1 and C2 are charged. The inductor and capacitors equations in this mode are as follows; 9 diL > ¼ Vin1  Vt > > > dt > > dvout1 Vout1 = ¼ iL  C1 : dt R1 > > > > dvout1 Vout2 > > ; ¼ iL  C2 : dt R2 L

ð5Þ

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Fig. 6 Equivalent circuit for switching state 2

2.2.3

Switching State 3 (DT2, DT3 < T < T)

In this state, switches S1 and S2 are OFF and S3 switch is turned ON. The diode D1 is forward biased and the diode D2 is reverse biased and does not conduct. Equivalent circuit of proposed converter for this state is shown in Fig. 7. In this state, inductor L is discharged and delivers its stored energy to C1 and R1 , so inductor current decreases. Also in this mode capacitor C1 is charged and capacitor C2 is discharged and deliver its stored energy to load resistance R2 . The inductor and capacitors equations in this mode are as follows (Fig. 8);

Fig. 7 Equivalent circuit for switching state 3

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Fig. 8 Steady state waveform for battery discharging mode

9 diL > ¼ Vin2  Vout1 > L > > dt > > dvout1 Vout1 = ¼ iL  C1: dt R1 > > > > dvout1 Vout2 > > ; ¼ C2: dt R2

ð6Þ

3 Simulation Results Simulations are done for both charging and discharging modes of battery are using MATLAB/Simulink software in order to verify the performance of the proposed converter. The simulation parameters of the proposed converter are listed in Table 1. The simulation input voltage sources value for the proposed converter is given as Vin1 = 45 V and Vin2 = 36 V. The fuel cell (FV) is considered as input voltage source (Vin1 ) with the combination of battery (Vin2 ) is suitable for electric vehicle applications. Depending on the utilization state of the battery, charging and discharging modes of operation are defined for the proposed converter. The

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Table 1 Simulation parameters for the proposed boost converter S. No.

Values

Parameters

1. 2. 3. 4. 5. 6.

2.5 mH 1000 lF 10 kHz 45 V 36 V 70 X

Inductance (L) Capacitors (C1 ; C2 ) Switching frequency (fs) Input voltage (Vin1 ) Input voltage Vin2 ðVbattery Þ Resistance (R1, R2)

(a)

(b)

(c)

Fig. 9 a The total output voltage (Vt), b the output voltage (Vout1 ), c the output voltage (Vout2 )

converter module consists of three controllable switches. The required gate signals are given to each switch through PWM. The PI controller acts as an error minimize, here the response of the PI controller is compared with carrier and reference signal to produce the desired PWM. For each switch, a specific duty ratio (DX ) is considered. A resistive load R1 and R2 is taken such that from the simulation results, the output voltages are obtained as Vout1 = 85 V and Vout2 = 37 V, respectively. So the total output voltage Vt is regulated as Vt ¼ Vout1 þ Vout2 , therefore Vt = 122 V. Figure 9 shows, (a) the total output voltage Vt , (b) the output voltage Vout1 and (c) the output voltage Vout2 . For battery discharging mode, two input power sources Vin1 and Vin2 are responsible for supplying the loads. Figure 10 shows the simulation results for battery discharging mode current. For battery charging mode, the input source Vin1 not only supplies to the load but also delivers power to Vin2 (battery). This condition occurs when load power is low and battery requires to be charged. Figure 11 shows the simulation results for battery charging current.

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Fig. 10 Battery discharging current

Fig. 11 Battery charging current

4 Conclusion A non-cloistered dual-input multioutput DC–DC boost converter with less number of power electronics components is proposed using generating power source solar cell (PV) and energy storage system (battery). Also, the converter can be utilized as single input multioutput converter. Based on the utilization state of the battery two power operation mods are defined. For each mode, transfer functions matrices/small signal average methods are obtained separately. The proposed converter produces two different voltage magnitudes in its output which is suitable for electric vehicle-based applications.

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References 1. P. Thounthong, Member, IEEE, A. Luksanasakul, P. Koseeyaporn, B. Davat, Member, IEEE, Intelligent model-based control of a standalone photovoltaic/fuel cell power plant with supercapacitor energy storage. IEEE Trans. Sustain. Energy 4(1) (2013) 2. S. Danyali, S.H. Hosseini, Member, IEEE, G.B. Gharehpetian, Senior Member, IEEE, New extendable single-stage multi-input DC–DC/AC boost converter. IEEE Trans. Power Electron. 29(2) (2014) 3. P. Thounthong, V. Chunkag, P. Sethakul, B. Davat, M. Hinaje, Comparativestudy of fuel-cell vehicle hybridization with battery or supercapacitor storage device. IEEE Trans. Veh. Technol. 58(8), 3892–3905 (2009) 4. M. Zandi, A. Peyman, J.P. Martin, S. Pierfederici, B. Davat, F. Meybody-Tabar, Energy management of a fuel cell/supercapacitor/battery power source for electric vehicular applications. IEEE Trans. Veh. Technol. 60(2), 433–443 (2011) 5. J.L. Duarte, M. Hendrix, M.G. Simoes, Three-port bidirectional converter for hybrid fuel cell systems. IEEE Trans. Power Electron. 22(2), 480–487 (2007) 6. A. Peyman, S. Pierfederici, F. Meybody-tabar, B. Davat, Anadapted control strategy to minimize dc-bus capacitors of parallel fuel cell/ultracapacitor hybrid system. IEEE Trans. Power Electron. 26(12), 3843–3852 (2011) 7. J. Lee, B. Min, D. Yoo, R. Kim, J. Yoo, A new topology for PVDC/DC converter with high efficiency under wide load range, in Proceedings of European Conference on Power Electronics and Applications (2007), pp. 1–6 8. J. Zeng, Student Member, IEEE, W. Qiao, Senior Member, IEEE, L. Qu, Member, IEEE, An Isolated three-port bidirectional DC–DC converter for photovoltaic systems with energy storage. IEEE Trans. Ind. Appl. 51(4)(2015) 9. A. Nami, F. Zare, A. Ghosh, F. Blaabjerg, Multi-output DC–DC converters based on diode-clamped converters configuration: topology and control strategy. IET Power Electron. 3, 197–208 (2010) 10. A.A. Boora, A. Nami, F. Zare, A. Ghosh, F. Blaabjerg, Voltage sharing converter to supply single-phase asymmetrical four-level diode clamped inverter with high power factor load. IEEE Trans. Power Electron. 25(10), 2507–2521 (2010) 11. A. Nahavandi, M.T. Hagh, M.B.B. Sharifian, S. Danyali, A non cloistered dual input multi output DC–DC boost converter for electric vehicle applications. IEEE Trans. Power Electron. 30(4)(2016) 12. R.W. Erickson, D. Maksimovic, Fundamentals of power electronics, 2nd edn. (Kluwer Academic Publisher, New York, NY, USA, 2015), p. 2000

Application of Nonlinear and Optimal Control Techniques to High Gain DC–DC Converter Nibedita Swain

Abstract In this paper, a power converter is designed to give up an output power of 50 W. The design method is based upon two boost converter connected in cascade that gives an output of 460 V, and a high gain is needed. For controlling the voltage output of power converter, various control approaches like sliding mode controller and linear quadratic Gaussian regulator are described. The proposed small-signal averaged models for boost converter are derived mathematically. Then, it is used for designing of two different feedback controllers, which accomplish the additional understanding in the converter dynamics. First, a nonlinear sliding mode (SM) controller is designed; the design method depends on the selection of the sliding surface and switching function. And second, the design of linear quadratic Gaussian (LQG) state-feedback controller, by calculating the state-feedback gain matrix and Kalman estimator gain, is presented for the same converter topology. Here, the output voltage regulation and an excellent dynamic performance are compared between two different types of controllers. All the simulations are done in MATLAB/Simulink environment.







Keywords Boost converter Double boost converter State-space averaging technique Sliding mode controller Linear quadratic Gaussian controller







1 Introduction This paper primarily focuses on the use of power electronics and control system; here, the output voltage has increased from a very small input voltage of 17.2 volts to a large output voltage of 460 V by use of double boost converter. Double boost converter is a connection of two boost converters in cascade so that the output voltage will be very large. Due to non-minimum phase structure in boost converter, controlling of output voltage is quite difficult. Different control mechanisms are applied to control voltage output of DC–DC converter. The application of linear N. Swain (&) Deptartment of EEE, Silicon Institute of Technology, Bhubaneswar, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_14

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N. Swain

control methods in power converter is not suitable as they contain the energy storing elements like inductor and capacitor, and also, it depends upon the system parameters. The use of double boost converter with sliding mode controller (SMC) and LQG controllers and their effectiveness have been described in brief in this paper. The use of double boost converter used in the circuit is to amplify the voltage to a suitable value [1, 2]. A control method convenient for power converters must have the ability to tolerate the wide input voltage and load variations, providing stability at any operating condition with less settling time. Here, sliding mode control method is a feasible choice to regulate the converter circuits as it is variable structure systems. The controller enhances the dynamic behaviour of the system against changing load and uncertain system parameters. It also overcomes the deficit of the control techniques which are based on small-signal models. A variable structure system approach is adopted for the system which is an optimal control, i.e., LQG controller. The LQG controller is an optimal control problem, and it is applied in many control fields [3] due to sub-optimum performance. In sub-optimum performance, the control technique reduces a predetermined performance index [4] that reshapes the system’s states and the control surface. The Gaussian controller technique tunes the parameter Q and R in such a way that the effect on shape of the states and control signal traces, respectively. Q and R matrix are calculated by classical methods. This paper compares two different control methods in which better voltage regulation is achieved with minimum overshoot and minimum settling time. The voltage output of the first boost converter remains fixed, and the voltage output of second boost converter is controlled by means of different control techniques. The main objective of this paper is to get a constant DC of 460 V at the converter output side because most of the AC loads are operating with a voltage of 230 V AC. So, a 460 V DC is required at the converter output to convert 460 V DC to 230 V AC. For achieving 460 V DC with minimum overshoot and minimum settling time, two different types of controllers are designed and controlled.

2 Mathematical Modelling of Boost Converter Boost converter is a DC–DC converter where the output and input voltage are related by the Eq. (1). Vo 1 ¼ Vin ð1  dÞ

ð1Þ

Vo is the output voltage across the load, Vin is the input voltage, and d is the duty ratio of the converter.

Application of Nonlinear and Optimal Control Techniques to High …

131

iL

L

io

Vc

S on

Vi

C

R

Fig. 1 ON state of boost converter

“Switch ON” state During switch ON state, diode is reversed biased and isolating the output stage. Figure: 1 shows switch ON state of boost converter. During switch ON, the equation is described in Eqs. (2) and (3). diL ¼0 dt

ð2Þ

Vc CdVc  ¼0 R dt

ð3Þ

Vi  L

iL is the inductor current, VC is the capacitor voltage, L is the inductance, C is the capacitance, and R is the load resistance of the converter. The state-space representations of the above equations are described as in (4). 2

3 " diL 0 4 dt 5 ¼ dV c 0 dt

0

#

1 RC

iL Vc

 þ

1 L

0

Vi

ð4Þ

“Switch OFF” state During switch OFF, the output capacitor is assumed to be very large to ensure a constant output voltage. Figure: 2 shows OFF state of boost converter. During switch OFF, the equations are described in (5) and (6), respectively. diL ¼0 dt

ð5Þ

Vc dVc þC ¼0 R dt

ð6Þ

Vi  Vc  L iL 

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N. Swain iL

L

D

io

Vi

C

Vc

R

Fig. 2 OFF state of boost converter

The state-space representations of the above equations are given as in (7). 2

3  diL 4 dt 5 ¼ 01 dV c C dt

1 L 1 RC



 1 iL þ L Vi Vc 0

ð7Þ

The output voltage during ON and OFF state remains same and is given in Eq. (8).   i V0 ¼ ½ 0 1  L Vc

ð8Þ

The steady-state representation of the average system is linear [6]. In this converter, ON state and OFF state matrix are shown in (9) and (10), respectively. A1 is the ON state matrix, A2 is the OFF state matrix, B1 is the ON state input matrix, B2 is the OFF state input matrix, C1 is the output ON state matrix, C2 is the output OFF state matrix, D1 and D2 are the ON state and OFF state transmission matrix, respectively.  A1 ¼

0 0

 A2 ¼

   0 1=L ; B1 ¼ ; C1 ¼ ½ 0 1=RC 0

0 1 C

1 L 1  RC

 ; B2 ¼

1 L

0

; C2 ¼ ½ 0

1 ; D1 ¼ ½0

1 ; D2 ¼ ½0

ð9Þ

ð10Þ

The average model of boost converter described in [7] is realized by taking a weighted average of ON state and OFF state matrices as given in (11), (12) and (13), respectively.

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A ¼ A1 d þ A2 ð1  dÞ

ð11Þ

B ¼ B1 d þ B2 ð1  dÞ

ð12Þ

C ¼ C1 d þ C2 ð1  dÞ

ð13Þ

Using small signal analysis as described in [8] the steady state dc voltage transfer function and ac transfer function are given by the Eqs. (14) and (15), respectively. vo ðsÞ ¼ CðSI  AÞ1 B þ D vi ðsÞ

ð14Þ

v0 ðsÞ ¼ C½SI  A1 ½ðA1  A2 Þx þ ðB1  B2 Þvin  þ ðC1  C2 Þx dðsÞ

ð15Þ

3 Converter Modelling For getting voltage output of 460 V, two boost converters are connected in cascade, and hence, the name is double boost converter. The input to the first boost converter is 17.2 V which operates with a fixed duty ratio of 0.9 with switching frequency of 25 kHz. The parameter specifications of two boost converters are given in Tables 1 and 2. The schematic diagram of double boost converter is depicted in Fig. 3.

Table 1 Component specifications of Converter-1

Circuit parameters

Specifications

Input vol. (Vin) Inductance (L) Capacitance (C) Duty ratio (d) Switching frequency (fs)

17.2 V 1 mH 33 lF 0.9 25 kHz

Table 2 Component specifications of Converter-2

Circuit parameters

Specifications

Input vol. (Vin) Inductance (L) Capacitance (C) Duty ratio (d) Switching frequency (fs)

172 V 1 mH 33 lF 0.63 25 kHz

134

N. Swain L1

L2 D1 D2

S1 Vin

C2

C1 S2

RL

Fig. 3 Schematic diagram of double boost converter

The aim of this paper is to derive the transfer function model [9, 10] of double boost converter with respect to duty cycle “d”. The transfer function of first boost converter and the second boost converter is calculated as in (15) and (16), respectively. CONVERTER-1: Vo ðsÞ 5:212  105 s3 þ 3:633  108 s2 þ 9:155  105 s þ 1:579  1014 ¼ 4 ð16Þ dðsÞ s þ 606:1  s3 þ 6:979  105 s2 þ 1:837  108 s þ 9:183  1010 CONVERTER-2: Vo ðsÞ 3:807  105 s3 þ 5:097  109 s2 þ 0:002686s þ 2:162  1016 ¼ 4 dðsÞ s þ 606:1s3 þ 8:389  106 s2 þ 2:514  109 s þ 1:721  1013

ð17Þ

By cascading two transfer functions, the resulting transfer function of the double boost converter is calculated as in (17). 1:984  1011 s6  2:795  1015 s5 þ 1:851  1018 s4  1:133  1022 s3 V0 ðsÞ ¼ dðsÞ

þ 8:66  1024 s2 þ 2:404  1012 s þ 3:415  1030 s2 þ 1212s7 þ 9:454  106 s6 þ 8:205  109 s5  2:479  1013 s4 þ 1:378  1016 s3 þ 1:324  1019 s2 þ 3:392  1021 s þ 1:58  1024 ð18Þ

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4 Different Control Strategies The need of controller for any system is evaluated in four matrices such as command tracking, disturbance rejection, line regulation and noise rejection. Command tracking is the strength of output to respond for varying reference input. Disturbance rejection is the ability to confine output from load variation. Line regulation is the ability to disconnect output from change in input, and noise rejection is the strength to reject measurement noise/errors. This paper is concentrating only on command tracking and disturbance rejection. Two different types of controllers are designed for this double boost converter: a nonlinear controller (sliding mode controller) an optimal controller (LQG controller). (A) Sliding Mode Controller (SMC) Sliding mode is a nonlinear control method [11] that develops the dynamics of nonlinear system by the use of discontinuous control signal that forces the system to slide along a cross section of systems normal behaviour [12, 13]. Multiple control architectures are established so that state trajectories always move towards an adjacent region with a different control design[14], and the ultimate trajectory will not remain absolutely within one control design. SMC design involves two steps: one is selection of the sliding surface; another is the switching function [5]. Sliding mode does not depend upon plant dynamics and is calculated by parameters of switching function only [15, 16]. The technique consists of reaching mode and sliding mode. In reaching mode, the sliding surface is drifted towards the state trajectory from any initial point, and system response is more sensitive to uncertain parameters and disturbances. In sliding mode, the state trajectory moves to origin along the switching surface, and the states never leave the switching surface [17]. The state equation with control input u and state vector x is given in (19). :

x ¼ AxðtÞ þ BuðtÞ

ð19Þ

“u” consists of two components: un represents discontinuous component and a continuous component ueq, and they are related by an equation given in (20) u ¼ un þ ueq

ð20Þ

While the system is on the surface, ueq ensures the motion of the system on the sliding surface. The equivalent control component can be estimated by taking time derivative of sliding surface is equal to zero and is given in Eq. (21). S_ ¼ 0 The solution of Eq. (21) gives equivalent control ueq and is given in (22).

ð21Þ

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ueq ¼ ðCBÞ1 ðCAxÞ

ð22Þ

The sliding surface for boost converter is given in (24). s ¼ Ke þ e_

ð23Þ

“e” is the error between reference voltage and output voltage and is given in (24). e ¼ Voref  Vo

ð24Þ

The general state equation of boost converter with control input u is obtained by considering the switching states and is shown in Eq. (25). diL Vi vc ¼ þ ðu  1Þ dt L L dvc vc iL ¼ þ ð1  uÞ dt RC C

ð25Þ

The sliding surface S and derivative of S are calculated and are given in (26) and (27), respectively. d dvC ðVref  Vo Þ ¼ KVref  KvC  dt dt  vC iL þ ð1  uÞ ¼ KVref  KvC   RC C   1 iL KVref  vC K   ð 1  uÞ RC C   1
ð1  uÞ
iL s_ ¼ 0  K  vC  RC C      1 vC iL ð1  uÞ Vin vC þ ð1  uÞ þ ðu  1Þ ¼ K   RC C RC C L L s ¼ K ðVref  Vo Þ þ

ð26Þ

ð27Þ

Solving the above equation, it is simplified and is given in (28). " s_ ¼ vC

#   KRC  1 1 ð 1  uÞ 2 ð1  KRCÞ ð1  uÞ ð1  uÞ  þ  Vin  þ iL RC RC RC C LC LC ð28Þ

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Putting s_ ¼ 0

ð29Þ

We get " 0 ¼ vC

#   KRC  1 1 ð1  uÞ2 ð1  KRCÞ ð1  uÞ  þ  þ iL RC RC RC C LC

ð30Þ

In this work, control input is considered as duty cycle [4] and is given by Eq. (33). u2eq

 2vc Vin ð1  KRCÞ þ  iL þ ueq RC 2 LC LC LC    Vin 1 ð1  KRCÞ ð1  KRCÞ  þ  þ vc iL ¼ 0 þ 2 2 LC R C RC 2 LC v 



c

ð31Þ

Comparing Eq. (31) with (32) au2eq þ bueq þ c ¼ 0

ð32Þ

We get a¼

Vo LC

2Vo Vin 1  kRC þ  iL RC 2 LC LC   Vin 1 ð1  kRCÞ ð1  kRCÞ  þ Vo c¼ iL þ 2 2 LC RC RC 2 LC b¼

By putting the above data as specified in Table 1 for second boost converter, the values of a, b and c are obtained as a ¼ 30:3  106 Vo b ¼ ð60:6  106 Vo Þ þ ð521:16  106 Þ  ð18:36  106  K  30:3  103 ÞiL c ¼ 521:21  106 þ Vo  ð30:3  106  ð367:3  103  K  606:66ÞÞ þ ð18:3  106  K  30:3  103 ÞiL

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Now, ueq ¼

b 

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi b2  4ac 2a

Ignoring negative roots and considering positive roots, let the equation be given in (33). b2  4ac ¼ x

ð33Þ

where b2 ¼ ð60:6  106 Vo  ð18:36  106  K  30:3  103 ÞiL þ 521:16  106 Þ2 4ac ¼ 4  ð30:3  106 Vo Þ  ð521:16  106 þ Vo  ð30:3  106  ð367:3  103  K  606:6Þ þ ð18:3  106  K  303:3  103 ÞiL ÞÞ Hence ueq ¼ 1 þ

 pffiffiffi 521:1  106 þ x 60:6  106 Vo

For designing a control law, the positive definite Lyapunov function V can be defined as in (34). V ¼ 0:5S2

ð34Þ

The derivative of V with respect to time must be negative definite to establish a stable system that makes the sliding surface S attractive. Such condition leads to the following inequality: _ SS\0 To satisfy such condition, Eq. (35) is defined. un ¼ signðSÞ

ð35Þ

(B) Linear Quadratic Gaussian (LQG) Controller It is one of the optimal control problems. LQG controller is the combination of an optimal estimator (Kalman estimator) and optimal regulator (LQR) [4]. In LQG design, the stability of closed-loop system must be guaranteed using separation principle [6]. The paper presents the performance analysis of LQG controller under closed-loop conditions. The paper describes the design and simulation of LQG optimal controller for a DC–DC converter system. The system description and the system model are given using state-space averaging technique.

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Basic Principle of LQG Optimal Control A linear dynamic system can be described by the following mathematical models as given in (36) and (37), respectively: :

x ¼ Ax þ Bu

ð36Þ

y ¼ Cx þ Du

ð37Þ

where A, B, C and D are state-space matrices of the linear system model and x is state vectors, and y is the system output. The LQG control signal u is a state feedback described below in Eq. (38). u ¼ Kx

ð38Þ

where the vector K is obtained from the solution of algebraic Riccati equation (ARE). u can be derived from the minimization of the quadratic cost function as given in Eq. (39). Z1 J¼

ðxT Qx þ uT RuÞdt

ð39Þ

0

where Q and R represent weight matrices, Q is a positive definite or positive semi-definite symmetry matrix; R is a positive definite symmetry matrix. The feedback gain matrix K in LQR is solved using the Eq. (40). K ¼ R1 BT P

ð40Þ

P matrix can be evaluated by algebraic Riccati equation (ARE). Three steps can be formulated to find feedback gain matrix “K” for LQR. (a) The matrices Q and R must be selected properly. (b) P must be solved by using ARE. (c) The feedback gain matrix K must be evaluated by using the relation (41).

K ¼ R1 BT P

ð41Þ

The optimal controller gain for this system is found to be K ¼ ½3:65990:5973: By using the command “Kalman”, the Kalman estimator is calculated, and using the command “F = lqgreg(Kest,K)”, the state-space model of LQG regulator is

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formed. The transfer function of LQG regulator is calculated and is given in Eq. (42). FðsÞ ¼

0:0024 s  4:214 4000 s þ 1:196  107

ð42Þ

The optimal control law which minimizes the cost function is given below u ¼ ½0:01380:0024:

5 Simulation Results The schematic diagram of cascaded boost converter is illustrated in figure: 4. The output of the converter is connected to the single-phase inverter which produces an AC output for a grid coupled system. The output voltage of the cascaded boost converter is shown in figure: 5. From the voltage waveform, it is observed that the steady state is reached after 0.2 s which is large for power electronic converter. Figure: 6 illustrates the block diagram of double step-up converter along with SMC. Figure: 7 shows the voltage output of the cascaded boost converter with SMC. The steady-state voltage (460 V) is reached with minimal overshoot, and it settles after 0.20 s. It is observed that the output voltage is always reached to the steady state with less time and less oscillation. So, better tracking is possible by using SMC. Figure: 8 shows the schematic diagram of cascaded boost converter with LQG controller. The voltage response of the cascaded boost converter along with LQG controller is presented in figure: 9. It is observed that the output voltage is settled at 460 V after 0.13 s with no peak overshoot. The smooth DC voltage is achieved by applying LQG controller. Table 3 shows the performance comparison between SMC and LQG controller.

Fig. 4 Schematic diagram of cascaded boost converter

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Output voltage of the cascaded boost converter

800 700 600

Vo(volt)

500 400 300 200 100 0 -100

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

time(sec)

Fig. 5 Output voltage of cascaded boost converter

Fig. 6 Schematic diagram of cascaded boost converter with SMC

6 Conclusion The system is controlled both by using nonlinear and optimal control approach. The simulation results of all the controllers are promising. Sliding mode control and optimal control is a new approach to this problem and hence is providing needful results. Sliding mode controller reduces the no. of oscillations and also the settling

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output voltage of boost converter using SMC

500

400

Vo(volt)

300

200

100

0

-100

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

time(sec)

Fig. 7 Output voltage of cascaded boost converter with sliding mode controller

Fig. 8 Schematic diagram of cascaded boost converter with LQG controller

time, and LQG controller also reduces the peak overshoot and setting time. By comparing these two controllers, it can be concluded that LQG controller is the best controller for this problem.

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Converter voltage with LQG controller

500

400

Vo(volt)

300

200

100

0

-100

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

time(sec)

Fig. 9 Output voltage of cascaded boost converter with LQG controller

Table 3 Performance comparison of different controllers Time-domain parameter

Without controller

With LQG

With SMC

Rise time Setting time Peak overshoot (%)

0.03 s 0.25 s 71.2

0.01 s 0.13 s 0

0.03 0.20 12.7

References 1. J.H. Su, J.J. Chen, D.S. Wu, Learning feedback controller design of Switching converters via Matlab/Simulink. IEEE Trans. Educ. 45, 307–315 (2002) 2. H. Guldemir, Sliding mode control of dc-dc boost converter. J. Appl. Sci. 5(3), 588–592 (2005) 3. A. Mohammadbagheri, N. Zaeri, M. Yaghoobi, Comparison performance between PID and LQR controllers for 4- leg voltage-source inverters. 2011 International Conference on Circuits, System and Simulation IPCSIT, vol. 7 (IACSIT Press, Singapore) 4. J. Matas, L.G. Vicuna, O. Lopez, M. Lopez, M. Castilla, Sliding-LQR based control of Dc– Dc converters. European Power Electronics Conference (EPE’99), Lausanne, 7–9 (1999) 5. V. Utkin, J. Guldner, J. Shi, Sliding Mode Control in Electromechanical Systems (Taylor and Francis, London, 1999) 6. B.N. Datta, Numerical Methods for Linear Control Systems (Elsevier Academic Press, San Diego, CA, 2004) 7. N. Mohan, T.M. Undeland, W.P. Robbins, Power Electronics: Converter, Applications and Devices, 2nd edn (Wiley, 1995)

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8. P. Mattavelli, L. Rosetto, G. Spiazzi, Small-signal analysis of dc-dc converters with sliding mode control. IEEE Trans. Power Electron. 12(1), 96–102 (1997). https://doi.org/10.1109/63. 554174 9. H. Guldemir, Modeling and sliding mode control of Dc–Dc buck-boost converter. 6th International Advanced Technologies Symposium (IATS’11), 16–18, Elazığ, Turkey 10. Y. He, F.L. Luo, Sliding-mode control for dc-dc converters with constant switching frequency. Control Theor. Appl. 153(16), 37–45 (2006) 11. G.C. Verghese, Dynamic modelling and control in power electronics, in The Control Handbook, ed. by W.S. Levine, Ed. Boca Raton (FL: CRC Press LLC), chap. 78.1, pp. 1413– 1424 12. H. Guldemir, Sliding mode speed control for dc drive systems, mathematical and computational applications. 8, 337–384 (2003) 13. S.C. Tan, Y.M. Lai, C.K. Tse, A unified approach to the design of PWM-based sliding-mode voltage controllers for basic DC-DC converters in continuous conduction mode. IEEE Trans. Circuits Syst. 53(8), 1816–1827 (2006) 14. J.J. Slotine, T.S. Liu, Applied nonlinear control (Prentice Hall, Englewood Cliffs, 1991) 15. J.Y. Hung, W. Gao, J.C. Hung, Variable structure control: a survey. IEEE Trans. Industr. Electron. 40(1), 2–21 (1993). https://doi.org/10.1109/41.184817 16. V. Utkin, Sliding mode control design principles and applications to electric drives. IEEE Trans. Ind. Appl. 40, 23–36 (1993) 17. B. Kavya Santhoshi, K. Mohana Sundaram, M. Sivasubramanian, S. Akila, A novel multiport bidirectional dual active bridge dc-dc converter for renewable power generation systems. https://doi.org/10.17485/ijst/2016/v9i1/85701

An Innovative Multi-input Boost Chopper for HEV A. Ranganadh and M. Chiranjeevi

Abstract In this paper, for efficient functioning of hybrid electric vehicles, a DC– DC converter which has multiple inputs is used. The output gain can be increased considerably than that of existing methods. The main input sources for these converters are fuel cell (FC), solar panel and battery. In which FC is treated as the main power supply and a roof-top PV is used which helps in charging the battery increasing the efficiency and reducing the fuel consumption. By using this converter, demanded power can be produced continuously even in the disconnection of either one or two sources. Moreover, in this, the strategy of power management is described. In order to authenticate and validate the prototype, this multi-input boost HEV can be considered. Which is implemented and is tested. This paper presents design and implementation of a system which gives the information about multi-input DC–DC converter for hybrid vehicle by using MATLAB/Simulink. Keywords Hybrid electric vehicle (HEV) Fuel cell and pv and battery


Multi-input DC–DC chopper


1 Introduction The main drawback of present-day vehicles which are powered by oil or diesel, etc., is that these fossil fuels are available in limited amount on the earth and is leading to environmental pollution. To eliminate the above-mentioned problems, most of the present-day innovators started showing interest in HEVs and PHEVs [1, 2]. As these are powered by renewable resources, EVs depend upon the energy which is maintained in the battery. The main problem which is faced by using this converter is that it consists of limited driving range, and large amount of time is required for charging. It can be overcome by using a bidirectional switch board charger. A. Ranganadh (&) Department of EEE, Guru Nanak Institute of Technology, Hyderabad, India M. Chiranjeevi Department of EEE, PACE Institute of Technology & Sciences, Ongole, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_15

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Solar-based EVs can also be recycled as a conventional energy source in this system, which are not being implemented due to the size and location of PV panels [2]. As a result, fuel cells are used for powering the device. The main problems of FCs are high cost, great density output current capability and poor transient performance. Clean electricity generation, and high efficiency operation. The vehicles which are mechanized by fuel cells are mixed by ESSs [1]. Cost of per unit power can be reduced, and problems such as transients can be reduced by hybridization technique. Boost-dual-half bridge topology-based chopper is recycled for this purpose which consists of three half-bridges and a three-winding transformer. It is applied for high step-up applications. The converter size cost is going to high due to the managing components such as active switches, input inductors and filter capacitors [2]. The system was proposed by FC and a battery unit. V2G is main advantages of proposed converter. However, the great number of power switches could reduce the reliability and increase the cost. In a multi-input DC-DC step-up chopper for hybrid PV/FC/Battery is proposed. This converter can be charged and discharged only by using PV and FC leading to the improper functioning A two-input DC–DC converter is used to connect two power sources with a DC bus or load. Due to turn-on zero voltage switching (ZVS) of all switches, it attains high efficiency, and also, it does not consists of bidirectional port.

2 Solar Energy and Boost Converter Silicon materials are used for building basic photovoltaic cell from its origin. Photovoltaic cells are made up of silicon material. In the process of photovoltaic cell structure, it consists of boron atoms from three valance electrons (p-donors) silicon to create a most affection to attract electrons (Fig. 1). If any one of the array is shaded, then automatically output will educe, whose variation of reduction in magnitude depends upon electrical configuration of the array, and its output will be corresponding to the high amount of light intensity

Fig. 1 Solar energy

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which it is subjected to. If the shadowed cell or shadowed module is connected electrically to other cells and modules which are unshaded, then there will be a reduction in the performance characteristics.

2.1

Maximum Power Point Tracking (MPPT)

They follow the MPPT’s maximum power cooperate photovoltaic (electronic) system module that allows power modules for everything. The main problem with this method is that it frequently, if it is necessary accordingly, finds the MPP Vmpp voltage or current or the maximum power given to it by the photoelectric and external temperature. In this segment, most useful MPPT techniques are described in any order [2]. A. Fractional Open-Circuit Voltage In this approach, it shows the relationship between the maximum voltage and the VOC is considered constant VMPP arrangement that made the observations. It was observed that K1 factor varies between 0.71 and 0.78. For this reason, the constant K1 is a measure of the VOC and VMPP with the help of periodic checking of those parameters. The implementation is simple and inexpensive to this effect, and the screening efficiency and its efficiency are low due to improper, which are in a constant K1 in the VMMP. B. Fractional Short-Circuit Current This method is based on the observation that the current at the maximum power point of the IMPP is approximately linearly related to the short-circuit current of the ISC. Here, it is not consistent with themselves and women, between 0.78 and 0.92. Circuit K2 is short, and accurate measurement of the current cycle of periodicals determines efficiency. DC–DC Converter DC-to-DC converter performs the operation of producing variable DC voltage under constant supply of DC voltage [3, 4]. Different ranges of output voltages can be achieved. They are used in power bus regulation, providing noise isolation, etc. Boost Converter The schematic in Fig. 2 depicts the basic boost converter. Boost converter increases the output voltage for the given input (Fig. 3). Buck-Boost Converter In order to required continuous current conduction operation Buck-Boost converter is preferred. the transistor will be on when Vx = Vin and will be in off position when Vx = V0. The duty cycle “D” range is from 0 to 1 for this convertor. As a result, the output voltage can vary the range of lesser or greater than input magnitude.

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Fig. 2 Boost converter

Fig. 3 Buck-boost converter

Among all the converters, only the buck converter exhibits linear graph between the duty ratio and output voltage. In order to get desired higher voltage, solar cells are assembled in series. In maximum cases this method of connecting cells in series. In such cases, boost converters are used which help in increasing voltage and decreasing of the cells [5, 6]. Battery It is a device which provides electrical energy to electrical devices in which electrochemical cells are associated with cascaded or concurrent as per the requirement. In a battery, electrons flow from negative terminal to the device and reach one end of the cell that may be positive in order to transfer the energy from battery to device. When a device is linked to a battery, electrolytes exist in a battery move as ions which helps in completing the chemical reactions required in order to transfer the energy. Lithium-Ion Battery A battery of calcium ion and lithium-ion (LIB) batteries consists of charging and discharging of their energy levels. The lithium batteries inter calculated lithium compound is recycled as the electrode material and compared with a non-calcium

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Fig. 4 Battery module for hybrid vehicle

metals are used in a rechargeable lithium battery. Electrolysis allows ionic movement, and two electrodes are a constituent component ion cell battery. Electrochemical reactions on electrolyte reagents and positive and negative lithium-ion electrolytes are providing a lithium-ion model into the conductive medium between the electrodes to move. The main important difference between a fuel cell and a battery is that fuel cell requires fuel and oxygen as a continuous source (Fig. 4). Fuel Cell An electrochemical cell which converts the electrical energy from chemical energy from a fuel by using an oxidizing agent by undergoing electro chemical reactions [7]. The main important difference between a fuel cell and a battery is that fuel cell requires of fuel and oxygen as a continuous source, whereas batteries do not require any such arrangement as it utilizes the chemicals that are previously exists in the battery. Hence, continuous supply of fuel is necessary for a fuel cell in order to produce electricity continuously.

2.2

Polymer Electrolyte Membrane Fuel Cells

Fuel cells with a polymer electrolyte membrane (PEM), also called proton membrane exchange fuel cells, are recycled to contribute high power density. It consists of advantages such as less mass and volume compared to other fuel cells [8]. In PEM fuel cells, a solid polymer is used as electrodes consisting of porous carbon containing a catalyst made of platinum or a platinum alloy. Work requires only hydrogen and oxygen from air and water.

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Direct Methanol Fuel Cells

They supply hydrogen to the fuel cell system or by transferring hydrogen-rich fuels such as methanol, ethyl, and hydrocarbon fuels, by developing hydrogen in the fuel cell system [8]. Due to the high energy density property of methanol to handle hydrogen, this fuel supply and transportation become easier, as a result of which direct fuel cells with methanol are used in many modern applications such as mobile phones and laptops.

3 Proposed System and Control Strategies For the performance verification of this converter, a 80 W prototype version of the circuit is built and tested in presented three states. Switching frequency is considered about 30 kHz. As mentioned earlier, the expected chopper had greatest capacity being used for different industrial and domestic applications such as HEV, DGs interface, smart homes. Power sources are mainly PV arrays, fuel cells and so on. Ignoring the transient time of the power sources, they could be replaced by DC power supplies to obtain experimental results. Li-ion type batteries are used widely due to their better performance in portable electronic gadgets. High reliability, high energy density, high temperature performance and being recyclable are main features of Li-ion batteries [6]. Due to high switching frequency, ferrite cores are chosen for the both inductors (Figs. 5 and 6) [9]. The structure of proposed multi-input step-up chopper is depicted. A conventional boost converters convert the voltage from lower level to higher level. Its characteristic converter is suitable for hybrid systems. In this work, the behavior of the source converter is in terms of objective managing to show the power of

Fig. 5 General structure of multi-powered EHV

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Fig. 6 Topology of the proposed convertor

management and control. The PV and FC are two independent power sources, and the output ratio is based on them. L1and L2 are inductance grams from the input panel of fuel filters and the cell. And in a series began to use L1 and L2 to change the sources of current Sources of photovoltaic modules to the FC. r1 and r2 are VPV’s and VFC’s equivalent resistance, respectively. Earth is the equivalent resistance of R-600 connected to the bus loads. S1, S2, S3 and S4 are power switches. Diodes D1, D2, D3 and D4 are used to establish modes. The increase of the output to the capacitor C1 to the capacitor Co and the result is that the gain of the voltage output of filter. The behavior of the continuous operation system (CCM) to produce smooth operation with less amount of vibration can be performed [9, 10].

3.1

Principle of Operation

In this section, principles of the suggested converter are discussed. Operation of the converter is divided into three states: (1) The load is supplied by PV and FC, and battery is not used. (2) The load is supplied by PV, FC and battery; in this state, battery is in discharging mode. (3) The load is supplied by PV and FC, and battery is in charging mode.

3.1.1

Topological Modes and Analysis

A. First operation state (The load is delivered by PV and FC, while battery is not used) In this state, there are three modes of operation. In this state, there is no operating system, the battery charge or discharge. Therefore, there are two paths for current to

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Fig. 7 Current flow path of operating modes in the first operating state

Fig. 8 Current flow path of operating modes in the third operating state

flow (through S3 and D3 or D1 and S4). In this paper, S3 and D3 are considered as a common path. However, D1 and S4 could be chosen as an alternative path. During this state, switch S3 is permanently ON and switch S4 is OFF (Figs. 7 and 8) [10].

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B. Second operation state (The load is equipped by photo voltaic, FC and battery) In this state, there are four modes of operation. During this state, the load is supplied by all input sources (PV, FC and battery). In the first mode, there is only one current path. However, in the other three modes, there are two current paths (through S3 and D3 or D1 and S4). In this state, current flows through D1 and S4. Switch S4 is permanently ON during this state. C. Third operation state (The load is given by PV and FC, while battery is in charging mode) This is illustrated in Fig. 10 four ways. In the state of FC, mass sends power to the battery. Both the first and second modes of operation, there are two S3 and S4 travels mainly D1 current or D3). D1 is selected to be in the S4 path, and the current flows in this state. During this state, switch S3 is permanently OFF and diode D1 conducts [9].

Fig. 9 Simulink for first mode operation

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Fig. 10 Output voltage graph for the first case

Fig. 11 Output current for the first case

4 Simulation Results Figure 9. shows the Simulink circuit for the proposed system for first mode of operation. Figures 10 and 11 show the output voltage and currents for the first mode of operation, respectively, output desired voltage is desired to be about 120 V, and output current is about 18A. As mentioned, in this state, battery’s power is zero.

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Fig. 12 Simulink model for the second mode of operation

4.1

For Second Mode of Operation (The Load is Supplied by PV, FC and Battery)

Figure 12 shows the Simulink circuit for the second mode of operation of the proposed system. During this state, the load is provided by all input sources (PV, FC and battery). The current paths are S3 and D3 or D1 and S4. Figures 13 and 14 show the graph of output current and voltages of the Simulink circuit for second mode of operation, respectively. The outputs of the system where the battery is in discharging mode are the voltage is around 125 V and output current is about 1.6A.

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For Third Mode of Operation (The Load is Dispensed by PV and FC, While Battery is in Charging Mode)

Figure 15 shows the Simulink circuit for the third mode of operation of the proposed system. During this state, PV and FC charge the battery and supply the energy of load. The current paths are through S3 and D3 or D1 and S4. Figures 16 and 17 show the output voltage and output current graphs of the proposed system for the third state of operation, respectively. In this mode of operation, the battery is in charging mode. The output voltage and currents of the mode of operation are 120 V and 1.6A, respectively.

Fig. 13 Output voltage for the second mode

Fig. 14 Output current for the second mode

An Innovative Multi-input Boost Chopper for HEV

Fig. 15 Simulink model for the third mode

Fig. 16 Output voltage for the third case

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Fig. 17 Output current for the third case

5 Conclusion In this paper, an emerging three-input DC/DC converter is proposed and analyzed thoroughly. The converter is able to supply the power load demanded in the search of one or two sources. Expectancy and performance patterns are also out of power of domestic and industrial processes with great reliability by stumbling the timer into the head and do an offering to use the converter. The converter is modeled for three different operational states and utilized to design a proper controller. MPPT algorithm is achieved and along power management is utilized to apply the commands of controller. Meanwhile, employing power management and MPPT procedure will enhance the performance of converter. Finally, a practical prototype of the presented converter is implemented, and results are taken and depicted. Results prove the analysis and performance of the converter.

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5. F.I.G. Maoosena, G-source inverter design, analysis and its applications, in The prose fuel cell vehicles search campaign (Michigan University, East Lansing, USA, 2007) 6. R.Y. Duan, J.D. Lee, High-efficiency bidirectional DC–DC converter with coupled inductor. IET Power Electron. 5(1), 115–123 (2012) 7. T. Markel, M. Zolot, K.B. Wipke, A.A. Pesaran, Hybrid fuel cell energy storage requirements for vehicles, in Battery advanced books miscarry (2003) 8. H.J. Chiu, L.W. Lin, A two-directional chopper for fuel cell electric vehicle driving system. IEEE Trans. Power Electron. 21(4), 950–958 (2006) 9. S. Danyali, S.H. Hosseini, G.B. Gharehpetian, New extendable single-stage multi- input DC–DC/AC boost converter. IEEE Trans. Power Electron. 29(2), 775–788 (2014) 10. Y.M. Chen, A.Q. Huang, X. Yu, A high step-up three-port dc-dc converter for stand-alone pv/ battery power systems. IEEE Trans. Power Electron. 28(11), 5049–5062 (2013)

Performance Evaluation of Transistor Clamped H-Bridge (TCHB)-Based Five-Level Inverter V. Kiranmayee and A. Sharath Kumar

Abstract Multilevel inverters are usually employed in high-power applications. Lower harmonics in the output make the multilevel inverters capable to handle high-power applications. Their main drawbacks are complex circuitry, with high number of power electronic devices and passive components. So, a structure called Transistor Clamped H-Bridge Multilevel Inverter (TCHB) is designed. This topology gives five-level voltage output with reduced number of switches. The simulation results employing level-shifted multicarrier modulation are presented, and THD is observed. Keywords Multilevel inverters (MLI) improvement THD Efficiency








TCHB five-level MLI
Reliability

1 Introduction Recently, by fast developing of high-power devices and controlling methods, multilevel inverters are becoming more popular in industrial companies. The multilevel inverters are kind of power electronic devices which convert a DC voltage to favourable AC voltage [1]. Multilevel inverters (MLI) normally combine the step voltage waveform of several levels of DC voltage sources. Power quality, less total harmonic distortion, reduced voltage gradient across the switches, good electromagnetic compatibility, less switching losses and low dv/dt stress are the supplementary benefits of MLI [2]. In the case of higher number levels in the output (>5), NPC and FC require complicated techniques for maintaining its voltage of capacitor at constant value while CHB requires many isolated DC sources [3]. Apart from the above problems, MLIs use a more number of semiconductor components which increase its cost and decrease the reliability.

V. Kiranmayee
A. Sharath Kumar (&) Electrical and Electronics Engineering, Kamala Institute of Technology & Science, Huzurabad, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_16

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This paper propounds a new fault-tolerant technique for five-level Transistor Clamped H-Bridge (TCHB) inverter which can be imprecise for higher levels by using cascading connections. TCHB inverter for symmetrical configuration was first proposed in [4]. However, the main drawback of this topology is the loss of modularity. Hence, the cascaded version for symmetrical topology is proposed for symmetrical configuration to achieve the modularity in TCHB inverter. In [5], the asymmetrical operation of the TCHB inverter is proposed for high-power quality applications. The disadvantages of these inverters are the fault intolerance capabilities. Fault on switches of its H-bridge will lead to total shut down of the operation. Hence, in [6], the fault tolerance for TCHB topology is proposed, but for DC/DC converter [7]. In this strategy, the third support is added to the topology which operated in case of a fault. Therefore, the TCHB topologies lag in applications where continuity is of utmost important as compared with CHB. Hence, in this paper, a new topology for fault tolerance improvement of TCHB inverter is presented [8, 9].

2 Operation and Principle of the Transistor Clamped H-Bridge (TCHB) Five-Level Multilevel Inverter Topologies Conventional Topology Figure 1 shows the new cascaded five-level H-bridge multilevel inverter. It has five output voltage levels that are 0, Vs/2, Vs, −Vs/2, −Vs (Table 1). The operating states of TCHB inverter are simulated for ten switching states. For the output voltage Vs, the switches S1, S4 should be in ON position.

Fig. 1 A configuration of the single-phase five-level PWM inverter

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Table 1 Output voltage based on the ON–OFF position of the switch ON switches

Node A voltage (VA)

Node B voltage (VB)

Output voltage (VAB = V0)

S1, S4, S2, S3, S2,

VS VS/2 0 (VS) 0 0

0 0 0 (VS) VS/2 VS

VS VS/2 0 −VS/2 −VS

S4 S5 S4 S5 S3

Table 2 Different switching states of modified topology Output voltage

ON state switches

Effect on capacitor if iL [ 0

Effect on capacitor if iL \0

Vs Vs =2 0 Vs =2 Vs

S1 ; S5 ; S6 S5 ; S6 ; S7 S4 ; S5 ; S7 S3 ; S4 ; S7 S2 ; S3 ; S4

No effect Capacitor discharges No effect Capacitor charges No effect

No effect Capacitor charges No effect Capacitor discharges No effect

For the output voltage Vs =2, switches S4 ; S5 should be in ON position. For the output voltage 0, either switches S3 ; S4 or S1 ; S2 should be in ON position. For the output voltage Vs =2, switches S3 ; S5 need to be turned on. For the output voltage Vs , switches S2 ; S3 need to be turned on. The operating states of TCHB inverter are simulated for ten switching states. The operational states of the standard inverter are shown in Fig. 2a, b, e, f, i and j, respectively, and the additional states in the proposed inverter operating at half level of DC bus voltage are shown in Fig. 2c, d, g and h. Modified Topology: The modified topology can generate five-level output voltage (i.e. Vs ; Vs =2; 0; Vs =2; Vs ). The two capacitors connected in series will get charged into half of the DC supply voltage. To achieve higher number of levels in the output, cascading of the modified topology is proposed (Fig. 3). Working levels: As the modified topology is a combination of CHB leg and NPC leg, the faults are classified based on the switches of above-mentioned leg, which are summarised in Table 3. The steps to be followed under fault condition are: (a) Isolate the leg connection of faulted switch with the help of fast fuses. (b) Modify modulation index to half. (c) Update the switching strategy in accordance with Table 3. For this modified topology, the fault tolerant strategies are fault on Leg-I (i.e. either both switches are open or one of the switches is opened or one switch open while the other being short). The switching states of the modified topology have been shown in Table 2.

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Fig. 2 a State 1: Vo ¼ Vs ; io ¼ ð þ veÞ, b State 2: Vo ¼ Vs ; io ¼ ðveÞ, c State 3: Vo ¼ Vs =2; io ¼ ð þ veÞ, d State 4: Vo ¼ Vs =2; io ¼ ðveÞ, e State 5: Vo ¼ 0; io ¼ ð þ veÞ, f State 6: Vo ¼ 0; io ¼ ðveÞ, g State 7: Vo ¼ Vs =2; io ¼ ð þ veÞ, h State 8: Vo ¼ Vs =2; io ¼ ðveÞ, i State 9: Vo ¼ Vs ; io ¼ ð þ veÞ, j State 10: Vo ¼ Vs ; io ¼ ðveÞ

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Fig. 3 Modified five-level Transistor Clamped H-Bridge inverter

Table 3 Different switching states of modified topology under fault Output voltage

ON state switches

Effect on capacitor if iL [ 0

Effect on capacitor if iL \0

VS =2 0 VS =2

S5 ; S6 ; S7 S4 ; S5 ; S7 S3 ; S4 ; S7

Capacitor discharges No effect Capacitor charges

Capacitor charges No effect Capacitor discharges

The current path for modified topology for each state along with effect of capacitor is shown in Fig. 4 (Figs. 5 and 6; Tables 4 and 5).

3 Simulation Results The conventional TCHB-based five-level MLI using LS-POD PWM has been simulated and the output voltage and current waveforms and THD have been observed (Figs. 7, 8, 9 and 10). The modified TCHB-based five-level MLI using LS-POD PWM under fault conditions (Leg-I) has been simulated and the output voltage and current waveforms and THD have been observed (Figs. 11, 12, 13 and 14). The modified five-level TCHB multilevel inverter using level-shifted phase opposition disposition PWM technique when under fault conditions (Leg-I, i.e. S1 open and S2 shorted) and the output voltage and current waveforms have been observed. The modified five-level TCHB multilevel inverter using level-shifted phase opposition disposition PWM technique when under fault conditions (Leg-I, i.e. S2

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Fig. 4 a Output voltage = Vs =2 (capacitor discharging), b output voltage = Vs =2 (capacitor charging), c output voltage = Vs, d output voltage = Vs, e output voltage = 0 V, f output voltage = 0 V, g output voltage = Vs =2 (capacitor charging), h output voltage = Vs =2 (capacitor discharging), i output voltage = −Vs, j output voltage = −Vs

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Fig. 5 a Output voltage = 0 V, b output voltage = 0 V, c output voltage = Vs , d output voltage = Vs

Fig. 6 a Output voltage = 0 V, b output voltage = 0 V, c output voltage = Vs , d output voltage = Vs

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Table 4 Switching state modified topology under the fault condition Output voltage

ON state switches

Effect on capacitor if iL [ 0

Effect on capacitor if iL \0

0 Vs

S4 ; S5 ; S7 S2 ; S3 ; S4

No effect No effect

No effect No effect

Table 5 Switching state modified topology under the fault condition Output voltage

ON state switches

Effect on capacitor if iL [ 0

Effect on capacitor if iL \0

0 Vs

S4 ; S5 ; S7 S1 ; S5 ; S6

No effect No effect

No effect No effect

Fig. 7 Output current and voltage waveform of conventional five-level TCHB multilevel inverter

Fig. 8 Conventional five-level TCHB multilevel inverter with multicarrier modulation, THD = 39.34%

Performance Evaluation of Transistor Clamped H-Bridge (TCHB) …

Fig. 9 Output voltage and current waveform five-level TCHB multilevel inverter

Fig. 10 Modified five-level TCHB multilevel inverter of modified THD = 4.44%

Fig. 11 Output current and voltage waveforms

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Fig. 12 Modified five-level TCHB multilevel inverter for a modified five-level TCHB multilevel inverter under post-fault conditions (Leg-I opened), THD = 2.97% under fault conditions (Leg-I opened)

Fig. 13 Output current and voltage waveforms for modified five-level TCHB multilevel under fault conditions (Leg-I, i.e. S1 open and S2 shorted)

open and S1 closed) has been simulated and the output voltage and current waveforms have been observed. The comparative analysis for a conventional five-level TCHB topology and modified five-level TCHB topology has been studied and the results are shown in Table 6.

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Fig. 14 Output waveforms for modified five-level TCHB multilevel inverter under fault conditions (Leg-I, i.e. S2 open and S1 closed) Table 6 Comparison of TCHB five-level topologies Parameters

Conventional five-level TCHB topology PD POD APOD

Modified five-level TCHB topology PD POD APOD

Switches of blocking voltage VS/2 Switches of blocking voltage VS Total number of switches DC source Capacitor Clamped diodes Reliability in case if fault on Leg-I Reliability in case if fault on Leg-II Efficiency (%)

1 4 5 1 2 0 Yes No

1 4 5 1 2 0 Yes No

1 4 5 1 2 0 Yes No

5 2 7 1 2 2 Yes Yes

5 2 7 1 2 2 Yes Yes

5 2 7 1 2 2 Yes Yes

91.2

97.46

97.65

91.25

95.42

91.25

4 Conclusion A five-level cascaded H-bridge multilevel with multicarrier pulse width modulation is presented. The simulation results show that the total harmonic distortion is low for multicarrier modulation method when compared with a conventional cascaded multilevel inverter. This circuit also decreases the number of switches, sources and losses. The total harmonic distortion for the presented model is observed.

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References 1. J. Lai, F.Z. Peng, Multilevel converters—a new source of power converters. IEEE Trans. Ind. Appl. 3, 509–517 (1996) 2. J. Rodriguez, J.-S. Lai, F.Z. Peng, Multilevel inverters: a survey of topologies, and applications. IEEE Trans. Ind. Electron. 4, 724–738 (2002) 3. J. Li, A.Q. Huang, Z. Liang et al., Analysis and design of active NPC (ANPC) inverters for fault-tolerant operation of high-power electrical drives. IEEE Trans. Power Electron. 2, 519– 533 (2012) 4. S. Ceballos, J. Pou, J. Zaragoza et al., Fault-tolerant neutral-point-clamped converter solutions based on including a fourth resonant leg. IEEE Trans. Ind. Electron. 6, 2293–2303 (2011) 5. S. Ceballos, J. Pou, E. Robles et al., Three-level converter topologies with switch breakdown fault-tolerance capability. IEEE Trans. Ind. Electron. 55(3), 982–995 (2008) 6. X. Kou, K.A. Corzine, Y.L. Familiant, A unique fault-tolerant design for flying capacitor multilevel inverter. IEEE Trans. Power Electron. 4, 979–987 (2004) 7. K. Mohana Sundaram, P. Anandhraj, V. Vimalraj Ambeth, PV-fed eleven-level capacitor switching multi-level inverter for grid integration, in Advances in Smart Grid and Renewable Energy, Lecture notes in Electrical Engineering (Springer, Singapore, 2018), pp. 57–64 8. M. Ma, L. Hu, A. Chen et al., Reconfiguration of carrier-based modulation strategy for fault tolerant multilevel inverters. IEEE Trans. Power Electron. 5, 2050–2060 (2007) 9. S. Ceballos, J. Pou, E. Robles et al., Performance evaluation of fault-tolerant neutral-point-clamped converters. IEEE Trans. Ind. Electron. 8, 2709–2718 (2010)

Enhancement of Power Quality by the Combination of D-STATCOM and UPQC in Grid Connected to Wind Turbine System M. Sumithra and B. C. Sujatha

Abstract Due to concern on the environment protection, eco-friendly technology is developed in different kinds of delicate and micro-scale non-conventional energy sources in our everyday life. In this paper, the WES generates power and voltage at PCC instantaneously with the grid, which is then simulated by considering the power quality problems occurring in grid system. To mitigate power quality issues, combination of D-STATCOM and UPQC is used. The goal of this study is concerned on power quality issues, i.e., voltage/current fluctuation, flickers, harmonic caused by WES and FACTS device. In this paper, the parameters like voltage/ current sag/swell are analyzed and simulated in SIMULINK/MATLAB software.




Keywords Wind energy system (WES) Battery energy storage system (BESS) Distributed static compensator (D-STATCOM) Unified power quality compensator (UPQC) Total harmonic distortion (THD) Point of common coupling (PCC) Current-controlled voltage source (CCVS)
















1 Introduction In recent days, requirement of electric power is increasing with exceeding sustainable growth and social progress, the production of electricity with renewable energy resources is more challenging task for generating systems to meet power requirements. Hence to meet the required power, non-conventional power generating systems are used. The non-conventional power resources like wind turbine, bio-fuel, water, and hybrid generating systems are used to fulfill required power for increased population growth, industrialization, and environment friendly as per the environment protection regulation guide lines [1, 2]. WES generates electricity which converts mechanical energy from wind into electricity. Induction generator is employed for run WES which is directly connected to grid. The wind energy systems present a technical challenge like instability, voltage deregulation, and M. Sumithra (&)
B. C. Sujatha Department of EEE, UVCE, Bangalore University, K R Circle, Bangalore 560001, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_17

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power quality issues [2]. The power quality problems, i.e., current/voltage raise/ drop, current/voltage disturbances, THD, imbalance, and short term voltage, which affects on the consumer instruments, cause mal-operation in their function. The more common power quality issues caused by several power electronic compensating equipments which are used for mitigation of the power quality issues, such as SVC, STATCOM, UPFC, UPQC, SSSC, etc., are used to minimize the effects caused by voltage variation [3]. In this paper, combination of D-STATCOM and UPQC is used to reduce power quality problems caused by WES connected to grid system.

2 Power Quality Problems 2.1

Voltage Variation

This is due to continuous variation of the air velocity/speed and generated torque. The variation in direct proportion to true power (P) and reactive power (Q). Voltage variation can be classified as voltage sag/dip, voltage swell/raise, voltage flicker/ fluctuation, and voltage unbalance.

2.2

Harmonics

Harmonics are generated by using power electronic device and non-linear load. It should be limited to acceptable limits. Usually, it should be within 5%.

2.3

Self-excitation of WES

The self-excitation of WES with an induction generator is carried out after WES is disconnected with non-linear load. Constant voltage block connected to the induction generator hence reactive power is compensated, however by balancing of the power system frequency voltage is regulated. The cons occurring in the self-excitation are safeties, real, and reactive power balance achievement [4].

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3 Analysis of Power Quality Enhancement 3.1

Power Quality Enhancement by D-STATCOM

The D-STATCOM consists of a 3-U VSC, and a capacitor is connected as DC link with BESS, connected at PCC. The D-STATCOM provides different magnitude and frequency component current for compensation at PCC. The D-STATCOM is based on CCVS converter that injects current into the power system or PCC so that the supply current (Is) become harmonics free and which is in-phase with source voltage. Figure 1 shown BESS with D-STATCOM [3].

3.2

Power Quality Enhancement by UPQC

The quality of power to the consumers is produced by the use of UPQC. UPQC consists of series and shunt compensators combined together which is connected via an electrical capacitor, which acts as a dc link so that the harmonic elements are within desired limits. The series element of the UPQC is known as dynamic voltage restorer (DVR) used to balance voltage level, and minimize the distortion at load side. The shunt element of UPQC is known as D-STATCOM used for load reactive power (Q) compensation; hence, source current is free from harmonic content and load is balanced. Generalized block diagram of UPQC is shown in Fig. 2. The equation of real and reactive power of the line is, P¼

Fig. 1 Interior structure of D-STATCOM

Vs Vr sin d X

ð1Þ

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Fig. 2 Interior structure of UPQC

Q¼

Vs Vr cos d  Vr2 X

ð2Þ

where Vs Vr X

Supply-side voltage. load-side voltage. System impedance.

3.3

Wind Energy System (WES)

Based on fixed speed topology with pitch control, wind turbine generates wind energy. In this scheme, the asynchronous generators are used because of simplicity and do not require field circuit separately. This can confess fixed and changing load and provide natural protection in opposition to short circuit. The obtained power generated by WES is given under Eq. 3, [2]. 1 Pout ¼ kCp qAU 3 2 where Pout k Cp q A Uwind

Output power in kW. Constant to yield power in kW. Maximum power coefficient. Air density in kg/m. Turbine blade area in m. Air speed in m/s.

ð3Þ

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4 Simulation and Result The WES connected grid system with combination of UPQC and D-STATCOM was modeled for a non-linear load in MATLAB/SIMULATION software. The system parameter is shown in Table 1. Figures 1, 2, and 3 show the interior structure of D-STATCOM, UPQC, and completed model of grid connected WES with UPQC and D-STATCOM, respectively. In this model, UPQC and D-STATCOM combined together with wind energy system; D-STATCOM provides compensation current to maintain the constant terminal voltage, elimination of harmonics, and balanced load. The D-STATCOM consists of three pairs of CCVS converter base IGBT, DC link capacitance, and AC inductance. The DC link capacitance is provided as an energy storage device to provide reactive power compensation. Whereas UPQC consisting of six thyristors connected altogether in each module with a dc link provided

Table 1 Simulation parameters S. No.

Parameter

Rating

1 2

Source Induction generator/motor

3

Inverter parameter

4 5 6 7

Shunt inverter LC filter (UPQC) Series inverter LC filter (UPQC) DC link for UPQC Load

3-Phase, 50 Hz, 415 V 1.5 MVA, 50 Hz, 415 V, p = 4, Rs = 0.0021, Ls = 0.06, Rr = 0.0019, Rr = 0.0016 Vdc = 700, Switching frequency = 28 kHz, Cdc = 0.750 µF, R = 1 Ω L = 15 H, R = 5 Ω, C = 100 µF L = 10 mH, R = 5 Ω, C = 10 µF C = 750 µF 40 kW

Fig. 3 Simulation model

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between the series and shunt filters. The UPQC provides compensated power to protect sensitive loads as well as enhances reliability of the network. Complete modeling of grid connected WES by using UPQC and D-STATCOM is shown in Fig. 3 (Figs. 4, 5, 6 and 7).

Fig. 4 a Supply current. b Wind current

Fig. 5 Swell mitigation. a D-STATCOM current. b UPQC current. c Load current

Fig. 6 Sag mitigation. a Supply current. b Wind current. c D-STATCOM current. d UPQC current. e Load current

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Fig. 7 FFT analysis. a with UPQC and D-STATCOM. b Without UPQC and D-STATCOM

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5 Conclusion This paper presents the WES, D-STATCOM, and UPQC with filter and non-linear load connected at PCC. Due to integration of WES, power quality problems exist and connecting non-linear load total harmonic distortion (THD) occurs in the system. Power quality problems like sag, swell, fluctuation, and flicker are mitigated by using D-STATCOM and UPQC with filter. Without D-STATCOM and UPQC, load current THD is 30.02% and then with D-STATCOM and UPQC, THD has been reduced to 0.90%.

References 1. S. Mohamm, Power quality improvement using a novel D-STATCOM-control scheme for linear and non-linear loads, in ICEEOT (2016) 2. M. Patel, Power quality improvement using D-STATCOM. Int. J. Electr. Eng. 3(5) (2016) 3. P.R. Kasari, M. Paul, B. Das, A. Chakraborti, Analysis of D-STATCOM for power quality enhancement in distribution network, in Proceeding of the 2017 IEEE Region 10 Conference (TENCON), Malaysia (2017) 4. S. Karare, V.M. Harne, Modelling and simulation of improved operation of D-STATCOM in distribution system for power quality improvement using MATLAB simulink tool, in ICECA (2017)

Transient Steadiness and Dynamic Response in Transmission Lines by SVC with TID and MPPT Controller Ajay Kumar and T. S. Prasanna

Abstract In order to maintain stable and efficient power system with growing demand, there is a quick progress of power electronics, which introduces the FACTS device. That is able to resolve the volatility problem easily. The FACTS device static var compensator (SVC) helps in improving the active and reactive power of the system. Maximum power point tracking (MPPT) is an external controlling device, which is used with FACTS device. The controller has been tested on a two-machine and three-buses in a control system using MATLAB software. The model consumes lesser runtime for maximum number of oscillation and to damp out the unwanted harmonics in the system. TID and MPPT controllers are used to control the machine and SVC device.







Keywords Static var compensators (SVC) Voltage regulator PID controller TID tuning MPPT controller Active power (AP) Reactive power (RP) Flexible AC transmission system (FACTS) MATLAB simulink
















1 Introduction In recent year, the customs power expertise, the low voltage counterpart of the more commonly known flexible AC transmission system (FACTS) technology, brings up with a feasible resolution to solve many problems relating to steadiness of supply at the consumer. To overcome these losses, we need to use different parameters which will reduce these losses with more stable operation. So that overall system performance can be increased. It is observed that the power control capability can be amplified by using static var compensation device [1, 2]. Most of the devices are control electronics based. FACTS devices are coupled in series for better performance. The power system is more affected by some factors such as AP, RP, reactance, susceptibility, control factor, quality, fluctuation of current, and voltage [3, 4]. The term steadiness specifies the capacity to sustain the machineries in A. Kumar (&)
T. S. Prasanna Department of Electrical Engineering, U. V. C. E. College, Bangalore, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_18

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synchronism. If the fluctuation in speed level of the machines, at that moment there will be a vibration in the system, which can be damp out by using PID controller [5]. The tilt integral derivative controller is a newly intended controller which has good performance and gives feasible results over PI or PID-based controller [6]. The analogous mode may work satisfactory. The main impartial of paper is to bring the system into stable condition by using MPPT algorithm [7, 8], which is employed in PV inverter endlessly to amend the impedance realized by the solar array to retain the PV system operating at, or close to, the peak control point of the PV panel under variable load conditions with change in solar irradiance, hotness, and load [9].

2 Static Var Compensator (SVC) The controlling device which controls the real and RP tends to make system to be continual which sets voltage at its termini by monitoring the amount of RP injected into or absorbed from the power network. The SVC is a shunt device of the FACTS family using power electronics to regulate power flow and progress transient steadiness in power networks [3]. The SVC sets voltage at its workstations by adjusting the sum of RP vaccinated into engrossed from the power network. If network voltage is low, the SVC generates RP (SVC capacitive). If network voltage is high, it absorbs RP (SVC inductive). The deviation of RP is analyzed by changing three-phase capacitor banks and inductor banks linked on the secondary side of a coupling transformer. Each capacitor bank is switched on and off by three thyristor switches (thyristor switched capacitor or TSC). Reactors may be switched on-off (thyristor switched reactor or TSR) or phase-controlled (thyristor controlled reactor or TCR). SVC has no rotary part unlike synchronous machine. SVC is used in voltage controller mode by controlling the reactive var in the network, where it is coupled. SVC can draw leading or lagging var to regulate the voltage variation or regulation in the network. If there is a drop in the voltage, then it delivers reactive control, and if there is a rise in the voltage, then it absorbs reactive control. So, the SVC can be used as a source or sink of reactive var in according to the necessity of user. Here the objectives are: a. b. c. d. e. f.

Very fast control response time Feasibility of individual phase control with parameter Reduced losses High reliability Less maintenance (absence of rotator parts) SVC voltage control.

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2.1

183

Configuration of SVC

It has distinct types of SVC: 1. Fixed capacitor-thyristor controlled reactor (FCTCR) 2. Thyristor switched capacitor (TSC-TCR). The second type is quite reliable than the first one and acquires reduced rating of the reactor and therefore generates fewer harmonic. The schematic diagram of a TSC-TCR type SVC is shown in Fig. 1. The illustrations of the TCR and TSC are allied on the secondary side of a step-down transformer. Tuned and high-pass screens are also allied in parallel which deliver capacitive RP at fundamental frequency. The voltage gesture is taken from the high voltage SVC bus by means of a potential transformer. A. Designed of PID Controller The procedure of selecting the control parameters to meet the given performance stipulations is called PID tuning. Here, PID controller is tuned by the triple integral differential (TID) method. It has three term control signal, UðtÞ ¼ Kp eðtÞ þ

Fig. 1 Typical SVC (TSC-TCR) structure

Kp Ti

ZZZ eðtÞdt þ Kp Td

d3 eðtÞ dt3

ð1Þ

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  UðsÞ 1 3 ¼ Kp 1 þ þ Td S EðsÞ Ti S3   1 3 Gc ðsÞ ¼ Kp 1 þ þ Td S Ti S3   1 3 Gc ðsÞ ¼ 0:6  Kcr 1 þ þ 0:125 Pcr S 0:5Pcr S3   1 1 2 Gc ðsÞ ¼ 0:6Kcr S  0:125Pcr Kp S þ þ 0:5Pcr S2 0:125Pcr S  0:125Pcr   16 8 Gc ðsÞ ¼ 0:075  Kcr Pcr S S2 þ 2 4 þ Pcr S Pcr S  Gc ðsÞ ¼ 0:075Kcr Pcr S S2 þ 2 

4 16 þ Pcr S2 P2cr S2

 Gc ðsÞ ¼ 0:075  Kcr Pcr S S þ  Gc ðsÞ ¼ 0:075  Kcr Pcr S

Gc ðsÞ ¼

4 Pcr S2

Pcr S3 þ 4 Pcr S2

ð4Þ ð5Þ ð6Þ

 ð7Þ

ð8Þ

2

 2 0:075  200  0:2 0:2S3 þ 4 Gc ðsÞ ¼ S 0:2  S  2 3 0:2S3 þ 4 S 0:2  S

ð3Þ

2

 2 0:075  Kcr Pcr Pcr S3 þ 4 Pcr S S

Gc ðsÞ ¼

ð2Þ

ð9Þ

ð10Þ

ð11Þ

ð12Þ

For choosing the appropriate controller constraints, TID is tuned as described above. In this process, limitation is selected as Ti = ∞, Td = 0 by using the PID controller action as shown in Fig. 2, Simply increase in Kp = 0 to a critical value Kcr. On which the output leading to continual alternations Fig. 4. Thus, the critical gain Kcr and the analogous period Pcr are experimentally carried out. It is advised that the values of the parameters Kp Ti Td would be fixed according to the formulation as same as Zieglar–Nicles method. Figure 5 shows the Simulink of interior assembly of PID controller with change in angular speed of input (Fig. 3).

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Fig. 2 Block diagram of PID controller

Fig. 3 PID controller is in proportional action

Fig. 4 Analysis of continuous swinging (Pcr)

Fig. 5 Interior assembly of PID controller with dx

3 Maximum Control Point Tracking (MPPT) The PV arrays are attached in series or in parallel. The PV array has V-I characteristic as alike to those of a single solar cell. Typical V-I characteristic of a solar cell array is shown in Fig. 5. The MPPT controller varies with irradiance as well as with temperature. A constant voltage load such as a battery cannot extract the maximum control under all conditions. MPPT is an algorithm implemented in PV inverters to endlessly alter the impedance seen by the PV arrays to keep the PV assembly functioning at, or near to the peak control point of the PV assembly under varying loading settings like changing solar irradiance, hotness, and load. MPPT

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Fig. 6 Model diagram of MPPT controller

systems are usually used in the governing projects of PV system. It accounts for different reasons such as adjustable irradiance (sunlight) and heat to confirm that the PV structure breeds extreme control at all the time (Fig. 6). The three communal MPPT algorithms are a. Perturbation and observation (P&O) b. Incremental conductance c. Fractional open-circuit voltage. Perturbation and observation (P&O): This algorithm agitates the operating voltage to ensure extreme control. While there are numerous progressive and other enhanced alternatives of this algorithm, a basic one is P&O MPPT algorithm. Incremental conductance: It relates the incremental conductance to the prompt conductance in a PV system. It depends on various factors (i.e., V, I), based on it will rises or falls. The extreme control point is reached; unlike as in the above P&O process, the voltage residues continue once MPPT is reached (Figs. 7 and 8). Fractional open-circuit voltage: This algorithm works on the maximum control at a point reached with a constant voltage. Depending on the voltage profile, its value hikes or drops.

4 Control System Model The modeling of a power network containing two hydraulic control plants with three-buses with SVC is used to mend the transient steadiness and in order to damp out oscillations. A single-line diagram represents a simple 500 kV conduction network as shown in Fig. 9.

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Fig. 7 Incremental conductance algorithm

Fig. 8 Model diagram of MPPT controller

To maintain network steadiness after fault, the conduction line is shunt compensated at its center by a 200 MVAR using FACTS device. The two machineries are coupled with a hydraulic turbine and governor (HTG), which will help to damp out the oscillation, so that the machineries should not come out of synchronism (Table 1).

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Fig. 9 Single-line diagram of two-machine three-buses control network using controller

Table 1 Parameter of single-line diagram of two-machines and three-buses S. No.

Parameter

Machine 1

Machine 2

1 2

Generated MVA Transformer

1000 MVA 13.8 kV/500 kV (D/Yg)

3 4 5

Generated MW At buses (MW) Distance between the buses

950 MW 944 MW (B1) 350 km (B1–B2)

5000 MVA 500 kV/13.8 kV(Yg/ D) 4046 MW 5000 MW (B2) 350 (B2–B3)

The SVC is controlled by external MPPT controller which will mend the transitory steadiness and dynamic load on the conduction line.

5 Model The above block diagram can be simulated using MATLAB. Model is carried out in three different forms 1. Model without using FACTS devices 2. Model with SVC device. Model using MPPT controller with FACTS device (Figs. 10, 11, 12, 13, 14, 15, 16 and 17).

Transient Steadiness and Dynamic Response in Transmission …

Fig. 10 Simulink diagram with SVC and MPPT Controller

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Fig. 11 Voltage across abc phases

Fig. 12 Current across abc phases

Fig. 13 Dynamic load

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Transient Steadiness and Dynamic Response in Transmission …

Fig. 14 Iabc, using SVC controller MPPT

Fig. 15 Vm, B, and Q using SVC controller MPPT Figure

Fig. 16 Control, voltage of M1

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Fig. 17 Control, voltage of M2

6 Results and Discussions The analysis of 2-machines, 3-buses system under various loading condition such as without SVC, with SVC and SVC with controlling device (MPPT controller). The following table shows different readings for voltage, current, active, and reactive control for different loading conditions (Tables 2 and 3).

Table 2 Three-phase load Parameters

Without SVC

With SVC

SVC with MPPT

Voltage Current M1 control M1 voltage M2 control M2 voltage

0.26 0.24 0.56 7V 0.57 2.57

0.18 s 0.184 s 0.57 W 7V 0.57 W 2.58 V

0.18 s 0.183 s 0.57 W 0.1 V 0.56 W 0.12 V

s s W W V

Table 3 Three phase with Dynamic load Parameters

Without SVC

With SVC

SVC with MPPT

Positive voltage Active (P) Reactive (Q)

0 0.22 s 0.2 s

0.14 s 0.15 s 0.149 s

0.22 s 0.21 s 0.198 s

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7 Conclusion From the simulation and analysis, we can conclude that SVC mechanism is controlled with MPPT and TID-based controller. The switch combines the advantages with cooperation of TID and MPPT controller. The two-machine three-bus Simulink model is tested in MATLAB. The design is performed on several factors like speed, angle difference, voltage, AP, and RP of the machineries. The model is related to the conventional SVC with MPPT controller. With the help of FACTS and other controlling device, we can improve the transient steadiness and dynamic reaction of the system. The performance of the designed controller is steadfast and is moderately constant. Upcoming work will be done on improving the transient steadiness and dynamic response of SVC using neural network-based TID controller or genetic algorithm with other controlling devices.

References 1. P. Kundur, Control system steadiness and control. McGraw Hill, (1994) 2. T. Sharma, A. Dahiya, Transient steadiness improvement in transmission line using SVC with Fuzzy Logic based TID controller, in 2014 IEEE 6th India International Conference on Control Electronics (IICPE), IEEE 1–5 December 2014 3. L. Gyugyi, Reactive power generation and control by thyristor circuits. IEEE Trans. IA-15(5), 521–531 (1979) 4. R. Das, D.K. Tanti, Transient Steadiness of 11-bus system using SVC and improvement of voltage profile in transmission line using series compensator. Am. J. Electr. Control Energ. Syst. 3(4), 76–85 (2014). https://doi.org/10.11648/j.epes.20140304.12 5. P.L. So, T. Yu, Coordination of TCSC and SVC for inter area steadiness enhancement. IEEE Trans. Control Delivery. 9(1), (2000) 6. M.H. Haque, Application of energy function to access the first swing steadiness of a power system with a SVC. IEEE Proc. Gener. Transm. Distrib. 152(6), 806–812 (2005) 7. M. Azab, A new maximum control point tracking for photovoltaic systems. WASET. ORG. 34, 571–574 (2008) 8. N. Femia, G. Petrone, G. Spagnuolo, M. Vitelli, Optimization of perturb and observe maximum control point tracking method. IEEE Trans. Control Electron. 20(4), 963–973 (2005) 9. C. Hu, R.M White, Solar Cells, McGraw-Hill Book

Three-Level DCMLI-Based Grid-Connected DSTATCOM D. Suresh and R. Chander

Abstract In this paper, the grid-integrated DCML inverter-based DSTATCOM is presented for elimination of harmonics caused by the nonlinear load and injection of active power harvested from PV arrays. It is well-known multilevel converters that can possess great advantages such as low switching losses and reduced size of filter requirement. The synchronous reference frame-based reference current estimation is presented with maximum power point tracking (MPPT). The MPPT algorithm based on perturb and observe is integrated with synchronous reference frame scheme. The simulated response of the DCML inverter-based DSTATCOM is effective for injecting active power from photovoltaic system. Keywords DSTATCOM Investor


Maximum power point tracking
PV arrays
DCML

In this paper, the grid-integrated DCML inverter-based DSTATCOM is presented for elimination of harmonics caused by the nonlinear load and injection of active power harvested from PV arrays. It is well-known multilevel converters that can possess great advantages such as low switching losses and reduced size of filter requirement. The synchronous reference frame-based reference current estimation is presented with maximum power point tracking (MPPT). The MPPT algorithm based on perturb and observe is integrated with synchronous reference frame scheme. The simulated response of the DCML inverter-based DSTATCOM is effective for injecting active power from photovoltaic system.

D. Suresh Vignan Institute of Technology and Science, Deshmukhi, India R. Chander (&) University College of Engineering, Osmania University, Hyderabad, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_19

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1 Introduction The increased use of power semiconductor devices in a wide variety of loads has given birth to numerous power quality problems in the electric power networks. These power quality problems, their causes and effects on the power system components are explicated in this paper. Threats posed by these power quality problems are being mitigated by installing DSTATCOM in shunt with nonlinear loads. Earlier, passive filters were in use, but owing to drawbacks pretense by them, viz. large size, resonance and fixed compensation, the focus of power quality engineers has shifted toward DSTATCOM. DSTATCOMs are capable enough to provide the solution related to harmonic compensation, reactive power compensation, balancing three-phase line currents, damping of oscillation in currents and voltage regulation. Since DSTATCOMs are connected in parallel with the load, thus they do not burden the source on account of displacement power factor and extra loading effect. The most of the DSTATCOMs are based on shunt-coupled active power filter, which is used for compensation of harmonics and volatile power of the nonlinear load. The family of DSTATCOMs is referred to single name that is custom power devices. The custom power devices are DSTATCOM, dynamic voltage restorer, compensator of integrated power quality, etc. The DSTATCOM is multifunctional device which provides the harmonics elimination, reactive power compensation, voltage regulation, power factor correction, load balancing and termination of the line. The execution of the DSTATCOM to a great extent relies upon the ongoing estimation of the remuneration current. The most usually utilized strategies in the literature are instantaneous and synchronous theory-based detection of the compensation current. Because of its ease of calculation of harmonics currents and self-learning ability, adaptive control scheme gains attention in the estimation of reference currents. In this paper, synchronous reference frame is arrived for harmonics current estimation. Nowadays, the grid integration of renewable energy-based generation is increasing with improved power quality feature such as harmonics elimination, reactive power compensation and load balancing. In recent studies, the two- and three-level inverters are compared based on semiconductor losses and filter consideration and evaluated that three-level inverter possesses lower semiconductor losses for higher switching frequencies than the two-level counterparts because three-level inverters have only one device commutated at each transition. In addition to that, ac output waveform of a multilevel inverter possesses a lower harmonic and reduced sizes of the AC filter components are possible [1–4].

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Fig. 1 Topology of PV cell integrated DSTATCOM

2 Configuration of DSTATCOM Topology The DSTATCOM configuration based on diode-clamped multilevel inverter is shown in Fig. 1. The DCMLI seems to be most suitable inverter topology for photovoltaic application without separate inverter. The DCMLI has common DC bus for easy integration of photovoltaic cell. The overall power circuit of DSTATCOM consists of PV array on DC side, DCMLI, interfacing inductor and DC-link capacitor. The interfacing inductor is used to suppress the switching high-frequency harmonics. The DSTATCOM is connected at point of common coupling through interfacing inductor.

3 Control Scheme Figure 2 shows the dynamic model of photovoltaic cell. To obtain the required level of power from photovoltaic cells, which are connected in series and parallel to form modules, the modules are connected array to obtain higher voltage and current level. The PV modules can be represented as approximate constant current source. The equation, which is used to describing the I–V characteristic of a practical PV cell, is

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Fig. 2 Equivalent circuit of photovoltaic cell


 qVoc Vout þ RS I 1  I ¼ IL  Id  Ish ¼ IL  ID e Rsh CkT

ð1Þ

where ID is the saturation current, q is the electron charge ð1:6  1019 CÞ, C is the diode emission factor, k is the Boltzmann constant ð1:38  1023 J/KÞ and T is the temperature. The power produced will be maximum at the knee point of the I–V characteristics of the PV module, and it is depicted in Fig. 3. Voltage and current properties are consistent with the knee point of I–V. The maximum power can be obtained from PV modules with maximum power point tracking (MPPT) algorithm. The maximum point algorithm is attached to the DSTATCOM control scheme. The DSTATCOM voltage is maintained with the MPPT algorithm at the correct value. In this paper, the incremental conductance method is used for tracking the maximum power from PV modules. This MPPT mechanism is the power line of the PV MPP (where dv/dt = 0), on the left is positive, and the opposite is negative. In the following equations, dv/di are sample delay values V dp dðviÞ di ¼ ¼ iþv ¼ 0 dv dv dv

Fig. 3 V–I characteristics of PV cell

ð2Þ

Three-Level DCMLI-Based Grid-Connected DSTATCOM

dv i ¼ di v (

dv [ i : left v di dv \ i : right di v

199

ð3Þ ð4Þ

The DC voltage regulator with MPPT algorithm used for estimation of reference voltage is shown in Fig. 3. The reference DC voltage is estimated with incremental conductance method and power generated from PV system also estimated for injecting active power from DSTATCOM to grid. The active power injected carries negative sign, which shows that the DSTATCOM injects active power into the DG set feeding system.

4 Simulation Results and Discussion The complete system of DSTATCOM consists of the power source, DCMLI DSTATCOM and nonlinear load. When DSTATCOM alone operating without photovoltaic modules connected on its dc side and it is used for elimination of harmonics caused by the nonlinear load. Before t = 0.05 s, the source current consists of integer multiple triplen harmonics current. When DSTATCOM is connected at t = 0.05 s, source current tends to sinusoidal and in synchronous with voltage waveform. The source current total harmonics distortion before compensation is 23.62%, and its value after compensation is found to be 2.04%. The simulated response of the DCMLI DSTATCOM is shown in Fig. 4. Before compensation with DSTATCOM, source current is highly nonlinear and contains harmonics which is result in heating of armature of the synchronous generator. When PV-DSTATCOM starts injecting active power harvested from PV module, the source current tends to be sinusoidal. The source current is in phase opposition to source voltage, which shows the active power flow from PV-DSTATCOM to the load. As load demand increases on grid, PV-DSTATCOM compensates the load current demand. This in turn results in reduction of fuel consumption. And also, DSTATCOM compensates harmonics and balances the current while feeding variety of the load. The power variation with solar insolation is shown in Fig. 6. The different waveforms of spectrum are identified as PV array voltage, PV array current and active power output of the PV array. With increase in solar insolation, the quantity of output active power also increases. The increase in power output from PV array the quantity of active power injected at point of common coupling also increases. This increase in active power can be observed from Fig. 5, and the value of source

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Fig. 4 Simulated waveform of DSTATCOM

Fig. 5 Simulated waveform with MPPT

current gradually increases with increase in compensating current of DSTATCOM. The active power injected from PV-DSTATCOM relieves the loading on the DG set and also compensates the harmonics and reactive power demand of the nonlinear load connected with DG set (Figs. 7 and 8).

Three-Level DCMLI-Based Grid-Connected DSTATCOM

Fig. 6 Simulated waveform with power variation

Fig. 7 Source current THD without DSTATCOM

Fig. 8 Source current with DSTATCOM compensation

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5 Conclusion In this paper, DCML inverter is connected to PV-DSTATCOM and is connected to the elimination of harmonics and reactive power compensation and active power injections. Synchronous reference frame integrated with LMS theory is used for MPPT controller. The algorithm is implemented based on the incremental conductance method to obtain the maximum power from the PV array. The active power control calculated from the PV range is used to introduce active power during joint coups in combination. DCML inverter-based DSTATCOM’s simplest response shows the potential compensation of active power injection from harmonics, reactive power and PV array. Acknowledgements Thanks to SERB-DST for ECR project file No: ECR/2016/000813.

References 1. B. Singh, K. Al-Haddad, A. Chandra, A review of active filters for power quality improvement. IEEE Trans. Ind. Electron. 46(5), 960–971 (1999) 2. D. Suresh, K. Venkateswarlu, S.P. Singh, T2FLC based CHBMLI DSTATCOM for power quality improvement, in IEEE conference, ICCCI (2018) 3. D. Suresh, K. Venkateswarlu, S.P. Singh, Adaptive control of three level active power filter, in IEEE conference, ICCCI (2018) 4. D. Suresh, K. Venkateswarlu, S.P. Singh, SIFLC based control implementation of DSTATCOM, in IEEE conference, ICCCI-2018, January (2018)

Reducing Number of Switches in Multilevel Inverter Using Diode Clamped and H-Bridge Inverters Karanam Deepak, M. Rama Prasad Reddy, K. Jaya Sree and P. Partha Saradhi Reddy

Abstract The multilevel inverters (MLI) are having more features to usage. In existing methods like diode clamped MLI and H-Bridge MLI, more number of switches are using compared to proposed MLI. So, a new method of 35-level MLI topology is a combination of diode clamped and cascaded multilevel inverters. In this method using the less number of switches and their pulse generating circuit. So, thereby ensuring the switching loss, reducing size and installation cost also less. So, the new proposed technology is well designed for renewable applications (RA) like PV cell and wind energy systems. Comparing to the other existing inverters, the switch count is very less. The results are validating by using MATLAB/Simulink design.







Keywords Multilevel inverters (MLI) H-Bridge MLII (HMLI) Diode clamped MLI (DCMLI) Photovoltaic cell (PV) cell Renewable applications (RA)







1 Introduction Power electronics’ switches play a vital responsibility in the electrical power conversions. Another advantage of MLI is control of the output power and uses in take out power commencing renewable energy applications like PV cell as well as wind power generation systems [1]. The conversion of DC to AC is possible to need inverter. In an electrical power, inverter is basic circuit that converts DC into AC. This conversion is important because AC is more useful in our daily applications. Conventional diode clamped and H-Bridge inverters having more switches compared to proposed system [2].

K. Deepak (&)
M. Rama Prasad Reddy
K. Jaya Sree G. Pullaiah College of Engineering and Technology, Near Venkayapalle, Pasupula Village, Nandikotkur Rd, Kurnool, AP 518002, India P. Partha Saradhi Reddy Guru Nanak Institute of Technology, Ibrahimpatnam, Telangana, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_20

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In present years, consumption of electric power is rapidly increasing day by day. So, the demand of electrical energy is increased. Since the greenhouse production are mostly consumes. The main reasons caused by the renewable resources a fast development in find another and a renewable resource has attained a major interest in research area to eliminate the lack of fossil fuels and also decreases the global warming [3]. Therefore, the renewable energy sources have to turn into a difficult field, and so many researchers have to focus on the renewable energy sources. So, this paper mainly focuses on to generate novel sustainable, normal wealth and environmental approachable natural renewable energy resources. In present, earth for the most part of environmental renewable energy sources can be used DC energy in environment such as solar system. In electrical power transmission systems is in AC and not all of the AC loads like machines, etc. are uses the DC power supply as their power sources. So, many applicants need an AC power as the main power source. Therefore, in this reason, we need conversion of energy DC into AC power [4]. MLI is a power electronic apparatus which converting direct current power supply to alternate power supply. In previous conventional systems, inverter is used to two-level inverter and is implemented using a few semiconductor switches. In present days, rapid increases the growth of industry and also introduces the high power applications devices which reaches the megawatt levels. So, the two-level inverter is not able of usage of high power applications. So, in this paper, introduce a more number of levels inverter like multilevel expand into an essential to overcome the short of existing two-level inverters and economically elevated power loads. Moreover, these reason multilevel inverters are designed to substitute the existing two-level inverters to grow high-quality power quality, and switching losses is less and capable of high voltage systems [5]. The most important scope of MLIs is to generate the multilevel output voltages with switching losses is very less. Comparing to the existing methods, the multilevel inverter has so many advantages. The advantages are: (1) The MLI inverter can be produced regular method voltage so the strain of the motor reduces. (2) MLI draws the input current with less distortion. (3) MLI can operate at both higher and lower switching frequency; it means, at lower switching, frequency having low switch losses and efficiency is high. (4) MLI reduces the total harmonic distortions (THD) [6]. This paper includes the performances of two types of MlLIs, and they are diode clamped inverter along with cascaded H-Bridge multilevel inverter. So, our proposed method is combination of diode clamped and H-Bridge circuits. The operation of the circuit and principle operation is discussed in the following secessions. The theory of multilevel inverter is to generate multilevel inverter output voltages with very less switching losses and harmonic distortions. In this proposed method the first, second diode clamped multilevel inverter (DCMI) and third one is the proposed MLI having combination of CHMI and DCMI. So, the proposed MLI applied for low power systems mainly photovoltaic systems.

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This paper describes many sections. Sect. 1 contains the introduction part of the MLI and Sect. 2 contains the background work of the DCMI and background work of the CHMI. Section 3 mentions the proposed MLI principle operation and explanation. Section 4 contains the simulation results of the proposed MLI.

2 Background Work The MLI general structure is toward making a sinusoidal voltage since of more than few voltage levels frequently achieve from capacitor voltage sources. Multilevel inverter has so many applications because of MLI having low THD and very low commutation loses. MLI develops into an efficient and valuable resolution for amplifying power and AC load also reduced. The multilevel inverters are divided into below categories: They are (1) cascaded type MLI, (2) diode clamped type MLI, (3) Flying capacitor type MLI. This paper discusses cascaded MLI and diode clamped type multilevel inverters. Compare to the diode clamped MLI and cascaded MLI, cascaded MLI is having the simplest arrangement.

2.1

Diode Clamped Type MLI (DCMI)

In largest parts generally use MLI method, that is, the diode clamped MLI (DCMI), and diode is used for clamping device. It can be used as clamp the DC voltage. So it attains the steps in the output voltage sources. Accordingly, the most important theory of this MLI is diode that is used for the power strategy voltage strain. Vdc is the DC voltage of apiece switch plus apiece capacitor. The No. of levels (N) inverters we need total (N − 1) no. of voltage source, and switching devices are need 2(N − 1) and the diodes are (N − 1)  (N − 2) diodes. After the increasing of the no. of voltage level, the superiority of the output AC voltage is increased and the waveform of the voltage has become nearer to a sinusoidal waveform. Figure 1 shows the five-level DCMLI. DC bus voltage having the four capacitors, C1, C2, C3 and C4. In a DC bus voltage of the Vdc, the apiece of capacitor voltage is Vdc/4 and the each device voltage strain will be partial and Vdc/4 is single capacitor voltage stage-level throughout clamp the diodes. The switches of the MLI are S1, S2, S3, S4, S01 ; S02 ; S03 and S04 . If the voltage output level is Vdc/2, then the switches can be conducted at S1–S4 next to the similar point. In an apiece voltage point, four switches conduct at a time. And the maximum output voltage level will get on partially of the DC source. So it is the main disadvantage of the DCMI. In this difficulty can be this difficulty preserve be solving by using a two times voltage supply or CHMLI two DCMLIs (Fig. 2).

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Fig. 1 Five-level MLI diode clamped inverter topology

Vdc/2

S1 C1 D1

S2

Vdc/4

D2 S 3

C2

Vdc

Vo

S4

D 1' n

a S 1'

C3 D 2'

S 2'

-Vdc/4

S 3' C4

D3' S 4' 0

-Vdc/2

Fig. 2 Single-phase cascade multilevel inverter topology

S1

S2 Hn

Va S3

S4

S1

S2

S3

S4

H2

S1 n S3

S2 S4

H1

Reducing Number of Switches in Multilevel Inverter … Table 1 Five-level DCMLI switching state table

207

V0

S1

S2

S3

S4

S01

S02

S03

S04

Vdc/2 Vdc/4 0 −Vdc/4 −Vdc/2

1 0 0 0 0

1 1 0 0 0

1 1 1 0 0

1 1 1 1 0

0 1 1 1 1

0 0 1 1 1

0 0 0 1 1

0 0 0 0 1

The five-level DCMLI amount produced voltage levels ought to the similar voltage significance. The switching operations can survive taken in a way that output voltage THD will become arrangement of
as
low 0as  possible.

The switches  0 0 0 the MLI is into four pairs S1 ; S1 ; S2 ; S2 ; S3 ; S3 ; S4 ; S4 . The sequence of the switches as specified in Table 1. The state Condition 1 means that switch ON position and 0 means switch OFF position. The following steps are the five-level DCMLI output voltage in this circuit as follows: • The output voltage V0 = 0, then the upper switches of S3, S4 and the lower switches S01 and S02 are in ON state. • And the output voltage V0 = Vdc/4, then the switches of upper switches are S2, S3, S4 and lower switch S01 is in ON state. • And the output voltage V0 = Vdc/2, the switches of S1, S2, S3, S4 the entire upper switches in turn ON position. • And the negative output voltage of V0 = −Vdc/4, the switch of upper side S4 and the lower side switches S01 , S02 and S03 are in turn ON position. • Another negative output voltage of V0 = −Vdc/2, and all the switches of the lower position in turned ON position.

2.2

Cascade H-Bridge Multilevel Inverter (CHMI)

The cascade MLI having no. of H-Bridge inverter units with separate DC source and each H-Bridge unit is connected in series. And the H-Bridge produces +Vdc, 0 and −Vdc voltage levels is connecting the input DC source to output AC side of dissimilar combination of the four switches S1, S2, S3 and S4. And the H-Bridge of each output is coupled in series. So, the synthesize output voltage waveform is nothing but a summation of all of the individual H-Bridge outputs. By linking enough number of H-Bridges in cascade and with suitable modulation format, almost sinusoidal output voltage waveform can be synthesized. The output voltages in number of levels having phase voltage (2s + 1) and line voltage (4s + 1) correspondingly. And s is nothing but a number of H-Bridges per phase. In a three H-Bridges, five bridges and seven H-Bridges per phase require

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seven levels, nine levels, respectively. The representative waveform created by seven-level CMLI. And the magnitude of the AC output phase voltage is the sum of the voltages created by H-Bridges.

3 Proposed Concept Figure 3 shows the multilevel inverter topology. The proposed circuit is the combination of H-Bridge and diode clamped inverter. Here, H-Bridge inverter can be used as alternating signal model like it gives the negative and positive polarities and in diode clamped inverter circuit having the capacitors also. So, the capacitor voltages are added and subtracted the operating power switches to generate the (N = 2n + 1 = 35) level output voltage waveform. And the basic operation of the main circuit is working principle of the topology as shown in Fig. 4. In this diagram, Vdc bus is the DC input voltage, and Vo is the output AC voltage, V01 is the fundamental voltage of Vo. In this proposed circuit having the So-S17 switches of the diode clamped inverters and another four

Fig. 3 Proposed topology of multilevel inverter

Diode clamped connection

CK SK DK C3 D3

S3

C2 S2 D2 C1

D1

P1 Vbus

S1

P3 Load

P4

P2

H-Bridge connection

Reducing Number of Switches in Multilevel Inverter … Fig. 4 Basic operations of switches

209

Vdc Vdc(K) Vdc(K-1) Vdc2 Vdc1 wt V0

wt

SK SK-1 S2

wt wt wt wt

S1 wt P 1P 4 wt P 2P 3 wt switches are power switches P1–P4 is giving signals of the H-Bridge inverters. And, the H-Bridge four power switches are operated at fundamental frequency of the output voltage.

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The proposed circuit having the total four switches of H-Bridge and seventeen diode clamped switches is used to generate output of N = 35-level voltage waveform. And also using the n capacitors and n diodes can be used to produce the output of overall very less switch count than an existing method.

4 Simulation Results Figure 5 shows the proposed MLI topology Simulink diagram. The output waveform of the staircase voltage waveforms for k different DC sources and its amplitude is Vdc and the equation is as follows: V 0 ðt Þ ¼

1 4Vdc X sinðnx0 tÞ ½cosðna1 Þ þ cosðna2 Þ þ


þ cosðnak Þ n p n¼1;3;5...::

ð1Þ

The magnitude of the Fourier transform coefficients can be calculated as follows Vn ¼

4Vdc ½cosðna1 Þ þ cosðna2 Þ þ


þ cosðnak Þ np

ð2Þ

In applied equations of the (1) and (2) are the proposed 35-level MLI topology are studied. The output of the MLI generates 220 V AC voltage waveform so we have to measure a DC bus voltage of the 340 V. Figures 6 and 7 show the simulated input waveforms of DC bus (A) voltage and (B) current of a 35-level multilevel inverter and simulated output waveforms of AC bus (A) voltage and (B) current of a 35-level multilevel inverter of the proposed MLI topology and the operating frequency of the proposed topology is 50 Hz. Table 2 is the comparison of different multilevel inverter topologies power switches count. This evaluation obviously demonstrates the superiority of our proposed MLI structure The voltage rating is high in cascaded H-Bridge MLI topology because this MLI using four switches. It should be pointed that the four switches used in the H-Bridge of our topology require a large voltage rating. These unlike switches are used in the conventional MLI topologies. Then all the conventional method switches having low voltage rating. The cost of the switch increases of its same low voltage rating and the driving circuits are frequently remaining the similar. So based on that, an important benefit in provisions of cost and easy functioning is still obtainable by our proposed MLI topology.

Reducing Number of Switches in Multilevel Inverter … Fig. 5 Simulink diagram of the proposed concept

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(A) DC INPUT VOLTAGE

(B) DC INPUT CURRENT Fig. 6 The simulated input waveforms of DC bus, a voltage and b current of a 35-level multilevel inverter

(A) AC OUTPUT VOLTAGE

B) AC OUTPUT CURRENT Fig. 7 The output waveforms of AC bus, a voltage and b current of a 35-level multilevel inverter

Table 2 Comparison of different types of MLI topologies

S. No

Circuit

No. of power switches used

1. 2.

Diode clamped MLI Cascaded H-Bridge MLI PROPOSED MLI

34 68

3.

21

Reducing Number of Switches in Multilevel Inverter …

213

5 Conclusion A new 35 MLI topology is proposed. The proposed method of 35-level multilevel inverter topology is combination of diode clamped and cascaded multilevel inverters. The proposed MLI is the better choice for renewable source applications like photovoltaic applications because proposed circuit having separate input DC sources is offered. This paper explains the operation and working principle of the proposed MLI topology. A complete study of efficiency and practical performance is still necessary to totally confirm the advantages of this new proposed MLI circuit.

References 1. J. Rodriguez, J.S. Lai, F.Z. Peng, Multilevel inverters: a survey of topologies, controls, and applications. IEEE Trans. Ind. Electron. 49(4), 724–738 (2002) 2. M. Malinowski, K. Gopakumar, J. Rodriguez, M.A. Perez, A survey on cascaded multilevel inverters. IEEE Trans. Ind. Electron. 57(7), 2197–2206 (2010) 3. M. Kavitha, A. Arunkumar, N. Gokulnath, S. Arun, New Cascaded H-Bridge Multilevel Inverter Topology with Reduced Number of Switches and Sources. Final year students/Dept. of EEE/DR.S.J.S Paul Memorial College of Engineering &Technology/Pondicherry/India, vol 2, issue 6. ISSN: 2278-1676, pp 26–36 (2012) 4. N.B. Zahari, Cascaded H-Bridge Multilevel Inverter [CHMLI]. A Proposal Submit in Biased Fulfilment of the Obligation for Bachelor of Degree in Electrical & Electronics Engineering (Electrical) Faculty of Electrical Engineering University of Teknologi Malaysia (2013) 5. A. Parkash, S.L. Shimi, S. Chatterji, Harmonics Elimination in Cascade Multilevel Inverters Using Newton-Raphson and Genetic Algorithm. Department of Electrical Engineering, National Institute of Technical Teachers’ Training and Research, Chandigarh-160019, India (2014) 6. Description of Multi Level Inverters and Advantages of MLI. https://www.elprocus.com/ multilevel-inverter-types-advantages/

Harmonic and Reactive Power Compensation with IRP Controlled DSTATCOM Haresh Nanda and Srinivas Reddy Chalamalla

Abstract The distribution system causes serious power quality problems with the more number of nonlinear loads. The nonlinear loads cause harmonics and reactive power problems. In this paper, a three-phase three-wire distribution static compensator (DSTATCOM) with instantaneous reactive power theory (IRP) control strategy is proposed for compensation of harmonics and reactive power problems. The proposed IRP-based control method is used to generate reference switching gate pulses for IGBT switches with a hysteresis control method. The proposed control technique using IRP improves the performance of the DSTATCOM under nonlinear voltage condition. The effectiveness of the proposed IRP theory-based three-phase three-wire DSTATCOM is investigated through MATLAB/Simulink. Keywords Power quality tion Reactive power





DSTATCOM
IRP theory
Harmonic compensa-

1 Introduction Power quality is a key issue in the customer electric power system, and it is given a special attention with the fast increase of modern industry. To improve the power quality, different treatment devices can be applied, such as active power filter (APF), dynamic voltage restorer (DVR), distribution static synchronous compensator (DSTATCOM) and unified power flow controller (UPFC) [1]. A statistical analysis shows that the harmonic is the most frequent one in all the problems of the power quality [2]. In recent, especially in the semiconductor manufacturing area, the harmonics are considered as a major cause of failure of the electronic devices which are sensitive to electrical disturbance. To reduce this disturbance, a corresponding compensation device is usually applied [3]. A distribution static synH. Nanda Electrical Engineerng Department, Q.Q.Government Polytechnic, Hyderabad, India S. R. Chalamalla (&) EEE Department, Guru Nanak Institutions Technical Campus, Telangana, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_21

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chronous compensator (DSTATCOM) is a series-connected device which is designed not only to compensate for the current harmonic disturbance problems but also to track and regenerate the current waveform fast, when harmonic distortion of the consumer side is outside the control range [4]. Several control algorithms have been proposed for the controlling of DSTATCOM [5]. In which most of control circuits are complicated and not easier to implement. In this paper, the simplified instantaneous reactive power theory has been proposed which uses three current sensing devices, two voltage sensing devices and a DC-link voltage measurement device for the controlling of DSTATCOM [6]. This proposed method is simple and total implementation cost is reduced. In this work, an IRP theory for three-phase three-wire systems for nonlinear load distribution system is proposed. This theory can be used for all balanced and three-phase systems. The aim of the system simulation is to verify the DSTATCOM effectiveness for a nonlinear load.

2 System Configuration Figure 1 shows the three-phase three-wire nonlinear load distribution system with proposed control strategy DSTATCOM. It consists of three-phase three-wire supply system connected through three voltage sources converter with eight-insulated-gate bipolar transistors (IGBTs) switches with interface inductor connected at the system. And, the interface inductor use to reducing the transients during switching the

Fig. 1 System configuration

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217

DSTATCOM, a ripple filter, which consists of inductor in series with a small resistance, is connected to the system. The DSTATCOM on the end bus provides harmonics elimination and reactive power correction.

3 Control Strategy Instantaneous reactive power theory is first introduced by Akagi in 1983. This theory depends upon converting the three-phase values to two-phase values based on a-b frame and calculating the active and reactive power in this frame. This IRP theory is also known as p-q theory. The p-q theory is based on the time domain. It is valid both steady-state and transient conditions of the system operation. The basic block diagram of p-q theory is shown in Fig. 2. For the compensated current Icomp is given by Eq. (1) Icomp ¼ Isource  Iload

ð1Þ

where Icomp is the compensation current, Isource is the source current, Iload is the load current respectively. p-q theory converting the three-phase system voltages, currents from 0-a-b co-ordinates by applying Clark’s transformation. This is represented as given in Eqs. (2) and (3).

Fig. 2 Basic block diagram of IRP theory

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3 rffiffiffi2 1pffiffiffi v0 2 4 v / 5 ¼ 26 4 1 3 vb 0

  1 pffiffiffi 1 pffiffiffi 32 3 va 2 2 1= 1= 7 4 vb 5 5 pffiffiffi2 pffiffi2 vc  3 3 2 2 8 9 rffiffiffi2 pffiffiffi pffiffiffi pffiffiffi 38 9 < i0 = 1= 2 1= 2 1= 2 < ia = 24 5 ib ia ¼ 1 1=2 1=2 pffiffiffi pffiffiffi : ; : ; 3 ib ic 0 3=2  3=2 2

ð2Þ

ð3Þ

Alternately, the power equation is given by Eq. (4) 3 2 v0 po 4 p 5¼40 0 q 2

0 v/ vb

32 3 i0 0 vb 54 i/ 5 va ib

ð4Þ

In the case of three-phase three-wire system, V0 = 0 and i0 = 0. So, zero sequence power P0 = 0, and consequently power equation by Eq. (5) [3].    v/ p ¼ vb q

vb va



i/ ib

 ð5Þ

Using Eq. (5), the instantaneous active and reactive load power can be obtained by Eq. (6). 

pl ql





v/ ¼ vb

vb va



il/ ilb

 ð6Þ

Which could be divided into AC component and DC component. The DC component is the first harmonic component consists of positive component and AC component is harmonic component consists of all harmonic component they compensated using DSTATCOM so that the DC components remain in the mains. After the average current, components can be calculated by using Eq. 7. 

is/ isb

 ¼

1 v2/ þ v2b



v/ vb

vb va

  p 0

ð7Þ

Applying Inverse Clark’s Transformation to the Eq. (7) then the compensated current is calculated by using Eq. (8). 2

3 rffiffiffi2 3   isa 1 pffiffi0ffi 2 4 isb 5 ¼ 4 1=2 5 is/ 3 =2 pffiffiffi isb 3 isc 1=2  3=2

ð8Þ

Harmonic and Reactive Power Compensation …

219

The obtained switching signal is applied to IGBT’S of DSTATCOM for proper switching operation of DSTATCOM.

4 Results and Discussion This proposed distribution system simulation results are observed with the new modified IRP control theory strategy. The simulation results are compared without and with DSTATCOM. The performance of the system is analyzed in terms of harmonic distortion and reactive power. The R-, Y-, B-phase are indicated as red, yellow and blue lines. The corresponding results are verified in MATLAB/Simulink software. The simulation results are verified under the following cases Case-1: Distribution system without DSTATCOM Case-2: Distribution system with DSTATCOM Case-3: Total harmonic distortion (THD) analysis.

4.1

Case-1: Distribution System Without DSTATCOM

When nonlinear load connected the distribution system, harmonic is developed in the system. The harmonics not only affect the source but also affect the other load points which are connecting at the PCC. The obtaining results with connecting the nonlinear load are observed and also without connecting DSTATCOM as shown in Fig. 3. The nonlinear load introduces harmonic in source current waveform as shown in Fig. 3b and its correcting source voltage is also shown in Fig. 3b. The corresponding system load current is shown in Fig. 3c. The per-phase representation of source current harmonic waveform is shown in Fig. 3d. The active and reactive power supply is shown in Fig. 3e, f. The load active and reactive power is shown in Fig. 3g, h.

4.2

Case-2: Distribution System with DSTATCOM

When DSTATCOM is connected to the distribution system at the PCC with the proposed control method, the harmonic and reactive power is compensated as shown in Fig. 4. The source voltage waveform is shown in Fig. 4a. The compensated source current waveform is observed from Fig. 4b. The per-phase representation of source current waveform is shown in Fig. 4c. The reactive power compensation is also observed from Fig. 4e–f. By connecting the DSTATCOM at

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Fig. 3 a Source voltage waveform, b source current waveform, c load current waveform, d per-phase source current waveform, e source active power, f source reactive power, i load active power, j load reactive power, without DSTATCOM

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221

Fig. 4 a Source voltage waveform, b source current waveform, c per-phase source current waveform, d DC-link voltage waveform, e source active power, f source reactive power, g DSTATCOM active power, h DSTATCOM reactive power, i load active power, j load reactive power, with DSTATCOM

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Fig. 4 (continued)

the PCC instead of source, the load observed the reactive power from the DSTATCOM as observed from Fig. 4e–f. So, the reactive power compensation is achieved. The load taking the active power from the source and DSTATCOM supplies the reactive power to the load. The DC-link voltage wave is as shown in Fig. 4b.

4.3

Case-3: Total Harmonic Distortion Analysis

The total harmonic distortion (THD) analysis without and with DSTATCOM is shown in Fig. 5. Figure 5a shows the THD without DSTATCOM and Fig. 5b shows the THD with DSTATCOM connected at the PCC. By connecting DSTATCOM, the harmonic is reduced from 27.90% to 2.51%.

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223

Fig. 5 a THD without DSTATCOM. b THD with DSTATCOM

5 Conclusions In this paper, the performance analysis of DSTATCOM with instantaneous reactive power theory has been carried out. Form the simulation output, it is observed that of DSTATCOM more active for the elimination of harmonics and reactive power in distorted source current compensation at the PCC. And also the simulation results without DSTATCOM and with DSTATCOM also observed using simulation software.

References 1. H. Tiwari, A. Agrawal, S. Agrawal, S. Shah, Power quality improvement using DSTATCOM in distribution system, in 4th International conference on “Advance Trend in Engineering, Technology and Research” (ICATETR-2015)

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2. S.R. Reddy, P.V. Prasad, G.N. Srinivas, Design of PI and fuzzy logic controllers for distribution static compensator. Int. J. Power Electron. Drive Syst. (IJPEDS) 9(2), 465–477 (2018) 3. R. Madhusudan, P.L. Reddy, Control strategies for DSTATCOM—a comprehensive review. Int. J. Innov. Technol. Exploring Eng. (IJITEE) 8(5) (2019) 4. D. Suresh, G. Sravanthi, R. Chander, DSTATCOM with Improved LMS based IRP theory, in E3S Web of Conferences, 87, 01013 SeFet (2019) 5. M. Kullan, R. Muthu, J.B. Mervin, V. Subramanian, Design of DSTATCOM controller for compensating unbalances. Circuits Syst 7, 2262–2272 (2016) 6. R. Dehini, C. Benachaiba, A. Bassou, Simulation of Distribution Static Compensator (D-STATCOM) to improve power quality. Arab. J. Sci. Eng. 38, 3051–3058 (2013)

Performance of Static VAR Compensator for Changes in Voltage Due to Sag and Swell M. S. Priyadarshini and M. Sushama

Abstract The deviations that occur in electrical power supplied by utilities to end users result in voltage decrease termed as sag and increase termed as swell. Due to voltage variations, change is evident for a short duration in voltage, current or frequency. In order to maintain constant voltage to the connected load, compensation devices are used based on flexible AC transmission systems (FACTS) technology. Based on an increase or decrease in voltage, suitable correction action can be taken by power electronic-based devices. The performance of static VAR compensator (SVC), which is a shunt connected FACTS device, is analyzed for voltage sag and swell. The SVC controller scheme, reactive power generated or absorbed, firing pulse generation and modes of SVC operation in MATLAB/ Simulink environment are explained.







Keywords Sag Swell Thyristor controlled reactor capacitor Static VAR compensator





Thyristor switched

1 Introduction Power system is defined as an interconnection between generator and load buses through transmission lines. If any generator is disconnected or taken out for service or maintenance, the lines fed by that generator will be disconnected and must be connected to other generator buses. This results in a change in voltage profile at the buses. Sudden increase in load also affects voltage. To achieve the aim of maintaining constant voltage, proper balance must be maintained between active and reactive power. Electric power supplied by utilities must be free of disturbances and

M. S. Priyadarshini (&) J.N.T.U Anantapur, Ananthapuramu, AP, India M. Sushama J.N.T.U.H C.E.H, Hyderabad, Telangana, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_22

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supply voltage must be within the range specified. Any violation of these conditions results in erroneous operation of power-consuming equipment. In flexible AC transmission systems (FACTS), control is increased and power transfer capabilities are increased [1]. In order to minimize the impact of changes in supply voltage, the quality of power supplied must be monitored. Static VAR compensator (SVC) is a shunt connected reactive power generator or absorber whose output is adjusted to exchange capacitive or reactive current [1]. It is used with the objective to maintain or control certain quantities which are subjected to a change of the electric power system. Based on requirement, compensation devices have to either absorb or generate reactive power. FACTS are defined by IEEE as AC transmission systems using controllers to increase controllability and power transfer capability. The aim of this paper is to study the performance of FACTS-based static VAR compensator for sag and swell. SVC and its controller operation are explained in Sect. 2 and performance of SVC is explained in Sect. 3 with suitable correction action that is taken and Sect. 4 ends with conclusion.

1.1

Voltage Sag and Swell

If 1 per unit (pu) is taken as reference, sag is a decrease in voltage to between 0.1 per unit (pu) and 0.9 pu for durations from half-cycle to 1 min [2]. Swell is an increase in voltage above 1.1 pu for durations from half-cycle to 1 min [2]. Due to momentary or persistent disturbances in supply voltage, the connected loads in the system can be severely affected. Some of the reasons for the disturbances to occur are load changes, faults, lightning and switching of loads with reactive components [3]. With respect to reference voltage of 1 pu, sag is defined with a decrease in voltage to 0.5 pu and swell with an increase to 1.5 pu out of the considered voltage waveform duration of 0.6 s as shown in Fig. 1.

Fig. 1 a Three-phase voltage signal with sag and swell b current corresponding to voltage with disturbances

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227

2 Static VAR Compensator (SVC) and SVC Controller SVC comes under the category of variable impedance type FACTS devices. SVC injects or absorbs reactive power to regulate voltage at a given bus. The two operation modes in SVC operation are voltage regulation and VAR control mode. In voltage control mode, the voltage at supply side of SVC is controlled, and in VAR control mode, the SVC susceptance is kept constant. SVC consists of parallel connection of: (i) A reactor whose operation is controlled by a thyristor (TCR). (ii) Three numbers of thyristor switched capacitors (TSC). In the case of TSC, switch has only ON and OFF possibilities and no control is possible. In the case of TCR, control of impedance is possible by varying firing angle of the pulse generators. Figure 2 shows the block diagram representation of SVC. In [4], a system is considered for analysis without compensation and with shunt and series compensation provided by SVC and thyristor controlled series compensators (TCSC). Load flow results are obtained and a comparison of apparent impedances of uncompensated case with 100 and 200% loading for SVC and TCSC compensated cases. In [5], advanced SVC and advanced static compensator are proposed and the optimum values of inductor, capacitor and proportional and integral gains are obtained using optimization techniques. IEEE 14 and 30 bus systems are considered in [5] with the variation in magnitude of voltage swell. For an improved and cost-effective operation of grid, distribution static VAR compensator (D-SVC) during internal, peak load and power losses profile is used in [6], considering the effects of total harmonic distortion (THD) on load profile. IEEE 14 bus system is modeled and simulated in [7] with magnitude of active power,

Generating Station

Transmission Line

Load Station

Coupling Transformer

Static var compensator SVC having one TCR and three TSCs in parallel

SVC Controller providing firing pulses to SVC Fig. 2 Block diagram showing interconnection of generators and load through a transmission line along with SVC

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reactive power and voltage magnitude with different types of variations in load. The waveforms shown in Fig. 3 depict currents drawn by TCR, three TSCs and resultant sum of currents drawn by TCR and three TSCs. The basic components of SVC controller are: voltage measurement, voltage regulator, distribution unit and firing unit which are all connected in sequence. The input signals for SVC controller are measured voltage and current as shown in Fig. 1 and output is firing angle controlled pulses for TCR and ON/OFF pulses for TSCs.

3 Performance of SVC When the reference voltage and measured voltages are same, reactive power will be zero. When there is a decrease in voltage for the duration between 0.1 and 0.2 s, reactive power generated is increased from 0 to 96.14 MVAR. During restoration of voltage to normal, the reactive power generated increases to a peak value of 417 MVAR at 0.222 s. Due to fault or overloaded conditions, when swell occurs from 0.4 to 0.5 s reactive power absorption takes place to −200 MVAR and becomes equal to zero when reference and measured voltages are same. The minimum value of reactive power is −224.7 MVAR at 0.461 s. The gate signals of TCR vary from a minimum value of 90° to a maximum value of 180°. From reactive power variation, the following are observed: 1. Due to decrease in voltage due to sag, TSCs must be switched into the power system to boost up the voltage and produce leading reactive power. Reactive power generation takes place. 2. Due to increase in voltage due to swell, TCR must be fired into the power system to control the voltage and produce lagging reactive power. Reactive power absorption takes place. As there is a change in voltage magnitude, corresponding susceptance changes resulting in variation of firing angle pulses. SVC controller provides suitable control action for reactive power generation and absorption based on variations in supply voltage with respect to reference voltage. Equation for amplitude of reactive current shown in Fig. 4c is given by (1). As the operation of TCR depends on firing angle delay a, the current and susceptance will be in terms of a. ILF ðaÞ ¼ VBL ðaÞ

ð1Þ


1 sin 2a 1  2a In (1), BL ðaÞ is the equivalent susceptance given by xL . p  p The primary voltage of transformer is fed to measurement system which converts the three-phase signal to magnitude of positive sequence component of voltage in terms of normalized values. The error signal which is the difference between measured and reference voltages is fed to discrete-time integrator along with droop which is slope measured in pu per 100 MVA. All the components in SVC controller along their sequence of connection are shown in Fig. 5. The input

Performance of Static VAR Compensator for Changes …

229

Fig. 3 Currents drawn by a TCR, b TSC1, c TSC2, d TSC3, e total current drawn by SVC which is the resultant sum of currents of TCR and the three TSCs

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Fig. 3 (continued)

for distribution unit connected to firing unit is susceptance and output is firing angle of TCR and ON/OFF control for TSCs. Distribution unit gives firing angle delay to TCR and pulses to TCS as shown in Fig. 3. Actual voltage is converted to pu and is compared with reference voltage 1 pu. Error signal is fed to voltage regulator which takes into consideration droop value and the susceptance required to provide necessary voltage is calculated. Droop is the slope of voltage–current characteristics of SVC. The three inputs of voltage regulator are measured voltage, reference voltage and reference value of susceptance Bref. This Bref value is taken to be equal to zero. Figure 6a–c shows reactive power generated or absorbed, measured and reference voltages and number of TSC’s that are switched on based on variations in voltage.

Performance of Static VAR Compensator for Changes …

(a)

231

(b)

Currents drawn by TCR and TSC are measured at the points shown by arrows

Fig. 4 a A portion of TCR bank, b a portion of one of the three TSC bank, c steady-state current waveform of TCR

Voltage measurement compares the normalized measured voltage with reference voltage of 1 pu Firing unit sends suitable firing signals to thyristors for control of TCR and on/ off of TSCs

Voltage regulator uses difference in voltages to find the susceptance that is required to maintain constant voltage

Distribution unit determines required firing angle delay of TCR

Fig. 5 SVC controller with the functions of its various components

By suitable switching operation and gain multiplication, the voltage regulator produces susceptance as shown in Fig. 6d. Firing angle delay control of TCR which consists of delta connected antiparallel thyristors in series with an inductor is shown in Fig. 6e. TSC is also delta connected antiparallel connected thyristor bank in series with a capacitor. During voltage sag, three capacitors are connected, and during swell conditions, no capacitor is switched on as shown in Fig. 6c. There exists a relationship between Figs. 4c and 6e. For highest and constant value of firing angle, steady current remains zero. There is no variation in current for minimum value of firing angle.

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Fig. 6 a Reactive power variation, b measured and reference voltages, c number of TSC’s that are switched on based on variations in voltage, d input and of distribution unit, i.e., susceptance, e firing angle delay control of TCR

4 Conclusion SVC injects reactive power in the line by thyristor switched capacitor. SVC absorbs reactive power from the line by thyristor controlled reactor. The supply or absorption of reactive power is done to regulate voltage against changes in voltage. An important benefit of using power electronic-based equipment is manifested in the form of increase in power transfer capability. As by switching ON or OFF capacitors, capacitive admittance can be directly connected or disconnected based on reactive power variation. SVC controller calculates the susceptance required to make voltage equal to reference voltage. The TSC components or banks which are made ON/OFF are decided and the delay angle provided by reactor is calculated.

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When voltage becomes as a reduced value than reference voltage, TSCs are turned on and when voltage is greater than reference voltage TCR operation is controlled by variation in firing angle. Corresponding to the variation in magnitude of supply voltage, necessary action is taken by reactive power absorption or generation.

References 1. N.G. Hingorani, L. Gyugyi, Understanding FACTS. IEEE Press, First Indian Edition (2001) 2. IEEE, Recommended Practice for Monitoring Electric Power Quality, IEEE Standard 1159-1995 (1995) 3. S. Joseph, The Seven Types of Power Quality Problems. White paper 18, Revision 1, Schneider Electric White Paper Library, pp. 1–21 (2011) 4. J. Piri, G. Bandyopadhyay, M. Sengupta, Effects of including SVC and TCSC in an existing power system under normal operating condition: a case study, in IEEE International Conference on Power Electronics, Drives and Energy Systems, pp. 1–6 (2018) 5. Y.M. Aboelazm, Y.E. Wahba, M.A.M. Hassan, Modeling and analysis of new advanced FACTS devices for voltage swells mitigation, in Twentieth International Middle East Power Systems Conference, pp-552–557 (2018) 6. M.S. Alvarez-Alvarado, C.D. Rodríguez-Gallegos, D. Jayaweera, Optimal planning and operation of static VAR compensators in a distribution system with non-linear loads, in IET Generation, Transmission and Distribution, pp. 3726–3735 (2018) 7. M. Priyadhershni, C. Udhayashankar, K. Chinnaiyan, Simulation of Static Var Compensator in IEEE 14 Bus System for Enhancing Voltage Stability and Compensation, Power Electronics and Renewable Energy Systems, Lecture Notes in Electrical Engineering, vol 326 (Springer, Berlin) (2015)

A New Efficient Z-H Boost Converter for DC Microgrids Ch. Sajan, T. Praveen Kumar and P. Balakishan

Abstract With the shortage of the vitality and regularly increasing of the oil value, look into on the sustainable and efficient power vitality sources, only the sunlight based exhibits and the energy units, turns out to be increasingly fundamental. Boost converters are all around used to accomplish high advance up and high effectiveness DC/DC converters and furthermore utilized as power-factor adjusted pre regulators. A Z-H boost DC-DC converter is proposed in this paper as there is no shoot-through exchanging state in this converter and the front-end diode is cleared out. The Z-H boost converter can be adjusted to DC-DC, DC-AC, AC-DC, and AC-AC power change. The simulation results confirmed the investigation and exhibited the huge capability of the Z-H boost converter. Keywords Z-H converter


Boost converter
PV module
Power losses
MPPT

1 Introduction In terms of energy, PV sources are one of the significant contender to the generation of power among all sustainable power source challengers continuously upto 2040 in light of the fact that it is completely spotless emanation free, inexhaustible electrical innovation with high accuracy. Boost converters are the most outstanding particularly for applications with higher DC bus voltage compared to the line input. These are commonly connected as preregulators or even joined with the last-stage circuits or rectifiers into single-stage circuits [1, 2]. The ease in circuit and frame work configuration decreased weight on gadgets and high transformations high control factor concern are the explanations behind utilizing support converter.

Ch. Sajan (&)
T. Praveen Kumar
P. Balakishan Department of Electrical and Electronics Engineering, Jyothishmathi Institute of Technology and Science, Karimnagar, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_23

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The regular strategy for decreasing current harmonics at the input stage utilizing a LC filter is never again used for all intents and purposes endurable to meet the necessities in some powerful applications. Most sustainable power sources, for example, PV power source frame works and energy units, have very low voltage output and require a voltage booster to give adequate output voltage [3, 4]. Be that as it may, there are a few abstractions for the ordinary interleaved help converter in high advance up DC-DC transformation [5, 6]. (1) The current ripples of the switches and the diodes at the output are enormous (2) The switch voltage stress is equivalent to the output voltage, which is huge in high-output voltage applications (3) The switching and reverse recovery losses are enormous because of strong switching operations and high voltage applications.

2 Design of the PV Module The frame work structure of the forthcoming DC microgrid comprises of the typical out spread DC Power line of 200V, to which the various circuits of the microgrid are associated [7, 8]. Sun-oriented PV is treated as the essential well spring of the intensity in this design. A sun-powered PV cluster is associated with the microgrid framework that supplies a variable DC load. The setup of the DC microgrid is planned so that it can work for AC power transformation. The two center purposes of the H-connect are associated with the two inductors separately with no association with the utility. The single diode model or the five-parameter model [9] is utilized for displaying the sunlight-based cell. The equal circuit of a solar PV cell is given in Fig. 1. It comprises of a present source, a diode, an arrangement obstruction, and a parallel opposition. The present source speaks to the photograph current (Iph) created inside the PV cell which is an element of the episode sun-based radiation (G) and cell temperature (T). The present Id is the diode current, and Ish speaks to the current in the shunt branch. Rsh and

Fig. 1 Equivalent circuit of a PV cell

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237

Rs are the shunt and arrangement protections. V and I speak to the yield voltage and current from the cell. Utilizing Kirchhoff’s law, the condition of the current can be composed as, I ¼ Iph  Id  Ish

ð1Þ

The diode current (Id) and the shunt current (Ish) are given by the following. Id ¼ fexp½ðV þ IRs Þ=nCKT   1g

ð2Þ

Ish ¼ ½V þ IRs =Rsh

ð3Þ

where q is the electric charge (1.6  10–19 C), K is the Boltzmann constant (1.38  10−23 J/K), C is the number of cells in a PV module, T is the cell temperature (K), and n is the diode ideality factor. In order to reduce complication, it is assumed that only photocurrent and the diode current depend on the working conditions [9]. The dependencies are specified by the Townsend equations which are given as, Iph ¼ ½G=Gref ½Isc þ lscðT  Tref Þ

ð4Þ


 Is ¼ Is ; ½T=Tref 3  exp qEg Ns=nK ð1=T  1=Tref Þ

ð5Þ

where G is the incident solar irradiation, Gref is the reference solar irradiation (taken as 1000 w/m2), lsc is the temperature coefficient of the short circuit current, and Eg is the band gap energy of the PV cell. The value of the reference diode saturation current is given by, Is ; ¼ Isc =fexpðqVoc =nCKT Þ  1g

ð6Þ

where Voc is the open-circuit voltage over the PV module made by associating a string of PV cells organized so as to create the required voltage and current yield. For an individual reference working condition, the estimations of the arrangement obstruction, parallel opposition, and diode ideality factor are resolved and these qualities are utilized for different conditions. Absolutely, a model of a sun-based PV module is developed utilizing (1–6) Simulink. For the reproduction of the DC microgrid, a PV cluster is utilized. This exhibit is associated with the DC dispersion framework by means of a lift converter. Here, the lift converter represents the Maximum Power Point Tracking (MPPT). This element of MPPT is an unquestionable requirement as it will permit the PV cluster to convey the greatest conceivable power for a specific illumination input. The Z-H help converter appeared in Fig. 2 [10, 11] is a voltage source converser which means there is no shoot-through exchanging state and longer. Every one of the switches appeared in Fig. 2 is bidirectional S1 and S3, S2 and S4 are accomplished, respectively. The control signals S2 and S3 can be in stage, or have

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Fig. 2 Z-H boost converter with load

Fig. 3 Sequence of the gate signals a S2 and S3 are in phase; b S2 and S3 are interleaved

180° stage move to bring down the voltage swells. The arrangement of door sign is appeared in Fig. 3. Here, D is the duty cycle for S2 and S3. The converter has two working stages: current charging (T0) and current releasing (T1). So as to create the switching signals, a triangular carrier wave is contrasted with a steady reference signal. In the event, if the triangular wave surpasses the reference signal, the switches S1 and S4 will be in ON condition, S2 and S3 are in OFF condition. Boost factor is given by B = [1 − 2D] − 1. where 0  D  0.5 for Vo  0 and 0.5  D < 1 for Vo  0 (Fig. 4). Fig. 4 Variation of the voltage gain versus duty cycle for the Z-H converter

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3 I2r Losses and Thermal Performances (a) Switching and conduction losses calculations At the point when the gadget is progressing from the blocking state to the directing state, the other way around exchanging misfortunes happens [12]. This period is portrayed by critical voltage over its terminals and huge current through it. Each progress should be increased by the changing recurrence to get the exchanging misfortunes for the vitality scattering. The exchanging misfortunes Psw are communicated as: Psw ¼ ðEon þ Eoff Þ  Fsw

ð7Þ

where Eon and Eoff are the energy losses during turn on and turn off of the switch and Fsw is the switching frequency. During the device is in full conduction mode, conduction losses occur. These losses are in direct relationship with the duty cycle. The average conduction losses Pco are expressed as: ZT Pavg:con ¼

1=T ½Vce ðtÞ  Ice ðtÞdt

ð8Þ

0

where Vce is the on-state voltage and Ice is the on-state current. The time period T is given as: T ¼ 1=Fsw

ð9Þ

where Fsw is inversely proportional to T. (b) Capacitor ESR losses calculations A perfect capacitors and inductors which are in series with resistance are called ESR(Equivalent Series Resistance). Its value is purely equal to value of set of losses of the energy which arises during the operating conditions. In a decent capacitor, the ESR is extremely little and in a poor capacitor, the ESR is huge. None the less the ESR is not just the resistance that would be estimated over a capacitor by the ohmmeter, the ESR is a determined amount with physical starting points in both the dielectrics conduction electrons and di-pole mechanisms. The losses in capacitors are communicated as: Pcap:loss ¼ I 2  ESR

ð10Þ

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(c) Magnetic core design calculations High flux centers offer the most outstanding biasing capacity of all powder center materials. The high immersion transition thickness and moderately low misfortunes of high flux centers make them very helpful for applications including, high influence and high DC predisposition. In this area, the attractive center structure is represented through the accompanying advances: (1) In order to choose an appropriate center size, the DC current and the inductance required with DC voltage to be known from the core selector outline as indicated by (11):   2 LIDC ¼ mH
A2

ð11Þ

A high flux 58337 core was selected for the Z-H boost converter in order to have fair comparison from an efficiency point of view. (2) Inductance core side and permeability are now known then calculating the no. of turns [13] by determine, the minimum inductance factor Almin by using the unconditional negative tolerance given in the core data sheet in

Almin ¼ Al  0:08 Al

ð12Þ

The DC resistance can be estimated after knowing the winding factor of the core, wire gauge, and the no. of turns. The DC resistance can be calculated as: RDC ¼ MLT  N

ð13Þ

4 Simulation and Results The Z-H boost converter has the capability of ideally giving an output voltage range from zero to infinity regardless of the input voltage. This utility of the Z-H converter for DC microgrid applications exhorts the superior suitability. The parameters of the impedance source work is chosen as L1 = L2 = 0.4 mH and C1 = C2 = C3 = 97 uf switching frequency used in the simulation is 20 kHz. The simulation results for Z-H converter is shown in Figs. 5, 6, and 7.

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Fig. 5 Simulink model of the Z-H boost converter with PV source for DC microgrids

Fig. 6 Current waveform at the output of the Z-H booster with load 1000 X

5 Conclusion This paper has conferred the presentation and appropriateness of a Z-H source support DC-DC converter for DC microgrid applications. PV framework with power electronic interface is demonstrated utilizing MATLAB/Simulink conditions. The PI controller outfits a controlled yield voltage for variable working states of the PV source. It is very well seen that the working states of the Z-H support DC-DC converter offers better execution and productivity with high efficiency and high output power with the low ripples.

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Fig. 7 Boosted voltage waveform at the output of the Z-H booster with load 1000 X

References 1. C.M. Wang, A new single-phase ZCS-PWM boost rectifier with high power factor and low conduction losses. IEEE Trans. Ind. Electron. 53(2), 500–510 (2006) 2. K.P. Louganski, J.S. Lai, Current phase lead compensation in single-phase PFC boost converters with a reduced switching frequency to line frequency ratio. IEEE Trans. Power Electron. 22(1), 113–119 (2007) 3. K. Kobayashi, H. Matsuo, Y. Sekine, Novel solar-cell power supply system using a multiple-input DC–DC converter. IEEE Trans. Ind. Electron. 53(1), 281–286 (2006) 4. S.K. Mazumder, R.K. Burra, K. Acharya, A ripple-mitigating and energy-efficient fuel cell power-conditioning system. IEEE Trans. Power Electron. 22(4), 1437–1452 (2007) 5. K. Ramtek, Y.N., Dynamic modeling and controller design for z-source DC-DC converter. Int. J. Scientif. Eng. Technol. 2(4), 272–277, Apr 2013 6. W. Li, X. Lv, Y. Deng, J. Liu, X. He, A review of non-isolated high step-up DC/DC converters in renewable energy applications. IEEE Trans. Power Electron. (2009). 978-1-422-2812-0/09 7. R.T. Naayagi, A.J. Forsyth, R. Shuttleworth, High-power bidirectional DC-DC converter for aerospace applications. IEEE Trans. Power Electron. 27(11), 4366–4379 (2012) 8. W. Li, X. He, Review of non-isolated high-step-up DC/DC converters in photovoltaic grid-connected applications. IEEE Trans. Industr. Electron. 58(4), 1239–1250 (2011) 9. F. Evran, M.T. Aydemir, Isolated high step-up DC-DC converter with low voltage stress. IEEE Trans. Power Electron, (early access) (2013) 10. F. Zhang, F.Z. Peng, Z. Qian, Z-H Converter. in Proc. PESC 2008 (2008), pp. 1004–1007 11. F.Z. Peng, M. Shen, Z. Qian, Maximum boost control of the Z-Source inverter. IEEE Trans. Power Electron. 20(4), 833–838 (2005) 12. N.V. Nguyen, B.X. Nguyen, H.H. Lee, An optimized discontinuous PWM method to minimize switching loss for multilevel inverters. IEEE Trans. Ind. Electron. 58(9), 3958–3966 (2011) 13. Y. Tang, X. Dong, Y. He, Active buck-boost inverter. IEEE Trans. Ind. Electron. 61(9), 4691–4697 (2014)

A Hybrid Power Conversion System Using Three-Phase Single-Stage DC–AC Converter Shaik Rafi, Simhadri Lakshmi Sirisha and Ravipati Srikanth

Abstract This research article emphasizes about incremental conductance method of tracking maximum power point and sliding variable structure control implemented for three-phase single-stage DC–AC converter for hybrid electric power generation. The variations in PV array have been reduced by MPPT converter and tied along with wind voltage to the DC bus. At a single stage, the three-phase voltage of 415 V, 50 Hz, has been obtained by using DC–AC converters. The sliding mode control used aims at reducing the power stages and also produces constant output voltage. In the proposed system, the signal harmonic components are found to be very low. The system is developed in MATLAB–Simulink environment, and the outputs have also been presented. Keywords Incremental conductance method mode control


Single-stage conversion
Sliding

1 Introduction The intensive demand for electrical power and the reduction in natural fuels like gas, oil, etc., always insists researchers to work on renewable energy sources [1] which are environmental-friendly, economical and feasible. The solar energy and wind energy play a prominent role in alternate energy sources. But the fragmentary behavior of these two sources has increased the necessity of combined solar–wind system. The integration of two or more alternate energy sources results in high efficiency, low cost and high reliability. Ghasemi [2] et al. presented the hybrid power system which combines solar and geothermal energy sources. Geothermal power plants use geologic deposits which are buried and combustible for generating the electric power, and it does not produce greenhouse gases. So, it can be harmful to the atmosphere. A combined S. Rafi (&)
S. L. Sirisha
R. Srikanth Vignans Nirula Institute of Technology and Science for Women, Guntur, Andhra Pradesh, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_24

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solar–wind power generation using DC–AC converter is analyzed. Solar power converters are designed by semiconducting power circuits associated with the power inversion, control and conditioning [3] the electric power. A single-stage three-phase combined power generation system has been suggested in the article which consists of MPPT solar charger, power conversion in one stage as shown in Fig. 1. The incremental conductance method of MPPT technique regulates the variable DC output voltage of PV module into a constant DC and can be stored in the battery. The three-phase DC–AC converter converts the low solar voltage to high AC voltage with fundamental frequency in a single stage. The topology shown in Fig. 1 maintains stable voltage under various load conditions. This research article mainly concentrates on IC MPPT controller, SVSC control and single-stage boosting and inverting property. The MPPT-based IC algorithm technique [4] is implemented to manage the turnout voltage of the solar, and the SVSC technique was [5] implemented to maintain the constant turnout voltage of the DC–AC converter to meet the heavy loads.

100V DC BUS

WIND

AC – DC

ENERGY

CONVERTER

SINGLE STAGE 3-ϕ DC-AC INVERTER

Grid

MPPT CONTROLLER

PV Array

NOT GATE

S DRIVER GATE

VPV IPV

δ IC

Fig. 1 Functional illustration of the 3-U system

Iref SVSC

vref

A Hybrid Power Conversion System Using Three Phase Single …

245

2 Three-Phase Single-Stage Conversion The proposed DC–AC converter has four switches in each phase which gives DC biased sinusoidal voltage in each phase. The phase shift of output voltage of each converter is p angle, which will deliver the maximum voltage differentially across the load [6]. Reference node of each converter is commonly connected, and load should be connected across the positive node of each converter. The circuit of the 3-U DC to AC modifier is shown in Fig. 2. The rectification of solar panel DC voltage to utility voltage and the boosting of low level dc to high level ac voltage in a single power conversion stage provides good benefit of DC-AC converter with minimum power switches and nil disturbance sine wave of the output voltage without involving filters. The proposed DC– AC converter of R-phase and its operation in two modes are shown in Figs. 3 and 4. Mode-1 (R-phase): Considering during positive half cycle, with switch SR1 open and SR2 closed, the inductor LR2 is charged increasing the loop current whereas with SR2 open and SR1 closed the capacitor CR1 gives electrical power to the load. Mode-2 (R-phase): Considering during negative half cycle, with SR3 open and SR4 closed, the inductor LR1 is charged and with SR4 open and SR3 close the CR2 supplies electric power to the load. The conduction mode of converter1 is VCR1 1 ¼ 1D VDC

ð1Þ

And the conduction mode of converter2 is VCR2 1 ¼ D VDC

LR2

SR3

LR1

SR1

LY2

SY3

LY1

R

Y

SY1

LB2

SB3

LB1

VDC

ð2Þ

B

SB1 SR2

SR4

SY2

SY4

SB2

SB4

CR1

CR2 D1

Fig. 2 Circuit of DC–AC converter

CY1 D2

CY2 D3

CB1 D4

CB2 D5

D6

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rL

IRL1

VDC

LR1

rL

SR1

CR1

SR2

SR1

IR1

rD

VDC

CR1

SR2

rD VCR1

VD

VD

Fig. 3 Operation of DC–AC converter (R-phase, Mode-1) LR2

rL

IRL2 VDC

SR4

LR2

rL

SR3

CR2

rD

SR3

IR2 VDC

SR4

VD

CR2

rD VCR2 VD

Fig. 4 Operation of DC–AC converter (R-phase, Mode-2)

The functionality of the converter (R-phase) can be easily evaluated with the equations given below VR ¼ VCR1  VCR2 ¼

VDC VDC  1D D

ð3Þ

VR 2D  1 ¼ VDC ð1  DÞD

ð4Þ

DiLRI VDC rLR ¼ I LR1 Dt LR1

ð5Þ

DVCR1 DVCR2 rLR ¼ I Dt rDCR LR1

ð6Þ

In the similar manner, the converter for Y-phase and B-phase can be evaluated with 120° phase shift each.

3 Incremental Conductance Method Incremental conductance method of tracking of maximum power point from solar panel will reduce the oscillations produced in P and O method. IC method uses the power expression to track MPP. The performance of the IC method can be considered to be better under rapidly varying atmospheric conditions. We have

A Hybrid Power Conversion System Using Three Phase Single …

247

Po = V0 * I0 where V0 is the solar panel unclosed circuit voltage and I0 is the PV panel current. Differentiating the power expression dP0 dðV0  I0 Þ dI0 ¼ ¼ I0 þ V0  dV0 dV0 dV0

ð7Þ

At MPP, ddPo ¼0 V0 From the above I0 þ V0  ddVI0 ¼ 0 and so ddVI0 ¼  VI00 . 0 0 If ddVI0  VI00 , then the MPP is obtained by increasing the duty cycle in steps, until 0 required point is reached. If ddVI0  VI00 , then the MPP is obtained by decreasing the duty cycle in steps, until 0 required point is reached. Once MPP is reached, it regulates the PWM control signals given to the converter. The fastness of MPP tracking is determined by the increment size [7]. In conventional IC algorithm, the PV array voltage Vo and current Io are to be measured to determine the optimal direction of perturbation. The flowchart of IC method is considered in Fig. 5, and the simulation diagram is considered in Fig. 6. Measurement of VPV(n),IPV(n)

( )=

( )−

( )=

( )−

( − ) ( − )

( )= 0

( + 1)

=

( ) + ( )

( )

( ) + ( )

( )

( )−

( + 1) = ( ) −

Fig. 5 Flowchart of IC method

( )

( )




( ) = 0

( + 1)

=

( )+

( + 1) =

( )

( + 1)

( )

= −

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Fig. 6 Simulation diagram of IC technique

4 Proposed Simulation Model The simulation diagram of single-stage 3-U DC–AC modifier is shown in Fig. 7. The design and modeling of three-phase single-stage DC–AC converter for hybrid energy system developed in MATLAB–Simulink environment are shown in Fig. 8 .

Fig. 7 Simulation diagram of 3-U DC–AC Modifier

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249

Fig. 8 Simulation diagram of three-phase single-stage DC–AC modifier for hybrid energy system

5 Simulation Results Solar power and wind energy sources are designed for charging the 100 V rated battery. Both energy sources are input of the battery. Figure 9 shows the solar panel output, and it is maintained at 100 V; Fig. 10 shows the wind energy output, and it delivers 104 V. The inverter input voltage of 104 V has been shown in Figs. 11, 12, 13, 14. 120

100

Voltage (V)

80

60

40

20

0

0

0.02

0.04

0.06

0.08

0.1

0.12

Time (sec)

Fig. 9 Solar voltage

0.14

0.16

0.18

0.2

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Voltage (V)

100

50

0 0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.14

0.16

0.18

0.2

Time (sec)

Fig. 10 Wind voltage

105

Voltage (V)

104.5

104

0

0.02

0.04

0.06

0.08

0.1

0.12

Time (sec)

Fig. 11 Inverter input voltage

The values of the parameters of the 3-U modifier are considered to be ideal as shown in Table 1. The 3-U output voltage so obtained and the corresponding current are shown in Figs. 15 and 16 which can then be connected to the grid for further utilization. The output harmonic component is found to be very less around 3.89% as shown in Fig. 17.

A Hybrid Power Conversion System Using Three Phase Single …

Fig. 12 Gate pulses for power switches (R-phase) of the proposed converter

Fig. 13 Gate pulses for power switches (Y-phase) of the proposed converter

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Fig. 14 Gate pulses for power switches (B-phase) of the proposed converter

Table 1 Parameter values of the 3-U modifier S. no

Parameter

Value (s)

1. 2 3 4 5.

Inductor Capacitor Switching frequency (Fsw) DC voltage Output voltage

750 µH 20 µF 400 kHz 104 V 415 V

6 Conclusion The model of a three-phase single-stage hybrid power generation using DC–AC converter has been evaluated in MATLAB–Simulink environment. Combined energy is synchronized through DC bus and connected to the battery. The turnout voltage of the solar panel is regulated by MPPT controller and maintained constant voltage for charging the battery. Present topology has replaced the two-stage conversion (low DC to high DC and then to high AC) to single-stage conversion improving the effectiveness of the system. The output signal harmonic component is also minimum around 3.89% only.

A Hybrid Power Conversion System Using Three Phase Single …

253

600

400

Voltage (V)

200

0

-200

-400

-600

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.12

0.14

0.16

0.18

0.2

Time (sec)

Fig. 15 3-U load voltage

600

400

Current (A)

200

0

-200

-400

-600 0

0.02

0.04

0.06

0.08

0.1

Time (sec)

Fig. 16 3-U load current

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Fig. 17 Total harmonic distortion

References 1. R. Lamba, S.C. Kaushik, Modeling and performance analysis of a concentrated photovoltaic thermoelectric hybrid power generation system. Energy Convers. Manag. 115, 288–298, Elsevier (2016) 2. H. Ghasemi, E. Sheu, A. Tizzanini, M. Paci, A. Mitsos, Hybrid solar–geothermal power generation: optimal retrofitting. Appl. Energy. 131, 158–170, Elsevier (2014) 3. Rong-Jong Wai, Wen-Hung Wang, Grid-connected photovoltaic generation system. IEEE Trans. Circuits Syst.-I 55(3), 953–964 (2008) 4. A. Dolara, R. Faranda, S. Leva, Energy comparison of seven MPPT techniques for PV systems. Electromagn. Anal. Appl. 3, 152–162 (2009) 5. W. Xu, Y. Cheng, Y. He, A novel scheme for sliding-mode control of DC-DC converters with a constant frequency based on the averaging model. J. Power Electron. 10(1), (2010) 6. P.M. Venkatesh, R. Velavan, A single stage hybrid electric power generation using dual leg DC/AC converter. J. Electr. Eng., 18(3), (2018) 7. P. Vital Rao, K.R. Sudha, S. Prameela Devi, Incremental conductance (IncCond) algorithm for Maximum Power Operating Point (MPOP) of Photo-Voltaic (PV) power generation system. Am. J. Eng. Res. (AJER) 02(12), 334–342. e-ISSN 2320-0847 (2013)

Enhanced Optimal Control Scheme for Attaining Improved Efficiency and Dynamic Response of WECS Using SVC C. Veeramani, A. N. Malleswa Rao, K. V. G. Aravind, M. Likhitha Reddy, M. Vipin Krishna and K. Maheswari Abstract In a WECS, it is significant to understand its characteristics and then find an optimal solution to produce electricity with greatest efficiency. The major issues in the wind energy conversion system (WECS) required to be improved with advantageous controller parameters of a DFIG to enhance efficiency factor and varying response of the system needs to be modified. A study on the FACTS device, namely SVC, is in the WECS and then to compute the output signal waves of the system with various trade-off between voltage (V), current (C), power (P), rotor speed (Rs) and pitch angle (/p). The work was experimented using MATLAB 7.5, and the results are compared conventional technique with the proposed approach for authenticating the robustness of the methods. Keywords SVC


DFIG
Compensator
CA

1 Introduction Current world is running with the service of electricity. This cannot be an amplification to ratify that provides the strength for technology development. Especially, it is the most required to uplift modern urban life. Keeping this in observation, renewable sources like wind energy is being preferred for production of electric power [1, 2]. The kinds of wind power generators are utilized in power stations which are classified in two ways: DFIG and SCIG. A doubly fed induction generator (DFIG) is preferred ahead of SCIG which has the capability to manage active and reactive power and also control voltage [3]. When a DFIG is being used sometimes, we find certain disturbances. Hence, proposing proper methods for enhancement of transient stability, DFIG scheme is more vital. The SVC balances the system voltage by consuming or feeding reactive power. Hence, the cultural C. Veeramani (&)
A. N. Malleswa Rao
K. V. G. Aravind
M. Likhitha Reddy
M. Vipin Krishna
K. Maheswari Department of EEE, Sri Indu College of Engineering and Technology, Sheriguda, Hyderabad, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_25

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algorithm (CA) is introduced for the adjustment of parameters of static VAR compensator and also functionality procedure [4, 5]. The simulation results and experimentation study authenticate the stability of DFIG with the measurable parameters potential difference, active power and rotor acceleration which is implemented in MATLAB IDE [6].

2 Perpetration of SVC The static VAR compensator is one of the classification of the FACTS to control power stream and for better transient soundness on power matrices. The inconsistency in responsive power is performing by exchanging 3ø capacitor banks and inductor banks associated with the optional side of a coupling transformer [3, 7] (Figs. 1 and 2).

3 Power System Model (PSM) Here, the PSM network is examined, and it includes windmills, matrix system and FACTS-based reactive power compensation devices [3] (Fig. 3).

4 Cultural Algorithm A CA is an accumulation of developmental populace of operators whose encounters are consolidated into a conviction space comprising of different types of representative learning. The different information sources in the conviction space can be seen as an outfit of classifiers with the acknowledgment capacity gathering test information utilizing methods, for example, sacking and boosting from the operator

Fig. 1 SVC voltage damper with variations in order to choose the peak rate of K

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257

Fig. 2 V-I characteristic of static VAR compensator

Fig. 3 Experimental setup of an integrated WECS—SVC DFIG scheme

population [7–9]. Selected people from the populace space add to social learning by methods for the acknowledgment work. The social learning dwells in the conviction space where it is put away and refreshed dependent on individual encounters and their triumphs or disappointments. There are five essential classifications of social learning that are significant in the conviction space of any social development model: situational, regulating, topographic, authentic or worldly and area knowledge [10].

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5 Simulation Result and Discussion Two scenarios of wind dynamics are depicted here to show the benefits of the proposed CA-SVC controlled model over the coordinated CA controlled model. In first situation, the breeze speed (WS) is at first set at 12 m/s and after that at t = 1.5 s breeze speed ascends to 15 m/s at 3 s. Here on, the WS is kept steady at 15 m/s from 3 to 10 s, while in second situation, wind speed is at first set at 15 m/s and after that at t = 1.5 s breeze speed tumbles to 12 m/s at 3 s. Here on, the breeze speed is kept consistent at meter/s from 3 to 10 s. The reenactment is performed for 10 s.

5.1

DFIG System Parameters

There are six wind turbines in the ranch; however, reenactment is finished by taking single wind turbine to obstruct at once and afterward on increasing by 6 we get the required parameters, as pursues: The ostensible breeze turbine mechanical yield: 6 * 1.5 MW, The generator evaluated control: 6 * 1.5/0.9 MVA (6 * 1.5 MW at 0.9 PF), The ostensible DC transport capacitor: 6 * 10000 uF. The subtleties of SVC parameters utilized in this recreation study are recorded as pursues: the system ostensible voltage (Vrms L-L) = 25 kV, frequency (f) = 60 Hz, 3/ base power (Phase) = 100 MVA, reactive power lower limits (Qc_min) = 100 MVA, reactive power lower limits (Qc_max) = 100 MVA. Scenario 1 In this case, an initial WS of 12 m/s is set meter/s at 3 s. Now, the WS is kept constant at meter/s from 3 to 10 s. The ideal controller addition esteems acquired from the proposed cultural algorithm for situation 1 is arranged in Table 1. System with SVC FACTS device and the system with coordinated CA controller and SVC device are tabulated in Table 2. Figure 1 shows the comparison waveform of DFIG output current for proposed CA-based controller, system with SVC FACTS device and the system with coordinated CA controller and SVC device when the breeze speed varies from 12 to

Table 1 Voltage regulator control mode, reference voltage (Vref) = 1 p.u, droop (Xs) = 0.03 p.u/ Phase Kp = 3, Ki = 500 Parameter

DC bus voltage regulator gains [Kp Ki]

Proposed CA Kp 8.3 403 Ki

Grid-side converter current regulator gains [Kp Ki]

Rotor-side converter current regulator gains [Kp]

Pitch controller gain [Kp]

0.85 5.97

0.58 8.3

145 –

Enhanced Optimal Control Scheme for Attaining Improved … Table 2 Response of generator current for unexpected change in wind speed

259

Parameters

CA

SVC

SVC + CA

Overshoot (p.u) Settling time (s)

0.7 8.8

0.75 8.2

0.77 7.8

15 m/s. Time values of DFIG output current for proposed CA-based controller, system with SVC FACTS device and DFIG-based WECS using cultural algorithm tuned controller and SVC gadget when exposed to the breeze speed changes from 12 to 15 m/s. From Fig. 1 and Table 1, similarly, the DFIG output current of test system with coordinated CA controller and SVC device has minimum overshoot of 0.82 (p.u), when compared to that of conventional controller-based test system and other intelligence controller-based test systems (Fig. 4). Figure 5 demonstrates the correlation waveform of converter DC-connect capacitor voltage for proposed CA-based controller, framework with SVC FACTS gadget and the framework with facilitated CA controller and static VAR compensator device. The framework with SVC actuality gadget and the framework with composed CA controller and SVC gadget when exposed to the breeze speed changes from 12 to 15 m/s. From Fig. 5 and Table 3, it is obviously that the converter DC-connect capacitor voltage of test framework with composed CA

Fig. 4 Examination of generator current waveform for abrupt change in wind speed from 12 to 15 m/s with SVC

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Fig. 5 Correlation of direct current voltage waveform for sudden variation in breeze speed from 12 to 15 m/s with SVC

controller and SVC gadget has settled rapidly with settling time of 6.8 s when contrasted with that of framework with proposed CA-based controller and SVC FACTS gadget. Figure 6 shows the comparison waveform of turbine blade pitch angle for proposed CA-based controller, system with SVC FACTS device and the system with coordinated CA controller and SVC device at the point when exposed to the breeze speed changes from 12 to 15 m/s. Table 4 demonstrates the overshoot/ undershoot and settling time estimations of turbine blade pitch angle for proposed CA-based controller, system with SVC FACTS device and the system with coordinated CA controller and SVC device when subjected to the WS varies from 12 to 15 m/s. Figure 7 demonstrates the correlation waveform of DFIG yield control for proposed CA-based controller, framework with SVC reality gadget and the framework with facilitated CA controller and SVC gadget when exposed to the breeze speed changes from 12 to 15 m/s. Table 5 demonstrates the overshoot/ undershoot and settling time estimations of DFIG yield control for proposed CA-based controller, framework with SVC actuality gadget and the framework with composed CA controller and SVC gadget when exposed to the breeze speed changes from 12 to 15 m/s. From Fig. 7 and Table 5, it is plain that the DFIG yield

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Table 3 Reaction of direct current voltage for sudden variation in WS Parameters

CA

SVC

SVC + CA

Overshoot (V) Settling time (s)

1149.92 4.2

1149.93 3.8

1149.95 3.5

Fig. 6 Response of turbine cutting edge pitch point waveform for blast change in WS from 12 to 15 m/s with SVC

Table 4 Response of pitch angle for blast variation in breeze speed Parameters

CA

SVC

SVC + CA

Overshoot (deg) Settling time (s)

8.4 3.6

8.3 3.5

8.2 6.3

intensity of test framework with facilitated CA controller and SVC gadget has settled rapidly with settling time of 7.8 s when contrasted with that of framework with proposed CA-based controller and SVC FACTS gadget. Additionally, the DFIG yield intensity of test framework with composed CA controller and SVC gadget has least overshoot of 9.7 MW, when contrasted with that of regular controller-based test framework and other knowledge controller-based test frameworks.

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Fig. 7 Feedback of doubly fed induction generator output power waveform for a rapid variation in breeze acceleration from 12 to 15 m/s with SVC

Table 5 Feedback of doubly fed induction generator gain power waveform for surge variation in breeze acceleration Parameters

CA

SVC

SVC + CA

Overshoot (MW) Settling time (s)

7.3 10

7.6 8.8

7.8 7.9

Scenario 2 For this situation, the most extreme breeze speed is set at 15 m/s and after that at t = 1.5 s wind speed tumbles to 12 m/s at 3 s. Presently, the breeze speed stays kept steady at 12 m/s from 3 to 10 s. Here, the recreation is accomplished for 10 s. In this way, the yield waveforms of current, control converter of DC voltage, rotor speed and pitch point of the DFIG are acquired. The ideal controller addition esteems for the DFIG-based WECS utilizing social calculation for situation 2 which is organized in Table 6. Table 6 Optimal controller gain values obtain scenario 2 Parameter

DC bus voltage regulator gains [Kp Ki]

Proposed CA 7.9 Kp 390 Ki

Grid-side converter current regulator gains [Kp Ki]

Rotor-side converter current regulator gains [Kp Ki]

Pitch controller gain [Kp]

0.85 5.97

0.58 8.3

150 –

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Figure 8 shows the comparison waveform of DFIG output current for proposed CA-based controller, system with SVC FACTS device and the system with coordinated CA controller and SVC device when wind speed changes from 15 to 12 m/s. Table 7 demonstrates the overshoot/undershoot and settling time estimations of DFIG yield current for proposed CA-based controller, framework with SVC actuality gadget and the framework with composed CA controller and SVC gadget when exposed to the breeze speed changes from 15 to 12 m/s. So also, the DFIG yield current of test system with coordinated CA controller and SVC device has minimum undershoot of 0.7 (p.u), when compared to that of conventional controller-based test system and other intelligence controller-based test systems. Figure 9 demonstrates the examination waveform of converter DC-connect capacitor voltage for proposed CA-based controller, framework with SVC certainty gadget and the framework with composed CA controller and SVC gadget when exposed to the breeze speed changes from 15 to 12 m/s.

Fig. 8 Analogy of generator current feedback for surge variation in breeze acceleration from 15 to 12 m/s with SVC

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Table 7 Outcome of generator current for surge variation in WS Parameters

CA

SVC

SVC + CA

Undershoot (p.u) Settling time (s)

0.7 10

0.72 9.5

0.74 9.8

Fig. 9 Analogy of direct current voltage feedback for gust variation in breeze acceleration from 15 to 12 m/s with SVC

Table 8 Feedback of direct current voltage for surge variation in WS Parameters

CA

SVC

SVC + CA

Undershoot (V) Settling time (s)

1149.92 4.2

1149.93 3.8

1149.95 3.5

Table 8 demonstrates the overshoot/undershoot and settling time estimations of converter DC interface capacitor voltage for proposed CA-based controller. Figure 10 and Table 9 show the overdamped system and settling time estimates of turbine blade pitch angle for both the proposed and coordinated models. In Fig. 10 and Table 9, it is clear that the turbine blade pitch angle of test system with coordinated CA controller and SVC device has got settled quickly to reference pitch angle of 0° with settling time of 3.6 s which is quick enough than proposed CA-based controller and SVC FACTS device.

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Fig. 10 Output waveform of pitch angle of turbine blade for sudden variation in airstream flow between 15 and 12 m/s with SVC

Table 9 Feedback of pitch angle for sudden variation in breeze acceleration Parameters

CA

SVC

SVC + CA

Undershoot (deg) Settling time (s)

8.4 3.6

8.3 3.5

8.2 3.3

Fig. 11 Output power waveform of a DFIG for sudden variation in WS from 15 to 12 m/s with SVC

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Table 10 Feedback of doubly fed induction generator output power for surge variation in breeze acceleration Parameters

CA

SVC

SVC + CA

Undershoot (MW) Settling time (s)

7.3 10

7.6 8.8

7.8 7.9

Figure 11 shows comparison waveform of DFIG output power for proposed CA controller, and from Fig. 11 and Table 10, we find the DFIG output power of the coordinated CA controller and SVC device has got settled quickly with settling time of 8.8 s which is quiet faster than that of system with proposed CA controller and SVC FACTS device. Similarly, the DFIG output power of test system with coordinated CA controller and SVC device has minimum undershoot of 7.5 MW, when compared to that of conventional controller-based test system and other intelligence controller-based test systems.

6 Conclusion The cultural algorithm controller-SVC FACTS-based model and control plan of DFIG joined WECS are displayed. The total reenactment procedure was performed in MATLAB programming. The outcomes in this manner profited from the proposed model gave improved framework proficiency and better unique soundness. It is likewise evident that the proposed CA controlled-SVC FACTS-based framework holds predominance and has numerous favorable circumstances over the organized CA controller-based test model, test model with GA-tuned controller, as far as framework effectiveness, soundness and dynamic reaction for the doubly encouraged enlistment generator utilized. In this manner, the proposed procedure can likewise be utilized for illuminating even the most confounded power framework advancement.

References 1. R.C. Pena, G.M. Asher, Doubly fed induction generator using back-to-back PWM converters and its application to variable speed wind-energy generation. IEE Proc. Electr. Power Appl. 143(3), 231–241 (1996) 2. D.E. Goldberg, Genetic Algorithms in Search (Optimization and Machine Learning. Addison-Wesley, Reading, Massachusetts, 1989) 3. C. Veeramani, J.P. Williams, P. Ramadevi, Evaluation of wind energy parameter pptimizationof A DFIG controllerBased on cultural algorithms. 2018 International Conference on Communication and Signal Processing (ICCSP) (2018) 4. Z. Jian, X. Ancheng, A new method to coordinate the PI controllers’ parameters of doubly-fed induction generator. 31st Chinese Control Conference (CCC), (25–27 July 2012), pp. 6747– 6751. ISSN: 1934–1768

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5. O. Anaya-Lara, N. Jenkins, J. Eknayake, P. Cartwright, M. Hughes, Wind Energy Generation, Modeling and Control (Wiley, 2009). ISBN: 978-0-470-71433-1 6. S. Abulanwar, Zhe Chen, Iov F., Enhanced LVRT control strategy for DFIG-based WECS in weak grid. International Conference on Renewable Energy Research and Applications (ICRERA) (2013), pp. 476–481 7. T. Ackermann, Wind Power in Power Systems (Wiley, UK, 2005) 8. P.M. Anderson, A. Bose, Stability simulation of wind turbine systems. IEEE Trans. Power Apparatus Syst. PAS-102(12), 3791–3795 (1983) 9. A. Tapia, G. Tapia, X. Ostolaza, J. Ramon, Modelling and control of a wind turbine driven doubly fed induction generator. IEEE Trans. Energy Convers. 18(2), 194–204 (2003) 10. A. Petersson, T. Thiringer, L. Harnefors, T. Petru, Modeling and experimental verification of grid interaction of a DFIG wind turbine. IEEE Trans. Energy Convers. 20(4), 878–886 (2005)

Design and Modelling of L-type Bi-directional Roller Conveyers for Glass Hauling S. Madhankumar, T. Vignesh, P. Anand Raj, Anirudh Varadarajan, T. Arul Praveen and S. Rajesh

Abstract In production industries and assembly sectors, the transportation of various parts within the plant and even outside the plant is crucial. For this, several types of shipment methods exist. These conveying methods are generally based on different materials that are transferred, their chemical and physical properties and so on. Glass is the best delicate substance that needs to be handled so cautiously in the manufacturing sectors. This paper provides the considerations and design calculations of the roller conveyer that is to be used for transporting the glass in the perpendicular direction. The roller convey or designed is powered, and different types of rollers are used rather than using conventional cylindrical rollers. The most important aspect of the design is to move the glass in the L-type conveyer without rotating the glass. Upon the successful completion of this project, a conveyer set-up is able to haul the glass in ninety degrees without changing the orientation and direction of the glass. Keywords Transport


Roller conveyer
Glass Haul
Rotation

1 Introduction The material handling system is a combination of mechanical and electrical systems that is used to transfer the components or substances in the production, assembly, packaging and shipment areas [1]. The handling of materials in the factory can be done using many ways. Some of the ways are by using forklifts [2], AGVs, trolleys, belt conveyers [3], gantry cranes, etc. These conveying methods are generally based on different materials that are transferred, their properties and so on. Glass is the S. Madhankumar (&)
T. Vignesh
P. Anand Raj
A. Varadarajan
T. A. Praveen Department of Mechatronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore 641008, India e-mail: [email protected] S. Rajesh Department of Mechanical Engineering, R.M.K. Engineering College, Tiruvallur 601206, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_26

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best substance that needs to be handled so cautiously in the manufacturing sectors. This paper provides the design and fabrication of the roller conveyer that is to be used for transporting the glass in the perpendicular direction. Each method has own advantage and drawbacks. Roller conveyers are generally used for the linear movement of materials within the plant. Similarly, glass is also transported using roller conveyers in industries. This glass must be carried out manually from one place to another in order to do the grinding process in all the four dimensions of the glass. This process involves more time consumption and hence reduces efficiency for the whole system. In order to avoid this, a new system based on various different features is proposed. In this system, a new conveyer bed is proposed which is used effectively to transport the glass in a perpendicular direction without changing the orientation of the glass. For this, special types of Omni wheels are used. The Omni wheels can be set up in such a way that the load can be moved in all possible directions. These wheels contain a sub-roller set-up in them which rotates freely in perpendicular direction to that of the original wheel rotation [4]. In this paper, a multi-directional driven installation based on interlacing two different sizes of Omni wheels, ninety degrees offset to one another, is proposed. This allows the shaft of both wheels to cross one another without interfacing while creating a common surface plane. Using such a system, the glass can be easily transferred in L-shape conveyers without changing its orientation.

2 Components and Their Properties 2.1

Bi-directional Omni Wheel Rollers

Omni wheels are generally made up of hard plastic or aluminium based upon different requirements. Their assembly is so complex from conventional wheels. These Omni wheels can be mated with another Omni wheel in order to provide double Omni wheel rollers. Similarly, there are also triple and quadric Omni wheel rollers [5]. A typical picture of triple Omni wheel is shown in Fig. 1. These wheels consist of discs mounted around the circumference which ensures the rotation of those discs in tangent to that of the normal direction of the main wheels. They are commercially available in markets up to a standard diameter of 200 millimetres [7]. The average load capacity of an Omni wheel roller depends on its diameter. For example, a 200-mm double Omni wheel can hold 25–30 kg of load.

2.2

Mild Steel Cylindrical Rods

Cylindrical rods are normally used in the conveyer systems as rollers [8]. The materials of the roller may be of stainless steel, mild steel or cast iron. Since the cast

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Fig. 1 Triple omni wheel [6]

iron is so heavy and stainless steel is so costly, mild steel material is preferred. These rollers can carry large-sized materials smoothly without any damage to the materials because of the absence of sliding friction. Figure 2 shows the view of a mild steel roller pipe. There are many rollers combined together to rotate and bring forth the linear movement of materials. The rollers can be a solid or hollow. But the hollow roller proves to be strong, and it does not bend or break when a huge load is applied.

2.3

Double-Reduction Helical Gear Box Motor

The motor is a device which is used to actuate the rollers [9]. Thus, the rollers rotate with a speed similar to that of the shaft of the motor. The motor as depicted in Fig. 3 is a double-reduction induction motor which contains a helical gear box.

Fig. 2 Mild steel roller

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Fig. 3 Double-reduction helical gearbox motor. Image courtesy Transtecno Group

This motor is chosen since the motor can be coupled directly to the shaft of the roller. When other types of motors are used, a chain drive must be separately provided to the shaft which in turn reduces efficiencies for the whole system.

2.4

Proximity Sensor

The sensor is used to sense the values and provide it to controller for further processing [10]. The proximity sensor as shown in Fig. 4 is used to detect objects without any physical contact. This sensor is classified into inductive and capacitive types based upon the detection principle. The inductive-type sensor can only detect ferrous material, so capacitive-type proximity sensor is used for the detection of glass.

Fig. 4 Proximity sensor. Image courtesy Baumer Industries

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3 Design Methodology The conveyer system is initially designed in one of the CAD software like SolidWorks or AutoCAD. In the design, various constraints are taken [11], which effectively increase the precision of the final outcome.

3.1

CAD Model

The system designed in CAD software is as shown in Fig. 5. In this design, an Lshape conveyer bed is made with the combination of both Omni wheel rollers and mild steel cylindrical rods. Both these rollers are powered, and they are actuated by the double-reduction helical gearbox motor. A chain drive is used in this system since belt drives will be not efficient and they need to be maintained periodically. Each rod attached with the frame by following means: • First, the rod is to be extended to about 10 cm outside the main frame. • Second, in order to fix the rod in the main frame, they must be fitted with plummer block bearing. To do so, the rod diameter needs to be step down and inserted into the bearing. • In order to provide transmission to all the rods, the rods are fitted with two sprockets in them so that the chain drive passes through each of the rod. A similar set-up is provided in the other side of the L-shape system. The junction part of the L-shape system contains the Omni wheel rollers which need to be

Fig. 5 CAD design

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Fig. 6 Sprocket set-up

powered with the help of two motors. In this case, the shafts which are interlaced must be combined with the help of chain drive, and these shafts are also provided with the sprocket set-up [12] as shown in Fig. 6.

3.2

Process Flow

The hauling process functions as a series of steps which occurs sequentially one after another. It starts with the glass being placed on the conveyer and ends at the place where sides of the glass are grinded. The process flow diagram is shown in Fig. 7. • Firstly, the total system is manually switched ON. This is the only manual action taking place in the overall system. • Secondly, the glass is lifted and kept on the conveyer bed by the bridge crane system. • Thirdly, the proximity sensor 1 senses the presence of glass in the system, and it transmits to the controller. The PLC [13] on other hand processes these signals, and outputs are produced which activate motors 1 and 2. Motor 1 is fitted with the initial cylindrical rods, and motor 2 is attached with the shafts of one of the Omni wheel rollers. As a result, the glass moves on the conveyer in the correct direction in a straight line. • Fourthly, a proximity sensor 2 is placed on the top-left corner of the conveyer system which detects the glass coming in the straight line. Now the glass needs to move in the L-direction without rotation. So, the sensor 2 detects the glass and transmit to the controller again. Now the controller processes this request, and turn ON the motors 3 and 4, thus switching OFF the motors 1 and 2. This implies that the glass will now be moved in

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Fig. 7 Process flow diagram

the horizontal direction. Motor 3 is attached with other set of Omni wheel rollers and motor 4 is attached with the horizontal cylindrical rods via chain drives.

3.3

Spacing Between Omni Wheels

The number of Omni wheels is decided based on the spacing rule. In case of our system, there are around 180 Omni wheels used. The rule depends on sense that at least three wheels must always be present below the glass. If the number of wheels

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Fig. 8 Spacing

below the glass becomes less than three in any instance, then the glass may fall in between the wheels because of lack of support. The rule is shown in Fig. 8. According to the rule, for any dimension of glass, the rule can be extrapolated, and the number of rollers or wheels can be found. Thus, the glass can remain stable on the conveyer bed, and the system ensures the safe haul of glass from one place to another.

4 Calculations Equations play an essential part in developing a successful working system. Since it is an automation unit, a number of factors and equations are to be made. An example system is considered, and the calculations are made below.

4.1

Bearing Calculations

Bearings withstand the total load exerted by the object placed on the conveyer bed [14]. So, this is best crucial calculation.
C¼

C L K P

is is is is

the the the the

l 106

1=k P

ð1Þ

capacity of dynamic load required life of bearing in million revolutions constant for ball bearings. Here k = 3 equivalent load in kilogram-force (kgf).

Using this formula, C is found. This C can now be checked with the data book, and the suitable value of inner and outer diameters of bearings can be calculated.

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According to this example, C value is found to be 880 kgf. Based on kgf rating, the diameter of bearing is calculated from data book as d = 35 mm D = 62 mm. Here d is the inner diameter of bearing, and D is the outer diameter of bearing. Thus, with the help of these diameters, suitable series of bearing are chosen. The chosen bearings are SKF 6007 and Series 60 bearing. Bearing calculation was made considering the load as a UDL and the beam as a simply supported beam.

4.2

Shaft Diameter Calculation

The following calculation for finding the suitable diameter of the shaft for the rollers is an example. The diameter can be found as a result of involving various formulas which is a hectic process [15]. This calculation involves momentum, torque, torsion and many more notations. Te ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi m2 þ T 2

ð2Þ

Here Te is the twisting moment, m is the bending moment, and T is the torque produced on the shaft. The bending moment of the shaft ‘m’ can be found by the formula below. m¼

wL2 8

ð3Þ

Here w is the weight acting on shaft (weight of the rollers + weight of the glass), and L is the total length of the shaft. The torque ‘T’ can be found by the formula shown below: T ¼Fr

ð4Þ

The torque is the multiplication of force ‘F’ and the perpendicular distance from the centre ‘r’. Using these formulas, Te can be found and is substituted in the consequent formula to obtain the shaft diameter. Te ¼

p 3 sd 16

ð5Þ

Here, s is the constant value for the given material. For mild steel, s is given by 96  106. From this equation, the shaft diameter can be found.

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Power of Motor

The power of the motor is the most important parameter in choosing the type of motor. If the power rating of the motor is found, then other specifications of motor like torque, service factor and efficiency rating can be found. P¼

2pNT 60

ð6Þ

Here P is the power of the motor, N is the speed of the motor, and T is the torque of the motor.

5 Results and Discussion The bi-directional L-type roller conveyer system results in a more efficient way of hauling glasses in the respective industries. This greatly enhances the productivity rate of the glasses that are machined. When compared with the manual methods of hauling the glass, the newly developed mechanism greatly reduces the human errors involved in handling of the glass. Some of the errors involved during manual hauling are scratches in the glasses, misalignment of glasses and angular tilt of the glasses. All these errors in the manual hauling can be successfully overcome by the Omni wheel roller conveyers which use highly accurate sensor unit and controllers like programmable logic controller unit.

6 Conclusions The paper is concluded with a system able to transport the glass in a tangential way without taking the orientations of glasses into account. The system designed is a cost-effective, fully automated system and involves human effort in supervision only. The system developed involves many unique features like Omni wheel set-ups and spacing rules. Thus, the paper opens a new perspective of designing a conveyer system which can be used for any type of objects to be transferred in any direction required.

References 1. B.S. Manda, U.S. Palekar, Recent advances in the design and analysis of material handling systems. J. Manuf. Sci. Eng. 119(4B), 841–848 (1997) 2. S. Ugale Sachin, S. Salvi Tushar, S. Lanjekar Sachin, R. Kshirsagar Prashant, Design, development and modelling of Forklift. Int. J. Eng. Res. Technol. 3(4), 1234–1238 (2014)

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3. D. Gupta, D. Dave, Study and performance of belt conveyor system with different type parameter. Int. J. Innovative Res. Sci. Technol. 2(6), 29–31 (2015) 4. A.P. Mohanraj, A. Elango, S. Karthick Karuppiah, M. Dinesh Babu, P. Manikanda Prabhu, Design and performance analysis on movement of square, triangular and octagonal structured omnidirectional mobile robot. Int. J. Sci. Eng. Res. 5(5), 513–518 (2014) 5. J.J. Parmar, C.V. Savant, Selection of wheels in robotics. Int. J. Sci. Eng. Res. 5(10), 339–343 (2014) 6. Patented Rotacaster omni directional floor wheels combine 360° movement with fixed orientation. http://www.rotacaster.com.au/inactive–multi-directional-wheels—mounts 7. Omni-Directional wheels. http://www.vexrobotics.com/228-2536 8. R.N. Yadav, N.K. Varshney, M. Mavi, Design and analysis of shaft and sprocket for power transmission assembly. Int. J. Innovative Sci. Eng. Technol. 3(3), 389–393 (2016) 9. Double Worm Gear Reducer. http://www.indiamart.com/hitech-transmission-solutions/ double-reduction-gearbox 10. I.K. Ahmad, M. Mukhlisin, H. Basri, application of capacitance proximity sensor for the identification of paper and plastic from recycling materials. Res. J. Appl. Sci. Eng. Technol. 12(12), 1128–1221 (2016) 11. T. Peng, J. Qian, B. Zi, J. Liu, X. Wang, Mechanical Design and Control System of an Omni-directional Mobile Robot for Material Conveying. Procedia CIRP 56, 412–415 (2016) 12. W. Suwannahong, C. Suvanjumrat, Analysis of roller chain drive system with multi-flexible body dynamics methodology. MATEC Web of Conferences, vol. 95(06007) (2017) 13. N. Thakur, M. Hooda, A review paper on PLC & its applications in robotics and automation. Int. J. Innovative Res. Comput. Commun. Eng. 4(4S), 209–214 (2016) 14. B. Ghalamchi, J. Sopanen, A. Mikkola, Simple and versatile dynamic model of spherical roller bearing. Int. J. Rotating Mach. Article ID 567542, pp. 1–13 (2013) 15. S. Pandit, A.G. Thakur, A review paper on redesign of gravity roller conveyor system for weight reduction through optimization. Int. J. Sci. Eng. Res. 6(2), 499–503 (2015)

Gyro-stabilized Platform in Ambulance T. Vignesh, S. Madhankumar, P. Anand Raj, Anirudh Varadarajan and T. Arul Praveen

Abstract The objective of this process is to develop a platform which is installed in an ambulance that can maintain its position perpendicular to gravitational force, irrespective of the position of the ambulance using servo motors which are placed perpendicular to each other. In this prototype, Arduino Mega 2560 Rev3 microcontroller is used to process the input from MPU-6050 inertial measurement unit. The code in Arduino 2560 Mega controller Rev3 analysis the input data, and when the C code receives the input from MPU and analyzes the angle and gives an output to the MG995 servo motor, it receives the signals and moves to the corresponding coordinates, so the movement of the vehicle will be nullified, for example, if the vehicle tilts 20 degrees with respect to x-axis, the servo motor moves −20 degrees with respect to x-axis. Keywords Gyro


MPU
Stabilized platform

T. Vignesh (&)
S. Madhankumar
P. Anand Raj
A. Varadarajan
T. Arul Praveen Department of Mechatronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore 641008, India e-mail: [email protected] S. Madhankumar e-mail: [email protected] P. AnandRaj e-mail: [email protected] A. Varadarajan e-mail: [email protected] T. Arul Praveen e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_27

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1 Introduction The self-enhanced gyro-stabilized platform atomically based on the sensor feedback is discussed [1]. Gyro-stabilized platform is a platform which maintains its position perpendicular to gravitational force which is acting on the body, to know the ordinates to get the coordinate value accelerometer sensor used. Based on this sensor feedback, dual-axis-stabilized platform is designed [2]. The stability armored flexible firing device system is developed [3]. The gyro-stabilized platform has the parallelogram and cardan frame. It has two rotating gyroscopes—the system having five generalized coordinates with two cyclic coordinates [4]. To find the x, y, and z axes displacements, the MPU-6050 sensor is used [5]. To process the given data, Arduino Mega 2560 is used which has 54 digital I/O pins in which 15 PWM pins are used as pulse width modulation (PWM) output, 16 pins as analog inputs, and 4 pins as universal asynchronous receiver–transmitter (UART) and has 16 MHz crystal oscillator which runs on dc power; it also has a USB connection, an ICSP header, a reset button, and a power jack [6, 7]. This processes the input and gives output for servo motor. The servo motor used is MG995 servo motor which is 55 g, the operating voltage of this motor is 4.8 V DC, torque created by this motor is 8.5 kgf.cm at 4.8 V, dead bandwidth is 5 ls, the operating temperature of this motor is 0–55 °C, and this servo motor has three pins—orange pin for PWM, red pin for +ve, and brown for ground [8, 9]. C language is used for coding. This platform can be placed inside an ambulance. When the ambulance moves in the inclining or declining road, the platform adjusts itself perpendicular to the gravity, irrespective of the position of the ambulance. As it maintains its position, the patient gets a comfortable and safe journey even when he is critical and being in a life support with help of medical equipment like ECMO [10]. The objective is to develop a platform which will be installed in an ambulance which maintains its position perpendicular to gravitational force, irrespective of the position of the ambulance using servo motor.

2 Methodology In this design, there are two servo motor connected perpendicular to each other. So that it provides stability for two axes, at first MPU 6050 [11] send the ordinates to Arduino Mega I2C bus is used to connect the IMU with the controller gyroscope gives fast but not accurate value and accelerometer gives slow but accurate value, therefore should combine both by using the complementary filter to acquire fast and accurate value. The I2C library is used in the program. When the microcontroller receives the input data, the code is executed and the output signal is sent to the servo motor; all the connection is made using jumper wire, and servo rotates opposite direction to nullify the tilt made by the vehicle. The connection is made as shown in Fig. 5.

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At the bottom, there are four DC motors connected with wheels for driving the vehicle setup; wheels are made up of plastic, and at the end of the wheel [12], it is coated with rubber to improve friction between the vehicle and the surface. All the four motors are connected to the frame which is made up of acrylic sheet. Acrylic sheet is used for building the platform because of its lightweight. The connection is made as shown in the image, and the signal given by MPU-6050 is successfully received by Arduino Mega, and IDE program is executed; servo motor receives the signal from Raspberry Pi, and the platform is maintained its position perpendicular to the gravitational force.

3 Components Used 3.1

MPU-6050

MPU-6050 is an IMU which gives the information to the microcontroller about the angle of the body; the sensor used here is MPU-6050. It has a ground and Vcc pins for power supply and analog pins which are used to get the signals from sensor. This analog value is sent to the controller based on the algorithm used; it gives the output for axis motors shown in Fig. 1.

3.2

Arduino Mega 2560 Rev3

Arduino has 54 digital I/O pins in which 15 will be used as PWM output, 16 analog inputs, and 4 universal asynchronous receiver–transmitter and has 16 MHz crystal Fig. 1 MPU-6050

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Fig. 2 Arduino Mega 2560 Rev3

oscillator which runs on DC power shown in Fig. 2; it also has a USB connection, a reset button, and a power jack [13]. Arduino Mega processes the given data and gives the output to servo motor.

3.3

Servo Motor

The servo motor used is MG995 servo motor shown in Fig. 3 which is 55 g, and operating voltage of this motor is 4.8 V DC, torque created by this motor is 8.5 kgf. cm at 4.8 V, dead bandwidth is 5 ls, the operating temperature of this motor is 0– 55 °C, and this servo motor has three pins—orange pin for PWM, red pin for +ve, and brown for ground [11]. Servo motor was chosen because high-speed operation is possible [14].

3.4

DC Motor

Dc motor was used for running the four wheels in the chassis; a DC motor was selected because of its reliability shown in Fig. 4 [15]. Fig. 3 Servo motor

Gyro-stabilized Platform in Ambulance

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Fig. 4 DC motor

Fig. 5 Circuit diagram

4 Calculation Complementary filter: h ¼ ð1  aÞ  ðh þ b  dÞ þ ða  cÞ For more accurate results, Kalman filter can be used.

ð9Þ

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Fig. 6 3D model

5 3D Model The model is created using modeling software. Figure 6 shows that the CAD model of gyro-stabilized platform in ambulance. For stabilization of platform, the proposed design has two motors which are attached for controlling three axes. It has four wheels controlled by the Arduino controller. It has a gyroscope for getting the feedback from the uneven surface. The feedback sent to the controller is based on the response from the axis motor controller output.

6 Result The body and the platform of the prototype are made using the acrylic sheet, and the servo motor is placed as per plan; this setup is placed on a chassis and the platform successfully. Figure 7 shows the prototype of the proposed design.

Gyro-stabilized Platform in Ambulance

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Fig. 7 Prototype

7 Conclusion The signal given by MPU-6050 is successfully received by Arduino Mega, IDE program is executed, servo motor receives the signal from the controller, and the platform maintains its position perpendicular to the gravitational force.

References 1. S. Turalkar, O. Padvekar, N. Chavan, P. Sawant, Self stabilizing platform. Int. J. Innov. Res. Technol. 3(11), 220–224 (2017) 2. S. Li, Y. Gao, G. Meng, G. Wang, L. Guan, Accelerometer-based gyroscope drift compensation approach in a dual-axial stabilization platform. Electronics 8(5), 1–12 (2019) 3. Z. Yao, J. Liu, Y. LI, X. LI, Z.-Y. Zhang, Design and implementation of a flexible GYRO stabilized platform. Adv. Comput. Sci. Res. 75, 810–816 (2018) 4. R. Votrubec, Stabilization of platform using gyroscope. Procedia Eng. 69, 410–414 (2014) 5. B. Ave, MPU-6000/MPU-6050 product specification. InvenSense 1(408), 1–54 (2012) 6. Bizdev, Arduino mega 2560. ardunio.cc (2019) 7. T. Vignesh, P. Karthikeyan, and S. Sridevi, Modeling and trajectory generation of bionic hand for dexterous task, in IEEE international conference on intelligent techniques in control, optimization and signal processing, pp. 1–6 (2017) 8. H. Speed, M. Gear, D. Ball, B. Servo, MG995 high speed metal gear dual ball bearing servo, vol. 6 (Electronicos Caldas, Columbia, 2018). P. 1–8 9. T. Vignesh, N. Manikandan, S. Kannaki, C. Prasath, M. Bhuvaneswari, S. Vignesh, Development of low-cost robotic arm for welding. Int. J. Innov. Technol. Explor. Eng. 8(8), 1238–1243 (2019)

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10. R.H. Bartlett, A.B. Gazzaniga, M.R. Jefferies, R.F. Huxtable, N.J. Haiduc, S.W. Fong, Extracorporeal Membrane Oxygenation (ECMO) cardiopulmonary support in infancy. J. Extra. Corpor. Technol. 11, 26–41 (1979) 11. M. Royal, Wavelet de-noising for IMU alignment. IEEE Aerosp. Electron. Syst. 19(10), 32– 39 (2004) 12. S. Madhankumar, M. Jishnu, J.K. Prithiv, S. Gowrishankar, S. Rajesh, R. Balamurugan, Design and modelling of disaster relief vehicle using Rocker Bogie mechanism. Int. J. Innov. Technol. Explor. Eng. 8(6), 1274–1276 (2019) 13. S. Jose, S.S. Weerasooriya, L. Forest, P.E.K. Wong, Servo writing a disk drive using a secondary actuator to control skew angle (2009) 14. E. Outreach, Servo Motor – Working, Advantages & Disadvantages, elprocus. pp. 1–7 (2019) 15. Mark, Brushed vs brushless RC motors. RC Roundup. pp. 1–10 (2018)

Power Systems

Performance and Comparison of Harmonics Using Active Power Filters and DVR in Low-Voltage Distributed Networks P. V. Kishore and D. Naveen Kumar

Abstract The objective of this paper is to figure out the harmonic deviations and suppress the harmonics in a power system due to nonlinear loads coupled at common coupling point (PCC). Harmonics are the major index for low power quality of the distribution system which may also effect the life expectations of the power converters. Passive harmonic moderation approach has better solutions in power converters which gives distinct harmonic accomplishments at a system level where plenty of units are allied in parallel nearer to point of common coupling. There are various modes for harmonic moderation techniques present in a system, but based on operation and excellence usage of the system the shunt active power filter (SAPF) and dynamic voltage restorer (DVR) methods are considered. This paper provides significant approach to the performance of DVR and SAPF and various compensation accomplishments which are used to evaluate the harmonics and its effect on nonlinear loads by using MATLAB/Simulink.




Keywords Dynamic voltage regulator (DVR) Harmonic mitigation Power quality Shunt active power filter (SAPF)





Inductor


1 Introduction As the technology is growing in a deadly manner, several inventions have come into presence. In order to make the life style simpler, several nonlinear loads have come into picture which introduces harmonics into the system, and also another severe reason for the harmonics is the use of power electronic devices for commercial as well as domestic loads which vastly affects the nature of devices in

P. V. Kishore (&)
D. Naveen Kumar Professor, Department of Electrical and Electronics Engineering, Guru Nanak Institutions Technical Campus, Hyderabad, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_28

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power system. Enhancing the power quality as dominant issue to alleviate the harmonics, several approaches are developed but in that passive harmonic moderation approach has better solution in power converters than earlier approach which gives distinct harmonic accomplishments at a system level where plenty of units are aligned in parallel to PCC. To alleviate the harmonic distortions, several approaches have been available, but the mostly used techniques are • Passive techniques • Multi-pulse rectifier techniques • Active harmonic cancellation techniques. The passive filters such as inductance, capacitance, and resistance elements are coordinated to control harmonics [1]. They are frequently used and inexpensive to alleviate the harmonics. But these are helpful to alleviate only single harmonic at a time so as to compensate more number of harmonics more number of devices have to be installed which makes the system so bulky and expensive. Some disadvantages are: • • • • •

Many components are required Bulky and depends on system impedance Tuned for certain loading conditions (i.e., cancels a single harmonic) Parallel and series resonance may occur for certain harmonics Affected by capacitor aging.

For that reason, multi-pulse filters are used to alleviate one or more harmonics using phase-shifting transformers along with passive filters which make the system bulky [2] and expensive which is not reliable for high loads. Based on the field of power electronics, another approach, active harmonic cancellation technique, is again classified into • Active front end (AFE) • Active harmonic filter (AHF). The active front-end (AFE) filter allows line current from the drives nearer to pure sine, but it has more losses and overpriced resolution of low-power appliances. Active harmonic filter (AHF) uses active switching components to inject equal and opposite currents or voltages in power system to cancel harmonics produced by other loads. It requires only one filter to eliminate all unwanted harmonics so it is also defined as active power filter (APF) which is again classified as shunt APF and series APF. Recently, a slim DC-link capacitor is used in converters as harmonic mitigation technique [3] which gave an unfavorable harmonics behavior to other conventional devices. However, the phase-angle deviations of the current harmonics of a single unit to a multi-unit system with various grid conditions have been evaluated and compared for each individual topology. Therefore, in this article, the harmonic analysis is studied without compensation and with SAPF and DVR at a system level, the THD analysis has been done at point of coupling, and the current harmonic cancellation of a combined single-phase and different three-phase systems’ topologies are shown by comparing the 3rd, 5th, and 7th harmonics at system level.

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2 System Description In general, plenty of nonlinear loads are aligned at translocations via diversified step-down transformers labeled in Fig. 1a having low- and medium-voltage networks. Microgrids having enormous amount of power electronic devices (three phases or single phase) are linked in parallel to PCC is shown in Fig. 1b. Let us

Fig. 1 a Layout of distribution network. b Detailed outline of low-voltage distribution network

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Fig. 2 Power converter topologies, three-phase diode rectifier a DC choke, b AC choke, c slim DC-link capacitor, and d single-phase diode rectifier with DC choke

consider a system inductance (Ls), which is a combo of grid inductance (Lg) and transformer inductance (Lt); Ls = Lg + Lt. To alleviate the line-current harmonics in three-phase diode rectifier, AC and DC chokes’ [4] configurations are used as shown in Fig. 2 which helps to analyze the low-order harmonic effects in system level which are helpful in addressing the configurations of a two kinds of generators and transformers with Ls = 2 µH (Lg = 1 µH and Lt = 1 µH) and Ls = 130 µH (Lg = 100 µH and Lt = 30 µH) having base impedance of 10–15% for microgrid generator and 5–15% to the transformers with short circuit ratios in the range of 120–230.

2.1

Distribution Network Analysis

The main theme of this article is to evaluate the consequences of grid system configuration on single-phase and three-phase power converter and a comparative analysis on phase-angle deviations [5] in line-current harmonics. For that reason, here, two cases are studied with the help of a mathematical expression derived for parallel systems: (a) Performance and analysis of three-phase power converter in system level (b) In order to alleviate the harmonic cancellation procedure in industrial area where a number of three-phase and single-phase units are aligned at PCC. In this scrutiny, one three-phase power converter is merged at the PCC, from Fig. 3, with the DC-choke topology stated in Fig. 2a. For evaluating the operation of the multi-parallel power converters at system level, consider a system as shown in Fig. 3. An idea of harmonic cancellation for the above system a mathematical equation has been evolved where a number of systems are aligned in parallel with a voltage source (Vs) and system inductance (Ls) shown in Fig. 4.

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Fig. 3 Module diagram of three-phase power converter

Fig. 4 Example of n number of parallel loads as current harmonic sources

From Fig. 4, ihð1Þ ; ihð2Þ ; . . .. . .. . .ihðnÞ are the n current harmonics having notations i as converter current and h as harmonics order.
 ihð1Þ ðtÞ ¼Ihð1Þ sin xh t þ uhð1Þ
 ihð2Þ ðtÞ ¼Ihð2Þ sin xh t þ uhð2Þ
 ihðnÞ ðtÞ ¼IhðnÞ sin xh t þ uhðnÞ

ð1Þ

where xh ¼ 2phf and ;h = harmonics phase angle i h ðt Þ ¼

n X h¼1

ihðnÞ ðtÞ ¼ ihð1Þ ðtÞ þ ihð2Þ ðtÞ þ


þ ihðnÞ ðtÞ

ð2Þ

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Now, let us consider only two converters of the system and evaluate the total harmonic current ih ðtÞ which is given as ih ðtÞ ¼ ihð1Þ ðtÞ þ ihð2Þ ðtÞ

ð3Þ

Hence, the total current harmonics for the two power converters is the vector sum of the above two harmonic currents which are expressed in Eq. (4). Therefore, the total current harmonic of a particular nth order is evaluated by the formula given below in Eq. (5). ih ðtÞ ¼ n X h¼1

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  ffi Ih2ð1Þ þ Ih2ð2Þ þ 2Ihð1Þ Ihð2Þ cos ;hð1Þ  ;hð2Þ

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! u n n uX X   t ihðnÞ ðtÞ ¼ IhðiÞ ðtÞIhð jÞ ðtÞ cos ;hðiÞ  ;hð jÞ i¼1

ð4Þ

ð5Þ

j¼1

Hence, in order to figure out the performance of current harmonics, the phase-angle value plays a major role in a multi-level system converter. Therefore, the simulation miniature of n three-phase power converters and single-phase converters is shown in Fig. 5 which is implemented by 90 power converters in order to study and analyze [6] the effect of load profiles and system configuration and overall harmonic distortion (THDi, phase-angle values) which is captured on primary side of transformer. From Eq. (5), it is clear that harmonic alleviation of multi-converter system is done by the vector sum and phase-angle values of total current harmonics at PCC by using 90 loads for simulation as shown in simulated results. In modern scenario low voltage distribution network consists of single phase loads and other domestic devices are coupled with power network along with the three phase non linear loads are connected as shown in Fig. 5. So, for this type of network, the harmonic analysis is done by coupling single phase and three phases at a common point and the results are analyzed, whereas better performance of the system is obtained when the THD is below 5% which is obtained by using shunt and series compensating devices such as shunt active power filter (SAPF) and dynamic voltage restorer (DVR), respectively [4].

3 Active Compensation Techniques 3.1

Dynamic Voltage Restorer (DVR)

The dynamic voltage restorer (DVR) is a power electronic device which works on the voltage unbalances in a system. It is very helpful in maintaining the voltages at a predominant level. This will inject the voltages into the transformers which are in

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Fig. 5 Distribution system with several single-phase and three-phase power converters connected at the PCC

Fig. 6 Schematic diagram of DVR

series in order to compensate the unbalances in voltages, and it will be in idle position during balanced conditions [5]. It is a series compensator which protects the critical loads from all the supply side deviations rather than outages is called dynamic voltage restorer. The DVR components are VSC inverter, injection transformer, harmonic filter, and energy storage device as shown in Fig. 6.

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Fig. 7 Equivalent circuit of DVR

The injection transformer is aligned in series with the sensitive load which is safeguarded by the DVR. The basic function of this transformer is to connect the DVR to the distribution system, and the injected voltages generated by the inverter are introduced into the distribution system. The basic operational principle of DVR is to interject proper series voltage to grid in order to restore the load voltage level to its desired level. As shown in Fig. 7, the Z-impedance, ZDVR, is dependent on the fault level of the load bus. When there is dip in system voltage, Vth, then DVR injects the voltage in series with the injection transformer so that the desired voltage magnitude, VL, can be maintained. The series-injected voltage of DVR can be written as VDVR or VINJ ¼ VL þ Zth IL Vth where VL Zth = Zline + ZDVR IL Vth

Desired load voltage magnitude Desired load impedance Desired load current System voltage during fault condition.

The current harmonic compensation and the total harmonic distortion of the system using DVR are given in MATLAB/Simulink results given in the next section.

3.2

Shunt Active Power Filter (SAPF)

We know that, passive filters do not reach the expected performance of the system since most of the systems are operated in an interlinked manner so large amount [7] of harmonics are generated by the nonlinear loads in the system. So, these harmonics are compensated by the help of the SAPF which interjects the currents in the opposite direction to the generated current harmonics. The basic structure of the

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Fig. 8 Working of shunt active power filter

Fig. 9 Schematic diagram of shunt active power filter

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SAPF is shown in Fig. 9, whereas Fig. 8 gives a detail idea of working of SAPF about current harmonics’ compensation. The current harmonic compensation and the total harmonic distortion of the system using SAPF are observed in MATLAB/ Simulink results given in the next section.

4 MATLAB/Simulink Results Because of enormous usage of power and electrical loads like single-phase and three-phase nonlinear loads, it has became a priority to alleviate the harmonics when large number of three-phase loads and single-phase loads are connected together at PCC. In this three phase loads like VFD for heating, ventilation and few single phase loads like computers florescent lamps are coupled at PCC which are selected for analysis 90 units are considered, out of them 45 units are three phase power converter having DC choke configuration each operates at 6KW. Each and every 15 single-phase loads is combined with the three phases in order to prevent any unbalance condition at PCC. To better figure out the harmonics cancellation process, three simulations have been performed in MATLAB, and the current wave

(a)

(b)

Fig. 10 a Line current of combination of three-phase and single-phase (90 units) loads of uncompensated system 10 and b %THD of uncompensated system of 90 loads

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(a)

(b)

Fig. 11 a Combination of three-phase and single-phase (90 units) loads of compensated system with DVR.and b %THD of compensated system with DVR of 90 loads

(a)

(b)

Fig. 12 a Line current of combination of three-phase and single-phase (90 units) loads of compensated system with DVR and b %THD of compensated system with SAPF of 90 loads

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Table 1 Comparative analysis of fundamental, SAPF, and DVR System configuration with 90 loads

%THD

3rd

5th

7th

Uncompensated system Compensated system with DVR Compensated system with SAPF

19.44 4.19

0.19 \ 72.1º 1.41 \ 152.3º

86.49 \ 68.8º 17.96 \ 257.2º

41.63 \ 104.2º 7.84 \ 180.5º

1.34

0.16 \ 173.7º

6.14 \ 35.3º

1.57 \ 203.3º

forms are provided in Fig. 10a. Line current of combination of three phase and single phase (90 units) loads of without compensated system. The numerical procedure of harmonic cancellation is expression (5). Therefore, magnitude of the fifth harmonic component is obtained from expression (5) which makes an error of *3% with the simulated value. For better and easy understanding, only fifth harmonic component is considered and expecting the same for other order. The performance of the system, THD analysis and line-current behavior of system without compensation and with DVR and with SAPF, is shown from Figs. 10a, b, 11a, b, 12a, b. Table 1 represents the 3rd, 5th, and 7th harmonic currents and its phase-angle behavior for uncompensated system and compensated system with DVR and with SAPF. The %THD is also calculated for system without compensation and with compensation (Fig. 11).

5 Conclusion In this paper the Dynamic voltage restorer and shunt active power filter are used either in loaded or unloading conditions are specified. The process of controlling these elements is easy while compared to others, and the harmonic cancellation in the system is nearly up to limits at the PCC. The most peculiar thing is that the harmonic performance of a system is generally based on some factors like grid inductor, transformer parameters, load profiles’ power converter topology, and the number of converters merged at the PCC. The significance of phase angle values of current harmonics has been discussed and the harmonic cancellation mechanism in large system including several different nonlinear power electronics based loads and subsequently from the case study a theoretical and practical analysis the control harmonic emission and control of THD is below 5% is obtained. However, from the analysis of combined three-phase and single-phase loads at PCC helps us for better understanding of harmonic cancellation and shows a vast difference in phase angle of current harmonics in multi-parallel power converter system.

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References 1. D. Kumar and F. Zare, Harmonic analysis of grid connected power electronic systems in low voltage distribution networks. IEEE J. Emerg. Sel.Top. Power Electron. 4(1) (2016) 2. D. Kumar, F. Zare, Analysis of harmonic mitigations using hybrid passive filters. in Proceedings 16th International Power Electronics Motion Control Conference and Exposition (PEMC), pp. 945–951 (2014) 3. T. Hruby, S. Kocman, Using broadband passive harmonic filters for harmonic mitigation in AC drives, in Proceedings IEEE 16th International Conference Harmonics and Quality Power (ICHQP), pp. 172–176 (2014) 4. X. Wang, F. Blaabjerg, W. Weimin, Modeling and analysis of harmonic stability in an AC power-electronics-based power system. IEEE Trans. Power Electron. 29(12), 6421–6432 (2014) 5. K.S. Chandragupta Mauryan, J. Manikanda Prabhu, N.K. Senapathi, D. Madhumitha, Literature survey on voltage profile management methods of distributed generation. Int. J. Eng. Res. Appl. 4, 94–98 (2014) 6. F. Meng, W. Yang, and S. Yang, Effect of voltage transformation ratio on the kilovoltampere rating of delta-connected autotransformer for 12-pulse rectifier system. IEEE Trans. Ind. Electron. 60(9), 3579–3588 (2013) 7. S. Rahmani, N. Mendalek, K. Al-Haddad, Experimental design of a nonlinear control technique for three-phase shunt active power filter. IEEE Trans. Ind. Electron. 57(10), 3364– 3375 (2010) 8. T.I. El-Shennawy, A.M.E. Moussa, M.A. El-Gammal, A.Y. Abou-Ghazala, A dynamic voltage restorer for voltage sag mitigation in a refinery with induction motors loads, Science Publications (2010) 9. P.T. Nguyen, T.K. Saha, Dynamic voltage restorer against balanced and unbalanced voltage sags: modelling and simulation. IEEE Trans. Power Deliv, 1–6 (2004)

Overview of Restructured Power System Prakash Vodapalli and Ramaiah Veerlapati

Abstract Power restructuring, a systematic running of modifying the rules and instructions that control the power market to impart consumers for the option of power producing, those are may be traders and allowing rivalry within the traders. Deregulation improves the stock rate and usage. Due to gain in the electric market, the power rates are likely to come down which welfare the consumers. Keywords Deregulation


Competition
Market
Efficiency
Cost

1 Introduction It is happening throughout the world, there which is a worry concerning about re-modelling and re-regulation of the property market over the aftermost decade. The rivalry in the wholesale generation market and the retail market combined with the open entry to the delivered circuit can tie many benefits to the extreme consumers, such as lower electricity rates and better favour. However, this rivalry also escorts different productive issues and oppositions to the operation of re-modelled power circuit. The re-modelling of the electricity branches is bracing by the economic opportunities to society resulting from the re-regulation of other communities such as communication, textiles, cement and airports. Presently, electrical utilities around the world are withstanding an extensive transformation from an essentially regulated and monopolistic industry to a new model distinguished by competition in generation/distribution [1, 2] with promised access to open transmission. Rivalries among the traders increases creation, thoughts and implementation efficiency. The target of re-regulation is to enable competitiveness based upon tropical efficiencies, and to erase the monopoly handling and market [3] imperfections that lie under the vertically integrated utility circuit.

P. Vodapalli (&)
R. Veerlapati Kakatiya Institute of Technology and Science, Warangal, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_29

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The electric power market is running by as a vertically run and controlled single entity shown in Fig. 2, they retain producing, supplying and dispersal amenities.

2 Properties of Deregulated Market Monopoly permit is the only regional electric utility that can generate, purchase or sell electric power within its resource area. This utility must operate by adopting their own styles and reduces its overall probable revenue chances. The utility’s rates are adjusted in accordance with government rules and guidelines. There is an advantage that guaranteed return on its actual amount. If it confirms to be the regulatory exercises and practices. The main intent of the deregulated power market: • To provide continued promised supply at reasonable cost. • To encourage the contention in the available supply and demand. • To provide quality of service to all types of consumers.

3 Stimulus for Restructuring the Power Market Advantages of Deregulation are: • • • •

Electricity rates may reduce due to new revolution and alternatives. Expected to result in broad consumer choice and more attention to Give better service. Deregulation also provides strong buyers and new producers.

A fierce environment will provide awards and bonus to risk takers and stimulate the use of new technologies and business methods. The structural components representing various segments of the power industry as generation companies or power producers, power marketers units of power exchange, schedule coordinators, transmission owners, independent system operator, ancillary services, retail service providers and local distribution companies. Power producers and power marketers are the primary and secondary generation sectors, respectively. Power producers are accountable for operating and maintaining plant in the generation area. Distribution companies are the ones restricted in running the distribution sector and providing various options for power merchandisers that are detached from distribution companies. In the regular process, designate a person who operates the system, to run the transmission network and facilitate delivery. Maintenance of the supplying network is own headache for the owners.

Overview of Restructured Power System

• • • • •

307

Maintains the security first. To maintain fairness, it should be dependent from others. Own computing. Non-profitable. Maximize the utilization.

Benefits of power exchange: The power exchange creating liquidity, encourage competition with hourly bids, standardized contracts with uniform pricing and more over is an unbiased unit. Figure 1 shows the components in a deregulated market • • • • •

Process flow: It receives bids from power producers and various customers. Match the bids then decide the market last clearing rate and prepare plan. Provide slots to the operator. Balance the desired plan and in such cases, system is overloaded.

Schedule coordinators—Customers in the power merchandising, they can voluntarily use their own trade rate. Secondary services—Secondary (Additional) service owners supply to the network brace services those were useful to system Response time is the significant factor that will determine whether the independent actions of participants in competitive markets can perform some reliability

Fig. 1 Components in a deregulated market

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Fig. 2 Electric power market vertically run and controlled single entity

functions. Time-to-time, depends on the market trends, rates and targeted revenue from the sale of electricity. A customer is an entity, in a fully deregulated market, where retail market is also open for competition [4, 5], and the end consumer has different available options for purchasing electricity. We can also buy power from the spot market by trading or contacting the owners or regional market. Sometimes, this also happens where exactly there is a demand in the markets at the wholesale level, only the big consumers have the option to select their supplier (Fig. 2).

4 Need for Computation Tools and Software Systems in Markets Paper Preparation New computational devices, tools and software systems are needed for generators, retailers, the ISO and all other market participants to have proper intimation about how to meet the planning objectives, controlling the existing one, future expansion and financial support to achieve the scheduled plans, appearing in the competitive market environment. For example, power producers need latest bidding systems to decide their bidding strategies and to properly communicate to their bidding information with the market operator; the retailers and distribution companies may need new billing systems and new load management system to meet the time-varying spot prices.

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It all depends on how resources are available in the market, present demand, stock available [6], any shortage of supply, any congestions market, skilled workers or workers having lack of experience, clearing price, transportation availability and weather conditions.

5 Conclusions Deregulation is an important aspect in the restructured electrical power system. It is an efficient, powerful tool and system will get benefited. This existing system is still modified in all the aspects.

References 1. S. Wu, T. Mei, J. Gong, D. Gan, Voltage fluctuation and flicker caused by distributed generation. Energy Eng. 4, 54–58 (2006) 2. E.A. Androulidakis, A.T. Alexandridis, H.E. Psillakis, P. Agoris, Challenges and Trends of Restructuring Power Systems due to Deregulation 3. K. Uhlen, L. Warland, O.S. Grande, Model for area price determination and congestion management in joint power market. IEEE International Symposium CIGRE, pp. 100–109 (2005) 4. J. Vora Animesh, Congestion management in deregulated power system—a review. Int. J. Sci. Res. (IJSR) ISSN (Online): 2319–7064 Impact Factor (2012): 3.358. MS University, Faculty of technology and Engineering, Kalabhavan, Vadodara, Gujarat-390001, India 5. A.J. Wood, B.E. Wollenberg, Power Generation, Operation and Control, 2nd edn. (Wiley Interscience, New York, 1996) 6. G. Mahesh Kumar, P.V. Satyaramesh, P. Sujatha, A review on congestion management in the restructured power system. Int. J. Appl. Eng. Res. ISSN 0973-4562, 13(10), (2018) (Special Issue) © Research India Publications. http://www.ripublication.com

Single-Phase PV System with Continuous H-Bridge Inverter Vodapalli Prakash, Mucherla Narasimha Rao and Chillappagiri Pavan Kumar

Abstract Continuous piercing of photovoltaic grid systems are increasing gradually in all most all applications. It enhances the efficiency and successful application of solar system. In this paper, an inverter was drafted and run only solar PV system. The given system utilises sinusoidal pulse width modulation (SPWM) control scheme in the inverter to modify steady-state voltage from the battery, given to alternating loads and MPPT. Thus lessening the problems of the system, these MPPT methods which required to employing disturbance and observe methods have been initiated with the PV panel by indicating economic handling current to get desired power. Here presented to demonstrate the given system of competent behaviour. Keywords Photovoltaic


Inverter
MPPT
SPWM

1 Introduction The demand of effective and continuous power is required in the electricity operation. Present power systems are highly condemnatory and require proper and effective control. Everyone’s moral duty is to the environmental kind of problems relating to the fossil resources and economic magnification. Among available natural sources, sunlight and wind energy have become very famous and demanding more due to the latest technology world. PV resources are used frequently nowadays with benefits such as free from pollution. Solar-electric source requirement slowly increases day-by-day. Because they are available at lower rates [1, 2]. Solar inverter is used to transform steady-state power which is used to get from PV modules, alternating power which is injecting to the load. Power electronic change is a pointer component to improve the total efficiency and generation levels of PV grid-connected system. In the existing work, a vast diversity of PV system models which are entered depends on various types of V. Prakash (&)
M. Narasimha Rao
C. Pavan Kumar Kakatiya Institute of Technology and Science, Warangal, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_30

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grid-connected inverter configuration. In single stage standalone PV connected system, oppose-type inverters are generally employed to connect solar to the grid.

2 Modelling of a PV Cell A photovoltaic cell is the backbone of a solar panel. A photovoltaic plate is formed by connecting large no. of cells in ground and vertical. Demonstrating a uni-diode representation of PV cell is shown in Fig. 1. IL ¼ Ish  Id Id ¼ Io ½eðqvaKT Þ  1     V þ I  Rs V þ I  Rs I ¼ Ilg  Ios  exp q  1  AKT Rsh Ilg ¼ fIscr þ Ki  ðT  25Þg  Iambda where I and V Cell output current and voltage Ios Cell reverse saturation current T Cell temperature in celsius K Boltzmann’s constant q Electron charge, 1.6  10−23 °C Ki Shorted current temperature coefficient Iscr Shorted current at 25 °C Ilg Light-generated current Ego Band gap for silicon A Ideality factor Tr Actual temperature Ior Cell saturation current Rsh Parallel resistance k Solar irradiation in W/m2 Rs Series resistance

Fig. 1 Uni-diode representation

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The current power and voltage curves of a PV cell are controlled by the solar visible light values. Due to the environmental changes from morning to evening, the solar irradiation is always floating, and still, there are some techniques to trace out the variation and can change the working of the PV cell to get the suitable load requirement. As more the irradiation, greater be the initiation to the cell and hence power magnitude would slowly improve for the identical voltage.

3 Influence of Changes of Temperature On the conflicting, as the temperature changes slowly on the cell, it has a reverse effect on the power producing capability. Reverse relation works between temperature and voltage. Increase in temperature leads to an increase in the band gap of the material, and thus, more energy is required to cross this barrier. Thus, the efficiency of the solar cell is reduced (Figs. 2, 3, 4 and 5).

Fig. 2 Observation of power–voltage

Fig. 3 Current–voltage plot curves

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Fig. 4 Power–voltage plot

Fig. 5 Current–voltage plot

4 Result of Changes in Temperature ‘The voltage at which panel can give resultant power is called key point’. Controller refers the voltage the suitable value from the module and generates required power. Here, Vpv (k) is the reference voltage generated after perturbation, i.e. present voltage, Vpv (k − 1) is the voltage prior to perturbation. Ppv (k) is the present power, Ppv (k − 1) is the previous power. Here, the used techniques for greater power point tracking are 1. Perturb and Observe Method. 2. Incremental Conductance Method. MATLAB-based embedded function block generates a reference voltage which is compared with the desired PV voltage to get perturbations. The continuous perturbation affects the net voltage of resist converter resulting in a change in the effective resistance of the converter. The solar PV array is optimised, when the resistances are traced.

Single-Phase PV System with Continuous H-Bridge Inverter

Cascaded H-Bridge Multilevel Inverter: See Figs. 6 and 7.

Fig. 6 Cascaded H-Bridge inverter

Fig. 7 H-Bridge multilevel inverter

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Fig. 8 Generation of changed waveforms

Modified Sinusoidal Pulse Width Modulation When taking SPWM waveform, the pulse width does not alter with the variation of modulation. The main uses of this method are improved fundamental characteristics, less no devices and reduce losses (Fig. 8).

Single-Phase PV System with Continuous H-Bridge Inverter WAVEFORM OF SPWM

VOLTAGE OF PV ARRAY

CURRENT OF PV ARRAY

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Parameters of PV Array Temperature 25 °C Irradiance 700 W/m2 Voc 31.2 V Isc 6.85 A Battery Voltage 40 V Load parameters R = 80 Ω, L = 20 mH.

5 Conclusion The implementation of the proposed standalone PV system is the simple method and is used for medium power application. The DC–DC boost/buck converter topology with perturb and observe control technique is a simple and accurate method to get efficient output. By using conventional two level inverters which has limitations such as switching failures, lack of reliability due to increased switching devices and capacitor voltages balancing issues. To overcome these limitations making modifications such as replacing a conventional two level inverter with multi level inverter. The MPPT control is, in general, challenging, because the sunshine condition that determines the amount of sun energy into the PV array may change all the time, and the current, voltage characteristic of PV arrays is highly nonlinear [3].

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References 1. A. Shukla, Modeling and simulation of solar PV module on MATLAB/Simulink. Int. J. Innovative Res. Sci. Eng. Technol. 4(1) (2015) 2. T. Salmi, MATLAB/Simulink based modeling of solar photovoltaic cell. Int. J. Renew. Energy Res. 2(2) (2012) 3. C. Liu, B. Wu, R. Cheung, Advanced algorithm for MPPT control of photovoltaic systems. Canadian Solar Buildings Conference Montreal (2014)

Comparison of Renewable Energy Generation in an Electrical Network with Energy Storage System N. Loganathan, A. Arvin Tony, T. Malini and S. Gobhinath

Abstract The optimum operation and amount of energy storage are operated by a buyer who faces unstable electricity costs and seeks to decrease its energy prices. The worth of storage is demarcated the consumer’s Internet profit obtained by optimally operative the storage. Model projecting management based mostly coordinated planning framework for different renewable energy generation then battery energy storing arrangements is accessible. On the idea of the short forecast of accessible renewable energy generation and cost info, a joint look-ahead optimization is performed by completely the various power plants and storage system to work out their internet energy booster towards the electrical network. In concurrence with moderate battery capability, the surplus unpredictable renewable power generation may be charging the battery storage and contrariwise. This paper presents an outline; in addition, overall educations of analysis and development within the field of various resolution strategies for energy storage systems and dynamic programming strategies are found within the literature. This paper has reviewed a number of the foremost common strategies together with various algorithms and computational simulation strategies. This paper provides help for the upcoming studies for those interested in the problem or proposing to do additional research in this area. Keywords Renewable energy systems


Storage system

N. Loganathan (&)
T. Malini
S. Gobhinath Department of Electrical and Electronic Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India e-mail: [email protected] S. Gobhinath e-mail: [email protected] A. Arvin Tony Department of Electrical and Electronic Engineering, KCG College of Technology, Chennai, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_31

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1 Introduction Electricity generation from predictable thermal generators is one of the greatest significant suppliers of worldwide greenhouse gas emissions. Renewable energy (e.g. solar, wind, etc.) offers a cleaner substitute for power production. However, it is acknowledged that renewable energy sources display vital unpredictability and uncertainty, which make it difficult to integrate renewable generation into power systems. The procedure of multiple storage strategies in an electrical network with vital generation from alternating renewable sources (like wind and solar). In each period slot, the scheme operative dispatches the output of conservative generators and energy storage devices to fulfil the inelastic load at different buses, subject to power network limitations. The system operator expresses difficult consecutive decision-making issues under uncertainty with an objective to minimize the expected total cost of conservative generation.

2 Review of Literature This paper gives overall circumstances of investigation than expansion in the arena of renewable energy system in power system network based on over 25 published articles. The following open literature presents the summary and application of each storage operation in a power network. The related assumptions made, métiers and faintness of each solution methods are highlighted. García-González et al. [1] gives a study the joint optimization of a wind power plant then a pumped-storage facility after the fact of opinion of a production corporation in a market atmosphere. The optimization model is expressed by way of a two-different stage stochastic programming problematic through two casual limits: market amounts and wind power generation. In this proposed system demonstrated that a joint short-range operation of a wind power plant then of an isolated pumped-storage plant can be found by resolving the accessible optimization model. The two-stage stochastic programming method has demonstrated to be an operative technique to classical the material supervisory procedure that wind park worker’s face in a spot-market framework below indecision. The wind farm proprietor has been demonstrated as a risk-neutral manager. Bitar et al. [2] in this proposed framework the primary issues of streamlining contract contributions for a self-regulating wind control maker taking an interest in ordinary day-ahead forward power markets for power. Utilizing a basic stochastic model for wind control generation and a model for the power industry, the issue of deciding ideal contract contributions for a WPP with co-found vitality stockpiling can be comprehended utilizing raised programming. Defining and resolving the issue of ideal contract estimating for a breeze control maker with assembled vitality stockpiling are taking part in traditional power markets. In this framework, strategy

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to distinguish effective computational methods is aimed at attractive attention of the convex contract estimating issue illustrated in this proposed framework. Xie et al. [3] the transient conjecture of accessible wind production and cost data, a joint look-ahead improvement is performed by the wind plant and storage system to decide their net power infusion to the electric power utility. Related to direct battery limit, the overabundance erratic wind production can be operated to control the battery stockpiling and the other way around. Figuring the model prescient control (MPC)-based composed planning as a quadratic programming issue, a few numerically proficient calculations to process the ideal control technique for wind production and BESS are proposed. The proposed method can build the joint benefit of wind production and BESS though levelling the available remaining energy infusion to the power utility. Su and El Gamal [4] energy storing can help decrease the power inequality because of the divergence between accessible renewable energy and demand. Considering the multi-time-scale grid activity, detail the power inequality issue for each timescale as an immeasurable horizon stochastic control issue and demonstrate that an eager arrangement limits the normal size of the remaining force irregularity. The decrease in power inequality can be accomplished with a moderately little storage size. This framework considered the multi-time-scale activity of the network and planned the power inequality issue for each timescale as an endless horizon stochastic control issue. Koutsopoulos et al. [5] the objective of this scheme is the ideal energy storage control issue since the cross of the utility operative. The goal is to devise a power storage control strategy that reduces long-term normal utility running price. The model, approach and structure of the ideal arrangement can be reached out to likewise represent an inexhaustible source that feeds the storing expedient. A capacity control strategy is asymptotically ideal for enormous capacity limit and performs very well limited capacity morals. Qin et al. [6] assessing the exchange worth of capacity is a significant issue in power system scheduling. Different investigations have testified various qualities based on numerical arrangements of differences of a fundamental model. The closed system highpoints the correct kind of gauging that is required and enables enormous prospect issues to be illuminated. The ideal control standard can be determined basically as value limits and the administrator needs just to contrast the present costs and the edge an incentive to choose how to buy and to sell. The control principle does not depend upon the state, for example, the sum of power in the capacity, and ideal purchasing and selling tasks dependably result in the storing ability be full or empty subsequently the process, separately. This proposed framework to register the normal ideal benefit, the payout of the capacity, the ideal storage sizing and estimation of the best achievable proportion against the oracle ideal benefit expecting the cost pursues a mean returning procedure. van de Ven et al. [7] proposed this work to incidentally store this economic power in a battery and use it to fulfil demand when power costs are high, along these lines enabling clients to misuse the price differences without moving their interest to the low-cost stages. The ideal strategy is appeared to have a threshold

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structure and determine this threshold in a couple of common cases. In this proposed system, the controller of end-user energy storing is under cost changes. With the assistance of determined the structure of the price limiting storage strategy, which ends up being a straightforward edge-based approach. Individually described the performance of these limits for some extraordinary cases, and appeared by methods for a numerical report that power storage can prompt critical cost investment funds. Xu and Tong [8] formulate the activity issue as a dynamic program. Under the supposition that customer utility (got from power utilization) is additively distinguishable after some time, towards the build up a beginning construction of the ideal procedure strategy for a customer who faces irregular power costs and stochastic demand. Consider the financial worth and ideal activity of power storage at purchaser areas, through a dynamic programming preparation. For a model with stochastic customer request and power costs, describe a significant edge structure of the ideal task strategy. The estimation of capacity reflects the normal net advantage gotten by the shopper scheduled the off coincidental that she ideally works the capacity. The estimation of capacity is autonomous of the buyer’s demand since it is ideal for them buyer to utilize the capacity just for exchange. Harsha and Dahleh [9] minimize the long-run normal expense of power utilized and asset in storage, assuming any, while fulfilling all the demand. This model is stockpiling with ramp constraints, adaptation losses, indulgence losses and an investment price. Demonstrate the presence of an ideal storage supervision approach under gentle suppositions and demonstrate that it has a double edge structure. Under this policy, derive operational outcomes that specify the marginal price from storage reductions with its size in which the optimum storage size is calculated expeditiously. The optimum energy storage management and size downside within the occurrence of renewable energy and dynamic valuation are related to electricity from the utility. Formulate the matter as an infinite horizon cost random dynamic program and procure varied structural results. Through elaborate process experiments, determining that energy storage will give important price and savings by cluster action renewable energy and decrease the employment of electricity from the utility. Sioshansi et al. [10] this method to evaluate the capability worth of storage. This proposed system customs a dynamic program towards typical the result of energy scheme outages on the procedure besides state of responsibility of storage popular sequent stages. To mix the optimized dispatch from the dynamic program with calculate system loss of demand possibilities to calculate a chance distribution for the state of charge of storage in every amount. This possibility distribution may be used as a compulsory outage rate for storage in common place dependability— based mostly capability worth estimation ways. This model and case study accept that storage is plainly worked to maximize energy profits solely, while not accounting for the likelihood of shortages and their result on sequent storage dispatch. The dynamic program might even be protracted to expressly model efficiency possibilities and embody future value uncertainty in optimizing storage dispatch.

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Kwon et al. [11] express the storing process problematic by way of a dynamic program through constraints calculated after practical load, source as well as price data. By way of the active program is computationally concentrated for significant issues, to explore algorithms supported estimated dynamic programming and apply them to a test information set. Though unreliably straight forward to state, the problem of determinative energy combines from the utility, renewable supply and storage the device is fairly advanced to answer. Applied six policies Markov decision method, one-step lookahead algorithmic program, one step roll-out algorithmic program, threshold-based approximation and naïve opportunistic algorithmic program and compared their performance victimization real data of energy demand, renewable energy production and electricity costs. Urgaonkar et al. [12] this proposed system thought-about the problem of resourcefully by means of these devices decrease the time average electrical grid bill in a data center. Victimization the technique of Lyapunov optimization, developed an online management algorithmic program that may optimally activity these devices to reduce the time price. This algorithmic program operates with none information of the statistics of the work or electricity price processes, creating it engaging within the presence of work and valuation uncertainties. Huang et al. [13] developed a low-complexity formula named DR-ESM. DR-ESM does not need any applied mathematics information of the system dynamics, as well as the renewable energy and also the power costs. During this system developed two actual light-weight energy management systems energy storage management and DR-ESM aimed at demand serving and demand response severally. Each scheme solely needs the user to solve a simple convex optimization program for deciding and permitting us to expressly calculate the specified energy storage size. This technique well tried that each scheme is able to realize near optimal performance. Lakshminarayana et al. [14] has dealt with a developed as a stochastic optimization problematic through the target of curtailing the period the normal price of energy exchange at intervals the utility. An algorithm to resolve the price minimization problem exploitation the technique of Lyapunov optimization is developed, and results for the recital of the algorithmic rule are provided. This modelled the set-up as a drawback to reduce the value of energy conversation among the utility for a given storage capability at the micro grids. This proposed system is extremely helpful for the facility grid expensive in terms of selecting the best grouping of storage size and collaboration so as to fulfil a particular price criterion. Since this work may be a primary stage towards exploring the trade-offs among collaboration and storage. Barthelmie et al. [15] comprehend the extent of the wind plant that short-term prediction becomes economically feasible and developed a prototypical aimed at wind energy. Prediction near-term wind generation output plays a crucial role in participating higher levels of wind power into the power market and is more recognized as a vital tool by electricity market members. The bulk observes the advantages related to prediction cover the associated prices and can assist wind power to penetrate the power industry to higher levels. The prices concerned in

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providing short-term predictions square measure for several players possible to be moderate compared to the advantages. Makarov et al. [16] dealt with an effect of desegregation wind power on the regulation and demand following necessities of California Independent System Operator (CAISO). Regulation and demands subsequent models were built supported hour-ahead and five-minute ahead demand and wind power forecasts. The methodology is created on a mathematical model mimicking the particular CAISO’s arrangement, period dispatch, and regulation processes and their timelines. Minute-to-minute differences and arithmetical interactions of the system parameters concerned in these processes square measure delineate with spare details to supply a strong and correct assessment of the extra capability, ramping and ramp period necessities that the CAISO regulation. Lakshminarayana et al. [17] give a framed as a stochastic optimization problematic through the impartial of curtailing the period normal price of energy exchange, subject to sufficient the user loads, the network power stream and storage restrictions. A direct current energy direction model is employed to formulate the network power flow constraints. A minimum complexity online solution to resolve this drawback is projected by supporting the Lyapunov optimization technique and analytical limits on the presentation of algorithmic rule derived. The result will be helpful for power utility designer to put out the best infrastructure in terms of storage and transmission lines to fulfil specific price criteria. A lot of general case with reactive power management associated an alternating current power flow analysis are the focus of the future investigation. Del Granado et al. [18] this proposed system developed a bottom-up approach, that specialize in the worth of energy storage and renewable micropower production in domestic homes. In the main specialize in the expectable inter-temporal differences of power request, wind velocity, and real time costs, and so accept that these limitations, however deterministic, are time varied. The purposes of the model are to reduce the overall energy consumption price, as realized from the utility, throughout a determinate horizon. Federgruen and Yang [19] have dealt with an optimal stationary policy, underneath each the long-term discounted and medium price criteria, and characterize its edifice. Presumptuous individual inventory level delivery is often approximated as a traditional, developed associate economical solution methodology identifying further structural properties. Commercially obtainable provider card systems tend to regulate combined score because the total or a weighted average various individual criterion. The price per unit ordered consists of two parts, one that is incurred for each ordered unit and a second element incurred just for effectively formed and delivered units. Bose and Bitar [20] the proposed system contemplates the system operator’s issue of minimizing the predictable price of generator dispatch after it has contact to spatially distributed energy storage resources. This technique expression that the expected good thing about storage derived underneath the optimum dispatch strategy is cupulate and non- decreasing within the vector of energy storage measurements. The characterization suggestions an easy methodology for the empirical

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formulation of locational marginal standards from net demand statistic knowledge. To get a particular characterization of the locational peripheral price of storage away from the derivation and for an additional general category of price parameters and network topologies. Gast et al. [21] give a concern about the trade-off between the custom of the reserves and the power loss. Power loss includes the power that’s either wasted, owing to the inadequacy of the storage cycle and the inevitable predicting mistakes or lost one the storage capability is unsatisfactory. The statistics of prediction errors construct a second policy active offset policy through stochastic optimization. The dynamic offset policy is scheduled a quantity of production adequate the making discrepancy, offset by a dynamic price that depends on the predicted storage level. This technique is often utilized in a multiple-stage optimization problem, primary by using local storage to resolve the local imbalance. Atzeni et al. [22] proposed a system to solve the utility optimization problematic by way of a noncooperative willing and evaluate the being of optimum polices. A distributed algorithmic rule to be run the user’s sensible metres, that offers the best production and/or storage methods, whereas conserving the privacy of the users and reduce the desired sign with the central unit. Developed the day-ahead grid optimization issues, where by every active user on the demand side egotistically minimizes his accumulative financial expense for buying/producing his energy wants, employing a game theoretical approach. Pavithra and Dahleh [9] has formulated the problem as a stochastic dynamic program that purposes to reduce the long-term normal price of electricity used then asset in storing, uncertainty slightly, whereas filling all the load. This proposed system storing through ramp limitations, alteration sufferers, overindulgence losses, and an investment price. The best storage the management strategy has a simple double threshold construction, and the marginal price of storage is reducing through storage size and so the best size under the best management strategy canister be calculated expeditiously. Xu and Tong [8] have formulated the problem as a stochastic dynamic program that purposes to reduce the long-term normal price of energy used then asset in storage, doubt slightly, whereas filling all the load. This proposed system is storing through ramp limitations, adaptation fatalities, dissipation sufferers and an investment price. The best storage management strategy has a modest double threshold construction, and the marginal price of storage is reducing through storage size and so the best size under optimum management strategy can be calculated expeditiously. Zhu et al. [23] have proposed another structure whereby adjacent home expressly shares power with one another to balance local energy gather and load in microgrids. This proposed system established a unique power sharing method to regulate homes that would share power and when to reduce system wide proficiency losses. System mistreatment empirical suggestions are of harvested photovoltaic power and residential power utilization.

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3 Conclusion In this article, summary and significant issues of diverse research studies for energy storage and renewable energy generation are presented. For a model with stochastic consumer demand and electricity prices, we characterize an important beginning construction of the optimum process policy. We describe the worth of storage for an excellent additional general setting that comes with the inter-temporal possessions in customer demand. The worth of storage replicates the predictable net profit obtained by the buyer if optimally activates the storage. It is strong, from the present literature, that around different solution methods for locating dynamic programming-based approaches, a Lyapunov optimization-based online algorithm. To exploit the procedure of many storage devices during a energy network with vital generation from alternating renewable sources provided, useful information and references for researchers will lead additional studies in this field.

References 1. J. García-González, Rocío Moraga Ruiz, L. de la Muela, M. Santos, A.M. González, Stochastic joint optimization of wind generation and pumped-storage units in an electricity market. IEEE Trans. Power Syst. 23(2), 460–468 (2008) 2. E. Bitar, R. Rajagopal, P. Khargonekar, K. Poolla, The role of co-located storage for wind power producers in conventional electricity markets, in American Control Conference (2011), pp. 3886–3891 3. L. Xie, Y. Gu2, A. Eskandari, M. Ehsani, Fast MPC-based coordination of wind power and battery energy storage systems. J. Energy Eng. 43–53 (2012) 4. H.-I. Su, A.E. Gamal, Modeling and analysis of the role of energy storage for renewable integration: power balancing. IEEE Trans. Power Syst. 1–9 (2013) 5. I. Koutsopoulos, V. Hatzi, L. Tassiulas, Optimal energy storage control policies for the smart power grid. IEEE Smart Grid Commun. 475–480 (2011) 6. J. Qin, R. Sevlian, D. Varodayan, R. Rajagopal, Optimal electric energy storage operation (IEEE, 2012), pp. 1–6 7. P.M. van de Ven, N. Hegde, L. Massoulié, T. Salonidis, Optimal control of end-user energy storage. IEEE Transa. Smart Grid 1–9 (2013) 8. Y. Xu, L. Tong, On the operation and value of storage in consumer demand response, in IEEE Conference on Decision and Control (2014), pp. 205–210 9. P. Harsha, M. Dahleh, Optimal management and sizing of energy storage under dynamic pricing for the efficient integration of renewable energy. IEEE Trans. Power Syst. 30(3), 1164–1181 (2015) 10. R. Sioshansi, S.H. Madaeni, P. Denholm, A dynamic programming approach to estimate the capacity value of energy storage. IEEE Trans. Power Syst. 1–9 (2013) 11. S. Kwon, Y. Xu, N. Gautam, Meeting inelastic demand in systems with storage and renewable sources. IEEE Trans. Smart Grid 1–11 (2015) 12. R. Urgaonkar, B. Urgaonkar, M.J. Neely, A. Sivasubramanian, Optimal power cost management using stored energy in data centers, in Sigmetrics’11 (2011), pp. 221–232 13. L. Huang, J. Walrand, K. Ramchandran, Optimal demand response with energy storage management. IEEE Smart Grid Commun. 61–66 (2012)

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14. S. Lakshminarayana, T.Q.S. Quek, H. Vincent Poor, Cooperation and storage trade-offs in power grids with renewable energy resources. IEEE J. Sel. Areas Commun. 32(7), 1386–1397 (2009) 15. R.J. Barthelmie, F. Murray, S.C. Pryor, The economic benefit of short-term forecasting for wind energy in the UK electricity market. Energy Policy 36, 1687–1696 (2008) 16. Y.V. Makarov, C. Loutan, J. Ma, P. de Mello, Operational Impacts of wind generation on california power systems. IEEE Trans. Power Syst. 24(2), 1039–1050 (2009) 17. S. Lakshminarayana, W. Wei, H. Vincent Poor, T.Q.S. Quek, Cooperation and storage tradeoffs in power-grids under DC power flow constraints and inefficient storage (2015) IEEE, pp. 1–5 18. P.C. Del Granado, S.W. Wallace, Z. Pang, The value of electricity storage in domestic homes: a smart grid perspective. Energy Syst. 5, 211–232 (2014) 19. A. Federgruen, N. Yang, Infinite horizon strategies for replenishment systems with a general pool of suppliers. Oper. Res. 62(1), 141–159 (2015) 20. S. Bose, E. Bitar, Variability and the locational marginal value of energy storage, in IEEE Conference on Decision and Control (2014), pp. 15–17 21. N. Gast, D.-C. Tomozei, J.-Y. Le Boudec, Optimal generation and storage scheduling in the presence of renewable forecast uncertainties. IEEE Trans. Smart Grid 5(3), 1328–1339 (2014) 22. I. Atzeni, L.G. Ordóñez, G. Scutari, D.P. Palomar, J. Rodríguez Fonollosa, Demand-side management via distributed energy generation and storage optimization. IEEE Trans. Smart Grid 1–11 (2012) 23. T. Zhu, Z. Huang, A. Sharma, J. Su, D. Irwin, A. Mishra, D. Menasche, P. Shenoy, Sharing renewable energy in smart microgrids, in ICCPS’13, (2013), pp. 219–228

Performance Analysis of Single-Phase Shunt Active Filter using Conventional PI Control Technique Rameshkumar Kanagavel and V. Indragandhi

Abstract In this manuscript, a conventional PI controller is used in voltage control loop of single-phase shunt active power filter (SAPF) for harmonic and reactive power compensation. And this manuscript discusses current extraction method based on PI control algorithms, SAPF design, and SAPF performance analysis using PI control technique. This control technique is demonstrated through MATLAB simulation and experimentation with FPGA controller-based prototype model.







Keywords DC-link voltage control PI control Single-phase shunt active power filter THD




1 Introduction Most of the commercial, industrial, and domestic loads such as fluorescent lamps, computers, uninterruptible power supplies, air conditioners, switched-mode power supplies, and medical equipments draw harmonic current from utility mains and cause poor power factor, poor efficiency, malfunctioning of medical facilities, misoperation of protective devices, and overheating of power system equipments. To prevent the issues listed above and to enhance the power quality, passive RLC filters have been widely used because of its low cost, large capacity, simple, and better efficiency, but they are having many drawbacks like huge in size, fixed compensation, and resonance problems. In addition, the passive filters having incapability to adjust to the system characteristic changes. So, in order to overcome those problems, the most effective method of improving power quality, the use of shunt Active power filter (SAPF), is preferred. The advantages include small size and ability to compensate both reactive power and harmonic currents [1–5]. Sasaki and Machida proposed the SAPF idea in 1971 [6]. The single-phase SAPF is widely used in medium- and low-power installation due to its low cost. In this case, R. Kanagavel (&)
V. Indragandhi School of Electrical Engineering, VIT University, Vellore, Tamilnadu, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_32

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reference current is generated based on regulating the DC-link capacitor voltage by using PI control algorithm directly without calculating reactive power required by the load [7–9]. The extraction technique is simple and straightforward when compared to other extraction techniques of SAPFs [6, 10]. For the proper operation of single-phase SAPF, the DC-link voltage to be maintained constant at any supply and load condition for the all-time. Even though the development of advanced controller, the classical PI controller is still have received greater attention by the engineers and researchers now days, due to its simple design, easy to implement, robust nature for irrespective of the variations in system parameters [11–13]. This manuscript consists of four parts. Initial starting with an introduction, the brief discussion about harmonic current extraction technique in Sect. 2. Discussion on simulation and experimental outcomes is presented in Sect. 3. At last, the conclusion is given.

2 Single-Phase SAPF System Model Shunt active power filter acts as an alternative solution to improve the power quality in electrical system. The SAPF operates by identifying the load harmonic current, those generated by the NLL, and subsequently, the SAPF injects a compensating current into the PCC to eliminate the current harmonics and compensate the reactive power caused by the NLL. Before connecting SAPF to the PCC, the supply and load current have same harmonics. When the active filter is switched on, source current becomes free of harmonics and sine in nature. The complete structure of SAPF system is illustrated in Fig. 1. The electrical grid supplies electric power to the nonlinear load (NLL). NLL is made up of full-bridge uncontrolled rectifier with a resistive and inductive (RL) load. The SAPF circuit is composed of an H-bridge inverter having four controlled power switching devices containing a DC-link capacitor (Cdc) and a coupling inductor (Lf). The SAPF is designed based on the previous study [15].

2.1

Reference Current Extraction

The extraction algorithm of SAPF is shown in Fig. 2. Basically, the algorithm consists of Vdc of voltage source inverter, source voltage, source current, and load current. Initially, by comparing Vdc with its reference value (Vdc, ref) then the voltage error is fed to a PI voltage controller. The controller output is considered as reference supply current peak value (Isp, ref). Subsequently, a unit amplitude of source voltage (Is, ref) is multiplied by a controller output. Then, the NLL current is added with the supply current reference which

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Fig. 1 Configuration of single-phase SAPF system

Vs

Unit vector Generation

Multiplier Vdc,ref Vdc

Is

Voltage Controller

Current controller

Gate Pulse

Fig. 2 PI control algorithm

gives filter reference current. Finally, the filter reference current and VSI output current signal are fed to current controller to generate gating signals for SAPF. The gain values of the PI controller state the voltage response and damping factor, and the minimum value of gains for the PI controller can be calculated using Eqs. 1 and 2 [14, 15]. kp  2ncx

ð1Þ

ki  cx

ð2Þ

where kp represents proportional gain, ki represents integral Gains, C represents the capacitance of DC-link capacitor, n represents damping factor (0.707), and x represents angular frequency.

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3 Results and Discussion The modeling and optimization of SAPF system are performed with MATLAB software. The MATLAB/Simulink model of single-phase SAPF is developed and executed to compensate the reactive power and harmonic compensation. A single-phase H-bridge inverter is considered as nonlinear load. Before compensation, the THD for the source current is 28.47%. After connecting single-phase SAPF to the point of common coupling (PCC), the harmonics are eliminated from supply current and THD is reduced to 3.8%. The detailed specification of single-phase SAPF is given in Table 1. The performance of conventional PI controller is evaluated in terms of its dynamic performance analysis for the two different conditions such as switch-on response and transient response. During switch-on response conditions, the single-phase SAPF is switched on at 0.05 s. The supply voltage (Vs), supply current (Is), load current (IL), filter current (If), and Vdc for conventional PI are shown in Fig. 3. From the results, the Vdc under conventional PI controller shows that the performance suffers from relatively long settling time of 670 ms. The single-phase SAPF is switched on at 1.25 s during transient response. During transient response, the load is changed from 28 ohm to 40 ohm. In conventional PI controller, longer settling time of 600 ms and critical overshoot 4V of Vdc were obtained due to a sudden variation of the load as shown in Fig. 4. In order to validate the work, a prototype model (1.5 kW) is used with same load configurations as in the simulation work with supply voltage of 100 V (141 Vpeak), 50 Hz which is developed by using single-phase autotransformer and the DC-link

Table 1 SAPF system specification Parameters Network Supply voltage Source resistance and inductance Supply frequency APF parameters DC-link voltage DC-link capacitance Filter resistance and inductance Average switching frequency Load parameters AC-side resistance AC-side inductance DC-side resistance DC-side inductance

Symbol

Value

Vs Rs and Ls F

100 Vrms 0.1 Ω and 1 mH 50 Hz

Vdc Cdc Rf and Lf Fsw

200 V 800 µF 0.01 Ω and 5 mH 10 kHz

Rc Lc RL, dc LL, dc

0.01 Ω 1 mH 28 Ω 160 mH

Performance Analysis of Single-Phase Shunt Active Filter …

Fig. 3 Switch-on response

Fig. 4 Transient response

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capacitor voltage which was set to 200 Vdc. FPGA controller is used to implement all the algorithms such as PI algorithm for reference current extraction and current control algorithm for generating proper gating signals to the VSI of SAPF. During switch response, the DC-link voltage reaches its steady-state value of 200 V with 1250 ms as shown in Fig. 5. The source current THD is reduced from 24.9% to 3.9%. The transient response (increasing load) has been obtained and is shown in Fig. 6. During transient condition, the DC-link voltage returns its reference value within 1200 ms. In both conditions, the controller takes longer setting time to settle the DC-link voltage.

Fig. 5 Switch-on response with Is = 5A/div, IL = 10A/div, Vdc = 100 V/div, and time 500 ms/ div

Fig. 6 Transient response, Vdc = 50 V/div, and time 100 ms/div

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4 Conclusion In this manuscript, a conventional PI controller-based single-phase SAPF has been proposed and validated. The simulation and experimental results were investigated. From the results, it has been found that the control technique compensates the current harmonics and reactive power generated by the NLL. But, the conventional PI controller takes longer settling time for both switch-on and transient conditions. So, in future work, the optimized PI controller needs to be used for better dynamic performance of SAPF.

References 1. M.V. Ataide, J.A. Pomilio, Single-phase shunt active filter: a design procedure considering harmonics and EMI standards, in Proceedings of the IEEE International Symposium on Industrial Electronics. ISIE’97, vol. 2 (IEEE, 1997) 2. Sajid Hussain Qazi, Mohd Wazir Mustafa, Review on active filters and its performance with grid connected fixed and variable speed wind turbine generator. Renew. Sustain. Energy Rev. 57, 420–438 (2016) 3. K. Chatterjee, B.G. Fernandes, G.K. Dubey, An instantaneous reactive volt-ampere compensator and harmonic suppressor system. IEEE Trans. Power Electron. 14(2), 381– 392 (1999) 4. F. Pottker, I. Barbi, Power factor correction of non-linear loads employing a single phase active power filter: control strategy, design methodology and experimentation, in 28th Annual IEEE Power Electronics Specialists Conference, PESC’97 Record, vol. 1 (IEEE, 1997) 5. B. Singh, A. Chandra, K. Al-Haddad, Power quality: problems and mitigation techniques (Wiley, 2014) 6. H. Sasaki, T. Machida, 2009–2019 A new method to eliminate AC harmonic currents by magnetic flux compensation-considerations on basic design. IEEE Trans. Power Apparatus Syst. 5 (1971) 7. R. Mahanty, Indirect current controlled shunt active power filter for power quality improvement. Int. J. Electr. Power Energy Syst. 62 (2014) 8. H. Afghoul et al., Design and real time implementation of fuzzy switched controller for single phase active power filter. ISA Trans. 58, 614–621 (2015) 9. M.K. Mishra, K. Karthikeyan, An investigation on design and switching dynamics of a voltage source inverter to compensate unbalanced and nonlinear loads. IEEE Trans. Ind. Electron. 56(8), 2802–2810 (2009) 10. R. Gupta, Generalized frequency domain formulation of the switching frequency for hysteresis current controlled VSI used for load compensation. IEEE Trans. Power Electron. 27(5), 2526–2535 (2012) 11. Hasan Komurcugil, Double-band hysteresis current-controlled single-phase shunt active filter for switching frequency mitigation. Int. J. Electr. Power Energy Syst. 69, 131–140 (2015) 12. K. Rameshkumar, V. Indragandhi, K. Palanisamy, T. Arunkumari, Model predictive current control of single phase shunt active power filter. Energy Procedia 117, 658–665 (2017) 13. R. Kanagavel, V.R. Indragandhi, K. Palanisamy, R. Kannan, A novel current control technique for photo voltaic integrated single phase shunt active power filter. Int. J. Renew. Energy Res. (IJRER) 7(4) (1709–1722)

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14. P. Karuppanan, K.K. Mahapatra, PI and fuzzy logic controllers for shunt active power filter— a report. ISA Trans. 51(1), 163–169 (2012) 15. M. Zainuri, A. Atiqi Mohd, et al., DC-link capacitor voltage control for single-phase shunt active power filter with step size error cancellation in self-charging algorithm IET Power Electron. 9(2), 323–335 (2016)

Calculation of VFTO and VFTC in the 550 kV GIS with Mitigation Techniques M. Naga Jyothi, C. V. K. Bhanu and CH. Ramya

Abstract The 550 kV gas-insulated substation is designed in the electromagnetic transient program (EMTP-RV) which simulated the design when the disconnector switch is operated. The operation of disconnector switch induces the VFTOs within a few nanoseconds, and the currents associated with VFTOs produce the electromagnetic fields. To reduce the very fast transients in the system, the mitigation techniques are to be implemented. In this paper, to reduce very fast transients in the system, the ferrite rings are installed at the disconnector switch. And it also have the impact on spark resistance and transformer entrance capacitance which will reduce the overvoltages.







Keywords VFTO VFTC Spark resistance Transformer entrance capacitance EMTP





Ferrite rings


1 Introduction A GIS is the high-voltage substation which consists of major structures of sulfur hexafluoride (SF6) which is used as insulating medium, disconnector, and circuit breakers for switching operations which results in VFTO [1]. The application of GIS is widely increased from the past few years due to the merits of compact in size, good environmental conditions, and good anti-contamination properties [2]. Due to advance technology in industries and the growth in population in recent years, energy demand has been increasing. The main purpose to implement the GIS M. Naga Jyothi
CH. Ramya (&) Department of EEE, VNR VJIET, Hyderabad, India e-mail: [email protected] M. Naga Jyothi e-mail: [email protected] C. V. K. Bhanu GVPCE, Vizag, Andhra Pradesh, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_33

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is for the environmental constraints and the reliability in the system. GIS requires less space to construct, when comparing with air-insulated substation (AIS), less maintenance, and pollution-free [3]. However, the GIS can also show the main problem on VFTO, which occurs due to the switching operations or circuit breakers. These transients will have very short rise time in the range of 1–100 ns and frequency in the range of 100 kHz to 50 MHz. The GIS have surge impedance of 75–95 Ω with the velocity propagation of 288 m/µs. The surge impedance of GIS is followed by an equation [4]. Z ¼ 60 lnðb=aÞ X

ð1Þ

where a and b are the outer diameter and inner diameter of a bus duct, respectively. GIS has the major problem of VFTO and VFTC, and these transient surges occur due to the opening or closing operation of disconnector and circuit breakers [5]. In this paper, a 550 kV GIS is modeled in the EMTP-RV software. Due to the disconnector switching operation, the VFTO and VFTC are obtained within the few nanoseconds. The ferrite rings are modeled with the shunt combination of inductor of 0.02 mH and resistance of 70 Ω. It is used to suppress the over-voltages in the system [6]. VFTCs are also generated due to the closing or opening operations of disconnector. The electromagnetic compatibility (EMC) problems due to VFTC are major risks for the source voltage of about 245 kV [7].

2 Modeling of 550 kV System A 550 kV GIS power system consists of underground GIS and grounded GIS. The two parts of the GIS are connected to the 550 kV cable. The power station has two 550 kV overhead transmission line which is connected to the power system with the length 37.36 km for the each line. For the overhead transmission line, the surge impedance (Z) is 350 Ω and the wave propagation (v) is 300 m/µs. For GIS bus duct, the surge impedance is 80 Ω and the wave propagation is 231 m/µs. For XLPE-800 cable, the surge impedance is 68.8 Ω and the wave propagation is 103.8 m/µs [2]. The power transformer is modeled with the lumped capacitance with grounding of 5000 pF. When the opening operation of circuit breaker is modeled with the series capacitance of 300 pF between the two contacts, the closing operation is modeled with the shunt capacitance of 230 pF. Electromagnetic potential transformer is modeled with grounded shunt capacitance of 400 pF. For closing or opening operation of disconnector, an ideal switch is in series with the series resistance of 2.5 Ω and arc resistance which is modeled with the staircase resistance. The equation of the dynamic arc resistance is given as

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Table 1 Tabular column for arc resistance S. no.

T

e(−t/T)

R0*e(−t/T)

1 3 5 7

1 10 20 30

0.367879441 4.53999E−05 2.06115E−09 9.35762E−14

3.68E+11 45,399,930 2061.154 0.093576

Table 2 Voltage–current characteristics of 420 kV MOA at the transformer side I (A) V (kV)

0.008 594.0

20.0 674.5

10,000.0 932.0

Table 3 Voltage–current characteristics of 444 kV MOA at the line side I (A) V (kV)

0.003 628.0

Fig. 1 Equivalent wiring diagram of 500 kV GIS [1]

20,000.0 1161.0

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R ¼ r þ R0  eðt=TÞ where r is series resistance of 2.5 Ω, R0 is having higher initial value of 1012 Ω, T is the time constant of 1 ns, and t is the time which varies from 1 to 30 ns. Table 1 data is provided for the staircase resistance which is equivalent model for arc resistance. The surge arrester or metal-oxide arrestor (MOA) is modeled with the nonlinear resistance. The voltage and current characteristics of surge arrester on the side of transformer and on the side of line are given in Tables 2 and 3. The equivalent wiring diagram of 550 kV GIS is as shown in Fig. 1.

3 Simulation Results In this paper, 550 kV GIS is modeled in EMTP, and the disconnector switch, DS-50543, was operated in opening condition and modeled with the exponentially decreased resistance or an arc resistance. For this study, consider the total simulation time of 30 µs and the step time of 2 ns. Always, the total simulation time must be greater than the step time. Pre-strikes and re-strikes occur in the system, and the sparks are modeled with the small resistance of 2.5 Ω. From the simulation, when the potential between the two contacts exceeds to the nominal value then the arc will re-strike. Otherwise, the arc will be extinguished. These re-strikes and extinguished arcs are simulated with the controlled switch in the EMTP-RV software.

3.1

VFTO Due to the Opening Operation of DS-50543

The DS-50543 switch is in opening condition when the DS-50546 and CB-5054 are already in the open operating mode. Due to the switching operation, the sudden change in frequencies and amplitudes to voltage will reach the peak value of 2 p.u. pffiffi where, 1 p.u = 550 * p2ffiffi3 kV = 449.073 kV. The simulated results are given in Table 4. Table 4 VFTO for opening operation of disconnector DS-50543 Voltage at bus bar (kV Voltage at transformer (kV) Voltage at surge arrester (kV)

14S 569 TR1 626 11UA 512

15S 614 TR3 728 12UA 492

TR4 661

17S 605 TR6 901 13UA 560

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Table 5 VFTC for opening operation of disconnector DS-50543 Curent at bus bar (kV) Current at transformer (kV) Current at surge arrester (kV)

3.2

14S 4000 TR1 1140 11UA 2700

15S 6750 TR3 983 12UA 2710

TR4 607

17S 2860 TR6 2500 13UA 412

VFTC Due to the Opening Operation of DS-50543

From Table 5, the very fast transient currents are shown during the opening operation of disconnector DS-50543. The VFTCs are very small after the switching operation which takes place in the system.

3.3

Impact of Trapped Charge

The opening operation of disconnector switch in GIS, the trapped charge will be occurred on floating section. The trapped charge is high at disconnector location of 2.1 and 1.7 p.u at transformer location (TR6). The simulation results for considering the trapped charge of −1.0 and −0.5 p.u are given in Table 6.

4 Mitigation Techniques 4.1

Impact of Ferrite Rings

Ferrite rings are modeled with the shunt combination of inductor and resistor, and the equivalent circuit is shown in Fig. 2 [8–11]. The impact of ferrite rings will suppress the transient surges in the system (Table 7).

4.2

Impact of Spark Resistance

Exponentially decaying resistance is known as spark resistance, which affects the damping overvoltages. The simulation results will show the spark resistance from 0.5 to 10 Ω [12]. The voltage is decreased from 638.5 to 551 kV at the location of bus bar (14S) (Table 8).

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Table 6 Simulation results for trapped charges Trapped charge (p.u.) Voltage at bus bar (kV) Voltage at transformer (kV) Voltage at surge arrester (kV)A Voltage at disconnector (kV)

14S 17S TR1 TR6 11UA 13UA DS-50543

−1.0 640 794 910 960 582 490 1155

−0.5 570 755 781 900 528 554 1113

0 569 605 626 901 512 560 1045

Fig. 2 Equivalent circuit of ferrite ring

Table 7 Suppression of VFTO using ferrite rings

Voltage at bus bar (kV) Voltage at transformer (kV) Voltage at surge arrester (kV)

Table 8 Simulation results for spark resistance

Spark resistance Voltage at bus bar (kV) Voltage at transformer (kV) Voltage at surge arrester (kV)

14S 547 TR1 598 11UA 475

14S 17S TR1 TR6 11UA 13UA

15S 588 TR3 TR4 689 655 12UA 460

0.5 638.5 677.6 626.5 1140.5 492.5 574.2

1 568.2 592.7 626 907 530.2 534.4

17S 591 TR6 867 13UA 538

10 551 588 623 898 472 506

Calculation of VFTO and VFTC in the 550 kV … Table 9 Simulation results for transformer entrance capacitance

4.3

Transformer entrance capacitance (pF) Voltage at transformer (kV)

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TR1 TR3 TR6

5000

10,000

15,000

626 728 901

608.5 714 900

605 713.1 898

Impact of Transformer Entrance Capacitance

The effect of transformer entrance capacitance will also lead to the suppression of very fast transient overvoltages. The influence of transformer entrance capacitance with the simulation results are given in Table 9.

5 Waveforms of VFTO and VFTC with the Operation of Disconnector Switch at Various Locations Figures 3, 4, 5, 6, 7, 8, and 9 are the waveforms observed VFTO and VFTCs with different disconnector switching operations at various locations.

Fig. 3 VFTO caused due to disconnector switch DS-50543 with the peak value of 901 kV at TR6

Fig. 4 VFTC caused due to disconnector switch DS-50543 with the peak value of 412A at 13UA

346 Fig. 5 Suppressed voltage of 547 kV at 14S by considering ferrite rings

Fig. 6 VFTO caused due to disconnector switch DS-50543 with trapped charge of −1.0 p.u., the peak value of 960 kV at TR6

Fig. 7 VFTO caused due to disconnector switch DS-50543 with trapped charge of −0.5 p.u., the peak value of 900 kV at TR6

M. Naga Jyothi et al.

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Fig. 8 VFTOs are suppressed with spark resistance of 10 Ω and the peak value of 472 kV at 11UA

Fig. 9 VFTOs are suppressed with transformer entrance capacitance of 15,000 pF, the peak value of 713 kV at TR6

6 Conclusion Due to the switching operation of disconnector or a circuit breaker, the very fast transients are observed in the system which leads to affect the internal or external parts of the substation, so that, to reduce the high transients in the system, the mitigation techniques are implemented. From the study of 550 kV GIS, when operating of disconnector switch, the high voltages are occurring at the transformer (TR6) of 1.7 and 2.1 p.u without and with considering of trapped charge. With the suppression techniques of ferrite rings, spark resistance, transformer entrance capacitance, the great change in voltages improves the system efficiency.

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References 1. V. Himasaila, M. Nagajyothi, T. Nireekshana, Review on analysis of very fast transient overvoltage in gas insulated substation. Int. J. Sci. Res. Eng. Technol. (IJSRET) 6(7) (2017). ISSN 2278–0882 2. L. Tiechen, Z. Bo, Calculation of very fast transient overvoltages in GIS, in IEEE Transmission and Distribution Conference & Exhibition (2005) 3. A. Tavakoli, A. Gholami, H. Nouri, Comparision between suppressing approaches of very fast transients in gas-insulated substations (GIS). IEEE Trans. Power Deliv. 28(1) (2013) 4. V.V. Kumar, M.J. Thomas, M.S. Naidu, VFTO computation in a 420 kV GIS, in High Voltage Engineering Symposium (1999), pp. 22–27 5. P. RamaKrishna Reddy, J. Amarnath, Nanocrystalline to suppress VFTO and VFTC of 245 kV gas insulated substation. Int. J. Comput. Appl. 70(13), 0975–8887 (2013) 6. M.A. Abd-Allah, A. Said, E.A. Badran, New techniques for mitigation in GIS. J. Electr. Eng. (2014) 7. M. Mohana Rao, M.J. Thomas, B.P. Singh, frequency characteristics of very fast transient currents in a 245-kV GIS. IEEE Trans. Power Deliv. 20(4) (2005) 8. J.V.G. Rama Rao, J. Amarnath, S. Kamakhaiah, Simulation and experimental method for the suppressing of very fast transient overvoltages in a 245 kV GIS using ferrite rings, in IEEE Conference on High Voltage Engineering and Application (ICHVE) (2010) 9. J. Lijunl, Z. Yuanbing, P. Ge, Y. Zheng, Z. Xianggong Estimating the size of ferrite rings for suppressing VFTO in GIS, in 8th IEEE Conference on Properties and Applications of Dielectric Materials (2016) 10. Y. Guan, G. Yue, W. Chen, Z. Li, W. Liu, Experimental research on suppressing VFTO in GIS by magnetic rings. IEEE Trans. Power Deliv. 28(4) (2013) 11. N. Pathak, Prof. T.S. Bhatti, Prof. J.M. Ibraheem, Study of very fast transient overvoltages and mitigation techniques of a gas insulated substation, in International Conference on Circuit, Power and Computing Technologies [ICCPCT] (2015) 12. M.A. Haseeb, M.J. Thomas, Disconnector switching induced transient voltage radiated fields in a 1100 kV gas insulated substation. Elsevier J. Electr. Power System Res. 161 (2018)

Energy Efficiency and Conservation Schemes Proposed for an Educational Building in Oman Ch. Ramya, Ch. Venkateswara Rao, Nurul Hasan Shaikh, Mohammed Kashoob, Syed Aqeel Ashraf and C. H. V. Suryanarayana Abstract The energy is substantial input for the economic growth of any country. In lieu of developing countries, increase in energy demand requires huge investments. Consequently, energy cost reduction, efficiency improvement and energy conservation utilization, management and audit are needed. In Sultanate of Oman, energy demand has been rising rapidly due to contemporary growing economic and population. The non-renewable natural gas produces energy. This prompts the move toward renewable energy sources and the implementation of Energy Efficiency and Conservation (EE&C) schemes such as energy audit and conservation. It is an analysis of the energy flow within a building that attempts to balance the energy input with its use not affecting the energy output. It is used to classify the entire energy stream. Energy management is used to attain and maintain an optimum energy flow to reduce energy costs and prevent the misuse of energy. Comprehensive EE&C measures are needed to conserve energy and for load leveling. In this paper, energy audit, conservation and management studies are conducted for Salalah College of Technology. The saving in the energy is achieved by implementing EE&C schemes through load observation, identifying the unnecessary usage of power and weak links in energy usage.




Keywords Energy audit Power consumption Energy efficiency Power factor correction





Energy conservation


1 Introduction In Oman, as shown in Fig. 1, the installed power capacity has been lagging behind the consumed power except for a short period from 2013 to 2016 [1].

Ch.Ramya (&)
Ch.Venkateswara Rao
N. H. Shaikh
M. Kashoob
S. A. Ashraf
C. H. V. Suryanarayana Swarnandra College of Engineering and Technology, Narsapuram, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_34

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Fig. 1 Installed power capacity versus electrical power consumption for the 2002–2107 [1]

It can also be seen from Fig. 1 that the consumption of electrical power is rising exponentially exclusively in residential, commercial and industrial premises. Increasing the installed power capacity will require an exponential increase in the investments in the sector of power which may be difficult. A different solution would be to implement conservation schemes and energy efficiency. In [2–5], different schemes for EE&C were proposed for Oman. In [6–8], methodologies based on smart grid for energy saving were studied. Many papers [3, 9–11] proposed and scrutinized the benefits of the migration to renewable energy sources in Oman such as solar and wind. In this paper, energy audit and conservation projects are carried out in Electrical Engineering building at Salalah College of Technology (SCT). Economic and efficient procedures of energy management have been surveyed subject to budget limitation. These measures result in decrease in cost of energy by taking certain precautions for safety environment. The scope and approach of the energy audit study also include time and cost estimates for carrying out the energy study. The selection of Salalah College of Technology has been considered because of (i) the higher amount of electrical energy consumed in such institutes and (ii) the constraint on the savings budget for energy conservation procedures.

2 Existing Load Conditions Institute Considered SCT is a government technical institute in Salalah, Oman. In this college, there are three departments, i.e., Engineering, Information Technology and Business. In

Energy Efficiency and Conservation Schemes …

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Engineering department, there are five sections in that one of the sections is Electrical & Electronics Engineering which is considered for energy conservation measures for year 2018–2019. In the Electrical building, there are two floors with five lines and 40 rooms.

2.1

Connected Load

SCT is considered a high-tension client, receives electricity from Dhofar Power Company (DPC) in Salalah, under government tariffs [12] with five 11 kV/433 V, 1000 kVA substations with two feeders of three-phase four-wire. One feeder is connected to the Electrical Engineering building. The energy consumption: 2500 units/day. The monthly power consumption will be 55,000 units. Load details, power consumption per load and annual energy consumption data are presented in Fig. 2.

Fig. 2 a Load details, b power consumption and c energy consumption per year

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2.2

Electricity Tariff: Maximum Demand (kVA) Charges

The total monthly cost for the energy consumed is presented in Table 1.

3 Energy Audit Following reasons for conducting energy conservation: • The rise in energy cost input and natural gas price rise due to the expected future tariff revisions by the authorities in every year. • The ministry of manpower is considered the option for renewable energy bases in the nigh imminent for financial reasons and also to avail to the government’s incentives. • To develop energy-efficient buildings, green energy buildings or smart homes. A. Energy auditing period For this institute’s energy conservation project, the EE&C team took six months for conducting the detailed energy audit. B. Energy Auditing procedures The EE&C team sets the criteria for energy auditing as • • • • • •

Crosschecking the machineries/equipment Analyzing the monthly electricity bills, Reading of load progress, loading design and demand control, Study of distribution schemes, cables, Checking of lighting, Air conditioning systems, etc.

The following areas through which energy conservation could be achieved: • • • •

Electrical services and distribution system, Lighting, Air conditioning, Energy supervision scheme.

Table 1 Total tariff for monthly electricity consumption [12] Tariff structure

Electricity cost

0–3000 3001–5000 5001–7000 7001–10,000 >10,000 Total cost (1000 Baisa = 1 OMR)

3000  10 = 30,000 Baisa 2000  15 = 30,000 Baisa 2000  20 = 40,000 Baisa 3000  25 = 75,000 Baisa 45,000  30 = 1350,000 Baisa 1,525,000 Baisa (1525 OMR)

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In this paper, the team completed (i) the annual energy savings, (ii) energy costs and annual cost savings, (iii) initial cost, (iv) payback period, return on investment.

4 Electrical Energy Conservation The following electrical energy conservation methods were done by the EE&C team (see Fig. 3): A. Incoming supply Parallel Cables Two underground cables, 4 core, 415 V, 250 A, XLPE/SWA/PVC/CU 4  240 mm2 are running from the sub-station; one cable of length 120 m to air conditioning control panel and another of 60 m to the departmental over-head bus bars. The EE&C team recommended, as one of the energy conservation actions, to run a cable in parallel. Total length of both existing cables was 180 m. Total resistance of the cable, R¼

3  0:0915  180 ¼ 0:0494 X 1000

ð1Þ

The cable losses for a total load current of 400 A are calculated as P ¼ I 2  R ¼ 4002  0:0494 ¼ 7:904 kW

Fig. 3 Energy conservation methods and energy savings

ð2Þ

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an average time of 3200 h is considered for operation annually. Then, the energy loss in the cables is calculated to be E ¼ P  t ¼ 7904  3200 ¼ 25292:8 kWh

ð3Þ

Considering equal load sharing for parallel cables, the current in each cable is (400/2) = 200 A. In each cable, power loss is 2002  0.0494 = 1.976 kW. Total power loss for both cables is 2  1.976 = 3.952 kW, and total energy loss is 31,616 kWh. Saving in energy loss per year is 31,616 kWh (50% saving as expected). Annual saving in energy cost due to parallel cables is at 10 Baisa/ kWh = 316.16 OMR. Cost of 4  240 mm2 cable per meter length = 25 OMR. Expenditure on additional 180 m cable = 25  180 = 4500 OMR. Labor on running additional cable is 300 OMR, and hence, total expenditure on running additional cable is 4800 OMR. Payback period (4800/316.16) = 15.1  16 months. The Return on investment (ROI) is 1/16 or 6.25% per month.

4.1

Power Factor Correction

The power factor of the institute varies between 0.89 and 0.93 usually since its origination, which was well above the requirement of the DPC. So, average power factor of 0.91 the load in kW was found to be 850  0.91 = 773.5 kW. The EE&C team decided to improve the power factor to an average value of 0.98. The targeted locations for placing the capacitors are at the sub-station for base load compensation and at the loads supply so that only when the load is connected, the capacitors will be ON. The 100 kVAR capacitors available at the sub-station were just sufficient to compensate the base load to a power factor of 0.91. 96 KVAR were mounted in order to improve the power factor of 0.98 (calculations not shown). For a load of 773.5 kW, the kVA demand at 0.98 power factor is (773.5/ 0.98) = 789.29. Therefore, saving in kVA is (789.29 − 773.5) = 15.79. Annual saving in cost due to kVA reduction is 15.79  3200  (10/1000) = 505.28 OMR. Cost on additional capacitors is 400 OMR per 100 kVAR (100 OMR per 25 kVAR). Payback period (400  12)/505.28) = 9.5  10 months. The ROI is 1/10 which is about 10% per month.

4.2

Changing Air-Conditioned Environment

The electrical building under our investigation has 62, 2650 watts of two-toncapacity air conditioners. The general purpose air-conditioned rooms were operating at a temperature of 17 °C frequently regardless of the seasons.

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Recent research [1–12] illustrates that computers can operate at 30 °C. When the temperature is greater than 28 °C, an energy saving of 3% has been noticed per 1 °C increase in temperature. Based on that, the controlled temperature only in the computer rooms at 22 °C. The energy consumption/air conditioner/hour to maintain a room temperature of 22 °C is initiated as (5  (3/100)  2650  62  3200) = 78,864 kWh with conventional airflow. Annual saving in cost due to change in air-conditioned environment is 78,864  (10/1000) = 788.64 OMR.

4.3

Voltage Control of Lighting

The energy conservation in lighting has resulted in a saving of about 15% according to their records. The lighting installation consisted of 220 with four lamp sets, each rated at 80 watts (Total power = 220  80 = 17.6 kW). Connecting a voltage control scheme for the lighting, which made use of one autotransformer for every lighting control zones, and finally connected to a central control panel. The total power rating including control gear was found to be 17.6 kW + (220  2 watts) = 18.04 kW. The total energy consumed is 18.04 kW  3200 = 57,728 kWh. The voltage regulator scheme has summary energy consumption in two ways: firstly, following start-up, the supply voltage was condensed by 12.5%, and secondly, using a photocell to display the daylight level in the institute, the supply voltage was reduced by up to a further 5%. For 3200 h of annual operation, due to the voltage control system, a 30% reduction in energy consumption (57,728 kWh  (30/100) = 17,318.4 kWh) by the lighting installation has been measured, resulting in an annual energy cost saving of 17,318.4  (10/1000) = 173.184 OMR. The cost of the control system and its installation was 450 OMR; so, the repayment period is worked out to be 31 months.

5 Conclusions Energy conservation and efficiency schemes such as energy audit and management have been conducted at the Electrical Engineering building of SCT. Solution to optimize and reduce the energy consumption was proposed. Incoming parallel supply cables, power factor improvement, changing of air conditioning environment and lighting voltage controlling systems were implemented at primary audit energy conservation. Based on the above energy conservation, the average time was 10 h. The percentage saved energy, 17.18%. It is expected that with a secondary energy audit implementing more energy conservation solution that the saved energy percentage will significantly increase.

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Future Scope: In the future, a secondary energy audit that includes the succeeding activities, phases and processes: • • • • • • • • • •

Switching off of the lamps and equipment, Renewable sources, Introducing LEDs to replace existing lamps, Introducing DC loads, Optical fiber cables, Soft switching, Sensor operated equipment, Green energy concept, Ventilation considerations, Software implementation.

References 1. National center for Statistics and Information. [Online]. Available: https://data.gov.om 2. Project for Energy Conservation Master Plan in the Power Sector, February 2013 3. A.H. Al-Badi, A. Malik, A. Gastli, Sustainable energy usage in oman—opportunities and barriers. Renew. Sustain. Energy Rev. 15(8), 3780–3788 (2011) 4. M. Chaichan, H.A. Kazem, Energy conservation and management for houses and building in oman-case study. Saudi J. Eng. Technol. 1, 69–76 (2016). https://doi.org/10.21276/sjeat. 2016.1.3.3 5. T. Sweetnam, Residential energy use in oman: a scoping study (2014). [Online] Available:. http://discovery.ucl.ac.uk/1425280/1/Oman%20Final%20Report%20v0%208_revised.pdf. Accessed 12 Dec 2018 6. A.S. Malik, M. Bouzguenda,”Smart grid capacity and energy saving potential—A case study of Oman, in IEEE PES Conference on Innovative Smart Grid Technologies—Middle East (Jeddah, 2011), pp. 1–6 7. T. Masood, et al., Smart grid operations and control challenge by implementing SSSC tailored to optimize performance in between United Arab Emirates and Oman on the GCC power grid, in 13th IET International Conference on AC and DC Power Transmission (ACDC) (Manchester, 2017), pp. 1–6 8. A. Malik, M. Albadi, A. Bani-Araba, M. Al-Jabri, A. Al-Ameri, A. Al Shehhi, Development of strategic plans and scenarios for the Smart Grid and their impact—a case study of muscat interconnected system, in 28th International Renewable Energy Congress (IREC) (Amman, 2017), pp. 1–5 9. A.H. Al-Badi, M.H. Albadi, A. Malik, M. Al-Hilali, A. Al-Busaidi, S. Al-Omairi, Development of a cost model for assessment of wind and solar power in Oman, in IEEE International Conference on Industrial Technology (ICIT) (Cape Town, 2013), pp. 700–704 10. H. Al Riyami et al., Power quality of Dhofar network with 50 MW wind farm connection, in 2016 Eighteenth International Middle East Power Systems Conference (MEPCON) (Cairo, 2016), pp. 33–39 11. A.H. Al-Badi, Wind power cost assessment in Oman, in 5th IEEE Conference on Industrial Electronics and Applications, vol. 2 (Taichung, 2010), pp. 634–638 12. Authority for Electricity Regulations, Oman, Permitted Tariffs (2018). [Online] Available: https://www.aer.om/en/tariffs

Design and Analysis of PV-Based DSTATCOM with LCL Filter for Localized Distribution System Pratap Ranjan Mohanty and C. V. Harshavardhan Reddy

Abstract In maximizing the power transmission from solar electricity to the grid, the use of power converters is very essential. In the early days, the problems in terms of power quality (PQ) have meant that both the voltage quality and current quality of the PV system linked to the grid are becoming increasingly crucial, particularly as nonlinear equipment has been widely used. This paper presents a PV-based DSTATCOM with LCL filter in power distribution network to reduce the power quality problem as a harmonic distortion presents in network. Low-frequency-induced harmonics can be reduced by an LCL filter and a satisfying grid-side current can be generated with a relative low induction in comparison with the L filter. The new poles implemented by LC part lead to resonance in the scheme, leading to stability problems. This paper introduces a compensation technique using the SRF theory to enhance such issues so that efficiency in the constructed LCL filter scheme can be enhanced. The use of the active damping technique can rectify this issue. The proposed system is simulated in MATLAB/SIMULINK to improve the performance of the system by harmonic distortion reduction.











Keywords Voltage source converter (VSC) LCL filter PV system Active damping Distribution static compensator (DSTATCOM) Synchronous reference frame (SRF)




1 Introduction The quality of power is now becoming a prevalent issue for distribution system operators (DSOs) and clients. Most important for the future network is maintaining high quality and reliability of supply [1]. Electrical power system (EPS) leadership and its reliability, effectiveness and energy quality are the primary goals of the intelligent grid. The development of high-power semiconductor systems permits the P. Ranjan Mohanty (&)
C. V. Harshavardhan Reddy Department of Electrical & Electronics Engineering, Madanapalle Institute of Technology & Science, Angallu, Madanapalle, Chittor, AP 517325, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_35

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control of transmission level energy flow. Static compensators with shunt-connected energy are often used as a reactive power and/or harmonic present source. They can be used for instantly controlling voltage, correcting power factor and controlling reactive power [2]. STATCOM is a shunt device that can regulate the reactive power produced using a converter. To extract the reference present from the distorted line current, the time domain-based synchronous reference frame theory is used [3, 4]. An LCL filter is essential for connecting converters to the network to minimize distortions associated with the high frequency of switching. The most prevalent alternative is a straightforward inductive filter. The structure of the filter is easy but it is not sufficient to attenuate high-frequency signals. Due to this nonlinear harmonically distortion, a number of issues arise in devices used in our field, such as: engine overheating, increasing loss of several kinds, continuous equipment harm at worst and elevated meter measurement error [5]. To solve this issue, the introduction of shunt capacitive components allows the implementation of a greater order filter. LC and LCL filters at elevated frequencies are very well attenuated, but resonance can be caused. In terms of DC voltage control, harmonic present removal and reactive power compensation, the modified SRF offers improved efficiency over the standard technique [6]. The converter control scheme may involve extra passive damping circuits or active damping algorithms. The use of large-scale, nonlinear devices (UPS, SMPS, rectifier, etc.) causing a severe voltage fluctuation and voltage drop in a electricity system is the most prevalent formal pollution on the lower voltage condition [7]. In this setup, it is essential to know that the current compensating filter supply on the PCC requires an active power line conditioner. The active power supplies are only required for the reactive power of the load in an ideal situation and thus the average voltage of the DC-link condenser is to remain constant [7–9]. The harmonics in the scheme cause several undesired problems, e.g., increasing transformer heating, a small energy factor, resonant overvoltage, harmonic voltage drop over the impedance of the network, a bad distribution plant usage and other charges linked to the same PCC [4]. Traditional schemes are not so efficient in reducing harmonics because their static action and no dynamic action or intervention on the removal of harmonics are taken in real time [2–4, 6]. But, compared to standard active and passive filters, the shunt active energy filter provides promising outcomes. Compare various control approaches for harmonic elimination in energy system network based on FFT assessment (a key tool for harmonic reduction assessment). Basically, this research work demonstrates the management strategy, i.e., synchronous frame reference method and PQ approach, which helps to decrease harmonics by MATLAB Simulation and Modeling when using the DSTATCOM PV System.

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2 PV-Based DSTATCOM with LCL Filter In the past decades, both on the consumer and on the grid side the use of power electronics has grown up substantially. In front of the dc connection, the PV system is linked to increase the electricity. The PV panel delivers the energy to the DC connection as shown in Fig. 1. The figure describes the PV-connected DSTATCOM. The presented PV system can continuously supply the power to the grid by supplying the ripple current to the nonlinear load. Generally, grid-connected PV system may be used as DSTATCOM for power quality improvement [10]. Basically, damping methods are used to damp resonant peak of the LCL filter [11]. In this research work, the active damping principle [12] is implemented to the PV-DSTATCOM-connected grid with LCL filter for improvement/advantages. Designing active damping method for LCL filter along with current controller is one of the key issues in the PV-based DSTATCOM circuit. This was the choice of DSTATCOM as a device for distribution networks accessible with the most efficient, flexible alternate present transmission scheme. It can be regulated dynamically to control voltage by either absorbing or injecting reactivity into the distribution network compared to other FACTS instruments as a static VAR compensator (SVC) [13]. It also has the ability to respond to subsequent modifications in PV production with the help of a subsequent reaction. DSTATCOM has a high-performing, mature scheme, with quick dynamic reaction which provides voltage stability, voltage adjustment, harmonic control, correction of power factor, suppression of flicker voltage and reactive electricity at a LV rate. DSTATCOM technology is a bidirectional power electronic (converter) shunt three-phase device that is attached near the load mainly for the production and absorption of reaction energy. That is, when the DSTATCOM output voltage is

Fig. 1 Circuit topology of grid-connected PV-DSTATCOM

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Fig. 2 Block diagram of proposed control algorithm

higher than the PCC (terminal voltage) voltage or in inductive compensation mode, if the PCC voltage is higher than the DSTATCOM voltage, DSTATCOM will function in the capacitive compensation mode. The progress of technology in the growth of low switching loss IGBTs has led to an adjustment of the paradigm of the basic frequency switching approach for DSTATCOM in the area of pulse width modulation [14]. The PWM algorithm for controlling the PCC voltage was modeled and simulated as shown in Fig. 2. Instantaneous energy theory is used to create a PWM carrier-based algorithm that transforms voltage and energy into a synchronous rotating frame with the help of park conversion [15]. The Id and Iq components in the carrying algorithm can be independently controlled in such a manner that the reactive power can be exchanged via the DC-bus voltage controller and the PCC or terminal voltage controller. The PWM pulse signal of the carrier algorithm is produced by the PCC’s terminal voltage measurement and control and the secondary voltage from DSTATCOM. Those measurements and the proportional–integral (PI) are used to produce the active and reactive elements for the comparison of the d − q element of the grid present and to make the Vd and Vq parts of the modulating signal (carrier signal) for the SPWM.

3 Control Design Different feeder sectors and load buses can usually be used before the PCC. At best therefore, the Thevenin equivalent acquired by examining the PCC network is the source and feeder impedances. Let us indicate Rs and Ls, respectively, for the feeder resistance and inductance. The PCC voltage represented as, Vt ¼ Vs  Is Rs

dis dt

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Because of the decline in discharge and replacement, we can shift the renewable energy resources to shunt compensation, because we can resolve this issue again. Instead of the standard L-filter, a high-order LCL filter was commonly used to smooth the VSI output currents. With the general weight and size decrease of the parts, the LCL filter achieves a greater attenuation along with cost savings. f ðsÞ ¼

L1 CL2

s3

CRs þ 1 þ ðL1 þ L2 Þs þ ðL1 þ L2 Þs2 RC

To extract the reference present from the distorted line current, the time domain-based Synchronous Reference Frame Theory is used. In both stable and dynamic states, the SRF control approach works perfectly for controlling the active energy line conditioner in real-time applications. The modified SRF technique involves streamlined units of vector orientation generation, a DC-link voltage regulator, and a fixed-rotating synchronous frame for extracting the reference current. Currently, LCL filter use has become a popular practice for soft power output voltage shaft and reduced injection of switching frequency components of current at grid-connected VSI applications. High-frequency attenuation LCL filter has excellent efficiency. To fulfill the necessary attenuation, a lower complete inductance can be implemented. Low DC-bus voltage and system expense also imply a smaller inductance. This also means that the resonant frequency needs appropriate damping. Passive damping is comparatively straightforward, but damping strength increases as attenuation capacity decreases. For internal disturbance, active damping is more robust than passive damping, particularly when a big quantity of harmonics is present in the grid voltage, whereas the energy loss between these two techniques is little different. We will discuss the procedure for the filter design and propose a minimum condenser system. With some changes in the VSC retardation and regulator design procedure, the present feedback on the condenser active damping approach is accepted. The resonance is traditionally damped by the use of passive damping (PD) circuits with resistance. Increased losses due to damping resistance led to a proposition of several topologies to reduce losses and sufficient damping while attenuating the frequency element of switching. The design of a PD-scheme becomes difficult in high-power apps where switching frequency is small, taking into account adequate management of energy loss and corner frequency placements. Overall, because of its simplicity and reliability, a PD technique is chosen for resonance damping. An alternative method, known as active damping (AD), where a VSI is controlled so that no additional resistive element is required for resonance damping. The inverter uses the PWM switching system to produce high-order harmonics on carrier frequencies and the sideband frequencies to be removed.

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4 Simulation Results The MATLAB/SIMULINK platform will examine the efficiency of the constructed grid-connected PV-DSTATCOM system simulation results with the LCL system. In this paper, we discuss the harmonic reduction in different cases. Case-A discusses the DSTATCOM with LCL filter without damping method and Case-B discusses DSTATCOM with LCL filter with damping method and Case-C discusses PV-based DSTATCOM with LCL filter with damping method. Case-A: DSTATCOM with LCL Filter Without Damping Method The highest frequency close to resonance has almost disappeared. The solution is easy and reliable, but it reduces the system’s thermal loads and significantly reduces the filter effectiveness. The active damping is able to solve this issue. The resistor decreases the voltage across the condenser by a voltage commensurate with the subsequent current. You can do this in the control loop, too. Through a virtual resistor without loss, the filter will be actively dampened. This technique has the disadvantage of requiring a further present sensor and may cause noise issues due to the amplification of high-frequency signals. Figures 3 and 4 show the current waveform and THD analysis of a DSTATCOM with LCL filter without damping controlling. Here we can observe the total harmonic distortion is 2.16%. Case-B: DSTATCOM with LCL Filter with Damping Method Resonant peak at resonant frequency is the key issue with the LCL filter and this can be solved by correctly designing the damping system. For removal of the resonant peak, we will address the active damping technique. Figures 5 and 6 show the current waveform and THD analysis of a DSTATCOM with LCL filter with damping controlling. Here we can observe the total harmonic distortion is 0.52%.

20

ISa

Source Current (A)

ISb ISc

10 0 -10 -20

0

0.1

0.2

Time(s)

Fig. 3 Source current waveform

0.3

0.4

Mag (% of Fundamental)

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Fundamental (50Hz) = 9.898 , THD= 2.16% 50 40 30 20 10 0

0

100

200

300

400

500

600

800

700

900

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Fig. 4 THD value of source current

Source Current (A)

20

ISa ISb ISc

10 0 -10 -20

0.4

0.3

0.2

0.1

0

Time(s)

Mag (% of Fundamental)

Fig. 5 Source current waveform

Fundamental (50Hz) = 10.69 , THD= 0.52% 3 2 1 0

0

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400

500

600

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Frequency (Hz)

Fig. 6 THD value of source current

Case-C: PV-Based DSTATCOM with LCL Filter Figures 7 and 8 show the current waveform and THD analysis of a PV-based DSTATCOM with LCL filter with damping controlling. Here we can observe the total harmonic distortion is 0.11%.

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Source Current (A)

20

ISa ISb ISc

10 0 -10 -20

0

0.1

0.2

0.3

0.4

Time(s)

Mag (% of Fundamental)

Fig. 7 Source current waveform

Fundamental (50Hz) = 14.4 , THD= 0.11% 1 0.8 0.6 0.4 0.2 0

0

100

200

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Frequency (Hz)

Fig. 8 THD value of source current

5 Conclusion In this paper, the application of grid-connected PV-based DSTATCOM with LCL system has been presented. The features and the design method for both passive and active damping of the LCL-type filter have been provided on the injected grid current regulator and voltage controls. The technique used for calculating the compensatory reference current can have a significant effect on the control system results. The sequence extract algorithm must be impermissible to harmonics, noise and changes of grid frequency in order to prevent deteriorating system efficiency because of the adverse sequence compensation loop. It has been shown that the suggested active damping systems satisfy the demand that filter inductance varies with excellent robustness without any further energy loss in the filter. The findings indicate a nice control system efficiency and confirm that the design of the suggested system is feasible and effective for all working conditions. For the control of the DSTATCOM with the LCL filter, a newly enhanced active damping with a decoupled dq-frame present controller is suggested to obtain an efficient load compensation.

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References 1. V. Srinath, Power quality issues in grid connected solar power system. Power Qual. (2016) 2. S. Choi, W. Choi, K. Lee, Improvement of LCL filter based grid connected inverter using SRF method. IEEE Power Electron. 25(5) (2011) 3. F.M. Flaih, J. Shrivastava, Improvement of power quality by DSTATCOM with LCL filter in power. Distribution system. IJMER. 3(6) (2013) 4. G. Zeng, T.W. Rasmussen, M. Lin, Modeling of LCL filter with active damping in PV based D-statcom. IEEE Smart Grid 4(4) (2014) 5. R. Uday, S. Kulkarni, An active damped LCL filter based grid connected PV system. IEEE Power Syst. (2014) 6. E.K. Tomaszewski (2015) Design and Analysis of PV Inverter for Unbalanced Load Compensation in Microgrid. W. University 7. E. Suzan, B. Alireza, Enhancement of stability for DC bus voltage controller in grid connected VSI wit LCLC filter. IEEE Power Electron. 4(3) (2014) 8. D. Badra, M. Rajnish, Improvement of power quality by reduction of harmonic using SRF control strategy. IIT (2015) 9. B. Frede, W. Huai, Improvement of reliability of capacitors for DC link in power electronic converters. IEEE Ind. Appl. 9(6) (2016) 10. A. Akella, N. Kumar, A case study on control of D-STATCOM with L and LC filters. ISA Trans. (2014) 11. M.V. Kumar, M.K. Mishra (2014) A new control strategy for DSTATCOM with SPWM switching technique. Electr. Power 12. P. Wang, Y. Tang, Anew generalized design of shunt active filter with LCL filter. IEEE Ind. Electron. 54(3) (2012) 13. M. Kumar, G. Nagesh, An active damping method with PI and PR regulators with SRF controller for DSTATCOM. IJEEPS. 4 (2013) 14. C.V.H. Reddy, C. Naresh, Analysis of DSTATCOM with LCL filter to improve the voltage stability. IJE. l-3(3) (2013) 15. K.V.M. Reddy, A.E.W.H. Kahlane, An LCL filter design for gird connected PV system. H&S Tech. 3(3) (2013)

Optimal Scheme and Power Controlling aspects in Shipboard System Vijay Raviprabhakaran and Teja Sree Mummadi

Abstract This paper deals with controlling DC power in shipboard power. The Shipboard power system (SPS) experiences disturbance due to variations in load. A DC bus distribution system developed for the USA. Coast Guard’s 270-ft Intermediate Endurance Harvester is simulated using MATLAB in this paper. Whenever a fault occurs in load, the system power varies. In this article, the DC power system is controlled automatically by detecting disturbances. The proposed method includes self-governing fault detection and controlling DC power. The shipboard power system consists of a challenge related to restoration. The reliability and flexibility of the system are improved with effective integrated energy storage devices (ESD) and solar power. A maiden attempt is made in the paper with a solar panel for the cost-effective operation of the SPS. Also, the SPS with and without the PV panel is tested for optimal operation. Furthermore, this shipboard management system may be implemented in the Indian shipboard system for optimal power management.







Keywords Optimal power management DC bus distribution system Shipboard power system Energy storage system Solar power Fault detection Energy storage device













1 Introduction The ships used for both military and commercial purposes which work based on electricity are enabled by integrated power systems (IPS), are to meet increasing demand. In critical conditions, i.e., once there is a variation in load or IPS component failure, these then provide real-time management for dynamic configuration to support the system. Recently, there has been much advancement in controlling and managing DC microgrid. These advancements have been implemented in applications such as V. Raviprabhakaran (&)
T. S. Mummadi Department of Electrical Power Engineering, CVR College of Engineering, Hyderabad 501510, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_36

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traction, smart buildings, shipboard power systems and many more. The drastic development in semiconductor devices and power electronic devices over the past two eras made a way for advanced electrical networks, which are useful in automotive, space and marine applications. These systems deliver high efficiency and reliability [1, 2]. Because of the progression in power electronics, DC distribution systems have gained more attraction than AC. Though there are many advantages to the DC-based integrated power system, it is difficult to maintain optimal performance without interruption with a dynamic load. The protection schemes can detect faults and isolate them, but they do not consider the optimization constraints or balance the power and after fault isolation. To meet the increasing DC shipboard power demand, all the electric ships are implementing the integrated power system [3]. They have to manage the power for the dynamic profile to support the system critical operations when there is a change in dynamic load or IPS failure. In the IPS architecture, all the loads are supplied by a common electrical power bus, which enables the handling of the loads and generation sources more optimal and efficiently. It is able to direct power to vital loads on demand [4]. A new method is introduced which controls and optimally reforms a DC power system while automatically detecting system disturbances. A dynamic approach is proposed using time-scale separation [5]. The aim is to provide coordination between system protections to validate that the system ruins constantly at all stages of operation even after there is a disruption. To implement and certify the approach, a DC-based, shipboard power system is employed. The reliability of electrical power plays a major role in this modern world. As the demand for electrical power is increased, there should be an alternative for restoration and recovery when an outage or error on the method [6]. In this case, the power system is provided with storage devices can deliver standby power or power throughout transition [7]. In this paper, DC SPS is integrated with the energy storage system, which acts as a feedback path. An energy storage device subsystem is very necessary for the shipboard power system and for terrestrial electrical systems. This element helps in storing a large amount of energy, which can be used as a backup. During any fault conditions, the electrical system can be fed by ESDs. The outage of electrical equipment and other parts or operational issue may occur if ESDs fail to provide energy during faults [8, 9]. The DC SPS is integrated with the energy storage system (ESS) wherever the battery is used as an energy storage device in this article. By integrating the ESS with the DC SPS, the system efficiency is improved. The ESS acts as a backup of the system. ESS technologies are technologically viable nowadays. A few of them are flywheels, the superconducting magnetic energy storage (SMES), battery energy storage system (BESS), the compressed air energy storage (CAES), supercapacitors and pumped hydro storage (PHS). Either AC or DC system, for charging and discharging purposes, the ESS requires power converters. Mostly used ESS is an uninterruptible power supply (UPS). In this paper, the battery is used for the energy storage devices. This article contrasts with the modeling of DC SPS with solar resources for power generation. Solar panel technology requires a power converter to boost the output power [10]. In this paper, a boost converter is used as a converter for the solar panel. The buck-boost converter is integrated with ESS as a power converter. The residual of this article is outlined as trails: Sect. 2 acquaints

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with SPS configuration. Section 3 is about SPS power control. Modeling and simulation circuit and results are shown in Sect. 4. Conclusions are presented in Sect. 5.

2 Shipboard Power System 2.1

Shipboard Configuration

An electrical power distribution system for the ship must be able to provide power generation and distribution, control and some basic power electronics operations. Space and weight parameters limit the amount of discharge in the system for restoration purposes. The resistive losses in SPS are nearly insignificant for the tightly coupled distribution network. Recent developments of SPSs include integrated circuits and which are trending nowadays. Due to the advantages of DC over AC, the DC shipboard came into existence. Usually, there are two categories of the distribution system that includes radial-type and ring-type distribution system. Convectional SPSs are of radial-type distribution systems. The radial-type system has a generating station at the center of the loads. The power flow is in only one direction. In radial type, if any fault occurs, then it would result in loss of supply toward many units up to the fault is situated and unfurnished. When there is a variation in a generation, it seriously affects the load side which results in voltage fluctuations. Due to this, reliability and stability decrease. But recently, to overcome radial distribution problems, the zonal distribution system is implemented. The researchers proposed a new technology that includes both radial distribution architecture and zonal approach. Zonal method hires a starboard bus (SB) and a port bus (PB), thus dividing the craft into numeral electric zones. The system characteristic of electrically integrated SPS is very analogous to the island microgrid except which is not automated. It will have a comparatively feeble power balance, then the generator capacity is closely sized to the load demand [11]. In order to maintain shipboard power system reliability, it has to be automated. It is also proved that automation gives better results than manual control. In this process, multiple power generation capabilities abode throughout the ship.

2.2

DC Shipboard Power System

The first DC shipboard was in the 1880s, but due to a lack of power electronic devices, this system failed [12]. With recent advancements in power electronic devices and storage technologies, the DC SPSs have gained attention. The main motivations for the development of DC SPS are fuel economy, and other advantages include

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(i) Implementation of parallel connection or disconnection for DC power sources will be simple. (ii) Dispensation from reactive power flow. (iii) Eliminating harmonic and imbalance problems. (iv) Unlike variable frequency drives, AC SPS in DC shipboard power systems (DCSPs) renewing energy might be effortlessly absorbed in further loads through the DC bus. (v) Due to the absence of a power factor in the DC distribution system, resistive loss in cables is reduced. There is no need for phase and voltage synchronization in DC SPS. As a result, the generators are quickly brought online and connected to the DC system. The fuel efficiency of the system operation can be improved by using DC networks by achieving the integration of advanced high speed and high-efficiency diesel generation. Hence, DCSPSs keep gaining an increase in research interests. The model of Onboard DC Grid has two arrangements. A prime is a multi-drive approach, and the second one is a completely distributed system [13]. Another new approach includes components include AC generators, inverter modules, AC motors, etc., but AC switchboard is excluded [14]. Fuel-saving is nearly 20% with the integration of variable speed diesel generator operating at the optimal speed [15–17].

3 Proposed DC Shipboard Power System Management 3.1

System Fault Monitoring

During steady-state operation systems, fault monitoring and protection are within the limits of healthy operations. Whenever there is any disturbance or sudden change in load, then power varies. The fluctuation in voltage/current has to be detected and cleared immediately so as to sustain reliability. In this paper, the modeled power system includes measuring devices to maintain continuously monitor the voltage and current parameters. The measuring devices are installed at the terminals of generator, loads and in DC bus. Current, voltage and power thresholds (e.g., a certain percentage of steady-state values) are set in each of the measuring devices to detect a disturbance at a specific location and give signals.

3.2

Coordinated Control

In this paper, for DC SPS for power generation, the diesel generators are used. DC SPS follows the HVDC working principle, where the generated AC is converted into DC through converters (i.e., rectifiers). The elementary block illustration of the DCSPS is shown in Fig. 1.

Optimal Scheme and Power Controlling aspects in Shipboard System

DIESEL GENER ATOR

VSC

D C

DIESEL GENER ATOR

VSC

DIESEL GENER ATOR

VSC

BATTERY BANK

LOAD CONVERTER

B U S

371

LOAD

LOAD LOAD CONVERTER

LOAD LOAD CONVERTER

DC/DC

Fig. 1 Basic block diagram of DC SPS

The DC bus power is again converted into AC near loads via an inverter. The battery is cast off as a backup, i.e., as an energy storage device. Power is supplied by three generators with diesel generators prime movers. The basic principle behind the DC ship power system and configuration is similar to the extension of multiple DC-links. But for these drives, electrical power consumption is more than 80%, in this regard; all DC-links are united in a common DC bus. This approach includes components like AC generators and motors, inverter modules, etc., but the AC switchboard is excluded. In AC SPS, to regulate the speed of the motor, a frequency converter consisting of a rectifier and an inverter is used. The AC voltage is first converted to DC in the rectifier and then inverted back to AC. DC/DC converters are lighter than power transformers, but have a greater efficiency (>98%), require less maintenance, and their price range is much lower. There are numerous customs of constituting a DC ship power network. The generators are associated with MV1 Switchboards, and the energy is transformed through transformers to the converters. In the multi-drive configuration, all converters are placed in the main switchboard. That simply means that the power cables from generators to the DC bus carry AC current.

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4 Simulation Results and Discussions 4.1

DC Shipboard Power System Without Solar Panel

The proposed method is implemented and modeled in MATLAB version 2016a with Intel Quad-Core 7th Generation as revealed in Fig. 2. Two-level pulse-widthmodulated (PWM) voltage source converters are used to convert the AC (3 /, 440 V, 60 Hz) power produced by the three-phase generators which are again stepping down using transformers. The DC bus reference voltage is set to 750 V. As the feedback path, energy storage system is used with the battery of 650 V, 400 Ah and connected to the DC bus through a bi-directional DC/DC converter. In MATLAB for DC/DC converter, the half-bridge converter is used. Pulse width modulation has been supplied to it. Two disturbances are created which occur commonly in the system to validate the feasibility of the proposed approach. Two disturbances are generator loss and load start. The three generators’ output power and battery power waveforms are shown in Fig. 3. Whenever there is a fault in the generation, protecting the device, i.e., a circuit breaker detaches the faulted part with the non-faulted path. At generator three, a fault is generated in 4 s gets isolated and does not generate power. In MATLAB, to produce fault, the circuit breaker is set to trip at 4 s. Whenever the disturbances occurred in generators and if power generation is lost, then the battery supplies the power to the load. In Fig. 3, it is witnessed that the third generator power is lost during 4 s due to generation fault, i.e., power does not get supplies to the load. But in this concept, all the bus bars are tied together, a reference value is set at 750 V, and ESS is set as feedback. Subsequently, every time, there is any fault occurred in power generation, the load gets supplies through the feedback path. Whenever there is a need for power, then the battery in the ESS injects the voltage into the system through a converter. However, the battery injects the power into the system, and then, the converter acts as a boost converter. In Fig. 4, it is realized that the load is disconnected at 2–3 s, and thus during that period, the excess generated power is wasted. Without ESS, this power gets wasted, i.e., whenever there are disturbances at load, then the circuit breaker quarantines the faulted path with the non-faulted path. At this instant, the power generated is not supplied to load. Satisfactorily not to waste the excess power, the feedback path absorbs the excess power. When extra power is generated, then the ESS device’s battery absorbs it through DC/DC converter which acts as a buck converter and absorbs the power. The battery waveform is observed in Fig. 7 that whenever it is observing the excess power generated the battery power. The battery uses extra power to charge itself. The battery is charged every time there is surplus power. The power is balanced by injecting and absorbing. The DC voltage waveform from the 750 V capacitor is observed in Fig. 5. Though there are some overshoots when disturbances occurred, it is comprehended that the DC bus voltage kept regulated at all points of the disturbance. The power electronics converters quickly recover the voltage regulation as depicted.

373

Fig. 2 DC shipboard power system model

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Fig. 3 Three generators output power along with the battery power output

4.2

Shipboard Power System with Solar Panels

Solar panels are used for power generation in place of generators. The method using solar panels is modeled and implemented in MATLAB is exposed in Fig. 6. By using solar panels, the battery and electric motors get charged, and then, the usage of fossil fuels can be reduced. Solar panels directly convert sunlight to electricity. These panels produce reliable electricity without using fossil fuels. The generator 1 output power and battery waveforms are shown in Fig. 7. Only one three-phase generator and two solar panels are used which generates 9 kW each. The generator 1 produces nearly 13 kW of power. The battery is charged as backup and support, and the same is connected with a DC/DC converter. The solar panel output power is simulated in Fig. 8. The ship needs approximately 10 kW of power for a load. Each panel produces 8 kW of output power. The output power of loads is nearly 10 kW. The three load output power is observed in Fig. 9. Whenever there is any interruption in power supply, the battery

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Fig. 4 The output power of three connected load

Fig. 5 DC voltage from capacitor

provides power to the loads. If there is any fault at load and if it is isolated, then the generated power is absorbed by the battery. The usage of solar panels in this system is because of their benefits. Solar panels are very reliable and require less maintenance. These are static devices, so there are no rotational losses. The solar panel system produces power in all types of weather

V. Raviprabhakaran and T. S. Mummadi

Fig. 6 DC SPS using the solar panel

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Fig. 7 Generator 1 and battery waveform

Fig. 8 Solar panel output power waveform

conditions. They produce nearly 80% of their potential energy on moderately gloomy days, and even in worst cases like extremely cloudy days, they produce 25% of their total potential.

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Fig. 9 Load output power waveform

5 Conclusion This paper proposes the controlling technique of DC power in the shipboard power system. The proposed method is applied to DC shipboard using solar panels, and outcomes are presented. The use of solar power reduces fuel cost and power generation losses. Moreover, solar power is eco-friendly and cheaper energy resources. The proposed energy storage system is capable of producing quality results in terms of controlling DC power in the shipboard. The results indicate that the system remains stable at every instance of time of disturbances, which are introduced at specific intervals of time. Furthermore, the disturbances and faults at generators for the Indian DC shipboard can be investigated.

References 1. R.G. Blakey, Power electronics in warships. Power Eng. J. 7(2), 65–70 (1993) 2. Webstar, Naval experience of power electronics maintenance. IEE Colloq. Power Electron. Reliab. 202 (1998) 3. Z. Jin, G. Sulligoi, R. Cuzner, L. Meng, J.C. Vasquez, J.M. Guerrero, Next-generation shipboard DC power system: introduction smart grid and dc microgrid technologies into maritime electrical networks. IEEE Electrif. Mag. 4(2), 45–57 (2016) 4. R. Vijay, Quorum sensing driven bacterial swarm optimization to solve bacterial swarm optimization to solve practical dynamic power ecological emission economic dispatch. Int. J. Comput. Methods 15(3), 1850089–24, (2018) 5. R. Vijay, Optimal and reliable operation of microgrid using enriched biogeography based optimization algorithm. J. Electr. Eng. 17(4), 1–11 (2018)

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6. R. Vijay, T. Pavithra, Cost optimization of energy storage systems based on wind resources using gravitational search algorithm. Int. J. Adv. Res. 5(5), 41667–1680 (2017) 7. R. Vijay, Transmission line outage detection and identification by communal spider optimization algorithm. CVR J. Sci. Technol. 14, 38–42 (2018) 8. G. Seenumani, J. Sun, H. Peng, Real-time power management of integrated power systems in all electric ships leveraging multi time scale property. IEEE Trans. Control. Syst. Technol. 20 (1), 232–240 (2012) 9. S.Y. Kim, S. Choe, S. Ko, K. Sul, A naval integrated power system with a battery energy storage system: Fuel efficiency, reliability, and quality of power. IEEE Electrification Mag. 3 (2), 22–33 (2015) 10. K.S,. Chandragupta Mauryan, T. Nivethitha, B. Yazhini, B. Preethi, Study on integration of wind and solar energy to power grid. Int. J. Eng. Res. Appl. 4, 67–71 (2014) 11. F. Shariatzadeh, N. Kumar, A.K. Srivastava, Optimal control algorithms for reconfiguration of shipboard microgrid distribution system using intelligent techniques. IEEE Trans. Ind. Appl. 53(1), 474–482 (2017) 12. E. Skjong, M. Rodskar, T.J. Molinas, J. Cunningham, The marine vessel’s electrical power system: from its birth to present day. Proc. IEEE 103, 2410–2424 (2015) 13. J.F. Hansen, J.O. Lindtjorn, U.U. Odegaard, Myklebust, Increased operational performance of OSVs by Onboard DC Grid, in 4th International Conference on Technology and Operation of Offshore Support Vessels (Singapore 2011) 14. ABB, Onboard DC grid. The step forward in power generation and propulsion, Technical report (2015) 15. ABB, The step forward onboard dc grid, Technical report (2014) 16. S. Chakraborty, M.G. Simões, W.E. Kramer, Power electronics for renewable and distributed energy systems. A Sourceb. Topol. Control. Integr. 99, 100 (2013) 17. K. Hutton, B. Babaiahgari, J.D. Park, A comparative study on electrical distribution systems for the US coast guard’s 270-ft medium endurance cutter, in North American Power Symposium (NAPS) (IEEE, 2016), pp. 1–6

Global Optimization Algorithm to Solve Economic Load Dispatch Problem Considering Equality and Inequality Constraints Prakash Arumugam and Anand Rajendran

Abstract This paper proposes a novel technique to solve various problems related to economic load dispatch. So far, lot of algorithms has been developed in order to obtain the optimal solution for the various problems related to ED. They are meta-heuristic in nature and exhibit their quality in terms of fuel cost reduction and computational time. The technique proposed in this article generates feasible solution than other algorithms. The efficacy of the proposed technique is proved by selecting various IEEE test systems involving 3, 6, 15, 40 generators which are analyzed and the results obtained are compared with recently reported algorithms. The obtained result shows that the proposed approach is efficient in producing lesser fuel cost and acts as an alternative algorithm to solve the ED problems in practical power systems.




Keywords Economic dispatch (ED) Global optimization algorithm (GOA) Meta-heuristic Valve-point loading (VPL)







1 Introduction Power system planning (PSP) is a major task involved in determining a minimum cost strategy for the purpose of supplying adequate load demand considering various constraints. In lieu, a global technique has been developed which significantly reduces the fuel cost. The input–output relation of generator is represented by a Please note that the LNNS Editorial assumes that all authors have used the western naming convention, with given names preceding surnames. This determines the structure of the names in the running heads and the author index. P. Arumugam (&) Department of Electrical and Electronics Engineering, QIS College of Engineering and Technology, Ongole, AP, India A. Rajendran Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_37

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single quadratic equation, whereas if the valve-point loading effect is included, the objective function becomes highly non-convex and non-smoother in nature. Representing the fuel cost function in a piecewise nonlinear form makes it to be realistic in nature. The solution gets trapped with multiple local minima, which causes difficulty for many algorithms. In addition, the inclusion of various constraints turns the problem even more complex and so obtaining the global optimum solution is impossible. Among various objectives, the important objective of ED is the reduction of fuel cost incurred for the power generation [1]. Based on the mathematical methods, many approaches have been framed and implemented in the past decades in the ED contest, for, e.g., gradient method, linear and nonlinear programming, quadratic programming, newton method, dynamic programming, and lagrangian relaxation techniques. Generally, the mathematical model requires the derivative function of the specific ELD problem without considering VPL effects. Many global optimizers have been developed at a later stage as an advancement of the previously developed mathematical models in order to solve the complex engineering problems. Some of them are differential evolution (DE) [2], modified DE (MDE) [3], bacterial foraging (BF) [4], harmony search (HS) [5], firefly algorithm (FA) [6], chaotic PSO (CPSO) [7], and chaotic ant swarm optimization (CASO) [8]. The aforementioned algorithms attain the near-global optimum solution which is not the exact solution of the optimization technique. Also, the stochastic optimization techniques suffer from the following problems: slow convergence; control parameters are sensitive; getting trapped in local minimum solution and premature convergence. Apart from the negatives listed, few positives are as follows: the improvement in the tabu search has a flexible memory for evaluating the local minima. PSO might produce a quality solution with less computational time. DE is better known for its faster convergence characteristics and its robustness. To overcome the limitations of the aforesaid algorithms, an alternative solution was developed which leads to the development of hybrid algorithms. The hybridization of two different algorithms helps in finding the minimum function. Few hybrid approaches developed in the past years are PSO-sequential quadratic programming (PSO-SQP) [9], chaotic-DE-SQP (CDE-SQP) [10], GA/PS-SQP [11], seeker optimization algorithm-SQP (SOA-SQP) [12], CPSO-SQP [13], bee colony optimization-SQP (BCO-SQP) [14], power search algorithm (PSA) [15], EP-SQP [16], QIPSO [17], MPSO [18], PSO [19], CPSO [20], VIKOR [21], PCFL [22], MVO [23], and NPSO [24]. In this paper, a new technique is proposed that involves the process of searching the global optimum solution in the entire search space in such a way that the search process does not get stuck at the local minimum. All the possible solvers are explored using this global optimization function. The time for execution obtained with the help of this global function is little bit higher than the other methods, since the minimum fuel cost is obtained only after the search criteria passes through all the solvers. The effectiveness of the technique is tested on standard IEEE test systems involving 3, 6, 15, 40 generator systems, wherein the proposed approach outperforms the other algorithms reported in the literature. The paper is organized in the following structure. Section 2 describes the problem statement of the ED problem considering VPL effects. In Sect. 3, the global

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optimization technique is described. The implementation of the algorithm to solve the standard IEEE test systems and its comparison with other algorithms are explained in Sect. 4. Finally, the observations of this paper are recapitulated in Sect. 5.

2 Problem Statement The objective of the ELD problem is to minimize the fuel cost incurred at the power plants and also meet the power system demand by satisfying the equality and constraints. The simplified fuel cost function of the generator can be represented by the quadratic equation Minimize C ¼

n X   C Pj

ð1Þ

j¼1

and   C Pj ¼ aj þ bj Pj þ cj P2j

ð2Þ

where aj, bj, and cj are the fuel cost coefficients of the generator. As the unit slowly varies with the operating region, the input and the output data are measured. As a result, the generator cost function is obtained with the help of the data point taken from the “heat run” test. The rippling effect on the unit curve occurs when the steam admission valve in a turbine begins to open. As a factor of considering this effect, the valve-point effects must be included to the basic fuel cost function, which is given by     C Pj ¼ aj þ bj Pj þ cj P2j þ jej ðsinðfj  Pjmin  Pj

ð3Þ

where ej and fj represent the VPL effect coefficients. The inclusion of the VPL effects causes a problem to all optimization methods as it rises the nonlinearity of the search space. The VPL effect is represented in Fig. 1. Constraints Equality Constraint: For obtaining the minimum fuel cost as per the Eq. (3), the equality constraint has to be satisfied. It is given by n X i¼1

Pj ¼ PD þ PL

ð4Þ

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5

Input - Mbtu/h

4 3 2 Quinary valve

1

Quaternary valve

Tertiary valve Secondary valve Primary valve fully open Output - MW

Fig. 1 Input–Output characteristics of a thermal unit illustrating valve-point loading effect

where PD and PL are the total demand and power loss, respectively, in MW. PL is calculated using the power loss coefficients and is expressed in the quadratic form as PL ¼

n n n X X X

Pi Bij Pj þ b0i Pi þ b00 i¼1

j1

ð5Þ

i¼1

where Bij, b0i , and b00 are the coefficients, given by the symmetric matrix n  n, length n and constant, respectively. Inequality Constraint: The power generated for each generator should lie between the upper and lower limits which is given by Pmin  Pj  Pmax j j

ð6Þ

where Pmin and Pmax are the lower and upper power limit of the jth generator in j j MW.

3 Proposed Technique In the past decades, many optimization algorithms including hybrid techniques have been developed for finding the minimal fuel cost for various ELD problems. Each defined algorithm exhibits its own behavior based upon its characteristics.

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All those have been applied to multiple engineering problems for finding the optimal solution. But still, the determination of optimal fuel cost gets retarded due to its trapping at local minima. To overcome this issue, a global technique has been developed which helps in determining the lowest fuel cost for various ELD problems. The advantage of this algorithm is that it travels throughout the entire search space so that global optimum solution can be obtained. Furthermore, the application of this algorithm simple to large systems provides the promising result when compared to other state-of-the-art algorithms. Pseudocode for Global Optimization Algorithm (GOA) 1. Input the Objective function for the GOA 2. Let the sample size be N 3. Initialize the power demand, minimum power and maximum power, power loss coefficients 4. Section 1 Initialize x0, lb Initialize r, s, t Define T1, T2, T3, F1, F2 and F3 Tc = T1 + T2 + T3 Problem = (createOptimproblem(‘fmincon’‘objective’, @(x)GlobalOptim(x), x0, lb, ‘options’, optimset(‘algorithm’, ‘sqp’)) gs = globalsearch; [x, feval] = run(gs, problem) 5. Section 2 Function(c, ceq) = GlobOptim(x) p(1) = {x(1) + x(2)*x(10) + x(3)*x(10)^2} p(2) = {x(4) + x(5)*x(10) + x(6)*x(10)^2} p(3) = {x(7) + x(8)*x(10) + x(9)*x(10)^2} C = {p(1)-pmax1 p(2)-pmax2 p(3)-pmax3; -p(1)-pmin1 - p(2) + pmin2; -x(10)} Ceq = sum(p)-pd-pl 6. End the loop 7. Return the minimum fuel cost.

4 Results and Discussion To test the efficacy of the algorithm, the following benchmark systems of the ELD problems have been considered. System 1: 3-generator system with PD = 850 MW neglecting transmission loss System 2: 6-generator system with PD = 1263 MW considering transmission loss

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System 3: 15-generator system with PD = 2630 MW considering transmission loss System 4: 40-generator system with PD = 10,500 MW neglecting transmission loss. The implementation of the algorithm was carried out in MATLAB R2016a on a personal computer with 4 GB RAM, corei3 processor using Windows 10 operating system.

4.1

Three-Generator System with PD = 850 MW Neglecting Transmission Losses

In this system, a 3-unit system with a demand of 850 MW is considered without transmission loss. As the dimension of the system is low, the search space is low and not very complex. Unlike GOA, the other algorithms failed to obtain the optimal solution with several trial runs. Moreover, this algorithm although takes higher computational time than the other methods, its primary objective is obtained by executing all the possible solvers. The obtained results are depicted in Table 1. The proposed global methodology yields the optimum fuel cost of 8198.06 $/h by executing all the 72 possible local solvers with an execution time of 3.12 s. When analyzed, the fuel cost gets reduced by 36.01 $/h with PSO-SQP [9], 36.04 $/h with GA-PS-SQP [11] and QIPSO [17], 36.66 $/h with PSO [19] and Fig. 2 pictures the comparison results between GOA and other reported optimization techniques for a demand of 850 MW.

4.2

Six-Generator System with PD = 1263 MW with Transmission Losses

In this section, the 6-generator system is considered with the demand of 1263 MW with transmission loss. The generator characteristic for 6-unit system is presented in Table 2. Table 3 epitomizes the result obtained for the 6-unit system with a load of 1263 MW considering the line losses. It is seen that the proposed methodology

Table 1 Comparison of the results obtained by GOA with other techniques/algorithms for three-generator system Algorithms

Minimum fuel cost ($/h)

Global Optimization Algorithm (GOA) PSO-SQP [9] GA-PS-SQP [11] QIPSO [17] PSO [19]

8198.06 8234.07 8234.10 8234.10 8234.72

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387

Fig. 2 Comparison of fuel costs in dollar per hour for three-generator case with PD = 850 MW

outperforms the other reported algorithms in terms of obtaining lesser fuel cost and transmission line losses. The GOA proves to be more efficient than MPSO [18] by 1.1 MW reduction in fuel cost and 1.108 kg/h of loss, 1.41 MW reduction in fuel cost and 1.333 kg/h of line loss than PSO [19], and 1.43 MW—1.397 kg/h of fuel cost and line loss, respectively. When compared with CPSO method 1 [20]—CPSO method 2 [20], the fuel cost gets reduced by 4 and 3 $/h, respectively. The line loss gets reduced by 0.0003 kg/h when compared with CPSO method 1 [20] and 0.0002 kg/h when compared with CPSO method 2 [20]. On execution, it is observed that a total 6 out of 8 solver runs converged with a positive local solver and the time taken for the execution is 4.32 s, which is slightly higher than the other reported algorithms. The graphical comparison of the results obtained for fuel cost, line loss, and its comparison with other algorithms is shown in Figs. 3 and 4, respectively.

4.3

Fifteen-Generator System with PD = 2360 MW with Transmission Losses

A 15-generator system with the demand of 2630 MW with transmission loss is considered for analysis. The characteristic of the 15-generator system is given in Table 4. The simulation was carried out for 15 unit system and the results obtained are presented in Table 5. From the results, it is evident that the proposed GOA outperforms the other reported algorithms in terms of fuel cost and line losses. The computational time required for obtaining the minimum fuel cost is found to be 9.36 s. On comparing with PSO [19], CPSO method 1 [20], and CPSO method 2

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Table 2 Characteristics of six-generator system Unit

Pmin i (MW)

Pmax i (MW)

ai ($/ MW2)

bi ($/ Mw)

ci ($)

P0i

URi (MW/h)

DRi (MW/h)

Prohibited zones (MW)

1

100

500

0.0070

7.0

240

440

80

120

2

50

200

0.0095

10.0

200

170

50

90

3

80

300

0.0090

8.5

220

200

65

100

4

50

150

0.0090

11.0

200

150

50

90

5

50

200

0.0080

10.5

220

190

50

90

6

50

120

0.0075

12.0

190

110

50

90

[210 240] [350 380] [90 110] [140 160] [150 170] [210 240] [80 90] [110 120] [90 110] [140 150] [75 85] [100 105]

Table 3 Comparison of the results obtained by GOA with other techniques/algorithms for six-generator system Algorithms Global Optimization Algorithm (GOA) MPSO [18] PSO [19] CPSO method 1 [20] CPSO method 2 [20]

Load supplied = 1263 MW Total output (MW) Line loss (kg/h) 1274.6 11.625

Fuel cost ($/h) 15442.23

1275.7 1276.01 1276 1276

15,447 15,450 15,447 15,446

12.733 12.958 12.9583 12.9582

[20], the total fuel cost gets reduced by 26 $/h, 3 $/h and 2 $/h, respectively. The total line loss gets reduced by 0.34 kg/h, 0.09 kg/h, and 0.0987 kg/h when compared with PSO [19], CPSO method 1 [20], and CPSO method 2 [20]. The computational time required for obtaining the minimum fuel cost is found to be 9.36 s. Figure 4 depicts the comparison of fuel cost obtained by GOA and its comparison with other optimization techniques.

4.4

Forty-Generator System with PD = 10500 MW Without Transmission Losses

This section deals with 40-generator system considering valve-point loading effects and neglecting transmission losses. The global optimization has been implemented on the system chosen to extract the effectiveness of the larger system. The cost

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389

Fig. 3 Comparison of fuel costs in dollar per hour for six-generator case with PD = 1263 MW

Fig. 4 Comparison of fuel costs in dollar per hour for fifteen-generator case with PD = 2630 MW

function data for the 40 units system are given in [9]. The proposed methodology has been applied, and the obtained results show that the fuel cost obtained remains superior when compared with the other reported algorithms. The minimum generation cost is found to be 121,411.623 $/h, which is comparatively less compared to other algorithms. Table 6 depicts the individual power generation.

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Table 4 Characteristics of fifteen-generator system Unit

Pmin i (MW)

Pmax i (MW)

ai ($/MW2

bi ($/Mw)

ci ($)

P0i

URi (MW/h)

DRi (MW/h)

1 2

150 150

455 455

0.000299 0.000183

10.1 10.2

671 574

400 300

80 80

120 120

3 4 5

20 20 150

130 130 470

0.001126 0.001126 0.000205

8.8 8.8 10.4

374 374 461

105 100 90

130 130 80

130 130 120

6

135

460

0.000301

10.1

630

400

80

120

7 8 9 10 11 12 13

135 60 25 25 20 20 25

465 300 162 160 80 80 85

0.000364 0.000338 0.000807 0.001203 0.003586 0.005513 0.000371

9.8 11.2 11.2 10.7 10.2 9.9 13.1

548 227 173 175 186 230 225

350 95 105 110 60 40 30

80 65 60 60 80 80 80

120 100 100 100 80 80 80

14 15

15 15

55 55

0.001929 0.004447

12.1 12.4

309 323

30 20

55 55

55 55

Prohibited zones (MW) [185 225] [305 335] [420 450]

[180 [305 [390 [230 [365 [430

200] 335] 420] 255] 395] 455]

[30 40] [55] [185 225] [305 335] [420 450]

Table 5 Comparison of the results obtained by GOA with other techniques/algorithms for fifteen-generator system Algorithms Global optimization algorithm (GOA) PSO [19] CPSO method 1 [20] CPSO method 2 [20]

Load supplied = 2630 MW Total output (MW) Line loss (kg/h)

Fuel cost ($/h)

2662.0316

32.0316

32,832

2662.4306 2662.1302 2662.1303

32.4306 32.1302 32.1303

32,858 32,835 32,834

The results of the proposed scheme have been compared with PSO-SQP [9], GA-PS-SQP [11], and NM-PS [25]. Moreover, the average time using global optimization technique is found to be 34.23 s which is less when compared with many techniques presented in Table 7. Figure 5 represents the fuel cost comparison values obtained by GOA with other optimization techniques.

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391

Table 6 Best solution obtained by GOA for the 40-unit system with PD = 10,500 MW Generator

Power (MW)

P1 110.7998 P2 110.7996 P3 97.3999 P4 179.7331 P5 87.7999 P6 140 P7 259.5999 P8 284.5999 P9 284.5997 P10 130 P11 94 P12 94 P13 214.7598 P14 394.2796 P15 394.2496 P16 394.2794 P17 489.2821 P18 489.2821 P19 511.2796 P20 511.2796 Expected Power Generated Total Power Generated (MW) Deviation in the power generation Total fuel cost ($/h) Computational time (s)

Generator

Power (MW)

P21 P22 P23 P24 P25 P26 P27 P28 P29 P30 P31 P32 P33 P34 P35 P36 P37 P38 P39 P40

523.2796 523.2796 523.2794 523.2795 523.2795 523.2795 10 10 10 87.8009 190 190 190 164.7998 194.4138 200 110 110 110 511.2802 10,500 10,500 0 121411.623 34.23

Table 7 Comparison results between GOA and other techniques for PD = 10,500 MW Algorithms

Minimum fuel cost ($/h)

Mean time (s)

PSO-SQP [9] GA-PS-SQP [11] NM-PS [25] Global optimization algorithm (GOA)

122094.670 121458.000 121412.574 121411.623

73.970 46.980 71.453 34.23

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Fig. 5 Comparison of fuel costs in dollar per hour for 40-generator case with PD = 10500 MW

5 Conclusion The improvement in the SQP with global search option is presented in this paper. Four different standard IEEE test systems are chosen for proving the robustness and efficacy of the algorithm. The results are epitomized from Tables 1, 2, 3, 4, 5, 6, and 7. The results show that the GOA has the best quality solutions in terms of reduced fuel cost and transmission losses for adopted systems. The computational time alone is a major concern and it is possibly due to the wide search of all the possible solvers in the search space. The proposed technique can be implemented for the large-scale power systems in the future.

References 1. J.B. Park, Y.W. Jeong, H.H. Kim, An improved particle swarm optimization for economic dispatch with valve-point effect. Int. J. Innov. Energy Syst. Power 1, 1–7 (2006) 2. N. Noman, H. Iba, Differential evolution for economic load dispatch problems. Electr. Power Syst. Res. 8, 1322–1331 (2008) 3. N. Amjady, H. Sharifzadeh, Solution of non-convex economic dispatch problem considering valve loading effect by a new modified differential evolution algorithm. Int. J. Electric Power Energy Syst. 8, 893–903 (2010) 4. B.K. Panigrahi, V.R. Pandi, Bacterial foraging optimisation: Nelder-Mead hybrid algorithm for economic load dispatch. IET Gener. Transm. Distrib. 4, 556–565 (2008) 5. L.S. Coelho, V.C. Mariani, An improved harmony search algorithm for power economic load dispatch. Energy Convers. Manag. 10, 2522–2526 (2009)

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6. X.S. Yang, S.S.S. Hosseini, A.H. Gandomi, Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl. Soft Comput. 3, 1180–1186 (2011) 7. J. Cai, X. Ma, L. Li, P. Haipeng, Chaotic particle swarm optimization for economic dispatch considering the generator constraints. Energy Convers. Manag. 2, 645–653 (2007) 8. J. Cai, X. Ma, L. Li, Y. Yang, H. Peng, X. Wang, Chaotic ant swarm optimization to economic dispatch. Electric Power Syst. Res. 77(10), 1373–1380 (2007) 9. T.A.A. Victoire, A.E. Jeyakumar, Hybrid PSO–SQP for economic dispatch with valve-point effect. Electric Power Syst. Res. 1, 51–59 (2004) 10. L.S. Coelho, V.S. Mariani, Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect. IEEE Trans. Power Syst. 2, 989–996 (2006) 11. J.S. Alsumait, J.K. Sykulski, A.K. Al-Othman, A hybrid GA–PS–SQP method to solve power system valve-point economic dispatch problems. Appl. Energy 5, 1773–1781 (2010) 12. S. Sivasubramani, K.S. Swarup, Hybrid SOA–SQP algorithm for dynamic economic dispatch with valve-point effects. Energy 12, 5031–5036 (2010) 13. J. Cai, Q. Li, L. Li, H. Peng, Y. Yang, A hybrid CPSO–SQP method for economic dispatch considering the valve-point effects. Energy Convers. Manag. 1, 175–181 (2012) 14. M. Basu, Hybridization of bee colony optimization and sequential quadratic programming for dynamic economic dispatch. Int. J. Electric Power Energy Syst. 1, 591–596 (2013) 15. A. Prakash, C.S. Ravichandran, Power search algorithm (PSA) for combined economic emission dispatch problems considering valve point effects in economic load dispatch. Turkish J. Electr. Eng. Comput. Sci. 6, 4647–4656 (2017) 16. T.A.A. Victoire, A.E. Jeyakumar, A modified hybrid EP–SQP approach for dynamic dispatch with valve-point effect. Int. J. Electric Power Energy Syst. 8, 594–601 (2005) 17. M. Ke, H.G. Wang, Z.Y. Dong, K.P. Wong, Quantum-inspired particle swarm optimization for valve-point economic load dispatch. IEEE Trans. Power Syst. 1, 215–222 (2010) 18. C.C. Kuo, A novel coding scheme for practical economic dispatch by modified particle swarm approach. IEEE Trans. Power Syst. 4, 1825–1835 (2008) 19. Z.L. Gaing, Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans. Power Syst. 3, 1187–1195 (2003) 20. C. Jiejin, Ma. Xiaoqian, L. Lixiang, P. Haipeng, Chaotic particle swarm optimization for economic dispatch considering the generator constraints. Energy Convers. Manag. 48, 645– 653 (2006) 21. O.P. Akkas, Y. Arikan, E. Çam, Load dispatch for a power system in terms of economy and environment by using VIKOR method. J. Sci. Eng. 57, 733–741 (2017) 22. B. Taheri, G. Aghajani, M. Sedaghat, Economic dispatch in a power system considering environmental pollution using a multi-objective particle swarm optimization algorithm based on the Pareto criterion and fuzzy logic. Int. J. Energy Environ. Eng. 2, 99–107 (2017) 23. H. Singh, S. Mehta, S. Prashar, Economic load dispatch using multi verse optimization. Int. J. Eng. Res. Sci. 6, 43–51 (2017) 24. N. Singh, Y. Kumar, Multiobjective economic load dispatch problem solved by new PSO. Adv. Electr. Eng., 1–6 (2015) 25. C. Zafar, M. Hasan, A. Mohammad, Design of reduced search space strategy based in integration of Nedler-Mead method and pattern search algorithm with application to economic load dispatch problem. Neural Comput. Appl. 12, 3693–3705 (2017)

Implementation of Conventional Controllers in HVDC Links for Improvement of the Power System Stability G. Ranga Purushotham, S. Satyanarayana and Ch. Saibabu

Abstract In recent years, the trend has been toward simpler models like DC system for stability programs. A DC link is highly controllable; this unique characteristic of the DC line is used to increase the value of the transient stability of the AC systems. Hence, it is proposed to incorporate the DC link with conventional controllers like proportional and proportional integral controllers to maintain the system transient stability by controlling the power flow through DC link. DC links can be represented as algebraic equations, and the interface between AC and DC systems is treated in a manner similar to that described for power flow analysis as in conventional power flow analysis. It is assumed that reactive power in injected at the AC terminals of the converters, the power flows are simulated by Newton– Raphson method, and the stability analysis is carried out by point-by-point method.







Keywords HVDC links Proportional controller Proportional integral controller Transient stability NR method Point-by-point method Power world simulator













1 Introduction Some of the early efforts to incorporate HVDC system models into stability programs used detailed representation which accounted for the dynamics of the line and the converter controls. In recent years, the trend has been toward simpler models. Such models are adequate for general-purpose stability studies of systems

G. Ranga Purushotham (&) Guru Nanak Institutions Technical Campus, Ibrahimpatnam, India S. Satyanarayana Raghu Institute of Technology, Visakhapatnam, India Ch. Saibabu JNTUK, Kakinada, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_38

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in which the DC link is connected to strong parts of the AC systems. However, for weak AC system application, requiring complex DC system controls, and for multiterminal DC systems, detailed models are required [1]. Therefore, the preference is to have flexible modeling with a wide range of control. The required degree of control depends on the purpose of the study and the particular DC systems. In transient stability studies, the generators are modeled as the classical model— two-axis model and one-axis model. The classical model is the simplest, and the synchronous machine is represented by a voltage source of constant magnitude in series with the direct-axis transient reactance. In this presentation, it is proposed to discuss the impact of dynamic system modeling of HVDC systems for the power system stability. The HVDC line modeled for a power system network is used for the transient stability analysis. In this presentation, the stability of the 9-bus power system network under transient fault conditions is proposed to be analyzed for the following cases: (a) When there are no HVDC lines in the power system network, i.e., the power system network is purely an AC network. (b) When HVDC lines with proper control methods are incorporated. For remote DC links, which do not have significant impact on the results of the stability analysis, very simple models are usually adopted. However, for nearby faults, one has to consider physical constraints on tap-changing ratios, reactive limits, etc., to be realistic. The DC links may be represented as constant active and appear as algebraic equations, and the interface between AC and DC systems is treated in a manner similar to that of power flow analysis in conventional power flow analysis (Fig. 1).

Fig. 1 IEEE 9-bus system

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2 Transient Stability of Network with AC Transmission Lines 2.1

Generator Data

S. no

Particulars

Generator 1

Generator 2

Generator 3

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Rated MVA KV PF Type Speed (rev/min) Xd Xd′ Xq Xq′ Xl (leakage) Stored energy at rated speed

247.5 16.5 1.0 Hydro 180 0.146 0.0608 0.0969 0.0969 0.0336 2364 MW–S

192.0 18.0 0.85 Steam 3600 0.8958 0.1198 0.8645 0.1969 0.0521 640 MW-S

128.0 13.8 0.85 Steam 3600 1.3125 0.1813 1.2578 0.25 0.0742 301 MW –S

2.2

Load Data

The data of the three loads present in the 9-bus power system network considered in this project is as shown [2]. S. no.

Load

MW

MVAR

1. 2. 3.

1 2 3

125 100 90

50 35 30

2.3

Power Flow Calculations

Power flows of the 9-bus system under fault condition: For the 9-bus with the power flows as described, it is assumed that a three-phase balance fault has occurred on the line between buses 4 and 6, and the fault is located near to the bus 6.

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Transient Stability of the 9-Bus System

Output power of generator 1 Po1 ¼ E12 Y1 cos h11 þ E1 E2 Y12 cosðh12 d1 þ d2 Þ þ


þ E1 En Y1n cosðh1n d1 þ dn Þ ¼K RE1 Ek Y12k cosðh1k d1 þ dk Þ

ð1Þ

Output power of generator 2 Po2 ¼ E2 E1 Y21 cosðh21 d2 þ d1 Þ þ E22 Y22 cos h22 þ


þ E2 En Y2n cosðh2n d2 þ dn Þ X ¼ E2 EK Y2k cosðh2k d2 þ dk Þ

ð2Þ

Output power of generator 3 Po3 ¼ E3 E1 Y31 cosðh31 d3 þ d1 Þ þ E3 E2 Y32 cosðh32 d3 d2 Þ þ E32 Y33 cosh33 þ


þ E3 En Y3n cosðh2n d2 þ dn Þ n ¼ RE3 EK Y3k cosðh3k d3 þ dk Þ ð3Þ From the general equations, described by Eqs. (1), (2), and (3), the output power at generators 1, 2, and 3 are given as below: Output of generator 1 Po1 ¼ E12 Y11 cos h11 þ E1 E2 Y12 cosðh12 d1 þ d2 Þ þ E1 E3 Y13 cosðh13 d1 þ d3 Þ

ð4Þ

Output of generator 2 Po2 ¼ E2 E1 Y21 cosðh21 d2 þ d1 Þ þ E22 Y22 cosðh22 Þ þ E2 E3 Y23 cosðh23 d2 þ d3 Þ

ð5Þ

Output of generator 3 Po3 ¼ E3 E1 Y31 cosðh31 d3 þ d1 Þ þ E3 E2 Y32 cosðh32 d3 þ d2 Þ þ E32 Y33 cosðh33 Þ gene

ð6Þ

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399

Generator Model

The inertia constants of the machines from the data given are calculated by the following formula M¼

GH 180 f

ð7Þ

where M = inertia constant in per unit. G = station rating in per-unit apparent power. H = kinetic energy at rated speed in megajoules per MVA of rating. f = frequency in hertz (it is assumed as 50 Hz). The inertia constants of the three machines are given in the following equations: M1 ¼

2:4759:57 ¼ 2:63175103 P:U 18050

ð8Þ

M2 ¼

1:92  3:922 ¼ 8:367  104 P:U 180  50

ð9Þ

M3 ¼

1:28  2:766 ¼ 3:934  104 P:U 180  50

ð10Þ

The time interval Δt for point-by-point calculations will be taken as 0.1 s. Then Dt2 ð0:1Þ2 ¼ ¼ 3:799 M1 2:632  103

ð11Þ

Dt2 ð0:1Þ2 ¼ ¼ 11:9517 M2 8:367  103

ð11Þ

Dt2 ð0:1Þ2 ¼ ¼ 11:9517 M2 8:367  103

ð12Þ

Dt2 ð0:1Þ2 ¼ ¼ 25:42 M3 3:934  104

ð13Þ

The results of the computations of swing curves, namely the angular positions of the three machines as functions of time, are given in Fig. 2.

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Fig. 2 Stability curves for the system with AC lines

3 Modeling of HVDC Transmission Line in Stability Studies Specification of DC Line between buses 4 and 5 [3]. Rectifier parameters: No. of bridges Base voltage (KV) XF ration XF tap XF min. tap XF max. tap XF tap step Commutating XF resistance Commutating XF resistance Minimum firing angle Firing angle

2 345.0 0.5578 1.5 0.51 1.5 0.000625 0.000 10.0 15.0 48

Inverter parameters: No. of bridges Base voltage (KV) XF ration XF tap XF min. tap XF max. tap XF tap step Commutating XF resistance Commutating XF resistance Minimum firing angle Firing angle

2 345.0 0.5578 1.5 0.51 1.5 0.000625 0.0001 10.0 15.0 15.6

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4 Power System Stability with Controllable DC Transmission Line Two stabilizing controls, a proportional controller (P controller) and a proportional integral controller (PI controller), are designed to stabilize the power system. The controls used are to alter power flow setting in the DC line [4].

4.1

Proportional Controller (P–Controller)

The proportional controller is a device that produces an output signal, u(t), which is proportional to the input signal, e(t) uð t Þ / e ð t Þ uð t Þ ¼ K p e ð t Þ

ð14Þ

where Kp = proportional constant or proportional gain. So, the transfer function of the proportional controller is given by GcðSÞ ¼

4.2

U ðsÞ ¼ Kp E ðsÞ

ð15Þ

Proportional Integral Controller

It is a device that produces an output signal, u(t) [5], consisting of two terms—one proportional to the input signal, e(t), and the other proportional to the integral for the input signal, e(t) i.e.,   Z uð t Þ / e ð t Þ þ eðtÞdt

ð16Þ

Z uðtÞ ¼ Kp eðtÞ þ Ki

eðtÞdt:

ð17Þ

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where Kp = proportional constant or proportional gain. Ki = integral constant or integral gain. The transfer function of proportional integral controller is given by GcðSÞ ¼

U ðsÞ Ki ¼ Kp þ E ðsÞ S

ð18Þ

Augmenting System Stability Using a Proportional Control. The power system stability of the 9-bus power system network with presence of the HVDC transmission line between buses 4 and 5 is augmented by using a proportional control. Based on the error signal defined, the power flow in the DC transmission line is given as Kþ1

Pdi

¼ Pkdi Kp ek

where Pdi = active power flow at the inverter terminals. K = time step. e = error signal. Kp = proportionality constant (Fig. 3). Fig. 3 Stability curves for the system with HVDC lines and P controller

ð19Þ

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Fig. 4 Stability curves for the system with HVDC lines and PI controller

From the figure (x-axis as time and y-axis as rotor angle), we can observe that all the generators are in synchronism and swing together. From this, we can conclude that the system is maintained in stable state even though there is a transient fault on the system between the lines 4 and 6. Augmenting System stability Using Proportional Integral Control. The power system stability of the 9-bus power system network with presence of the HVDC transmission line between buses 4 and 5 is augmented by using a proportional integral control. Based on the error signal defined, the power flow in the DC transmission line is given as Z Pdi ¼

Pkdi Kp ek Ki

eðtÞdt

ð20Þ

The system stability with presence of proportional control is calculated by the point-by-point method and the results of generator angles with respective to the time computed (Fig. 4). From the figure (x-axis as time and y-axis as rotor angle), we can observe that all the generators swing together. Hence, we can conclude that the system is maintained in stable state even though there is a transient fault on the system between the lines 4 and 6. On comparing proportional integral control with the proportional control, the system stability obtained by the proportional integral control is considered better than the proportional control.

5 Conclusion By defining an error signal as power mismatches at generator buses, the generator 1 is made to accelerate. The system stability is augmented by the proportional control and proportional integral control separately, and the plots of generator angles are plotted. The system stability is considerably improved by including a feedback

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proportional controller and further enhanced by a feedback proportional integral controller, which adapt the HVDC line settings during transient. On comparison between the controllers, the proportional integral controller performs better dynamically than the proportional controller.

References 1. B. Scott, J. Jardim, O. Alsaç, DC power flow revisited. IEEE Trans. Power Syst. 24(3) (2009) 2. H. Rahman, B.H. Khan, Possibility of power tapping from composite, AC–DC power transmission lines. IEEE Trans. Power Deliv. 23(3) (2008) 3. J. Arillaga, High Voltage Direct Current Transmission, 2nd edn. IEEE Power and Energy Series 29 (1998) 4. C. Lu, J. Si, X. Wu, P. Li, Approximate dynamic programming coordinated control in multi-infeed H V D C power system. IEEE Trans. Neural Netw. (2006) 5. G.M. Huang, V.K. Swamy, H V D C controls for P.S stability. IEEE Trans. Power Syst. (2002)

Low Voltage Ride Through (LVRT) Capability Enhancement of Axial Flux Induction Generator-Based Wind Energy Conversion System V. Ramesh Babu and A. Ganapathi

Abstract With the remarkable increase in wind energy installed capacity, the wind energy conversion systems are also needed to be treated as conventional energy resources. These wind farms are required to be connected to grid as per grid codes during and as well as after the occurrence of short fault. In this paper, the different techniques have been proposed to enhance the low voltage ride through (LVRT) capability to axial flux induction generator (AFIG)-based wind energy conversion system (WECS). A mathematical model is developed, and simulation studies have been carried out in the environment of MATLAB/Simulink to study the effectiveness of the techniques. Also, the proposed system is compared with doubly fed induction generator-based WECS.







Keywords Axial flux induction generator Low voltage ride through Doubly fed induction generator Superconducting magnetic energy storage Wind energy conversion system







1 Introduction The stability and security are the vital considerations for assessing the performance of any energy supply utility. The frequency of the power outages can be minimized by providing a well-organized control and protection mechanisms. Being a considerable contributor for power generation to meet the power demand, the renewable energy resources need to contribute to the grid stability. The wind farms are required to be tied to grid as per grid codes to satisfy the reliability considerations during and after a short-term fault. In the recent past, the DFIG-based wind energy system is being the most popularly used. To enhance the continuity and reliability of the wind systems, the low voltage ride through (LVRT) capability needs to be increased which provides the protection instead of tripping off the WECS from the circuit. Many ways are proposed to enhance the LVRT capability of the wind V. Ramesh Babu
A. Ganapathi (&) EEE Department, VNRVJIET, Hyderabad, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_39

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systems in the literature like the crowbar method to suppress the overvoltage and overcurrent during the fault condition, but it requires the large reactive power from the supply [3–5]. Some of the methods are related to controlling the converter circuits from the grid side and stator side [6–8], and it has disadvantages like voltage drops. The super-capacitors or energy storage devices are used to eliminate problems of low voltages during faults occurs [12]. The axial flux induction generator (AFIG) is a special type of machine which has both the characteristics and advantages of the induction machines and the axial flux machines. The axial flux is the flux which acts along the axis of the rotor. The advantages of the AFIG compared to the DFIG are as follows. 1. 2. 3. 4. 5.

Higher power density. Large surface area compared to size of the machines. More efficient topology in the electromagnetic sense. Windings are fully active. Efficient cooling because the windings are directly in contact with the exterior aluminum outside casing.

In axial flux machine, reactive power exchange with the grid is not determined, but it can be determined by the behavior of the generator characteristics of the grid-side power electronic converter. The axial flux machine is generally disconnected from the grid to control the power factor of axial flux machine and the grid-side converter independently. This paper is proposed for the new method of enhancing LVRT capability of AFIG with SMES for the power quality issues’ compensation. It is useful for the generation of desired output voltage with SMES on stator side. The SMES is useful for the compensation of the overvoltage and overcurrent during the low voltage fault condition. The properties of the AFIG in normal condition and at low voltage fault are obtained, and a control strategy of the VSC-SMES will be given. The paper consists of the detailed modeling of the AFIG, modeling of SMES and the controlling of the SMES circuit. And the model is analyzed with the MATLAB/Simulink tool by the analysis of the simulation results.

2 Mathematical Model of AFIG Having high pole number, the axial flux machines are well alternatives for low-speed applications. The axial flux induction generator is connected to the circuit through the converter topology proposed. The stator is directly connected to the grid. When fault occurs, the stator voltage decreases and stator current increases. This low voltage and overcurrent at stator can be minimized with a series connection of SMES at stator side. The equations which define the electromagnetic and mechanical performance do not depend on the direction of flux in the air gap. The dynamic model of an AFIG is got from basic three-phase machine

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Fig. 1 Equivalent circuit of induction generator

equations which got transformed to the d–q frame. Figure 1 demonstrates the equivalent circuit of induction generator. did  xLq2 iq2 dt

ð1Þ

diq þ xLd1 id1 þ xWsM dt

ð2Þ

ud1 ¼ Rs id1 þ Ld1 uq2 ¼ Rs iq2 þ Lq2

where Ld1, Lq2, Rs and wsM are the stator inductances of d and q axes, stator resistance and the stator flux linkages, respectively. Equation (3) relates the electromagnetic torque with the electrical parameters in AFIG.
 Te ¼ 1:5p Ld1 id iq  Lq2 id iq þ iq WsM

ð3Þ

Back-EMF voltage can be derived as follows:   urdq¼ jðLm =Ls Þxs Ws2dq  jðLm =Ls Þxr Ws0dq  Ws2dq ert ejxs t

ð4Þ

where ws0dq and ws2dq are the stator flux at normal state and fault state of stator flux. The back-EMF voltage is generated by SMES in order to overcome overvoltages during fault condition.

3 MATLAB Model of AFIG with SMES The MATLAB model of the proposed system is as shown in Fig. 2 in which the SMES output voltage can be controlled through the gate switching of the converter circuit. The model for proposed topology is as shown in Fig. 2 having the grid-side converter (GSC) and the stator-side converter with the VSC-connected SMES circuit. The VSC-SMES for the active and reactive power transfer is shown in Fig. 3. The superconducting magnetic energy storage (SMES) is used for the power exchange with fast response and independent. Superconducting magnetic energy storage (SMES) systems are capable of storing bulk amount of electrical power in superconducting coils in the form of a steady magnetic field. And these systems are

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Fig. 2 MATLAB model of AFIG with SMES

Fig. 3 Circuit of VSC-SMES

having higher efficiencies (>90%) and the fast response ( < PGk  PGk  PGk min max k ¼ 1; 2; . . .; NG VGk  VGk  VGk > : min max QGk  QGk  QGk tkmin  tk  tkmax

k ¼ 1; 2; . . .; NT

ð7Þ

ð8Þ

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T. Srihari et al. max Qmin ck  Qck  Qck

k ¼ 1; 2; . . .; NC

ð9Þ

min max  VLk  VLk VLk

k ¼ 1; 2; . . .; LB

ð10Þ

Slk  Smax lk

k ¼ 1; 2; . . .; nl

ð11Þ

3 Genetic Algorithm with Multi-Parent Crossover (GA-MPC) From the last three decades, different GAs have been developed to find solution for various optimization problems. But the efficiency of these algorithms relies on complexity of the problem. However, in few situations, GA algorithms are not performed well in comparison with other algorithms [11]. Therefore, recently the performance of GA is enhanced by replacing the normal crossover with new multi-parent crossover and diversity operator [12]. The new crossover utilizes three parent chromosomes to generate three new child chromosomes, in this out of three, two for exploitation, while the third one is for enhancing exploration. The diversity operator may help to avoid local optima. The procedure for GA-MPC takes place by means of three genetic operators, which are expressed below

3.1

Parent Selection

In this selection process, the parent chromosomes are used to generate new child chromosomes. Here, roulette wheel-based selection has been used to pick the chromosomes based on their fitness value relative to the fitness value of other population [12].

3.2

Proposed Three-Parent Crossover

Crossover operator is the heart of genetic algorithm. The crossover used in the current work is based on random crossover and the procedure steps are given below [12]; 1. Select the three chromosomes by using roulette wheel selection. 2. If one of the selected individual is same with another, then it is replaced with a random chromosome from the selection pool. 3. Rank the above-selected chromosomes based on their fitness value.

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4. A random number ‘a’ is generated between 0 and 1. 5. New offsprings are generated by using below equations

O1 ¼ x1 þ aðx2  x3 Þ O2 ¼ x2 þ aðx3  x1 Þ

ð12Þ

O3 ¼ x3 þ aðx1  x2 Þ

3.3

The Diversity Operator

In order to enhance the diversity in the population, diversity operator developed in [12] used here. The new diversity operator consists of selecting a random individual from the selection pool that will be the new chromosome.

3.4

The Complete Procedure for Proposed GA-MPC Algorithm

Step 1: Define GA parameters and generate initial population as given below. Xi ¼ ½PGi;2 ; . . .; PGi;NG ; VGi;1 ; . . .; VGi;NG ; ti;1 ; . . .; ti;NT ; bCi;1 ; . . .; bCi;NC 

ð13Þ

The whole search space for GA-MPC with N chromosomes is given below: 3 X1 . 6 . 7 6 . 7 6 7 X ¼ 6 Xi 7 6 . 7 4 . 5 . X 3 2 P PG1;2 ; . . .; PG1;NG ; VG1;1 ; . . .; VG1;NG ; t1;1 ; . . .; t1;NT ; bC1;1 ; . . .; bC1;NC 7 6 .. 7 6. 7 6 7 ð14Þ P ; . . .; P ; V ; . . .; V ; t ; . . .; t ; b ; . . .; b ¼6 Gi;2 Gi;NG Gi;1 Gi;NG i;1 i;NT Ci;1 Ci;NC 7 6 7 6. 5 4 .. PGP;2 ; . . .; PGP;NG ; VGP;1 ; . . .; VGP;NG ; tP;1 ; . . .; tP;NT ; bCP;1 ; . . .; bCP;NC 2

Step 2: Calculate the fitness of each chromosome using below equation [4].

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2         þ wV VL  V lim  2 þ wQ QG  Qlim  2 þ wS Sl  Slim  2 jF j ¼ f þ wP PG1  Plim G1 L G l

ð15Þ where, f indicates TFC objective, wP ; wv ; wQ ; ws denotes penalty coefficient of respective state variable. Step 3: Arrange all the chromosomes in descending order. Apply the roulette wheel selection and fill the selection pool. Step 4: Select the three consecutive chromosomes and apply new multi-parent crossover by using (12) to generate new chromosomes. Step 5: For each oij , generate a random number u 2 ½0 1. If u 2 ½0 1\q then oij ¼ xij : Step 6: if any variable is violated its limits, set it to corresponding boundary value. Step 7: If maximum generations are reached, then stop the procedure, take the optimal value from the last generation is a best solution, or else, go to step 2.

4 Simulation Results The performance of the proposed GA-MPC method is tested on IEEE 30-bus system with the aim of minimizing TFC, APL, and sum of voltage deviation. The bus data, line data, cost coefficients, generator voltage, min and max values of load bus voltages are referred from [4]. The current code is implemented in MATLAB 2010a and is worked on the computer with 2.2 GHz and i3 core processor. The number of generations and population is considered 200 and 50 respectively and the minimum and maximum values of the crossover probability (Pc) are considered as 0.1 and 0.9, respectively.

4.1

3-Unit System

The IEEE 30-bus system has been solved with GA-MPC algorithm. With the selected parameters, the best, mean, and worst values, for all the objective functions, over 30 trials are delineated in Tables 1 and 2, from which it is inferred that GA-MPC algorithm gives better statistical values over other techniques. The best combination of control variables evaluated using GA-MPC for minimum TFC, SVD, and APL is shown in Table 2. The lower and higher load bus voltages attained from all the objectives shown in Fig. 1 confirm the compliance of voltage inequality constraints at all load buses. Figure 2 indicates the percentage of TFC savings of the GA-MPC in comparison with other methods shown in the same figure, which clearly indicates that, the GA-MPC technique provides highest cost

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Table 1 Statistical analysis of all the objective function models Different models Model 1 Model 2 Model 3

Best ($/h) 799.0767 0.0924 2.9044

Table 2 Comparison of objective function models attained using GA-MPC with the other methods

Mean ($/h) 800.0874 0.9411 3.1026

Different models

Worst ($/h) 805.2372 0.0967 3.8173

Method

SD 8.2731 0.0015 0.7962

TFC ($/h)

Model 1

GA-MPC 799.0767 FIDE [4] 799.0943 DSA [4] 799.0943 CBO [5] 799.1294 LCA [6] 799.1974 DE [5] 799.2891 SA [5] 799.45 EGA [5] 799.56 AGA POP [5] 799.8441 BHBO [7] 799.9217 GEADHDE [5] 800.1579 TMM [4] 801.0119 GM [4] 804.853 Model 2 Method (P.U.) GA-MPC 0.0924 CBO [5] 0.0932 TLBO [5] 0.0945 BBO [5] 0.0951 DSA [4] 0.0977 Model 3 Method APL (MW) GA-MPC 2.9044 CBO [5] 2.9369 ABC [5] 3.1078 EGA [5] 3.2008 MDE [5] 3.2400 BHBO [7] 0.1262 ABC [5] 0.1379 The bold values address the results obtained by the proposed algorithms in the present article

savings. The SVD values attained using GA-MPC for objectives, M1 and M2 are 1.7796 and 0.0924, from which it can be observed that the SVD value obtained for M2 is lesser by 94.8% as compared to M1, hence, voltage profile is improved.

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Fig. 1 Minimum and maximum voltages in different models

Fig. 2 % of TFC Savings attained in different methods

Only M1 that is the minimization of TFC is considered, the convergence characteristics achieved using GA-MPC is depicted in Fig. 3, from where it is seen that GA-MPC quickly brings down to the optimal objective value. All the abovementioned results reveal that, the GA-MPC is efficient to solve OPF problems (Table 3).

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820

Fig. 3 Convergence characteristics attained in M1

GA-MPC

Total Fuel Cost ($/h)

815

810

805

800 X: 200 Y: 799.1

795

0

50

100

200

150

Number of Generations

Table 3 Best combination of control variables obtained in different models

Control variables

Model 1

Model 2

Model 3

Pg1 (MW) Pg2 (MW) Pg5 (MW) Pg8 (MW) Pg11 (MW) Pg13 (MW) Vg1 (p.u.) Vg2 (p.u.) Vg5 (p.u.) Vg8 (p.u.) Vg11 (p.u.) Vg13 (p.u.) t6-9 (p.u.) t6-10 (p.u.) t4-12 (p.u.) t28-27 (p.u.) bsh10 (p.u.) bsh12 (p.u.) bsh15 (p.u.) bsh17 (p.u.) bsh20 (p.u.) bsh21 (p.u.)

177.0560 48.8267 21.3035 20.9196 11.9253 12.0000 1.1000 1.0878 1.0615 1.0693 1.1000 1.1000 1.0395 0.9174 0.9930 0.9705 0.0490 0.0500 0.0494 0.0500 0.0500 0.0499

176.333 48.4464 21.4459 23.1456 12.0315 12.0068 1.0372 1.0268 1.0168 1.0104 0.9931 0.9910 1.0083 0.9000 0.9438 0.9708 0.0499 0.0023 0.0499 0.0001 0.0500 0.0500

51.3358 79.9686 50.0000 35.0000 30.0000 40.0000 1.0995 1.09883 1.0884 1.0892 1.1000 1.1000 1.0907 0.9000 1.0001 0.9694 0.0461 0.0090 0.0223 0.0500 0.0455 0.0140 (continued)

426 Table 3 (continued)

T. Srihari et al. Control variables

Model 1

bsh23 (p.u.) 0.0353 0.0499 bsh24 (p.u.) 0.0231 bsh29 (p.u.) TFC ($/h) 799.0767 SVD (p.u.) 1.7796 APL (MW) 8.6312 The bold values address the results algorithms in the present article

Model 2 0.0499 0.0499 0.0264 804.3416 0.0924 10.0093 obtained by

Model 3 0.0460 0.0450 0.0162 967.1280 1.9198 2.9044 the proposed

5 Conclusion In the current work, a new genetic algorithm with multi-parent crossover is successful implemented to solve single-objective OPF problems. With the introduction of multi-parent crossover and diversity factor concepts, the present method attained the optimal solutions. The performance of the developed GA-MPC is tested on IEEE 30-bus by considering different objective functions. The obtained results identified that the GA-MPC algorithm is better as compared to other methods reported in the paper.

References 1. J. Carpentier, Contribution to the economic dispatch problem. Bull. Soc. Fr. Electr. 3, 431– 447 (1962) 2. K. Lee, Y. Park, J. Ortiz, A united approach to optimal real and reactive power dispatch. IEEE Trans. Power App. Syst. 104, 1147–1153 (1985) 3. M.S. Kumari, S. Maheswarapu, Enhanced genetic algorithm based computation technique for multi-objective optimal power flow. Int. J. Electr. Power Energy Syst. 32(6), 736–742 (2010) 4. H. Pulluri, R.N. Sharma, V. Sharma, An enhanced self-adaptive differential evolution based solution methodology for multi-objective optimal power flow. Appl. Soft Comput. 54, 229– 245 (2017) 5. H. Pulluri, R.N. Sharma, V. Sharma, Preeti, A new colliding bodies optimization for solving optimal power flow problem in power system. Int. Conf. Power Syst., 1–6 (2016). https://doi. org/10.1109/icpes.2016.7584138 6. H.R.E.H. Bouchekara, M.A. Abido, A.E. Chaib, R. Mehasni, Optimal power flow using the league championship algorithm: a case study of the Algerian power system. Energy Convers. Manag. 87, 58–70 (2014) 7. H.R.E.H. Bouchekara, Optimal power flow using black-hole-based optimization approach. Appl. Soft Comput. 24, 879–888 (2014) 8. A. Sloiman, H. Abdel-Aal, Modern Optimization Techniques with Applications in Electric Systems (Springer Publications, 2011). https://doi.org/10.1007/978-4614-1752-1 9. T.N. Malik, A. Asar, M.F. Wyne, A new hybrid approach for the solution of nonconvex economic dispatch problem with valve point loading effects. Electr. Power Syst. Res. 80, 1128–1136 (2010)

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10. K.P. Womg, Y.W. Wong, Genetic and genetic/simulated-annealing approaches to economic dispatch. IEE Proc-Gene. Trnams. Distr. 141(5), 507–514 (1994) 11. H.R.E.H. Bouchekara, A.E. Chaib, M.A. Abdio, Optimal power flow using GA with a new-multi parent crossover considering: prohibited zone, valve-point effect, multi-fuels and emission. Electr. Engg. (2016). https://doi.org/10.1007/s00202-016-0488-9 12. M. Saber, A. Ruhul, L. Dary, GA with a new multi-parent crossover for constrained optimization, in IEEE Congress on Evolutionary Computation, pp. 857–864 (2011)

Genetic Algorithm with Multi-Parent Crossover Solution for Economic Dispatch with Valve Point Loading Effects Harish Pulluri, M. Vyshnavi, Patange Shraddha, B. Sai Priya, T. Sri Hari and Preeti Abstract During recent times, different researchers have presented various techniques for different optimization problems. In current literature on genetic algorithms (GAs), a genetic algorithm with multi-parent crossover (GA-MPC) has been found to be more efficient than other hybridized or modified GAs. In this paper, a GA with a new multi-parent crossover has been proposed to get solution economic dispatch (ED). The current algorithm is verified on 3-unit and 5-unit generator systems by considering minimization of fuel cost. The obtained results are proved the effectiveness of the GA-MPC over the other methods mentioned in the paper.







Keywords Genetic algorithm Economic dispatch New multi-parent crossover Valve point effect




1 Introduction Recently, as the demand for power is increasing day by day, economic dispatch (ED) method plays a major role in optimal operation in modern energy system. The basic idea behind ED lies in the determination of real power outputs of the generators for the short-term load in the system at the most economical price [1].

H. Pulluri
M. Vyshnavi
P. Shraddha
B. Sai Priya Department of Electrical and Electronics Engineering, Geethnajali College of Engineering and Technology, Hyderabad, TS, India T. Sri Hari Department of Electrical & Electronics Engineering, Guru Nanak Institution Technical Campus, Hyderabad, TS, India Preeti (&) Electrical & Electronics Engineering Department, Shri Vishwakarma Skill University, Palwal, Haryana, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_41

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ED is a nonlinear optimization problem. Therefore, several classical algorithms are employed to solve the above-said problem, namely quadratic programing [2], linear programing (LP) [3], and non-linear programing (LP) [4]. All the mentioned methods are outstanding characteristics for convex ED problems. However, these methods are difficult to get a better solution in solving nonconvex ED. So, to overcome the above-said drawbacks and to find an near optimal solution heuristic methods, such as differential evolution [5], particle swarm optimization (PSO) [6], simulated annealing (SA) [7], pattern search [8], diffusion particle optimization [9], harmony search (HS) [10], teaching learning-based optimization (TLBO) [11], colliding bodies optimization [12], social spider algorithm (SSA) [13], krill herd (KH) [14], and exchange market algorithm (EMA) [15], are developed to solve ED problems. Genetic algorithm is an evolutionary algorithm developed in 1960 by John Holland [16]. It is based on random selection from a population of individuals and natural genetics. Till now, GA and several variants of GA are developed and successfully applied to solve the ED problem [17, 18]. Similarly, in this paper a new multi-parent crossover-based genetic algorithm with diversity operator is proposed to solve the ED problem. The obtained results are proved that the GA-MPC is an effective algorithm to solve the optimization problems. The remaining article is structured as below, Sect. 2 explains about mathematical formulation. Section 3 gives about proposed genetic algorithm with new multi-parent crossover. Section 4 explains about simulation results and discussion. Conclusion of the article is given in Sect. 5.

2 Mathematical Formulation The idea of ED is to minimize a fitness function by varying some of control variables to meet the various constraints, which is expressed below min f ðx; uÞ ( gðx; uÞ ¼ 0 Subjected to hðx; uÞ  0

ð1Þ ð2Þ

where f is a fitness function, x gives dependent variable matrix, and u gives independent variables matrix. The fitness function and constraints which are used in the current work are given below [10].

Genetic Algorithm with Multi-Parent Crossover Solution …

2.1

431

Fitness Function

(a) Minimization of total fuel cost (TFC): The TFC of each generator is defined as the combination of a quadratic TFC with valve point loading effect that is given below [10] min f ¼

NG X

      am þ bm Pgm þ Cm P2gm þ dm  sin em  Pmin  P  gm gm

ð3Þ

m¼1

where f gives TFC of the thermal generators; am ; bm ; cm ; dm and em indicate cost coefficients mth generating unit. Pmin gm represents lower active power limit of the mth generator.

2.2

Constraints

During the minimization of TFC, various constraints considered that are expressed as below [10]: (a) Equality constraints: The equality constraints gðx; uÞ are given active power balance and is given as follows Ng X

PGm ¼ PD þ Ploss

ð4Þ

k¼1

where PD indicates total active power demand. (b) Inequality constraints: The inequality constraints hðx; uÞ are expressed between their defined limits given as follows: max Pmin gk  Pgk  Pgk

k ¼ 1; 2; . . .; Ng

ð5Þ

Power loss produces in dispatch lines of the system, which is calculated using B-matrix formula that is expressed as [10] Ploss ¼

NG X NG X m¼1 n¼1

PG;m :Bmn :PG;n þ

XNG m¼1

B0m :PG;m þ B00

ð6Þ

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3 Genetic Algorithm with Multi-Parent Crossover (GA-MPC) From the last three decades, different GAs have been developed to find solution for various mathematical and optimization problems. But the efficacy of the algorithms depends on complexity of the problem. However, in few situations, the GA algorithms not performed well in comparison with other evolutionary algorithms [19]. Therefore, recently the performance of GA is enhanced by replacing the normal crossover with new multi-parent crossover and diversity operator [20]. The new crossover utilizes three-parent chromosomes to generate three new child chromosomes, in this out of three, two are help exploitation, while the third one is for enhancing exploration. The diversity operator may help to avoid local optima. The procedure for GA-MPC takes place by means of three genetic operators, which are expressed below.

3.1

Parent Selection

In this selection process, the parent chromosomes are used to generate new child chromosomes. There are different types of selection processes available in the literature. In the present work, roulette wheel-based selection has been used, and it picks the individuals based on their fitness value relative to the fitness value of other population [20].

3.2

Proposed Three-Parent Crossover

Crossover operator is the heart of genetic algorithm. The crossover used in the current work is based on random crossover and the procedure steps are given below [20]; 1. Select the three chromosomes by using roulette wheel selection. 2. If one of the selected individual is same with another, then it is replaced with a random chromosome from the selection pool. 3. Rank the above selected chromosomes with respective their fitness value. 4. A random number ‘a’ is generated; it follows Gaussian distribution with mean µ and standard deviation r. 5. New offsprings are generated by using below equations

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O1 ¼ x1 þ aðx2  x3 Þ O2 ¼ x2 þ aðx3  x1 Þ O3 ¼ x3 þ aðx1  x2 Þ:

3.3

ð7Þ

The Diversity Operator

To improve the diversity in the population, diversity operator developed in [20] used in the present work. The new diversity operator consists of selecting a random individual from the selection pool that will be the new chromosome.

3.4

The Complete Procedure for Proposed GA-MPC Algorithm

Step 1: Define GA parameters and generate initial population as expressed below. Xk ¼ ½ Xk;1

Xk;2

. . . Xk;m

. . . Xk;Ng 

ð8Þ

The whole search space for GA-MPC with N chromosomes is given below: 2

X1 X2 .. .

3

2

Pg1;1 Pg2;1 .. .

7 6 6 7 6 6 7 6 6 7 6 6 X¼6 7¼6 Pgk;1 6 Xk 7 6 6 . 7 6 .. 4 . 5 6 4 . . XNK PgNK;1

Pg1;2 Pg2;2 .. . Pgk;2 .. . PgNK;2







.. .


.. .




Pg1;m Pg2;m .. . Pgk;m .. . PgNK;m







.. .


.. .

Pg1;Ng Pg2;Ng .. . Pgk;Ng .. .

3 7 7 7 7 7 7 7 7 5

ð9Þ




PgNK;Ng

Step 2: Calculate the fitness each chromosome using below equation [10].  2 jF j ¼ f þ wP jPG1  Plim G1 j

ð10Þ

where, f indicates TFC objective, wP denotes penalty coefficient of slack bus active power. Step 3: Arrange all the chromosomes in descending order. Apply the roulette wheel selection and fill the selection pool. Step 4: Select the three consecutive chromosomes and apply new multi-parent crossover by using (7) to generate new chromosomes.

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Step 5: For each oij , generate a random number u 2 ½0 1. If u 2 ½0 1\q then oij ¼ xij . Step 6: If any variable is violated its limits, set it to corresponding boundary value. Step 7: If maximum generations are reached, then stop the procedure, take the optimal value from the last generation is a best solution, or else, go to step 2.

4 Simulation Results The functioning of the proposed GA-MPC method is tested with two test systems, namely 3- and 5-unit systems with an objective is to minimization total fuel cost by considering transmission losses. The proposed code has been written on MATLAB 2010a, and run in a computer with 2.2 GHz, i3 core processor. The number of chromosomes and generations is considered as 40 and 200, respectively. The lower and higher values of crossover probability (Pc) is 0.1 and 0.9, respectively.

4.1

3-Unit System

Initially, the current approach is applied on 3-unit system with a load of 210 MW. The lower and higher values of active powers, cost coefficients of the generators B-coefficients for evaluation of transmission losses are taken from [10]. The best combination of generator powers achieved with GA-MPC method is compared with genetic algorithm (GA) [17], GA with active power optimization (GA-APO) [17], Newton’s approach [17], and the respective results are mentioned in Table 1. It is identified that the TFC achieved with the proposed method is optimal in comparison with other given methods mentioned above. It means, the GA-MPC is 6.877 $/ h better when compared to the GA-NSO with the optimal value in the literature. The convergence graph achieved with GA-MPC method is depicted in Fig. 1, and it is identified that GA-MPC is reached optimal solution in early stage only.

Table 1 Comparison of optimal real power output attained in 3-unit system with the other methods Methods/unit

GA [17]

GA-APO [17]

NA [17]

GA-MPC

1 2 3 APL TPD TFC

53.2604 88.9645 74.7693 6.9939 216.9942 3252.46

61.6467 95.1632 60.5402 7.3460 217.3501 3341.77

50.0000 86.0678 79.7119 5.7797 215.7797 3205.99

76.0196 90.8411 50.0033 6.8641 216.864 3199.1123

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3340

Fig. 1 Variation of TFC with iteration number for 3-unit system

3320 GA-MPC

Total Fuel Cost ($/h)

3300 3280 3260 3240 3220

X: 200 Y: 3199

3200 3180

0

50

100

150

200

Total Number of Generations

4.2

5-Unit System

To identify the effectiveness of the proposed GA-MPC in medium systems 5-generator systems is selected. The current test system has 5 generators and a load of 259 MW. The optimal cost obtained with different techniques such as GA [17], GA-APO [17], NA [17], PSO [10], modified subgradient harmony search (MSG-HS) [10] for current system has been reported in Table 2. From Table 2, it is identified that the GA-MPC is capable to provide the least cost in comparison with 900

Fig. 2 Variation of TFC with iteration number for 5-unit system

890

Total Fuel Cost (S/h)

GA-MPC 880

870

860

850 X: 200 Y: 834.1

840

830

0

50

100

150

Number of Generations

200

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Table 2 Comparison of real power outputs attained GA-MPC with the other methods for 5-unit system Methods/ unit

GA [17]

GA-APO [17]

NA [17]

PSO [10]

MSG-HP [10]

GA-MPC

1 2 3 4 5 APL TPD TFC

172.765 26.6212 24.8322 23.4152 19.1885 7.8250 266.8217 926.5530

172.765 26.6212 24.8322 23.4152 19.1885 7.8250 266.8217 926.5530

181.129 46.7567 19.1526 10.1879 10.7719 8.9977 267.9977 905.5437

197.4696 20.0000 21.3421 11.6762 17.7744 9.2623 268.2623 836.4568

199.9623 20.0000 20.8157 15.5504 12.5069 9.5654 268.5653 834.363

199.5997 20.000 20.9786 15.4929 12.4840 9.560 268.56 834.1302

Table 3 Statistical analysis for 3- and 5-unit systems Different units

Best ($/h)

Medium ($/h)

Worst ($/h)

SD

3-Unit system 5-Unit system

3199.1123 834.1302

3208.8232 835.8090

325.9612 866.8288

17.501 7.3015

all the other methods. The variation of TFC with iteration number with GA-MPC method is depicted in Fig. 1, and it is understood that the change in TFC remains same after 53 iteration with proposed algorithm. The best, medium, worst costs, and standard deviation with GA-MPC for 25 independent trails are delineated in Table 3. From this table, it is identified that the GA-MPC provided good statistical values. Moreover, the optimal TFC achieved in 20 independent trials for current

870

Fig. 3 Deviation of optimal cost of 6-unit system for 1200 MW with 20 trials

GA-MPC

Total Fuel Cost ($/h)

865 860 855 850 845 840 835 830

0

5

10

15

Number of Indenpendent Trials

20

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system is depicted in Fig. 3, and it is confirmed that GA-MPC gave optimal or near optimal value for 19 trials. From the above results, it is understood that GA-MPC is efficient to find the solution for economic dispatch problems (Fig. 2).

5 Conclusion In the current research work, a new genetic algorithm with multi-parent crossover has been implemented to find the solution of ED with valve point. With the introduction of multi-parent crossover and diversity factor, the current algorithm is effectively obtained the optimal solutions. The efficiency GA-MPC is verified on 3and 5-generator systems by considering the minimization of TFC. The achieved results are proved that GA-MPC is better in comparison with the evolutionary methods mentioned in the literature.

References 1. X. Xai, A.M. Elaiw, Optimal dynamic economic dispatch of generation: a review. Electr. Power Syst. Res. 80, 975–986 (2010) 2. A. Pathom, K. Hiroyuki, T. Eiichi, J. Hasegawa, A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function. IEEE Trans. Power Syst. 17(2), 411–416 (2002) 3. D.W. Wells, Methods for economic secure loading of a power system. Proc. IEE. 115(8), 1190–1194 (1968) 4. A.M. Sasson, Nonlinear programming solutions for load-flow, minimum loss, and economic dispatching problems. IEEE Trans. Power Apparatus. Syst. 88(4), 399–409 (1969) 5. Y. Xiaohui, W. Liang, Z. Yongchuan, Y. Yanbin, A hybrid differential evolution method for dynamic economic dispatch with valve-points. Expert Syst. Appl. 36, 4042–4048 (2009) 6. G. Bhaskar, M.R. Mohan, Security constrained economic load dispatch using improving particle swarm optimization suitable for utility system. Electr. Power Energy Syst. 30, 609– 613 (2008) 7. C.K. Panigrahi, P.K. Chattopadhyay, R.N. Chakrabarti, M. Basu, Simulated annealing technique for dynamic economic dispatch. Electr. Power Compon. Syst. 34(5), 577–586 (2007) 8. J.S. Alsumait, M. Qasem, J.K. Sykulski, A.K. Al-Othman, An improved pattern search based algorithm to solve the dynamic economic dispatch problem with valve point effect. Energy Convers. Manage. 51, 2062–2067 (2010) 9. L. Han, E. Carlos, Y. Zheng, Economic dispatch optimization algorithm based on particle diffusion. Energy Convers. Manage. 105, 1251–1260 (2015) 10. C. Yasar, O. Serdar, A new hybrid approach for nonconvex economic dispatch problem with valve-point effect. Energy 36, 5838–5845 (2011) 11. S. Banerjee, M. Deblina, K. Chandan, Teaching learning based optimization for economic load dispatch problem considering valve point loading effect. Electr. Power Energy Syst. 73, 456–464 (2015) 12. H. Pulluri, R.N. Sharma, V. Sharma, Preeti, A new colliding bodies optimization for solving optimal power flow problem in power system. Int. Conf. Power Syst. https://doi.org/10.1109/ icpes.2016.7584138

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13. J.Q. James, Y. Victor, O.K. Li, A social spider algorithm for solving the non-convex economic load dispatch problem. Neurocomputing 171, 955–965 (2016) 14. H. Pulluri, N. Gouthamkumar, U.M. Rao, Preeti, Krill herd algorithm for solution of economic dispatch with valve point loading effect. Lect. Notes Electr. Eng. 553, 383–392 (2019) 15. N. Ghorbani, E. Babaei, Exchange market algorithm for economic load dispatch. Electr. Power Energy Syst. 19–27 (2016) 16. A. Sloiman, H. Abdel-Aal, Modern Optimization Techniques with Applications in Electric Systems (Springer Publications). https://doi.org/10.1007/978-4614-1752-1 17. T.N. Malik, A. Asar, M.F. Wyne, A new hybrid approach for the solution of nonconvex economic dispatch problem with valve point loading effects. Electr. Power Syst. Res. 80 (1128), 36 (2010) 18. K.P. Womg, Y.W. Wong, Genetic and genetic/simulated-annealing approaches to economic dispatch. IEE Proc. Gener. Trans. Distrib. 141(5), 507–514 (1994) 19. H. Bouchekara, A.E. Chaib, M.A. Abdio, Optimal power flow using GA with a new-multi parent crossover considering: prohibited zone, valve-point effect, multi-fuels and emission. Electr. Eng. (2016). https://doi.org/10.1007/s00202-016-0488-9 20. M. Saber, A. Ruhul, L. Dary, GA with a new multi-parent crossover for constrained optimization. IEEE Congress on Evolutionary Computation, pp. 857–864 (2011)

Teaching Distance Relay Protection and Circuit Breaker Co-ordination of an IEEE 9 Bus System Using MATLAB/SIMULINK Cholleti Sriram and Muppalla N. R. Kishore

Abstract In this paper, co-ordination of impedance (Z) relay is set with faults are at different locations of IEEE 9 bus system. Multiple relays with circuit breakers are connected at different buses far from each other and the tripping of these relays is observed with respect to the distance of fault occurrence from the bus. Resistance (R) versus reactance (X) graphs are shown with zone detection before and after fault occurrence, also with fault detection using impedance at the bus. A comparative analysis is carried out with different fault locations on IEEE 9 bus system in MATLAB/Simulink GUI environment generating graphs plotted with respect to time. Keywords MATLAB (matrix laboratory) Co-ordination Relay R-X characteristics





Distance protection
Circuit breaker

1 Introduction Day-by-day power demand is increasing, the distribution system is also increasing with increase in number of loads and sources which are connected to the interconnected grid system. Because of these increment of load, occurrence of faults also increasing on both transmission as well as distribution lines. These faults make tremendous effect on equipment as well as on customers and economy in general. By using conventional relay [1], circuit breakers like overcurrent relays or inverse time overcurrent relays are used to eliminate the fault from the system. These traditional relays sense the breakers to trip off the power supply to the nearest fault occurring on bus and also the breaker connected very far from the fault. This discontinuous supply from the source to the loads may reduce the production and C. Sriram (&) Department of EEE, Guru Nanak Institute of Technology, Hyderabad, Telangana, India M. N. R. Kishore Department of EEE, Vignan’s Nirula Institute of Technology & Science for Women, Guntur, Andhra Pradesh, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_42

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effect the consumer end products and not economical. The relays need to be working in co-ordination [2] with respect to the distance of fault from the distance relay and trips the circuit breaker which is near to the fault but not the breaker which is far away from the location of fault. To achieve this relay co-ordination, impedance relays are used where the tripping is done with respect to the impedance measured at the particular bus. The outline of the paper is: Sect. 1.1 elaborates about the operation of a distance relay, Sect. 2 discusses the modeling of an IEEE 9 bus system, Sect. 3 discusses the modeling of the distance relay, Sect. 4 discusses the results and discussions and Sect. 5 gives conclusion of the paper.

1.1

Distance Relay

A simple mho function [3] along with a two-bus test system can be seen in Fig. 1. All the vectors in the given figure are operated by current I in the transmission line with variation in R, X and Z values. The R value is considered to be real value and the X is considered to be imaginary value. The impedance plot is the plotted with respect to the voltage measured and current measured at the transmission line. The value of the impedance is calculated by comparing the angle between polarizing quantity and conducting quantity which is said to be V and IZ. The impedance value or the R-X plot [3] varies with change in diameter when a transient state is occurred on the transmission like fault at any end of the line. As the relation between current and impedance is given as V = IZ, current and impedance are inversely proportional to each other. During normal operating conditions, the value of Z is high as voltage V is very much greater than the current I in medium voltage transmission lines. During fault condition, the value of V drops to a low Fig. 1 Mho relay function

Teaching Distance Relay Protection and Circuit Breaker …

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value and the value of I increases making the impedance Z value to drop [4, 5]. The circuit breakers connected on the transmission lines are tripped with change in the impedance value with respect to faults on the line, source side or load side. To see the impedance relay co-ordination of different circuit breakers an IEEE 9 bus system is considered with faults applied at different locations and denote the tripping operation of the breakers with respect to the distance of fault from the relay.

2 Modeling of an IEEE 9 Bus System An IEEE 9 bus system [6] consists of three conventional sources at buses 1, 2 and 3. Three loads are connected at bus 5, 6 and 8. The transmission line is operating at 230 kV which is medium transmission line voltage. The total system is ring main system where three sources and loads are interconnected to each other sharing the power from all the three sources. Single line diagram of an IEEE 9 bus system can be seen in Fig. 2. Each source generates powers at different voltage [6] and different capacity which are interconnected to each other with step-up transformers. All the transformer secondaries are maintained at 230 kV. Below is the power table for the IEEE 9 bus system (Table 1). The total active power generated by the three sources is 313.831 MW and generated reactive power is 143.78 MVAR. The total power consumption by the load is 315 MW and 115 MVAR.

Fig. 2 IEEE 9 bus system

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Table 1 Load flow report Bus No.

Bus (kV)

Gen (MW)

Gen (MVAR)

Load (MW)

Load (MVAR)

1 2 3 4 5 6 7 8 9

16.5 18 13.8 230 230 230 230 230 230

73.831 155 85 0 0 0 0 0 0

9.738 92.091 41.951 0 0 0 0 0 0

0 0 0 0 125 90 0 100 0

0 0 0 0 50 30 0 35 0

3 Modeling of an Impedance Relay Impedance of any system is considered with two values: R, resistance and X, reactance which is given as Z ¼ R þ jX

ð1Þ

Here, j is the imaginary axis as the reactance is measured for reactive power measured consumed by inductance of the line. As mentioned in previous Sect. 1, the relation between Voltage V, current I and impedance is given as V ¼I  Z

ð2Þ

The impedance can be calculated as Z¼

V I

ð3Þ

However, the values of voltage and current are not integers but are in complex sinusoidal format. A Fourier transformation is used to calculate the magnitude and angle of voltage and current individually for each phase |Va| and ∟Va for phase A. Now the complex sinusoidal signal is converted to polar format where the magnitudes are divided and angles are subtracted. jV j jI j ∟Z = ∟V - ∟I jZ j ¼

ð4Þ ð5Þ

For our consideration, a three-phase to ground fault is applied for intense current change in all the lines. The modeling of IEEE 9 bus system with two circuit breakers with fault at bus 4 in MATLAB/Simulink environment is shown in Fig. 3.

Teaching Distance Relay Protection and Circuit Breaker …

Fig. 3 Impedance relay modeling for each individual phase

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Table 2 Settings of relay placed at bus 7 S. No.

Zones of protection

% Of transmission line

Impedance in Ohms

1 2 3

Zone 1 Zone 2 Zone 3

80% of bus 7–8 100% of bus 7–8 + 20% of bus 8–9 100% of bus 7–8 + 100% bus 8–9

25.71 49.03 116.60

Table 3 Settings of relay placed at bus 4 S. No.

Zones of protection

% Of transmission line

Impedance in Ohms

1 2 3

Zone 1 Zone 2 Zone 3

80% of bus 4–5 100% of bus 4–5 + 20% of bus 5–7 100% of bus 4–5 + 100% bus 5–7

30.30 61.45 155.78

Two circuit breakers CB1 and CB2 are connected at bus 4 and bus 7 to ensure protection of the system. A three-phase to ground fault is occurred at bus 4 at 0.3 s and cleared at 0.5 s. The value of impedance is fed to relay block from which a logic ‘1’ (high) or ‘0’ (low) is generated. A counter is connected to count the number of restricting of the circuit breaker and is compared to zero. The signal generated is either 1 or 0 which changes with change in the voltage and current values of the phase. To make the circuit breaker remains in closed position the value generated by the counter is transposed using NOT gate and generated a logic ‘1.’ When the counter trips during fault condition, the NOT gate generates a logic ‘0’ turning the circuit breaker OFF. The modeling of impedance relay for each individual phase is shown in below figure. The circuit breakers are operated using impedance relays where the impedance is measured by taking voltage and current of each line individually. The impedance is calculated with respect to time and change in the value is observed during normal and fault conditions. The circuit breakers are tripped with respect to the change in the impedance values at the measured buses 4 and 7. Tables 2 and 3 show the setting of an impedance relay placed at bus 7.

4 Results and Discussions The simulation of the test system in Fig. 2 is run for 0.7 s with three-phase to ground fault occurred from 0.3 to 0.5 s. The simulation is run for two different cases (1) Fault occurred at bus 7 and (2) Fault occurred at bus 4.

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In both the cases, the position of circuit breakers is not changed and all parameters of the model are maintained same. It is to be observed that the circuit breaker near to the fault is tripped OFF rather the other breaker far from the fault is not tripped as there is no much change in the impedance of the line. Below are the graphs plotted for case 1 and case 2. Figure 6b is the R-X plot of three zones at bus 4 when a fault is occurred between bus 7 and bus 8. The impedance is not entering into the relay characteristics. Therefore, the relay will not operate and the corresponding CB2 is tripping as shown in Fig. 5, and it says the relay and CB are coordinated. But the relay placed at bus 7 is tripped as shown in Fig. 6a. Figure 7b is the R-X plot of three zones at bus 7 when a fault is occurred between bus 4 and bus 5. The impedance is not entering into the relay characteristics. Therefore, the relay will not operate and the corresponding CB1 is tripping as shown in Fig. 4, and it says the relay and CB are coordinated. But the relay placed at bus 4 is tripped as shown in Fig. 7a.

Fig. 4 Circuit breaker 1 and 2 tripping signals of three-phase during fault at bus 7

Fig. 5 Circuit breaker 1 and 2 tripping signals of three-phase during fault at bus 4

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Fig. 6 R-X characteristics at a bus 7 b bus 4

Fig. 7 R-X characteristics at a bus 4 b bus 7

5 Conclusion With the above results, it can be seen that the circuit breaker which is connected close to the fault is permanently tripped OFF but the breaker connected far from the fault is not tripped. The impedances at the buses 4 and 7 different when the fault location is changed and the relay operated only when the impedance drops below the threshold value. A comparative R-X plots are also shown with change in impedance curve during normal condition and fault condition.

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References 1. D. Tziouvaras, Relay performance during major system disturbances (2006), http://www. selinc.com 2. S.B. Wilkinson, C.A. Mathews, Dynamic characteristics of Mho Distance Relays (GE Publication GER-3742), General Electric Power Management 3. G.E. Alexander, J.G. Andrichak, Ground Distance Relaying: Problems and Principles (General Electric Publication GER-3793), General Electric Company 4. U.K. Jethwa, R.K. Bansal, N. Date, R. Vaishnav, Comprehensive load-shedding system. IEEE Trans. Ind. Appl. 46(2) (2010) 5. M. Begovic, D. Novosel, M. Milisavljevic, Trends in power system protection and control. Decis. Support Syst. 30(3), 269–278 (2001) 6. R.G. Farmer, Power system dynamics and stability, in The Electric Power Handbook (CRC Press LLC, 2001)

Necessity of Power System State Estimation: A Generalized Linear State Estimation Solution with Application of PMU Measurements M. Ravindra, R. Srinivasa Rao, V. Srinivasa Rao, N. Praneeth and Vasimalla Ashok Abstract This paper presents a review on major blackouts occurred in power grid across the world and importance for the need of state estimation (SE) solution. This work introduces the applications of phasor measurement units (PMU) to reduce the occurrence of blackouts in power system. The blackouts in power system can occur due to overload, light load conditions, heavy storms or due to line outages. The cascade failure due to line outages, i.e., measured as N-1 outages can lead to power system blackout. The necessity of power system state estimation (PSSE) solution for protection in view of blackouts is discussed in this paper. A Generalized Linear State Estimation (GLSE) method is proposed to obtain robust and accurate states of power system network. A matrix integrating phasor and conventional measurements are formulated to obtain accurate states. IEEE transmission bus network such as 14-bus network is considered as test case for proposed GLSE programming in

Please note that the LNCS Editorial assumes that all authors have used the western naming convention, with given names preceding surnames. This determines the structure of the names in the running heads and the author index. M. Ravindra Department of Electrical and Electronics Engineering, Aditya College of Engineering, Suram Palem, Kakinada, India R. Srinivasa Rao Department of Electrical and Electronics Engineering, University College of Engineering, JNTUK, Kakinada, India N. Praneeth (&) Department of Electrical and Electronics Engineering, Gurunanak Institutions, Hyderabad, India V. Ashok Department of Electrical and Electronics Engineering, Anubose Institute of Technology, Khammam, India V. Srinivasa Rao Department of Electrical and Electronics Engineering, Aditya Engineering College (A), ADB Road, Surampalem, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_43

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MATLAB. The accurate states are computed and compared with standard weighted least squares (WLS) method to show its effectiveness.







Keywords Blackouts Linear state estimation Phasor measurement unit (PMU) State estimation Weighted least squares (WLS)







1 Introduction A long period of power loss or power failure in a region greater than 2–3 days is called blackout. Mainly, the blackout may occur due to outage of transmission power line, sudden shutdown of power generation, overloading conditions, low withdrawing power, etc. If the power generated does not meet the load demand or power supplied is greater than power demand, It can lead to power system blackout. Real-time control of power system with security assessment is required to avoid blackouts [1]. Introduction of synchrophasor technology in power system with allocation of phasor measurement units (PMU) can synchronize data available at different network places with help of global position system (GPS) servers connected to the satellites [2, 3]. As data availability is dynamic through PMUs, the power system can be brought into balanced condition through real-time control such that the generation and load demand can be balanced equally. The dynamic information or data at all the buses is required for protection of power system. The synchrophasor technology incorporated in PMU devices helps to obtain the data of phasors that link with time [3]. The data available at all the buses can make system complete observable. When we compare the PMU data with SCADA, PMU takes 10–60 samples per second, whereas supervisory control and data acquisition (SCADA) systems through remote terminal units (RTU) take 1-sample every 2–4 s [3]. PMU helps to monitor wide-area networks, whereas SCADA helps to monitor local monitoring and control. Time can be synchronized through PMU devices whereas through SCADA, RTU units it cannot. PMU measures both magnitude and phase angle, whereas SCADA can measure only magnitude. From the above differences, it can be concluded that PMU devices are more advanced compared to RTU devices in SCADA systems. The problem with PMU devices is the average cost per PMU for allocating, installing and assigning is $40,000 to $180,000. Allocation of PMU devices at every bus in power networks is infeasible. In order to obtain complete information of bus networks, the PMU devices should be placed optimally at certain buses that make network complete observable. With allocation of PMU devices at optimal places, we know the information or states of only few buses where PMUs devices are placed. In order to obtain all states of the buses in the network, it is essential to perform SE. The obtained SE values (calculated) at all the bus networks are compared with true measured values to obtain bad data in case of any contingency occurred in network can be identified through this. The single line outages or multiple line outages and cascade outages (N − 1) can be detected through SE process.

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2 Review on Blackouts In India, severe blackout occurred on July 30 and 31, 2012, in northern grid and northeastern grid, which affected more than 700 million people in nine states of northern part including capital of India. The main reason behind the blackouts is major grid disturbances due to high load and changes in monsoon. During the incident, the load demand required in one region is high and in other region is very low than generated supply. For balance condition, load demand should be equal to load supplied, i.e., the power required at the load end should be equal to generated power [4]. On November 1, 2014, a national wide power blackout took place in Bangladesh. Unexpected outage in HVDC station and control failure of spinning reserve led to this blackout [5]. On November 10, 2009, major parts of Brazil and whole Paraguay with nearly 67 million people are affected. Heavy rains along with strong winds destroyed three transformers on a HV—transmission line creating a short circuit [6]. On August 20, 2005, Indonesia is affected with blackout due to lack of generation capacity. Almost half of the country is affected due to power failure. The transmission lines in the middle of Cilegon and Saguling in West Java failed leading shutdown of two generator units affecting 120 million people nearly half of country’s population [10]. This followed the country to attempt deal with growing energy crisis [7]. On September 28, 2003, Italy, during storm conditions, the power line which supplied electricity from Switzerland to Italy has been destroyed by uprooted trees which led to power cut as long as 18 h [8]. On August 14–15, 2003, the worst blackout is occurred in Northeast USA. The blackout occurred due to sudden shutdown of power line after it came in contact with overgrown trees. A faulty alarm of the generator failed to give signal to the operators which resulted in shutdown of other three lines. This affected nearly 50 million people in southeastern Canada and eight-northeastern states in the USA[9]. Due to poor and insufficient transmission, equipment led to collapse of northern grid on January 2, 2001, which effected approximately 230 million north Indians causing an estimation loss of 500bn INR. This occurred due to sudden power surge in transmission network [10]. On March 11, 1999, due to lightning struck on electricity substation in Sao Paulo created a sequence reaction that resulted in shutting down Itaipu hydropower plant. Approximately, 97 million people left in dark across south and southeastern Brazil [10]. On March 13, 1989, entire Quebec and Canada underwent a blackout for 12 h. A solar geomagnetic storm is responsible for collapse of transmission system. The geomagnetic storm caused a variation in earth’s magnetic field, thus tripping power grid [11]. On July 13–14, 1977, lightning bolt created power outage in New York city. The nuclear power plant is rendered offline due to tripping, while a second lightning

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strike caused two more 345 kV transmission lines to shut down. Subsequent power surges, malfunctioning safety equipment and human error left nine million people into dark [12]. On November 9, 1965, a blackout occurred in power system due to improper setting of safety relay at Sir Adam back station near to Niagara Fall resulted in tripping of 230 kV transmission line [13]. In the view of blackouts occurred till now, we can observe that due to lack dynamic information and failure of operators to control the system are the main causes of power failure for longer period of time, i.e., blackouts. This can be minimized by providing dynamic information to the control centers. Dynamic information of the states of the network can be attained by allocating of PMUs at different parts of the network through which accurate states of the network can be estimated.

3 Application of Synchrophasors in Power System Synchrophasors are measured by PMU devices that are connected to GPS servers to establish real-time monitoring and controlling actions in electric grid. It measures electrical signals on power grid using time synchronization. PMU measurements at different locations can be synchronized through GPS systems as shown in Fig. 1.

3.1

Synchrophasor Application Across World Wide

With the development of synchrophasor technology, recent application for PMU deployment expansion in country has two significant goals: supporting smart security techniques in substations and improving state measurement accuracy based on latest standards for PMUs.

Fig. 1 PMU devices connected to GPS server

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Northern region—14, Western region—16, Eastern region—12, Southern region—12, Northeastern region—8. PMUs measure immeasurable measurements such as phase angles of the buses located at different system networks. Total 62 no of PMUs are allocated in India by 2012 [14, 15]. The information regarding PMU deployment in various regions in India is shown in Fig. 2. The obtained phasor data delivers dynamic information on power systems, which can help operators to pledge corrective actions to develop power system reliability with application of SE. Worldwide several networks from Russia, Europe, North America and Brazil are in progress using/developing the novel PMU applications to connect the possible benefits of this emergent technology in operating very large-scale power networks [16]. In the USA, department of energy and industry partners invested a total amount of more than $357 million to implement synchrophasor technology which can provide grid operators with record wide-area vision to measure the performance of transmission network and get better reliability [16, 17].

Fig. 2 Deployment of PMUs in India [14, 15]

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In overall, there are 1700 PMUs deployed on NAG, 1380 PMUs deployed with funding of ARRA, in addition 150PMUs allocated with private funding utilities or project participants [17]. The allocation of PMUs in power grid of USA is shown in Fig. 3. In 2009, 166 PMUs deployed in North America, allocated in few areas inside the grid. Figure By 2015, the ARRA projects had deployed 1380 more PMUs and 226 PDCs that present operators the vision of transmission networks that supply around 88% of total USA load and cover roughly two-thirds of continental USA [17]. Another valuable application of synchrophasor measurement data is detection of apparatus letdown, utmost of which is not identified by SCADA system. System stability estimation is conceded utilizing synchrophasor data particularly capturing information of an interconnected system like low-frequency oscillations due to generation control problem or other reasons. Apart from above nations, other countries like South Africa, Brazil, USSR, WECC whose service territory extends from Canada to Mexico and some European countries have deployed/planning to deploy a large no. of PMUs in their system. As on 2013 in china, around 2400 PMU devices had been allocated in power network grids, covering the entire 500-kV substations in country and important power networks covering total 220/110-kV substations [18]. Moreover, in addition, 30 WAMS control area stations in service give dynamic information of power system behavior. The majority of PMU devices were installed after 2006. More than 80% of these PMUs were deployed in past ten years [18].

Fig. 3 Deployment of PMUs in the USA [16, 17]

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This indicates very fast growth in PMU installation after the 2008 initiate of CSG projects. Moreover, the CSG has deployed 342 PMUs, including 211 allocated in substations and 162 in power networks. The allocation of PMUs in power grid of China is shown in Fig. 4.

3.2

Need of State Estimation Solution with PMU Measurements

The state of AC power system is expressed by voltage magnitudes and phase angles at buses. It is impossible to measure all voltage phasors in power system with a limited number of measurement units. However, they can be calculated by SE using partial real-time measurements acquired from the system. The PSSE depends on available measurements in power system, the estimation criterion and the measurement unit or topology errors. By executing PSSE, not only all voltage phasors could be calculated but also the bad measurements could be detected. When compared with conventional and asynchronous measurement units, PMU which is based on the GPS technique provides power engineers with synchronous and more accurate measurements which are capable of improving SE in robustness and accuracy. The integration of PMU measurements in SE can make the estimation solution more accurate, improve network observability and enhance ability to detect bad data.

Fig. 4 Deployment of PMUs in China [18]

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The traditional and commonly used approach in PSSE is weighted least squares (WLS). The solution of the traditional PSSE is not accurate enough since the measurements are all conventional measurements based on the asynchronous measurement units. In [19], Abur presented a multi-area SE utilizing PMU measurements. In this method, system is attempted to be divided into several zones based on boundary sensitive buses. The SE runs in each zone separately. In this method, the measurement vector is composed of both the conventional measurements and PMU measurements. The only drawback of this method is it takes more time for convergence. In [20], the author proposed a hybrid state estimation (HSE) in which the measurement vector should be a combination of the TSE solution and all the PMU measurements available in power system. Since PMU measurements are accurate enough and readily used, voltage phasor at the neighboring bus of a PMU bus could be expressed by a linear equation based on Ohm’s law. So, the SE equation is still a linear equation. The disadvantage in process is the HSE is recommended only for on-line bus sensitivity analysis. In [21], multistage state estimation (MSE) technique is presented to integrate PMU measurements without disturbing existing SCADA system. This procedure requires more number of PMUs which is against the economic criteria. In [22], three different techniques are investigated to include PMU measurements into SE problem. From the review of SE methods in literature survey, we observe that an efficient SE with optimal PMU measurements is required to provide, • • • • •

Accurate states of the system in less time Complete observability of power system Reliable measurements for power system Observability in case of contingencies or PMU loss Avoid block outs by providing accurate DSE.

To obtain accurate SE, in this paper, we propose GLSE solution technique with PMU measurements.

4 Problem Formulation 4.1

GLSE with PMU Measurements

The SE problem involving measurements such as power injections and power flows is nonlinear, whereas formulation of the measurement function with PMU measurements is linear which includes voltage and phase angles. The measured matrix (Z) and function matrix (H) are formulated to obtain states are as follows

Necessity of Power System State Estimation: A Generalized …

2


 3 2
VR I 6 6 Vim WLS 7 0  7 6
6
7 6 6 VR 7 ¼ 6 H11 6 7 6 H21 6 Vim 6
6
 PMU 7 5 4 Gij 4 I R Bij Iim PMU Z

 3 0 I 7 7
 H12 7 7 VR þe H22  7 7 Vim X Bij 5 Gij H

457

ð1Þ

where I is identity matrix of dimension n  n, the zero elements of the sparse matrix are replaced by 1 to form H11 and H22 . While H12 and H21 are represented by null matrix. Currents in transmission network are related to voltages by series admittance Yij ¼ Gij þ jB. Transmission lines are represented by an equivalent p-model. Mathematically, real and imaginary parts of injected currents can be formulated as IR ¼ ViR Gii  Vim Bii þ

n X

ðVjR Gij  Vjim Bij Þ

j¼1;j6¼1

Iim ¼ ViR Bii þ Vim Gii þ

n X

ð2Þ ðVjR Bij  Vjim Gij Þ

j¼1;j6¼1

The solution of linear model is computed directly as X ¼ ð½HT ½R1 ½HÞ1 ½HT ½R1 ½Z

ð3Þ

Here, R is diagonal co-variance matrix.

4.2

GLSE Procedure

The proposed GLSE can be computed without iterations with proper design of matrices as shown in Eq. (1).

1. 2. 3. 4. 5. 6. 7.

%Psudo code% Steps to obtain Linear State Estimation start Formation of Y bus matrix with IEEE bus network data The true states of IEEE network are obtained through Newton Raphson load flow analysis. Design of matrices such as Measured Matix (Z) and Function matrix (H) with PMU measurements and conventional measurements as shown in equation(1) Final GLSE is obtained by computing the equation (3) Compute the accurate states of the network. end

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5 Results and Analysis The GLSE along with BILP is programmed in MATLAB, and it is run on Intel(R) Core(TM), an i3 processor at 2.20 GHz with 4 GB of RAM. The PMU locations considered for 14-bus network are computed through binary integer linear programming (BILP) in [23]. The single line diagram of 14-bus network is considered as shown in Fig. 5. The locations obtained through this measurement are buses— (2, 6, 7 and 9). The total locations obtained to place PMUs are four for 14-bus network which is less than one third of the total buses in network. The true values obtained through N − R method and traditional WLS states are considered to compare with proposed GLSE. Figures 6 and 7 show comparison of the p.u voltage and phase angles of proposed GLSE method with other true (N − R method) and WLS methods. From the figure, we can observe that the voltage and phase angle obtained by GLSE are closer to true values which shows the accuracy of the results provided by proposed GLSE method.

12

13

14

11

10

6 C 8

9 7

C G

1

5

2 G

Fig. 5 Single line diagram of 14-bus network

4

3

C

Necessity of Power System State Estimation: A Generalized …

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1.08

GLSE with PMU WLSE without PMU

Voltage Magnitude in P.U

1.06

True values (N-R method)

1.04 1.02 1 0.98 0.96 0.94

2

1

0

3

4

5

6

7

8

9

10

11

12

13

14

13

14

Bus Number Fig. 6 Comparison of voltage magnitude of 14-bus network with and without PMU

Phase angle in degrees

0 True values (N-R method) WLSE Method GLSE with PMU

-5

-10

-15

-20

0

1

2

3

4

5

6

7

8

9

10

11

12

Bus Number Fig. 7 Comparison of phase angle of 14-bus network with and without PMU

6 Conclusion The review on power system blackouts occurred across the global network is presented in this paper. Application of synchrophasor technology in power system and the need of state estimation with PMU measurements are demonstrated. The proposed Generalized Linear State Estimation (GLSE) in this paper is robust

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technique that obtained accurate results. The results obtained through proposed technique are closer to true values. IEEE 14-bus system is considered for test case, and the results obtained through MATLAB programming are validated by comparing with standard weighted least squares (WLS) method. Results show the effectiveness of proposed GLSE method.

References 1. J. Yan, A new emergency control method and a preventive mechanism against cascaded events to avoid large scale blackouts. Grad. Thesis Diss. 10334 (2011). https://lib.dr.iastate. edu/etd/10334 2. A. Abur, A.G. Exposito, Power System State Estimation: Theory and Implementation. (CRC press, 2004) 3. A.G. Phadke, J.S. Thorp, Synchronized Phasor Measurements and Their Applications, (Springer, New York, NY, 2008) 4. L.L. Lai, H.T. Zhang, C.S. Lai, F.Y. Xu, S. Mishra, Investigation on July 2012 Indian blackout, in 2013 International Conference on Machine Learning and Cybernetics, vol. 1 (IEEE, 2013), pp. 92–97 5. M.A. Kabir, M.M.H. Sajeeb, M.N. Islam, A.H. Chowdhury, Frequency transient analysis of countrywide blackout of Bangladesh power system, in 2015 International Conference on Advances in Electrical Engineering (ICAEE) (2014), pp. 267–270 6. W. Lin, H. Sun, Y. Tang, G. Bu, Y. Yin, Analysis and lessons of the blackout in Brazil power grid on November 10, 2009. Dianli Xitong Zidonghua (Autom. Electr. Power Syst.) 34(7), 1–5(2010) 7. F.A. Shaikh, et al. International grid: new way to prevent blackouts. Int. J. Comput. Sci. Inform. 1(4), 2231–5292 (2012) 8. S. Corsi, C. Sabelli, General blackout in Italy sunday September 28, 2003, in IEEE Power Engineering Society General Meeting, pp1691–1702 (2004) 9. J.F. Hauer, N.B, Bhatt, K. Shah, S. Kolluri, Performance of WAMS East in providing dynamic information for the North East blackout of August 14, 2003, in IEEE Power Engineering Society General Meeting, pp. 1685–1690 (2004) 10. N.C. Chakraborty, A. Banerji, S.K. Biswas, Survey on major blackouts analysis and prevention methodologies, pp. 51–56 11. D. Larose, The Hydro-Québec System Blackout, in Report of Special Panel Session on Effects of Solar-Geomagnetic Disturbances on Power Systems. IEEE Power Engineering Society Summer Meeting (Long Beach, California, USA, 1989) 12. R. Sugarman, Power/energy: New York City’s blackout: A $350 million drain: Ripple effects off the July 13, 1977, lightning stroke cost the public dearly in lost property, services, end income. IEEE Spectr. 15(11), 44–46 (1978) 13. G.D. Friedlander, The great blackout of’65. IEEE Spectr 13(10), 83–88 (1976) 14. V.K. Agrawal, P.K. Agarwal, H. Rathour, Application of PMU based information in improving the performance of Indian electricity grid, in 17th National Power System Conference-2012 (India, 2015) 15. P.K. Agarwal, N.D.R. Sarma, Synchrophasors and WAMS–an indian experience. IFAC-Papers On-Line 49(27), 66–72 (2016) 16. A.G. Phadke, R.M.D. Moraes, The wide world of wide-area measurement. IEEE Power Energy Mag. 6(5) (2008) 17. Advancement of synchrophasor Technology in projects funded by American recovery and reinvestment act 2009. U.S Department of energy (A Report), March 2016

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18. L. Chao, et al. Advancing china? Smart grid: Phasor measurement units in a wide-area management system. IEEE Power and Energy Magazine, 13(5), 60–71 (2015) 19. L. Zhao, A. Abur, Multi-area state estimation using synchronized phasor measurements. IEEE Trans. Power Syst. 20(2), 611–617 (2005) 20. Nuqui, R.F., Phadke, A.G. Hybrid linear state estimation utilizing synchronized phasor measurements. In. Proc. Power Tech, 2007 IEEE Lausanne, pp. 1665–1669 (2007) 21. A.S. Costa, A. Albuquerque, D. Bez, An Estimation Fusion method for including phasor measurements into power system real time modeling. IEEE Trans. Power Syst. 28(2), 1910–1920 (2013) 22. S. Chakrabarti, et al. Inclusion of current phasor measurements in a power system state estimator. IET Gen. Transmission and Distribution, 4(10), 1104–1115 (2010) 23. M. Ravindra, R.S. Rao, Dynamic state in presence of load changes, in 2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI), pp. 163–168SS (2016)

Sensorless Operation of PMBLDC Motor Drive Using Neural Network Controller Poonam M. Yadav and S. Y. Gadgune

Abstract In this paper, a position sensorless permanent magnet brushless direct current (PMBLDC) motor drive is presented. For acknowledgment of sensorless activity, zero intersection of back emf is recognized. So as to produce the correct terminating beats for substitution of inverter circuit and to expel the clamor from the back-emf signals, low pass channels are utilized. The total drive framework is displayed in the MATLAB/Simulink programming. The sensorless drive is tested for various operating conditions.




Keywords BLDC motor Back-EMF detection Estimator Hall-effect sensors





Sensorless operation
Speed


1 Introduction A brushless DC (BLDC) engine is a pivoting self-synchronous framework whose stator is same as that of an enlistment engine, and the rotor has surface set up perpetual magnet. In BLDC engine, winding is situated on stator curl that is stationary, and perpetual magnets are set on rotor that is turning. In DC engine, the present extremity is modified by commutator and brushes; however, in brushless DC motor there are not any brushes and commutator. The present polarity reversal is managed through switches (MOSFET, IGBT) in synchronization with rotor role. So, sensored BLDC motor uses role sensors to experience the particular rotor role or the position may be detected without sensors [1]. Brushless DC automobiles are maximum famous over widespread DC motor due to its high performance, silent operation, compact length, reliability, and coffee upkeep. But the velocity manage of these cars is not a simple project; the P. M. Yadav
S. Y. Gadgune Dr. Babasaheb Ambedkar Technological University, Lonare, India P. M. Yadav (&)
S. Y. Gadgune Electrical Engineering, Padmabhooshan Vasantraodada Patil Institute of Technology, Budhgaon, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_44

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improvements in microcontroller, power electronics, and electric drives over the last decade have made reliable and rate powerful decision for adjustable velocity application. The BLDC vehicles are used in domestic appliances, replacing the traditional motor packages;, everyplace there is a quick developing market for BLDC automobiles for decades to return because all and sundry wishes dependable and price effective answer. There is demand for low running fee, high overall performance, decreased acoustic noise, and a whole lot of comfort alternatives. Those traditional technologies cannot offer price powerful answer. One of the methods to get higher performance is by deciding on the right corridor sensor which might significantly have an effect on reliability and overall performance of the many important packages as well as robotics, clinical device, heating, ventilation, and air conditioning gadget enthusiasts. These packages all name for a fantastically green and quiet motor. BLDC cars are electronically commutated cars, also referred to as synchronous cars due to the fact stator flux and rotor flux both rotate at equal frequency which might be powered by way of a DC deliver through an inverter [2].

2 Methodology for Back-Emf Detection Sensing of returned emf during the closing situation for figuring out the rotor position to execute the sensorless operation of BLDC motor is a not unusual technique. In this paper, also equal basic approach is employed. Keep in mind a three-segment famous person-related PMBLDC motor force. The motor is fed via a three-segment inverter. The switching pulses for the inverter circuit are generated based totally at the rotor position [3–12]. At any time, c language only of the three levels are engaging in and the 0.33 one remains floating. The voltage of phase A dia þ ean dt

ð1Þ

Vbn ¼ Rib þ L

dib þ ebn dt

ð2Þ

Vcn ¼ Ric þ L

dic þ ecn dt

ð3Þ

Van ¼ Ria þ L where L R ia

the phase inductance, the stator resistance, the phase current of A. Similarly for phase B and C

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The line voltage Vab can be obtain by subtracting Eq. 2 from 1 as, dðia  ib Þ þ ean  ebn dt

ð4Þ

Vbc ¼ Vbn  Vc ¼ Rðib  ic Þ þ L

dðib  ic Þ þ ebn  ecn dt

ð5Þ

Vcn ¼ Vcn  Va ¼ Rðic  ia Þ þ L

dðic  ia Þ þ ecn  ean dt

ð6Þ

ean  ebn ¼ Vab  Rðia  ib Þ  L

dð i a  i b Þ dt

ð7Þ

ebn  ecn ¼ Vbc  Rðib  ic Þ  L

dð i b  i c Þ dt

ð8Þ

ecn  ean ¼ Vca  Rðic  ia Þ  L

dðic  ia Þ dt

ð9Þ

Vab ¼ Van  Vbn ¼ Rðia  ib Þ þ L Similarly for Vab and Vca

Equations 4, 5, and 6 can be written as,

From Eq. 7, zero crossing of phase B is regularly recognized. Further Eqs. 8 and 9 identifies zero crossing of phase C and A, respectively. As soon as the motor is started out, it shifted to sensorless operation by way of making use of accurate commutation instants calculated through zero crossing factor of lower back emf. The sensorless commutation instants are behind schedule by means of 30 electrical degrees from the zero crossing immediate of again emf in an effort to excite, the segment windings throughout that portion of returned emf.3.

3 Neural Network (ANN) Controller ANNs are numerical frameworks, including the many weighted interconnected task parts (neurons). A processing detail is an equation generally referred to as a switch function. This detail of processing receives warnings from separate neurons, combines and transforms them, and generates numerical results. As is well established, the processing element approximately corresponds to the real neurons. x¼

N X i¼1

A i Wi þ h

ð10Þ

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Equation 10 displays artificial neuron single. ANNs’ structure includes three main segment neurons, the association that provides information and yield course, and association loads that demonstrate the performance of those associations. The partner ANN’s plan (structure) is regularly drawn up, and weight estimates expected to improve yield accuracy are resolved using one of several numerical calculations. The ANNs disentangle a connection between the information factors and determined factors by deciding the loads utilizing past models. At the end of the day, ANNs are “prepared.” When these connections are resolved, partner ANN might be worked with new information and estimations might be made. A system’s presentation is estimated on the grounds of the pointed sign and blunder. The blunder edge is obtained by the system yield examination and, in addition, the point yield. In such a strategy, a back-spread equation is used to guide the loads to lower the blunder edge. The system is generally ready by continuing this handling. The point of preparation is to achieve an optimal arrangement based on estimates of execution. ANNs have a wide range of uses, considering all stuff, problems. They are efficiently used by a few sectors [13–16].

4 System Description Figure 1 displays the BLDC motor speed control block diagram. It shows that once the DC input voltage is provided to the inverter, three phase output will be generated and the BLDC engine will be supplied. Inside the motor will be developed back emf. This will be used to create regulated door beat from the replacement circuit for such tasks. The speed can be identified at this stage and it tends. This can discovered mistake signal and adjusted then the yield of the controller is given to include voltage. Drive parameters might be estimated utilizing reproduction diagram.

Fig. 1 PMBLDC motor speed control by using back-emf zero crossing detection method

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The less drive of the sensor relies on the place of the back electro magnetic force (BEMF) initiated by the growth of a durable magnet rotor before the winding of the stator. This approach also needs a trapezoidal sign to be used in order to have a zero BEMF intersection. For a set, setup of the BEMF amplitude is relative to the rotor velocity of the motor (number of stator winding turns, mechanical rotor characteristics, and rotor magnet characteristics). The less strategic sensor uses BEMF’s zero intersection to synchronize phase replacements. To recognize BEMF, the particular 120° six-advance drive is utilized. “120° six stage drive” powers zero current twice in each stage during a six-stage period. This permits BEMF zero intersection to be identified and read. Back emf, NlrB ¼ x

ð11Þ

where N l r B x

number of windings per stage length of the rotor inner range of the rotor rotor attractive field angular velocity.

Since the controller should coordinate the rotor turn, the controller needs a few methods for deciding the rotor’s direction/position (with respect to the stator loops.) Some plans use hall-effect sensors or a rotating encoder to straightforwardly gauge the rotor’s position. Others measure the back emf in the undriven curls to derive the rotor position, dispensing with the requirement for isolated hall. Effect sensors and thus less controllers are frequently referred to as sensors. Controllers that sense rotor stance based on back emf have additional problems in beginning motion because after the rotor is stationary, no back emf is produced. This can be ordinarily cultivated by beginning turn from a self-assertive stage, and after that jumping to the best possible stage in the event that it is observed to not be right. This may make the engine run quickly in reverse, adding significantly greater unpredictability to the startup grouping. Different sensors less controllers are fit for estimating winding immersion brought about by the situation of the magnets to derive the rotor position. This may cause the motor to run briefly backward, adding even more complexity to the startup sequence. Other sensors less controllers are capable of measuring winding saturation caused by the position of the magnets to infer the rotor position.

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5 Simulation Circuit Diagram Figure 2 demonstrates the simulation for PMBLDC motor drive speed control by using back-emf detection method. It is additionally called as sensorless control of BLDC engine. In this strategy, the door heartbeats are produced by utilizing back-emf location technique to control exchanging of inverter switches. By this technique, we can expel hall-effect sensors and decrease the size of motor. Artificial neural system controller is utilized to ascertain the mistake and send to inverter.

6 Simulation Results Figure 2 shows reproduction circuit chart of the proposed framework. The simulation should be possible by utilizing MATLAB Simulink. Simulink, created by MathWorks, is a business apparatus for displaying, reproducing, and investigating multi area dynamic frameworks. Its fundamental interface is a graphical square diagramming gadget and a customizable plan of square libraries. It offers tight uniting with the rest of the MATLAB condition and can either drive MATLAB or be scripted from it. Simulink is commonly used in control theory and mechanized sign taking care of multi zone reenactment and plan. The accompanying figure indicates reproduction consequences of, stator back emf, stator current, and electromagnetic torque (Figs. 3, 4, 5 and 6).

Fig. 2 Simulation for PMBLDC motor drive speed control using back-emf detection method

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Fig. 3 Back emf (volts) versus time (seconds)

Fig. 4 Stator current (Amp)

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Fig. 5 Rotor speed (rpm)

Fig. 6 Electromagnetic torque

7 Conclusion A new sensorless drive scheme by detecting ZCP of BEMF difference is analyzed. Optimal performance of the motor drive system is improved by using this scheme. From the recreation results, it is inferred that the ideal yield of the machine can acquire by this strategy as on account of ordinary sensorless method. The nonpartisan voltage is not required in the proposed strategy; just the three engine

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terminal voltages should be detected. Here, I utilized artificial neural network controller. By utilizing this, the engine rpm is encouraged to the ANN and the controlled info voltage is produced naturally as indicated by the yield.

References 1. B. Singh, S. Singh, Territory of art on permanent magnet brushless DC motor drives, India. J. Power Electron. 9(1), 1–17 (2009). JPE 9-1-1 2. Y.K. Chauhan, B. Kumar, Circuitous back EMF detection based sensorless operation of PMBLDC motor drive, in International Conference on Power Electronics Intelligent control and Energy systems, ICPEICES (Delhi, India, 2016) 3. Y. Yi, D.M. Vilathgamuwa, M.A. Rahman, Execution of an artificial-neural-network-based constant versatile controller for an inside permanent magnet engine drive. IEEE Trans. Ind. Appl. 39(1), 96–104 (2003) 4. S. Mondal, A. Mitra, M. Chattopadhyay, D. Chowdhury, A new approach of sensorless control methodology for achieving ideal characteristics of brushless DC motor using MATLAB/Simulink, in Third International Conference on Computer, correspondence, Control and Information Technology (C3IT) (Hoogly, India, 2015) 5. B. Li, R. Ma, F. Fu, X. Jin, W. Chen, A new sensorless control method for brushless permanent magnet DC motors. in, Sensorless control for Electrical Drives and Predictive control of Electrical Drives and Power Electronics (Munchen, Germany, 2013) 6. J.C. Gamazo-Real, E. Vázquez-Sánchez, J. Gómez-Gil, Position and speed control of brushless DC motors using sensorless techniques and application trends. Sensors 10, 6901– 6947 (2010) 7. A. Nair, K.R. Rajgopal, A novel back-EMF detection scheme based sensorless control of permanent magnet brushless DC motor drive, in, International Conference on Electrical Machines and Systems (Incheon, South Korea, 2010), PP. 978–983 8. Shubham Sundeep, Bhim Singh, Hearty position sensorless technique for PMBLDC motor. IEEE Trans. Power Electron. 33(8), 6936–6945 (2018) 9. K. Giridharan, R. Gautham, FPGA based advanced controllers for BLDC engine. Int. J. Eng. Explor. Appl. 3(4), 1615–1619 (2013) 10. J. Kuruvilla, K. Deepu, B. George, Speed control of BLDC motor utilizing FPGA. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 4(4), ISSN (Print): 2320–3765, ISSN (Online): 2278–8875 11. G. Haines, N. Ertugrul, Wide speed range sensorless operation of brushless permanent-magnet motor using flux linkage increment. IEEE Trans. Ind. Electron. 63(7), 4052–4060 (2016) 12. Shih-Chin Yang, Robert D. Lorenz, Correlation of resistance-based and inductance-based self-sensing controls for surface permanent-magnet machines using high-frequency signal injection. IEEE Trans. Ind. Appl. 48(3), 977–986 (2012) 13. T. Govindaraj, R. Rasila, Development of fuzzy logic controller for DC – DC Buck Converters. Int J. Eng. Techsci. 2(2), 192–198 (2010) 14. H.-G. Yeo, C.-S. Hong, J.-Y. Yoo, H.-G. Jang, Y.-D. Bae, Y.-S. Park, Sensorless drive for interior permanent magnet brushless DC engines, in IEEE International Electric Machines and Drives Conference Record (1997) 15. M.A. Rahman, M.A. Hoque, On-line self-tuning ANN based speed control of a PMDC engine. IEEE/ASME Trans. Mechatron. 2(3), 169–178 (1997) 16. M.N. Uddin, M.A. Abido, M.A. Rahman, Research facility usage of a fake neural system for web based tuning of a hereditary calculation based PI controller for IPMSM drive, in Proc. Int. Conf. Displaying, Simul. Choose. Mach., Converters Syst. (Montreal, QC, Canada, 2002)

Comparative Performance Analysis of Active- and Resistive-Type SFCL in Reducing the Fault Current G. Ganesh, Ravilla Madhusudan, L. Vamsi Narasimha and B. Sambasiva Rao

Abstract Nowadays, an efficient alternative to expensive enhancement of protective equipment is superconducting fault current limiters (SFCLs), which provide economic remedies to intercept the existing protective devices in the power system from being severely affected by excessive currents. In this paper, resistive- and active-type SFCLs are applied separately to reduce the fault current. The active SFCL is a combination of a transformer which is lossless (superconducting) and a voltage-controlled PWM converter. The converter equivalent impedance is controlled for current suppression, whereas the resistive-type SFCL will compare the fault current with the reference value and introduces some resistance based on the increase in temperature. Both resistive- and active-type SFCLs are designed in MATLAB and added into a test system of 100 MVA, 33 kV. The results presented show that active SFCL reduces more fault current in comparison with the resistive SFCL in the considered network. Keywords Superconducting fault current limiters Fault current limiter RSFCL ASFCL








Pulse-width modulation


1 Introduction The fault levels of power system protection equipment are exceeding the handling limit of installed devices because of the increase in generation and consumption of electricity. The security, stability, and reliability of the network get decreased if it is overlooked in the mask of increasing the generation [1–10]. Multiple solutions are suggested to decrease the effect of increasing fault current issues. The most straightforward way would be upgrading all the conductors, switchgear, and protection devices in existing power systems to raise their fault current ratings and interrupting speed [1, 2]. However, the process of replacing G. Ganesh (&)
R. Madhusudan
L. Vamsi Narasimha
B. Sambasiva Rao Department of Electrical and Electronics Engineering, Sir C R Reddy College of Engineering, Eluru, Andhra Pradesh, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_45

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equipment is expensive, complicated, and time-consuming. Moreover, many of the, in effect, switchgear and protection equipment need several cycles to interrupt the fault current. Unfortunately, faults will not wait, and within this period damage the system. So, the research for effective suppression of abnormal current with in less time has taken its gear up [3].

1.1

The fault current limiter (FCL) solution

Because of the urgency of the fault current limitation and issues with the other techniques, fault current limiters (FCLs) have become the preferred option to costly system upgrades. The advantages of FCLs are that they generally do not affect power system operation during normal conditions, respond, and act very fast to mitigate the destructive and other undesirable effects caused by fault currents [2, 11]. Out of many types of fault current limiters developed, superconducting fault current limiter (SFCL) is the most inventive fault-current-limiting device. The performance of SFCL to improve reliability of system with in effect devices was studied in this paper by using resistive-type and active-type SFCL. Both resistive-and active-type SFCLs are designed in MATLAB and introduced into test system of 100 MVA, 33 kV supplying a resistive load. The performance characteristics of resistive- and active-type SFCLs are checked under different fault conditions, and the results are presented.

2 Theoretical Analysis 2.1

Construction and Operation of Active SFCL

Figure 1 shows active SFCL, which consists of a lossless (superconducting) transformer and a voltage-type PWM converter. Ls1 and Ls2 are the selfinductances, and Ms is the mutual inductance. Z1 is the impedance of the entire circuit, and Z2 is the impedance of the load. Ld and Cd act as filters of harmonics in output. As the AC voltage side of the converter circuit is controlled, it is viewed as a regulated voltage source [1, 2]. Under no-fault conditions, the current (I2) induced will be made zero, so the active SFCL has nothing to do on the network. After the identification of fault, the current produced will be periodically adjusted in phase angle and amplitude to control the primary voltage of lossless transformer in series with the main circuit. In this way, the fault current is decreased to a significant value. The two voltage equations obtained are cs ¼ Ib1 ðZ1 þ Z2 Þ þ jxLS1 Ib1  jxMS Ib2 U

ð1Þ

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Fig. 1 Single-phase voltage compensation-type active SFCL. a Connection diagram and b circuit equivalent of transformer

Fig. 2 Simulink model of active-type SFCL

cp ¼ jxMs Ib1  jxLs2 Ib2 U

ð2Þ

By the control of I2, the voltage Up will be made equal to zero. Therefore, impedance SFCL is zero (ZSFCL = Up/I1). During abnormal operation (Z2 faulted), the line current will increase to I1f, and the voltage of primary circuit increase to Upf [3, 5] (Fig. 2).

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 Ic 1f ¼

cs þ jxMs Ib2 U

 ð3Þ

ðZ1 þ jxLs1 Þ

cs ðjxLs1 Þ  Ib2 Z1 ðjxMs Þ U d b U1f ¼ jxLs1 Ic 1f  jxMs I2 ¼ ðZ1 þ jxLs1 Þ

ð4Þ

The ZSFCL (current-limiting impedance) is given by: ZSFCL ¼

d U1f jxMs Ib2 ðZ1 þ jxLs1 Þ ¼ jxLs1  cs þ jxMs Iba Ic ðU 1f

ð5Þ

With I2 in normal state, impedance of SFCL is ZSFCL1 ¼

Z2 ðjxLs1 Þ ðZ1 þ Z2 þ jxLs1 Þ

ð6Þ

Suppressing I2 to none, the ZSFCL2 ¼ jxLs1

2.2

ð7Þ

Structure and Principle of Resistive SFCL

Under the typical operating condition, SFCL does not influence the network due to zero resistance. During faulted condition, the current rises above the typical value of superconductors, forcing the device to go into resistive state. This kind of nonlinear characteristic of SFCL is used to bring down fault current. In this paper, quench and recovery characteristics are designed based on the resistance of SFCL which is expressed by

RSFCL

8 0; > > i12 > h < 0 ðtt Tsc Þ R 1  e ; m ¼ > > a1 ð t  t 1 Þ þ b1 ; > : a2 ð t  t 2 Þ þ b2 ;

ðt0 [ tÞ ðt0  t\t1 Þ ðt1  t\t2 Þ ðt2  tÞ

ð8Þ

Rm is the highest value of resistance offered by SFCL, and Tsc is the transition time of the SFCL. Also, t0 time is the start time of to suppress. Finally, t1 and t2 are the retrieval times, respectively. Extinguishing and regaining characteristics of the SFCL modeled in MATLAB using the above mathematical equation are shown in

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Fig. 3 Operating characteristics of resistive SFCL

Fig. 4 Simulink model of resistive-type SFCL

Fig. 4. In standard condition, resistance of SFCL is zero, which is shown in Fig. 3. When time is one second, the resistance offered increases to value required for quenching of current and then the resistance of SFCL reduces to zero [8–10].

3 Simulation and Results For a test circuit with the source of 100 MVA, 33 kV, the resistive transmission line of 10 km with a balanced resistive load is modeled in MATLAB environment (Fig. 5). For the above test circuit, when different faults are applied at t = 0.2 s, the results are shown in the following figures. It is observed that the fault current in the faulted phase, when an L-G fault is applied, has risen to 2700 A, which is very much higher than the standard current without fault. Similarly, for other faults like L-L, L-L-G, and L-L-L, the fault current magnitudes are 2400 A, 2700 A, and 2800 A, respectively. The variation of the currents for all the above faults is shown in Figs. 6, 7, 8, and 9.

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Fig. 5 Simulink model of the test circuit

3.1

Current Wave Forms for Various Faults

In order to limit the high fault current, we now insert the resistive SFCL into the test circuit. The resistive SFCL increases the resistance when the fault occurs, and it is observed that fault currents for different faults are 1700 A, 1500 A, 1800 A, and 1700 A which indicates a reduction of around 37% in the magnitude of fault current without SFCL. The variation of the currents for all the above faults with resistive SFCL is shown in Figs. 10, 11, 12, and 13. Now, in the place of resistive SFCL, we introduce active SFCL into the test network. We can reduce the fault current magnitudes for different faults to 1300 A, 1100 A, 1200 A, and 1300 A, respectively, for all the faults without SFCL. This indicates a reduction of around 62% in the magnitude of fault current without SFCL. The variation of the currents for all the above faults with active SFCL is shown in Figs. 14, 15, 16, and 17 (Fig. 18 and Table 1).

Comparative Performance Analysis of Active- and Resistive- Type …

Fig. 6 Current waveform for L-G fault

Fig. 7 Current waveform for L-L fault

Fig. 8 Current waveform for L-L-G fault

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Fig. 9 Current waveform for L-L-L fault

Fig. 10 Current waveform of L-G fault

Fig. 11 Current waveform of L-L fault

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Fig. 12 Current waveform of L-L-G fault

Fig. 13 Current waveform of L-L-L fault

Fig. 14 Current waveform of L-G fault

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Fig. 15 Current waveform of L-L fault

Fig. 16 Current waveform of L-L-G fault

Fig. 17 Current waveform of L-L-L fault

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Fig. 18 Current values of various faults

Table 1 Current values of various faults Type of fault

Without SFCL (Amps)

With resistive SFCL (Amps)

With active SFCL (Amps)

L-G L-L L-L-G L-L-L

2700 2400 2700 2800

1700 1500 1800 1700

1300 1100 1200 1300

4 Conclusion The suppression characteristics of resistive- and active-type SFCLs are studied under different fault conditions in a balanced three-phase power system with only resistive load and resistive lines. The resistive SFCL increases the resistance when the fault occurs, and a reduction of around 37% in the magnitude of fault current for different faults without SFCL is observed. By placing the active SFCL, a reduction of around 62% in the magnitude of fault current without SFCL can be observed. From the observations, it is concluded that active SFCL is more effective (i.e., almost double) in suppressing excessive current magnitude than resistive SFCL for a particular system with a resistive load which is not a practical case. For further studies, these two types of SFCLs can be applied in a real-time system with RL load, and their performance could be analyzed. Within the scope of this paper, the active-type SFCL is superior to the resistive-type SFCL.

References 1. A. Nageswara Rao, P. Rama Krishna, The fault level reduction in distribution system using an active type SFCL. Int. J. Eng. Comput. Sci. 5(8), 17392–17396 (2016). ISSN: 2319-7242 2. L. Chen, C. Deng, F. Guo, Y. Tang, J. Shi, L. Ren, Reducing the fault current and overvoltage in a distribution system with distributed generation units through an active type SFCL. IEEE Trans. Appl. Supercond. 24(3), 1051–8223 (2014)

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3. L. Chen, Y. Tang, J. Shi, Z. Sun, Simulations and experimental analyses of the active superconducting fault current limiter. Phys. C 459(1/2), 27–32 (2007) 4. L. Chen, Y. Tang, J. Shi, Z. Li, L. Ren, S. Cheng, Control strategy for three-phase four-wire PWM converter of integrated voltage compensation type active SFCL. Phys. C 470(3), 231– 235 (2010) 5. L. Chen, Y.J. Tang, J. Shi, L. Ren, M. Song, S.J. Cheng, Y. Hu, X.S. Chen, Effects of a voltage compensation type active superconducting fault current limiter on distance relay protection. Phys. C 470(20), 1662–1665 (2010) 6. J. Wang, L. Zhou, J. Shi, Y. Tang, Experimental investigation of an active superconducting current controller. IEEE Trans Appl. Supercond. 21(3), 1258–1262 (2011) 7. A. Rama Devi, J. Nani Kumar, Simulation of resistive super conducting fault current limiter and its performance analysis in three-phase systems. Int. J. Eng. Res. Technol. 2(11), 411–415 (2013). ISSN: 2278-0181 8. U.M. Mohana, S.T. Suganthi, Performance analysis of superconducting fault current limiter (SFCL) in single phase and three phase systems. Int. J. Commun. Eng. 1(1), 56–61 (2014) 9. S. Nemdili, S. Belkhiat, Modeling and simulation of resistive superconducting fault-current limiters. J. Supercond. Novel Magn. 25(7), 2351–2356 (2012) 10. S.M. Blair, C.D. Booth, G.M. Burt, Current-time characteristics of resistive superconducting fault current limiters. IEEE Trans. Appl. Supercond. 22(2), 5600205 (2012) 11. S.M. Blair, The analysis and application of resistive superconducting fault current limiters in present and future power systems. A thesis submitted for the degree of Doctor of Philosophy (April 2013)

Squirrel Search Optimizer for Solving Economic Load Dispatch Problem V. P. Sakthivel, M. Suman and P. D. Sathya

Abstract Economic load dispatch (ELD) is one of the most imperative problems to be solved for the economic operation of a power system. In this context, a new meta-heuristic swarm intelligence algorithm named squirrel swarm optimizer (SSO) for solving the ELD problems is proposed. SSO mimics the foraging behavior of squirrels which is based on the dynamic jumping and gliding strategies. The proposed SSO approach is implemented for two-test power systems encompassing 6 and 15 units systems and compared with genetic algorithm (GA), particle swarm optimization (PSO), artificial immune system (AIS), chaotic PSO (CPSO), bacterial foraging algorithm (BFA), biogeography based optimization (BBO), firefly algorithm (FA), glowworm swarm optimization (GSO), and exchange market algorithm (EMA). Results reveal the supremacy of the proposed SSO approach in terms of solution quality and convergence speed. Keywords Economic dispatch optimizer Ramp rate limits





Prohibited operating zone
Squirrel search

1 Introduction ELD is one of the most important concerns to be solved for a power system to operate smoothly and economically. It is a process of sharing the total load on a power system between different generating plants in order to achieve the greatest operating economy. Conventional techniques such as linear programming algorithms [1], quadratic programming algorithms [2], nonlinear programming algorithms [3], dynamic programming algorithms [4, 5], and Lagrangian relaxation V. P. Sakthivel (&) Department of EEE, Government College of Engineering, Dharmapuri 636704, India M. Suman Department of EEE, FEAT, Annamalai University, Chidambaram 608002, India P. D. Sathya Department of ECE, FEAT, Annamalai University, Chidambaram 608002, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_46

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algorithms [6, 7] have been implemented to ELD issues. The classical calculus-based methods cannot perform satisfactorily to solve ELD problems due to highly nonlinear features of the problem and a large number of constraints. For instance, recent meta-heuristic algorithms, PSO [8–12], adaptive PSO [13], chaotic PSO [14, 15], differential evolution (DE) [16], evolutionary programming (EP) [17], GA [18, 19], real-coded GA [20], BFA [21], BBO [22], gravity search algorithm (GSA) [23], pattern search technique (PSM) [24], AIS [25], artificial bee colony (ABC) [26, 27], FA [28], and GSO [29] are promising alternatives to solving complicated ELD issues. An opposition-based learning idea is employed to enrich GSA’s performance [30]. Liao provided GA algorithm based on niche immune isolation to solve dynamic ELD (DELD) problem [31]. Modified chaotic DE (MCDE) is suggested to solve the DELD issue of a large-scale integrated power system [32]. Chaotic map update mechanism and metropolis rule are used in the MCDE to improve normal DE features. Modified shuffled frog jumping algorithm is implemented to solve the ELD problem [33]. Iteration-based PSO algorithm is introduced to solve the ELD issue [34]. Lately, different hybridization techniques such as GA-SQP [35], PSO-SQP [36, 37], hybrid EP-PSO-SQP [38], hybrid DE (HDE) [39, 40], hybrid DE-PSO [41], hybrid shuffled differential evolution (SDE) [42], hybrid differential harmony search [43], and modified shuffled frog leaping algorithm (MSFLA) with genetic algorithm [33] are developed to provide better solutions for ELD problems. Ghorbani and Babaei [44] presented a new heuristic algorithm for solving ELD problems, by employing EMA. A novel modified PSO (MPSO), which includes the advantages of BF and PSO, was proposed for constrained dynamic ELD problem [45]. Bacterial foraging PSO-DE (BPSO-DE) algorithm [46] was developed by integrating BFO, PSO, and DE for solving static and dynamic ELD problems of different test systems. Squirrel search optimizer (SSO) is a recently developed powerful swarm intelligence algorithm proposed by Jain et al. [47] to solve constrained optimization problem. The proposed SSO approach is based on the foraging activities of squirrel individuals. Each squirrel individual modifies its position using four processes, namely (1) distributing the population, (2) dynamic foraging behavior, (3) seasonal adapting intelligence, and (4) random repositioning of individuals at the end of winter season. The dynamic foraging and seasonal adapting mechanisms balance the exploration and exploitation searches of the algorithm which make SSO a powerful technique. In this paper, a new swarm intelligence algorithm, SSO, is developed for solving the ELD problems. The SSO technique is applied on two different test cases (6-unit and 15-unit systems) with varying degree of complexity for verifying its performance with other methods such as GA, PSO, AIS, CPSO, BFA, BBO, FA, GSO, and EMA. The rest of this paper is organized as follows: The problem formulation is presented in Sect. 2. Section 3 describes the brief overview of the proposed SSO approach and the pseudo-code of the proposed method applied to ELD problem.

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The simulation results and comparisons are discussed in Sect. 4. The conclusion is summarized in Sect. 5.

2 Formulation of ELD with Generator Constraints The goal of the ELD problem is to find an optimal power generation schedule while minimizing fuel costs and also satisfying the operating constraints of different power systems.

2.1

Objective Function

The problem with ELD is formulated as follows: Minimize F ¼

ng X

Fi ðPi Þ

ð1Þ

i¼1

The generator’s total fuel cost is defined by: Fi ðPi Þ ¼ ai P2i þ bi Pi þ Ci where Fi total fuel cost of the generators ai ; bi ; ci cost coefficients of generator i.

2.2 2.2.1

System Constraints Power Balance Constraints

The generators’ complete power output must be equal to the sum of power requirements and complete transmission losses and is provided by: ng X i¼1

Pi ¼ PD þ PL

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The transmission losses are expressed as PL ¼

ng X ng X

Pi Bij Pj þ

i¼1 j¼1

ng X

B0i Pi þ B00

i¼1

where PD power demand PL transmission losses Bij line loss coefficients.

2.2.2

Generator Capacity Constraints

Each unit’s output power needs to be restricted by limiting inequality between its limits. This constraint is represented by Pi;min  Pi  Pi;max where Pi;min ; Pi;max minimum and maximum generation of unit i.

2.2.3

Ramp Rate Constraints

The actual working range of all generating units is restricted by the ramp rate constraint and is provided as follows: Pi  P0i  URi P0i  Pi  DRi where Pi ; P0i current and previous power output of ith unit, respectively

2.2.4

Prohibited Operating Zone

Prohibited operating zones constraint is defined by Pi;min  Pi  P1 L PU i;k1  Pi  Pi;k

PU i;nz

k ¼ 2; . . .nz

 Pi  Pi;max

where URi, DRi up and down ramp limits of ith unit, respectively k index of prohibited zone

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3 Proposed Approach Based on Squirrel Search Algorithm SSO is a recently developed swarm intelligence algorithm based on the foraging behavior of squirrels. It has been first proposed by Jain et al. [47]. It is a population-based approach consisting of a large number of squirrels in which each squirrel moves through a multi-dimensional search space to look for food. In this optimization algorithm, the positions of squirrels are considered as different design variables and the distance of the food from the squirrel individual is analogous to the fitness value of the objective function. In SSO, the individual squirrel alters its position and moves to the better positions. It involves that there is n number of squirrels in a deciduous forest and only one squirrel at each tree. It is assumed that the three types of trees, namely normal, acorn, and hickory trees, are available in the forest. The forest area is supposed to contain N trees in which one hickory tree, Na acorn trees, and remaining are normal trees which have no food. The hickory tree is the best foraging area for the squirrels. The movement of each individual is influenced by the four processes, namely (i) distributing the population, (ii) dynamic foraging behavior, (iii) seasonal adapting intelligence, and (iv) random repositioning at the end of winter season.

3.1

Distributing the Population

The positions of N squirrel individuals are randomly generated. Then, the population is sorted in ascending order for minimization problem and vice versa. Then, the squirrel individuals are distributed into three types: individuals located at hickory trees (Fh), individuals located at acorn trees (Fa), and individuals located at normal trees (Fn). Fh is the squirrel individual with the minimum fitness value, Fa includes the individuals that have the fitness rank from 2 to Na + 1, and the remaining individuals are denoted as Fn.

3.2

Dynamic Foraging Behavior

The dynamic foraging behavior can be mathematically modeled as follows: The positions of individuals which are gliding from acorn trees to the hickory tree are updated as follows. Xait þ 1

 ¼

  Xait þ dg Gc X th  Xait Random location

if r1  Pdp otherwise

 ð2Þ

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The positions of remaining individuals which are gliding from normal trees to the acorn and hickory trees are updated by the following equations, respectively. Xit þ 1 ¼ Xit þ 1 ¼

 

   Xit þ dg Gc X tai  Xit if r2  Pdp Random location otherwise   Xit þ dg Gc X th  Xit Random location

if r3  Pdp otherwise

ð3Þ

 ð4Þ

where r1, r2, and r3 are random numbers in the range of [0, 1], Xh is the location of squirrel individual which reached the hickory tree, and t indicates the current iteration. Gliding constant, Gc, is used to balance the exploration and exploitation searches in the SSO algorithm. Its value notably influences the performance of the proposed algorithm. dg is the gliding distance which can be expressed as dg ¼

hg tanð/Þ

where hg is the constant valued 8 and tan (u) represents the gliding angle which is defined by tanð/Þ ¼

D L

where D and L are the drag and lift forces which can be expressed by the following equations, respectively: D¼

1 2qV 2 SCD

L¼

1 2qV 2 SCL

where q, V, and S are density of air, speed, and the surface area of body, respectively. CD and CL are drag and lift coefficients, respectively.

3.3

Seasonal Adapting Intelligence

The foraging behaviors of squirrels are significantly affected by the seasonal fluctuations. The squirrels are more active in autumn as compared to winter. To avert

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the SSO algorithm from being abused into local optimal solutions, the seasonal adapting intelligence is introduced. The seasonal constant is given by vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u d  2 uX t t t Sc ¼ t Xai;k  Xh;k

i ¼ 1; 2; . . .; Na

ð5Þ

k¼1

The minimum value of seasonal constant is expressed as Smin ¼

10e6

ð6Þ

ð365Þt=ðtmax =2:5Þ

where t and tmax are the current and maximum iteration values, respectively. The larger Smin value facilitates the exploration while smaller one improves the exploitation ability of the algorithm.

3.4

Random Repositioning at the End of Winter Season

If Stc  Smin , winter season is completed. Then, the locations of the flying squirrel individuals are randomly repositioned by the following equation. tþ1 Xinew ¼ XL þ Le0 vyðxÞ  ðXU  XL Þ

ð7Þ

where XL and XU are the lower and upper bounds of squirrel individual. Levy distribution improves global exploration ability of the algorithm and finds new candidate solutions far away from the current best solution. The Le’vy flight is calculated as Le0 vyðxÞ ¼ 0:01 

a  ra 1

jrb jb

where ra and rb are two randomly distributed numbers in [0, 1], b is a constant, and a is expressed as   3b1 Cð1 þ bÞ  sin pb 2 5  a¼4  b1 C 1 þ2 b  b  2ð 2 Þ 2

where CðxÞ ¼ ðx  1Þ

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Algorithm: Pseudocode for SSO based ELD Begin: Read input parameters of SSO algorithm and system data Generate random positions for n number of squirrels Evaluate fitness of each squirrel’s location using Eq. (1) Sort the locations of squirrel individuals in ascending order depending upon their fitness value Distribute the squirrel individuals on hickory nut tree, acorn nuts trees and normal trees Randomly select some squirrel individuals which are on normal trees to move towards hickory nut tree and the remaining will move towards acorn nuts trees while(Stopping criterion is false) For t = 1 to n1 (n1 = Number of squirrel individuals which are gliding from acorn trees to hickory nut tree) if r1 ≥ Pdp Update the position of squirrel individual using Eq. (2) else Randomly generate the position of squirrel individual within the search domain. end end For t = 1 to n2 (n2 = Number of squirrel individuals which are gliding from normal trees to acorn trees) if r2 ≥ Pdp Update the position of squirrel individual using Eq. (3) else Randomly generate the position of squirrel individual within the search domain. end end For t = 1 to n3 (n3 = Number of squirrel individuals which are gliding from normal trees to hickory tree) if r3 ≥ Pdp Update the position of squirrel individual using Eq. (4) else Randomly generate the position of squirrel individual within the search domain. end end Calculate seasonal constant (Sc) using Eq. (5) if

S ct ≤ S min

Randomly reposition the squirrel individuals using Eq. (7) end Update the minimum value of seasonal constant (Smin) using Eq. (6) end The location of squirrel on hickory nut tree is the final optimal solution of the ELD problem End

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4 Simulation Results and Comparisons In order to examine the effectiveness of SSO-based ELD problems, numerical simulations are performed on 6 and 15-unit schemes and the outcomes acquired are compared with those of GA, PSO, AIS, CPSO, BFA, BBO, FA, GSO, and EMA approaches. The numerical assessments are conducted by SSO approach based on MATLAB simulation. The parameters used in SSO approach are as follows: • • • • • • • • • •

Number of hickory tree = 1 Number of acorny trees, Na = 3 Number of trees (population size) = 20 Maximum number of generations, tmax = 100 Gliding constant, Gc = 1.9 Density of air, q = 1.204 kg m−3 Speed, V = 5.25 ms−1 Surface area of body S = 154 cm2 Drag coefficient CD = 0.6 Lift coefficients CL=0.675  CL  1.5

4.1

Test System 1

The proposed SSO approach is applied to a small test system composed of six generating units with a load demand of 1263 MW. For this test scheme, transmission loss, ramp rate restrictions, and forbidden working areas are regarded. The system information for this test case is provided in Table 1. Table 2 shows the optimum schedule of generation and the total cost of generation obtained by various approaches.

Table 1 System data for 6-units Unit (i)

Pimin

Pimax

ai

bi

ci

PUR

PDR

Piprev

POZs

1 2 3 4 5 6

100 50 80 50 50 50

500 200 300 150 200 120

240 200 220 200 220 190

7.0 10.0 8.5 11.0 10.5 12.0

0.0070 0.0095 0.0090 0.0090 0.0080 0.0075

80 50 65 50 50 50

120 90 100 90 90 90

440 170 200 150 190 110

[210, 240], [350, 380] [90, 110], [140, 160] [150, 170], [210, 240] [80, 90], [110, 120] [90, 110], [140, 150] [75, 85], [100, 105]

GA [12]

PSO [12]

P1 474.8066 447.4970 178.6363 173.3221 P2 262.2089 263.4745 P3 134.2826 139.0594 P4 151.9039 165.4761 P5 74.1812 87.1280 P6 13.0217 12.9584 PL Minimum cost ($/h) 15,459 15,450 Bold designates the results of suggested approach

Unit (MW)

Table 2 Best solution of 6 units’ system 434.4236 173.4385 274.2247 128.0183 179.7042 85.9082 12.9583 15447

CPSO [15] 458.2904 168.0518 262.5175 139.0604 178.3936 69.3416 12.655 15448

AIS [25] 449.46 172.88 263.41 143.49 164.91 81.252 12.4437 15443.8164

BFA [21] 447.3997 173.2392 263.3163 138.0006 165.4104 87.0797 12.446 15443.0963

BBO [22]

447.3872 173.2524 263.3721 138.9894 165.3650 87.0781 12.443 15443.075

EMA [44]

446.892 175.4966 262.4621 137.0965 164.5297 89.3483 12.5273 15,448

GSO [29]

447.0936 172.9299 263.9487 138.6932 164.9655 87.7593 12.3902 15442.4

SSO

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It is found from the table that the proposed SSO approach provides lesser fuel cost than the other approaches.

4.2

Test System 2

The SSO is also implemented on a bigger test system that consists of the 15 generating units. Transmission losses and forbidden area of operation are included. The system’s complete load demand is regarded to be 2630 MW. Table 4 presents the generator coefficients, capacity limits, ramp rate limits, and forbidden areas. In Table 5, the ideal generation plan, cost, and power loss acquired by the suggested SSO approach are contrasted with other approaches (Table 3).

4.3

Convergence, Statistical, and Computational Analyses

The convergence behavior of SSO is depicted in Figs. 1 and 2. It is seen from figures that SSO converges more quickly. The 50 independent trials are executed for the two test cases, and the obtained statistical results are presented in Tables 3 and 6. From the statistical analysis, it is manifested that the generation costs obtained by different trials are closer to the best solution. The average execution time taken by the SSO algorithm is the least among the other compared approaches. Thus, the proposed SSO approach confirms the excellent solution quality, computational efficiency, and convergence characteristics.

5 Conclusion In this paper, a new meta-heuristic swarm intelligence algorithm named squirrel swarm optimizer (SSO) for solving the ELD problems is introduced. The proposed SSO approach has been implemented in two power systems and compared with other approaches available in the literature. From this comparative study, it is evident that the proposed SSO algorithm has acquired better solutions in terms of solution quality and convergence characteristics. In the vein of ELD problem, this proposed SSO approach may also be urged for solving other constrained power system optimization problems.

Min. cost ($/hr) Mean cost ($/hr) CPU time/iteration (sec) Bold designates the results

Compared items

15,459 15469 0.22 of suggested

GA [12]

15,450 15454 0.066 approach

PSO [12]

Table 3 Statistical analysis of 6 units’ system CPSO [15] 15447 15449 –

AIS [25] 15448 15459.7 –

BFA [21] 15443.8164 15446.95383 –

15443.0963 15443.0963 0.0325

BBO [22]

15443.075 15443.075 0.0244

EMA [44]

15,448 15450 0.189

GSO [29]

15442.4 15442.6 0.023

SSO

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Pimin

150 150 20 20 150 135 135 60 25 25 20 20 25 15 15

Unit(i)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

455 455 130 130 470 460 465 300 162 160 80 80 85 55 55

Pimax

Table 4 System data for 15-units

671 574 374 374 461 630 548 227 173 175 186 230 225 309 323

ai 10.1 10.2 8.80 8.80 10.4 10.1 9.80 11.2 11.2 10.7 10.2 9.90 13.1 12.1 12.4

bi 0.000299 0.000183 0.001126 0.001126 0.000205 0.000301 0.000364 0.000338 0.000807 0.001203 0.003586 0.005513 0.000371 0.001929 0.004447

ci 80 80 130 130 80 80 80 65 60 60 80 80 80 55 55

PUR 120 120 130 130 120 120 120 100 100 100 80 80 80 55 55

PDR 400 300 105 100 90 400 350 95 105 110 60 40 30 20 20

Piprev

[30, 40], [55, 65]

[180, 200], [305, 335], [390, 420] [230, 255], [365, 395], [430, 455]

[185, 225], [305, 335], [420, 450]

POZs

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PSO [12]

GA [12]

415.31 439.12 359.72 407.97 104.42 119.63 74.98 129.99 380.28 151.07 426.79 459.99 341.32 425.56 124.79 98.56 133.14 113.49 89.26 101.11 60.06 33.91 50.0 79.96 38.77 25.0 41.94 41.41 22.64 35.61 38.2782 32.4306 33113 32858 of suggested approach

Unit (MW)

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 PL Minimum cost ($/h) Bold designates the results

Table 5 Best solution of 15 units’ system 450.05 454.04 124.82 124.82 151.03 460 434.53 148.41 63.61 101.13 28.656 20.912 25.001 54.418 20.625 32.1302 32835

CPSO [15] 441.1587 409.5873 117.2983 131.2577 151.0108 466.2579 423.3678 99.948 110.684 100.2286 32.0573 78.8147 23.5683 40.2581 36.9061 32.4075 32854

AIS [25] 455.00 380.00 130.00 130.00 170.00 460.00 430.00 72.0415 58.6212 160.00 80.00 80.00 25.00 15.00 15.00 30.6626 32704.4503

EMA [44] 455.00 380.00 130.00 130.00 170.00 460.00 430.00 71.745 58.9164 160.00 80.00 80.00 25.00 15.00 15.00 30.6614 32704.45

FA [28] 455 380 130 130 170 460 430 72.0672 60 158.487 80 80 25 15.274 15.0592 30.927 32706.9

GSO [29]

455.00 380.00 130.00 130.00 170.00 460.00 430.00 72.5623 59.6765 157.487 80.00 80.00 25.004 15.262 15.0542 30.046 32698.3

SSO

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Squirrel Search Optimizer for Solving Economic … Fig. 1 Convergence of SSO algorithm for 6-unit system (Test system 1)

Fig. 2 Convergence of SSO algorithm for 15-unit system (Test system 2)

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Table 6 Statistical analysis of 15 units’ system Compared GA PSO CPSO AIS items [12] [12] [15] [25] Min. cost 33113 32858 32835 32854 ($/h) Mean cost 33228 33039 33028 33021 ($/h) Mean CPU 49.31 26.59 13.31 – time (sec) Bold designates the results of suggested approach

EMA [44]

FA [28] 32704.4503

GSO [29] 32704.45

32784.5024

SSO 32698.3

32976.81

32704.4504

32856.1

32700.23

–

–

–

5.63

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Voltage Sag Mitigation for PMSG System Using DVR Based Hybrid Fuzzy Logic Controller Basagonda Chandrika and B. C. Sujatha

Abstract Nowadays, many industries are using non-conventional energy sources to generate a huge amount of power/electricity, as these are non-pollutant. But connecting wind turbine to the power system or grid may lead to power quality issues such as voltage sag/swell, flicker, and harmonic and inter-harmonics. DVR is one of the CP devices used to mitigate voltage drop/raise (Srivastav and Sharma in Int J Adv Res Electr Electron Instr Eng 6(5), 2017 [1]). Sag is a more critical issue than swell. In this paper, sag and harmonic problems have been addressed. It demonstrates that how DVR injects voltage to compensate sags and reduces harmonics during fault. Here, the performance of DVR with PI controller, fuzzy controller, and hybrid fuzzy logic controller has been compared. The demonstration is done in MATLAB/Simulink.








Keywords Custom power (CP) Dynamic voltage restorer (DVR) Wind energy system (WES) Permanent magnet synchronous generator (PMSG) Fuzzy logic controller (FLC) Hybrid fuzzy logic controller (HFLC) MATLAB/Simulink










1 Introduction In modern days, power quality problems have become more critical issues not only for industries also for commercial people. So, maintaining a good quality of the power without any disturbance is very important. If any failure occurs in the system, it effects on the whole system; hence, it is essential to clear all failures and disturbances. In the proposed system, WES is connected to the grid, which is based

B. Chandrika (&) Power and Energy System, UVCE, Bangalore University, K R Circle, Bangalore 560001, India B. C. Sujatha Department of EEE, UVCE, Bangalore University, K R Circle, Bangalore 560001, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_47

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on PMSG. There are many types of wind turbine. Voltage dip leads to an increase in the current beyond the inverter limitation. Due to this, DC capacitor voltage increases vulnerably. Hence, the fast removal of voltage sag is important [2]. There are many alternative ways to mitigate voltage sag, such as STATCOM, DVR, UPQC. In [3], the dynamics of wind energy system is analyzed with and without DVR and STATCOM. The modeling and construction of PMSG have been explained in the literature [3]. This PMSG is very efficient than induction generator, as it is excited without any energy supply. Because of connecting wind turbine harmonics will be generated in the system, DVR is used to mitigate harmonics and voltage dip. DVR injects the constant voltage and helps to enhance power quality in the network. Here, DVR is connected with fuzzy logic controller, as it can able to check the error of 0.1–0.9.

2 DVR DVR is one of the CP devices which are used to inject voltage to compensate load terminal voltage. It is designed to connect in series to the distribution system via injection transformer. It injects constant voltage to the load terminal for mitigating sag/swell. Sag is a more critical issue than swell.

2.1 1. 2. 3. 4.

Main Components of DVR

Injection transformer Harmonic filter Storages devices/control system VSC/VSI. Voltage injection from the DVR can be written in equation form [4] as, VDVR ¼ Vload þ ðZline  Iload Þ  Vsource

where Vload Zline Iload Vsource VDVR

Load voltage Impedance of line Load current Source voltage at fault condition DVR injected voltage (Fig. 1).

ð1Þ

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505

Fig. 1 Schematic diagram of DVR

2.1.1

Injection Transformer

Injection transformer is used for the purpose of boost Vac, supplied by voltage source converter to the required voltage levels.

2.1.2

Harmonic Filter

Harmonic filter eliminates the harmonic components which generated by the VSI. Filter can be connected either high voltage side or converter side of the injection transformer.

2.1.3

Storage Devices/Control System

DVR is used for compensation purpose. For compensation, DVR requires real power during voltage disturbances. Hence, storage devices are used to supply real power to DVR. In this system, controllers used are PI Controller, FLC, and HFLC.

2.1.4

VSC/VSI

VSC converts the DC voltage into AC voltage. Firing pulses to VSC is given from PWM generator.

3 Proposed Control Scheme In the proposed method, WES is integrated into the grid. In this method, PMSG-based WES is connected to the source side. WES is used as an external source. Here, wind speed is maintained at 12 m/s. Mitigation of sag and harmonics during fault condition is very important. To mitigate these problems, DVR is

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designed and proposed control strategy using HFLC. It has been shown that HFLC has more advantages than PI. FLC analyzes analog input value in terms of logical variables which may be any real numbers between 0 and 1.

3.1

Mathematical Equation for Conversion of Energy in WES

The KE of wind is written as 1 Ec ¼ mv2 2

ð2Þ

m ¼ qvS

ð3Þ

m = air mass; v = speed of the wind q = density of air; S = surface area of turbine. Power in the wind is expressed as 1 1 P W ¼ E c  m  v2  q  S  v3 2 2

ð4Þ

The above equation can be modified as 1  q  A  v3  Cp 2   Cp ¼ 0:5 c  0:022b2  5:6 e0:17c Pw ¼

ð5Þ ð6Þ

where Cp Rotor power coefficient b Blade pitch angle.

4 Test System A 3U, 415 V, and 50 Hz programmable voltage source is connected to three winding transformers which feeding two transmission lines. DVR is connected in series between the point of common coupling and load through an injection transformer. Voltage sag is created using three-phase faults. DC link is supplying DC voltage to inverter to get AC voltage. Discrete PWM generator is used to produce firing pulses to inverter (Table 1).

Voltage Sag Mitigation for PMSG System Using DVR Based … Table 1 Test system parameter

Parameter

Rating

Supply voltage DC voltage Line resistance Line inductance Stator phase resistance

Three-phase, 50 Hz, 415 V 120 V 0.001 O 0.005 H 0.425 O

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Fig. 2 DVR control system with HFLC

Vital role of dynamic voltage restorer is to inject voltage at load terminal during fault condition. Simulation is carried out for duration of 0.1–0.2 s. DVR with HFLC scheme [5] is given in Fig. 2. The proposed system configuration of DVR with hybrid fuzzy logic controller is given in Fig. 3.

5 Simulation and Results Simulation is done for DVR with PI controller, fuzzy logic controller, hybrid fuzzy logic controller for voltage sag from 0.1 to 0.2 s. Nominal voltage taken here is 415 V, 50 Hz. It is observed that DVR has injected the required voltage for

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Fig. 3 Proposed system configuration

Fig. 4 Supply voltage of the proposed system

compensation in all three phases during sag condition. Discrete PWM generator has been used for pulse generation (Figs. 4 and 5). Case 1: Compensated System for PI Controlled DVR See Figs. 6 and 7. Case 2: Compensated System of DVR with Fuzzy Logic Controller See Figs. 8 and 9. Case 3: Compensated system of DVR with Hybrid Fuzzy Logic Controller See Figs. 10 and 11; Table 2.

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Fig. 5 Uncompensated system with three-phase faults

Fig. 6 Injected load voltage for DVR with PI controller

6 Conclusion In this proposed system, simulation is carried out for DVR with PI, FLC, and hybrid fuzzy logic controller for mitigating voltage dip and harmonics. Three-phase 415 V, 50 Hz generation system has been developed with wind turbine, and PWM

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Fig. 7 THD for DVR with PI controller

Fig. 8 Injected load voltage for DVR with fuzzy logic controller

generator is used for pulse generation. From the simulation result, it is shown that DVR with hybrid fuzzy logic controller has effectively compensated voltage sag at the load side and reduces harmonic distortion that compared simulated outputs with PI controller and FLC. This system can be implemented further by using SVPWM for better results.

Voltage Sag Mitigation for PMSG System Using DVR Based …

Fig. 9 THD for DVR with FLC

Fig. 10 Injected load voltage for DVR with hybrid fuzzy logic controller

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Fig. 11 THD for DVR with HFLC

Table 2 Performance comparison

System

Load current THD (%)

DVR with PI controller DVR with FLC DVR with hybrid fuzzy controller

4.77 0.38 0.30

References 1. M. Srivastav, R. Sharma, Power quality improvement of distribution networks using dynamic voltage restore. Int J Adv Res Electr Electron Instr Eng 6(5), (2017). https://doi.org/10.15662/ ijareeie.2017.0605034 2. M.N. Eskander, S.I. Amer, Mitigation of voltage dips and swells in grid connected wind energy conversion systems. IETE J Res 57(6), 515 (2011)

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3. M. IIyas, Y. Rana, J.S. Khan, Analysis of the grid connected wind energy system using MATLAB/Simulink. Int J Adv Res Electr Electron Instr Eng 4(7), (2015) 4. D. Francis, T. Thomas, Mitigation of voltage sag and swell using dynamic voltage restorer. in International Conference on Magnetics and Drives (AICERA-2014 iCMMD) 5. P. Keertana, A. Chandana, Power quality enhancement for DFIG system by using HFLC based UPQC. Int J Adv Res Eng 7(02), (2018)

Different Types of Energy Storage Systems: A Literature Survey Rama Rao Bomma, J. Jayakumar and T. Bogaraj

Abstract Increasing renewable energy penetration into integrated energy storage systems (ESS) requires more efficient methods to store the energy in an effective way. Possibly various energy storage system (ESS) technologies faces various problems such as charging and discharging, reliability, economy, compactness, and safety. This paper audits the diverse sorts of ESS innovations, structures, features, and classifications. Also gives the clear idea about applications, advantages, and limitations of all technologies in grid and transportation system. It also provides a general review of performance capabilities of Li-ion battery and also other advanced ESS for small satellite applications. A hybrid ESS which consists of a battery and a supercapacitor is used in pure electric vehicles.










Keywords Energy storage system (ESS) ESS technologies Microgrid Electric vehicle Energy management system




1 Introduction The fast development of vitality utilization, CO2 emanations, and request supply bungle all inclusive is because of the rising populace development rate and urbanization levels [1]. These challenges expect improvement to enhance vitality use and limit fuel utilization and dangerous outflows [2]. Different options in contrast to the utilization of petroleum products have been proposed to accomplish supportable vitality frameworks [3, 4]. Sustainable power source (RE) innovations

R. R. Bomma (&)
J. Jayakumar Research Scholar, Karunya Institute of Technology and Sciences, Coimbatore, T.N, India R. R. Bomma
J. Jayakumar Professor, Karunya Institute of Technology and Sciences, Coimbatore, T.N, India R. R. Bomma
T. Bogaraj Assistant Professor, PSG College of Technology, Coimbatore, T.N, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_48

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with vitality stockpiling frameworks (ESSs) have turned out to be broadly supported arrangements among these choices [5–7]. ESS helps sustainable power source combination from numerous points of view and deals with the average power balance amid a power emergency; hence, the solidness of the framework significantly affects the general electric framework by putting away vitality amid off-top hours with diminished expense [8–12]. Subtleties on the utilizations of vitality stockpiling innovations have been explored in [13–15]. The reduced life cycle of batteries has been perceived as the key limit of ESSs that impedes the improvement of the microgrid (MG). To deal with this impediment, numerous scientists have prescribed half breed vitality stockpiling frameworks (HESSs) that intend to develop the future of batteries [16]. The MG thought is projected by the Consortium for Electric Reliability Technology Solutions (CERTS) [12]. CERTS can be portrayed as a constrained substance that includes scattered essentialness resources (DERs) and controllable warm and weights. These stacks are related with the upstream system for power age using photovoltaic (PV) sheets, wind plants, vitality parts, diesel generators, and scaled down scale turbines with a limit contraption (e.g., batteries or supercapacitors (SCs)) [17]. From the utility perspective, microgrid can be considered as a controlled cell of the power structure. From the customer point of view, MG can be expected to meet their essentials of trustworthiness, reduced feeder hardships, enhanced capability, voltage hang minimization, or reliable impact supply [18]. Microgrid with ESS has turned into a capable segment for future shrewd matrix sending [19–21]. Be that as it may, because of the discontinuous idea of sustainable power source assets and fluctuating burden profiles, the power supply in MG a few times neglects to alleviate the heap requests framework recurrence change [17, 22]. In this manner, fluctuating sustainable control sources must be smoothed with limit structures to give better-control quality [23, 24]. Microgrid (MG) has versatile working characteristics in structure related and islanded modes and there is a improvement of cross section capability and security [25, 26]. In the framework associated method of activity, MGs can keep up stable framework recurrence by trading power with a fundamental lattice [27]. In any case, in remote islands, MGs are arranged as off-cross section systems [28] where the fundamental repeat control is essential [22]. Figure 1 shows the structure of MG, where PV boards give vitality and a battery vitality stockpiling gadget (BESS) balances the interest of vitality [25]. MG associates with the power framework through the purpose of normal coupling (PCC). Specified the expanded MG establishments, circulation frameworks present critical changes in attributes contrasted and the present dispersion framework. Subsequently, reasonable control procedures must be received to deal with these distinctions and improve by and large proficiency [26]. Numerous vital contemplations exist for the vitality stockpiling framework in MGs. Effective administration of ESS, control electronic interfaces, charging and releasing, transformation component of intensity, unwavering quality, and security from perils are the serious challenges for the improvement of the vitality stockpiling

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Fig. 1 Structure of MG [25]

framework in the applications of MG. Figure 2 portrays the effect of a vitality stockpiling framework in a power framework organize [15]. ESS can be connected for vitality exchange [29], crest shaving [30], load streaming [31], turning hold [32], voltage support [33], dark begin [33, 34], recurrence guideline [24], quality of power [35, 36], control dependability [37], sustainable power source frameworks (RESs) moving [38, 39], smoothing and firming [39], transmission and circulation update deferral [40], blockage alleviation [41], and off-lattice administration [38, 41], as appeared in Fig. 2 [42]. The choice and the executives of vitality stockpiling frameworks and vitality assets altogether lessen the abnormalities in a power framework organize. The goal of this audit is to exhibit the present report of ESSs, assess problem and obstructions, and give chosen proposals to promote advancement by concentrating on the earth and wellbeing issues. This investigation talks about different existing vitality stockpiling gadgets, which incorporate their activities and attributes for proficient MG use. Therefore, the key commitment of this investigation is the exhaustive

Fig. 2 Application summary of energy storage system [42]

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Fig. 3 Shape of flywheel energy storage system [61]

examination of choosing future ESSs to accomplish the practical advancement of MGs. Along these lines, this audit gives critical data to executing ESS in MG applications and getting better the current innovation.

2 Summary of Energy Storage System ESS setups, their characterizations, and shapes are delineated in the accompanying subsections. A. Energy Storage System (ESS) Configuration Regularly totaled and disseminated ESS are the two fundamental designs of ESS innovation for MG applications, as portrayed in Fig. 4. For the accumulated framework, the measure of intensity stream from DERs to PCC transport stays at a consistent esteem. Besides, the absolute limit of this ESS can be connected to alleviate control stream vacillations [43]. On the off chance that the limit of a vitality stockpiling gadget builds, the expense likewise increments. Assembling and controlling expansive ESS are troublesome. Therefore, little scale and conveyed vitality stockpiling gadgets can be utilized to accomplish the dependable and successful power guideline. ESS gadgets in disseminated stockpiling designs are straightforwardly associated with explicit distributive sources with various interfaces. Be that as it may, controlling force stream is the fundamental challenge looked by the disseminated framework. In addition, the capacity procedure still endures misfortunes through influence electronic interfaces for conveyed assets and ESS [12].

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Fig. 4 CAES simplified diagram [55]

B. Classification of Energy Storage System ESS is grouped dependent on use of vitality in explicit structure. ESS can be comprehensively ordered as an electrochemical, warm, mechanical, compound, electrical, and crossover vitality stockpiling framework. Additionally, these frameworks can additionally be grouped relying upon the procedure of developments and materials utilized. Figure 5 shows the subtleties on the grouping of ESS [44]. Batteries [45], packed air vitality stockpiling (CAES) [46], flywheel vitality stockpiling (FES) [47], SCs [16, 48], superconducting attractive vitality stockpiling (SMES) [49], hydrogen stockpiling [50], and mixture vitality stockpiles (HESs) [44, 51, 52] are the most much of the time utilized capacity advancements for MG applications.

3 Different Classifications of Energy Storage Systems A. Mechanical storage systems Mechanical vitality stockpiling frameworks (MSS) are beneficial in light of the fact that they can work adaptable to change over and store vitality from sources [52]. In addition, they can convey the put away power when it essential for mechanical work [53]. In view of the running standard, MSS can be named pressurized gas,

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Fig. 5 View of redox flow battery [55]

constrained spring, motor vitality, and potential vitality. In any case, from an innovative perspective, mechanical capacity frameworks include three frameworks: flywheel, siphoned hydro capacity, and packed air vitality innovations. Among the three frameworks, siphoned hydro-capacity frameworks (PHS) contribute the most on the planet power stockpiling limit with their long life cycle. In spite of the fact that mass vitality frameworks still depend on PHS, PHS has serious disadvantages, for example, high capital cost, negative ecological effect, and decreased land utilization. Subsequently, the future improvement of PHS is restricted [53–56]. Subtleties on different innovations, for example, flywheel vitality stockpiling frameworks, CAES, and gravity vitality stockpiling frameworks (GES) are talked about as pursues. (1) Flywheel Energy Storage Systems Flywheel, as the fundamental part mainly current fast FESS, is a gigantic turning barrel (plate) that is bolstered on a stator by attractively suspended course [58]. FESS can be portrayed into two fundamental classes: fast and less-speed FESS [59]. Flywheels with speed of fewer than 10,000 rpm are measured as less-speed flywheels, which are progressively well known in enterprises [60]. The chief shape of a flywheel framework and an empty barrel type are appeared in Fig. 6 [61]. It can be utilized for the smooth operation of machines and can precisely store dynamic vitality from the rotor mass turning at more speeds [59, 62]. The put away motor vitality in FESS is identified with speed and latency. Less-speed FESS contains a steel plate with large idleness and less speed. Then again, fast FESS has a composite circle with moderately lower dormancy and rapid. As the pivoting pace of rotor increments, put away vitality likewise increments proportionally, and energy storage changes in a square with angular momentum.

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Fig. 6 Charging and discharging of a lead-acid battery [49] Fig. 7 Charging and discharging of Li-ion battery

This put away vitality can be utilized further by decelerating rotor torque (release mode) and restoring the active vitality to the electrical engine, which goes about as a generator [52, 62]. The efficiencies of flywheel stockpiling gadgets go from 90 to 95%, while appraised control ranges from 0 to 50 MW [62–64]. A commonplace examination can be created between these two kinds of flywheels, and the distinctions are condensed in [66] (Fig. 3). (2) Compressed-Air Energy Storage Systems (CAES) CAES by and large stores the weight vitality with the press of gas (generally air) into the repository. Turbine is utilized for development of the packed gas; it can be changed into mechanical vitality [69]. Figure 8 outlines the rearranged schematic of a CAES plant [55]. Amid less power request, abundance control drives a reversible engine or a generator unit, which thusly runs a chain of blowers to infuse the air into the capacity unit. This stockpiling unit can be as an underground cave or an over ground supply. Nevertheless, in the midst of low power age for the pile demand, the put away packed air is discharged and afterward warmed by the warmth source. The packed air vitality is later exchanged to the turbine. A recover unit is utilized here to reuse the waste warmth vitality, which further decreases fuel utilization and cycle productivity. A far reaching audit of CAES until 1985 was researched in [70].

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Fig. 8 Chraging and discharging of NaS battery

The main function of CAES plant on the planet is the Huntorf control plant, which was created with two salt arches as the underground stockpiling caves (300,000 m3 at 50 °C and 46–66 bar). The power rating of this plant is 290 MW. This plant was intended to work at 8 h every day life cycle by accusing of packed air. Moreover, it can work for 2 h with full power rating [71, 72]. A propelled plant named MacIntosh plant was created in 1991 in Alabama. Its ability is 110 MW; the plant can work at a cycle of 26 h with full power [71, 73]. Figure 9 demonstrates the rearranged structure of the MacIntosh plant [74]. The plant reliably demonstrates great performance with a scope of 91.2–99.5% beginning and running reliabilities [75]. The CAES framework can be worked for little to expansive scale control limit. In any case, it is appropriate for a huge scale unit that includes framework applications for burden moving, crest shaving, voltage, and recurrence control [55]. The reaction time of CAES is more. CAES can smoothen the power yield of now and again shore wind plants. Accordingly, CAES has pulled in the consideration of the scholarly and modern parts [76]. The ongoing improvement in the field of CAES is the application of supercritical compacted air or packed CO2, which has expanded the proficiency of the plant [76, 77] by conquering the issues of customary CAES. A noteworthy test to execute the substantial scale CAES innovation is choosing the reasonable topographical positions with underground characteristic caverns [58]. To address this issue, progressed adiabatic CAES plant was planned, which likewise faces a problem of small release productivity. A joined cooling; warming, and power framework was explored in 2016 to tackle these disadvantages [78].

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Fig. 9 Schematic view of SC

Fig. 10 Principal diagram of SMES system [69]

B. Electrochemical Storage Systems In the electrochemical stockpiling frameworks (EcSS), substance vitality in the dynamic material is changed over into electrical vitality [81]. This change method is finished by substance response, and vitality is put away as electric flow for a particular voltage and time [75]. The dimension of voltage and current are produced through the arrangement or parallel connections of cells [80]. This is the biggest gathering of vitality stockpiling gadgets explored by [77]. Ordinary battery-powered batteries and stream batteries (FBs) are two strategies that store vitality in electrochemical structure. Be that as it may, substance response diminishes the future and vitality of battery albeit insignificant support is required for these batteries [58].

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Electrochemical capacity gadgets are accessible in various sizes, which is the primary preferred standpoint of this innovation [66]. Lead-corrosive [67], lithium-particle [69], sodium-sulfur (NaS) [12, 32, 42], nickel-cadmium (NiCd) [55], nickel- metal hydride (NiMH) [71], and FBs [8, 69], are test of this stockpiling framework. Some regular EcSSs that can be connected in MG are examined in the accompanying subsections. (1) Redox Flow Battery Storage Systems FBs, which are normally called redox stream batteries (RFBs), work in charged or released mode by a (reversible) concoction response. The reaction occurs between the electrolytes of the battery. These two electrolytes of RFBs are contained in autonomous tanks. As far as possible is direct in respect to the furthest reaches of batteries, and the point of confinement of battery is affected by the amount of battery cells and materials. Power is delivered when redox mixture response (reduction– oxidation) occurs in the midst of action [42]. RFBs have high efficiency (up to 85%) with a long life cycle. It has high soundness and limit with versatile operational characteristics in the electrical system. In this manner, RFB winds up advantageous for application in a self-ruling and independent system [43]. A typical and develop test of redox stream battery is vanadium redox stream battery (VRFB) [58]. Figure 11 represents the essential perspective on vanadium battery [55]. It demonstrates that two fluid electrolytes (V 2+/V 3+ and V 4+/V 5+) with broke down metal particles have been siphoned to the contrary sides of the battery. Stream

Fig. 11 Topology of hybrid MG system with HESS [58]

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battery has just a single dynamic component out of the two permeable anodes, anolyte and also the catholyte. Amid charging/releasing mode, H + is traded through the particle detachment of the layer [55]. The concoction responses are as per the following when the cell voltage is around 1.4 V. (2) Battery Energy Storage Systems BESS is generally pertinent for different purposes in all areas (age, transmission, and dissemination) of electrical power frameworks and in this way gives advantages to buyers [80]. In [21] and [41], the extensive audit of the capacity arrangement of various battery stockpiling advances, for example, lead-corrosive, lithium-particle, redox stream, NaS, and nickel-cadmium battery has been examined. The recurrence of MG is foreseen to be constrained by BESS innovation. A basic proportional circuit of a battery is introduced in Fig. 12 [68]. The working point is the convergence of the source line. Vb is the terminal voltage drop, and VL is the heap line voltage. Figure 12 portrays the regular power profile of BESS for one day. The power bend over the even pivot (time) means the releasing qualities of battery to control the recurrence. Power underneath the time pivot portrays the charging condition of the cell to keep up the recurrence inside the sensible range [52]. Battery capacity is an important determinant in selecting a storage device. The limit of a battery might be characterized as the all out amount of electrical charges that can be conveyed in a solitary release by the cell. The condition of charge (SoC) can be portrayed as the proportion of outstanding ability to the ostensible limit. Distinctive examinations uncover that a semi Z-source inverter is an appropriate procedure for the parallel activity of the battery. In Reference [13], a semi Z-source

Fig. 12 Capacity of global cumulative storage installations

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inverter for BESS has been proposed for the application in MG. In this model, the shoot-through obligation cycle of the semi Z-source inverter is connected to share the heap current between the batteries worked in the islanded association conspire. In any case, on account of a framework associated mode, to get the autonomous guideline of current in both battery frameworks, the proposed model relies upon the inverter tweak file and the shoot-through obligation cycle. The consequence of this investigation demonstrated that microgrid voltage stays adjusted in the unequal burden conditions. Different battery advances are delineated in the following subsections. a. Lead-Acid Storage Systems Lead-corrosive (PbA) battery is the most broadly utilized battery-powered capacity with different sizes and structures in various applications [58]. Among all electrolyte batteries, the PbA battery demonstrates high effectiveness (70–80%) and has the most elevated cell voltage [58]. The cathode and anode are made of PbO2 and Pb, individually. Sulfuric corrosive is utilized as the electrolyte. They are more affordable contrasted and other battery advances, for example, NiCd and NiMh, and are profoundly appropriate for extensive scale MG applications [81]. Different focal points of this innovation are that PbA battery gives incredible charge maintenance and vitality thickness with quick reaction and long life cycle (5–15 years) [42]. In any case, customary PbA battery has a short cycle lifetime (500–2000 cycles), low explicit vitality, occasional water support, and untimely disappointment because of sulfation. To conquer the constraints referenced, progressed PbA batteries have been created, which have multiple times higher power dealing with capacity and four to multiple times expanded life cycles [33, 58]. PbA batteries can be classified into overflowed and valve-directed (VRLA) batteries. The last has turned out to be progressively well known because of its high explicit power, generally low establishment and support cost, and fast charging attributes [44]. VRLA incorporates the adsorbed glass material (AGM) and GEL. AGM batteries have com-settlement volume and recombine hydrogen and oxygen to frame the water in the charging mode; hence, water utilization is constrained [45]. Be that as it may, GEL batteries need the controlled instrument for charging. The fundamental weakness of this GEL battery is that inside the GEL electrolyte, gas air pockets might be delivered. b. Lithium-Ion (LI-ION) Storage Systems Despite the fact that lithium-particle batteries were first popularized during the 1990s, this vitality stockpiling innovation has turned into the quickest developing innovation lately [33]. A Li-particle stockpiling gadget can store vitality at the megawatt scale. The huge headway of this innovation in expanding the dimensions of vitality stockpiling limit is because of the attributes of high productivity (>90%), high vitality thickness, quick reaction time (in milliseconds), and alluring self-release rate (5% per mount) [52]. A schematic of the Li-particle battery alongside the charging and releasing strategy is exhibited in [11]. The cathode and

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anode are produced using lithium metal oxide (LiCoO2) and graphite carbon cell, separately. Amid the charging time frame, Li-particle goes from cathode to anode. The procedure is turned around on account of the release time frame. The electrolyte utilized here can be framed utilizing a natural dissolvable with disintegrated lithium salt or strong polymer [55] (Fig. 7). The proposed strategy was considered in the accompanying situations, for example, dark begin activity, the dismissal ability of positive and negative current unsettling influence amid voltage guideline, and low voltage shortcoming. The test result uncovers that the proposed technique displays an adequate act under regular MG situations. To drag out the battery life, the present dimension must keep up the scope of most extreme unique charge present and greatest powerful release current. Additionally, the battery voltage ought to likewise keep up the scope of most extreme charge voltage and greatest release voltage. The impediments of the Li-particle battery are its cycle profundity of release (DoD) and surprising expense. Be that as it may, the expense of the Li-particle cell is relied upon to diminish with expansive scale creation. Table 2 represents the highlights of various vitality stockpiling gadgets and aides in the choice of Li-particle battery as a vitality stockpiling gadget given its improved execution [42]. Li-particle batteries are intended for high-temperature applications. The plan of batteries relies upon better than ever sciences (e.g., LiFePO4 and Li4Ti5O12). In this manner, these batteries are portrayed by high gravimetric and volumetric vitality thickness (75–200 Wh/Kg and 200–500 Wh/L). It likewise demonstrates improved proficiency (90–95%), high power capacity (multiple times concerning ostensible power), broadened lifetime (of roughly 20 years), delayed cycle activity (8000 full cycles), and a wide temperature extend (20–55 °C) [33, 58]. Accordingly, this innovation needs to turn out to be progressively prevalent because of its little size, light weight, and potential. MGs are little power frameworks that work autonomously from the appropriation matrix, and Li-particle batteries can be the most appropriate stockpiling innovation for the islanded task of MG [36]. Additionally, a solid thought for lithium-iron-phosphate (LiFePO4) battery is talked about in [57]. In any case, Reference [58] suggested that a lithium-sulfur battery can be a decent option because of its high explicit vitality, dependability, relatively minimal effort, and diminished natural danger. As of late, Tesla has executed the world’s biggest stockpiling innovation with Li-particle battery. The limit of this Hornsdale wind plant is 100 MW. In this manner, a propelled Li-particle battery can be created by consolidating every one of these qualities, which show satisfactory execution with great effectiveness, huge storeroom, long date-book life, and low release rate. C. Sodium-Sulfur (NaS) Storage Systems NaS battery includes liquid terminals (both sodium and sulfur) and non-fluid beta alumina electrolyte. Sodium is utilized as the negative anode and sulfur is treated as the positive terminal. Figure 8 demonstrates the charge and discharge responses of the NaS battery. Amid the releasing period, sodium (Na) is oxidized at the Na-beta between face to create sodium particle Na+ when going through the electrolyte.

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This particle is joined with sulfur to shape sodium polysulfide (Na2Sx). The particle is likewise seen to ace duce the ideal yield voltage. Electrons move through the outer circuit. Invert instrument happens when the battery is energized [33]. The in general electrochemical response in the NaS battery can be composed as Na þ xS ¼ Na2Sx; where the estimation of x ought to be inside 3–5. This innovation is generally material for burden leveling, voltage droop minimization, and balancing out sustainable power source control age [29]. Be that as it may, as referenced, this sort of electrochemical vitality stockpiling gadget needs to work at high temperature (350 °C/623 K) to keep up high reactivity and guarantee that sodium and sulfur transform into fluid [32, 59]. This component prompts challenges in utilizing the NaS battery in different applications given that the cost increments because of its usage [79]. In any case, with the progression of innovations since 1980 and applying the particular fabrication process, the vitality thickness of this battery turns out to be a lot higher (multiple times from lead-corrosive battery), and cost moves toward becoming lower contrasted and other capacity gadgets. Increasingly finished, look into is continuous to control the point of confinement of temperature and keep up high vitality thickness, as introduced in [20]. As a potential gadget to actualize in MG, it demonstrates high efficiency, a long cycle period as long as 15 years, and quick reaction (in millisecond) amid full charging and releasing operation. Consequently, nations, for example, Japan and China are putting resources into extensive scale mechanical uses of this innovation [21]. The utilization of NaS battery in certain pieces of the world is introduced in [19, 22]. This particle is joined with sulfur to frame sodium polysulfide(Na2Sx). The particle is additionally seen to master duce the ideal yield voltage. Electrons move through the outer circuit. Turn around system happens when the battery is energized [13, 33, 38]. D. Electrical Storage Systems Electrical vitality stockpiling framework (EESS) might be characterized as the limit of putting away electrical vitality to deliver power what’s more, providing it to the heap for use when essential. Vitality can be put away by adjusting the electrical or attractive fields with the assistance of capacitors or superconducting magnets [52]. The present power organize framework faces the test of integrinding the transmission and dispersion framework with restore capable vitality sources. Thusly, EESS has been treated as a reasonable innovation to alleviate this issue because of the various appealing highlights in the framework organize. These highlights may help in working the power framework organize, load balancing, improving the power quality, supporting the MG, and lessening the need of bringing in electrical vitality in the pinnacle request period [55]. Ultracapacitors (UCs) and SMES frameworks are instances of EESS [58]. They can be utilized as momentary stockpiling gadgets if there should be an occurrence of high stream current given

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that the limit of the ordinary capacitor is constrained. In this way, a supercapacitor with high stockpiling limit may supplant the ordinary capacitor, which has high capacitance. SMES are favored at the exit of the power plants to balance out the yield or in the modern part, where top vitality consumption must be suited [52]. The subtleties of these two stockpiling frameworks alongside their ongoing improvement are depicted broadly in the accompanying subsections. (1) Supercapacitor Storage Systems SCs, additionally called UCs or electric twofold layer capacitors (EDLCs), can be characterized as capacity gadgets that can store electrical vitality between two leading cathodes. This innovation has no concoction responses. It has turned into the option in contrast to a traditional capacitor utilized in various electronic applications and general batteries. This innovation has the attributes of high power thickness and high pinnacle control yield; the long logbook life cycle can be revived and released up to a huge number of times contrasted and the conventional battery [29]. The vitality thickness of SC has been expanded because of the utilization of a high-surface zone material, for example, initiated carbon. In the uses of the power framework, for example, correspondence and shuttle innovation, beat burden may exist. This kind of burden may cause extreme power and thermal aggravations in MG applications; this is the principle purpose behind presenting the SC, which has a quick reaction in power leveling and power offsetting establishments with the best possible control framework to beat these issues [30, 31, 69]. Figure 9 outlines the essential structure of a supercapacitor. The capacitance of SC is not steady; rather, it differs with the difference in the voltage, which relies upon the dog lease request and supply from SC. the electrolyte. This particle is joined with sulfur to shape sodium polysulfide (Na2Sx). The particle is additionally seen to professional duce the ideal yield voltage. Electrons move through the outside circuit. Switch system happens when the battery is energized [33]. The by and large electrochemical response in the NaS battery can be composed as Na þ xS ¼ Na2 Sx;

ð1Þ

where the estimation of x ought to be inside 3–5. In [33], the use of the supercapacitor for the best possible activity of MG in lattice associated and islanded methods of activity for typical and broken conditions were represented. In [34], another utilization of SC in the rail-street was talked about, where 55.5% framework effectiveness was recorded. In different applications, the productivity of SC is nearly in the scope of 84%– 97%. In spite of having every one of these preferences, this SC has a few downsides, which incorporate the high self-release rate (up to 40% every day) and costs (6000 dollars/kWh). To defeat these difficulties, the continuous investigation centers around the savvy multi-layer SCs that comprise of materials, for example, carbon, graphene, or paper [55, 35]. The scientists currently center around the development of cathode dependent on ultra-little Si nano particles in polyaniline for SC [36].

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SMES frameworks basically work dependent on the primary of electrodynamics [37]. In this stockpiling framework, vitality is put away in the attractive field by the course of current in a superconducting loop with the assistance of an AC to DC converter (charging mode). Be that as it may, the put away vitality can be discharged back to the network utilizing the DC to AC converter (releasing mode). Ohmic misfortunes in this innovation can produce heat in the framework and along these lines cause the warm insecurity of SMES [38]. (2) Super Magnetic Energy Storage (SMES) Systems The two sorts of SMES frameworks can be portrayed as follows: more-temperature SMES (HTS) that works at approximately 70 K and low-temperature SMES (LTS) that works at roughly 7 K. Figure 10 demonstrates the fundamental outline of the SMES framework [69]. The LTS framework is a more develop innovation than the HTS framework. This strategy can give quick reaction to charging and releasing wonders, which are restricted to couple of milliseconds. In addition, this framework has high vitality thickness (4 kW/l) and high productivity (95–98%) with a long lifetime of roughly 30 years. Vitality put away in the SMES gadgets can be communicated as pursues: WLS ¼ 1L  I2;

ð2Þ

where L indicates the self-inductance of the curl, I is the measure of current that moves through the loop, and WLS is the measure of put away vitality in the loop. SMES frameworks are accessible in the scope of 0.1–10 MW for business use. With the progression of technology, the limit is relied upon to increment to around 100 MWh in the following decade. In any case, because of the unpredictability of the cooling framework and curl material, the expense of the SMES framework establishment is still high ($10,000/kWh) [58, 71]. Besides, visit changes in the working current in this innovation make SMES precarious. This issue was fathomed in [38]. SMES innovation is essentially pertinent in UPSs and improves control quality. It has turned out to be stylish for MG applications because of the adaptable attributes it offers in trading genuine and responsive power [39]. Current examinations on SMES gadgets depend on decreasing the expense of loops and cooling frameworks to make this stockpiling gadget exceedingly alluring to shoppers. Also, a half and half SMES framework could be created to expand capacity limit [40]. F. Hybrid Energy Storage Systems Half and half ESS (HESS) alludes to the joining of at least two ESSs that were connected to accomplish the upsides of each ESS for getting brilliant qualities in a single specific application. It is beyond the realm of imagination to expect to give every one of the highlights by one ESS type. In this way, the mix of ESS has turned into the interest for current innovation, for example, MG. As per [48], high-control

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ESS gadgets are helpful for quick reaction at high rates for brief term, while high vitality gadgets demonstrate the moderate reaction with the more expanded period. MG needs an ESS that joins the attributes of more power and high vitality stockpiling framework to improve the dependability and capacity of the framework with the decrease of the power quality issues [49]. The control technique of HESS is more convoluted than that of a solitary ESS, and numerous highlights are included, such writing audit on the HESS innovation demonstrates that, for MG applications, the reconciliation of battery/SC [16], battery/SMES [22, 50], battery/FC [51], FC/SC [32], and SC/RFB [49] is conceivable. Battery/SC innovation is presently very prevalent and generally pertinent. Battery/SMES HESS topology has been researched to improve the productivity of a breeze plant [52], which repays the variance of burdens in railroad applications [53], the all-encompassing life cycle of battery [54], and recurrence control in MG [50]. For application in MG, HESS demonstrates better execution in frequency adjustment contrasted and the battery-just framework. In this application, the battery life cycle is improved on the grounds that it acquires assurance from high recurrence charging or discharging cycles and pinnacle flows. Reference [22] uncovers that the life of battery can be stretched out from 5.7 to 9.2 years by the proposed HESS topology. Reference [51] exhibited a HESS topology of battery/ FC, where the battery was utilized as an essential stockpiling gadget for short to medium span, and HFC was connected as a long haul stockpiling gadget. The impediment of moderate reaction in the battery can be overwhelmed by the quick reaction qualities of HFC. Additionally, this HESS method demonstrates higher explicit power than HFC alone. FC/SC HESS for MG applications likewise show better performance (8.5% more productivity) than FC stockpiling framework just [32]. a. Application of Battery/Supercapacitor Energy Storage Systems in Microgrid Numerous examinations have explored on the hybridization of battery/SC for a long time. This theme is generally main stream with the scientists since it can furnish nearly substantial capacity limit with quick charging and releasing characteristics [55]. A dynamic model for this structure has been projected in [56]. This model is fit for balancing out the recurrence change in MG application. The utilization of SC gives the battery alleviation from worry by limiting motions and abrupt homeless people Apart from these qualities, ensuring the framework interior power and making full utilization of vitality are likewise the critical contemplations for HESS [57]. A network incorporated cross breed MG framework with HESS has been created, as appeared in Fig. 11 [58]. MG assumes crucial double job characteristics by going about as a rectifier from the AC-side and as an inverter from the DC-side. This HESS innovation improves the expansion of life go up to 75% through pinnacle shaving and related warm weight response [59]. All the related research on battery/SC uncovers this is a much improved, dependable, and effectively available

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innovation that fulfills the interest of the framework and enhance battery task. Consequently, framework productivity and future of the battery have been improved with this HESS system.

4 Issues and Challenges of Ess in Mg Applications The present status of ESS innovation alongside the development of HESS can alleviate numerous difficulties looked by the past innovation, for example, productivity or capacity limit. Be that as it may, the extent of the further improvement of this technology for the future application in MG innovation given that BESS innovation has lost his appeal because of calendric maturing and cyclic maturing [60]. Research is presently centered based around the measuring, costing, wellbeing, or effective administration of vitality in the framework. Along these lines, the key issues and difficulties can be recognized with respect to material determination, control electronic interfacing, vitality balance among ESS and MG, natural effect, and the security of this innovation. The accompanying subsections give a point by point review of these key issues alongside the particular proposals. A. Selection of Materials Material choice, cost of materials, and accessibility of crude materials are the most critical criteria for ESS framework. Materials decide the existence cycle of the capacity gadget. A few stockpiling materials and their improvement methodology for different capacity, for example, mechanical, warm, hydrogen, gravity, electromagnetic, and electrochemical gadgets, have been talked about in various examinations. In any case, the material determination is not ideal as a rule [81]. For the further advancement of ESS in MG application, the execution of high-grade ESS materials with their critical commitment must be tended to [61]. Charging and releasing attributes, limit, vitality and power thickness, life cycle, and destructiveness can be significantly impacted by the materials [8]. The current ESS component with extensive capacity limit, for example, flywheel, siphoned hydro, SMES, lithium-particle battery, NaS battery, and stream battery is still expensive in the power showcase. In addition, half and half ESS, for example, a battery/ supercapacitor, gives an expansive limit storeroom; however, the effectiveness of this HESS innovation can additionally be created. In this manner, a financially savvy long haul cutting edge innovation can lead the material choice of ESS in MG application with improved vitality effectiveness and solidness. B. Power Electronic Interface The power electronic interface manages the procedure to guarantee the power quality, execution, control guideline, dependability, solidness, and productivity of the framework [15, 63]. To build the handiness of the MG framework, control electronic interface (PEI) might be utilized to coordinate MG with ESS and the current electrical power organize. PEI has different attributes since it has the

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important plan for power change with the assistance of a rectifier and an inverter. For PEI applications, distinctive converters, for example, buck, support, buck-help, cûk, half-connect, fly back, H-extension, and Z-source converter, might be utilized, which can be sorted under DC-DC, DC-AC, AC-AC, and AC-DC converter topology. In view of the capacity innovation, for example, SC, FC, FESS, BESS, or SMES, and their applications, a power converter permits the association among the two DC transport of unequal voltage, a DC transport, and an air conditioner transport or even the association of a present source to a voltage transport [15, 68]. The current PEI framework has detriments in size, swells, cost, adaptability, and effectiveness [15, 74]; thus, advanced research may be required on the PEI framework to conquer the difficulties for the effective activity of the capacity innovation. C. Energy Management System Enhancement in dispersing the power in the ESS topology for MG applications can be performed by sharing the intensity of the vitality the board framework (EMS). A few ESS, for example, CAES, GES, and Li-particle battery can be demonstrated for huge scale coordination, though TES, SMES, FBs, and power devices are effective for medium-scale vitality the executives [58]. To plan an effective EMS, the minimization of the general framework misfortune and the control of SOC can assume an essential job in enhancing the productivity and keeping the save for future interest, separately [65]. Also, HESS can control vacillation, which improves control quality and limits the greatest dynamic power change rate. Consequently, they can be a superior option than a solitary ESS framework. The variant topologies of HESS have been talked about in this audit. Battery/SC HESS topology is seen to be a decent decision for future improvement. In this manner, present day ESS the board for MG applications with solid and stable attributes could be enhanced by a quality administration framework, which builds the general proficiency and decreases the expense. D. Size and Cost of ESS The size and economy of various ESS innovations are very high. In the event that the size increments, so does the expense. As examined in various examinations on compacted air, flywheel, HFC, gravity, and warm or battery stockpiling, measure relies upon the vitality rating and power rating [45, 66]. Curiously large ESS is not reasonable. Cost consolidates establishment and maintenance costs. The per unit cost of vitality is additionally an essential factor in vitality innovation. Cost relies upon the capacity materials, limit, charging/releasing rate, DoD, and life cycle [67, 80]. In spite of the fact that the expense of various ESS is high in various classifications and acquires steady and dependable task, ESS is an unavoidable answer for MG. Given the normal value decrease of some new advances, (e.g., GES, Li-particle, stream battery, NiCd, or Ni-Zn) sooner rather than later and inspecting the current stockpiling, for example, PHS, CAES, FES, FC, and TES, balancing out voltage and recurrence variance of single ESS in MG has numerous confinements. In this way, with the progression of innovation, HESS has been created to

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incorporate more advances to accomplish a proficient musical dramation with vitality exchange, ring shaving, time moving, and voltage support [42]. The limit of the capacity framework can be expanded with their joining, for example, battery/ SC, battery/flywheel, battery/SMES, CAES/SC, CAES/flywheel, and FC/SMES, which decreases the general size and economy of the plant by dodging the consideration of more stockpiling gadgets independently. It additionally has a solid commitment in expanding the future of capacity [48]. Along these lines, receiving a comprehensive vitality stockpiling arrangement to adjust the ability to lessen the expense and increment unwavering quality would be a noteworthy test for sustainable and customary system frameworks. E. Ecological Impact Research on the natural effect has officially demonstrated that, as the vitality created by inexhaustible sources builds, the discharge of ozone harming substance or other lethal emanations decline [68]. Natural perils happen from the burning of non-renewable energy source (CAES), attractive field (SMES), recyclable materials, or synthetics of the capacity framework during assembling and transfer time. HESS can incorporate the irregular sustainable power sources in power matrix and in this manner can lessen fuel utilization and lethal emanation [2]. Albeit 100% RE generation is exorbitant [68], specialists expect to lessen the establishment and support expenses of the RE sources to guarantee manageable improvement. F. Security Issues The security of ESS has turned into the interest for present day MG applications. For protected and secure tasks, different factors, for example, the attractive qualities of materials, life cycle, temperature, cut off, cheating, and over-releasing attributes of ESS, must be tended to proficiently. This procedure can diminish the vulnerability and discontinuity of the framework. SMES ought to have the control to lessen the ohmic misfortunes; CAES, TES, and NaS batteries require temperature control instrument; SC stockpiling experiences high self-release rate; energy components request wellbeing from erosion with less-and high-temperature the executives; lead-corrosive batteries need customary upkeep amid task; and Li-particle batteries need cheating and over-releasing security [68–71]. In this manner, late research can concentrate on conquering these issues to make the innovation exceedingly easy to use.

5 Discussion and Conclusion ESSs advancements are an elective answer for the potential usage of sustainable power source in MG applications. Numerous analysts are engaged with the improvement of ESSs and their uses in MG to deal with the OK control balance by putting away vitality amid off-top hours with decreased expense. In this way, the flawlessness in the demonstrating of ESSs with streamlining qualities are the key

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highlights of cutting edge ESS innovations. In any case, the improvement of an effective ESS for MG applications is a difficult issue. In addition, practically all examinations and surveys are restricted in the ESS types, qualities, and their arrangements with points of interest and hindrances. The present investigation features the diverse advances of ESS, their developments, tasks, and vitality change components to give a solid review to guaranteeing the supportability of future ESS frameworks in taking care of natural and financial issues. This examination likewise checked on the execution of individual ESS, for example, flywheel, packed air, battery, power module, supercapacitor, very attractive, redox stream, lithium-particle, and the cross breed ESS, for example, battery/supercapacitor, battery/SMES, and battery/F in MG activity. Besides, vitality and power thickness, reaction time, estimate, effectiveness, cost, life cycle, and material determination have been clarified in different pieces of this audit. This survey additionally foreseen the propelled power gadgets interface among ESS and MG toward the develop half breed ESS with ideal highlights. This thorough audit recommends that the improvement of ESS materials and substance arrangements can expand the capacity limit, life cycle, and proficiency of the gadget. To guarantee better execution with solid task, this examination uncovers that crossover ESS is profoundly alluring in MG applications. This audit featured numerous elements, challenges, and their conceivable arrangements and proposals for cutting edge ESSs in MG applications, which may support scholastics, scientists, and businesses to change and improve the current ESSs into a propelled dimension. Therefore, the key commitment of this examination is the far reaching investigation of various ESS mix in MG applications to give a thorough thought on the progressed ESSs and their future sending in the MG organize. The survey has proposed essential and specific recommendations for the further mechanical advancement on ESS in MG applications. Propelled explore is required to improve cutting edge ESS in MG applications. A few issues of ESS exist regarding materials, size, and cost. Control interface, condition, and wellbeing must be routed to achieve legitimate framework functionality and market acknowledgment. The long haul plan for ESS is to structure a practical, dependable, and limit office to lead the maintainable utilization of ESS in MG task. A propelled power electronic framework may continue to defeat the exchanging difficulties and security hardware issues and address the overheating and over charging/releasing wonders for effective ESS task. An ideal EMS and progressed ESS topology could be a decent decision for future advancement to expand by and large proficiency and decrease cost. Suitable strategies must be created to discover the optimal size of the ESS to accomplish an effective task with vitality exchange, ring shaving, time moving, and voltage support. The vitality stockpiling approach is embraced to adjust power and increment dependability, which would prompt a significant potential for ESS in MG applications.

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• The improvement of an appropriate model for the ESS that considers different sub-models, for example, charging/releasing, ideal size, plan controller, wellbeing, and assurance, must be considered further. • Further research must be directed on the ESS materials and synthetic arrangement improvement to build stockpiling limit, life cycle, and effectiveness. • For an ecological effect of investigation, emanation decrease and cost-sparing models must be created to guarantee sustainable ESS advancement and lessen negative environmental effects assuming any. • Examination ought to be attempted on ESS reconciliation into MG to defeat the synchronizing unpredictability, improve the incorporation execution or islanded activity, and increment the computational speed. • Further research ought to be performed on the protected and secure ESS task, which thinks about temperature, hamper, and cheating and over-releasing qualities. • The proposals would be wonderful commitments toward the development of ESS innovations, which are required to dominate the power showcase later on. In this way, propelled explore dependent on this survey may fundamentally beat the confinements of the current ESS advances in MG applications to meet further maintainable vitality usage.

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Renewable Energy

Design and Implementation of Efficient Energy Management System in Electric Vehicles R. Gauthami, Vineeth V. Nair, Aswin Sathish, K. Vishnu Soureesh, K. Ilango, R. S. Sreelekshmi, S. A. Ilangovan and S. Sujatha

Abstract Pollution and its related problems are degrading the planet along with its residents at an exponential rate. The transportation system is the most potential sector for this and electric vehicles the foremost solution. Most innovations in EVs today concern user comforts. But, the need currently is to focus on energy and its optimal utilization. This requires concentrating on the vehicle source side. Hybrid energy storage systems (HESS) possess the perfect remedy to fit into this need. This paper focuses on the development of a control algorithm for the proper designing and the implementation of an efficient energy management system in EVs with active HESS of battery and supercapacitor by bringing in load sharing to this hybridization during the various load demand conditions. Energy management algorithm has been designed. and performance has been analyzed using MATLAB. Hardware prototype for testing was also made and the results were verified.





Keywords Electrical vehicles (EVs) Hybrid energy storage system (HESS) Control algorithm Supercapacitors Power sharing Varying loads










1 Introduction Electrifying our transport system and decarbonizing it has become a global race against pollution and the growing concern for stability. EVs, which can be plug-in hybrid electric vehicles, battery electric vehicles, or hybrid electric vehicles, are the best solution to these problems [1]. Extensive research works are hence going on in R. Gauthami
V. V. Nair (&)
A. Sathish
K. Vishnu Soureesh
K. Ilango
R. S. Sreelekshmi Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Clappana, Kollam, Kerala 690525, India S. A. Ilangovan
S. Sujatha Vikram Sarabhai Space Centre, Indian Space Research Organisation, Thiruvananthapuram 695022, Kerala, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_49

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all areas related to EVs. With this comes the requirement for energy and the efficiency in meeting it. Due to the unpredictability in the availability of renewable sources, there is a necessity for alternative solutions like energy storage systems (ESSs) and thus comes the requirement for the proper management of these systems [2]. Hence, bringing in proper energy management to the EV system is the main aim of this work. To achieve this, a system with active HESS of battery and supercapacitor connected to the DC link will be utilized to work on. In this paper, Sect. 2 discusses the state of the art related to the work. Section 3 deliberates about the selected test system, and Sect. 4 covers the explanation about the energy management algorithm. The simulation design and analysis of the test system is reported in Sect. 5. The prototype implementation of the test system and its results have been discussed in Sect. 6. Section 7 concludes the work.

2 State of Art Current EV market has seen numerous innovations come up in different areas such as vehicle range and charging time. However, looking deep into the technological sides, these innovations barely scratch the surface of what the real necessity in the current time is. Energy and its proper utilization is the most important factor now. Lithium-ion batteries are one of the most popular energy storage systems owing to its high energy density property. However, there are number of limitations for Li-ion batteries like low power density, temperature issues, weight, and high cost [3]. Though alternative energy storage devices like supercapacitor and fuel cells are paving ways, no single storage system is able to satisfy the market demand completely [4, 5]. Supercapacitor–battery hybrid combination perhaps is the best that can be offered. Supercapacitors are electrostatic energy storage devices with advantages such as high power density and quick charging/discharging cycles [6]. Thus, they are a good option to meet the sudden peak demands of a vehicle, whereas batteries are handy for supporting the normal demands of the vehicle [7, 8]. Battery–supercapacitor hybridization supports a number of benefits such as increasing the battery life, extending range, and decreasing vehicle cost [9]. High energy density device is combined with a high power density device, hence complementing its disadvantages. In this paper, designing, development, and implementation of an effective energy management algorithm with the active HESS configuration of battery and supercapacitor have been proposed for efficient energy management in EVs.

3 Test System Description The block diagram of the selected test system which consists of EV motor drive system and active HESS topology of battery and supercapacitor connected across a DC link through the DC–DC converters individually is shown in Fig. 1. Here, the

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test system does not consider recharging of supercapacitor, and hence, a DC–DC converter is sufficient in place of a bidirectional DC–DC converter. The DC link voltage is maintained at constant system voltage. From the DC link, the connection goes to the motor driver and the motor. To achieve efficient energy management, HESS system with EMU is used.

4 Energy Management Control Algorithm The energy management control mechanism for the EMU is shown as flowcharts in Figs. 2, 3, and 4. Here, the entire control mechanism is based on the amount of load current that is required for the vehicle operation. This has been implemented practically using Arduino UNO for hardware validation. The EMU senses the battery voltage, battery current, supercapacitor voltage, supercapacitor current, the load voltage, and the load current. Based on the sensed parameter values and the control algorithm given, EMU gives the appropriate switching pulse to the DC–DC converters, and hence, load sharing is effectively brought in. Initially, the voltage of the battery is checked by the algorithm. If the battery voltage is lower than the minimum cutoff, the vehicle does not start and the battery needs to be charged, whereas if the voltage of the battery is above the minimum cutoff, the vehicle starts. When the vehicle starts, generally there will be a high current requirement. Hence, this demand will be met by the supercapacitor. If the vehicle is accelerating, the system undergoes the load sharing strategy. In this strategy, if the load current required is below ‘x’, where ‘x’ is the nominal current that can be supplied by the battery at 1C rate, the entire load is supplied by the battery alone. Now, if the load requirement is above a value ‘y’, where ‘y’ is a high transient current, the entire load

Fig. 1 Block diagram of active HESS topology with EMU in electric vehicles

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Fig. 3 Flowchart continuation

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Fig. 4 Flowchart continuation

is met by the supercapacitor alone. But, this will be performed only after checking the voltage available in the supercapacitor. If the voltage is sufficient, supercapacitor will be made to meet the demand. Otherwise, the battery itself acts as the source. For the conditions in-between these, load current is met by both the sources together. This case is further divided here into two conditions. If the load demand is greater than a specific value ‘a’ (where ‘a’ can be fixed according to the designer’s requirement), supercapacitor is made to meet the majority of the requirement while the battery meets the rest. For conditions below ‘a’, battery meets the majority of the requirement while supercapacitor meets the rest of the requirement. Again, here

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also, the voltage available in the supercapacitor is checked, and it is only if the voltage is sufficient that the supercapacitor is allowed to aid in the load sharing. Now, if the vehicle is free running, the current drawn would be small. Hence, battery will supply the entire demand. If the vehicle is in decelerating mode, the same conditions as during acceleration will be checked and correspondingly the load requirements are met from the two sources.

5 Simulation and Performance Analysis The simulation design of the proposed test system which has been modeled in MATLAB Simulink platform is shown in Fig. 5. The components used for simulations are a battery of nominal voltage 12 V and rated capacity of 2.5 Ah. Supercapacitor rated at 120 F and rated voltage of 12 V. Loads used are RL load.

5.1

System with Battery and Supercapacitor

The test system has been modeled, and its performance has been validated using MATLAB Simulink. EMU controls the supercapacitor and battery side DC–DC converter separately with PWM gate pulses. Whenever a particular source is required, the circuit breaker at the corresponding DC converter is turned ON and is turned OFF at all other times. The blocking diodes are kept at the output of the converters to avoid the reverse flow of current. The load demand is simulated from zero to the maximum demand which is 8 A, with varying loads. The load is varied for 0.8 s in steps of 1 A per 0.1 s. The condition given is such that whenever the load current is less than 3 A, the battery delivers the entire demand. If the load demand is more than 5 A, supercapacitor delivers the entire demand. For the

Fig. 5 MATLAB circuit diagram for test system (0–8 A)

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condition in-between, the load demand is shared by the two sources (here, for a load current of 3–4 A, battery supplies major load, and for a load current of 4–5 A, supercapacitor supplies major load). From Figs. 6, 7, and 8 it is clear that for varying load current of 0–8 A, the battery shares maximum current till 3 A and supercapacitor supplies current from 5 A onwards. For the conditions in-between, load is shared between the two sources. Here, it is observed that during load switching, the system experiences fluctuations in the load voltage and current. Hence, only the mean values of voltage and current are considered [10]. The performance of EV current, battery, and supercapacitor current behaviors during 0–0.005 s is shown in Fig. 9. Since the load requirement is less than 3A, the

Fig. 6 Load voltage and load current

Fig. 7 Battery SoC, voltage, and current

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Fig. 8 Supercapacitor SoC, voltage, and current

Fig. 9 CASE: 1 load, battery, and supercapacitor current for load 5A

Fig. 13 Output voltage and current of battery and supercapacitor at buck converter end (load *5A)

because, both the converters are parallel connected and voltage at that point will be equal. Hence, even if one of the converter end voltage is increased, it tries to maintain at reference voltage (which is the rated voltage of the load, here 6 V) and thus drawing more current.

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System with Battery Alone

The system has been simulated with battery-alone condition to compare the amount of stress that the battery takes when it is a single source, with that of the hybridized battery–supercapacitor condition during high demands. The supercapacitor in the source side has been removed, and the battery was made to meet the same load conditions alone. Figure 14 shows the load current and the load voltage for battery-alone condition. Here, during the high load demand (from 0.5 to 0.9 s), there is a slight drop in the load voltage. This shows that the battery is not able to meet the high load current requirements properly (Fig. 15).

Fig. 14 Load voltage and load current—battery-alone condition

Fig. 15 Battery SoC, voltage and current for 0–0.9 s

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Fig. 16 SoC comparison of battery-alone and battery-in-hybrid condition

In Fig. 16, it is clear that for simulation of 0.9 s, the SoC of battery when used alone is 99.988%, whereas it is 99.997% when the supercapacitor is also used. For a full-scale system, the SoC drop would show huge differences and the stress on the battery would be proportionally higher.

6 Hardware Realization of the Active HESS Configuration with EMU The hardware model for the proposed system consisting of active HESS topology and EMU together was implemented as shown in Fig. 17. Here, the source side consists of a battery and supercapacitor both rated at 12 V which is connected to a motor driver (MD10C) via a DC–DC buck converter. This motor driver is used to

Fig. 17 Hardware prototype block diagram

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run the motor. Arduino UNO microcontroller module acts the EMU for this system. Voltage and current sensors were used to sense the signal parameters. Balancing circuits were also used to assist during the charging of the battery and the supercapacitor. The digital storage oscilloscope (DSO) outputs of the hardware prototype testing in no-load have been shown in Figs. 18, 19, 20, and 21. The load voltage was maintained at around 6 V for all cases. Due to the losses created during the practical implementation of the system, slight variations have been observed. When these losses were neglected, the system was found to behave as expected by the control algorithm. In Fig. 18, the load current is 318 mA while the battery current is 392 mA and the supercapacitor current is 34.7 mA. It was observed that since the load was less, battery supplied the majority of load. Figure 19 shows that when load demand is 363 mA, battery supplies 234 mA and supercap supplies the rest. Figure 20 shows the output when the load demand is 475 mA. Here, battery supplies 198 mA while supercapacitor supplies 284 mA, hence reducing the stress on battery. The load demand in Fig. 21 is 527 mA. Since the load requirement is high, the supercapacitor contributes majority (481 mA) while battery supplying 65.1 mA.

Fig. 18 DSO output—Case 1: load voltage, battery, and supercapacitor current for load *0.3 A

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Fig. 19 DSO output—Case 2: load voltage, battery, and supercapacitor current for load around 0.3–0.4 A

Fig. 20 DSO output—Case 3: load voltage, battery, and supercapacitor current for load around 0.4–0.5 A

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Fig. 21 DSO output—Case 4: load voltage, battery, and supercapacitor current for load >5 A

7 Conclusion This work is focused on developing efficient control method to have proper load sharing between battery and supercapacitor in an active HESS topology, by using an energy management unit. The proposed control algorithm required for the EMU has been designed, developed, and successfully implemented into the test system. Simulation of the test system has been done on MATLAB Simulink. The simulation results showed that based on the control algorithm given to the EMU, the DC–DC converter output is controlled, and hence, the load is shared by the two sources. The hardware prototype model has been designed and implemented to validate the performance of the test system. The results showed that by controlling the EMU, the load demand can be shared by the hybrid energy storage system. Furthermore, it can be inferred from the results obtained that a hybrid combination of battery and supercapacitor has a number of advantage such as decreasing the stress on the battery, reducing the size of battery, increasing battery life, and regenerative braking. Acknowledgements We would like to express our deepest appreciation to all those who provided us the support to complete this work. We thank all the staff at the Applied Power Systems Division (APSD), Chemical Systems Group (CSG), Vikram Sarabhai Space Centre (VSSC), and Indian Space Research Organization (ISRO), Thiruvananthapuram for assisting us with the hardware prototype. We also thank all the staff of the Dept. of Electrical and Electronics Engineering and the Principal and Management of Amrita School of Engineering, Amritapuri, Kerala. A special mention to our family and friends for their unabated encouragement.

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References 1. X. Shi, X. Wang, EV transformation in Beijing and the comparative eco-environmental impacts: a case study of electric and gasoline powered taxis. J. Clean. Prod. 137, 449–460 (2016) 2. S. Sivanandan, V.R. Pandi, K. Ilango, S. Sivanandan, V.R. Pandi, K. Ilango, Stateflow based implementation of energy management for a DC grid using analog and digital control techniques. in International Conference on Technological Advancements in Power and Energy (TAP Energy), (2017) 3. B.G. Pollet, I. Staffell, J.L. Shangc, Current status of hybrid, battery and fuel cell EVs: from electrochemistry to market prospects. J. Electrochim. Acta 84, 235–249 (2012) 4. K.T. Chau, Y.S. Wong, Hybridization of energy sources on electric vehicles. Energy Convers. Manag. 42(9), 1059–1069 (2001) 5. K.T. Chau, Y.S. Wong, C.C. Chan, Energy conversion and management. Energy Convers. Manag. 40(10), 1021–1039 (1999) 6. E. Faggioli, P. Rena, V. Danel, X. Andrieu, R. Mallant, H. Kahlen, Supercapacitors for the energy management of electric vehicles. J. Power Sour. 84(2), 261–269 (1999) 7. R.S. S, R. Anusree, V. Raveendran, M.G. Nair, Solar fed hybrid energy storage system in an electric vehicle. in Solar Fed Hybrid Energy Storage System in an Electric Vehicle, (2018) 8. K.R. Bharath, R. Kodoth, P. Kanakasabapathy, Application of supercapacitor on a droop-controlled microgrid for surge power requirement. in International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT), (2018) 9. H. Yoo, S.K. Sul, J. Jeong, System integration and power-flow management for a series hybrid ev using supercapacitors and batteries. IEEE Trans. Ind. Appl. 44(1), 108–114 (2008) 10. V.V. Nair, K. Ilango, Microgrid control strategies for enhanced storage management. in International Conference on Technological Advancements in Power and Energy (TAP Energy), (2017)

A Cost-Effective PV-Based Single-Stage Conversion System for Power Backup B. Kavya Santhoshi and K. Mohana Sundaram

Abstract Shortage of power is a severe problem in rural areas of India. This work aims at providing a feasible solution to this problem. When a power outage occurs, inverters are used as a backup power source in residential applications. In general, an inverter has to go through a two-stage process for power conversion from DC to AC since a special converter is required to boost the voltage to higher level. The inverters that are used for residential purpose consume electricity from grid in order to charge or discharge the battery of the inverter that usually causes overloading. These main disadvantages can be overcome with the use of Quasi-impedancesource inverter (QZSI). With the use of a single-stage conversion circuitry for AC application, an attempt to provide a cheap and economic renewable energy system has been made in this work.





Keywords Photovoltaic Renewable Single-stage conversion Sine PWM


Quasi-Z source
Inverter


Acronyms Used QZSI VSI CSI ZSI DCM CCM MPPT SL-QZSI SPWM

Quasi-impedance-source inverter Voltage-source inverter Current-source inverter Impedance-source inverter Discontinuous conduction mode Continuous conduction mode Maximum power point tracking Switched-inductor Quasi-Z-source inverter Sinusoidal pulse width modulation

B. Kavya Santhoshi (&)
K. Mohana Sundaram Department of Electrical and Electronics, Vel Tech Multi Tech Engineering College, Chennai, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_50

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1 Introduction Renewable energy is the area of interest of researchers, and solar energy has gained importance due to various advantages that it possesses compared to others; the main advantage being that it is available always. Since solar energy obtained from a PV panel possesses harmonics, there has been a search for a remedial action to overcome harmonics. Also, energy from the sun is not available in all seasons, and there exists intermittency in power produced using solar panels. Continuity of supply and power backup is the possible solution to the power production using solar panels. Consecutively, batteries are used in a PV-based system, and various harmonic reduction techniques, pulse width modulation techniques and MPPT techniques had been proposed so that the efficiency on a total scale is improved. Traditional inverters such as VSIs and CSIs suffer from few limitations, and hence, an alternate topology (Z-source inverter) was proposed for single-stage conversion. Thereafter, research has focused on different topologies of Z-source inverters and their evolution. The concept of Z-source inverters was proposed by F. Z. Peng. It brought to the limelight the advantages of using Z-source inverters instead of VSIs and CSIs. In general, inverters and generators provide energy backup during power outages. For residential applications, inverters are most preferred. But these inverters draw power from grid for charging or discharging of battery. In the proposed work, a standalone inverter drawing power from solar panel and having single-stage conversion with low harmonics at the inverter output is proposed. A unique circuitry is proposed with a modulation strategy that is commonly used, i.e. sinusoidal pulse width modulation.

2 The Evolution of Z-Source Inverters F. Z. Peng, in the year 2003, introduced the concept of Z-source inverter [1]. In a ZSI, there is a distinctive impedance (Z) arrangement that can act as a median point between the main inverter circuit and the power source. The ZSI has superior characteristics in comparison with VSIs and CSIs. The most significant facet of ZSI is that a ZSI can behave like a buck–boost inverter and have wide variation in voltage range. In the same year, F. Z. Peng also proposed a Z-source inverter that found application in adjustable speed drives. The output voltage required for an application can be achieved despite the magnitude of line voltage because ZSIs can boost capacitor voltage to high levels. Some more literature works based on Z-source inverters were proposed between 2003 and 2007 [2]. Efficiency seems to be a problem, and hence, Quasi-Z-source inverters (QZSIs) with more advantages than Z source were introduced [3]. Ample advantages exist by using them, in particular for PV applications.

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Joel Anderson and F. Z. Peng, in the year 2008, proposed four different topologies using Quasi-Z-source inverters [3]. Operation in DCM and CCM for voltage-fed and current-fed QZSIs was depicted, and the results were compared. Inference obtained from the paper was that QZSIs function better and this work uses this inference importantly [3]. Due to the stochastic fluctuations in PV, energy storage had to be the only preferable option. Many works in literature focused on PV-based systems with energy storage (batteries) thereafter [4, 5]. J. G. Cintron and F. Z. Peng, in their paper regarding Quasi-Z-source inverter comprising energy storage, have depicted the benefit of energy storage in a PV-based system. When PV power is less than grid power, battery discharges, and when the PV power is more than the grid power, battery charges [6]. The harmonics in the load, however, are still present to a considerable level [5]. Thus, the application of MPPT techniques, optimization methods and PWM techniques to solar-based impedance-source inverters gathered interest for researchers. Many papers, with unique control techniques, were proposed. Some of them are explained in the following section. The Maximum Power Point Control (perturb and observe method) and the point of common coupling current control of the ZSI are suggested. In order to obtain the constant DC-link voltage, have good disturbance rejection and increase the dynamic response of the system, a novel DC-link PID compensator is proposed. Arghyadip Bhattacharya and Bidyut Kr. Bhattacharyya, in their paper, proposed an MPPT algorithm that varies the reference voltage in each step of computation, and when compared to the triangular waveform, it gives variable duty ratio for the switch S1 of the boost converter, connected to the solar PV array, thus extracting maximum amount of energy from the PV array. The simple QZSI is applied for a standalone system for water pumping and electricity. The system consists of a centrifugal pump coupled to the shaft of a motor accompanying a QZSI fed by PV arrays. Embedded adaptive FL-IC-based MPPT algorithm is used. A. Battiston, E.-H. Miliani, S. Pierfederici and F. Meibody-Tabar proposed a flatness-based controller on a QZSI in an automotive application. In this, the current is shared between battery systems by adjusting the duty cycle in shoot through mode of inverters. However, it is an expensive strategy for PV-based systems. Later, a modified SPWM with two carrier signals in order to obtain maximum boost condition was proposed. Although the harmonics are reduced considerably, circuit topology and implementation become complex. From Quasi-Z-source networks, there was an evolution to switched-inductor- and switched-capacitor-based impedance-source networks. Switched-capacitor (SC) and switched-inductor (SL) structures have been projected in order to improvise the performance level of conventional converters in either step-up or step-down mode [7]. The proposed inverters, in this paper, have the following main characteristics: continuous input of current, lesser voltage stress on capacitors, lower stress on inductors and higher boost voltage inversion ability. They are applicable for photovoltaic applications, where a low input voltage is inverted to a high AC output voltage. The new topology proposed here is asymmetric. A unique cell is employed at one side of the network, in which a capacitor is inside the original switched-inductor cell. The suggested inverter, in the paper, SL-QZSI, has a common ground point for

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DC-source voltage and no start-up inrush current. The proposed inverter has a higher voltage gain with respect to the SL-qZSI for the same modulation and source. Then, further research on ZSIs leads to the development of trans-Z-source and TZ-source inverters. In trans-Z-source inverter, the inductors in impedance network of a conventional ZSI are replaced with either transformers or coupled inductors. In this configuration, the voltage gain is raised when transformer turn ratio is lowered. Hence, for low-cost applications, transformers with lower turn ratio are required. In [8], lesser components were used in the circuit.

3 Proposed Standalone PV-Based Single-Stage Conversion System The traditional two-stage inverters suffer limitations such as high component cost and less efficiency [9]. Thus, ZSI with a single-stage structure is used as an alternate. The proposed topology will act as an effective solution to economical inverters used for backup of power. The PV panel and battery unit provide the input supply to QZSI network that is placed between the input and the three-phase inverter. Using SPWM, the pulses are generated to activate the inverter. These gate pulses make the switches in the inverter to get turned on in a specific pattern. Based on this ON and OFF functionality of the switches, output AC voltage waveform is traced. In this case, the application can supply power at residences. So during the absence of power or solar energy, the battery which is charged during the availability of solar energy will provide input. The topology of QZSI used in this work is inherited from [10, 11]. Simulation is carried out with tested values. They have shown the advantages as follows: 1. the rating of the capacitor of the impedance network is greatly reduced, 2. the ripples of output obtained are reduced significantly and 3. PV panel produced constant current. The schematic of proposed configuration is presented in Fig. 1. The power to charge the battery is taken from PV panels. The DC energy obtained from PV is fed to the Quasi-Z-source network. The boosting of voltage is obtained at this juncture. The control of three-phase inverter switches through SPWM takes place. The output obtained has fewer harmonics compared to the traditional inverters. A comparison in voltage stress on switches and efficiency between these two inverters is shown in Figs. 2 and 3, respectively. It is based on Eqs. (1) and (2) VDC/VPV ¼ 1:732G/2

ð1Þ

VDC/VPV ¼ 1:732G  1

ð2Þ

where VDC is the DC-link voltage, VPV is the PV output voltage and G is the overall gain of the inverter.

A Cost-Effective PV-Based Single-Stage Conversion System … PV PANEL AND BATTERY

QZSI NETWORK

THREE PHASE INVERTER

565 LOAD

SINUSOIDAL PWM CONTROL

Fig. 1 Schematic of proposed system

Fig. 2 A comparison of voltage stress between two-stage inverter and QZSI

Fig. 3 A comparison of efficiency between two-stage inverter and QZSI

4 Simulation Results 52 V input from PV is fed to the QZSI. Initially, a resistive load of 25 X is taken as load. The key index of measurement of harmonics is total harmonic distortion (THD). Without application of PWM control, THD measured 12.73%. After application of SPWM, the harmonics at the output have reduced. The final results obtained from MATLAB have proven significant reduction in THD to a total of 1.9% from the first simulation carried out without PWM control which yielded 12.73%. The parameters that have been used for simulation are shown in Table 1. The THD values obtained through FFT analysis in MATLAB before applying SPWM and after applying SPWM are made known in Figs. 4 and 5, respectively.

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Table 1 Entities used for simulation using MATLAB 7.10.0 (R2010A) Parameter

Value

Input voltage Input current L1 L2 C1 C2 R

52 V 18 A 1 mH 1 mH 100 lF 100 lF 25 X

Fig. 4 Total harmonic distortion before applying SPWM

Fig. 5 Total harmonic distortion after applying SPWM

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Fig. 6 Hardware accomplishment of the proposed work

Table 2 Components used for hardware implementation of the proposed model Circuit

Components used

Power supply unit

DC supply, step-down transformers, full-bridge rectifier and voltage regulator MOSFET IRF840 AT89C51 (40 PIN) IR2110 R load

Power electronic unit Controller unit Driver circuit Load

Quantity 1 12 1 6 25 X

Therefore, it could be noted that THD has been mitigated by approximately 10% with the assistance of SPWM control. Hardware realization of the proposed work is exposed in Fig. 6, and the components used for the same have been shown in Table 2.

5 Conclusion An attempt to bring forward a standalone PV-based system to act as a cost-effective solution for power outages in rural areas was made. The evolution from a traditional Z source to the latest T-source inverters is clearly described. It can be inferred that qZSI inverters have more advantages than ZSIs. Special reference was made to Quasi-Z-source inverters used in photovoltaic applications. They have proven to be a viable solution to the stochastic fluctuations that occur in DC power obtained from PV modules. The proposed work uses few components and hence cheaper than

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standard inverters in market in terms of electricity charges. A unique QZSI fed from photovoltaic panels is proposed. It could be used like a standalone system that acts as an efficient backup system during power outages in rural areas. Further, SPWM technique is used to minimize the harmonics of the output. Simulation was done with MATLAB, and the results have proven to show significant improvement in terms of harmonic reduction.

References 1. F.Z. Peng, Z-source inverter. IEEE Trans. Ind. Appl. 39(2), 504–510 (2003) 2. X. Ding’, Z. Qian’, S. Yang’, B. Cuil, F.Z. Peng, A direct peak DC-link boost voltage control strategy in Z-source inverter. IEEE (2007) 3. J. Anderson, F.Z. Peng, A class of quasi-Z-source inverters, in IEEE Conference (2008) 4. Z. Rasina. M.F. Rahmanb, Control of bidirectional DC-DC converter for battery storage system in grid-connected quasi-Z source PV inverter. IEEE (2015) 5. J.G. Cintron, Y. Li, S. Jiang, F.Z. Peng, Quasi-Z-source inverter with energy storage for photovoltaic power generation systems, in Proceedings of 26th Annual IEEE Applied Power Electronics Conference and Exposition (2011), pp. 401–406 6. K.S. Chandragupta Mauryan, V. Jayachitra, A. Nivedita, V.M. Parvathy, A study on intelligent control for smart grid. Int. J. Adv. Res. Comput. Sci. Electron. Eng. 3, 163–167 (2014) 7. A. Chub, O. Husev, J. Zakis, J. Rabkowski, Switched-capacitor current-fed quasi-Z-source inverter. IEEE (2014) 8. P.C. Loh, D. Li, F. Blaabjerg, T-Z source inverters. IEEE Trans Power Electron 28(ll), 4880– 4884 (2013) 9. B. Kavya Santhoshi, S. Divya, M. Sasi Kumar, Selective harmonic elimination for a PV based quasi-Z source inverter for drive systems. IEEE (2014) 10. B. Kavya Santhoshi, K. Mohana Sundaram, S. Padmanaban, J.B. Holm-Nielsen, K.K. Prabhakaran, Critical review of PV grid-tied inverters. Energies 12, 1921 (2019) 11. B. Kavya Santhoshi, K. Mohana Sundaram, Hybrid converter with simultaneous AC and DC output for nano-grid applications with residential system. J. Eng. Appl. Sci. 13, 3289–3293 (2018)

Solar Tracking System Using IoT Krishna Chaitanya Diggavi, Manidhar Thula and B. Pakkiraiah

Abstract The preeminent goal of this project is to elucidate about the maximum power generation through solar tracking system and it has been noticed that the yield of solar cell is more than static tracking system. The furthermore supremacy add on points from our project is that it does not affect the environment, shrinkage the cost of fossil fuels and the total tracked power generation results have been contemplated in IFTTT account via E-mail. This proposed system design basically consists of two sections, software programming which is interlaced with hardware components to stir the position of solar collector at different angles, certain shadings are done such that output of it may differ with respect to shading on the solar collector. Keywords Solar energy


Internet of things
Thing speak
Node MCU

1 Introduction The natural energy sources, which are known as renewable energy sources which continuously produced by natural processes and forces occurring in our environment, the energy sources which are in-exhaustible and naturally replenished once used. Renewable energy is an indigenous resource available in considerable quantities to all developing nations and capable in principle of having a local, regional or national economic impact; usage of it is financially and economically competitive for certain applications because when it is used in the rural areas, the need of

K. C. Diggavi (&)
M. Thula Department of EEE, Guru Nanak Institute of Technology, Hyderabad, Telangana 500050, India B. Pakkiraiah Department of EEE, Gokaraju Rangaraju Institute of Engineering and Technology-Autonomous, Bachupally, Hyderabad, Telangana 500090, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_51

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transmitting electrical power or transporting the non-conventional fuels are not necessary. As the renewable energy has evolved a lot in these days because the sun is the main provenance of energy at the earth, the energy reaches the earth from the sun in the form of electro-magnetic radiation, where the sun’s heat is solar energy, earth’s heat is the geothermal energy, energy in waves is tidal energy and energy in wind is wind power, as the conversion technology is modular, it will be easy to add new capacity and these can be developed rapidly.

2 Solar Energy In this modern world, energy has become an integral part of our daily life. The energy source mainly we use is supplied to us in the form of diesel, petrol, coal, LPG, CNG and electricity. As the need for an alternative energy source is increasing at an alarming rate, solar power generation can be a solution for all of our energy concerns. The irradiance of solar energy is the suns radiant. We receive solar radiation in a range of 4–7 kwh/m2 per a day and in that, only 71% reaches the earth and remaining energy is observed by the atmosphere [1]. Solar photovoltaic technology converts sunlight into electricity directly without any other additional energy conversion step [2]. Such amount of energy is good enough to generate electricity from the solar photovoltaic collector.

3 IoT The devices which communicate with the IoT technology provide all the required information and instructions of the working of the devices under given treatment of this technology. IoT technology is used in automation of home to monitor and control either locally or remotely using smart phones through mobile applications. Most typical IoT devices are lights, fans, security alarm, camera, sensors, door lock and other electrical and electronics appliances [3]. As IoT is managed and run by multiple technologies, in this paper, Internet of things technology is used for supervising solar photovoltaic power generation which can greatly improve the maintenance, performance and monitoring of the solar panel [4]. This paper will facilitate the maximum output voltage characteristics of the tracking and turning the solar panel in the direction of the optimal angle of receiving the sunlight using LDR. It also automatically keeps track of the amount of voltage supply received by the solar panel in optimal angle.

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4 Fabrication Solar tracking system is fabricated through simple circuit diagram which is given below and it gives complete structure and detailed connection of the components used in the solar system [5, 6] (Fig. 1).

4.1

Working

The sun-tracking solar panel consists of three LDRs: solar panel, a servo motor and Node MCU. Three light-dependent resistors are arranged beside of the solar panel. LDR produces low resistance if the intensity of light falls on them is more. The solar panel angle changes with the help of servo motor in the sun direction where the light intensity is more by comparing the light intensity on 3 LDR sensors, i.e., low resistance compared to other [7]. The flow chart gives detailed explanation about the sensing of the LDR sensor and changing the position the solar collector that is panel according to the position of the sun (Fig. 2). Panel moves towards 1st angle of 30 degrees towards the east in morning session, as the intensity of light falls on the LDR 1 increases and if intensity on the second LDR is more, panel slowly moves towards second angle that is. In the noon time and if the intensity on the third LDR is more, panel slowly moves toward third angle that is, in the evening session, sun is ahead and intensity of light on the all three sensors is same. In such cases, panel is constant and there is no rotation. Finally, when the intensity of the sun does not fall on any of the LDR sensors then the panel moves to initial position for the sunrise for high intensity of the light.

Fig. 1 Circuit diagram

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Fig. 2 Flowchart of LDR sensor

These all signals are given to the controller that is Node MCU which is interfaced with the Arduino Uno, this acts as the main controlling unit which passes the signal to servomotor which is annexed to the solar panel. In our project, the signal is received from the LDR sensors by the intensity falling on them, respectively, and these data is monitored in the cloud as data sheet in an excel sheet through the software called thing speak, by the technology called Internet of things.

5 Results The designed solar tracker was operated for a whole day from 8:00 AM to 5:00 PM and voltage generated was read at hourly interval. At the same time, the same solar panel of identical specification was set at a fixed position and voltage reading was taken throughout the day. During the experiment, two digital multi-meters were used for reading data. Open circuit voltage and short circuit current at each hour were determined.

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Determination of Output Voltage Characteristics

According to the changing conditions of light in a day, we split the day into three sessions that are morning (8 AM to 11 AM), afternoon (11 AM to 2 PM) and evening (3 PM to 5 PM) which approximately have 3 h of time period each and we spent 20 min for the experimental output in each session, which completely take 1 h of time for all sessions to get experimental result. Our intension is to calculate the output voltage for the solar panel of 12 V which consists of 36 cells with three different variation of exposure of panel to sun in all three divisions of a day. In the first and foremost, the panel is in initial position that is toward the east, as the sun rises in east. We took the output voltage of 12 V solar panel is noted down in every tilted position of solar panel and then we further move on for shading process (Fig. 3). As it is a prototype, that the solar panel which is annexed with the LDR sensors, which cannot resist the continuous intensity of solar heat, because these sensors are not industrial. So this experimental prototype setup cannot be performed continually in light, so we consider 20 min in each session so that every session’s output voltage of a solar panel can be calculated in one hour a day. Case I In the morning session, the panel is in particular angle that is 45° which is perpendicular to horizon of sun path. Step 1: We shaded first 12 cells out of 36 cells of 12 v solar panel about 6.5 min in an overall time period of 20 min, and all remaining 24 cells are exposed to the sun, and the output voltage of solar panel with shaded region is noted down (Fig. 4). Step 2: Similarly for output voltage of second shaded region, we shaded the “24” cells for the next 6.5 min here about 13 min are accomplished in time and again all remaining 12 cells are exposed to the sun (Fig. 5). Step 3: In the final step of shading process where all the “36” cells are shaded, so that intensity of the light does not fall on it, this process is shaded up to remaining

Fig. 3 Solar panel

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Fig. 4 First shaded region

Fig. 5 Second shaded region

Fig. 6 Final shaded region

6.5 min in total time period 20 min and output voltage is noted down for all these three different variation in shaded region (Fig. 6). Case II In the afternoon session, the panel is tilted to another particular angle of 90° is exactly perpendicular to the sun position. Further steps will be performed and output of solar panel is noted as Case I.

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Case III In the evening session, the panel is again tilted to 135° so that the panel is exactly perpendicular to evening position of a sun. Further steps will be performed ad output voltage of panel is noted as Case I. Day 1 Voltage Readings Voltage is measured with tracking of the sun’s path. In day 1, the voltage is noted in a table for all three mornings, afternoon and evening and graph of the voltage with respective time is shown below. Here, the time is taken in minutes that is about 20 min (Table 1). From the above table for the time 6.5 min is taken as first shaded region in all the three different session with their particular angles, 13.0 min is taken as second shaded region in all the three different angle variation in every session and 19.5 min is taken as final shaded region in every session according to their angles, curves of each session according to their shaded regions is shown in graph with respective of time versus voltage (Fig. 7). From the above graph, we observed three curves with different colors, that the each color shows three sessions, i.e., the red colored curve shows the voltage curve of morning session, the green colored curve shows the voltage curve in afternoon session and finally, the blue colored curve shows the voltage curve in the evening session.

Table 1 Day 1 voltage is measured with tracking Time (min)

Morning-tracked voltage readings

Afternoon-tracked voltage readings

Evening-tracked voltage readings

0 6.5 13.0 19.5

10.42 8.9 5.9 3.82

10.62 10.1 8.25 7.2

10.27 9.62 8.3 4.9

Fig. 7 Day 1 maximum voltage detection from graph

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The maximum voltage is found at 0.4379 A, 10.968 V point on the curve. The maximum power at that point is 4.71 W. Day 2 voltage is measured with tracking of the sun’s horizon (Table 2). Curves of each session according to their shaded regions are shown in graph with respective to time versus voltage (Fig. 8). Comparison Between the Solar Tracing System and Without Tracking Finally, we did the comparison between the solar tracing system and fixed system. We have taken readings for three sessions that is at 9 AM in morning session, 1 PM at afternoon session and 4 PM at evening session (Table 3). The graph represents the voltage curves of the tracked system and fixed system, we noted the voltage of the solar collector which is noted for another day and graph of the voltage with respective time is shown below, here, the time taken in hours

Table 2 Day 2 voltage is measured with tracking Time (min)

Morning-tracked voltage readings

Afternoon-tracked voltage readings

Evening-tracked voltage readings

0 6.5 13.0 19.5

10.2 9.14 7.35 5.3

10.51 9.62 8.7 7.0

10.27 8.1 6.7 4.9

Fig. 8 Day 2 maximum voltage detection from graph

Table 3 Voltage of tracking system Time (min)

With tracking voltage readings

Without tracking voltage readings

9 AM 1 PM 4 PM

10.96 10.62 10.27

11.62 10.23 8.34

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Fig. 9 Comparison of tracking system

Fig. 10 Power analysis graph

from the above table 9 AM is taken as 0, 1 PM is taken as 1 and 4 PM is taken as 2 which is shown in time versus voltage graph (Fig. 9). By solar tracking, the power consumption data is received in Gmail through the thing speak account with the help of the in Internet of things (Fig. 10). Power analysis graph is shown above which is received in the Gmail through the IFTTT account and software called thing speak and technology said to be Internet of things.

6 Conclusion In this paper, we demonstrate a simple and concise overview of the solar tracking mechanism to increase the efficiency of the solar energy, as this system continuously track the sun’s trajectory over the course of the day which is used to collect the far greater amount of solar power. The system is constructed to be autonomous such that user need not to do lot of configuring once the system is assembled.

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We designed in order to compare the voltage waveforms of the tracking system and rigged system, and to display the daily, weekly and monthly analysis of the solar energy from the collector. This project can be further enhanced by using these current parameters of the amount of solar energy which is able to store in battery. This proposed can facilitates the fault detection in real-time monitoring in solar power generation plants. From the above conclusions, a final conclusion could be made that in any environmental condition, the automatic solar tracking system is a way much better implementation than the fixed panel.

References 1. M.R. Patel, Wind and Solar Power Systems Design, Analysis and Operations, 2nd edn. (CRC Press, Boca Raton, 2006) 2. E. Anderson, C. Dokan, A. Sikora, Solar panel power tracking system (Worcester Polytechnic Institute, 2003). Z. Bundalo, Microcontroller based solar tracking system, in Proceedings of TELSIKS Conference (Niš, Serbia), pp. 518–552 3. P.G. Pachpande Internet based embedded data acquisition system. Int. J. Electron. Commun. Comput. Eng. 5(4) (2014) July, Technovision, ISSN 2249-071X 4. B. Kang, S. Park, T. Lee, S. Park, IoT-based monitoring system using tri-level context making model for smart home services, in IEEE International Conference on Consumer Electronics (ICCE) (2015) 5. Solar Technologies | Photovoltaic Solar Panels | Thin Film Solar Panels | Solar Thermal. Solar Panels, Photovoltaic Systems, Solar Solutions for Home, Business & Utility-Scal-Sun Power Retrieved August 29 6. Introduction to the arduino microcontroller, in Hands-on Research in Complex Systems (Shanghai Jiao Tong University June 17–29) 7. A.H. Yamin, M.N. Ibrahim, M. Idoras, Embedded solar tracking instrumentation system, in Power Engineering and Optimization Conference, vol. 7 (2013), pp. 223–227

Demand Management System for OFF-Grid PV System Mrutyunjay Das, Kuldip Singh and Ch. Laxmi

Abstract In an efficient and stabilized energy distribution system, proper synchronization between supply and demand is highly desired. OFF-grid PV system is playing vital role for fulfilling the electrical demand with solar power plant and DG synchronization. In the generation side, renewable energy sources are generally tied with DG to meet the peak demand and providing the reference voltage and frequency for string inverters. Due to the variable nature of the load, it creates lots of reverse power flow problems in DG, which lead to the system instability. Demand side management is using load and generation control for integration of PV power plant with DG in OFF-grid mode. The study is carryout at 75 kW power plant with synchronization of 125 kVA DG in real time for understanding the instability parameters and control of generation and load demand with DMS.




Keywords Demand side management Solar photovoltaic system DG integration Peak period OFF-peak period








OFF-grid


1 Introduction Due to rapid urbanization, the demand for energy is increasing at a faster rate which puts a thrash on the generating units. But due to gradual depletion of fossil fuels and rapid deterioration of environment widens the difference between supply and demand of energy. To curb with the situation, solar PV system synchronized with DG is integrated with the OFF-Grid so as to supply energy during the peak periods through grid. Due to the requirement of reference voltage and frequency for the grid string inverter, DG needs to be synchronized in OFF-grid mode. But due to the variable nature of load, reverse current flows through DG in the absence of grid supply which makes the unstable. So there is a need for demand side management

M. Das (&)
K. Singh
Ch. Laxmi Department of EEE, Guru Nanak Institute of Technology, Hyderabad, Telangana State 500050, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_52

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to stabilize the system. This paper deals with the demand side management by generation scaling method [1]. The electrical energy generated in solar PV mainly depends on solar irradiation, which is not constant throughout the day. During the peak load period, total generation of solar PV is supplied to load and during OFF-peak period, the balance generated energy is supplied through grid. But during the grid failure, the excess energy generated flows to the DG in the form of reverse current creating transient instability which deteriorates the condition of DG set. Demand side management is one of the methods for stabilization of the system during transient period [1, 2]. Demand side management has been employed to manage the factors responsible for instability of the system, e.g., loads shaping and consumption nature of the end users. This can provide the consumers about flexibility in their consumption of electrical energy. We know that the load is always of variable nature. Broadly, the entire load can be classified as scheduled and unscheduled loads. Similarly in terms of generation also some are of independent nature and others are dependent. For a stable power system, load should always match with that of generation. To attain stability, either the generation should be changed with respect to load or load should be rescheduled with respect to generation. But rescheduling of generation has its own limits. Hence, to attain the stability in power system, rescheduling of loads is the effective solution in the present scenario which requires the implementation of demand side management [1, 3].

2 OFF-Grid PV System The maximum solar irradiance is used to be measured by reference PV cells at STC, i.e., solar radiation of 1000 W/m2 and ambient temperature of 25 °C. Due to the variable nature of solar irradiance, the solar electricity produced is also variable. In order to overcome this variation in the output of PV, an energy storage system is necessary to be included which can release energy during peak periods and maintain effective power supply. In India, energy storage concept is used in industrial as well as commercial sectors for shaving the peak load along with the function as backup power supply. Among the challenges that are holding back, the widespread use of energy storage systems is the cost. Despite declining prices for energy storage, it remains higher than the price per unit of current obtained from the grid. For that reason, the integration of PV systems with grid is a better option for continuity of supply and meeting the demand. To meet the energy demand, some form of renewable energy must be integrated with grid [4–6]. In case of grid failure, the grid losses its communication with PV system and the system is termed as OFF-grid system.

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Hence, IL ¼ IPV þ IDG Now, if IL [ IPV , the excess energy required by the load is drawn from the DG. This is not cost effective. Also if the load is more, the DG comes to stress leading to DG failure. If IL \IPV , the excess energy generated will be drawn by the DG due to which a negative current will flow through DG. So IL ¼ IPV þ ðIDG Þ This leads to over excitation of DG making the system instability.

3 Demand Management System The generated power should be equal to the amount of power consumed due to the following reasons. First, for improving the power quality, the power consumption needs to be balanced with the electricity produced. If the electricity produced is not sufficient to meet the demand, it causes instability in grid, voltage fluctuation and may be total brownout. Second, if the produced electricity is significantly more than the demand, it causes increase in production cost [2]. The design of power grid is based on the maximum projected demand which also includes the peak demand. As peak demand is intermittent in nature, the economical grid design should not have much higher capacity than the average demand. This makes it necessary to reduce the peak demand. In terms of residential customers, low price of electricity is prime consideration. In terms of commercial customers, cost of electricity should be competitive. But cost of production and operation is affected by even small increase in cost. With all these considerations, electricity cost is a prime factor for economic development [3]. Demand side management (DSM) is generally applied to energy efficiency measures that can modify or reduce end-user’s energy demand which ultimately reduce energy costs for a given output. From the utility point of view, it is sensible to promote consumption by increasing sales. But this would be possible only if there will be surplus energy. Due to the energy crisis, this seems to be impossible. Hence to reduce energy costs, demand side management is the only solution in present scenario. This can be done by decreasing or shifting of consumption of energy through efficient improvements or load shifting on the customer side of the electric meter [2, 6, 7]. Residential load is the largest contributor for the increase in peak demand. The residential loads are mainly responsive to weather factors and life style of the people. The demand side management allows the consumers to know their load profile and measure to control their loads. During the peak load period, most of the generators generate near to their maximum capacity. This stresses the system and leads to a system failure. All the loads related to residential, commercial and industrial consist of scheduled and unscheduled loads. By the load shifting method, the unscheduled loads can be shifted or controlled which can reduce the peak load.

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In case of less load period, the generated energy by solar PV can be supplied to grid. But in case of grid failure, the excess energy flows through the generator which comes to over excitation mode causing instability of the system.

4 Case Study for OFF-Grid System The study is carryout in an Educational Institution with 75 kW solar power plant integration with 125 kVA DG set for fulfil the electrical demand in OFF-Grid mode. In Educational Institutions, the critical task is PV power balance w.r.t load demand from solar power plant, due to variability and uncertainty of output power w.r.t time. For balance, the load demand solar power plant is integrated with DG in OFF-grid mode [4]. The stability of OFF-grid solar power plant determines, whether the generation system can settle down to a new or original steady state after transient disappear due to sudden load changes. OFF-grid solar PV power plant has small capacity, so it needs to set aside DG generation to compensate for sudden load demand as well as for providing the reference voltage and frequency for string inverters in solar power plant. The study is carry out on different generation condition as shown in Table 1 for analysis of instability in generation system at different load demand [4, 7]. As the data given in Table 1, the generation from solar PV power plant is increasing the reverse power flowing in the DG set, and due to more reverse power flow, the positive accelerating torque is acting in the same direction of mechanical torque. The DG set is experiencing the positive angular acceleration torque (Ta) in the same direction of applied mechanical toque and increasing the speed of DG set above synchronous speed (Ns). The synchronization of solar PV power plant with DG set for fulfill the load demand is shown in Fig. 1 without control.

Table 1 Generation data for PV power plant and DG S. No.

Load current (IL ) (A)

Generation current from PV (IPV ) (A)

Generation current from DG (IG ) (A)

Frequency (Hz)

Speed of DG set N (RPM)

1 2 3 4 5 6 7 8 9 10

58.4 48.98 50.1 51.7 50.1 60.4 54.98 49.1 55.7 51.1

0 21 92.1 108.1 122.6 0 25 87.1 107.1 120.6

58.4 27.98 −41 −57.4 −72.5 60.4 29.98 −38 −51.4 −69.5

48.9 49.6 51.59 51.60 51.62 48.9 49.8 51.59 51.59 51.62

1485 1475 1508 1535 1567 1483 1472 1507 1534 1566

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Fig. 1 Synchronization of solar PV power plant with DG set with DSM control

Ta ¼ Tm  Te

ð1Þ

  The rate of change of frequency ddft is df T a f 0 ¼ dt 2H

ð2Þ

Here, f0 is base frequency (50 Hz) and H is system inertia constant Or F¼

PN 120

ð3Þ

Here, F is frequency is number of poles and N is speed in RPM.

5 DMS Control The integration of solar PV power plant and DG in OFF-grid mode is optimized by DMS control as shown in Fig. 2. In this case study, the reverse power flow is control with inverter control method. The DG system is taking the 20% of total load in non-load condition for stabile generation. The generation from solar power plant is controlled with inverter control system. Based on the load demand, the inverter is synchronized with DG set as shown in Table 2. In this case study, Inv1-33 kVA, Inv2-33, Inv3-10 kVA.

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Fig. 2 Demand management control for OFF-grid Table 2 Synchronization of solar plant and DG with DMS S. No

Load Current (IL ) (A)

Generation current from PV (IPV ) (A)

Generation current from DG (IG ) (A)

Frequency (Hz)

Inverter state Inv Inv 1 2

Inv 3

1 2 3 4 5

58.4 48.98 50.1 60.4 75.2

0 21 38.6 46.9 58.7

58.4 27.98 11.5 13.5 16.5

48.9 49.6 49.5 48.9 49.8

OFF ON ON ON ON

OFF OFF ON OFF ON

OFF OFF OFF ON ON

Fig. 3 Demand management control for generation control

The excess generation for solar power plant is control with Demand management system control unit, which is controlling the excess generation from solar and synchronizing the solar PV Power plant with DG. The DG set operating in normal condition at normal operating frequency as shown in Figs. 2 and 3.

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6 Conclusion The solar power plant is working in two modes ON-grid and OFF-grid. The solar string inverter requires the reference frequency and voltage to start the generation. In ON-grid power plant, the reference voltage and frequency are provided by grid and excess generation of solar power plant will flow into grid through bidirectional meter. In OFF-grid mode, the solar power plant will integrate with DG set, the excess generation will flow in DG set during less load demand; due to excess generation, the frequency of DG set is increasing. The excess generation will damage the DG set. The demand management system is useful for synchronization of solar power plant in OFF-grid with DG set for scaling the generation. The excess generation is controlled with different operation of inverter controls

References 1. K. Singh, M. Narendra Kumar, S. Mishra, Stability analysis of isolated hybrid microgrid for village electrification. Int. J. Eng. Technol. 7(2.23), 235–237 (2018) 2. C.U. Eze, D.D. Agwu, L.O. Uzoechi, A new proposed demand side management technique. Int. J. Eng. Sci. Emerging Technol. 8(6), 271–281 (2016) 3. K. Singh, M. Narendra Kumar, S. Mishra, Load flow study of isolated hybrid microgrid for village electrification. Int. J. Eng. Technol. [S.l.] 7(2.23), 232–234 (2018). ISSN 2227-524X. http://dx.doi.org/10.14419/ijet.v7i2.23.11925 4. C.C.A. Rajan, Demand side management using expert system, in IEEE Conference on Convergent Technologies for Asia-Pacific Reigon TENCON (2003) 5. K. Kusakana, Optimal schedule power flow for distributed photovoltaic/wind/diesel generators with battery storage system. IET Renew. Power Gener. 8(9), 916–924 (2015) 6. K. Singh, M. Narendra Kumar, S. Mishra, Stability analysis of isolated hybrid microgrid for village electrification. Int. J. Eng. Technol. [S.l.] 7(2.23), 235–237 (2018). ISSN 2227-524X, http://dx.doi.org/10.14419/ijet.v7i2.23.11926 7. K. Singh, M. Narendra Kumar, S. Mishra, A study on economic power dispatch grid connected PV power plant in educational institutes. in AIP Conference Proceedings 1952, (2018), p. 020047. https://doi.org/10.1063/1.5032009

PV-Wind-Integrated Hybrid Grid with P&O Optimization Technique R. Rekha, B. Srikanth Goud, Ch. Rami Reddy and B. Nagi Reddy

Abstract Renewable energy sources are alternative sources playing an important role in the generation of power in order to satisfy the huge requirements of electricity by various utilities. This paper presents new modeling of MPPT controller for solar and winds integrated grid with solar irradiance and temperature as parameters. MPPT controller like P&O is used to derive the parameters like maximum power using a simulation model. Outputs obtained from MPPT controller are fed to DC-DC boost converter and then to the inverter to enhance maximum power as output from PV and wind system, and results are proposed through MATLAB/Simulink. Keywords PV array Inverter


Wind
MPPT controller
DC-DC boost converters


1 Introduction Renewable energy is an inexhaustible resource available in considerable quantities for economic improvement for developing nations. Among various sources, solar and wind are prominently used in generating electrical energy in order to meet the huge demand from the utilities. As solar and wind both are intermittent in nature, it is very difficult to predict the availability of maximum energy available for conversion into electrical energy. In order to overcome such issues, various MPPT’s were developed for tracking maximum energy. In this proposed paper, Perturb and Observe-based MPPT has been used for both PV and wind-integrated grid [1, 2] R. Rekha
B. Srikanth Goud (&) Department of Electrical and Electronics Engineering, Anurag College of Engineering, Ghatkesar 501301, India Ch. Rami Reddy Nalanda Institute of Engineering and Technology, Guntur 522438, India B. Nagi Reddy Koneru Lakshmaiah Education Foundation, Guntur 522502, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_53

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Solar energy in the form of irradiance and temperature is the input to PV, wind is in the form of kinetic energy, and this energy is converted into mechanical energy by using wind turbine and then fed to doubly fed induction generator to convert into electrical output. Boost DC-DC converters along with MPPT are used to develop the duty pulses which are needed to operate and give an output of fixed DC when both the systems are integrated to a common bus. The input to the wind is in the form of kinetic energy and this energy is converted into mechanical energy by using wind turbine and then fed to squirrel cage induction generator to convert into electrical output [7]. In this paper, modeling and control strategy of PV and wind-integrated hybrid grid system is proposed. MATLAB/Simulink software is used to check the operation of system proposed [3, 4].

2 Proposed Block Diagram of PV-Wind-Integrated Grid with P&O MPPT Integrated gird consists of three layers generation unit, control unit, and application unit. In the proposed system, renewable energy sources PV and wind are integrated together by using DC-DC converters as we know that PV output is in the form of DC, whereas wind output is in AC. In order to make them integrated together, power electronics-based DC-DC converters are employed and converted to constant DC, given to a common DC bus and then converted into AC using an inverter and hybrid power is generated. Generally, PV and wind are nonlinear in nature due to which maximum power tracking is difficult. To overcome such constraint, we adopt optimization techniques like Perturb and Observe MPPT which is utilized to track maximum power from both the sources during its availability. IRef, a reference current is generated during maximum power point tracking and a duty pulse is generated from the proposed MPPT and output of constant voltage from both the sources and fed to a common DC bus and then converted to AC by using a switched-mode inverter and then to the grid. Figure 1 shows block diagram of proposed system.

Fig. 1 Block diagram of proposed system

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3 Description and Mathematical Modeling of PV A. PV Equivalent Circuit Design The equivalent circuit of the solar cell is being studied for many years. It generally consists of photocurrent, diode, series, and shunt resistor. General PV model is built in MATLAB/Simulink and verified the characteristics. Figure 2 shows solar cell equivalent circuit. Photovoltaic current expression is as follows:     V þ IRS VJ IPV ¼ IL  IO exp 1  nVT RSH IPV ¼ ILGC  ID  ISH

ð1Þ

Vj ¼ V þ IPV RS

ð2Þ

where ILGC ID IPV ISH

VPV Vj IPH RS

light generated current diode current photo voltaic current shunt current

voltage across output terminals voltage across both diode and resistor Rsh (V) output current (A) series resistance     VJ ID ¼ IO exp 1 nVT

ð3Þ

IO reverse saturation current n diode density factor

Fig. 2 Solar cell equivalent circuit

I PV +

I l gc

Id

VPV

RSh −

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Boltzman’s constant absolute temperature VT ¼ KT=q

ð4Þ

By Ohm’s Law ISH ¼ IPV IPV

VJ RSH



   V þ IRS VJ ¼ ILGC  IO exp 1  nVT RSH     V þ IRS V þ IRS ¼ ILGC  IO exp 1  nVT RSH

ð5Þ

From the above equation, we get the output as IPV which is shown in Fig. 3. B. MATLAB/Simulink of PV Model Solar energy is intermittent and nonlinear due to changes in climatic conditions, so we need to adopt new topologies like MPPT to track maximum power from these available resources. Irradiance and temperature are the inputs to the PV panel whose output continuously changes as they are nonlinear in nature. In order to overcome such constraint, a new approach of P&O MPPT algorithm is adopted to

Fig. 3 General PV model is built in MATLAB/Simulink

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Fig. 4 Equivalent simulink diagram of PV module

attain maximum power as output. In MATLAB, control structure is designed. The outputs are Ipv and Vpv which are given to the proposed MPPT’s which periodically functions by raising or decaying the operating current of a PV array and compares previous value output, and if the condition is satisfied, the control mechanism moves the PV operating point in the same direction or else in the opposite direction. It generates the duty pulses if the condition is satisfied and gives it to the DC/DC converter which further increases its voltage output to higher voltages by using boost converters [5]. Figure 4 shows equivalent Simulink diagram of PV module C. Design of Wind Turbine System The wind is a form of energy which is produced due to heavy blow of airs which is in form of kinetic energy, and this is fed to the turbine to convert into mechanical energy which in turn converts into electrical energy by using DFIG. The inputs to the wind turbine are wind speed and generator speed which is fed as feedback and pitch angle; here, we assumed pitch angle to be zero. DFIG consists of stator and rotor windings to which mechanical torque is fed as input. Stator windings are grid-connected and turbine drives rotor which converts mechanical torque into electrical power and this is transferred to the grid through stator windings [3]. Wind turbine output power is given by 1 3 Pm ¼ qA Vwind CP ðk; bÞ 2 where Pm = mechanical power Cp = coefficient of turbine k = tip speed ratio

ð6Þ

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b = pitch angle A = turbine swept an area q = air density. Coefficient of Cp (k, b) used is considered from and given by   c5  C2 k  C3 b  C4 e 1 þ C6 k CP ðk; bÞ ¼ C1 ki

ð7Þ

where C1 and C6 depends on the WTR and design of blade, and k1 is given by the following equation 1 1 0:035  ¼ ki k þ 0:08b b3 þ 1

ð8Þ

Further Eq. (6) can be written for specific values of A and q as shown below 3 Pmpu ¼ Kp Cppu Vwindpu

ð9Þ

D. MATLAB/Simulink of Wind System Due to change in Wind Speed, Power developed undergoes a change in frequency and amplitude and in order to eliminate such constraints and maintain Constant DC voltage three-phase two winding transformer with six input ports with appropriate phase angles for the double bridge ac/dc rectifier whose firing angle is controlled by PI controller (Fig. 5). Shows Simulink Diagram of Wind system.

Fig. 5 Simulink diagram of wind system

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4 DC-DC Converters Power electronics-based DC-DC converters are employed in both PV and wind system. Fluctuated output is produced from PV, and to maintain constant DC output voltage we use DC-DC converter which is operated by maximum power point tracker which produces the duty pulsed required for the converters used near both PV and wind which is to be operated and maintained at a constant voltage of 400 V DC. The output produced from DFIG is in the form of AC. Whenever both the outputs of PV-wind are to be integrated together, the outputs of both systems should be same. The double bridge rectifier is used to rectify and maintain constant DC voltage of 400 V and connected to a common grid. Figure 6 shows MATLAB/ Simulink Diagram of DC-DC Converter,

5 Inverter Design The output of DC bus voltage of 400 V is fed to the inverter designed and constructed in MATLAB/Simulink and converted to 400 V line-to-line voltage at a frequency of 60 Hz. Losses are included due to ROP and IIP. Figure 7 shows MATLAB/Simulink of Inverter.

6 Perturb and Observe the MPPT Algorithm At various time periods of irradiance and temperature, maximum power is tracked and delivered to load. It calculates power (P(t)) by measuring I and V and continuously it compares with the previous power; once if there is an increment in step size then the output voltage is varied and the duty pulses are generated which is given to the controlled switch IGBT of DC/DC boost converter [6]. Figure 8 shows Proposed P&O MPPT.

Fig. 6 Matlab/Simulink diagram of DC-DC converter

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Fig. 7 Matlab/Simulink of inverter

Fig. 8 Proposed P&O MPPT

7 Simulation Results and Discussions The I-V and P-V curves are shown in Fig. 9a, b; the obtained power and current of the PV model depends on its inputs and operating voltage (Table 1).

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(a)

(b)

Fig. 9 a solar system I-V curves, b solar system P-V curves

Table 1 PV panel specifications

Name

Range

No. of connections Maximum power Pm Voltage at Pm Current at Pm ISC VOC

60 225 W 29.67 V 7.55 A 8.27 A 36.88 V

WTIG Parameters Characteristics of wind turbine model corresponding to various values for generator speed and generator power in per unit are shown in Fig. 10. WT output depends on wind speed and generator speed (Tables 2, 3, 4 and 5).

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Fig. 10 Characteristics of wind speed Table 2 WTIG parameters

Name

Range

Base wind speed Pm at base wind speed Coefficient (C1–C6) Performance coefficient Rotor type Voltage (L-L) Nominal frequency Nominal power RPM RS Rr LS Lr Lm Inertia constant Friction factor Pairs of poles

9 (m/s) 1 (PU) [0.516, 116, 0.4, 21, 0.0068] 0.48 (p.u) for [b = 0°, k = 8.1] Squirrel cage 440 V 60 (Hz) 200 (HP) 1785 rpm 0.01282 (p.u) 0.00702 (p.u) 0.05051 (p.u) 0.05051 (p.u) 6.77 (p.u) 0.3096 (s) 0.0114 (p.u) 2

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Table 3 Boost type DC/DC converter parameters Name

Range

Initial capacitor voltage Capacitance Input port RS Switching loss current

400 V 200 µF 0.5 0.025

Table 4 Parameters for double bridge rectifier Name

Range

Reference voltage SR of one SCR SC of one SCR RI of one SCR LF (f-filter) CF (filter) PW of synchronized 12-pulse generator KP of PI voltage control system KV of PI voltage control system

400 (V) 2 (k-X) 0.1 (µF) 1 (mH) 66 (mH) 3300 (µF) 80 (°) 2 20

Table 5 Transformer parameters used in the double bridge rectifier Name

Range

Input winding parameters (Yg) [V1 R1 L1] Output winding parameters (Y) [V2 R2 L2] Nominal power Nominal frequency

[460 (V) 0.00025 (p.u) 0 (p.u)] [230 (V) 0.00025 (p.u) 0.0024 (p.u)] 120 (KW) 60 Hz

Proposed system characteristics during the simulation process over a period of time. The inputs to the PV and wind are irradiance and wind speed which are gathered from [6] and shown in Fig. 11a, b. Even though the inputs fluctuate over a period of time in both the systems, they are maintained constant voltage shown in Fig. 12a, b by using proper power electronics-based converters. The purpose of using P&O MPPT’s is to set the DC/DC and double bridge rectifier reference current (Iref) so that PV array and wind output operates at maximum power point by sequentially increasing or decreasing the operating currents. From Fig. 10a, we could observe that during time period 8.40 am and 19.00 pm the power from solar is very less due to the nonexistence of radiation. To overcome such constraint, we integrated wind system to PV by choosing from wind speed profile as shown in Fig. 11b as input to the wind turbine and integrated both together to a common DC

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(a)

(b)

Fig. 11 a solar irradiance of PV panel, b wind profile

bus and maintained at a stable voltages show in Fig. 12a, b and by using inverters converted power is transferred to the loads which are maintained at 60 Hz 440 V line to line and the hybrid power delivered is shown in Fig. 13 [7].

8 Conclusions PV/wind are the two predominantly used sources which are integrated together to meet the supply and demand as PV alone cannot meet the demand for supply. In this paper, both are designed and modeled using MATLAB and their performances are studied. The total data has been collected from [7] and its performance characteristics were observed by implementing P&O MPPT.

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(a)

(b)

(c)

(d)

Fig. 12 a DC/DC converter output voltage using P&O, b wind DC output voltage using P&O, c solar power output using P&O, d wind output power using P&O

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Fig. 13 Hybrid power delivered to load using P&O

References 1. D. Pilakkat, S. Kanthalakshmi, An improved P&O algorithm integrated with artificial bee colony for photovoltaic systems under partial shading conditions. Sol. Energy 178, 37–47 (2019) 2. B. Srikanth Goud, B. Loveswara Rao, Review of optimization techniques for Integrated distribution generation. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 8(4) (2019). ISSN: 2278-3075 3. B. Pakkiraiah, G.D. Sukumar, Research survey on various MPPT performance issues to improve the solar PV system efficiency. J. Solar Energy (Hindawi Publishing Corporation, 2016) 4. D.K. Geetha, P. Pramila, A survey on efficiency in PV systems with DC-DC converter. Commun. Appl. Electron. (CAE) 6(1) (2016) 5. A.F. Cupertino, J.T. De Resende, H.A. Pereira, S.I. Seleme Jr., A grid-connected photovoltaic system with a maximum power point tracker using passivity-based control applied in a boost converter, in Proceedings of the 10th IEEE/IAS International Conference on Industry Applications (INDUSCON’12) (Fortaleza, Brazil, 2012) 6. E.M. Natsheh Member IEEE, A. Albarbar, Member, IEE J. Yazdani, Member IEEE, Modeling and control for smart grid integration of solar/wind energy conversion system, in 2nd IEEE PES International Conference (2012) 7. K.S. Chandragupta Mauryan, M. Abuvatamizhan, V. Balaji, R. Mani, Improved efficiency of large capacity renewable energy—integration with grid. Int. J. Eng. Sci. 3, 12–17 (2014)

A Practical Approach in Design and Fabrication of Solar-Powered Four-Wheeled Electric Vehicle S. Gobhinath, S. Boobalan, R. Ashwin, Jan Meshach and K. Rajkumar

Abstract In the twenty-first century transportation, industry has undergone rapid changes, with the advent of electric vehicles (EV), and the world is moving towards a clean and green future. The electric vehicles depend on electrical energy from coal-powered power plants for their charging; this dilemma has sought the attention of researchers and engineers to look out for a cleaner technology to power up the batteries. One of the possible solutions is solar-powered electric vehicle (SPEV); the solar-powered EV can self-charge its batteries without any turbulence caused to the environment. This paper enumerates multifarious design and fabrication strategies involved in building a practical off-road four-wheeled solar-powered electric vehicle.








Keywords Solar panels BLDC motor Maximum power point tracking algorithm (MPPT) Battery management system (BMS) Solar-powered electric vehicle (SPEV) Electric vehicles (EV)







1 Introduction The world is alarmed by climate change, and this undesired change is a result of prolonged exploitation of non-renewable natural resources. The greenhouse gases are the automobile sector which primarily depends on diesel or petrol for transportation [1]. Solar-powered electric vehicle can be considered as an alternative to such cars; however, this technological leap is in its infancy, practical fabrication; S. Gobhinath (&)
S. Boobalan
J. Meshach Department of Electrical and Electronics, Sri Krishna College of Engineering &Technology, Coimbatore, India R. Ashwin Department of Mechatronics Engineering, Sri Krishna College of Engineering &Technology, Coimbatore, India K. Rajkumar Department of Electrical Engineering, National Institute of Technology, Trichy, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_54

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successful charging and driving a solar-powered EV are an intense task; it involves numerous calculations, design considerations, time and dedicated workforce to deliver better performance. The projected system has solar panels whose outcome is regulated by the solar charge controller, i.e. maximum power point tracker, and it in turn supplies the regulated output to the battery and the battery supplies the required power to the BLDC motor which is regulated by the BLDC motor controller [2, 3].

2 Proposed System Description The overall design and the manufacturing processes are divided and assigned into different subsystems, namely chassis, steering, braking, suspension, transmission and electrical [1]. This model consists of solar PV array either connected at series or connected at parallel according to the needs of the load. The MPPT comes in with a built-in algorithm to derive the maximum power that can be taken out from the panels. The battery is connected to the MPPT via the battery management system [2, 4]. Further, the motor controller is connected to the battery.

3 Mechanical System Overview The mechanical system constitutes the body of the vehicle and is responsible for the effective movement of the vehicle [3].

3.1

Chassis Design

The idea of the vehicular design was to ensure a rigid construction that supports an effective robust suspension and designing of a versatile steering system. The solar-powered electric vehicle’s body was to be tapped with the polycrystalline solar cells at a maximum scale without any disturbance to other systems [4, 5]. The base design of the chassis was to maintain the efficiency of the solar cells, thereby providing support to other systems of the vehicle. The design primarily focused upon on safety aspect of the driver, and a separate fireproof wall was provided behind the driver that separates the driver from the battery [4]. A four-wheeler design was chosen over the three-wheeled design in order to increase the overall stability and to enhance the seating of panels upon the chassis [5]. The overall dimension was approximately 123*64*50 inches. Commercial welding methods can be used to weld and form the chassis (Fig. 1).

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Fig. 1 Chassis—top view without cover and vehicular design of the proposed SPEV

3.2

Steering

The steering system ensures proper tire-to-road contact. The steering system helps to maintain a proper angle between both the tires during turns and straight-ahead driving. The driver should be talented to turn the vehicle with little effort. So, the rack and pinion steering with a 6:1 steering ratio amongst various types of available steering systems in the market to meet our objective of having a minimum turning radius was opted.

3.3

Braking

Brakes translate friction to heat [4, 5]. The static thermal analysis was carried out upon the disc rotor to estimate and relate their performance and their temperature distribution. The disc brakes were chosen for the vehicle (Table 1).

Table 1 Specifications of brake

Materials

Value

Axle capacity Wheel size Bolt pattern Rotor diameter Brake flange configuration Wheel stub diameter

1800 lbs 9″ to 12″ 5 on 3-1/3 7.2” 4 bolt ½

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4 Electrical System Design The electrical system is responsible for powering the solar vehicle. This is the main system in the solar vehicle that ensures that the vehicle runs properly and smoothly.

4.1

Solar Panels

The usage of polycrystalline silicon was opted as it suited the requirements of the vehicle’s need (Fig. 2; Table 2).

4.2

Motor

Brushless direct current motors were preferred for the vehicle; since they produce high torque, better efficiency and a lower inertia operation at higher speeds, they tend to dissipate heat better than conventional and require less maintenance and are more reliable when it derives to special machine application. In addition, BLDC motors do not present the risk of sparks or arcs. It also offers a low weight at a relatively small size [5] (Table 3).

Fig. 2 Solar panel dimensions

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Table 2 Solar panel electrical characteristics

Parameter

Value

Maximum power (Pmax) Open circuit voltage (Voc) Maximum power voltage Short circuit current Maximum power current (Iamp) Maximum system (DC) voltage Operating temperature Normal operating temperature Temperature coefficient of Isc Temperature coefficient of Voc

120 W 44.1 V 35.0 V 3.69 A 3.37 A 24 V −40 °C/85 °C 50 °C 0.080%/°C −0.350%/°C

Table 3 Motor specifications

Parameter

Value

Motor type Frame size Power Current (Amp) Voltage Speed Motor poles Hall sensor Mounting Degree of protection Class of insulation Ambient temperature/Max temperature

BLDC Motor LBC 05 2 KW 35 A 48 V 3000 rpm 8 5V Flange IP 55 F 50 °C/70 °C

4.3

Electrical Connections

Figure 3 gives the exact connection of all electrical components in the system and the methodology followed to connect them and orient them in such a way that the system works properly [4].

4.4

Battery

A high energy density, better self-discharge and less weight were the parameters that were considered in order to choose lithium-ion battery [6] (Table 4).

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Fig. 3 Electric vehicle with solar-powered connection diagram

Table 4 Battery specifications

Parameter

Value

Rated voltage Rated current Length Breadth Depth Weight

48 V 100 Ah 800 mm 315 mm 115 mm 35 kg

5 Conclusion In future, adoption of green technology will become inevitable, and solar-powered electric vehicle could be the future when proper research and development is vested upon this technology. This technology can cure a monopoly that is prevalent in the automotive industry.

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References 1. R. Seyezhai, A. Sankar, Simulation and implementation of solar powered Electric vehicle. Circ. Syst. 8(12), 643–661 (2016) 2. N.A. Rahim, R. Passarella, Z. Taha, Driving force characteristic and power consumption of 4.7 kW permanent magnet motor for a solar vehicle. ARPN J. Eng. Appl. Sci. 5(1), January 2010 3. J. Prasanth Ram, T. Sudhakar Babu, N. Rajasekar, A review on solar PV maximum power point tracking techniques. Renew. Sustain. Energy Rev. 6(7), 826–847 (2017) 4. S. Gobhinath, H. Adhithyan, S.K. Arvindadhithya, Hybrid regenerative braking system for electric vehicles using BLDC motor. Int. J. Pure Appl. Math. 119(12), 1895–1904 (2018) 5. Z. Taha, R. Passarella, H.X. How, J.Md. Sah, N. Ahmad, Application of data acquisition and telemetry system into a solar vehicle, in International Conference on Computer Engineering and Applications (2016) 6. S. Gobhinath, V. Aparna, R. Azhagunacchiya, An automatic driver drowsiness alter system by using GSM, in IEEE Explore ISCO Conference Publication, pp. 125–128 (2017) 7. W.F. Milliken, D.L. Milliken, “Race Car Vehicle” Dynamics, SAE International (2015) 8. M. Giannouli, P. Yianoulis, Incorporation of photovoltaic systems as an auxiliary power source for hybrid and electric vehicles. Solar Energy 8(6), 441–451 (2017)

Survey on Security Aspects in Smart Grid: Performance and Parametric Analysis V. V. Vineeth, S. Sophia and S. Jayanthy

Abstract Smart grid is a great new aspect of the power industry. It integrates various advanced technologies and information and communication capabilities to deal with problems found to occur with the existing electrical networks. Such integration facilitates and improves efficiency and accessibility of the electric power system with the additional features of regularly supervising, calculating, and administrating customer demands. This leads to the excessive deployment of smart meters in order to recognize the actual benefits. But, the deployment of smart meters brings up different concerns on the security of information among both consumers and service providers. This paper aims to focus on the major attacks on smart meter security which challenges the overall grid security. Keywords Smart grid Security solutions


Smart meter
Cyber attack
Physical attack


1 Introduction The several improvements and better new capabilities of the smart grid environment make the grid architecture more complex and expose it to various kinds of attacks. Smart meters tend to be an important component of the grid by acting as a central gateway located on customer’s site supporting two-way communication. Smart V. V. Vineeth (&) Department of Electrical and Electronics, Sri Krishna College of Engineering & Technology, Coimbatore, India e-mail: [email protected] S. Sophia Department of Electronics and Communication, Sri Krishna College of Engineering & Technology, Coimbatore, India S. Jayanthy Department of Electronics and Communication, Sri Ramakrishna Engineering College, Coimbatore, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_55

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meters gather a large amount of information and transfer to companies, service providers, and consumers. Collected data includes information about a private consumer which may be made use to deduce activities of consumer, devices used, and the times when home is vacant [1]. Smart meter security aspects include customer security, physical security, and implicit trust that exist among traditional power devices. Customer security issues arise when the private consumer information collected by a meter is prone to attack. The vulnerability to physical access and attacks of various distributed components comprises the physical security aspect [2]. The security aspect of communication among power devices includes the data spoofing attack which may affect the device-to-device communication which can affect more devices.

2 Smart Meter Overview Smart meters are key components in the grid infrastructure, the idea being emanated due to the wide-scale deployment of smart grid environment. Smart meter systems are available in smart grid environment not just to stipulate instant meter information on supplies, including water, electricity, and gas to service providers, but it, in turn, use this information so as to make it available to end users who are the final users of electricity and some of them even include the measurement of worthiness of power and essential control features [3]. Smart meter devices can adapt the power generation as on demand and thus can enable the balancing of power production and its distribution in a smart grid environment. Such devices are actually the point of contact among the two components such as the electric utility and the end user. Smart meter acts as a point of managing being located at the end use premise and physically managing the same. Smart meters have the added characteristics including identification of power usage patterns and behaviors by tracking the usage of data, capability to disconnect an end user from smart grid, generating alerts for the service providers in case of any problem, and monitoring and controlling of smart home devices at peak times [4]. Deploying a large quantity of software aspects and techniques is involved in the integration of smart meters to smart grid concept. These techniques depend primarily on the aspects of the demanding situations. The National Institute of Standards and Technology developed smart grid infrastructure composed of seven domains as shown in Fig. 1. This design and also the implementation of the smart meters thus depend on the specific requirements of service providers and that of end users. Different control devices and sensors are employed in a smart meter for the purpose of identifying the various devices and parameters so as to permit transmission of command signals and information. By active monitoring of performance and also the electricity usage features of smart grid load, smart meters can carry out a vital role in power grid in the future. Gathering of power consumption information from end users helps providers to analyze and manage power demands and the same can be made use of to notify the customers about efficient way so as to make use of their smart devices. It also helps service providers to detect stealing of

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Fig. 1 Smart grid domains (NIST)

electric power and unauthorized access which in turn helps in improving the power distribution and power quality [5]. Since smart meters can identify profitable end users depending on overall energy consumption and power generation sources, service providers can provide such customers with advanced voluntary value-added services. These all require the collection of large amount of real-time data from end users. Though there is an enormous list of various features and capabilities provided by a smart meter, its deployment raises many security and privacy concerns. It comes up with major concerns on overall security including data security(regarding end user privacy data), data integrity issues, availability, access control, to name a few. These security concerns are due to the reality that smart meters often act as the weakest link in grid environment [6]. That is, they can be easily attacked through other networks since they operate on wireless means for communications. These attacks launched on smart meters can in turn affect the overall security of the grid which can cause data corruption, mistakes in accounting, power blackouts, etc (Fig. 2).

3 Security Solution Challenges The different problems and challenges concerned in the implementation, design, exploitation, and maintenance of smart meters are outlined in paper [7]. The deployment of smart meters in distributed systems is analyzed which incur huge cost, and it faces more difficulties with the increasing number of customers.

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Fig. 2 Basic network architecture

The gathering and transmission of power consumption data raise privacy and security risks. Data can also disclose the location of customers, and the authentication of such information is vital [8, 9]. DNP3 and its enhanced versions can be used for communication network, but it cannot offer needed security [10]. Power consumption information being carried over cellular networks has security risks [11] which tend to other problems like poor protocols and authentication. This is added up by data concentrators. The paper identifies different design issues including technology aspects, physical aspects, cost of device, communication, and identification for all devices. The maintenance issues were classified as being of network failures, communication network, smart meter, and base server. Further, it investigates the challenges with data transfer as being the quantity of information to be transmitted, the access criteria and also the kind of modulation to be employed. Certain sections of people are also interested in collecting data from smart meter including illegal customers and attackers [12], and since the gateways can be compatible with other appliances, it brings up cyber security and also physical security risks [13]. The weaknesses of current smart metering systems are highlighted in paper [14]. It focuses on the methods used to secure transmission of data, including symmetric and asymmetric means. Both the approaches are found to have problems: communication security (where whole information will be lost) for symmetric and there are problems of the reduced speed of systems in case of asymmetric approach. It was outlined that weak security could be resulted by using symmetric algorithm at both parts of communication, whereas though some sort of security can be attained in asymmetric case, it is a lot of waste of time. The process of generation and management of keys for the algorithm is also a tedious process. The possibilities that can be modeled on a smart meter system are discussed in paper [15]. Two types of derived attacks have been launched; assuming an attacker can have system-level access, and an abstract model has been designed to extract

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and analyze the attacks. The first attack being launched is communication interface attack targeting on communication link. This is done by writing fake processes and using ports, which indeed resulted in fake consumption of data. The second attack is physical memory attack, which takes into consideration the fact that power consumption data will be written to flash memory in case of network unavailability. A script has been written to deactivate scripts and to overwrite data file with fake data. Power consumption could be modified by activating the script. The effect of these two attacks has been studied in the paper by considering the CPU and memory overhead involved, and it is found to have a great impact. This paper [16] focuses in detail on the security and privacy issues of smart meters. The potential attackers, their model of attacks and threats caused, are presented. Different types of attackers on smart meters are identified including eavesdroppers, marketing agencies, customers, novice attackers, and active attackers. Security attacks launched by these attackers include eavesdropping, denial of service, packet injection attacks, man-in-the-middle attack, remote connect/disconnect, malware injection attacks, and firmware manipulation. It is identified in the paper that the security and privacy issues are closely related. The consequences are also highlighted. A collective analysis of the reading of smart meter among a clustered group of meters based on a detection model is done in paper [17]. The attacker is aimed at producing faulty readings by the compromised meters. The meters of neighborhood network are grouped into clusters, and they multicast their reading values. The compromised meters can either report fault readings to its peers in a cluster thus avoiding detection, or it can report it to both peers and central unit at the same time. The results of the attacks have been identified, and an effective peer-monitoring system is analyzed to be appropriate to find out the misbehaving of meters in a cluster. The possible attacks on smart grid infrastructure are identified in paper [18] focusing on smart meter perspective. Different types of attacks affecting the confidentiality, integrity, availability, and non-repudiation are identified and analyzed in detail. The physical attacks as well as their cyber counterparts are listed out, and it is identified that smart meters are the possible targets of attack in a grid environment.

4 Smart Grid Security Goals Security of smart grid security can be examined as being a group of main goals such as availability, integrity, confidentiality, and also accountability. • Availability: This goal makes sure that information be accessed on reliable and also in a timely fashion. Collection of the data, refinement, and the sharing of data is important and is to be maintained by security solutions. • Integrity: Data should be highly accurate and reliable. It should be ensured that the data is accurate and should be free from manipulation in order to prevent harmful attacks and fraud.

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• Confidentiality: Huge volumes of data generated by the grid should be collected, stored, and analyzed. It includes sensitive data regarding consumers and utilities. Care should be taken so as to avoid unauthorized access to sensitive information. • Accountability: Since proper care is needed while disclosing sensitive information, the communications of the user with the systems are logged properly and are linked with specific users. That is, smart grid users are responsible for the activities they carry out. Logs should be such that it may not be manipulated, and integrity is to be maintained.

5 Proposed Solutions 1. Powerful authentication methods are required to verify the integrity. Explicit requests are needed to permit access to network, and all other accesses are denied implicitly. 2. Smart grid infrastructure may have embedded and also general-purpose systems. Both these should be secured from the malwares. Embedded systems generally run on software from manufacturer that must necessarily have a secure storage which should contain keys that can be used for the validation of the particular software. Key can be made use to validate any software that is newly downloaded. Third party generally provides general-purpose systems, and its security is assured by means of updated antivirus solutions which regularly get updated and also by way of intrusion prevention systems. 3. Host-based security measure should be supplemented with intrusion prevention and detection systems (IPS and IDS) so as to guard the system against inside and outside attacks. 4. At least once in a year, vulnerability evaluation should be done to ensure that the interfacing elements are secured. 5. Awareness programs are to be conducted so as to educate network users about the security aspects on using the network so as to avoid possible system threats. 6. Common authentication mechanisms like Internet Protocol Security (IPSec) and Transport Layer Security (TLS) should be employed by the source and destination systems in order to know each other. 7. For safe and secure communication, devices should enable virtual private network (VPN). 8. For reliable and secure communication, devices must use public key infrastructure (PKI) [7]. While using cryptography for security, certain constraints exist like the storage and processing power to execute authentication and encryption mechanisms [8]. This is of major concern in smart grid environment since communications involve various channels of various bandwidths to which all the devices, other entities, and servers will be connected all the time.

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9. Data filtering should be done by the utilities in order to obtain the relevant data for their processing. 10. For achieving smart grid security, both control system engineers and software engineers for security must equally be involved.

6 Conclusion The various attack strategies and its effects on smart metering systems have been identified in this paper. The detailed analysis on the attack regimes indicates a line open up for the future research to introduce secure and appropriate model for smart meter architecture so as to make it secure and can function by ensuring that the model is free from all the various kinds of attacks. The model should be such that it considers all the various categories of attacks including cyber and physical categories. This is of primary importance since the loopholes open up for launching attacks on a smart meter affect the overall functionality of smart grid infrastructure and may lead to the overall destruction of the grid.

References 1. J. Naruchitparames, M.H. Güne, C.Y. Evrenosoglu, Secure communications in the smart grid, in Consumer Communications and Networking Conference (CCNC), pp. 1171–1175 (2011) 2. S.S.S.R. Depuru, L. Wang, V. Devabhaktuni, N. Gugi, Smart meters for power grid: challenges, issues, advantages and status, in Power Systems Conference and Exposition (PSCE), pp. 1–7 (2011) 3. S. Jaarsma, R. van Gerwen, R. Wilhite, Smart metering. Leonardo Energy (2006) 4. Y. Tanaka, Y. Terashima, M. Kanda, Y. Ohba, A security architecture for communication between smart meters and han devices, in IEEE Third International Conference on Smart Grid Communications, pp. 460–464 (2012) 5. H. Li, R. Mao, L. Lai, R. Qiu, Compressed meter reading for delay-sensitive and secure load report in smart grid, in 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 114–119 (2010) 6. U. Greveler, P. Gloesekoetter, B. Justus, D. Loehr, Multimedia content identification through smart meter power usage profiles, in Proceedings of the International Conference on Information and Knowledge Engineering IKE’12, Jul 16–18, Las Vegas, Nevada, USA (2012) 7. S.S.S.R. Depuru, L. Wang, V. Devabhaktuni, N. Gudi, Smart Meters for Power Grid— Challenges, Issues, Advantages and Status, IEEE (2011) 8. C. Bennett, D. Highfill, Networking AMI smart meters, in Proceedings of the IEEE Energy 2030 Conference, Atlanta, GA, pp. 1–8 (2008) 9. D. Silva, New ‘Smart’ Electrical Meters Raise Privacy Issues (Online). Available: http:// www.physorg.com/news176703307.html 10. T. Mander, H. Cheung, A. Hamlyn, W. Lin, Y. Cungang, R. Cheung, New network cyber-security architecture for smart distribution system operations, in Proceedings of the

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V. V. Vineeth et al. IEEE Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy, Pittsburgh, PA, pp. 1–8 (2008) F.M. Cleveland, Cyber security issues for advanced metering infrastructure, in Proceedings of the IEEE Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy, Pittsburgh, PA, pp. 1–5 (2008) M.F. Foley, The Dangers of Meter Data (Part 1), (Online). Available http://www. smartgridnews.com/artman/publish/industry/The_Dangers_of_Meter_Data_Part_1.html G.N. Ericsson, Cyber security and power system communication—essential parts of a smart grid infrastructure. IEEE Trans. Power Deliv. 25, 1501–1507 (2010) R. Rashedi, H. Feroze, Optimization of process security in smart meter reading, in 2013 Smart Grid Conference (SGC), December 17–18, Tehran, Iran (2013) F.M. Tabrizi, K. Pattabiraman, A Model for Security Analysis of Smart Meters, IEEE (2012) O. Ur-Rehman, N. Zivic, C. Ruland, Security Issues in Smart Metering Systems (2015) Z.A. Baig, A. Al Amoudy, K. Salah, Detection of compromised smart meters in the advanced metering infrastructure, in Proceedings of the 8th IEEE GCC Conference and Exhibition, Muscat, Oman (2015) R. Vigo, E. Yüksel, C.D.P.K. Ramli, Smart grid security a smart meter-centric perspective, in 20th Telecommunications Forum TELFOR 2012, Serbia, Belgrade (2012)

A Literature Survey on Renewable Energy Sources in India Praveen Mannam and R. P. Singh

Abstract In monetary and ecological motivating forces, the advances are reshaping the conventional perspective on power frameworks. Presently in a multi-day’s vitality source, a sustainable power source is quickly turning into a favored one all-around the globe. Network vitality ventures democratizing access to the advantages of renewables on and off the lattice, developing markets driving the organization of renewables on track to advancement, and enterprises growing the extent of their sun-oriented and wind obtainment. Renewables are achieving cost and execution equality on the framework, and at the attachment, the new advances are sharpening the focused edge of wind- and sunlight-based. Wind- and sun-based powers have shown to be aggressive with regular age advances over the top of worldwide markets, even without endowments. The sending of new advances will help further diminish in expenses and improve coordination. This will empower a developing number of vitality purchasers to acquire their favored vitality source and quicken national vitality changes over the world. As India grows, clean defensible electrical supply avails to control for homes, structures, and furthermore bigger networks. Circulated age (DG) has been taken as decentralized age and appropriation of intensity particularly in the country regions. The distributed age (DG) innovations in India identify with miniaturized scale turbines, wind turbines, biomass, and gasification of biomass, sunlight-based photovoltaic, and mixture frameworks. The expanded reaping of nature energy steam from inexhaustible clean sources comes to the overwhelming test dependably developed to effectively suit the power foundation. The power age from different accessible sustainable power source structures with appropriated age must be associated with customers through legitimate incorporation. Such a kind of joining brings the idea of small-scale framework (MG). Along these lines, this paper proposed the significance of micro-grid innovation in the present days in India, and furthermore, it is the overview of lasting power core belongings with vitality frameworks organizations in India and on the planet.

P. Mannam (&)
R. P. Singh Electrical Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Madhya Pradesh, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_56

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Keywords Renewable energy resources (RES) Micro-grids (MG) generation (DG) Solar systems Wind energy Biomass energy








Distributed

1 Introduction The significance of sustainable power sources in the progress to a maintainable vitality base was perceived in the mid-1970s. The current sustainable power source is being utilized progressively in four unmistakable markets, and there are control age, warming and cooling, transport, and rustic/off-lattice vitality services. While assets, for example, consolidated cycle gas turbines (CCGT) have greater adaptability to pursue the heap bend, progressively reasonable battery stockpiling and different advancements are helping smooth the impacts of wind- and sun-oriented irregularities, giving them a greater amount of the unwavering quality required to contend with ordinary sources. From a value point of view, inland wind has turned into the world’s most reduced cost vitality hotspot for power age, with an unsubsidized LCOE (levelized cost of energy is a term which portrays the expense of the power delivered by sun oriented over some stretch of time, normally the justified existence of the framework) scope of US$ 30–60 (MWh) [1]. An aggregate of 121 nations had sent almost 495 GW of coastal breeze control, driven by China, the USA, Germany, India, Spain, France, Brazil, the UK, and Canada, and inland wind had achieved value equality in these nine nations [2]. In the USA, the least expenses are in solid breeze locales, for example, the Great Plains and Texas, while the most noteworthy are in the upper east [3]. All-inclusiveness, the most minimal expenses are in the nine driving nations, just as Eurasia and Australia [4]. Utility-scale sun-powered PV is hot on wind’s heels: It is the second-least expensive vitality cause. An expensive of sun-powered PV’s LCOE extend (US $43–53/MWh) is less than that of some other age source [5]. A record 93.7 GW—more than the absolute limit in the year of 2011, i.e., 69 GW was included all-inclusiveness in 2017 crosswise over 187 nations, carrying the all-out ability to 386 GW, driven by China, Japan, Germany, the USA, Italy, India, and the UK [6]. Solar has achieved value equality in every one of these business sectors aside from Japan, and it is the world’s most elevated cost sun-based markets, essentially because of high capital expenses. As Japan advances to aggressive closeouts, sun-oriented value equality is normally somewhere in the range of 2025 and 2030. In the USA, the most reduced expenses are in the southwestern states and California [7]. Internationally, Australia has the most reduced expenses for sun-powered PV and Africa has the most elevated because of speculation costs [8]. Commercial sunlight-based PV has come to unsubsidized attachment equality in parts of all the top sun-powered markets that are at framework equality, aside from India [9]. US property holders conveyed the same number of private stockpiling frameworks in the first four months of 2018 as in the previous three joined, generally in California and Hawaii [10]. Private sun-powered in addition to capacity is as of now less expensive than utility retail rates in 19 US states, just as in a few areas of Australia and Germany, where,

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Fig. 1 UNITS = GW. Source IRENA, renewable capacity statistics 2018

separately, 40 and 50% of private sun-oriented PV frameworks introduced in 2017 included capacity [11] (Fig. 1). Australia and Europe have more private and business housetop sun-based than utility-scale sunlight-based limit, raising the possibility of dispersed versus utility-scale sun based on the addition to capacity turning into the characterizing vitality asset rivalry when network and attachment equality are come to.

2 Renewable Energy Sources in India The Ministry of New and Renewable Energy (MNRE) in India has been encouraging the execution of wide range projects including outfitting sustainable power, sustainable power source for country territories for lighting, cooking and intention control, utilization of tenable powering in urban, mechanical and business applications, and improvement of substitute powers and applications. A sustainable power source is an incredible mix of empowering patterns and request patterns. Sustainable power source assumes a significant job in the long-haul vitality supply security, broadening of vitality blend, vitality gets to, ecological security and maintainability. Sustainable power source will undoubtedly assume an expanding job in the future vitality frameworks. India has a tremendous supply of supportable power core assets, and it has one of the biggest projects on the planet for conveying sustainable power source items and frameworks. To be sure, it is the prevailing domain on the planetoid to have an elite service for sustainable power source advancement, the MNES. India is

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currently the eleventh biggest economy on the planet, fourth regarding obtaining power. Power age from sustainable sources is on the ascent in India, with the portion of durable power supply in the nation’s absolute introduced limit ascending from 7.8% to around 13% in the span of 2008–2014 as per the IREDA. Currently in our country, it has about 36.4 GW of introduced sustainable power source limit. Of these, wind is the biggest giver and stands at around 23.7 GW of introduced limit making India the world’s fifth biggest breeze vitality maker. Little hydro control (4.1 GW), bio-vitality (4.4 GW), and sunlight-based vitality (4 GW) establish the rest of the limit (MNRE, 2015). It has been accounted for that as far as power age, roughly 70 billion units for every year are being created from inexhaustible sources (MNRE, 2014). Figure 2 beneath demonstrates the sustainable power source blend in the all-out introduced limit in India. The Indian sustainable power source division is the fourth most appealing [12] sustainable power source showcase on the planet. Indeed October 2018, India positioned fifth in introduced sustainable power source limit. As indicated by 2018 Climatescope report, India positioned second among the developing economies to prompt progress to clean energy. Installed sustainable power age limit has expanded at a quick pace in the course of recent years, posting a CAGR of 19.78% between FY14 and 18. The centerpiece authority of India has moved to clean vitality after it sanctioned the Paris Agreement. With the expanded help of government and improved financial matters, the area has something to be alluring from speculator’s point of view. As India hopes to satisfy its vitality need individually, which is relied upon to reach 15,820 TWh by 2040, sustainable power source is set to assume a significant job.

Fig. 2 Share of renewables in total grid installed capacity. Source CEA-MNRE report

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3 Solar Systems in India India has tremendous sun-powered potential because of normal yearly temperature shrinks from 26 to 28 °C. The bright segments are arranged in the sinister/eastern strand, from Calcutta to Chennai. Photovoltaic (PV) cells are set on the rooftop of houses or business structures to collect the energy from sun and to convert the collected energy into electricity. For example, mirrors or illustrative bowl that came over and step the shine for the period of the day are additionally utilized. Sunlight-based vitality is an appealing prospect for India, as the nation gets sun-oriented radiation of 5–7 kWh/m2 for 300–330 days in a year. This means a power age capability of roughly 20 MW/km2 for sun-based photovoltaic (SPV) applications and 35 MW/km2 for sun-oriented warm age. This suggests India gets sun-oriented vitality comparable to about 5000 trillion kWh/year, which is proportional to 600 GW. This far surpasses the nation’s present vitality utilization. India is positioned fifth in SPV establishments and ninth in sun-oriented warm application installations on the planet. India has 10–12 maker’s creating around 100 MW of SPV cells and roughly 20 producers with a complete installed capacity of 120 MW in module fabricating. The proposed National Solar Mission under the National Action Plan on Climate Change (NAPCC) tries to give long-haul vision to the improvement of solar energy in India. The draft objectives of the proposed mission include: • 10–12% of total power generation capacity estimated for year. • Solar power cost decreases to accomplish framework tax equality by 2020. • 4–5 GW of introduced sun-based assembling limit by 2017 of integrators-cumspecialist organizations (around 80), with an all-out limit of roughly 245 MW [13].

4 Wind Energy Wind vitality age was a significantly credited to the provision of quickened deterioration. Wind vitality is the undisputed market pioneer in India, representing almost 70% of all-out lattice interactive renewable limit in the country. India is outperformed distinctly by Germany as one of the world’s quickest developing markets for wind energy. By the mid-1990s, the subcontinent was introducing more wind creating limits than North America, Denmark, Britain, and the Netherlands. The ten machines, close Okra in the region of Gujarat, were a portion of the primary breeze turbines introduced in India [14].

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5 Biomass Energy India is extremely wealthy in biomass. It has a capability of 19,500 MW (3500 MW from bagasse-based cogeneration and 16,000 MW from surplus biomass). Right now, India has 537 commissioned and 536 MW under development. The actualities strengthen the possibility of a responsibility by India to build up these assets of intensity creation. The following are the rundown of certain states with greater opportunities for biomass creation: • • • • • • • •

AP (200 MW) Bihar (200 MW) Gujarat (200 MW) Karnataka (300 MW) Maharashtra (1000 MW) Punjab (150 MW) Madras (350 MW) UP (1000 MW) [14].

Government initiatives A few activities by the Government of India to support the Indian tolerable source area are as per the following: • A new hydropower has been delineated for the developmental of hydras enlarged in the community. • To execute a 238-million-dollar communal assignment on advancement hypercritics innovations for disinfectant char control. • The sun-based housetop area (Table 1).

Table 1 India’s existing and projected renewable energy capacity additions. Source Mericom India IEEFA Estimates (GW) Source Utility-scale solar Rooftop solar Wind Biomass + RoR Floating solar Hybrid wind and solar Total

Installed capacity as of FY201B/19

Capacity additions FV2018/19

Estimated capacity additions FY2019/ 20

FY202Q/ 21

FY2021/ 22

Total

26.7

6.5

7.5

11.6

14 0

59.8

3.9

1.6 1.7 0.5

2.0 5.0 0.5

3.0 6.4 0.5 0.1 0.4

40 6,4 0.5 1A 0.7

12.9 53.1 15.3 1.5 1.2

10.3

15.0

22.0

27.0

143.8

13.8 0.1 79.8

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6 Micro-Grids (MG) in India In creating nations like India, a great many people in remote territories are not ready to infer the advantages of the progressing jolt process. Since there is no power that organizes accessible to associate the secluded towns to the focal or state networks, more ventures are required. In this association, the government had started sometime before, the procedure of country zap through sustainable and other locally accessible conveyed age assets. The current ongoing provincial jolt projects are for the most part with a sustainable age. In the point of view of the manner in which that the conventional vitality sources are rapidly depleting and speak to a danger to the earth, a short investigation of political, financial, specialized, and ecological viewpoints for the arrangement of RESs-based miniaturized scale frameworks (MG) in India is finished. In India, however, there is an activity for the consolation of micro-grids and there is as yet far to go in defeating certain obstacles. There are many secluded DGs of composed task existing in the nation. Well known among them is the Sagar Island micro-grid [15]. This specific venture is by and large together subsidized by MNRE, the Indian Government, Indo-Canadian Environment Facility (ICEF), and West Bengal Renewable Energy Development Agency (WBREDA). “Asia-Pacific association on Urban Development and Climate: APP” is the territorial participation structure built up by the initiative of the USA to enhance the capacity of Kyoto Convention [16]. At present, seven nations (Japan, USA, Australia, Korea, China, India, and Canada) are taking part in this action. They framed Renewable Energy and Distributed Generation Task Force (REDGTF) to direct primer and achievability contemplates for improvement of keen vitality arrangement utilizing different sustainable power sources in different nations. One such investigation has been completed in Maharashtra, India, for extensive assessment criteria for circulated power utilizing micro-grid [17].

7 Conclusion According to the information referenced by Indian CEA (Central Electricity Authority), the yearly development in power age during ongoing FY 2018–19* (*up to February 2019) is 24.77% which is uncommon improvement contrasted with 6.47% in 2015–16 FY. At last, the use of maintainable source of energy resources for power age is giving more advantages to the nation and individuals, particularly contamination-free living conditions and expanded lifetime.

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References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.

Lazard, Levelized Cost of Energy Analysis—Version 11.0 (2017) M. Motyka, A. Slaughter, C. Amon, Global Renewable Energy Trends US Department of Energy, Wind Technologies Market Report (2016) REN21, Renewables 2018: Global Status Report (2018) Lazard, Levelized Cost of Energy Analysis—Version 11.0 Capacities Calculated from IRENA, Renewable Capacity Statistics 2018 US Department of Energy, 2016 Wind Technologies Market Report REN21, Renewables 2018: Global Status Report CSIS, BNEF’s New Energy Outlook 2018 W. Mackenzie, US Energy Storage Monitor, June 5, 2018 J. Farrell, Reverse power flow. Institute for Local Self-Reliance, July 2018; REN21, Renewables 2018 Global Status Report According to Renewable Energy Country Attractiveness Index 2018 by EY India Ministry of Non-Conventional Energy Sources (MNES). http://mnes.nic.in/ Global Energy Network Institute (GENI). www.geni.org (Online) Available: http://www.iset.uni-kassel.de/abt/FBA/publication/2008/2008MitraSesi. pdf, Nov 2009 (Online) Available:http://unfccc.int/kyotoprotocol/items/2830.php, Nov 2009 F. Ahmad, M.S. Alam, Economic and ecological aspects for microgrids deployment in India, in Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2017.11.027

Designing of Solar Hybrid Electric Vehicle from Source to Load P. Ajay Sai Kiran and B. Loveswara Rao

Abstract Electric vehicles have gained large attention everywhere throughout the world due to rising global gas emissions and with increased fuel cost and depletion of fuel of conventional transportation. With the widespread EVs, there is an impact created by them not only on the transportation sector but also on the power sector. With various types of configuration of EVs, the scope of smart grid and microgrid and also the reliability of the grid resilience and reliability can be improved. In this paper, solar-based hybrid electric vehicle has been designed and the impact of EVs and utilization and scope of EVs from source to load is analyzed.

1 Introduction Electric vehicles have been adopted by many countries due to its advantages over conventional ICE in the aspect of fuel and tailpipe emissions. This adoption has both advantages and disadvantages on the distribution of power this paper is framed completely discussing the scope of electric vehicles and also different case studies of electric vehicles and schemes adopted by different countries over the globe to increase EVs. Adverse environmental conditions in the sense pollution is been increased due to tail pipe emissions from conventional vehicles. Electric vehicles gain an advantage over internal combustion engine because the efficiency of the electric motor is high compared to the IC engine, and speed control will be easier and regenerative braking will be an additional advantage. The increase of electric vehicles will give the scope of “prosumer” where the consumer will be acting as a producer too with the energy that has been stored in the battery and energy that can P. Ajay Sai Kiran (&)
B. Loveswara Rao Department of EEE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur 522502, Andhra Pradesh, India e-mail: [email protected] P. Ajay Sai Kiran SASI Institute of Technology and Engineering, Tadepalligudem 534101, Andhra Pradesh, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_57

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be produced by hybrid electric vehicle combination of internal combustion and electric generator. The popularity of EVs during 1900 is also a key to know the development of personal vehicle and the other available options. Steam was the most dependable source for driving vehicles and powering load too. A portion of the main self-impelled vehicles during the late 1700s depends on steam, yet it took until the 1870s for the innovation to grab hold in automobiles. This is because steam is not the best option for personal vehicles as it requires longer time to start up and water needs to be replaced. An electric vehicle does not have any of the issues related to steam or gas. They were simple to drive and did not discharge any pollutants like the other cars. The downfall of the electric vehicles started due to the back step taken by companies due to the limitations in charging infrastructure and the lack of sale due to a limited range.

1.1

Type of Electric Vehicles and Charging Infrastructure

Depending upon the type of configuration, fuel electric vehicles are classified as shown in the table (Table 1). In BEV, the required energy to propel the vehicle has been produced by the battery management system solely. BEV does not have charging capability; it completely depends upon battery swapping technology. Battery swapping is the technology in which the discharged battery will be supplanted by charged battery depending upon the specifications of the vehicles (Fig. 1). In HEV, there are two sources that are responsible for the propulsion of the vehicles. The energy will be supplied depending upon the load requirement; the main advantage of HEV internal combustion engine has the capability to generate the electric energy required for charging the battery. The main disadvantage of HEV does not have the capability of charging infrastructure (Fig. 2). As illustrated in the figure, the drive train configuration is the combination of fuel and Electric energy, the HEC can work in the following pattern [1]: 1. Fuel drivetrain propels the load alone. 2. Electric energy source drives train alone. Table 1 Comparison of different drive trains S. No.

Name of the technology

Drive train

1 2 3

Battery electric vehicles (BEV) Hybrid electric vehicles (HEV) Plug-in hybrid electric vehicles (PHEV)

4 5

Fuel cell EV (FCEV) All electric vehicle

Battery Battery + ICE Battery + ICE charging supported Fuel cell + ICE Battery

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Fig. 1 Rear axle configuration of battery electric vehicle

Fig. 2 Hybrid electric vehicle configuration

3. Both fuel and electric drivetrain propel the load at the same time. 4. Electric drive is been charged from the load (regenerative braking). 5. Electric drivetrain obtains power from fuel drivetrain (ICE charging battery). The disadvantage of hybrid electric vehicles can be overcome by plug-in hybrid electric vehicle (Fig. 3). By the combination of ultra-capacitor, battery will provide many advantages as an attractive storage system; the main advantage of ultra-capacitor is high power density whereas the battery has a high energy density; and both the battery and the UC provide power to the motor and power electronic DC/AC inverter during acceleration (Fig. 4). The fuel cell EVs have been the focus of auto manufacture as an alternative source. In major, the difference between a battery and fuel cell is that fuel cell generates electrical energy rather than giving out stored energy. There are five types of FC, which are currently being developed, the alkaline fuel cell (AFC), the proton Fig. 3 Plug-in HEV drive train configuration

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Fig. 4 All electric vehicle configuration

exchange membrane fuel cell (PEM), the phosphoric acid fuel cell (PAFC), the molten carbonate fuel cell (MCFC), and the solid oxide fuel cell (SOFC) [2].

1.2

Charging Methods

Charging in general aspect is the action of putting energy back to the battery; that is, for restoring energy, different charging methods have been discussed in this session and different charging infrastructure. 1. Constant Voltage Charge The name itself clears that the constant voltage charge is when a constant voltage is supplied across the battery pack. This level of voltage is a preset value given by the manufacturer. This method is accompanied by a current limiting circuit most of the time, especially during the beginning of charging where high currents are drawn comparing to its capacity. The voltage value is chosen depending upon the type of battery. 2. Constant Current Charge The constant current charging method is supplying a constant current to the battery with a low percentage of current ripples independent of the battery state of charge or temperature. This method is achieved by varying voltage to the battery by using control techniques such that current through it will be constant. This constant current charging can be done in two methods: “single rate current” or “split rate current.” 3. Taper Current Charge This method is implemented when the source cannot be controlled or source is nonregulated. This is usually implemented with a source (transformer) having high

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voltage when compared with the capacity of the battery voltage. Resistance is used to limit the current flowing to the battery. A diode can also be used to ensure unidirectional power flow to the battery. In this method, the current starts at the full rating and gradually decreases as the cell gets charged. 4. Pulse Charge This technique involves using a short time current or voltage pulses for charging. By changing the width of pulses, the average of the current or voltage can be controlled. Pulse charging provides two significant advantages. It reduces charging time. The conditioning effect of this technique highly improves the life cycle. The intervals between the pulses are known as rest time which plays a crucial role; they provide some time for chemical reactions inside a battery to take place. 5. Reflex Charge During charging procedure, some gas bubbles appear on the electrodes, especially amplified during fast charging. This phenomenon is called “burping.” By applying very short discharge pulses or negative pulses the phenomenon of Burping can be achieved, for example, by short-circuiting the battery for small intervals compared to charging time intervals in a current limited fashion, typically 2–3 times bigger than charging pulses during the charging rest period resulting in depolarizing the cell will speed up the stabilization process and hence the overall charging process. This technique is also called as “burp charging” or “negative pulse charging.” 6. Float Charge Due to self-discharging of batteries, they get discharged over time. 100% SOC should be maintained by batteries for a long time to be ready for the time of use. But they may lose 20–30% of charge due to self-discharging. To compensate this self-discharging, a constant voltage which is been determined by battery characteristics has been applied permanently. This voltage is known as “float voltage.”

1.3

Charging Infrastructure [3]

The charging infrastructure will depend upon the type of charging it is whether slow, fast, or superfast charging. Depending upon the above-stated criteria, charging infrastructure is classified into three types (Table 2).

1.4

Batteries Available for Battery Electric Vehicles [4]

Batteries are source required to drive the electric vehicles, the review of different batteries available has been reviewed, and the status of the battery technology has been discussed.

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Table 2 Different types of charging S. No.

Types of charging

1

Alternate current charging: it is also known as level 1 or level 2. In this system, an inbuilt car inverter converts AC to DC DC charging: it is otherwise called level 3 or direct-current quick charging (DCFC). This charging framework changes over the AC from the lattice to DC before it enters the vehicle and charges the battery without the need of inward inverter. It works at the power from 25 kW to excess of 350 kW Wireless charging: this system uses electromagnetic waves to charge batteries. It is generally a charging cushion associated with a divider attachment, and plate is appended to the vehicle. Current technologies line up with level 2 chargers and can give catalyst to 11 kW

2

3

1. Lead–Acid (Pb–Acid) Lead–acid batteries are widely used for many applications over a century. It has the advantage of high efficiency in the range of 95–99%. Its main disadvantage that has limited its adoption is their weight, and they have low specific energy (30–40 Wh/kg). 2. Nickel–Cadmium (Ni–Cd) Nickel–Cadmium is widely used for traction applications. Their specific energy is low within the range of 45–60 Wh/kg. They are recommended when high instantaneous currents must be provided. 3. Nickel–Metal Hydride (Ni–MH) Nickel–metal hydride is most widely used in EV and PHEV applications. Comparing with the above types of specific energy at expense of lower life cycle, Ni–MH batteries have been up to two or three times more energy than a Ni–Cd type. The typical value for the specific energy of present technology is 75–100 Wh/kg. 4. Lithium–Ion (Li–Ion) This type has noticeably high specific energy, specific power and great potential for technological improvements providing EVs and PHEVs with perfect performance characteristics such as acceleration performance. Their specific energy is in the range of 100–250 Wh/kg. Because of their nature, Li–Ion batteries can be charged and discharged faster than Pb–acid and Ni–MH batteries, making them a good option for EV and PHEV applications. 5. Lithium Polymer (Li–Po) Li–Po batteries have the same energy density as the Li–Ion batteries but at a lower cost. This specific chemistry is one of the most potential choices for the applications

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in EVs and PHEVs. There have been significant improvements in these technologies. Formerly, the maximum discharge current of Li–Po batteries was limited. 6. ZEBRA Batteries Mostly used batteries after Lithium–Ion is ZEBRA batteries; they are exclusively modeled for EV applications. It is a sodium–nickel chloride battery; this technology is first developed in South Africa in the 1980s, later Switzerland has adopted this.

1.5

Motors Used for Propelling

Electric vehicle industry is now using mainly three motors for propelling the vehicle. 1. 2. 3. 4. 5.

Brushed DC motor. Brushless DC motor. Permanent magnet synchronous motor. Three-phase induction motor. Switched reluctance motor.

Out of the above classifications, BLDC has majority application because of its following advantages. I. II. III. IV. V. VI.

Low maintenance. 85–90% conversion efficiency. High operating speeds. Since no brushes are available, there will be no chances of sparking. The size will be compact and occupy less space. Simple to control for forward and reverse actions.

2 Determination of SoC and SoH State of charge and state of health are important parameters that will influence the run time of EVs. SoC and SoH are defined as

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3 Battery Pack Calculations In this paper, we have taken 3 kW motor and controller and 250 W solar panel to design a PHEV. We use Samsung 18,650 Lithium–Ion batteries of 500 No. For constructing the suitable amount of battery capacity, calculations are shown below. The capacity of each battery = 2600 mAh Motor for Power Train = 3000 W Solar Panels@250 W = 2 * 250 W Controller = For operation of BLDC motor mosfet switiching For constructiong the battery bank A. Battery Bank Calculations The voltage of one battery = 3.6 V Rated Capacity of one = 2.6 Ah Battery C-Rate = 0.5

Rated Capacity of One battery in Wh ¼ Voltage of one battery * Rated capacity of one battery ¼ 3:6 V  2:6 Ah ¼ 9:36 Wh

ð1Þ Total number of batteries used to construct a battery bank = 500 The total capacity of battery bank in Wh = 9.36 * 500 = 4.68 kWh It means the battery bank can feed 4.68 kW of load for one hour B. Series and Parallel Combination calculation of Li–Ion to feed load of 3 kW Number of batteries in series = 13 Number of series in parallel = 25 Total number of series–parallel Combination = 13S42P Generally, battery bank for EVs is defined in series-parallel combination as XsYp. Battery Life ¼

Battery capacity in mAh  0:70 load curren in mA

ð2Þ

The factor 0.70 makes allowance for the external factor that will affect battery life.

Designing of Solar Hybrid Electric Vehicle from Source to Load Table 3 Technical specifications of solar panel

Type of module Maximum power Tolerance Open-circuit voltage Short-circuit current Maximum power voltage Maximum power current Module efficiency Solar cell efficiency

Runtime ¼

633 250 W 250 W ±3% 37.8 V 8.7 A 31.5 V 7.94 A 15.3% 17.2%

10  Ampere Hours Load in Watts

ð3Þ

C. Solar Panel for EVs In this paper, we have taken two number of 250 W solar panels, which survive two purposes: one is to drive EV and other to charge the battery bank, and it depends upon the requirement specifications of the panel as shown below (Table 3).

4 Comparison Between ZEBRA and Li–Ion Batteries See Table 4.

5 EVs Scope of Research A. EVs As Distributed Generation

Table 4 Comparision between ZEBRA and Li–Ion batteries ZEBRA batteries

Lithium–Ion

High energy density >110 Wh/kg Indefinite, maintenance free, storage life at ambient temperature 100% coulombic efficiency, accurate charge capacity

High energy density Low maintenance

Expensive: The production of ZEBRA batteries can be an expensive affair

Quick charging: Lithium–Ion batteries take a fraction of the time taken by other batteries to charge Expensive: The manufacturing of Lithium–Ion batteries can be a expensive affair

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With the increase in the sale of plug-in EV globally, it includes both battery EVs that drive on electric power and an internal combustion engine. It contains battery packs with a storage capacity between 4 kWh and 100 kWh. EVs are becoming an important distributed energy resources (DER) that utilities, microgrid, smart grid, and many industries can use to help efficient energy balance between supply and demand, provide ancillary services, and support critical energy needs during outages. Overseen and upgraded by framework administrators, these portable “storage-on-wheels” system can give the huge potential to increment overall. Energy demand will be served with the help of plug-in hybrid electric vehicles which will make electrical system more resilience and robust. The emergence of PHEV and battery electric vehicles will be a source of distributed energy storage in the power sector. The potential exists for this storage to bring benefits from the ability to shift net demand depending upon response to power sector needs. However, there are some constraints by a range of factors including their mobility, need to serve transport energy requirements, and the availability of physical charging opportunities. B. Optimized Charging Scheme: Along with the charging infrastructure and charging schemes, there is another method of how the battery will charge and it is known as revive capability as it is discussed below (Table 5). Since charging of electric vehicles is unplanned and unsystematic, it cannot be controlled and predicted based upon the load curve, because the charging of EVs depends upon customer interest. So, it requires some optimizations and predictions to avoid the burden on the existing grid. By adopting a load forecast method for fast charging of electric vehicles considering information interaction such as state of charge of the battery, this minimizes the time cost of the EV user [5]. A methodology has been developed to determine the battery charging load on the power system load profile; different conditions have been considered for controlled and uncontrolled charging; and uncontrolled charging will impose peak or nearly peak in off-peak time. With smart and planned charging methods for EVs, burden on power systems can be reduced to a maximum extent [6]. Table 5 Specifications of different types of chargers Types of charging schemes

Specifications

Rapid chargers

The work on two types supplies AC and DC as well. Rapid chargers on AC will be rated at 43 kW, for DC it will be 50 kW at least. Both will charge a vehicle to 80% SoC within 30–60 min; it depends upon battery capacity The capacity of fast chargers will be between 7 and 22 kW. Typically, full SoC can be obtained in 3–4 h The range will be up to 3 kW; the main application is for overnight charging usually between 6 and 12 h for pure EV and between 2 and 4 h for PHEV

Fast chargers Slow units

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The electrical vehicle routing problem (EVRP) tries to limit the charge utilization of electric vehicles. The far-reaching computation of charge utilization utilized by electric vehicles is given in this model. Target capacity is encircled limiting charge utilization and directing electric vehicles [7]. Charging and battery limitations are responsible for the downfall of EVs. With the advancement in charging infrastructure again, there is the adoption of EVs. With regenerative braking, some amount of power can be obtained to charge up the battery in BLDC motor driven. EVs now have some advanced technologies known as reviving by which some amount of energy will flow back to batteries when brakes are applied. EV an advanced braking mechanism is designed combining various regenerative methods and plugging. Different methods such as stopping time, energy recovery for different running conditions can be obtained.

6 Application of EVs in Power system See Table 6.

7 Results See Figs. 5, 6, 7, 8, and 9.

Table 6 PHEV at different levels S. No.

Level

Support by PHEV to power system

1

Generation level Generation level Transmission level Distribution level Distribution level

Bulk-energy services: electric-supply capacity

2 3 4 5

Ancillary services: regulation, spinning, not-spinning, and supplemental reserves, voltage support, black start Transmission infrastructure services: transmission-upgrade deferral, transmission-congestion relief Distribution infrastructure services: distribution-upgrade deferral, voltage support Customer energy management services: power quality, power reliability, retail electric energy time-shift, demand-charge management

636 Fig. 5 Solar hybrid electric vehicle on test drive

Fig. 6 13S42P Lithium–Ion battery bank

Fig. 7 Program developed to monitor charging and discharging

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Fig. 8 Battery bank discharge when SoC is >40%

Fig. 9 Battery bank charge when SoC is 0 or DG < 0). 8: Based on the results of the variable check, increment or decrement the duty cycle and end the process (Fig. 5).

b. Flowchart for MPO:

4 Modified Multi Level Inverter Topology The nine-level multilevel inverter circuit is proposed for photovoltaic applications. The number of power switches in different multilevel inverter is listed below in Table 2 [8, 10].

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Table 2 Power switches in multilevel inverter Name of the inverter

No. of switches

Level of output

Cascaded inverter Diode clamped inverter Proposed MLI

20 power switches 24 power switches Seven power switches

Nine level Nine level Nine level

Fig. 6 Schematic circuit of the boost converter with nine-level modified multilevel inverter

The nine-level multilevel inverter input voltage Vdc is split up into four levels (each Vdc/4 magnitude) by using DC link capacitor, thus we include the four capacitors (C1, C2, C3 and C4) for voltage dividing in the nine-level proposed modified multilevel inverter which is shown in Fig. 6. In order to generate PWM signal for MLI switching circuit, there is a need of four numbers of equal reference signals; hence in our proposed system, triangular carrier signal with different DC offset and pulsating DC supply is used to generate the switching pulse. Table 3 shows the corresponding switching sequences and the operation of proposed MLI topology, and it may also be observed that the [8] nine-level voltage can be generated. In order to achieve nine-level output , the power switches of MLI are operated in nine different modes. In mode 1, the switches S1 and S4 are in on state, and then MLI operated is at maximum positive voltage, i.e. Vdc. In mode 2, the switches are

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Table 3 MLI switching sequence Mode

Switching sequence

Voltage level

1 2 3 4 5 6 7 8 9

S1 and S4 D1, S3, D2 and S4 D5, S6, D6 and S4 D9, S7, D10 and S4 S3 and S4 S2, D3, S5 and D4 S2, D7, S6 and D8 S2, D11, S7 and D12 S2 and S3

+Vdc +3Vdc/4 +Vdc/2 +Vdc/4 Vdc = 0 −Vdc/4 −Vdc/2 −3Vdc/4 −Vdc

Fig. 7 Schematic of the typical output voltage in nine-level inverter

operating in the following sequence D1-S5-D2-S4, where 3/4th level of the voltage has been reduced. Figure 7 shows the schematic of the typical nine-level inverter output voltage, and modified PWM switching patterns to the nine-level are shown in Fig. 8 [11, 12]. In order to mitigate the drawback of the conventional topology, the proposed modified nine-level multilevel inverter topology has been implemented. The requirement of number of power switches in the proposed system can also be reduced with low power rating, due to that it reduces the switching losses as well as maintains the THD level within permissible limit (less than 5%). c. Simulation Results: MATLAB version 2015a is used to develop the simulation model as shown in Fig. 9 and also the verified simulation results such as nine-level inverter output voltage, THD level and power quality. Here, CHSM6612P-320 W panel parameters are assigned as PV panel model input parameter. The output of model is varied which depends upon climatic

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Fig. 8 Modified nine-level inverter PWM switching patterns

changes such as temperature and irradiation level as shown in Figs. 10 and 11, respectively. This simulation results show the proposed system that achieved higher efficiency and also mitigate the switching losses. Figure 12 shows the converter output voltage maintained 230 V for different input condition. At various points, the modified nine-level inverter is analysed. It seems that there is the reduction in the output harmonic level by increasing the output voltage magnitude steps with help of PWM control. The nine-level inverter output current and voltage waveform without using the filter are shown in Figs. 13 and 14, respectively [10].

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Fig. 9 MATLAB/Simulink model for the proposed system

Fig. 10 I−V curves

Fig. 11 P−V curves

The nine-level inverter has been operated at a frequency range of 1–2 kHz, in order to maintain the coordination between the nine-level inverter and the boost converter. So, there may be a reduction in the switching losses and in the THD rate. The FFT analysis of multilevel inverter with 100 cycles is shown in Fig. 15. The boost converter performance remains same, even in the fast-varying atmospheric condition. The THD level of the nine-level inverter is given as 4.90% as shown in Fig. 16.

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Fig. 12 Converter output voltage

Fig. 13 Nine-level inverter output voltage waveform

Fig. 14 Nine-level inverter output current waveform

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Fig. 15 FFT analysis of multilevel inverter

Fig. 16 THD rate of MLI

5 Conclusion The boost converter and nine-level inverter for solar photovoltaic system have been designed and developed. The function of boost converter and modified nine-level MLI are modelled using MATLAB software and also carried out the simulation for various input conditions such as temperatures, irradiation levels and switching frequencies. The MPO algorithm is implemented for MPPT control of boost converter and new PWM control to the nine-level multilevel inverter. Simulation results verified the control scheme effectiveness and also analysed the THD level (THD-4.90%), overall converter efficiency and switching losses. This modified nine-level inverter topology results in reducing the switching losses with the output voltage level increases. The added advantage of the proposed has quiet better features over the conventional topology. The output result is better in terms of required switches, dc supplies, number of levels in output voltage, power loss, cost and switching algorithm. Thus, the extension of the proposed system will help us to mitigate the problem of power outages.

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References 1. J. Prakash, S.K. Sahoo, S.P. Karthikeyan, Sinusoidal output voltage h-bridge multilevel inverters,in Proceeding on SEISCON 2012, p. 264 (Chennai, India, 2012) 2. E.M. Ahmed, M. Shoyama, Variable step size MPPT using a single variable for stand alone battery storage systems. J. Power Electron. 11(2), 218–227 (2011) 3. H.N. Zainudin, S. Mekhiler, Comparison study of maximum power point tracker techniques for PV systems, in Proceedings of the 14th International Conference (MEPCON’ 10), pp. 750–755 (Cairo University, Egypt, 2010) 4. J. Prakash, S.K. Sahoo, A Review on power electronics converters for solar photovoltaic system, in Proceeding on 5th International Conference, on Science, Engineering and Technology (SET) (Vellore, Tamil Nadu, India, 2012) 5. B.K. Santhoshi, K. Mohana Sundaram, M. Sivasubramanian, S. Akila, A novel multiport bidirectional dual active bridge dc-dc converter for renewable power generation systems. Indian J. Sci. Technol. 9(1), (2016). https://doi.org/10.17485/ijst/2016/v9i1/85701 6. J. Prakash, H. Saisidhartha, S.P. Raghavel, A. Shanmugasundaram, Coordinated control scheme in solar PV fed Boost converter and hybrid multilevel inverter, in Proceedings of the Conference on International Intellectual Convergence on Advances in Science and Engineering (IIASE-2017) (Chennai, India, 2017) 7. J. Prakash, S.K. Sahoo, S.P. Karthikeyan, I.J. Raglend, Design of PSO-fuzzy MPPT controller for photovoltaic application. Power Electron. Renew. Energy Syst., Lect. Notes Electr. Eng. (Springer India) 326, 1339–1348 (2015) 8. J. Prakash, S.K. Sahoo, Design of soft switching interleaved boost converter for photovoltaic application. Res. J. Appl. Sci. Eng. Technol. (Maxwell Scientific Organization) 9(4), 296 (2014) 9. J. Prakash, S.K. Sahoo, K.R. Suguvanam, Design of coordinated control scheme for hybrid resonant boost converter and multi level inverter. Indian J. Sci. Technol. 9(11), (March 2016). https://doi.org/10.17485/ijst/2016/v9i11/89389 10. K.M. Sundaram, P. Anandhraj, V.V. Ambeth, PV-Fed eleven-level capacitor switching multi-level inverter for grid integration. Adv. Smart Grid Renew. Energy, Lect. Notes Electr. Eng. 57–64 (2018) 11. S.R.K. Suresh, Analysis of PD, POD, APOD, CO AND VF PWM techniques for cascaded multilevel inverter. South Asian J. Eng. Technol. 2(16), 46–55 (2016) 12. J. Prakash, S.K. Sahoo, S.P. Karthikeyan, A novel technique for common mode—voltage elimination and DC—link balancing in three-level inverter. Int. Rev. Model. Simul.S (IREMOS), 5(2), 840–845 (2012) 13. M. Nupur, B. Singh, S.P singh, R.D. Dasharath Kumar, Multilevel inverters: a literature survey on topologies and control strategies, in IEEE 2nd International conference on power, Control and Embedded systems (2012), pp. 1–11 14. M. Murugesan, R. Pari, R. Sivakumar, S. Sivaranjani, Technical review on different multilevel inverter topology. IJAET E-ISSN 0976-3945 15. I.D. Kim, E.C. Nho H.G. Kim, J.S. KO, A generalized Undeland Snubber for flying capacitor multilevel inverter and converter. IEEE Trans. Ind. Electron. 51(6), 1290–1296 (2004) 16. P.P. Dash, M. Kazerani, Harmonic elimination in a multilevel current source inverter-based grid-connected photovoltaic system, in Proceedings of IEEE Industrial Electronics Society IECON 2012, pp. 1001–1006 (Montreal, QC, 2012) 17. B. Kavya Santhoshi, K. Mohana Sundaram, S. Padmanaban, J.B. Holm-Nielsen, K.K. Prabhakaran, Mohana sundaram sanjeevikumar padmanaban critical review of PV grid-tied inverters. Energies 12(10) 18. C. Mauryan, K.S.M. Abuvatamizhan, V. Balaji, R. Mani, Improved efficiency of large capacity renewable energy—integration with grid. Int. J. Eng. Sci. 3, 12–17 (2014)

A Novel Technique to Observe the Performance of Virtual Solar PV Module System G. Suresh Babu and N. R. Sai Varun

Abstract Photovoltaic (PV) energy source or a PV emulator is required to analyze the performance of PV equipment under fluctuating conditions. Typical PV modules are costly and static with limited customization abilities. A PV emulator can realize the characteristics of various PV modules under various test conditions (type of locality, climatic conditions, different irradiations, varying temperatures, and various maximum power point tracking (MPPT) algorithms) virtually. A switch mode power supply (SMPS)-based cloud-connected PV emulator is used to validate the Perturb-and-Observe (P&O) which is effectively illustrated.




Keywords Solar PV emulator Maximum power point tracking Perturb-and-Observe Table mode Buck converter










1 Introduction The exponential growth of generation of electric energy with renewable energy source compared to its counterpart is phenomenal because of the fact that fossil fuels are getting depleted. Among all the renewable energy sources, solar-based energy conversion is simple. No doubt solar energy has occupied center of attraction in renewable energy arena because of low carbon emission and decreased capital investments. However, the research on PV modules is paralyzed due to requirement of costly equipment and complexity in testing procedures [1]. Another limitation in the research of PV module is not attaining stable and repeatable conditions. Also, factors like unguaranteed irradiation, unrepeatability due to varying climatic conditions, and requirement of huge space for installation make research no way near in reality [2]. In order to address the above issues, solar PV emulator is one of the solutions in the present conditions. G. Suresh Babu (&)
N. R. Sai Varun Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_59

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Main theme of MPPT techniques is to provide an ideal duty cycle to converter such that maximum power is extracted from PV panel. Many MPPT techniques and algorithms are proposed [3–6], and among those Perturb-and-Observe is most commonly used due to its simplicity in implementation practically with high efficiency. Though there are various P&O algorithms based on mathematical modeling simulations available in solar world, a nelson’s eye is manifested on real-time conditioning. The main aim of this paper is to provide practical conditions of the implementation of algorithm “P&O method,” by using a SMPS-based cloud-connected PV emulator. By this emulator, various parameters can be considered (location, time duration, tilt angle, irradiations, and temperature) while algorithm is simulated experimentally.

2 System Description Block diagram of SMPS-based emulator is shown in Fig. 1. It mainly consists of programmable power supply, data logger, and personal computer with ecosence_50 application. PV emulator consists of four channels which can be operated in individual connection, in series connection, and in parallel connection. PV emulator is configured in table mode. In table mode, various parameters of PV system can be tested throughout the day. Using the software application, data of irradiations and temperature are fetched from database. Based on this data, IV tables are generated. This table contains 1000 pairs of voltage and current values which represent operating point on IV curve. In table mode, each of the channels can be assigned with particular PV module characteristics. Simulated mode is similar to table mode except that in simulated mode it possible to accumulate ten different characteristics for each channel.

Fig. 1 Block diagram of PV emulator

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3 Perturb-and-Observe Method P&O MPPT algorithm works based on PV array which is perturbed of radiation of direction. As the power drained from array increases, operating point shifts toward maximum power point which states voltage is also working in similar direction. On other hand, if power drained from array decreases, operating point shifts away from MPP and working voltage perturbation has to be upturned [5–7]. Figure 2 presents the flowchart of P&O method on the function of duty cycle. The performance of the above algorithm is evaluated at various test conditions by using PV emulator.

Fig. 2 Flowchart of P&O method

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4 Experimental Result All the experiments are conducted at load of 40 Ω/5 A. Figure 3 shows the experimental setup of PV emulator connected to personal computer. PV emulator is feeded to load through buck converter whose duty cycle is controlled by MPPT shown in figure. PV emulator is set for the parameters as listed in Table 1. Figures 4 and 5 show the P versus T curves and V versus T highlighting the power with MPPT and without MPPT. Power values are observed for period of five minutes. Figures 6 and 7 illustrate the power versus time curves and voltage versus time for increased load current with MPPT and without MPPT, and Figs. 8 and 9 depict curves for increased load current of 0.73 A. All the cases were studied for selected parameters presented in Table 1. Variation of voltage and power over time with MPPT and without MPPT is observed.

Fig. 3 PV emulator feeding resistive load through DC–DC converter

Table 1 Selected parameters for PV emulator through ECOSENCE software

Parameters

Value

Solar panel vendor Selected solar panel Latitude of location Longitude of location Day and time Albedo factor

Vikram Solar Pvt. Ltd. ELV 40 17.3921 78.3195 7/03/2019–10.00 0.2

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Fig. 4 Power versus time curves with MPPT and without MPPT for load current of 0.53 A

Fig. 5 Voltage versus time curves with MPPT and without MPPT for load current of 0.53 A

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Fig. 6 Power versus time curve with MPPT and without MPPT for load current 0.63 A

Fig. 7 Voltage versus time curve with MPPT and without MPPT for load current 0.63 A

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Fig. 8 Power versus time curve with MPPT and without MPPT for load current 0.73 A

Fig. 9 Voltage versus time curve with MPPT and without MPPT for load current 0.73 A

5 Conclusions A SMPS-based cloud-connected PV emulator is used to test Perturb-and-Observe MPPT algorithm. From the experimental analysis, it clearly points up the difference of with and without MPPT, and the P&O algorithm extracts more power when compared against latter. Under given test conditions, while using the tracker in the system, a certain level of unstability in power arises as the algorithm is checking MPP point continuously. On observing voltage versus time curves, there is certain

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drop in voltage in either of the conditions with and without MPPT as the load current is increased. Also, parameters like irradiations, temperature effect, and location effect can be considered while using PV emulator. This makes an effective study on algorithm with various test conditions.

References 1. P.J. Binduhewa, M. Barnes, Photovoltaic emulator. 2013 IEEE 8th International Conference on Industrial and Information Systems, ICIIS 2013, 18–20, Sri Lanka (2013) 2. D. Chariag, L. Sbita, Design and simulation of photovoltaic emulator. 2017 International Conference on Green Energy Conversion Systems (GECS) 3. S. Mule, R. Hardas, N.R. Kulkar, P&O, IncCon and Fuzzy Logic implemented MPPT scheme for PV systems using PIC18F452. IEEE WiSPNET Conference 4. F. Locment, M. Sechilariu, I. Houssamo, Energy efficiency experimental tests comparison of P&O algorithm for PV power system. 14th International Power Electronics and Motion Conference, EPE-PEMC (2010) 5. M.L. Azad, S. Das, P.K. Sadhu, B. Satpat, A. Gupta, P. Arvind, P&O algorithm based MPPT technique for solar PV system under different weather conditions. International Conference on circuits Power and Computing Technologies (ICCPCT) (2017) 6. O. Singh, S.K. Gupta, A review on recent Mppt techniques for photovoltaic system. IEEE IEEMA Engineer Infinite Conference (eTechNxT) (2018) 7. D. Sera, T. Kerekes, R. Teodorescu, F. Blaabjerg, Improved MPPT algorithms for rapidly changing environmental conditions. IEEE 2006 12th International Power Electronics and Motion Control Conference (2006)

Power Quality Analysis for Brushless DC Motor Drive Fed by a Photovoltaic System Using SRF Theory S. R. Rajasree, V. Ravikumar Pandi and K. Ilango

Abstract Harmonic contents present in power network have adversely affected the power quality and this reduces the stability of the system. In this paper, active filter-based compensation has been developed using the PV source. A nonlinear BLDC load is considered which injects more harmonic current in the system whereby increasing the total harmonic distortions (THD). This THD value needs to be maintained below the permissible limit by properly controlling the shunt active power filter (SAPF) to get effective compensation. In this work, synchronous reference frame (SRF) theory is used to produce the compensating current required for canceling the harmonic content produced by the BLDC load and for compensating the reactive power. The energy to active filter is obtained from PV array and the intermediate DC-to-DC converter stage is used to smoothen the operation of BLDC motor drive. The complete system is modeled and simulated in MATLAB Simulink. From the simulation analysis, it shows that the reduction of THD (%) is within the limits and VAR compensation is achieved thereby improving the power factor.




Keywords Active filter BLDC motor drive quality Synchronous reference frame theory





Harmonics
MPPT
Power

S. R. Rajasree (&)
V. R. Pandi Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India e-mail: [email protected] V. R. Pandi e-mail: [email protected] K. Ilango Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_60

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1 Introduction The increasing use of nonlinear loads has produced an essential impact on the quality of power being supplied. Passive filters [1–3] were used for compensation, but it eliminates only a particular harmonic range. Active filters [4, 5] were used to overcome the limitations of passive filter. Active power filters (APF) are widely used for enhancing power quality by reactive power compensation and harmonic filtering. Among APF, shunt APF (SAPF) was considered as the best solution to current harmonics. Various control techniques [6, 7] for implementing active power filtering include instantaneous PQ theory, synchronous detection algorithm, DC bus voltage algorithm and SRF theory [8]. In some cases, reactive power is also supplied by the APF along with harmonic compensation, and then the source should provide only the active part of the load current. Because of their efficiency and longer existence, BLDC motors are used in applications that require the motor to run continuously. This paper uses SAPF to provide compensation in BLDC drive application. The system considered has photovoltaic array and DC–DC converter [9], which is connected to BLDC type load. SRF theory is considered as one of the simple and most preferred techniques, which can fastly extract the harmonic content and of the VAR component of current. Hence, the SRF technique has been developed to effectively control the SAPF.

2 Proposed Method The proposed system is shown in Fig. 1, where the conventional source is connected to a BLDC type nonlinear load [10] expected to supply only the real power demand. The photovoltaic array provides required energy to fulfill the VAR demand by the nonlinear load and the power required for harmonic compensation. The PV side output voltage is given to a DC/DC converter from which it is connected to the APF through the DC-link capacitor. The DC-to-DC converter tracks the point corresponding to maximum power (MPPT) so as to fully make use of the PV power. The inverter control technique is aimed to neutralize the nonlinear load harmonics and reactive power demand by load. Perturb and Observe MPPT algorithm [11, 12] is used whose main advantage is that the check for the MPP will be independent to the environmental condition. A DC-link capacitor is connected between active filter circuit and PV converter system, which is used to downsize high-frequency harmonics. The DC-link voltage of the filter circuit is provided through the PV arrangement. Input from PV is boosted to 700 V and fed to the capacitor. Specification of BLDC motor is given in Table 1. The wattage rating, open-circuit voltage and short-circuit current and of PV array is 550 W, 400 V and 2.2 A, respectively.

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Fig. 1 Block diagram of compensation system

Table 1 Specifications of BLDC motor

Voltage Resistance of stator Inductor value Flux induced by magnets Pole pairs Back emf Rated speed

415 V 2.875 Ω 8.5 mH 0.175 4 Trapezoidal 3000 rpm

3 Design of SAPF SAPF, which is parallelly connected with the nonlinear load, introduces a compensating current to the system that is in opposition to the nonlinear load harmonic current. Hence, the net current drawn from the network at the load side will be a sinusoidal current of only fundamental frequency. The power circuit design of a SAF includes filter inductor value selection, DC-link capacitor value selection and DC-link capacitor voltage selection. The DC-link capacitor maintains the voltage with minimum ripples and acts as a storage element. The energy balance principle determines the size of the capacitor (Cf).

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VS  DIL  T2 Cf 
2
2 DVcf  DVcf ;ref

ð1Þ

where, DVcf is the minimum or maximum DC bus voltage, DVcf ;ref is the reference DC bus voltage, Vs is the source voltage, DIL is the reactive and harmonic load currents and T is the fundamental period of the input voltage. The minimum interfacing inductor (Lf, min) can be calculated as in Eq. (2) Lf ;min ¼

Vcf 2  DIsw  fsw;max

ð2Þ

where, fsw;max is the maximum ripple side frequency and DIsw is the maximum switching ripple of the compensation current.

4 Synchronous Reference Frame Theory The block diagram representation of SRF theory technique [7] is shown in Fig. 2. The input currents ia, ib, ic are transformed into a–b axes and then into the synchronous rotating reference frame. Here, fundamental frequency currents are considered as DC values and the harmonic elements are transformed into AC quantities. The harmonic currents are then extracted using low-pass (LP) filter, then correlate to the reference values and finally supplied to a PI controller to retrieve the effective control gain values. The application of inverse d − q and Park’s transformations gives out the harmonic currents in the three phases. Compared to the instantaneous PQ theory, this algorithm provides accurate calculation of harmonic currents under balanced load and unbalanced source conditions. In d e  qe reference frame, the basic frequency components are converted to DC values and the harmonic components are converted to non-DC values. Compared to other controllers, SRF controller is not sensitive to phase errors. The PI regulator’s output is summed up to the extracted DC current part within the d e  qe reference frame to obtain current references for the SAPF.

5 Modeling of PM-BLDC Motor Drive Permanent magnet (PM) motors serve a wide range of applications because of their reliability, efficiency and high-power density. A brushless DC motor [13] has a permanent magnet rotor and a stator windings connected to the control unit. A minimum cost means of electronic commutation of currents is six-step commutation (Fig. 3).

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Fig. 2 Block diagram of SAPF control technique

Fig. 3 Basic PM-BLDC drive scheme

The voltage equations for the BLDC motor are derived as: vab ¼ Rs ðia  ib Þ þ Ls

d ðia  ib Þ þ va  vb dt

ð3Þ

vbc ¼ Rs ðib  ic Þ þ Ls

d ðib  ic Þ þ vb  vc dt

ð4Þ

vca ¼ Rs ðic  ia Þ þ Ls

d ðic  ia Þ þ vc  va dt

ð5Þ

where Rs Ls ia; ib and ic

per phase stator resistance, per phase stator inductance, instantaneous value of stator phase currents,

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vab ; vbc ; vca instantaneous value of stator line voltages, va ; vb ; vc instantaneous value of phase back emfs The current equation is written as ia þ ib þ ic ¼ 0

ð6Þ

ic ¼ ðia þ ib Þ

ð7Þ

Equation (6) is rewritten as

Using Eq. (7), the equations for line voltage are reordered as vab ¼ Rðia  ib Þ þ L

d ðia  ib Þ þ va  vb dt

ð8Þ

vbc ¼ Rðia þ 2ib Þ þ L

d ðia þ 2ib Þ þ vb  vc dt

ð9Þ

vca ¼ Rð2ia  ib Þ þ L

5.1

d ð2ia  ib Þ þ vc  va dt

ð10Þ

Inverter Switching

Table 2 shows the interval of switching, current direction and the Hall effect sensor signals. One of the phases is open at any instant of time. The rotor position is detected from the point at which, the open phase back emf crosses zero. Hall effect sensors are used to identify position of rotor. The gates of the inverter are controlled by the Hall effect switches, passing through a decoder circuit.

Table 2 Switching sequence Switching interval

Sequence 0

Position sensor HS2 HS3 HS1 1 0 0

0°–60° 60°–120° 120°–180° 180°–240° 240°–300° 300°–360°

S1

S4

Phase current a b c +ia −ib Off

1 2 3 4 5

1 0 0 0 1

S1 S3 S3 S5 S5

S6 S6 S2 S2 S4

+ia Off −ia −ia Off

1 1 1 0 0

0 0 1 1 1

Switch on

Off +ib +ib Off −ib

−ic −ic Off +ic +ic

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6 Simulation and Results The APF along with BLDC drive load and the complete control circuit was implemented using MATLAB/Simulink platform. The overall response of the designed system has been tested for load torque in the form of step signal as well as continuous change in loading levels.

6.1

Case 1: Step Input as Load Torque

The load torque is supplied in the form of a step signal to the BLDC drive. Figure 4 shows the Simulink model of the proposed system. Figure 5 represents the source side FFT analysis current without using shunt active filter whose The THD level is 25.01%. Figure 6 represents the FFT analysis of source current with filter whose THD value is now reduced to 4.74%. Figure 7a shows the power analysis at source/load side before introducing the filter. In addition to the active power, the source is producing the reactive power which needs to be eliminated. Figure 7b shows the power analysis at source side after including the shunt active filter. The SRF control technique is used by the APF to generate the compensating currents which helps to minimize the reactive power to zero. Figure 8 shows that the filter generates the reactive power thereby making the source reactive power to zero value. Figure 9a shows that the phase angle difference between the voltage and current waveforms at source side is considerable when the control algorithm is not used. While Fig. 9b represents the waveforms of source side voltage and current after

Fig. 4 MATLAB model of the proposed system

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Fig. 5 Analysis of supply current with no filter

Fig. 6 Analysis of supply current with filter

Fig. 7 Real and reactive power a at source/load without filter b at source with filter

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Fig. 8 Filter reactive power

Fig. 9 Voltage and current waveforms at source side a without filter b with filter

implementing the control algorithm. It shows that there is no phase angle difference between them thereby improving the power factor.

6.2

Case 2: Continuous Change in the Loading Levels

The load is varied in three segments. From 0.1 to 0.4 s, the load is increased from 0 to 3 Nm linearly. Then from 0.5 to 1 s, the load is decreased to 2 Nm. Further, from 1 to 1.5 s the load is linearly decreased to 1 Nm. After 1.5 s, the load is kept constant at 3 Nm. The results obtained demonstrate that the variations in load torque are followed satisfactorily by the shunt active filter. Figures 10a, b shows the power analysis at load side and source side using the shunt active filter. According to the torque variation, the real power gets adjusted at both sides. Figure 11 shows that the filter produces the required reactive power thereby making the source reactive power to zero value.

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Fig. 10 Real and reactive power a at load side with filter b at source side with filter

Fig. 11 Filter reactive power

7 Conclusion The system proposed has been simulated with a highly nonlinear BLDC type load, which simultaneously injects harmonics as well as draws a considerable amount of reactive power. The performance of proposed control techniques using SRF theory in SAPF is compared without any compensation in the system. The SRF control method is very effective in providing compensation and requires less computations. Using this method, the compensating currents are generated which results in harmonic elimination and VAR compensation. MATLAB/Simulink model is developed and BLDC drive application is included for the simulation purpose. It is observed that the THD level is reduced within the standard limits, reactive compensation is performed and also power factor correction is achieved.

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References 1. P. Salmeron, S.P. Litran, Improvement of the electric power quality using series and shunt passive filters. IEEE Trans. Power Deliv. 25(2) (2010) 2. V.R. Pandi, H.H. Zeineldin, W. Xaio, Passive harmonic filter planning to overcome power quality issues in radial distribution systems. IEEE Power and Energy Society General Meet (2012) 3. V. Ravikumar Pandi, H.H. Zeineldin, Weidong Xaio, Ahmed F. Zobaa, Optimal penetration levels for inverter-based distributed generation considering harmonic limits. Electr. Power Syst. Res. 97, 68–75 (2013) 4. B. Singh, K. Al-Haddad, A. Chandra, A review of active filters for power quality improvement. IEEE Trans. Industr. Electron. 46(5), 960–971 (1999) 5. P.K. Kumar, K. Ilango, Design of series active filter for power quality improvement. International Conference on Electronics, Communication and Computational Engineering (ICECCE), Hosur (2014) 6. D. Aiswarya, K. Ilango, M.G. Nair, A comparative performance analysis of PV grid interface STATCOM control algorithms. International Conference on Innovations in Power and Advanced Computing Technologies (i-PACT) (2017) 7. K. Bhattacharjee, Design and simulation of synchronous reference frame based shunt active power filter using SIMULINK. National Conference on Challenges in Research & Technology (CRT) (2013, September) 8. V.D. Kore, M. Fernandes, N.R. Ahire, Design of shunt active filter using reference frame theory for power quality improvement. International Conference on Advance in Electrical, Electronics, Information, Communication and Bio-informatics (AEEICB) (2018) 9. M.R, Sindhu, M.G. Nair, Photovoltaic based adaptive shunt hybrid filter for power quality enhancement. IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) (2016) 10. R. Kumar, B. Singh, BLDC motor driven PV array fed water-pumping system employing zeta converter. IEEE International Conference on Power Electronic (2010) 11. K.R. Bharath, P. Kanakasabapathy, Implementation of enhanced perturb and observe maximum power point tracking algorithm to overcome partial shading losses. International Conference on Energy Efficient Technologies for Sustainablity (ICEETS) (2016) 12. D. Beriber, A. Talha, MPPT techniques for PV systems, International Conference on Power Engineering, Energy and Electrical Drives (2013) 13. M.U. Deepa, G.R. Bindhu, Performance analysis of BLDC motor drive with power factor correction scheme. IEEE International Conference on Power Electronics, Drives and Energy systems (PEDES) (2016)

Energy Management Scheme for Green Homes Using Artificial Neural Network A. Naresh Kumar, P. Shiva Kumar and Thati Mahesh

Abstract A novel computing energy management scheme (CEMS) for green homes using a mixed grid solar power is introduced here. The aim of this paper is to build an optimal, robust, and smart controller for home energy consumption by increasing the usage of solar power and minimizing the effect on the electric grid system while satisfying the power demand of home appliances. In order to minimize the consumed power of the house, an artificial neural network (ANN)-based energy management scheme is proposed. ANN handles the impacts of the changes in electricity price and backup unit load and controls the energy storage accordingly using a computing scheme. Keywords Artificial neural network


Solar grid
Energy management scheme

1 Introduction In the recent world, the increasing demand for power and the factors, viz. limited reserves, increasing power costs, environmental pollutions leads the renewable power to be the attractive power sources. Since this power source has an unlimited supply and they do not cause any environmental pollutions, they are focused extensively and used increasingly every day. Government puts in novel legislation and feed-in-tariff to support the investor to install novel renewable power usage site [1]. However, most of the power and environmental gains were obtained by focusing effort on enhancing the energy efficiency in commercial and residential buildings. Data calculated by the information energy administration explains that buildings account for 34% of the power utilized in India, and of that power, 66% is consumed by buildings [2]. The energy efficiency of appliances, ventilation, lighting, heating, air conditioning, and all home appliances must continue to enhance [3]. In order to

A. Naresh Kumar (&)
P. Shiva Kumar
T. Mahesh Department of Electrical and Electronics Engineering, Institute of Aeronautical Engineering, Hyderabad, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_61

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cover customer limits and preference and to attain the features of reduced energy consumption of all electrical aspects, an efficient scheme is required [4]. The hybrid performance of grid and solar is far difficult than running parts individually. In a scheme with only grid or solar, just single element is regulated. In a combined system, both sources are regulated separately and at the same time based on the running condition and power demands. During very low sunlight condition, the solar cell cannot supply consistent energy. Similarly, the grid will not employ in all conditions like power cuts. So the power must have the model to improve the lack of power in all conditions when the scheme does not employ regularly or composition gives less power than the requirements. ANN designing is clearly explained in Ref. [5]. Further, the paper is structured as follows: Sect. 2 describes training procedure for ANN, and Sect. 3 presents the summarized results. Section 4 contains the conclusion of the proposed algorithm.

2 ANN-Based Energy Management System The diagram of the power system using solar power and electrical grid is depicted in Fig. 1. The proposed system consists of photovoltaic (PV) and utility electrical grid as a combined electrical source. Data selected from source sides is send to computer to be validated for computing method of the energy management scheme. Outline of proposed algorithm is illustrated in Fig. 2. Components of ANN are input layer, weight, bias, hidden layer, output layer, and transfer function. Normal ANN structure is shown in Fig. 3. The input data of ANN is electricity price, and backup unit load. The output data of ANN is energy storage. The number of neurons in the input layer and output layer is based on the dimension of the input and output data. The architecture of ANN for energy

Fig. 1 Power system using solar and grid power

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Fig. 2 Proposed algorithm

Fig. 3 ANN structure

management scheme in green homes is selected based on trial and error method. The transfer function of the hidden and output layer is ‘tansig.’ The weights are updated after each epoch during training. The number of neurons in each hidden

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layer is selected by experimenting with different ANN configurations. When the ANN network is trained, the ANN network provides the exact output when checked. The ANN was trained by Levenberg–Marquardt (LM) as the learning algorithm. The best performance is attained with one hidden layer, and twenty neurons in the first hidden layer depend on a series of trials and modifications. Among them, the ANN network with 2-20-1 architecture was chosen because it had the lowest errors. The MSE is 9.89e-06 in around three minutes computation time on a computer (i5, 2.4 GHz, 4 GB RAM). ANN training is depicted in Fig. 4. Similarly, ANN for energy management structure is given in Table 1. When the training and testing process are finished, MATLAB found 20 different valid ANN networks to operate.

Fig. 4 ANN training

Table 1 ANN structure information ANN module

ANN

ANN architecture No. of hidden layers No. of neurons in hidden layer Transfer function in hidden layer and output layer Mean square error (MSE) Computation time Epochs Learning rule technique

2-20-1 1 20 Tansig 9.89e−06 3.16 min 1015 Levenberg marquardt

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3 Scheme Evaluation The test results of the electrical utility estimation typical Indian home are listed in Table 2. There exists three time range(s) during the daytime characterizing peak electricity demand as illustrated in Fig. 5. The hourly electricity price curve of a typical day is represented in Fig. 6. The maximum hourly price is from 7.00 a.m. to 11.00 a.m. and from 5:00 p.m. to 7:00 p.m. The curves illustrate the off-peak (when electricity demand is the less); mid-peak (when electricity demand is medium); and on-peak (when electricity demand is the more). Figure 7 depicts the load curve of home without using the solar power or only when the home employs the distributed grid (red curve), then when the home is equipped with a solar but without CEMS scheme (blue curve), and lastly when the home is equipped with both solar and ANN-based CEMS scheme (green curve). Figure 8 depicts the hourly cost of electrical power consumption of the individual

Table 2 Typical house power consumption pattern Appliance name Refrigerator Refrigerator Freezer Cooking Electric range Microwave Coffee maker Toaster oven Laundry Dryer Washer Iron Entertainment Color TV Stereo Computer Heating Water heating HVCS Other Lighting Cell charging Car block heater

Power consumption (W)

Usage per day (h)

Daily power consumption (W/h)

40 50

24 24

960 1200

360 50 40 40

5 12 12 12

1800 600 480 480

1600 400 40

1 1 12

1600 400 480

120 120 50

5 3 3

600 360 150

190 200

18 24

3420 4800

320 9 220

5 3 3

1600 27 660

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Fig. 5 Typical house power consumption pattern

Fig. 6 Typical hourly price

Fig. 7 Load curve for the individual schemes

Fig. 8 Hourly cost curve of individual schemes

configurations. The proposed ANN-based CEMS scheme normally resulted in cost-saving superior than the calculated conventional and HSES system. It can estimate the electricity monthly bills based on test results for the individual systems. While the bill is about Rs. 100 for the conventional system, it decreases to Rs. 80 for the home which is equipped with a solar but without CEMS scheme system. However, the green home could decrease more electricity bill if the system uses a CEMR scheme (Rs. 75). This saving indicated around of 25% of the starting bill.

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4 Conclusion This paper presents artificial neural network-based energy management scheme for green homes. The simulation results explained that the proposed scheme provides a better scheme compared to the traditional technique for cost saving. Furthermore, the error level data attained via the proposed controller will enhance the usage of reserves more strongly in the green home. Future work will develop the adaptive neuro fuzzy inference system design details of the power management scheme based on the input of this computing algorithm for the same green home.

References 1. T.M. Hansen, R. Kadavil, B. Palmintier, S. Suryanarayanan, A.A. Maciejewski, Enabling smart grid cosimulation studies: rapid design and development of the technologies and controls. IEEE Electrification Mag. 4, 25–32 (2016) 2. A. Atmaca, Life cycle assessment and cost analysis of residential buildings in south east of Turkey: part 1—review and methodology. Int. J. Life Cycle Assess. 21, 231–246 (2016) 3. W. Zhou, K.W. Li, Y. Chan, Y. Cao, X. Kuang, Liu, Smart home energy management systems: concept, configurations, and scheduling strategies. Renew. Sustain. Energy Rev. 61, 30–40 (2016) 4. M.S. Ahmed, A. Mohamed, R.Z. Homod, H. Shareef, A.H. Sabry, K.B. Khalid, Smart plug prototype for monitoring electrical appliances in home energy management system. IEEE Stud. Conf. Res. Dev. 10, 32–36 (2015) 5. A. Nareshkumar, M. Chakravarthy, Simultaneous fault classification and localization scheme in six phase transmission line using artificial neural networks. J. Adv. Res. Dyn. & Control. Syst. 10, 342–349 (2018)

Control Systems

Comparative Analysis Between Conventional and Neuro-Fuzzy Control Schemes for Speed Control of Induction Motor Drive Shubhangi Kangale, B. Sampathkumar and N. Raut Mrunmayi

Abstract Controlling speed of induction motor is very difficult during light load conditions because it has very poor power factor and high input surge current. As also, it is a constant speed motor. Conventional controllers have poor control performance and are unable to have smooth control of the speed for nonlinear loads. The intention of the proposed scheme is to design neuro-fuzzy control scheme for controlling the speed of highly nonlinear loads like induction motor to overcome the lacunas of conventional controllers. This project uses a combination of neural network and fuzzy logic controllers so that it has the advantages of both. Back propagation algorithm is used to remove the neuro-fuzzy trial and error complexity. To design and study the performance, MATLAB software is used.







Keywords Artificial intelligence Neuro-fuzzy controller MATLAB software PI controller Back propagation (BP) Real-time implementation Self-tuning













1 Introduction Today, the controlling speed of induction motor drives is an important aspect of research. It has been valued more not only because it is the most used motor in industries but also due to their varied operation modes. Also it is a self-starting motor, simple and robust in construction, and the cost is low and reliable. Though it is more advantageous to have use of induction motor drive, it is very difficult to control the speed because of its nonlinear characteristics and parameters changes with working conditions. Conventional controllers have fixed gain like (PI) proportional integral and (PD) proportional derivative controllers. These controllers suffer from load disturbances, steady state error, and parameter variations.

S. Kangale (&)
B. Sampathkumar
N. Raut Mrunmayi Department of Electrical Engineering, Fabtech Technical Campus College of Engineering and Research, Sangola, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_62

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Design of proposed neuro-fuzzy controller (NFC) has been introduced with the main purpose of controlling the speed of induction motor drive. The actual neuro-fuzzy controller mixes fuzzy controller and a layered sensory neural network design. The pace error is taken by NFC to reduce calculating anxiety, and it also suited for practical outfit drive purposes. A back propagation criterion is used. Weights and membership features of the designed NFC can be configured by using unsupervised self-tuning method. Performance is going to be analogous way as compared to that of dual-input NFC.

2 PI Controller The proportional integral controller (PI controller) operates the plant with the control of weighted addition of error and the integration of the error. PI controller decreases steady state error near to zero. Figure 1 shows the basic block of PI controller. The PI controller is very popularly used in industries because of its features like robust design simple structure and low cost. But at certain conditions when the device is highly nonlinear in characteristics and uncertain, PI controllers fail to operate [1]. These are mostly preferred when rapid response of the system is not required and speed response is not an issue and large transport delay is acceptable. Figure 2 shows the simulation of PI controller. As shown in the result of PI controller, it decreases the steady state error near to zero, which is not possible in case of proportional controller. PI controller reduces maximum overshoot and reduces damping. It also decreases the bandwidth and improves the rise time. But as induction motor is nonlinear in nature, the operation of induction motor is very sensible to load disturbances, and PI controller cannot sustain such large disturbances. Figure 3 shows the speed response of PI controller [2].

Fig. 1 Basic block of PI controller

Comparative Analysis Between Conventional …

Fig. 2 Simulation of PI controller

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Fig. 3 Speed response of PI controller

3 Fuzzy Logic Control System Fuzzy control is invented by Zadeh, which is a rule-based controller. It uses “if– then” strategy for the control process. In this strategy, to form if then rules, we can assign number of variables [3]. It can be applied to nonlinear systems as the analytical model of the system is not required in fuzzy control [4]. Figure 4 shows the block diagram of a fuzzy logic controller. Fuzzy technique is also part of artificial intelligence because of unique characteristics like sustainable level of tolerance, rapid adaptation, and smooth operation, minimization in the effect of nonlinearity, and high approximation adaptability.

Fig. 4 Block diagram of a fuzzy logic controller

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From the result comparisons of fuzzy control and conventional controller, it shows that the nonlinearity in an induction motor can be reduce by fuzzy controller, and performance can be improved and having smooth controlling of a speed than conventional one (Figs. 5, 6, 7, 8 and 9).

Fig. 5 Simulation of fuzzy controller

Fig. 6 Speed response of fuzzy controller

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Fig. 7 Structure of neuro-fuzzy controller

Fig. 8 Simulation of neuro-fuzzy controller

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Fig. 9 Speed response of neuro-fuzzy controller

4 Neural Network Controller Neural network is branch of artificial intelligence which works on biotic systems, such as the brain. Neural networks have unique method for solving problem. Earlier computers are programed with a code to solve a specific problem. Until having a designed code, computer is unable to provide solution. This limits the problem-solving power of conventional computers. Neural networks work in an analogous way as the brain of human work. The neural network is the combination of different processing elements (neurons) which works together to solve a specific problem. Neural networks can be trained by examples. It is not having fixed program for particular task. The examples for training should be selected carefully such that network should function correctly. As the network has its own way of problem solving, its working is not predictable. Neural network can be used for applications such as data classification or pattern recognition through a learning process

5 Neuro-Fuzzy Controller The designed NFC consists of fuzzy controller and neural network. It mainly includes four layers which are fuzzification layer, input layer, rule layer, and defuzzification layer [5].

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It shows some transient during starting but having very constant speed control during working condition in spite of load disturbances. It also improves transient behavior and reduces steady state error.

6 Comparative Analysis The analysis of the designed simplified NFC-based control strategy for induction motor is simulated with MATLAB/Simulink. From the result figures, the execution of the designed NFC is robust, simple in computation similar to that of the two inputs NFC. As a result, neuro-fuzzy controller shows dynamic and smooth control of speed over wide range as compared with conventional controller. This is because it overcomes the constrain of fuzzy controller as well neural network controller. A fuzzy controller has asymmetric membership functions in motor applications which require large manual adjustment with trial, and also it is tough to create rules for particular applications. For neural network, it is tremendously a rigger process to create a sequential instruction data. NFC controller has advantages of both (neural and fuzzy logic) controller which results into superior performance of speed response with parameters of settling time and maximum peak overshoot compared with conventional (PI) controllers.

7 Conclusion From the analysis of the designed NFC and typical PI controller-based IM drive, the designed system observed superior to that of the conventional one. The designed neuro-fuzzy controller induction motor drive system is observed rugged and appropriate solution for practical implementation of industrial drives.

References 1. E.C. Shin, T.S. Park, W.H. Oh, J.Y. Yoo, A design method of PI controller for an induction motor with parameter variation, in Proceeding IEEE IECON, vol. 1, pp. 408–413 (2003) 2. A. Miloudi, A. Draou, Variable gain PI controller design for speed control and rotor resistance estimation of an indirect vector controlled induction machine drive, in Proceeding IEEE IECON, vol. 1, pp. 323–328 (2002) 3. C.M. Liaw, J.B. Wang, Y.C. Chang, A fuzzy adapted field-oriented mechanism for induction motor drive. IEEE Trans. Energy Convers. 11(1), 76–83 (1996)

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4. M.N. Uddin, T.S. Radwan, M.A. Rahman, Performances of fuzzy-logic-based indirect vector control for induction motor drive. IEEE Trans. Ind. Appl. 38(5), 1219–1225 (2002) 5. F.-J. Lin, R.-J. Wai, Adaptive fuzzy-neural-network control for induction spindle motor drive. IEEE Trans. Energy Convers. 17(4), 507–513 (2002)

Estimation of Nonlinear Hybrid Systems Using Second-Order Q-Adaptive Self-switched Derivative-Free Estimators Sayanti Chatterjee

Abstract This paper introduces the adaptive versions of proposed self-switched estimators for a class of nonlinear hybrid systems. This proposed estimation scheme can eliminate the common disadvantage of conventional state estimators, that is the requirement of fairly accurate information about process noise covariances. To obtain a good compromise about computational complexity and estimation accuracy, a Q-adaptive (QA) state estimator based on derivative-free estimators like second-order CDKF and first-order CDKF has been proposed and employed in this work. The efficacy of the proposed estimators in comparison with QAEKF has been demonstrated through simulation studies on a benchmark problem, namely chemical stirred tank reactor (CSTR).













Keywords CDKF Q-adaptation Estimation Nonlinear hybrid system CSTR

1 Introduction This paper presents a novel scheme for the estimation of nonlinear hybrid systems (NLHS) using adaptive estimators. The main focus of this thesis work is ‘hybrid system’ which is a special kind of complex systems containing two distinct kinds of subsystems, namely time-evolving and event-driven subsystems, which interact with each other and operate in real time [1, 2]. Hybrid systems, therefore, embrace a wide range of applications ranging from embedded real-time systems to large-scale manufacturing, from aerospace control to traffic control. The basic structure of the hybrid systems comes from discrete event dynamical systems (DEDS). The main disadvantage of modeling of a nonlinear hybrid system is to generate some process noises due to mismatch of the state equation according to the mode changes, This newly developed estimation scheme can eliminate the common S. Chatterjee (&) Department of Electrical and Electronics Engineering, Narsimha Reddy Engineering College, Hyderabad, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_63

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disadvantage of conventional state estimators, that is the requirement of fairly accurate information about process noise statistics. Considering the conjecture that the theory of adaptation for existing linear and nonlinear signal models may also be extended for nonlinear hybrid systems, adaptive self-switched estimators have been proposed and their performances have been demonstrated using non-trivial case studies in simulation. To obtain a good compromise between computational complexity and estimation accuracy, adaptive state estimators (based on extended Kalman filter and derivative-free estimators like first- and second-order central difference Kalman filter) have been employed in this paper for NLHS. Intercomparison studies of different self-switched estimators using benchmark test problem are also done in this context. As stated earlier, modern real-world engineering systems exhibit complex hybrid behaviors. State estimation and mode determination of hybrid systems for accurately and timely online monitoring is a difficult task. In this paper, the existing literature on the estimation of hybrid systems has been reviewed to get an idea to choose appropriate estimation technique for further use. Although emphasis has been given on nonlinear estimators, observers and linear estimators for hybrid systems have been also surveyed in this chapter. Very early works have been intentionally omitted to keep the section within page limit. In the present work, a Q-adaptive nonlinear estimator [3, 4] is employed which obviates the need of complete knowledge about the plant dynamics and disturbances. An adaptive state estimator is initialized with a guess value of the unknown noise covariance and iteratively improves the estimate of the unknown noise covariance. In the present work, CDKF has been used [5–7] as the core nonadaptive nonlinear state estimation algorithm. For the state estimation of hybrid systems, self-switched estimation algorithm [8] has been adopted here to track the system states as well as to infer the system modes. Thus, a separate mode estimator [9] was not necessary. As the state dynamics of the filters modify themselves with mode transitions, such filters may be termed as self-switched filters [8] and proposed by the same author. The estimation [10–12] schemes of nonlinear hybrid systems (NLHS) using non-adaptive nonlinear filters have been described in literature, where noise statistics is assumed to be known. Though [13] mentions the possibility of employing Q-adaptive filters for a nonlinear hybrid plant, no results specifically for adaptive filters have been reported therein. This section deals with another important estimation technique, known as central difference filter (CDF). CDF is an important member of derivative free estimator family [14]. The main advantages of using CDF-based estimator over the widely used extended Kalman filter (EKF) are some known limitations of the EKF, such as singularity problems and complex Jacobian calculations [15]. The linearization process based on square root factorization of the output covariance matrices has been proposed in [4, 5]. The advantage for this type of linearization technique has been stated as:

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no Jacobian or Hessian matrix need not to be computed; the linearization process is more accurate than linearization technique used for EKF. In [10], state estimators for nonlinear systems have been derived based on polynomial approximations using this technique. A conference paper containing similar material has appeared in [4]. So, in one line, the novelty of the present contribution lies in proposing a self-switched second-order nonlinear Q-adaptive filter, namely QA-CDKF-2 for state estimation of a benchmark hybrid system. Here, we have chosen CSTR liquid level system [16, 17]. The following sections are a blend of algorithm based on Q-adaptive second order, plant description, results and simulation, and conclusion.

2 Algorithm of Self-switched Adaptive State Estimator In this paper, the situation where the process noise and measurement noise statistics are improper or unknown is considered for estimation purposes. The non-adaptive estimators cannot provide satisfactory estimation accuracy in such a situation. For NLHS, as process equations modify themselves according to the modes, the estimation errors due to unknown noise covariances become very large. Adaptive versions of proposed self-switched estimators provide solution to this problem [8, 12]. The general structure of self-switched estimators which has been demonstrated in previous work of same author [8] has been reformed here for unknown noise covariances. The adaptive version of the self-switched estimator algorithm [8, 12] developed in this dissertation has been discussed below: General algorithm of adaptive self-switched estimator Step 1: Initialize the estimator (filter) state, noise covariance, and error covariance. Step 2: Determine modes based on initialization. Step 3: Estimate states using filter algorithm. Step 4: Scaling-based Q-adaptation (for Q-adaptive filter) or residual-based R-adaptation (for R-adaptive filter) is performed. Step 5: Switch to correct modes based on estimated states. Step 6: Modify filter equations according to the estimated modes. Step 7: Repeat from Step 3.

2.1

Estimation of NLHS using self-switched CDKF

The general structure of self-switched estimators which has been demonstrated in the previous work of the same author [7] has been reformed here for unknown noise covariances. The flowchart of Q-adaptive second-order CDKF has been presented in Fig. 1.

696 Fig. 1 Flowchart of CDKF-2

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Though second-order QACDKF has been described here, first-order CDKF and EKF (basic structures have been discussed in the appendix section) follow the same algorithm and modify their basic structures accordingly.

3 Plant Descriptions In this section, another nonlinear hybrid system is used as a benchmark problem. In this section, the benchmark test problem is considered for demonstrating advantages and disadvantages of proposed estimation techniques. This problem has been taken from the literature [16, 17]. The system as shown in Fig. 2 consists of a tank containing a fluid whose level is controlled by three control valves (V1, V2, V3). V1 and V2 are dedicated to control inflow, whereas V3 controls the outflow. S1 and S2 are supply. Each valve opens or closes depending upon the predefined thresholds (hlv and hlp) as shown in Fig. 2. The fluid in the tank is uniformly heated by a thermal power source, under adiabatic conditions.

3.1

Plant Mathematical Model

With the discretization of the system dynamics, and the additional simplifying physical assumptions, the state equations can be described by the following nonlinear difference equations, determined by the mass and energy conservation laws. The fluid level is considered as first state (x1) and temperature is considered as second state (x2). Q1 and Q2 are nominated as inflow rates, and Q3 is the outflow rate. Vm is the assigned inlet fluid temperature; Ts is the time step; and wk and vk are the process noises and measurement noises, respectively. x1 ¼ x1 þ Ts ½a1 Q1 þ a2 Q2  a3 Q3 þ w1 

Fig. 2 Liquid level control system

ð1Þ

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Table 1 Value of parameters of liquid level control system Sl. No.

Name of the parameter

Value of the parameter

1. 2. 3. 4. 5. 6.

Inlet flow (Q1) Inlet flow (Q2) Outlet flow (Q3) Inlet fluid temperature (Vm) Lower threshold height (hlv) Upper threshold height (hlp)

1 m/h 4 m/h 4.5 m/h 15 °C 4m 10 m

x2 ¼ x2 þ

Ts ½a1 Q1 þ a2 Q2  ðVm  x2 Þ þ 24 þ w2  x1

ð2Þ

The temperature of the fluid has been considered as the measurement of the system. The values of the plant parameters are given in Table 1.

3.2

Mode Discrimination

The hybridness of the plant depends upon the liquid level. When the liquid level crosses the lower threshold value, the valves V1 and V2 remain open and liquid flows through inlet pipes. When the liquid level is higher than the higher threshold value, the outflow occurs through outlet valve V3. There are two modes. Mode 1: If water level is less than hlv or (hlv < water level < hlp and water level is increasing from below hlv): In this case, a1 ¼ 1; a2 ¼ 1; a3 ¼ 0. If water level is more than hlp: In this case, a1 ¼ 0; a2 ¼ 0; a3 ¼ 1. Mode 2: If water level is more than hlv or (hlv < water level < hlp and water level is decreasing from above hlp): In this case, a1 ¼ 0; a2 ¼ 0; a3 ¼ 1.

4 Results and Simulation The mathematical modeling of this case study has been also elaborated in the previous section. Same model has been revisited here to fulfill the aim of the thesis. In this case study, we assume, the initial condition of liquid level is x1= 15 cm; and temperature is x2 = 10 °C Sampling time is assumed 0.05 s. The process noise covariances and measurement noise covariances are given below [16]:

Estimation of Nonlinear Hybrid Systems Using Second-Order …



0:02 0 Q¼ 0 0:01





0:16 ;R ¼ 0 2 unit

0 0:05

699

 : unit2

In this case study, mode changes occur and the plant behaves like a nonlinear hybrid plant. The initial condition of the estimator is assumed to be [0 0] unit. No. of Monte Carlo run is 1000. The liquid level has been considered as output (Fig. 3). From the above figures, it can be deduced that the RMSE of second-order CDKF (CDKF-2)-based estimation is comparable and shows much less error than EKF and first-order CDKF (CDKF-1).

5 Performance Study of Q-Adaptive Self-switched Estimator It is well-known that it is difficult to prove convergence of nonlinear adaptive filter especially for hybrid system, and there appears to be no publication in this area. It is therefore expedient to thoroughly test any candidate nonlinear filter to the target hybrid system through simulation before any plan to deploy the same. Convergences studies of the estimation scheme using adaptive estimators have also been carried out using the CSTR benchmark problems. In this work, convergence validation of the second-order QACDKF has been exhibited by (i) Adding an unknown small disturbance in tank [9]. In this scenario, it is assumed that initial height of each tank: 15 cm. Initial height of estimator: 0 cm. MC run = 1000. The modified process equation of second state by adding a small disturbance D1 sin wt is given below (Fig. 4). (ii) When inflow is 10% perturbed. In this case, it is assumed that initial height of each tank: 10 cm. Initial height of estimator: 0 cm. MC run = 1000 perturbation of inflow = 10%. Process noise and measurement noises are given below (in cm2) (Fig. 5). When the inflow is perturbed, a noticeable change in RMSE is found in case of EKF, whereas the change in RMSE is less in case of second-order CDKF (CDKF-2). As the measurement of state 2 is not present, the RMSE of second state has been taken to prove the convergence and adequacy of the proposed algorithm. In both the cases, it can be shown from figures that the RMS error for second-order QACDKF is consistently small and change in RMSE is very little which proves the convergence of second-order QACDKF. On the other hand, when the unknown disturbance of same amplitude is added or inflow is same amount perturbed, a noticeable change in RMSE is found in case of QAEKF. As the measurement of state 2 is not present, the RMSE of third state has been taken to prove the convergence of the proposed algorithm.

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Fig. 3 RMSE of (a) state 1 (b) state 2 (c) mode

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Fig. 4 Convergence study of Q-adaptive estimators in the presence of external disturbance (in logarithmic scale)

Fig. 5 RMSE of third state when inputs are perturbed (in logarithmic scale)

Performance Comparison of Q-Adaptive Self-switched Estimators For this case study, the benchmark CSTR has been elected. The process and measurement noises are white noises whose mean = 0 and covariances are Q and R, respectively. The other parameter values are taken as stated in Sect. 3. Table 2 compares the mean values of RMSE of first state within time scale 30–50 s using Q-adaptivebased estimation for the different initial value of process noise covariance when window size is: 20. The table provides a comparison study of the mean values of RMSE of first state within time scale 30–50 s using Q-adaptive-based estimation for different window sizes and initial value of process noise covariance is 100 times of truth.

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Table 2 Comparison of mean values of RMSE for different initial values of process noise covariances Process noise covariance initialization RMSE_QACDKF-2 (in cm) RMSE_QACDKF-1 (in cm) RMSE_ QAEKF (in cm)

0.01 * Q 0.1189 0.254 0.2347

1*Q

10 * Q

0.0311 0.0330 0.0320

100 * Q

0.1129 0.2349 0.2145

0.1283 0.2337 0.2332

Comparison of mean values of RMSE for different window size Window size

5

10

15

RMSE_QACDKF-2 RMSE_CDKF-2

0.2507 0.3523

0.1305 0.4312

0.1581 0.5881

6 Conclusion State estimation and mode determination for a class of nonlinear hybrid systems have been presented in this section using adaptive self-switched estimators. The adaptive estimators have been employed with satisfactory performances of state estimation and mode determination of nonlinear hybrid systems over non-adaptive ones. While comparing adaptive state estimators in the presence of unknown process noise covariance, it has been established that the proposed Q-adaptive second-order CDKF can outperform the EKF and the first-order CDKF versions in respect of state tracking. The proposed filter can consequently determine the true modes of the hybrid system quickly. It is also inferred from the results that the estimation scheme using QACDKF (second order) is more robust than QAEKF in the presence of the unknown disturbance and perturbed input.

References 1. C.G. Cassandras, J. Lygeros, Stochastic Hybrid Systems (CRC Press, Taylor & Francis Group, LLC, 2007) 2. M. Buss, M. Glocker, M. Hardt, O. von Stryk, R. Bulirsch, G. Schmidt, Nonlinear hybrid dynamical systems: modeling, optimal control, and applications, in Modelling, Analysis, and Design of Hybrid Systems, ed. by E.G. Frehse, E. Schnieder (Springer, 2010) 3. A. Almagbile, J. Wang, W. Ding, Evaluating the performances of adaptive Kalman filter methods in GPS/ INS integration. J. Glob. Positioning Syst. 9(1), 33–40 (2010) 4. A.H. Mohamed, K.P. Schwarz, Adaptive filtering for INS/GPS. J. Geodesy 73, 193–203 (1999)

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5. H.E. Soken, C. Hajiyev, A Novel Adaptive Unscented Kalman Filter For Pico Satellite Attitude Estimation. PHYSCON 2011, León (2011, September, 5) 6. K. Ito, K. Xiong, Gaussian filters for nonlinear filtering problems. IEEE Trans. Autom. Control 45(5), 910–927 (2000) 7. T.S. Schei, A finite-difference method for linearization in nonlinear estimation. Automatica 33 (11), 2053–2058 (1997) 8. S. Chatterjee, S. Sadhu, T.K. Ghoshal, Fault detection and of non-linear hybrid system using self-switched sigma point filter bank. IET Control Theor. Appl. 9(7), 1093–1102 (2015) 9. W. Wang, L. Li, D. Zhou, K. Liu, Robust state estimation and fault diagnosis for uncertain hybrid nonlinear systems. Nonlinear Anal. Hybrid Syst. 1(1), 2–15 (2007) 10. S. Chatterjee, Improved fault detection and for nonlinear hybrid systems using self-switched CDKF. Selected for Presentation IEEE Indicon 2015 (New Delhi, India, 17–20 2015) 11. S. Chatterjee, S. Sadhu, T.K. Ghoshal, Improved estimation and fault detection method for a class of nonlinear hybrid systems using self switched sigma point filter. In 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC) (IEEE, 31 Jan 2014), pp. 578–582 12. S. Tafazoli, Hybrid system state tracking and fault detection using particle filters. IEEE Trans. Autom. Control 14(6), 1078–1087 (2006) 13. A. Mirzaee, K. Salahshoor, Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller. J. Process Control 22(3), 626–634 (2012) 14. S. Chatterjee, S. Sadhu, T.K. Ghoshal, Improved estimation and fault detection scheme for a class of non-linear hybrid systems using time delayed adaptive CD state estimator. IET-Signal Process. 11(7), 771–779 (2017) 15. S. Chatterjee, S. Sadhu, T.K. Ghoshal, Self-switched R-adaptive extended kalman filter based state estimation and mode determination for nonlinear hybrid systems. Computer, Communication, Control and Information Technology (Kolkata, India, 2014), pp. 1–6 16. F. Cadini, E. Zio, G. Peloni, Particle filtering for the detection of fault onset time in hybrid dynamic systems with autonomous transitions. IEEE Trans. Reliab. 61(1), 130–139 (2012) 17. C. Andrieu, A. Doucet, E. Punskaya, Sequential Monte Carlo methods for optimal filtering, in Sequential Monte Carlo Methods in Practice (Springer, New York, 2001)

Control Quality Enhancement of Inverted Pendulum Using Fractional Controller K. Muralidhar Goud and C. Srisailam

Abstract This manuscript deals with a fractional PID controller which is proposed for inverted pendulum on a cart system (IPCS). The mathematical equivalent of the system is done by Euler–Lagrange’s method with the consideration controller and system frequency response specifications controller parameters like Kp , Ki , Kd ; k and µ are determined using FPID optimization toolbox. Compared to classical optimization techniques, this FPID technique provides better phase and gain margins for the system. FPID optimization tuning helps to obtain iso-damping property. It is a property where the open-loop phase https://en.wikipedia.org/wiki/Bode_ plotis constant, means the derivative of open-loop phase is zero at a frequency called tangent frequency (xc ). Open-loop phase is flat which indicates that the plant exhibits the property of robustness for gain variations. Systems which exhibit iso-damping properties, overshoots the closed-loop step signal have nearly constant for assorted values of the system gain. This ascertains that the system demonstrates the robustness properties to gain variations.










Keywords Fractional order calculus Cart Pendulum Gain margin Phase margin Robustness Iso-damping Underactuated













K. Muralidhar Goud (&)
C. Srisailam Department of EEE, Vardhaman College of Engineering, Hyderabad, Telangana, India e-mail: [email protected] C. Srisailam e-mail: [email protected] K. Muralidhar Goud
C. Srisailam Department of EEE, Chaitanya Bharathi Institute of Technology (A), Hyderabad, Telangana, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_64

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1 Introduction The system under consideration is the best example of nonlinear, unstable system [1]. The same exhibits the property of underactuated system. The underactuated systems are mostly referred for robotic applications; they are mostly confined to mechanical systems [2–10]. As the preferred system is nonlinear, standard linear techniques cannot be applied to linearize it. The manuscript goal is to equilibrate the IPCS perpendicularly. In this, the pendulum is controlled by using FOPID controller. The fractional order controllers have been applied in rigid robots and hexapod robot successfully [11–13]. FOPID controller parameters can be tuned using genetic algorithm and particle swarm optimization technique. Here in this paper tuning of FOPID controller parameters is done by using FPID optimization toolbox with different optimization algorithms. The mathematical modeling is done by using Lagrangian equations, and system is controlled using fractional order PID controller. The three parameters Kp , Ki , and Kd are calculated using PID controller; with these values, remaining k and µ can be obtained (Fig. 1).

2 Modeling of IPCS Inverted pendulum is a single-input multi-output system. The pendulum will fall if the cart remains in static position. So, to avoid pendulum unbalance the cart must be moved. Many classical controllers are designed only to control pendulum’s position as they are confined of only single-input single-output systems. The novelty is that, the design of controller to control the pendulum position or to balance it even though there is no movement in the cart. Here are some design specifications while designing controller like, • Settling time for x and h of less than 5 s • Rise time for x of less than 0.5 s Fig. 1 Inverted pendulum with cart

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• Pendulum angle h never greater than 20° from the vertical • Steady-state error of less than 2% for x and h. Summing the horizontal forces in the CS diagram, then the under mentioned equation of dynamics is ::

:

Mx þbx þN ¼ F

ð1Þ

There would not be any useful information for summing vertical forces. The sum total of horizontal forces in the IP diagram then the under mentioned equation of dynamics force N is; ::

::

N ¼ m x þ ml h cos h  ml h2 sin h

ð2Þ

On substituting the above equation into the first equation, two system equations will be obtained. ::

::

:

ðM þ mÞ x þ b x þ ml h cos h  ml h2 sin h ¼ F

ð3Þ

To acquire the second equation of dynamics in this system, the sum total perpendicular forces to the pendulum.


::

::

P sin h þ N sin h  mg sin h ¼ ml h þ m x cos h



ð4Þ

To eliminate of the P and N terms in Eq. (4), sum total of the moments about the pendulum centroid, the following equation comes.


:: 

Pl sin h  Nl cos h ¼ I h

ð5Þ

After combining expressions (4) and (5), the other governing equation is   :: :: I þ ml2 h þ mgl sin h ¼ ml x cos h

ð6Þ

The system analysis is done, and systems are linearized in vertically upward equilibrium position, and h ¼ p under assumption the system oscillate within a small neighborhood of this equilibrium. After assuming some system considerations like h ¼ / þ p, cos h ¼ cosðp þ /Þ  1 sin h ¼ sinðp þ /Þ  / :

:

h2 ¼ / 2  0

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After substituting above conditions in system, force equations are given by 

 :: :: I þ m l2 x mgl/ ¼ ml x ::

:

::

ðM þ mÞ x þ b x ml / ¼ u

2.1

Transfer Function

Apply the Laplace transform to linearized system equations with all initial conditions which are zero. Now 

 I þ m l2 /ðsÞs2  mgl/ðsÞmlXðsÞs2 ðM þ mÞXðsÞs2 þ bXðsÞs  ml/ðsÞs2 ¼ UðsÞ   I þ ml2 g XðsÞ ¼  2 /ðsÞ s ml

ð7Þ

Then, substitute Eq. (7) into Eq. (2). 

   I þ ml2 g I þ ml2 g 2  2 /ðsÞs þ b  2 ðM þ mÞ s s ml ml

ð8Þ

After rearranging, the mathematical model is in the following form. /ðsÞ ¼ UðsÞ s4 þ

ml 2 q s bðI þ ml2 Þ 3 s  ðM þqmÞmgl s2 q

 bmgl q s

where h i   q ¼ ðM þ mÞ I þ ml2  ðmlÞ2 From the mathematical model above, there is both a zero and a pole at the origin. After eliminating pole and zero, the resulting mathematical functions as shown below: /ðsÞ ¼ PPend ðsÞ ¼ UðsÞ s3 þ

ml q s bðI þ ml2 Þ 2 s  ðM þqmÞm/ s q

  bmgl q

 rad N

ð9Þ

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Similarly, the transfer function with the cart position X(S) then yield is shown in Eq. (10) XðsÞ ¼ PCart ðsÞ ¼ UðsÞ s4 þ

2.2

ðI þ ml2 Þs2 gml bðI þ ml2 Þ 3 s q

q  ðM þqmÞmgl s2

 bmg q

ð10Þ

State Space

The state-space form of motion equations is rearranged into first-order LDE and then converts into the typical state-space matrix form which is shown below. 2 : 3 2 0 x 6 x::: 7 6 0 6 7¼6 4/5 40 :: 0 /

1

0

ðI þ ml2 Þb I ðM þ mÞ þ Mml2

m gl I ðM þ mÞMml2

mlb I ðM þ mÞ þ Mml2

mgðM þ mÞ I ðM þ mÞ þ Mml2

0

 y¼

1 0

2

0

0 0 0 1

2

32 3 2 3 0 0 x : 2 I þ ml 6 7 6 7 07 76 x 7 þ 6 I ðM þ mÞ þ Mml2 7u 5 4 5 4 5 0 /: 1 ml 0 / I ðM þ mÞ þ Mml2

2 3    x: 7 0 6 6 x 7þ 0 u 0 0 4u5 : u

The C matrix two rows, first second elements for cart’s position and pendulum’s position respectively and are part of the yield. Specifically, the cart’s position is the first element of the output y and its deviation from equilibrium position is the second element, i.e., pendulum’s deviation.

3 Controller Design 3.1

FOPID Controller

On applying some controller design specifications like Phase margin ;m and gain crossover frequency xgc specifications: Being gain and phase margins have always considered as important measures of robustness. It is known that the phase margin is related to the damping ratio of the

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system and hence can also function as a recital measure. The equations to define the gain crossover frequency and phase margin are   Cðj xgc ÞGðj xgc Þ ¼ 0 dB ð11Þ argðCðj xgc ÞGðj xgc ÞÞ ¼ / þ /m Robustness to variations in the gain of the plant: The next constraint can be considered in this case which is as follows  d argðFðsÞÞx ¼ xgc ¼ 0 dx

ð12Þ

The open-loop system F(s) = C(s) G(s) phase to be horizontal at xgc and so, almost unbroken within a recess around xgc . That means the system is more vigorous to changes in gain and the peak overshoots of the response are almost unbroken inside a gain range, also known as iso-damping property of the response. It’s to be noted that the range of gain for which the system robustness is not motionless with this circumstance. The assortment is influenced on the crossover frequency around xgc for which the open-loop system phase keeps flat. This frequency assortment will be liable on the ensuing controller and the frequency characteristics of the plant. High-frequency noise rejection: The opposite sensitivity function constraint on T (jx) can be established:   T ðjxÞ ¼ Cðj xgc ÞGðj xgc Þ  1 þ Cðj x ÞGðj x gc

gc Þ

  dB  A dB 

8x\xt rad/s ) jT ðjxtÞjdB ¼ A dB; Let A be the desired value of the thoughtfulness role for frequencies x < xt rad/s (desired frequency range). To ensure a good output disturbance rejection: The opposite sensitivity function constraint on T (jx) can be defined:     1 SðjxÞ ¼ dB  B dB  1 þ Cðj xgc ÞGðj xgc Þ 8x\xs rad/s ) j SðjxsÞjdB ¼ B dB; (with B the anticipated value of the sensitivity function for frequencies x < xs rad/ s desired frequency range). The parameters of FOPID controller are estimated after considering the above specifications. Also, the estimation of FOPID controller

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Table 1 Parameter values of controller for controlling cart’s position Controller

Kp

Ki

Kd

k

l

FOPID PID

7.63  107 7.8  107

109 1010

14,050 14,050.45

0.5 1

0.5 1

Table 2 Parameter values of controller for controlling pendulum’s angle position Controller

Kp

FOPID PID

7.13  10 7.3  107

Ki 7

3  10 3.2  1010 10

Kd

k

l

4800 4900

0.5 1

0.5 1

parameter values using FPID optimization toolbox by using different techniques inbuilt in FPID toolbox is done. Transfer function of FOPID controller is: GcðsÞ ¼ KP þ

Ki þ K d Sl Sk

4 PID Controller The PID controller parameters are obtained using PID tuner in MATLAB toolbox therefore on substituting PID controller parameter values in transfer function given as (Tables 1 and 2). GcðsÞ ¼ KP þ

Ki þ Kd s s

After assigning values to transfer function of controller, the controller is used for the system for controlling operation.

5 Results Controller for pendulum’s angle control: See Fig. 2.

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Fig. 2 Inverted pendulum response for both PID and FOPID controller

Controller for cart’s position: See Fig. 3.

Fig. 3 Cart response for both PID and FOPID controller

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6 Conclusions In this paper, the central objective is to design a fractional order controller, which stabilizes the underactuated system, considered here an IPCS system. These papers approach successfully stabilized the IPCS system using fractional order (FO) PID controller. The objective is set to control the locus of the cart and angle of the pendulum implemented successfully. The outcomes acquired from FOPID controller are much healthier than traditional PID controller. From the results’ graph, it reveals that the fractional PID controller is better for stabilizing the underactuated robotic system. As an overall, the results got based on fractional order controller are much more superior to integer-order controller.

References 1. A.N.K. Bin Nasir, Modelling and Controller Design for an Inverted Pendulum System. (Department of Electrical, Universiti Teknologi Malaysia, 2006–2007), pp. 1–14 2. D.P.M. de Oliveira Valrio, User and programmer manual. Ninteger v. 2.3 Fractional Control Toolbox for MatLab (UNIVERSIDADE TÉCNICA DE LISBOA INSTITUTO SUPERIOR TÉCNICO, 2005, August) 3. J.R. White, U. Mass-Lowell, Lecture notes. Introduction to the Design and Simulation of Controlled Systems, 24.509 (Spring, 1997) 4. M.F. Silva, J.A. Tenreiro Machado, Fractional order PD_Joint control of legged robots. J. Vibr. Control 12(12), 1483–1501 (2006) 5. M. Reyhanoglu, A. van der Schaft, N. Harris McClamroch, I. Kolmanovsky, Dynamics and control of a class of underactuated mechanical systems. WSEAS Trans. Syst. Control. 10 (2015). E-ISSN: 2224-2856 6. J.W. Grizzle, C.H. Moog, Nonlinear Control of Mechanical Systems with an Unactuated Cyclic Variable. IEEE Trans. Autom. Control (2003) 7. S. Padhee, A. Gautam, Y. Singh, G. Kaur, A novel evolutionary tuning method for fractional order PID controller. Int. J. Soft. Comput. Eng. 1(3) (2011). ISSN: 2231-2307 8. D. Valrió, J. Sá da Costa, Optimisation of non-integer order control parameters for a robotic arm. In 11th International Conference on Advanced Robotics, Coimbra, 2003. 9. H.-S. Ahn, V. Bhambhani, Y.Q. Chen, Fractionalorder Integral and Derivative Controller Design for Temperature Profile Control, in Proc. 2008 CCDC-2008, 4767–4771 10. J.L. Adams, T.T. Hartley, C.F. Lorenzo, Fractional-Order System Identification Using Complex Order-Distributions. IFAC 2, prt1 (2006) 11. I. Podlubny, Fractional Differential Equations, vol. 198, 1st edn (Elsevier) 12. C.A. Monje, Y.Q. Chen, B.M. Vinagre, D. Xue, V. Feliu, in Advanced Industrial Control Series. Fractional Order Systems and Control-Fundamentals and Applications (Springer, Berlin, Germany, October 2) 13. M.F. Silva, J.A. Tenreiro Machado, A.M. Lopes, Fractional order control of a hexapod robot. Nonlinear Dyn. 38(1–4), 417–433 (2004)

A Review on Interference Management in Millimeter-Wave MIMO Systems for Future 5G Networks E. Udayakumar and V. Krishnaveni

Abstract Millimeter-wave (mm-wave) communication systems provide data rates in gigabits-per-second, due to large bandwidth availability used or 5G applications. Where the absorption and path loss are high in mm waves, it introduces poor propagation of sending the information. The shadowing can affect the millimeter wave to travel at long distances. Because of the high attenuation, the receiver provides low SNR value. Due to the oscillators, it causes a high interference. The interference occurred in mm wave such as inter-carrier interference, inter-block interference, phase noise, IQTM, etc. The various techniques are implemented at transmitter and receiver side to reduce the interferences. These waves are called a shorter wavelength wave, also has need to use a more number of antenna elements. Anyway, this mm-wave system has more spectral efficiency which was used for reducing the traffic demands. To overcome these problems, the beamforming was used with MIMO technology. This survey shows the effect of various interference and the cancelation techniques in downlink communications. Keywords IQ imbalance


Phase noise
PAPR
Zero forcing
Beamforming

1 Introduction The aim of 5G systems is to bring higher speeds in radio access providing 30–300 gigahertz frequency range [1]. The millimeter-wave communication considered a promising key technology for future wireless communication systems. It has higher traffic density, higher connection density, higher capacity, spectral efficiency and E. Udayakumar (&) Department of ECE, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamil Nadu, India e-mail: [email protected] V. Krishnaveni Department of ECE, PSG College of Technology, Coimbatore, Tamilnadu, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_65

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provides application services as mobile networks, augmented reality, virtual reality, high definition videos. It provides larger bandwidth when compared with 4G systems and more vulnerability to signal blockages from buildings. The base stations are provided with higher number of antennas for coverage of co-channel users, also named as massive MIMO systems. A typical challenge in millimeter-wave communication is severe path loss, propagation loss and blockage effects. Also, it was affected by rain causes high inter- and intra-cell interferences. The base stations are used to provide with higher number of antennas for coverage of co-channel users, also represented as massive MIMO systems. The multi-input multi-output (MIMO) systems have more number of transmitting and receiving antennas, which are used to increase the channel capacity in the reception side. The error rate performances are improved by these techniques. The MIMO which is used in 5G technology has 5G base stations to improve the transmissions technology for optimization of data connections. The major problems arise are reflection, attenuation, shadowing and refraction of signals at millimeter wave with a blockage [2]. For reducing the energy consumption, poor signal quality and to improve the communication quality, we focus on three parameters namely path loss, delay and capacity in 5G-green communication [3]. Millimeter waves have large antenna arrays at both receiver and transmitter side. While using the large arrays, the link margin has been realized in directional beamforming. Likewise, for canceling the interference in millimeter-wave communication, a hybrid array architecture has been used [2]. In terahertz communication systems, the highest level of interference and SINR occurs. The analytical model is designed for radiation pattern, antenna directivity to absorptions and blockage of interference. The main motivation of this paper is to explore various interferences arise in millimeter-wave communications and also show the various methods to reduce such interference. The millimeter-wave massive MIMO system where uplink communication in the respected cell is named to inter-cell interference [4] is shown in Fig. 1.

2 Related Work The path loss was raised in mm-wave communication. The directional antenna is used to get a high antenna gain for reducing the path loss. The sidelobes are detected using half- and full-duplex. The efficient energy-based resource allocating algorithm is used for relaying to self-interference suppression [5]. Also, attenuation exists due to atmospheric causes namely vapor, raindrops and oxygen molecules. For this, allocation of dynamic channel and frequency selection are used as interference model to manage the interference [6]. The interference alignment (IA), coordinating multi-point and treating interference as noise (TIN) were the several ways to reduce the interference. The TIN is used to breakdown strength of interference, CoMP is used to turn the inter-cell interference and IA is used to increase the capacity and remove the interference [7]. The intra-cell interference suppression

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Fig. 1 Inter-cell interference in mm-wave massive MIMO system

is carried out by zero forcing (ZF) and suppression of interference in inter-cell takes place by CoMP schemes in mm-wave overlaying networks [8]. The Interference-based Ranging Model (IRM) is used to increase the accuracy. The transmission outage probability is used to analyze the signal interference noise ratio (SINR). The SINR depends on control of medium access protocol, transmission power, network topology, antenna pattern and performance analysis; also, it is very complex. The interference model depends on channel model, antenna direction, network topology and MAC protocols [9]. The Interference-based Ball Method (IBM) is used to solve the Interference-based Ranging Model (IRM) and Protocol Model (PRM). In mm-wave beamspace MIMO systems, the inter-carrier interference got reduced by beam selection, by utilizing beamspace in mobile stations (MBs) [10]. The multi-user mm-wave system uses several antennas at base stations. It causes higher propagation loss and interference. The quasi-based deterministic (Q–D) model is employed for reducing degree of various antennas with adaptive arrays. For transmitter side, regularized channel inversion minimum mean squared error headings, (RI-MMSE) and for receiver side, interference cancelation (IC) MMSE MIMO detector was used. Figure 2 shows the system model for interference cancelation [11]. The inter-carrier and inter-block interference takes place in heterogeneous networks that are not completely removed. The novel interference alignment (IA) method is used to eliminate the ICI and IBI [12]. The OFDMA systems in heterogeneous networks have good cellular capacity and coverage, also use equalizers to remove ICI. A macro-cell users (MUs) at small and macro-cell base stations cause co-channel interference, ICI and IBI [13].

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Fig. 2 System model

3 Interferences The E-band and Q-band in mm-wave frequency bands produce nonlinear distortion by transmitters due to power amplifiers (PAs). The received signal regrowth introduces in-band distortion affects the adjacent channels. A novel channelized sideband noise method is used to cancel the unwanted emissions [14]. The RF distortions in mm-wave MIMO-OFDM systems exist as phase noise (PN), carrier frequency offset (CFO), phase and quadrature imbalance (IQI), cyclic prefix (CP), peak-to-average power ratio (PAPR) and power amplifiers (PA) nonlinearity [15]. The orthogonal frequency multiple access (OFDM) has high sidelobes and computational complexity. The local oscillator (LO) noise is also known as phase noise, addition of phase and amplitude noises. Due to wideband signals, SNR degrades with increasing LO noise [16]. The oscillator-based phase noise is the random mismatch between the phases of input passband carrier signal and carrier signal generator [17]. The effect of phase noise in mm wave causes inter-carrier interference, common phase error (CFO) due to the usage of common oscillators at both transmitter and receiver. In independent oscillator case, usage of more antennas at transmitter and receiver side, the phase noise occurred is low. The advanced mitigation scheme is employed for reduction of phase noise to get better error vector magnitude (EVM) [18]. The IQ imbalance is due to loss of orthogonally among adjacent sub-carriers. It is due to mismatch of amplitude and phase and error in 90-degree phase shifter. It can be evaluated by ZF, MMSE and maximum likelihood detection and estimation and phase-interpolation-based techniques [17]. The IQ timing variations are the mismatch between in-phase and quadrature routes at TX and RX. Due to CMOS process, it causes damage in channel length and produces image rejection ratio (IRR). A novel pilot design and estimators, IQTM estimators and compensators are required to compensate for the RF distortions [19]. The carrier frequency shifting and transmitter/receiver I/Q imbalance takes place in mm wave cause severe RF impairments. The preamble-based estimation and balancing techniques were used to reduce the CFO [20]. The digital pre-distortion (DPD) and TX receiver scheme are used for compensation of IQ imbalance [21].

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Fig. 3 The hybrid beamforming structure for downlink transmission

The PAPR caused by power amplifiers having computational complexity and severe propagation path loss [22]. PAPR is defined by relation between peak powers with average power [23]. The ISTA framework, constrained-squash method and random channel estimator with LTS were used to decrease the value of PAPR in mm-wave MIMO-OFDM systems [24]. The mm-wave radio links are affected by rain scatters produce cross-polarization effects. The mm waves are affected due to rainfall rate increased during 0.01% of the year is approximately equal to 75 and 120 mm/h. The extended boundary condition method is used to find scattering properties of raindrops [25]. The hybrid beamforming is used to decrease the number of radio frequency chains, cancel the inter-channel interference and used to increase the quality of the signal. The directional beamforming is used to ignore the interference and to increase the signal power. The line-of-sight interference was also canceled by maximal ratio combining (MRC); also, partial zero forcing (PZF) is used to mitigate the interference, which improves the coverage and throughput [26]. The hybrid beamforming structure for downlink transmission [27] is shown in Fig. 3. The analog beamforming-based Kronecker decomposition is used to enhance the signal quality and nullify the interferences, where it uses Orthogonal-Matching-Pursuit Algorithm. It needs higher phase shifters to decrease the hardware complexity [4]. The comparison of bandwidth and delay spread for millimeter-wave channels [28, 29] is shown in Table 1.

4 Conclusion This survey paper had shown several interference techniques which are implemented in TX and RX sides. Many algorithms were used to mitigate the interference in millimeter-wave systems. The interference compensation and estimation

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Table 1 Comparison of bandwidth, delay spread for millimeter-wave channels Carrier frequency (GHz)

28 38 60 73 86 RDA—Rotated Directional NLOS—Non-Line-of-sight

RF bandwidth

Antenna used

Max. TX–RX distance (m)

Mean RMS delay spread (ns) LOS NLOS

800 MHz SISO 500 28.8 17.4 800 MHz RDA 200 1.2 23.6 250 MHz RDA 50 0.8 7.4 800 MHz RDA 200 249 – 5 GHz RDA 685 0.125 – Antenna; SISO—Single Input Single Output; LOS—Line-of-sight;

techniques are used to solve these problems. Also, the beamformers will reduce the interference. The PLL-based oscillators, ZF, MMSE were reduced the interference and improved the probability of coverage for 5 g future systems.

References 1. G. Zhu, K. Huang, V.K.N. Lau, B. Xia, X. Li, S. Zhang, Hybrid interference cancellation in millimeter-wave MIMO systems, in 2016 IEEE International Conference on Communication Systems (ICCS), pp. 1–6 (Shenzhen, 2016) 2. E.U. Dayakumar, V. Krishnaveni, Analysing of various interference in Mmwave comm system: a survey, in Proceedings of IEEE International Conf on Computing, Comm & Networking Technology (ICCCNT ‘19), pp. 1–6 (Kanpur, 2019) 3. T. Wu, T. Chang, Interference reduction of MmWave technology of 5G-based green Comm. IEEE Access 4, 10228–10234 (2016) 4. G. Zhu, K. Huang, V.K.N. Lau, B. Xia, X. Li, S. Zhang, Beamforming the Kronecker decomposition of interference cancel in the analog domain, in 2017 Global Communication Conference, pp. 1–6 (Singapore, 2017) 5. G. Yang, M. Xiao, Performance analysis of MMwave relaying, beamwidth and self-interference. IEEE Trans. Comm. 66(2), 589–600 (2018) 6. S. Niknam, B. Natarajan, R. Barazideh, Interference analysis for finite-area 5G millimeter-wave net consider block effect. IEEE Access 6, 23470–23479 (2018) 7. C. Wang, C. Qin, Y. Yao, Y. Li, Low complex interference alignment for millimeter wave MIMO channels in 3-cell mobile net. J. Areas Comm 35(7), 1513–1523 (2017) 8. A. Maltsev et al., Performance of interference mitigation in the overlaying Millimeter Wave small cell net, in IEEE Conference on Communications and Networking (CSCN), pp. 130– 136 (Tokyo, 2015) 9. E. Modiano et al., Interference model index and applications to MMwave networks. IEEE Access on Wireless Comm 17, 71–85 (2018) 10. A. Hu, Channel estimation for interference mitigation in millimeter-wave multi-cell beamspace MIMO systems. J. Comm and Netw. 19(4), 371–383 (2017) 11. Y. Zhang, Y. Liu, J. Gao, Decorrelating receiver of interference mitigation in MMwave small cells networks. IEEE Access. 6, 7772–7779 (2018)

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12. H. Wang, R. Song, S. Leung, Removal of ICI & IBI of wireless heterogeneous net. Timing Mismatch. IEEE Comm. Letters. 21(5), 1195–1198 (2017) 13. H. Wang, S. Leung, R. Song, Analysing of the uplink IBI & ICI in heterogeneous net with multiple macrocells. IEEE Comm Lett. 21, 212–215 (2017) 14. C. Yu, H. Sun, X.-W. Zhu, W. Hong, A. Zhu, A channel sideband distortion model to suppress Q-band mMwave TX, in 2016 MTT-S Microwave Symposium, pp. 1–3 (San Francisco, CA, 2016) 15. A. Khansefid, H. Minn, Q. Zhan, et al., Waveform design and comparisons for MmWave Massive MIMO system to RF distortions, in 2016 Globecom workshops, pp. 1–6 (Washington, DC, 2016) 16. J. Chen et al., Influence of white local oscillator noise on wideband Comm. IEEE Microw. Theory & Tech. 66(7), 3349–3359 (2018) 17. N. Al-Dhahir et al., Reduced complex joint compensate phase noise & IQ imbalance of MIMO-OFDM systems. Trans. Wirel. Comm. 9(11), 3450–3460 (2010) 18. Svensson, T. et al., A Beamforming MIMO system in the phase noises at Mmwave frequencies, in 2017 Conference on Wireless Communication & Networking Workshops (WCNCW), pp. 1–6 (San Francisco, CA, 2017) 19. H. Minn, H. Huang, In phase and quadrature mismatch estimation and compensation of MmWave comm system. Trans. Wirel. Comm. 16(7), 4317–4331 (2017) 20. F. Wu, Y. Li, M. Zhao, Estimation of transmitter IQ imbalance at the receiver with receiver IQ imbalance & carrier offset of OFDM systems, in 2014 Globecom Workshops, pp. 960–965 (Austin 2014) 21. A. Chung, M. Ben Rejeb, Y. Beltagy, A.M. Darwish, H.A. Hung, S. Boumaiza, IQ imbalance & digital predistortions of Mmwave TX using reduced sample rate observations. Trans. Microw. Theory & Tech. 66(7), 3433–3442 (2018) 22. E. Udayakumar, P. Vetrivelan, PAPR Reduction of OQAM-OFDM Signals using neural network. Int. J. Appl. Engg Res. (RI Publications) 10(41), 30292–30297 (2015) 23. E. Udayakumar, P. Vetrivelan, PAPR reduction of OQAM-OFDM signal using optimized clipping and filter technique, in 2015 International Conference on Soft-computing & Network Security, pp. 1–6 (Coimbatore, 2015) 24. Z. Guo, Y. Yılmaz, X. Wang, TX centric channel estimation & low PAPR Precode for Mmwave MIMO systems. IEEE Trans. Comm. 64, 2925–2938 (2016) 25. K.M. Mora Navarro, E. Costa Interference between millimeter wave radio links due to rain scatter, in 2016 Radio and Science Meeting, pp. 115–116 (Fajardo, 2016) 26. A.H. Jafari, J. Park, R.W. Heath, Analysis of interference mitigation in mmWave communications, in 2017 International Conference on Communications (ICC), pp. 1–6 (Paris, 2017) 27. Y. Liuv et al., Multiple-beam selection with limit F/b of hybrid beamforming for massive MIMO systems. IEEE Access. 5, 13327–13335 (2017) 28. C. Wang, J. Bian, J. Sun, W. Zhang, M. Zhang, A survey of 5G channel measurements and models. Comm Surv. Tutor. 20, 3142–3168 (2018) 29. S. Kutty, D. Sen, Beamforming for mmWave comm: an inclusive survey. Comm Surv. Tutorials. 18, 949–973 (2016)

Preventing the Vehicle Accidents on Highways and Implementing Safety and Automation P. Parthasaradhy and K. Manjunathachari

Abstract In this paper, a mechanism for vehicle control and accident avoiding system is proposed. The salient features of this project are prevention of forward collision mechanism with alert, braking, lane changing and speed governing, i.e., when the vehicle attains high speed limit, it takes over the speed governing mechanism without the permission of the driver. The vehicle-to-vehicle communication is implemented to send the alert signals to the nearby vehicles whenever an accident occurs. The vehicle working environment for the driver is to avoid the forward collision and conditioning, collision avoidance and condition which are main factors to improve the safe traffic conditions. To implement this system, ARM7 is used. This microcontroller provides more efficient, reliable and effective working. This system estimates the speed and distance of the existing vehicles around, and the targeted vehicle measures these values using ultrasonic sensors. This results the speed of that particular vehicle to avoid collisions. MEMS provide facility for accident detection system. GPS is used to transmit the location of the accident and nearby other vehicles or static things like. ZigBee is used as an alternate to the network areas. Keywords Speed adaptation ZigBee


Collision detection
GPS
Ultrasonic sensor and

1 Introduction Self-controlled driven vehicles are progressively adding the features of collision prevention and alert mechanism to predict the probable occurrence of collision with external obstacles such as another vehicle or a pedestrian. Upon detecting a

P. Parthasaradhy (&) Guru Nanak Institutions Technical Campus, Hyderabad, India K. Manjunathachari ECE Department, GITAM University, Hyderabad, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_66

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probable collision, such systems typically initiate an action to prevent the collision or sending an alert to the vehicle operator. To minimize the accidents on highways and connecting roads, vehicular network will play a vital role in intelligent transportation systems (ITS). ITS modules like road safety and security, fleet management will rely on data exchange between vehicle and infrastructure (V2I) or in between vehicle to vehicle (V2V) [1]. It is observed that many of these accidents are specifically due to collision or at cross roads. Sensors are serving as the vehicle eyes, identifying the lane position, vehicle speed and whereabouts of other vehicles. The fact is that motor accidents are found to be common cause of death as compared to other reasons like, cancer or heart attack [2, 3]. For economical, technical and legal reasons, vehicle automation is not directly brought to market [4]. It is incrementally introduced through advanced driver-assistance systems (ADASs) such as adaptive cruise control (ACC). The author states that highly automated vehicle for intelligent transport system. Figure 1 indicates the conceptual diagram of vehicle-to-vehicle communication. In this concept, the communication is between the vehicles to send an emergency alert. In this proposed work, sensors are placed at strategic positions around the vehicle except on rear side. These sensors regularly scan the road ahead for impediments or vehicles, and then alert signal is given to the driver accordingly.

Fig. 1 Conceptual diagram of V2V communication

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2 System Architecture The data collected are structured in a packet and forwarded to a remote control unit in association with V2V and V2I wireless communication. Based on this information, this system directly estimates the accident severity, comparing the received data with the data bank of the previous accidents available in the database. This information is of utmost importance, for example, to determine the most suitable set of resources for rescue operation. Figure 2 shows the architecture of proposed system. A. On-Board Unit The proposed On-Board Unit lies in receiving the available information from the inside sensors and identify the happening of the dangerous situation, then reporting the same to the nearest Control Unit, as well as to other nearby vehicles which may face the problem. B. Forwarding System This system is installed in all vehicles. This unit receives the exigent information or position of nearby vehicles and transmits this information to all nearby vehicles and to the base station.

Fig. 2 Architecture of proposed system

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C. Base Station Main station is having modems like ZigBee and GSM, which serve to collect the exigency information. Main station receives the critical messages from the vehicle and arranges emergency support to the respective vehicle at the earliest. The base station also displays the alert message on the LCD module of the vehicle placed inside.

3 Prototype Implementation and Design The sensors used are accelerometer, force resistive sensor, vibration sensor, power supply and GPS. When an accident occurs, these three sensors collect the information of severity of the accident and the GPS is used to detect the locus of the vehicle. Later, this information is sent to the microcontroller. The data collected are structured into packets and are accelerated through ZigBee. Figure 3 shows block diagram of microcontroller-based system. The following sections explain about modules in the prototype system. 1. Microcontroller: The LPC2148 microcontrollers is a 32/16 bit ARM7TDMI-S CPU that combines the microcontroller with embedded high-speed flash memory ranging from 32 to 512 kB. 2. Ultrasonic Sensor: It is a distance estimation and mapping sensor of minimized size, higher range and flexible. The vehicle sensor detects the item with no physical contact. This settles on the vehicle to take the choice either to escape or to go up against and find out the deterrent according to its programming. Ultrasonic transducers were picked for this since they are more dependable and have a more prominent range than IR sensors.

Fig. 3 Microcontroller-based system

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3. Motor Driver: This device is a monolithic combined high-voltage, high-current four-channel driver intended to accept standard DTL or TTL logic levels and drive inductive loads and switching power transistors. This device is appropriate for use in switching applications at frequencies up to 5 kHz. 4. This is a LCD display module with 16  2 characters and yellow/green LED backdrop illumination. LCD show utilizes STN innovation so it has an extraordinary complexity and a wide survey point. Show module is constrained by SPLC780D (same as regular HD44780) parallel interface chipset that is anything but difficult to utilize. 5. GPS: The Global Positioning System (GPS) is a space-based sailing method that provides location and indication entropy in all withstand conditions, anywhere on or neighboring the ground where there is an open connector of reach to digit or more GPS satellites [5]. 6. ZigBee: ZigBee is a specification for a suite of high-level connection protocols utilized to create personal country networks improved from dwarfish, low-power digital radios. ZigBee chips are typically mainstreamed with radios and with microcontrollers that hump between 60 and 256 KB radiate memory. 7. RF Transmitter and Receiver: The RF communicator ability interfaced to the microcontroller through the encoder IC HT12E then modulates the digital information from the encoder IC into RF wireless communicator by ASK inflection model and transmits via RF out aerial. The wireless sender ability can be victimized to transport information at up to 3 kHz from any prescriptive CMOS/TTL publication.

4 Conclusion In this prototype hardware kit, automatic accident notification and assistance based on V2V and V2I communications along with self-speed control and regulation were implemented. The efficacy of this knowledge can be upgraded with the assistance of intelligent systems. A preliminary assessment of the severity of an accident is needed to adjust resources as required. The results achieved on the real tests indicates severity estimation algorithms are robust enough to allow a mass deployment of the proposed system.

References 1. N. Watthanawisuth, T. Lomas, A. Tuantranont, Wireless black box using MEMS accelerometer and GPS tracking for accidental monitoring of vehicles, in 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 847–850 (2012) 2. Z. Zhang, J. Zhang, A novel vehicle safety model: vehicle speed controller under driver fatigue. Int. J. Comput. Sci. Netw. Secur. 9(1), 355–362 (2009)

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3. W. Wei, F. Hanbo, Traffic accident automatic detection and remote alarm device, in IEEE proceedings on ICEICE, pp. 910–913 (2011) 4. S. Moon, I. Moon, K. Yi, Design, tuning and evaluation of a full range adaptive cruise control system with collision avoidance. Control Eng. Pract. 17(4), 442–455 (2009) 5. J. Lee, An accident detection system on highway using vehicle tracking trace, in 2011 International Conference on ICT Convergence (ICTC), pp. 716–721 (2011)

MRI-Based Medical Image Enhancement Technique Using Particle Swarm Optimization S. Sakthivel, V. Prabhu and R. Punidha

Abstract The recent work tends to a complexity improvement strategy, which joins the established difference upgrade approach. The primary targets of this paper are to expand the data substance and upgrade the subtleties of a picture utilizing the examination procedure of parameters bolstered by Particle Swarm Optimization (PSO) calculation. Here, PSO from swarm intellect (SI) has applied to appraise the consideration values. In the proposed technique, the edge closeness of data parameters, for example, mean, standard deviation, and difference have utilized to detail the improvement strategy. These strategies defeat the past Level-3 disintegration to extricate highlights from pictures of PSO methods. A reproduction result is a proposed particle swarm optimization based contrast enhance strategy that improves the general picture differentiate and enhances the data content in the picture. Additionally, constraints of Peak Signal-to-Noise Ratio (PS-to-NR) and Mean Squared Error (MSE) have investigated the Particle Swarm Optimization (PSO) image in Fig. 1. We contrast and other difference upgrade procedures, the proposed technique gives hidden information of a picture and it is progressively reasonable for applications in early tumor location. Keywords MRI images


PSO technique
Edge similarity index
Parameters

S. Sakthivel (&) Anna University, Chennai 600025, Tamil Nadu, India S. Sakthivel Department of CSE, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai 600062, Tamil Nadu, India V. Prabhu Department of ECE, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai 600062, Tamil Nadu, India R. Punidha Department of CSE, Bharathiyar Institute of Engineering for Woman, Salem 636112, Tamil Nadu, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_67

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Fig. 1 PSO image that represents the particle orientation

1 Introduction Particle Swarm Optimization (in short PSO) is a computational strategy with respect to a given amount of value [1]. PSO adopts the real numeric value, it is useful for constant estimation for better solution [2]. This strategy is utilized for upgrading the issue by iteratively attempting to improve an applicant arrangement amid every age every molecule is quickened toward the particles past crest position “pCrest” and the global crest position “gCrest.” At every cycle another speed, an incentive for every molecule is determined dependent on its present speed, the separation from its past crest location, and separation from the global crest location [3]. The new speed esteem is then used to ascertain the following location of the molecule in the hunt space. This technique is repeated a lot of times or until a base blunder is accomplished. Image enhancement techniques are separated into few strategies like point, spatial, transformation, and pseudo-coloring methods. In this research, we focused only on spatial method. In this method, we directly work with image pixels [4–6]. Histogram transformation process is used for dissimilarity improvement of grayscale images, which facilitate consequent high-level techniques such as recognition and classification [7, 8]. Segmentation of an image is a procedure of image apportioning from a picture to numerous arrangements of pixels. As improved algorithms of PSO techniques like Fractional-Order and Standard Darwinian’s Particle Swarm Optimization. These two calculations have been considered as existing approaches for image segmentation [9, 10]. PSO has been effectively applied in numerous zones: work enhancement-based function optimization, artificial neural system preparing, fluffy framework control, i.e., fuzzy, and different zones where genetic algorithm (GA) can be connected.

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2 Methods The current framework shows a multi-goal strategy for dark dimension picture upgrade utilizing Particle Swarm Optimization (PSO). The upgrade enhancement technique is a non-direct issue with different requirements. The calculation (MGE-PSO) [4] creates an entire pyramid of diversely measured picture to use more data for development process. An MRI cerebrum picture is characterized into typical and irregular utilizing an FFNN utilizing dimension 3-decay to separate highlights from pictures. Henceforth, we defeat this Level-3 technique by exploring the parameters in the MRI pictures [11]. In this PSO calculation is guided to decide every one of the parameters esteem. An MRI cerebrum picture is arranged into the typical and strange utilizing Feed-forward Neural Networks (FFNNs) in utilizing Level-4 deterioration to extricate highlights from pictures, trailed by use of PSO. The dimensionally decreased highlights are given to FFNN, and Particle Swarm Optimization finishes an image enhancement [9]. Feed-forward Neural Network (FFNN) is a managed classifier that is prepared in this conceptual effort utilizing PSO. Computationally, more affordable PSO needs many positions of usage codes and less computerized accounting. To acquire FFNN’s ideal parameters and for execution improvement, a PSO technique was utilized [12, 13]. a. Phase-I: We consider the MRI images as input image. The DICOM image of the brain images is converted into JPEG image. b. Phase-II: MRI brain tumor images are proceeded for further preprocessing task like 1. 2. 3. 4. c.

Grayscale conversion, Filtered image [14], Transformation, and Enhancement. Phase-III:

The way toward transforming or changing over an image into gatherings of its highlights is called quality removal. Surface is to be shown as a 2D exhibit of gray point variety. d. Phase-IV: Segmentation issue is changed over into a grouping issue and a brain tumor is fragmented by order and preparing dependent on the parameter utilized, the edge content is characterized, and the tumor is distinguished. A large number of iterations are performed so as to differentiate the tumors in the brain images with the PSO algorithm and produce the PSO image output in the grayscale level

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Fig. 2 Basic flow diagram of image analysis using PSO

MRI Image

Pre-processing Stage

Parameter Analysis

Feature Extraction

Classification (PSO Algorithm)

without distortions [15]. The basic flow diagram of PSO image analysis is shown in Fig. 2. e. Phase-V: The parameters are analyzed which were Edge Similarity Index (ESI)-Mean, Variance and Standard Deviation, Peak Signal-to-Noise Ratio (PS-to-NR), and Mean Squared Error (MSE) [16, 17]. In this phase, we produced output values of MSE and PS-to-NR which are shown in Fig. 5.

3 PSO Algorithm Algorithm: • Each arrangement has considered as molecule or particle. • The entire elements have a robustness assessment. The fitness values are to be determined utilizing target work. • The entire elements protect their individual most excellent presentation. • They also know the excellent presentation of their collection. • They change their rapidity thinking about their most excellent performance, and • Furthermore the best performance of the best molecule or particle. In PSO calculation, two ideal qualities characterize the wellness of resolution work starting one is that the best goals of each molecule accomplished up to this point. This worth is named as “pCrest” resolution. Another is that the best goals caterpillar-followed by any molecule among the whole populace. This best worth is thought as “gCrest” resolution [18, 19]. Si ðt þ 1Þ ¼ wSi ðtÞ þ c1:rand1ðpCrest; iðtÞ  Yi ðtÞÞ

)

þ c2:rand2ðgCrest; iðtÞ  Yi ðtÞÞ Yi ðt þ 1Þ ¼ Yi ðtÞ þ Si ðt þ 1Þ where “Yi(t), Si(t)” Shows the speed position of ith molecule or particle. “pCrest, I” demonstrate the individual peak position of ith molecule.

ð1Þ ð2Þ

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“gCrest, I” demonstrates that the worldwide peak position achieved up until this point and “c1 and c2” demonstrate that the position accelerating of steady worth. “rand1 and rand2” are unpredictable characteristics delivered somewhere in the range of 0 and 1. “w” is idleness weight used to give balance among present and universal search. In our PSO strategy, every solution is a particle. The flowchart diagram shown in Fig. 3 represents the above process of PSO algorithm for optimizing the MR image [5, 20]. i. Local Crest PSO: The local crest PSO (shortly lCrest PSO) utilizes a ring social network topology where littler neighborhoods are characterized for each molecule. The social segment reflects data traded inside the area of the molecule, reflecting local information of the environment. With reference to the rapidity equation, the social contribution to molecule rapidity is proportional to the distance between a molecule and the crest position found by the neighborhood area of molecule.

Fig. 3 Flow diagram of Particle Swarm Optimization algorithm

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ii. Global Crest PSO: In the global crest PSO (shortly gCrest PSO), the area for every molecule is the whole swarm. The social network utilized by the gCrest PSO that mirrors the star topology. In star neighborhood topology, the social segment of the molecule rapidity update reflects data acquired from every one of the molecule in the swarm. For this situation, the social data is the crest spot found by the swarm.

4 Pseudo-code 4.1

PSO Coding Algorithm

Input: Problem Size, Population Size. Output: Pg_Crest MI= Molecule_Initialization(); For k=1 to it_max For each Molecule p in MI do Fp=f(p); If fp is better than f(pCrest) pCrest = p; end end gCrest = crest p in MI; For each particle p in MI do v=w+cl*rand*(pCrest-p)+c2*rand*(gCrest-p); MI=p+v; end end

4.2

PSO Flowchart

See Fig. 3

5 Parameter Settings To analyze the parameters using PSO technique to distinguish the quality of the image information which are listed as follows:

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1. Edge Similarity Index (ESI), (a) Mean, (b) Variance, and (c) Standard Deviation. 2. Mean Squared Error (MSE), 3. Peak Signal-to-Noise Ratio (PS-to-NR).

6 Results and Inference To illustrate this idea, 50 samples of MRI brain tumor images have been collected. The MRI images are enhanced and classified to assess the parameter analysis of usual and unusual images [21]. The Particle Swarm Optimization technique was proposed so as to enhance the images. The future method provides high accuracy and time-consuming compared to previous research. As further work, we analyze the parameter values based on Edge Similarity Index, PS-NR and Mean Squared Error for determining the quality information of brain images by tabulating it and it will be useful for the radiologist to diagnose different grades of tumors and its disorders. The resultant images from the original MRI grayscale images, PSO image and other error-related images are shown in Fig. 4a–e, respectively.

Fig. 4 a Original MRI image of the human brain, b PSO image from original image, c original grayscale image, d squared error image, and e parameters’ output

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Fig. 5 Parameters’ output of PS-NR and MSE values

Fig. 6 Result of the Edge Similarity Index (Mean, Standard Deviation, and Variance)

The most important output of ESI parameters (in Fig. 6) like Mean = 0.510, Variance = 1.2957e+03 and Standard Deviation (SD) = 1.1383.

7 Conclusion In order to analyze and properly detect the brain tumor and its grades, the known practice called Particle Swarm Optimization is deliberating as best selections, it can be efficiently classified the usual and unusual images. To accomplish this task, the acquired MRI brain tumor images and Particle Swarm Optimization strategy were involved to generate an effective result. In PSO technique, image preprocessing

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strategy determines the specific feature and to be essential to certify that the precision of methods. The parameter analysis (ESI, PS-NR, and MSE) of the method results is auspicious and inspiring future works. Acknowledgements One of the authors would like to notify that there is no conflict on brain MRI image dataset in this paper and authors to thank the reviewer for their valuable suggestions.

References 1. A. Gorajand, A. Ghosh, Grey-level Image Enhancement By Particle Swarm Optimization. World Congress Nat. Biologically Inspired Comput. (NaBIC) 978-1-4244-5612-3/09/$26.00 @ 2009 IEEE 2. V. Selvi et al., Comparative analysis of ant colony and particle swarm optimization techniques. Int. J. Comput. Appl. 5(4), 1–6 (2010) 3. A. Sharma et al., Recent trends and techniques in image segmentation using Particle Swarm Optimization-a survey. J. Sci. Res. Public. 5(6), 6 (2015) 4. A.M. Nickfarjam et al., Multi resolution gray level image enhancement using particle swarm optimization. Springer 47(4), 1132–1143 (2017) 5. G. Qinqing, C. Dexin, Z. Guangping, H. Ketai, Image enhancement technique based on improved PSO algorithm, in 2011 6th IEEE Conference on Industrial Electronics and Applications (Beijing, 2011, pp. 234-238). https://doi.org/10.1109/ICIEA.2011.5975586 6. G. Mohan, M. Subashini, MRI based medical image analysis: Survey on brain tumor grade classification. Biomed. Signal Process. Control. 39, 139–161 (2018). https://doi.org/10.1016/ j.bspc.2017.07.007 7. J. Kennedy, R. Eberhart, Particle swarm optimization. IEEE (1995), 0-7803-2768-3/95/$4.00 0 @ 1995 8. L.L.Z. Haiyin, A method of image enhancement based on genetic algorithm. Math. Theory Appl., Category Index:TN911.73 9. R. Punidha, S. Sakthivel et al., Segmentation of brain tumor and its area calculation in brain MR images using K-Mean clustering and fuzzy C-Mean algorithm. Indian J. Pure Math. 116(23) 10. R.S. Kabade, M.S. Gaikwad, Segmentation of brain tumor &its area calculation in brain MR images using K-Mean clustering and fuzzy C-Mean algorithm. Int. J. Comput. Sci. Eng. Technol. 4(05) (2013) 11. S. Bauer, R. Wiest, L.P. Nolte, M. Reyes, A survey of MRI based medical image analysis for brain tumor studies. Phys. Med. Biol. 58, R97–R129 (2013) 12. H. Chen, J. Tian, Particle swarm optimization algorithm for image enhancement. Int. Conf. Uncertainty Reasoning Knowl. Eng. 1, 154–157 (2011) 13. P. Mohan et al., Intelligent based brain tumor detection using ACO. Int. J. Innov. Res. Comput. Commun. Eng. 1(9), 2143–2150 (2013) 14. Suneetha et al., Enhancement techniques for gray scale images in spatial domain. Int. J. Emerg. Technol. Adv. Eng. 3(2), 13–20 (2012) 15. P.G. Kuppusamy et al., A full reference morphological edge similarity Index to account processing induced edge artefacts in magnetic resonance images. Sci. Direct 37(1), 159–166 (2017) 16. M. Kanmani, V. Narshimhan, An image contrast enhancement algorithm for gray-scale images using particle swarm optimization (2018) 17. M.G.H. Omran, Particle swarm optimization methods for pattern recognition and image processing. University of Pretoria etd (2005)

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18. M. Bashir et al., Performance analysis of particle swarm optimization algorithm-based parameter tuning for fingerprint image enhancement. 1(7) (2016) 19. N. Singh et al., Parameter Optimization in Image Enhancement using PSO. Am. J. Eng. Res. 2(5), 84–90 (2013) 20. S. Dhariwal, Comparative analysis of various image enhancement techniques. Int. J. Electron. Commun. Technol. 2(3), 91–95 (2011) 21. M. Abdullah Al Wadud et al., A spatially controlled histogram equalizations for image enhancement. IEEE (2008)

Health Monitoring System Using IoT A. Selvanayakam, A. C. Varishnee, M. Kalaivani and G. Ranjithkumar

Abstract Health monitoring is a major issue found in recent days. Due to a lack of proper health monitoring systems, the patient may suffer from various health issues. Internet of things (IoT) devices are hosted nowadays to screening the health of patients over the Internet. Specialists are manipulating these keen gadgets to watch out for the patients. In human service innovations, IoT is quickly inconsistent with the healthcare industry. Our framework embraces the sensors which are associated with a microcontroller to monitor tolerant comfort. In this mission, we will create many features using an IoT-based health monitoring system which chronicles the patient heartbeat rate, body temperature, and unconsciousness. As well, IoT gives an email/SMS alert at whatever points those readings goes past basic qualities. Heartbeat rate and body temperature readings are noted, so tenacious welfare can be tartan from anywhere on the planet over the web.







Keywords Pulse sensors Arduino GSM module rate Body temperature Unconsciousness








Wi-Fi module
Heartbeat

1 Introduction Today quantity of personalities is inclined with long-lasting illnesses; this is because of several hazard factors, for instance, dietary propensities, physical inertia, and liquor utilization, among others. As per figures from the World Health Organization, 4.9 million individuals pass on from lung malignancy from the utilization of snuff, overweight 2.6 million, 4.4 million for raised cholesterol, and 7.1 million for hypertension [1]. It is said that in the following 10 years, passings from incessant ailments will increment by 17%, which means in figures of around 64 A. Selvanayakam (&) Sri Krishna College of Engineering and Technology, Coimbatore, India e-mail: [email protected] A. C. Varishnee
M. Kalaivani
G. Ranjithkumar Easa College of Engineering and Technology, Coimbatore, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_68

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million individuals will endure because of endless illnesses [2]. Which are profoundly factor in their side effects just as their development and treatment. If not observed before they can spare the patient’s life [3]. Patients would not acknowledge the truth of illness long haul because of handicaps. For this gathering of individuals with sort of infections must need consistent checking by the specialist to talk about its condition and set the proper medications [4]. Because of innovative advances in today, there is extraordinary assortment running sensor perusing indispensable signs, for example, body temperature and pulse screen [5]. This paper thinks about the patient’s signs day by day, in view of the need for anomalous the outcomes put away reliably which shed everyday tests so they can be the subject of restorative investigations. Essentially likewise the readings that do for all time to patients reports, specialists suggest you additionally exercise schedules that enable them to improve the personal satisfaction and defeat such illnesses. The web of things down to earth to the consideration and assimilation of patients is logically normal in the well-being part, searching for improving the nature of an individual’s life. Modernization in the Internet of things is new and is categorized as the resolution of all devices that interface with the system, which can be supervised from the web and which tolerate the expense of the measurements in an unpretentious stage to permit connotation with individuals they practice it. For regular issues, which are intelligible, unambiguous, locatable, addressable, and controllable by means of the Internet—either through RFID, remote LAN, extensive section establish or by different techniques. Internet of things is established in three standards, which are Internet-situated middleware, things’ sensors arranged, and learning-focused semantics. Such limitation on the grounds that the interdisciplinary idea of the subject. The suitability of the IoT is reproduced when going between the three standards in the progression of uses. In the equipment layer, whose intent is to permit the interconnection of physical articles utilizing sensors and related improvements. The complications related to this layer are recognized with scaling down, while today there are gadgets with capacity, handling, inward parts ought to be miniature and to improve expertise. Another test is found in the trade’s layer, which is depended on billion gadgets related to the system, which fuses improving the data move limit and the electromagnetic range. Confronted with the previously mentioned, the application layer and organizations are displayed endless possible results that license to procure, process, and endorse critical information for patients to the treatment of ailments and improve their lifestyles.

2 Proposed Method Checking your relatives with heath issues turns into a troublesome undertaking in the cutting-edge life. Particularly seniority patients have to be occasionally checked, and their family should be educated about their well-being status every now and then while at work. So we propose an innovative system that automates the task with ease. Our system progresses splendid patient prosperity following structure

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Fig. 1 Block diagram

that uses sensors to pursue tolerant prosperity and uses the web to send their nuances to the family if there ought to be an event of any issues. Our structure uses temperature similarly as heartbeat identifying to screen diligent prosperity. The sensors are related to a microcontroller to pursue the status which is accordingly interfaced with an LCD show similar to Wi-Fi affiliation to transmit cautions. On the off chance that the framework distinguishes any unexpected changes in patient heartbeat or body temperature, the framework consequently alarms the client about the patient’s status over IoT and besides demonstrates nuances of heartbeat and temperature of patient live over the web. In this way, IoT-based patient well-being following framework adequately utilizes the web to screen understanding well-being details and spare lives on schedule (Fig. 1).

3 Arduino Uno UNO is a microcontroller dependent on ATmega328P. It has 14 advanced info yield pins; 6 pins are for PWM yields; 6 pins go about as simple information pins. 16 MHz precious stone USB connector power jack is joined comprising LCSP header and reset catch. UNO contains everything expected to help any typical microcontroller (lC). In UNO, associations can be built up by interfacing Arduino to PC with a USB link, and control with AC-to-DC connector can be given or battery to begin (Fig. 2). Arduino is a firm which structures gear, lC-based units for structure propelled contraptions and natural articles that can percept, and control physical devices. It develops a successive correspondence interface for stacking programs from PC through USB. The power source is picked thusly. External (non-USB) power can

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Fig. 2 Arduino Uno

come either from an AC-to-DC connector (divider mole) or battery. The connector can be related by ceasing a 2.1-mm center-positive fitting into the board’s ability jack. The leads from a battery can be inserted in the ground and VIN stick headers of the power connector.

4 Transformer A stage down transformer changes over the high voltage (HV) and low current from the basic side to the low voltage (LV) and high current motivating force on the helper side. This transformer type has a wide application in electronic contraptions and electrical systems. Concerning the undertaking voltage, the movement up transformer application can be for the most part parceled in two social events: LV (voltages up to 1 kV) and HV application (voltages more than 1 kV). The first LV application implies transformers in electronic devices. Giving the electronic circuits requires low voltage regard (e.g., 5 V, even lower esteems these days). A stage down transformer is utilized to give this low voltage esteem which is appropriate for hardware provides. It changes home voltage (230/120 V) from essential to a low voltage on the auxiliary side which is utilized for the electronic providing. In the event that electronic gadgets are intended to have higher ostensible power, transformers with high working recurrence are utilized (kHz-s). The transformers with higher ostensible power worth and 50/60 Hz ostensible recurrence would be excessively enormous and substantial. Likewise, the everyday utilized battery chargers utilize the progression down transformer in its structure. The progression down transformers has significant capacity in the power framework. They bring down the voltage level and adjust it for vitality buyers (Fig. 3).

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Fig. 3 Transformer

5 Pulse Sensor The working of the pulse/heartbeat sensor is straightforward. The sensor has two sides: On the one side, the LED is placed along with an ambient light sensor, and on the other side, we have some circuitry. This hardware is in charge of the enhancement and commotion crossing out work. The LED on the front side of the sensor is put over a vein in our human body. This can either be your fingertip or your ear tips, yet it should be put really over a vein. Presently, the LED emanates light which will fall on the vein legitimately. The veins will have bloodstream inside them just when the heart is siphoning, so in the event that we screen the progression of blood, we can screen the pulses too. In the event that the progression of blood is identified, at that point the surrounding light sensor will get all the more light since they will be reflected by the blood; this minor change ingot light is examined after some time to decide our heart pulsates. Using the pulse sensor straight forward, but positioning it in the right way matters. Since all the hardware on the sensor is legitimately uncovered, it is additionally prescribed to cover the sensor with heated glue, vinyl tape, or other non-conductive materials. Additionally, it is not prescribed to deal with these sensors with wet hands. The level side of the sensor ought to be set over the vein and a slight presser ought to be connected over it; typically, clasps or Velcro tapes are utilized to achieve this weight (Fig. 4).

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Fig. 4 Pulse sensor

6 DHT11 The DHT11 humidity and temperature sensor module gives aligned readings utilizing a solitary advanced stick on your microcontroller. The DHT11 sensor gives 8 bits of accuracy to both dampness and temperature. It utilizes a solitary line for bidirectional sequential correspondences. An Arduino library is accessible to improve the task. It can quantify temperatures from 0 to 50 °C (32 to 122 °F) and stickiness from 20% to 95% relative humidity. This sensor works with both 3.3 and 5 V microcontroller frameworks. The fascinating thing with regard to this module is the convention that utilizations to move information. All the sensor readings are sent utilizing a solitary wire transport, which lessens the expense and broadens the separation. So as to send information over a transport, you need to portray the manner in which the information will be moved, with the goal that transmitter and recipient can comprehend what says one another. This is the thing that a convention does. It depicts the manner in which the information is transmitted. On DHT11, the 1-wire information transport is destroyed up with a resistor to VCC. Along these lines, if nothing has happened the voltage on the transport is equivalent VCC (Fig. 5).

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Fig. 5 DTH11

7 Accelerometer Sensor An accelerometer estimates legitimate quickening, related with the heaviness of a test mass. An accelerometer carries on like a damped mass on a spring. At the point when an accelerometer encounters increasing speed, the mass is uprooted to the point of at which the spring can quicken the mass at a similar rate as the packaging. The displacement is then measured in order to give the acceleration. In semiconductor devices, piezoresistive, piezoelectric, and capacitive parts’ area unit normally employed in order to convert the mechanical motion into associate degree electrical signal and area unit unmatched in terms of their low packaged weight, warm temperature varies and higher frequency varies (Fig. 6). Fig. 6 Accelerometer sensor

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8 GSM A GSM module is essentially a GSM modem (like SIM 900) associated with a PCB with diverse sorts of yield taken from the board—say TTL yield (for Arduino, 8051, and other microcontrollers) and RS232 yield to interface straightforwardly with a PC (individual computer). The board will moreover have pins or arrangements to connect mike and speaker and to require out +5 V or other values of control and ground associations. These sorts of arrangements shift with distinctive modules 3 (Fig. 7). A lot of assortments of GSM modem and GSM modules are accessible within the and get SMS utilizing Arduino—it is continuously great to choose an Arduino consistent GSM module—that is a GSM showcase to select from. For our venture of interfacing a GSM modem or module to arduino and consequently send module with TTL Yield provisions (Fig. 8).

9 Wi-Fi MODULE The ESP8266 Wi-Fi module could be a self-contained SOC with coordinates TCP/ IP convention stack that can grant any microcontroller get to your Wi-Fi organize. The ESP8266 is competent in either facilitating an application or offloading all Wi-Fi organizing functions from another application processor. This module incorporates an effective sufficient on-board preparing and capacity capability that permits it to coordinate with the sensors and other applications in particular gadgets Fig. 7 GSM module

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Fig. 8 Interface of Arduino and GSM module

Fig. 9 Wi-Fi module

through its GPIOs with negligible improvement up-front and negligible stacking amid runtime. Its tall degree of on-chip integration permits for negligible outside circuitry, counting the front-end module, is outlined to involve negligible PCB range (Fig. 9). The ESP8266 underpins APSD for VoIP applications and bluetooth coexistence interfacing; it contains a self-calibrated RF permitting it to work beneath all working conditions and requires no outside RF parts. The applications of ESP8266 are keen control plugs, domestic robotization, Wi-Fi location-aware gadgets, industrial wireless control, and security ID tags.

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Power Supply

A large fundamental method to manage gets a 12 and 5 V DC control supply using a single circuit. The circuit uses two ICs 7812 and 7805 for securing the required voltages. The AC main voltage will meander someplace close to the transformer, changed by expansion and sifted by the capacitor to get a dependable DC level. The 7812 deals with this voltage to get a steady 12 V DC. The yield of the IC1 will be managed by the 7805 to get a relentless 5 V DC at its yield. Along these lines, both 12 and 5 V DC are gotten. At first, little advance down transformer is used to reduce the voltage level 230 V AC into 12 V AC (Fig. 10). The yield of the transformer may be a throbbing sinusoidal AC voltage, which is changed over to throbbing DC with the assistance of a rectifier. This yield is given to a channel circuit that diminishes the AC swells and passes the DC components. 7812 regulator is used to converts 12 V DC study voltage. And 7805 regulator is converting constant 5 V DC voltage (Fig. 11).

11

Hardware Output

The sensors are related to a microcontroller to pursue the status which is along these lines interfaced with an LCD show similar to Wi-Fi affiliation to transmit alerts. In the case, system recognizes any unexpected changes in patient heartbeat or body temperature; the structure thus alerts the customer about the patient’s status over IoT; what is more, it demonstrates subtleties of heartbeat and temperature of patient live over the web. Consequently, IoT-based patient prosperity following system sufficiently uses the web to screen tireless prosperity subtleties and extra lives on the timetable (Fig. 12). Fig. 10 Power supply

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Fig. 11 Circuit diagram of power supply

Fig. 12 Hardware output

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Conclusion

This project focuses on a real-time pervasive healthcare monitoring system using IoT and cloud computing service which is more beneficial for elders and chronic disease patients. The current methods available for the realization of healthcare services are surveyed and the challenges that are part of realization are also highlighted. This paper proposes an intelligent real-time patient monitoring system that monitors the subject’s vital parameters such as temperature, pulse, fall detection model as well as detects any abnormality accurately. Appropriate medications are suggested based on the diagnosis of the provided set of symptoms. The system sends an alert message to the caretakers and doctors in case of any abnormality through WBAN. The system enables the clinicians to optimize the usage of available medical resources and minimize the costs in monitoring the patients. In the future, we will focus on improving wearing sensor experience by using softer materials and enabling controlled sharing of information among the doctors, the patient, and the patients’ families through the social networking paradigm.

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References 1. Y. Zhang, H. Liu, X. Su, P. Jiang, D. Wei, Remote mobile health monitoring system based on smart phone and browser/server structure. Healthc. Eng. 6(4), 717–738 (2015) 2. M. Pustiek, A. Beristain, A. Kos, Challenges in wearable devices based pervasive wellbeing monitoring, in Proceedings—2015 International Conference on Identification, Information, and Knowledge in the Internet of Things, IIKI 2015, pp. 236–243 (2016) 3. J. Gómez, B. Oviedo, E. Zhuma, Patient monitoring system based on Internet of Things, in Procedia Computer Science, vol. 83, no. Ant (2016), pp. 90–97 4. I. Chiuchisan, I.M. Dimian, U. Street, Internet of Things for e-Health: an approach to medical applications, in 2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM) (2015), p. 5 5. T. RajeshKumar, G.R. Suresh, Examination of militants utilizing NAM microphone and wireless handset for murmured speech in view of concealed Markov model. Int. Innov. Res. J. Eng. Technol. 02(04), 112–119 (2017)

Measurement of Fuel Level in Tank Using IR Sensors and Reporting Over IoT M. L. S. N. S. Lakshmi and Chandrasekhar Reddy

Abstract Raw cotton in the spinning mills is cleaned using petrol. Petrol is one of the essential fuels. This project is designed for the spinning industries to measure the level of the liquid content in the tanks. The usage of IR sensors and magnetic sensors evade the problem of oxidizing the level probes. Thermistor is a resistive device, which is used as a temperature sensor. The content of liquid/fuel in the tank is monitored by microcontroller. This level is displayed and updated using the IoT module interfaced with the microcontroller.













Keywords Keil software IR sensors Microcontroller Embedded system IoT

1 Introduction Embedded system is the mix of hardware and programming for devoted purposes. In an embedded system, the microcontroller is totally exemplified inside the system. These embedded systems dislike broadly useful PC, for example, PC, an embedded system performs one-to-numerous predefined assignments, as a rule with quite certain prerequisites. The purpose of embedded systems ranges from portable devices such as calculators and digital cameras to large stationeries like ATM’s, personal digital assistants, or the systems controlling nuclear power plants. As far as multifaceted nature embedded frameworks can go from exceptionally basic with a solitary microcontroller chip, to various units, peripherals and systems mounted inside an enclosure. Internet of Things [1] is connecting the physical devices to your network. These connections make your day-to-day life easy. Here, this IoT [2] device monitors the fuel level and warns about the fuel level in the tank. These devices are

M. L. S. N. S.Lakshmi (&)
C. Reddy Department of Electronics and Communication Engineering, QIS College of Engineering and Technology, Ongole, Andhra Pradesh, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_69

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communicated using Keil software with IoT [3] interface. As fuel is the most essential thing used to run the automobile, monitoring of the fuel is prominent step in any automobile industries. The fuel management systems [4] are dedicated to maintenance and measurement of the fuel level of vehicles or systems. Fuel management can be done using following technologies RFID tags, IR sensors, handheld scanners, Bluetooth, and nozzle-based technologies. In this paper, the Fuel executives framework that estimates tank’s fuel level to be shown through online application and structure of camera observation framework for station utilizing IR sensors [12] is proposed. The paper is organized in the following structure. Section 2 describes the block diagram of the system. In Sect. 3, the system description is described. The implementation of the system using Keil software is explained in Sect. 4. Finally, the observations of this paper are recapitulated in Sect. 5.

2 Block Diagram This petrochemical pumps [4] the liquid from tank to the process container by utilizing an AC motor as shown in Fig. 1. The limits of these process containers are up to 2000 litres. To fill the container, the motor takes more than one hour. An operator has to check the level of petrochemical in the tank to avoid overflow. In this project, magnetic sensors are used to notice the level of petrochemical in the tank. These magnetic sensors [8] are fixed on the walls of the tank. A floating magnet floats on the liquid and activates the magnetic sensors [11] one after the other based on the liquid level. This magnetic sensor is monitored by microcontroller. If the liquid level in the tank is maximum, it turns off the motor switch else waits for the fluid to be of maximum level. Also, this microcontroller displays the level in the LCD. The working of the system is as shown as above in the block diagram Fig. 1. The driver circuit is activated by sensing “Level Full” and “Low Level” indicators and operates the motor automatically [9, 10]. A buzzer is connected to the driver circuit, and it is activated during “Level Full” and “Low Level” of the tank [11]. IR sensors [6] are used to eliminate the problem of oxidization of level probes. This is an advanced and automated approach for industrial applications. This project uses regulated 5 V, 500 mA power supply. 7805 three-terminal voltage regulators are used for voltage regulation. Bridge type full wave rectifier is used to rectify the AC output of secondary of 230/12 V step-down transformer. After successfully connection of kit as shown in block diagram, first, switch on the power supply then bridge rectifier takes this 230 V AC supply and delivers the circuit required power that is 12 V and it also converts AC to DC, and this is because the kit can work only for DC power. After this process, LCD screen displays the waiting for network. At that time of power supply, the Wi-Fi IoT module in the kit automatically switches ON. It gives the Wi-Fi network with this network which will connect the kit to our mobile through IP address and app name is “Telnet”. After making of Wi-Fi connection, LCD displays “connected” message

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Fig. 1 Block diagram of fuel management using IR sensor

on its screen. Now, proceed to do the experiment. Now the IR sensors [5, 7] sense the petrol flow from top to bottom and switch the motor ON and OFF through the relay automatically. The filling percentage of the petrol tank is displayed on the LCD screen like 15%, 50%, 75%, etc., and this filling percentage is also sent to our mobile through IoT. By using this, petrol in the tank cannot be overflow and it cannot be empty means that it saves the wastage of petrol and decreases the manpower.

3 Software Details: Keil Keil software development tools for the 8051 Microcontroller Architecture support every level of software developer from the professional applications engineer to the student just learning about embedded software development. The industry-standard Keil C Compilers, Macro Assemblers, Debuggers, Real-time Kernels, Single-board Computers, and Emulators support all 8051 derivatives to execute different applications (Fig. 2).

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Fig. 2 On-board diagram

4 Results and Discussion of the System After switches on the power supply, the Wi-Fi module also switches on and sends the Wi-Fi signals to certain range. Using these signals, pair our mobile using Wi-Fi network through the app named as Telnet. Now open the app it asks IP address which is mentioned below after entering that click on connect then LCD shows connected which means mobile is connected to the circuit. The output is shown in Fig. 3. This procedure is explained below. Now the fuel passing through the sensors, a beep sound will be occurred after that the filling percentage of the tank is displayed like TANK 50% FULL, TANK 75% FULL which is shown in Fig. 4. The output of this circuit has displayed on the LCD as well as in our mobile through Wi-Fi IoT module and Telnet app downloaded in Play Store and paired to the circuit using IP address 191.164.1.18. The above results are the outputs of this circuit which will show in the mobile. The liquid in the tank flows from bottom to top, and it crosses the IR sensors at the particular level in the task as required. After giving the power, the LCD displayed the fuel percentage in the tank, i.e. 15%, 50%, 75%, etc., which shows temporarily, and the same output was displayed in paired mobile. The output shown in our mobile was displayed until the Telnet app was closed. This is shown in Fig. 4. By using this Wi-Fi module, there is no need to visit the tank because the filling percentage of tank is displayed in mobile. By using this technology, wastage of fuel is less; hence, it is mainly used in spinning mills to clean the cotton.

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Fig. 3 On-board connections

Fig. 4 Output of the module

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5 Conclusion This paper improvement depends on fuel in the board frameworks to quantify the tank’s fuel level, and it is to be shown through electronic application and structure of a camera supervision framework for the station. The task is utilized to quantify the oil level in the tank without the wastage of oil. It stores the data about filling the level of the tank. It uses solar and nuclear power for the functioning of the system. The charged power acquired from the board is given to the circuit. The power stays for the duration of the day and even in overcast days moreover. The circuit or framework will support various kinds of sensors, i.e. IR sensors just as well as magnetic sensors because the operation is the same. In view of the application, necessity one can choose the sensors. In spite of the fact that this is less expensive, the yields delivered by the ultrasonic sensor experience the ill effects of defectiveness brought about by the vapours of fuel. The synthetic ETAPA, being costly, settles this brokenness and gives a higher resolution.

References 1. O. Bello, S. Zeadally, Intelligent Device-to-Device Communication in the Internet of Things, Ieeexplore.Ieee.Org (2015), pp. 1–11 2. J. Carretero, J.D. García, The Internet of Things: Connecting the world. Personal Ubiquitous Computing (2013) 3. D. Rountree, I. Castrillo, The Basics of Cloud Computing (2014), pp. 123–149 4. M. Saravanan, T. Krishna Priya, S.R. Lavanya, P. Karthikeyan, Fuel level indicator for petrol bunk storage tanks/oil industries. Int. Res. J. Eng. Technol. (IRJET) 05(10). e-ISSN: 2395-0056, Oct 2018 p-ISSN: 2395-0072 (2018) 5. R. Gogawale, S. Sonawane, O. Swami, S.S. Nikam, Petrol level detection using ultrasonic sensor. Int. Eng. Res. J. (IERJ) 2(2), 848–850 (2016). ISSN 2395-1621 6. P. Veenasheela Rao, S.B. Chaudhury, Smart active liquid level measurement using infrared sensors suitable as consumer products. Int. J. Res. Eng. Technol. Sci. VII, Special Issue, Feb 2017, ISSN 2454-1915 7. T. Mohammad, Using ultrasonic and infrared sensors for distance measurement, in Int. J. Mech. Aerosp. Ind. Mechatron. Manuf. Eng. 3(3) (2009) 8. H. Canbolat, A novel level measurement technique using three capacitive sensors for liquids. IEEE Trans. Instrum. Meas. 58(10), 3762–3768 (2009) 9. A. Mane, R.C. Gadade, S. Gandhi, Smart fuel level indication system.GRD J. 2(6) (2017) 10. G. Ravichandran, V. Palanivel, A highly intelligent into system to monitor the petrol flow the petrol tank. Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET) 3(VIII), August 2015, IC Value: 13.98, ISSN: 2321-9653 11. O. Deokate, A. Kolekar, N. Shinde, A. Khanapure, Digital fuel level indicator system, in National Conference on Information, Communication and Energy Systems and Technologies 2019, vol. 5, issue 7 (2019). Print ISSN: 2395-1990, Online ISSN: 2394-4099 12. Method for measuring fluid level, Dec 24, 2002, US 6,823,731B1, Liquid Level Sensing Assembly and Method for Measuring Using Same (2004) 13. S. Kumari, G. Rathi, P. Attri, M. Kumar, Types of sensors and their applications. Int. J. Eng.178 Res. Dev. 10(4), 72–85 (2014). e-ISSN: 2278067X, ISSN: 2278-800X, April 2014

Miscellaneous

Image Denoising Using Spatial and Frequency Domain Filters: A Tool for Image Quality Enhancement Santhi Krishnamoorthi, Nirmala Madian and Dhanasekaran Rajagopal

Abstract Image denoising plays a vital role in image analysis. Image denoising helps in removing noise in the image by which the quality of the image is improved. Image denoising is mainly used in photographic images for enhancing the quality, medical image analysis where inbuilt noises are present due to images obtained from various imaging modalities. These noises should be removed to get a better understanding of medical images to detect tumor, cancer, and other diseases. Denoising helps in improving the image fidelity in satellite images. This work focuses on performing image denoising by various spatial and frequency domain filters on photographic images to enhance the image quality. The comparative analysis of all these filters is analyzed based on quality metrics like peak signal-to-noise ratio (PSN)R and mean square error (MSE). Keywords Image denoising


Spatial and frequency filters
PSNR
MSE

1 Introduction Image denoising helps in removing the noise and also helps in preserving the visual quality of the image. The noise in the image degrades the quality of the image; therefore, image denoising plays a vital role for image quality improvement. Image denoising is a pre-processing procedure which is used for filtering operation. The filtering can be linear of nonlinear. Spatial and frequency domain filters are used for denoising and frequency domain filters are more effective as they preserve the image information better than the spatial filters. S. Krishnamoorthi (&) Guru Nanak Institutions Technical Campus, Hyderabad, India N. Madian Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India D. Rajagopal Guru Nanak Institute of Technology, Hyderabad, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_70

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Various denoising algorithms for removing the noise in the image are median filter [1–7], wavelet decomposition [8–12], Complex wavelet transform [9], anisotropic diffusion [10, 11], average homomorphic Wiener filter [3, 12], NLM filter [13], waveshrink method with Bayesian map [14], adaptive denoising using wavelet [15], curvelet transform [16, 17, 23], linear and nonlinear filters [19–21], soft thresholding [22], and average filters [24]. The paper proposes a comparative analysis of image denoising using spatial and frequency domain filters. This analysis will help the researchers to understand relation between filters and noise model. Image denoising algorithms are to be carefully reviewed based on the image type. Depending on the image type, the algorithms are to be derived. Various image types include biomedical, photographic, satellite images, etc. Each image type requires specific algorithms for removing noise in that image. The quality of the image is analyzed based on quality metrics like PSNR and MSE.

2 Methodology The input image considered is a photographic image. The noise is added to the image. The filtering operation is applied to remove the noise. Spatial and frequency domain filters are considered. The output obtained is the denoised image with improved quality. The methodology is shown in Fig. 1. The noise in the image is modeled based on histogram or probability density function. The common noise models [18] used for the study are Gaussian, salt and pepper, speckle, Poisson, and additive white Gaussian noises. Gaussian noise model is used both in spatial and frequency domains due to its deterministic representation. Salt and pepper noise have dark and bright pixel representation in bright and dark regions, respectively. This noise is due to the error when converting the analog function to digital function and bit errors during transmission. Speckle is considered as manipulative noise due to which the interpretation of the image is difficult and these types of noise are dominated in laser holography and ultrasound image. Poisson noise is a form of electronic noise and is modeled using Poisson process. An uncorrelated random value results in white noise in an image. These noises in images are to be eliminated and this is done using spatial and frequency domain filters.

Fig. 1 Work flow methodology

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Spatial domain filters considered for the study are mean, median, Wiener, and Gaussian filters. Mean filters are called as average filter and it is a type of linear filtering. Each pixel in the image is replaced with the average value of its neighborhood pixels. The result of the filter is based on the window function. Larger the window size smoother the image results in blurring of image. Another linear filter is Wiener filter and works on statistical approach. Median filters are nonlinear filters effectively remove noise and also preserves the edges in the image. The problem associated with median filter is that it removes the fine details in the image. Gaussian filter uses 2D convolution operation for filtering the noise in the image. Gaussian kernel is used for convolution and the output is smoothened and blurred image. Frequency domain filters considered are wavelet and curvelet transforms and filters like Lee, Kuan, and frost are also used for denoising image. Discrete wavelet transform (DWT) analyzes the data using mathematical function based on the resolution or scale of the input data. Initially, the wavelet coefficients are extracted by decomposing the image using wavelet transform. The coefficients are called as approximation and detailed coefficients. A proper threshold function is selected and thresholding is performed on the detailed coefficients which gives a modified coefficient. The original image is obtained by performing the inverse transform on the modified coefficients. DWT obey multi-resolution analysis property which helps in analyzing the images at various resolutions. This transform is implemented by digital filters and decimators. A sub-band decomposition helps in decomposing the data into low and high pass filter stages and the iteration of this process is performed using the output of the low pass filter stage and can have many decomposition levels. Each stage generates approximation and detailed coefficient of the image. The complex valued extension to the standard DWT is the complex wavelet transform (CWT). This transform helps in removing the problems associated with phase information. The magnitude and the phase information are obtained using the real and imaginary coefficient of the image after applying CWT. The CWT works on dual-tree concept where one side of the tree deals with real values and other side of the tree is the imaginary component. Dual-tree CWT is better as this gives a shift invariant and directionally selective output. This is achieved using redundancy factor 2d, where d is the dimension. Curvelet transform is a multi-scale pyramid structure which holds many directions and position location and gives a needle-shaped foundation at the fine scale. This transform gives the sparse representation of the image by computing the inner product of the image and curvelet kernel. A trous algorithm is applied on the image with J scale. The image is divided into sub-band with appropriate block size using digital ridgelet transform on each block. The length of the kernel window is doubled at every dyadic sub-band which helps in preserving the fundamental property of curvelet transform. Thresholding and inverse curvelet are performed to obtain the original denoised image.

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Frost, lee, and Kuan filters help in removing the multiplicative noise in the image. Frost filter is a linear convolution filter and it is also an exponentially weighted averaging filter. The filter works based on the variation in the coefficient obtained with respect to the ratio of standard deviation to mean of the noisy image. The center pixel value of the filter kernel is replaced with weighted sum of the neighboring pixel. The weight value decreases when moving away from the target pixel which increases the variance. Lee filter works based on the variance value. The filter helps in preserving the edges in the image and is adaptive in nature. Kuan filter is a linear minimum mean-square-error filter. This filter also works on calculating the standard deviance and variance in the image.

3 Results and Discussion The noises considered for the analysis are salt and pepper, Poisson, Gaussian, speckle, and white Gaussian noises as in Fig. 2. The output of various filters is shown in Fig. 3. The performance of the filters is analyzed based on the quality metrics like PSNR and MSE. The PSNR is a measure of quality between the original and the denoised image. Higher the PSNR, better the quality of the image and is given as PSNR ¼ 10  log10

ð 2n  1Þ 2 MSE

where n is the number of bits in the image, MSE is the cumulative squared error between the original and the denoised image and is given as MSE ¼

M X N  1 X X MN j¼1 k¼1 j; k



 X j; k

2

where x and x is the restored and original image, M and N are the number of rows and columns in images. As MSE increases, PSNR decreases, respectively.

Fig. 2 Input images with noise

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Fig. 3 Spatial and frequency domain filters

The comparative analysis of spatial and frequency domain filters are given in Tables 1 and 2. From the performance analysis based on PSNR and MSE, the following observations are performed. For salt and pepper and Poisson noise, median filter and curvelet transform are suitable for removing the noise. For Gaussian noise, Gaussian and wavelet transform are preferred. For speckle and white Gaussian, the suitable filters are Wiener and wavelet transform.

4 Conclusion Image denoising helps in reducing the noise in the image and also improves the quality of the image. The accuracy of the filter in denoising determined based on quality metrics like PSNR and MSE. This paper helps in understanding the filter performance for various noises. From the performance analysis, frequency domain filters prove to be better compared to spatial filters. Among frequency domain filters, wavelet and curvelet outperform compared to other filters. Future scope of the work is to perform the performance analysis of spatial and frequency domain filters on medical images and satellite images and also uses convolution neural network for image denoising. Denoising is an important pre-processing method in medical image processing as it helps in differentiating the normal and pathogenic condition and in satellite, they are used for understanding the image fidelity.

NV Noise variance

Additive white Gaussian noise

Speckle noise

0.01 0.04 0.08 0.1 0.01 0.04 0.08 0.1 10 20 40 80

0.01 0.04 0.08 0.1

Salt and pepper noise

Poisson noise Gaussian noise

NV

Types of noise

30.78 26.49 22.89 21.34 32.79 25.09 20.65 16.69 15.41 29.50 25.56 22.46 21.23 22.51 29.18 32.74 32.79 38.81 38.40 38.05 37.80 38.92 25.69 18.80 14.75 13.68 28.67 21.64 16.85 15.51 20.67 30.47 38.11 38.92

Quality metrics PSNR Types of filters Mean Median

Table 1 Performance analysis of spatial domain filters

Gaussian 32.37 27.19 23.51 22.71 33.27 28.45 22.30 18.64 17.26 29.74 25.67 22.53 21.28 22.58 29.45 33.20 33.27

Weiner 31.13 26.69 22.94 21.40 36.83 26.02 20.65 16.67 15.36 32.58 28.14 24.44 22.13 24.71 32.77 36.82 36.84

54.26 145.4 334.3 476.55 34.22 168.09 559.99 1393.5 1869.2 73.016 180.12 368.84 490.21 364.57 78.49 34.58 34.22 8.56 9.40 10.20 10.78 8.33 175.36 856.48 2179.42 2786.26 88.33 445.94 1342.77 1827.26 557.13 58.41 10.04 8.33

MSE Types of filters Mean Median Gaussian 37.71 124.16 289.93 348.13 30.64 92.98 382.46 889.83 1222.46 69.03 176.18 361.15 483.91 359.22 73.79 31.09 30.66

Weiner 50.10 139.44 329.72 471.03 13.48 162.76 559.80 1401.4 1892.9 35.88 99.77 233.82 398.28 220.05 34.33 13.53 13.47

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41.09 31.85 28.79 30.61 24.08

Salt and pepper Poisson Gaussian Speckle White Gaussian

39.25 32.76 29.21 28.77 24.08

PSNR Types of filters Lee Kuan

Noise

33.86 36.31 34.51 33.95 24.08

Frost

Table 2 Performance analysis of frequency domain filters

Wavelet 64.58 64.53 63.93 64.55 64.07 66.85 78.02 62.32 61.88 63.60

Curvelet 5.06 42.47 86.01 56.56 254.06

7.72 34.45 77.94 86.24 254.06

MSE Types of filters Lee Kuan

26.71 15.18 23.01 26.21 254.06

Frost

0.02 0.02 0.03 0.02 0.02

Wavelet

0.01 0.01 0.04 0.04 0.03

Curvelet

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References 1. J.C. Church, Y. Chen, S.V. Rice, A spatial median filter for noise removal in digital images, in IEEE, pp. 618–623 (2008) 2. R.H. Chan, C.-W. Ho, M. Nikolova, Salt and pepper noise removal by median-type noise detectors and detail preserving regularization. IEEE Trans. Image Process. 14(10), 1479–1485 (2005) 3. S. Kumar, P. Kumar, M. Gupta, A.K. Nagawat, Performance comparison of median and wiener filter in image de-noising. Int. J. Comput. Appl. 12(4), 27–31 (2010) 4. T. Huang, G. Yang, G. Tang, A fast two-dimensional median filtering algorithm. IEEE Trans. Acoust. Speech Sign. Process. 27(1), 13–18 (1979) 5. H. Ibrahim, N.S. Kong, T.F. Ng, Simple adaptive median filter for the removal of impulse noise from highly corrupted images. IEEE Trans. Consumer Electron. 54(4), 1920–1927 (2008) 6. S. Akkoul, R. Ledee, R. Leconge, R. Harba, A new adaptive switching median filter. IEEE Signal Process. Lett. 17(6), 587–590 (2010) 7. A. Fabiaska, D. Sankowski, Noise adaptive switching median-based filter for impulse noise removal from extremely corrupted images. IET Image Process. 5(5), 472–480 (2011) 8. S.D. Ruikar, D. Doye, Wavelet based image denoising technique. Int. J. Adv. Comput. Sci. Appl. 2(3) (2011) 9. S.A. Murugan, K. Karthikeyan, N.A. Natraj, C.R. Rathish, Speckle noise removal using dual tree complex wavelet transform. Int. J. Sci. Technol. Res. 2(8) (2013) (ISSN: 2277-8616) 10. P. Perona, J. Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans Pattern Anal. Mach. Intell. 12(7), 629–639 (1990) 11. J. Weickert, A review of nonlinear diffusion filtering, in Scale-Space Theory in Computer Vision (Springer, LNCS, 1997), vol. 1252, pp. 1–28 12. M. Kazubek, Wavelet domain image denoising by thresholding and Wiener filtering. IEEE Sign. Proces. lett. 10(11), 324–326 (2003) 13. A. Buades, B. Coll, J Morel, A non-local algorithm for image denoising, in IEEE International Conference on Computer Vision and Pattern Recognition (2005) 14. G. Andria, F. Attivissimo, G. Cavone, A.M.L. Lanzolla, Selection of wavelet functions and thresholding parameters in ultrasound image denoising, in Medical Measurements and Applications Proceedings, IEEE International Conference, pp. 49–52 (2013) 15. S. Khera, S. Malhotra, Survey on medical image de noising using various filters and wavelet transform. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(4), 230–234 (2014) 16. A.K. Bains, P. Sandhu, Image denoising using curvelet transform. Int. J. Curr. Eng. Technol. 5(1), 490–493 (2015) 17. M.M. Bobby, Performance comparison between filters and wavelet transform in image de noising for different noises. Int. J. Comput. Sci. Commun. 2(2), 637–639 (2011) 18. C. Boncelet, Image noise models, in ed. by A.C. Bovik. Handbook of Image and Video Processing (2005) 19. R. Srinivas, S. Panda, Performance analysis of various filters for image noise removal in different noise environment. Int. J. Adv. Comput. Res. 3(13), 47–52 (2013). (ISSN (online): 2277-7970) 20. P. Agrawal, J.S. Verma, A survey of linear and non-linear filters for noise reduction. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 1(3) (2013) 21. N.P. Bhosale1, R. Manza, K.V. Kale, Analysis of effect of Gaussian, salt and pepper noise removal from noisy remote sensing images, in Proceedings of the International Conference on Emerging Research in Computing, Information, Communication and Applications, Elsevier, pp. 386–390 (2014) 22. D.L. Donoho, De-noising by soft-thresholding. IEEE Trans. on Inf. Theory 41(3), 613–627 (1995)

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23. R. Biswas, D. Purkayastha, S. Roy in Denoising of MRI Images Using Curvelet Transform, eds. by A. Konkani, R. Bera, S. Paul. Advances in Systems, Control and Automation. Lecture Notes in Electrical Engineering, vol. 442 (Springer, Singapore, 2018) 24. S. Gupta, S. Roy, Medav filter—filter for removal of image noise with the combination of median and average filters, in Recent Trends in Signal and Image Processing. Advances in Intelligent Systems and Computing, vol. 727, eds. by S. Bhattacharyya, A. Mukherjee, H. Bhaumik, S. Das, K. Yoshida (Springer, Singapore, 2019)

Comparative Study and Analysis of Human Knee Angle Measurement System S. Boobalan, K. Lakshmi and K. N. Thirukkuralkani

Abstract Human gait analysis is one of the most important tools to drive the actuator of the bionic leg, which is designed and acts on the command received from the gait system. In this article, we implemented the inertial measurement unit (IMU) and image processing system using Kinovea software to measure the human knee angle. A traditional tool called goniometer was used to measure the human knee angle. An updated new modified goniometer is introduced in this project to analyze the human gait system. IMU sensors are interfaced with Arduino, and the data was acquired and stored in the PC for the purpose of further analysis. The Kinovea is one the powerful sports analysis software, which was introduced here to measure the human knee angle measuring in different lightening condition. The comparative human knee angle measurement was studied, in that the acquired data was compared with each other system.







Keywords Human gait system Goniometer Inertial measurement unit Kinovea sports software Image processing system Arduino










1 Introduction For a normal human being, it is not a difficult thing to walk but incase of abnormalities like amputee lower limb person, affected by Parkinson’s disease (PD) and Paralysis person, it is the very difficult job to do walk. In recent years, the development of digital controller evolution plays a major in many different interdisciplinary areas [1, 2]. The contribution of digital controller in the domain of S. Boobalan (&)
K. Lakshmi Department of Electrical and Electronics, Sri Krishna College of Engineering and Technology, Coimbatore, India e-mail: [email protected] K. N. Thirukkuralkani Department of Electronics and Instrumentation, Sri Ramakrishna Engineering College, Coimbatore, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_71

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biotechnology and bionics engineering technology is more. If the control signal to the controller is more than perfect means, the controller could able to achieve its output without any lag. If the input is not up to the accuracy level means, the effect of controller will not achieve its output. In recent years, many companies are manufacturing and marketing the bionic leg for amputee person [3, 4]. The controllers are used in that bionic leg is an intelligent controller, and its output passes to the actuator driver circuit system. The input to the digital controller is received from the muscle sensor, electrodes, IMU sensor. The acquired data is preamplified and filtered, and then, the signals are classified using MATLAB. The human gait analysis is the major work to copy the nature of human walking style [5, 6]. It involves the study of locomotion of human walk. Human gait cycle involves the major two phases like stance phase, swing phase (Fig. 1). During stance phase, the human foot is always on the ground. During swing phase, the human air is in always above the ground. The human gait cycle angle is measured and calculated as follows (Table 1), The bionic leg consists of DC servo motor, onboard controller, signal conditioning circuit, battery, driving circuit, etc., and the motor is used to move the artificial limb looks like normal human walking gait cycle [7–9]. So that it should move forward and backward by the angle which is copied from the normal human gait cycle. To capture the normal human gait cycle, there are more methods are available to capture the human gait cycle. The various methods like digital goniometer, IMU, vision system [10, 11]. These are all methods are considered and

Fig. 1 Human gait cycle pattern

Table 1 Human knee movable joint angle

Movable joint range Joint angle range

In degree Pitch

Knee Ankle

0–90 −30 to 50

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implemented and capture the human gait cycle. From the comparative analysis, it has been concluded that the IMU-based capture system yields the good result. Even though it has its own drawback.

2 Human Gait Cycle System A. Goniometer Goniometer is the one traditional method to measure inclined angle between any two points. Nowadays, a digital goniometer is available in the market. The digital goniometer will able to measure the angle, and it could be able to show the result as digital data [3, 2]. The acquired digital data could be interfaced with the signal condition system. The procedure follows, a ten normal human beings are trained in the treadmill walking, and their human gait cycle captured. The trained subjects were asked to walk on the treadmill, before that the cables and digital goniometer were fixed accordingly do not get moved from fixed place. The data acquisition card is placed inside the subject’s packet. The data logger is then communicated with the controller, and data is stored in the laptop. The trained subjects were asked to do walking on the treadmill in a normal selected speed, the following functions carried out, 1. 2. 3. 4. 5. 6. 7. 8. 9.

Heel Strike Loading Response Mid Stance Unloading stance Terminal Stance Pre-Swing Toe Off Mid Swing Terminal Swing.

These are all the above functions carried out while the trained subjects on the treadmill walking period. Then, acquired data stored in the storage device for further analysis. B. Inertial Measurement Unit (IMU) A micro-electro mechanical system (MEMS) is the combination of mechanical system and electronic system. After the greater evolution of MEMS sensor, the measurements of velocity, acceleration, angle and orientation are becoming more easier than ever before. These MEMS device could easily interface with any kind of external hardware like Arduino, Raspberry, etc., for further analysis [12, 13]. Once the data acquired from the sensor through wired or wireless, then it could be sent to preamplifier, filter and analog to digital conversion process (ADC).

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The ten normal subjects were asked to wear the IMU sensor to each their right leg thigh. In this article, the author implemented wireless communication between IMU sensor to receiver. From the measurement side, IMU sensor is connected to Nano Arduino board. The whole setup is packed and placed inside the subject’s packet while the subject on the treadmill walking. On the other hand, receiving side one Nano Arduino board is to receive the IMU signals. The receiving end Arduino board is connected to external PC for further process [14, 15]. The communication distance may vary about the environment condition, and under normal environmental condition, the distance is about 500 meters. The gait cycle data was drawn once the IMU sensor reads the angle of human knee. The same trained subjects are asked to wear the IMU Sensor kits and follow the same functions carry out from the goniometer process. Then, the data was acquired and stored in PC for further process. C. Kinovea Vision System—Image Processing Gait Analysis Kinovea is one of the free open ware sports analysis software, which is available in the market at free of cost. Kinovea software has the in-built feature of video analysis tool in it [16, 17]. It can able to analyze the image at very low speed with the high accuracy rate. The trained subjects were asked to walk on the treadmill with the speed of 3.0 km per hour. Then, the video has been recorded, and the captured videos are sent to Kinovea software system. Then, it could be processed by frame per frame. The human gait cycle system is analyzed, and the knee angles are measured clearly by using Kinovea software.

3 Results and Discussion A. Goniometer Results The traditional method is the goniometer method, which is the best conventional method to measure angle by ourself. It is the purely conventional and off-line method. The measured data could be tabulated in off-line mode only. From the data, we can study the human gait cycle pattern only. We could not able to interpret the measured data to any controller. So, goniometer methods have shown the better results even though this method will not be applicable in digital controller circuit. Because this conventional system could not able to communicate with the environments itself. B. Inertial Measurement Unit IMU Results IMU sensor-based knee measurement system could able to measure the data accurately. But IMU system will introduce the more artifact into the system. Due to wired and wireless system, it could introduce the more artifact to the system. So, we need to introduce the more filtering circuit to remove those artifacts. So, it would get more complexity to readout the exact knee angle values from the sensors.

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Fig. 2 Human gait cycle pattern analysis using IMU

But the response from the sensor is too high but the artifact introduces more complexity to system. Trained subjects were asked to walk on the treadmill for about 2 min. Then, the image processing has been on Kinovea software. The following Fig shows the two-dimensional graph for the knee angle value about 4 s of human gait cycle (Fig. 2). C. Vision System based Image Processing System Results The following Fig. shows the human gait cycle system with knee angle values. The captured values are tabulated, and it will be stored in the storage devices for further process (Fig. 3). The following shows the knee angle values in degree by using Kinovea software. The subject was trained, and treadmill speed was at 3.0 km per hour for 1 min. The knee angle values are acquired at stored. The knee angles were acquired for all the trained subjects. But the gait pattern or style of walking is different from one to another person. Each and every person does have his/her own gait pattern or walking style. The following table shows the knee angle values, which it could be common to all type of normal people (Table 2).

4 Conclusion In this article, the author implemented the various human knee angle measurement system like digital goniometer system, IMU sensor-based measurement system and vision system. From the results, it has been concluded that the above all methods have its own advantage and disadvantages itself due its characteristics. Goniometer has produced good results, but communication between external environments is

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Fig. 3 Human gait cycle pattern analysis using Kinovea software

Table 2 Human gait cycle pattern analysis using Kinovea software

Gait state position

Minimum

Maximum

Heel strike Loading response Mid stance Unloading stance Terminal stance Pre-swing Toe off Mid swing Terminal swing

4 8 13 19 26 48 55 60 10

8 12 17 23 31 57 62 66 15

more difficult. So, the conventional measurement system is not worked in the entire system of human gait cycle controller system. From the IMU sensor system, the more artifacts are introduced to the controller circuit. The filtering circuits need to introduce to remove those artifacts. Then, the complexity of the system gets increased. Among the all three human gait cycle measurement system, the vision-based system produces the good results because it could able to communicate with external environmental system through computer. The vision-based system could not introduce any artifacts to the system. Only the major difficulty is that the image processing technique and environmental lightening condition alone. In future, the author is used a deferent image processing analyze tool to improve the accuracy of human gait cycle pattern.

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Acknowledgements Ethical approval—The authors express their sincere thanks to the Management and the Principal of Sri Krishna College of Engineering and Technology, Coimbatore, for providing the necessary facilities for the completion of this paper. This study was approved by the ethics committee of Institutional Human Ethics Committee (IHEC), PSGIMS&R Coimbatore, India. The authors state that this study conforms to the ethical standards contribute to human welfare by ensuring a research process that combines the highest integrity and safety of human research participants.

References 1. C.N. Teague et al., Novel methods for sensing acoustical emissions from the knee for wearable joint health assessment. IEEE Trans. Biomed. Eng. 63(8), 1581–1590 (2016) 2. U.-J. Yang, J.-Y. Kim, Mechanical design of powered prosthetic leg and walking pattern generation based on motion capture data. Adv. Robot. 29(16), 1061–1079 (2015) 3. J.J. Castañeda, et al. Knee joint angle monitoring system based on inertial measurement units for human gait analysis, in VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th-28th (Springer, Singapore, 2017) 4. P.J. Rowe et al., Knee joint kinematics in gait and other functional activities measured using flexible electrogoniometry: how much knee motion is sufficient for normal daily life? Gait & posture 12(2), 143–155 (2000) 5. Ehsan Sobhani Tehrani, Kian Jalaleddini, Robert E. Kearney, Ankle joint intrinsic dynamics is more complex than a mass-spring-damper model. IEEE Trans. Neural Syst. Rehabil. Eng. 25(9), 1568–1580 (2017) 6. B. Koopman, E.H. van Asseldonk, H. van der Kooij, Estimation of human hip and knee multi-joint dynamics using the lopes gait trainer. IEEE Trans. Rob. 32(4), 920–932 (2016) 7. L.L. Flynn et al., VUB-CYBERLEGs CYBATHLON 2016 Beta-Prosthesis: case study in control of an active two degree of freedom transfemoral prosthesis. J. Neuro Eng. Rehabil. 15 (1), 3 (2018) 8. B. Hwang, D. Jeon, A method to accurately estimate the muscular torques of human wearing exoskeletons by torque sensors. Sensors 15(4), 8337–8357 (2015) 9. N. Shaari, I. Md Isa, T. Jun, Torque analysis of the lower limb exoskeleton robot design. ARPN J. Eng. Appl. Sci. 10, 19 (2015) 10. A. Pagel et al., Bio-inspired adaptive control for active knee exoprosthetics. IEEE Trans. Neural Syst. Rehabil. Eng. 25(12), 2355–2364 (2017) 11. M.F. Eilenberg, Hartmut Geyer, Hugh Herr, Control of a powered ankle–foot prosthesis based on a neuromuscular model. IEEE Trans. Neural Syst. Rehabil. Eng. 18(2), 164–173 (2010) 12. S. Pfeifer et al., Model-based estimation of knee stiffness. IEEE Trans. Biomed. Eng. 59(9), 2604–2612 (2012) 13. S. Pfeifer et al., Actuator with angle-dependent elasticity for biomimetic transfemoral prostheses. IEEE/ASME Trans. Mechatron. 20(3), 1384–1394 (2014) 14. V. Rajťúková et al., Biomechanics of lower limb prostheses. Procedia Eng. 96, 382–391 (2014) 15. E.J. Rouse et al., Design and testing of a bionic dancing prosthesis. PLoS ONE 10(8), e0135148 (2015) 16. H. Vallery et al., Complementary limb motion estimation for the control of active knee prostheses. Biomedizinische Technik/Biomedical Engineering 56(1), 45–51 (2011) 17. A.N. Amirudin et al., Biomechanics of hip, knee and ankle joint loading during ascent and descent walking. Procedia Comput. Sci. 42, 336–344 (2014)

Voice and Image BER Analysis of the OFDM System with MECCT and MLNST Companding Techniques Over Mobile Radio Channels B. Sarala, M. Zaheer Ahamed, S. Sree Hari and V. Bhagya sree

Abstract In a single carrier system, a deep noise of fading can destroy the whole link. However, for the multicarrier scheme, a minute proportion of subcarriers are only being damaged. Using the multicarrier modulation overlapping technique, half of the bandwidth can be saved. Orthogonal frequency-division multiplexing (OFDM) is a multicarrier modulation and is widely used in broad band communications, 3rd to 5th generation networks, WLAN, WIMAX, multimedia transmission, etc. The OFDM system minimizes multipath effects such as inter-symbol interference (ISI) and inter-carrier interference (ICI) thereby increasing system capacity to accommodate a higher number of users. The objective of this paper is to improve the power efficiency (bit error rate (BER)) of the OFDM system using companding techniques. The BER performance analysis of the OFDM system over mobile radio channels, such as additive white Gaussian noise channel (AWGN) and Rayleigh fading channels are studied, analyzed, and implemented using MATLAB for voice and image. The bit error rate (BER) of the OFDM system with modified exponential companding with clipping transform (MECCT) and also with modified linear non-symmetrical transform (MLNST) companding techniques are compared with that of the OFDM system and the OFDM system with linear non-symmetrical transform (LNST) companding technique over AWGN and Rayleigh fading channels. The MECCT-based system has less BER and it saves bandwidth of 2.0 dB over AWGN channel. Keywords OFDM


System
BER
Companding
MECCT
MLNST

1 Introduction The OFDM is a type of frequency-division multiplexing (FDM) system. However, it is resistant to multipath fading and has high spectral efficiency as compared to other multiplexing techniques. It separates the assigned frequency spectrum into B. Sarala
M. Zaheer Ahamed (&)
S. Sree Hari
V. Bhagya sree Maturi Venkata Subba Rao Engineering College (MVSREC), Nadergul, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_72

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subchannels which are orthogonal to one another. This decreases the probability of cross-channel interference (CCI), thereby permitting the subcarriers to intersect. The OFDM system offers data rate up to 54 Mbps in a 20 MHz channel for IEEE 802.11a. Fifth-generation networks support OFDM system for very high-speed broadband connectivity at affordable price. This decreases the total frequency spectrum occupied, and therefore, capacity is increased. However, the OFDM system is more sensitive to frequency offset and phase noise. There is a decrease in the power efficiency of radio frequency amplifier due to the high PAPR [1]. Companding compresses the dynamic range of the input signal and expands the dynamic range of the output signal so that the noise and distortion are suppressed while the signal is maintained. Since the first patent by A.B Clark in 1928, companding has different types of applications in analog and digital communications, audio, image, and video compression in multimedia applications. The merits of the companding have lesser BER, less distortion, and less complexity than clipping. Companding increases the signal-to-noise ratio (SNR) when the input signal is low and therefore reduces the effect of a system’s noise and distortion [2]. The main objective of this paper is to reduce the BER and improve the power efficiency of the OFDM system without additional bandwidth. This paper is organized as follows: Sect. 1 describes the introduction of the OFDM system and companding function. Section 2 describes the block diagram of the OFDM system. Sect. 3 presented the MECCT and MLNST companding techniques. Computer simulation results are presented in Sect. 4. Finally, the conclusions are listed in Sect. 5.

2 OFDM System The printing area is 122 mm  193 mm. The use of the IFFT in multicarrier modulation (OFDM) transmitter is to convert the signal from frequency domain to time domain signal. The IFFT can be expressed as x ð nÞ ¼

N 1 2pjkn 1X X ðkÞe N ; n ¼ 0; . . .; N  1 N k¼0

ð1Þ

where xðnÞ describes the signal after subcarrier mapping and N is the IFFT length. The fast Fourier transform (FFT) is given by X ðK Þ ¼

N 1 X

xðnÞe

2pjkn N

; k ¼ 0; . . .; N  1

ð2Þ

n¼0

where n = 0, …, N − 1. The complex numbers xðnÞ distinguish the amplitude and phase of the various sinusoidal components of input signal X ðkÞ. The FFT converts xðnÞ to X ðkÞ and IFFT converts X ðkÞ to xðnÞ to compute xðnÞ as a sum of sinusoidal

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parts (1/N) X ðkÞe2pjkn=N with frequency K/N cycles per sample. The FFT at the receiver converts the received signal to the frequency domain to pick up N subcarriers [3]. The transceiver block diagram of the OFDM system with the MECCT and MLNST companding techniques is shown in Fig. 1. The input information is voice and image, and it is converted to the binary information. A short algorithm description of the system is as follows 1. Consider speech/voice and image as the input signal. 2. Source encoding is employed to transmit the speech and image signals into its respective information bits so as to signify the corresponding sequence in digital form. 3. These bits have been modulated using BPSK modulation, fed to IFFT block, and proposed MECCT and MLNST companding techniques. 4. Instigate noise to simulate channel errors. Signals are transmitted through AWGN and Rayleigh fading channels. 5. At the receiver de-companding, FFT and demodulation techniques are employed to recover the transmitted speech and image. 6. Calculate BER as a function against various values of SNR and plot it accordingly [4].

Data

BPSK

S/P

De-CT

ADC

Remove CP

, FFT/

Channel

S/P DCT/ DWT

Fig. 1 OFDM transceiver blocks diagram

Add CP

IFFT, P/S

P/S

BPSK Demodula tor

CT & DAC

HPA

Received data

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3 Companding Techniques The companding technique describes the compression in the transmitter and the expansion at the receiver. The main objective of this study is to reduce the BER and improve the power efficiency of the OFDM system without additional bandwidth using MECCT and MLNST companding techniques [5].

3.1

MECCT Companding

The MECCT technique combines the clipping concept and the exponential concept. It uses an algorithm known as modified exponential companding with clipping transform (MECCT). The MECCT companding algorithm is explained below xm ¼ xn when 0  jxn j  T1 ¼ T1 þ logðjXn j  T1 þ 1Þ when jXn j [ T

ð3Þ

The final output is expressed as Xmn ¼ jXm jeih where h ¼ tan1

ð4Þ

b

and xn are in the form of axn þ jbxn , the threshold value at the ðjxn jÞ , where rxn is the standard deviation, transmitter is calculated as T1 ¼ median rxn jxn j is the modulus of the OFDM transmitted symbol, and T1 is the threshold value. At the receiver, the de-companding transform operates on the received signal to obtain an estimation of the transmitted signal. The MECCT de-companding algorithm is as given below The original received signal after de-companding is a

  ^xn ¼ rn ejh ; when jrn j  T2 ¼ T2  1 þ 10jrn jT2 ejh when jrn j [ T2

ð5Þ

The threshold value at the receiver is given by T2 ¼

medianðjr n jÞ rr n

where rrn is the standard deviation and jrn j is the modulus of the MC-CDMA received symbol.

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781

The MLNST Companding

The modified linear non-symmetrical transform (MLNST) is an idea of the LNST with phase angle, which can outperform the LNST companding. The MLNST algorithm is as follows 1 Y ðnÞ ¼ X ðnÞejh ; when jX ðnÞj  TM l

ð6Þ

¼ lX ðnÞejh ; when jX ðnÞj  TM where h ¼ tan1 ba, xðnÞ is in the form of axðnÞ þ jbxðnÞ, where the ranges of l and TM are 0  l  1 and 0  TM  maxfjX ðnÞjg:. Since X ðnÞ is complex valued, the companding function should be applied to real and imaginary parts separately [5]. At the receiver, the unique signal can be recovered as stated by ^ ðnÞ ¼ lRðnÞejh ; when ne/1 ðTM Þ X 1 ¼ RðnÞejh ; when ne/2 ðTM Þ l

ð7Þ

4 Results The OFDM system with MECCT and MLNST companding schemes are applied in MATLAB with the following specifications—128 IFFT size and 64 subcarriers and the type of modulation used are BPSK with LNST and MLNST companding with the µ value of 0.65. The designed method performance can be evaluated in terms of the BER. The results are compared to OFDM system, OFDM system with LNST, MECCT, and MLNST companding schemes. The BER performance is analyzed over AWGN and Rayleigh fading channels. Figure 2 shows the speech signal which is transmitted and received over OFDM system with MECCT and MLNST companding techniques over AWGN and Rayleigh fading channels. The speech BER performance of the OFDM system with companding techniques are measured over AWGN and Rayleigh fading channels as given in Figs. 3, 4 and 5. Figures 3, 4, and 5 show the BER analysis of the speech signal which is transmitted over the OFDM system with MECCT, and also with MLNST and LNST companding techniques over AWGN channel. The OFDM system with MECCT technique has less BER over AWGN channel as compared to OFDM system and OFDM system with LNST and MLNST companding techniques. MECCT-based system saves bandwidth (Eb/N0) of 2.0 dB and 4.0 dB, respectively, as compared to the OFDM system with LNST and MLNST companding techniques

782 Fig. 2 Speech signal is transmitted and received over AWGN channel [5]

B. Sarala et al. original speech Signal 0.01

0.01

0.005

0.005

0

0

-0.005

-0.005

-0.01

Fig. 3 BER analyses of the speech signal over AWGN channel

10

0

1000

500

-0.01

500

0

1000

0 OFDM OFDM+LNST µ=0.65

10

-1 OFDM+ MLNST µ=0.65 OFDM+ MECCT

BER

10

10

10

10

-2

-3

-4

-5

0

5

10

15

20

Eb/No (dB)

at µ = 0.65 and 0.5 over AWGN channel. The MLNST is LNST with phase. The BER of the LNST based system is equal to that of the MLNST based system. The OFDM system with LNST companding requires more bandwidth at µ = 0.5 as shown in Fig. 4. The µ value of the LNST companding-based system decreases which requires more bandwidth. Figure 5 shows the BER analysis of the speech signal which is transmitted to the OFDM system with MECCT and MLNST companding techniques over Rayleigh fading channel. The OFDM system with MECCT algorithm has less BER over Rayleigh fading channel as compared to the OFDM system and OFDM system with MLNST companding and slightly higher BER than that of the OFDM with LNST companding technique. Figure 6 shows the image which is transmitted and received over OFDM system with MECCT and MLNST companding techniques over AWGN channel. The image BER performance of the OFDM system with MECCT, LNST and also MLNST companding techniques are measured over AWGN and Rayleigh fading channels.

Voice and Image BER Analysis of the OFDM System … Fig. 4 BER analyses of the speech signal over AWGN channel

10

783

0

OFDM OFDM+LNST µ=0.5

10

OFDM+ MLNST µ=0.5

-1

OFDM+ MECCT

BER

10

10

10

10

-2

-3

-4

-5

0

5

10

15

20

Eb/No

Fig. 5 BER analyses of the speech signal over Rayleigh fading channel

0.52

OFDM OFDM+LNST µ=0.65 OFDM+ MLNST µ=0.65 OFDM+ MECCT

0.5

BER

0.48

0.46

0.44

0.42

0

5

10

15

20

Eb/No (dB)

Fig. 6 Image is transmitted and received over AWGN channel [6, 7]

source image

output image

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Figure 7 demonstrates the BER analysis of the image that is transmitted with the OFDM system with MECCT, LNST, and MLNST companding techniques over AWGN channel. OFDM system with MECCT algorithm has less BER over AWGN channel as compared to the OFDM system and OFDM system with LNST and MLNST companding techniques. Figure 8 shows the image BER (linear scale) analysis over Rayleigh fading channel, and the BER values are equal for all the companding-based systems.

Fig. 7 BER analysis of the image with AWGN channel

10

0

OFDM OFDM + MECCT

10

-1

OFDM+LNST µ=0.65

BER

OFDM+ MLNST µ=0.65

10

10

10

-2

-3

-4

0

5

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15

Eb/No (dB)

Fig. 8 Image BER analyses over Rayleigh fading channel

0.5

BER

0.48

0.46 OFDM

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OFDM+ MECCT OFDM+LNST µ=0.65 OFDM+proposed MLNST µ=0.65

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5 Conclusion The major global demand intended to have high speed and high data mobile and personal communications which is quickly expanding OFDM technology also assures to be a key technique for acquiring the high data capability and high spectral efficiency requisites for high-speed wireless systems of the present and future development. Consequently, the effect of the companding scheme on considered OFDM system in AWGN and Rayleigh fading channels has been examined. The BER performance can be increased by applying MECCT companding to OFDM system. It is seen that MECCT-based OFDM system counters the noise in channels to attain improved BER for AWGN and Rayleigh fading channels. Based on simulation results, it is examined that MECCT companding scheme with OFDM system outperforms the LNST and MLNST companding-based OFDM system for speech and image signals.

References 1. T.F. Smith, M. Waterman, Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981) 2. A. Deshmukh, S. Bodhe, Comparison of DCT and wavelet based OFDM system working in OFDM signals. Int. J. Adv. Technol. 3(2), 74 (2012) 3. B. Sarala, D. S. Venketeswarlu, B. N. Bhandari, MC-CDMA PAPR reduction using a modified exponential companding transform with clipping. Global J. Res. Eng. 13(10), version 1.0 (2013) 4. A.A. Suleiman, E.F. Bardan, D.A.E. Mohamed, Linear companding transform for the reduction of peak-to-average power ratio of OFDM signals. IEEE Trans. Broadcast 55(1), 155–160 (2009) 5. Md. Golam Rashed, M. Hasnat Kabir, Md. Selim Reza et.al, Transmission of voice signal: BER performance analysis of different FEC schemes based OFDM system over various channels. IJAST 34(89–99) (2011) 6. http://en.wikipedia.org/wiki/wav 7. R.C. Gonzalez, R.E. Woods, Digital image processing, 2nd edn. (2005), p. 362 8. http://en.wikipedia.org/wiki/file:lenna.png

Enhancement of Performance and PAPR Reduction Using Combination of PTS and SLM Scheme with Opposition-Based GWO in MIMO–OFDM K. Aruna Kumari and K. Sri Rama Krishna Abstract Among various PAPR decrease systems, picked mapping (SLM) is a complimented methodology that achieves exceptional PAPR decay execution without sign mutilation. Furthermore, partial transmit sequence (PTS) is other than single incredible frameworks diminish the PAPR of OFDM. Regardless, an ideal region in PTS framework is surveyed to be an essential disquiet. To move the current PAPR decrease technique, we have consolidated perfect SLM and PTS-based PAPR decay methodology in analogous. Using, the OGWO figuring spread development was picked by slightest PAPR correspondence gathering gadgets. The anticipated PAPR decrease advance is related autonomously on each transmitted receiving wire, in this manner, the PAPR can be extraordinarily diminished. What is more, OGWO streamlining PAPR decay strategy give enhanced execution advanced unfussy path PAPR decrease. The anticipated methodology investigated by different narrative PAPR reducing means to demonstrate the plentifulness. Keywords MIMO–OFDM


PAPR
SLM
PTS
GWO

1 Introduction Symmetrical repeat (OFDM) is routinely utilized high-order pace remote exchanges of inborn screwup power in a multipath space. [1]. Combination of MIMO system with OFDM has a major consideration for the subsequently making broadband utilization suitable to their probable of provide soaring data rate, toughness to desertion channels and consistent communication. Place up in different isolated values, for K. Aruna Kumari (&) Department of Electronics and Communication Engineering, PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, Krishna, A.P, India K. Sri Rama Krishna Department of Electronics and Communication Engineering, VR Siddhartha Engineering College, Kanuru, Vijayawada, Krishna, A.P, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_73

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example, remote urban systems, as a rule, perability for wave get to (WiMAX), and 3GPP fruition [2]. Control profitability canister is recovered by boosting variety. By shifting a repeat specific MIMO conduit keen on a great deal of equivalent repeat level MIMO, OFDM lessens the multifaceted [3]. The disaster of raised common control extent to result in the accepted on development at lifted control [4]. The source holder of run of the mill control which is in essence decreased, with location to a fixed submersion manage. In these cutting edge business remote structures, the PAPR issue is huger in uplink [5] in light of the fast that this is the limiting association to the extent extention and range [6], and as the portable terminal is compelled in battery control, the adequacy of the power enhancer is basic.

2 Proposed Methodology The major challenging concern in MIMO-OFDM system is the high PAPR to limit this problem different reduction algorithms were proposed. Selective mapping SLM and Partial Transmit Sequence (PTS) are the most efficient technique used to reduce the PAPR in multicarrier transmissions without causing distortion in the signal. SLM and PTS techniques parallel in MIMO-OFDM system to reduce the PAPR proposed. Framework, in which its sections the trade speed into different bearers; everyone is adjusted. OFDM resembles FDMA, by subdividing the accessible transmission limit into different channels that the unmistakable customer access is drilled and a short time later allotted to customers. OFDM utilizes the range altogether more beneficially by isolating the channels a great deal closer together. Peak-to-conventional power degree (PAPR) decrease in various remote structures the degree between run of the mill power and pinnacle lively power is used. High top-to-normal power degree certified testing. There are three basic solicitations to be explicit coding, standard distortion and flag scrambling in the PAPR diminishment. Fundamental framework found Straight Square coding in any case to process a significant number of sub-transporters this strategy is not appropriate. The PAPR decay methodologies are likewise used to lessen the power use and keep away from the nonlinear bowing impacts appear in the fundamental flag. Much advancement is done in PAPR diminishing frameworks recently. With high PAPR, OFDM flag has wide assortment of amplitudes. In any case, PTS and SLM for PAPR diminishment process are altogether related to MIMO–OFDM structure. PAPR ¼

max0  t  T jxðtÞj2 h

i E
xðtÞ2


ð1Þ

That authentic and dream regards in beyond what many would consider possible theory state of is ordinarily scattered. The arrangement of PAPR condition CCDF work, Nyquist inspecting speed preserve be unmistakable for apiece PAPR of measurements obstruct as takes after

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 N P ¼ Pr ðPAPRðxðnÞÞÞ [ PAPR0 ¼ 1  ePAPR0

ð2Þ

Consequently, much emphasis on finest likelihood appropriation. The CCDF is characterized by,  N Pr ðPAPR [ PAPR0 Þ ¼ 1  ePAPR0

ð3Þ

PAPRMIMO ¼ max PAPRi ; =; i ¼ 1; 2; . . .; NT

ð4Þ

where NT is the number of transmission antennas. Thus, the segment of banner tops happens which conveys in-band and out-of-band twisting in this manner the Q-manage pushes toward the inundation territory in light of the high PAPR. So to remain the Q-signal in the immediate area the vibrant degree of the power intensifier ought to be lingering which again lessens its practicality and updates the expense.

3 PAPR Reduction Technology For PAPR, an extensive proportion of methodologies has been presented underneath (Fig. 1). The principal data discourage different choice OFDM sign is banner least significant thought of this system. This is a convincing and turning less methodology utilized diminishing. The name of this strategy demonstrates that one gathering must be chosen of various plans. As per the likelihood of discrete-time OFDM transmission we should affect a data square considering number of pictures from the star gathering plot.

X1

Serial data ‘X’

S/P

X2

Converter XM

IFFT IFFT

b1l

IFFT

Grey Wolf optimization

Fig. 1 Block diagram of PTS

+

b2l bM l

X’l(opti)

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PAPR reduction by optimum PTS and SLM Both the PTS and SLM are the promising strategy for abatement of the PAPR. Midway transmit sequences (PTS) conveys a banner. The computational multifaceted nature of PTS procedure depends upon the amount of stage turn elements allowed and consequently achieve the OFDM signals with low PAPR. Picked mapping (SLM) is used to make different self-governing OFDM blocks from a lone data square and after that select one having PAPR. The free OFDM strategies can be found with discovering self-sufficient stage game plans. The underneath figure shows the square diagram of MIMO–OFDM structure using PTS and SLM computations for PAPR diminishment. The serialized information is changed over into parallel information squares, which are tweaked by utilizing any regular regulation plans. The resulted symbols are allocated to orthogonal sub carrier mapping. And after that they are gone parallel through PTS and SLM strategies to diminish the PAPR for MIMO–OFDM structure. PTS and SLM are various strategies connected in parallel structure. Along these lines, PTS transmits the square, which is the summation of the duplicated sub-hinders by ideal stage factors in time area to acquire a limited PAPR. SLM selects the block, which is the resulting of multiply the copies of original signal with different phase sequence in frequency domain and has the lowest PAPR. Since the stage factor is an irregular worth comprise of 1 or −1. Proposed Gray Wolf Optimizer (GWO) algorithm-based opposition method The GWO count, proposed is cheerful with the pursuing behavior and social organization of diminish frauds. It takes after other metaheuristics, and in GWO count the interest starts by a people of discretionarily made wolves (contender courses of action). The social overwhelming movement is instanced as in Fig. 4. The chasing (improvement) is guided by a, b and d in this calculation. The x wolves are required to encompass a, b and d to discover upgraded arrangements. From the above chain of importance, the prevailing pioneers are named alpha (a), which is generally responsible for making choices about chasing, dozing spot, etc. Alpha passes on its choices to the pack. In choice creating, betas are subordinate wolves and are consultants to alpha. Representation of GWO algorithm-based opposition The fundamental portions of GWO are encompassing, chasing and attacking the prey. The algorithmic steps of GWO are presented in this section. Optimization algorithm-based opposition instating both the populace and parameters (a, b, d) of quest operators for OGWO calculation, at that point ascertain the wellness esteem for each hunt specialists and update its position appropriately. For each refreshed hunt specialists, process the wellness esteem. Presently, update the last estimations of a, b and d to supplant the most exceedingly awful wolf and after that compute. When the end criteria get fulfilled, show the best arrangement. Something else, if the condition is not satisfied recurrent the means until the rule has been resolved.

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Steps in GWO-based Opposition The various steps involved in GWO-based opposition are described briefly with the following equations; Step 1 Initialization The initial population of search agents is produced randomly. Stage 2 Generate inverse arrangement According to restriction-based GWO, at the same time, to hint at upgrade figure for introduce wolves’ answer the adjacent wolves and its converse wolves are considered. It is given that an opposite wolf’s answer has a superior chance to be closer to the worldwide ideal arrangement than discretionary wolf’s answer. Each arrangement has a selective inverse arrangement. The contrary arrangement is ascertained in light of the condition; every solution Yi has an exclusive opposite Yopi solution. The opposite solution OPðY1 ; Y2 ; . . .Yn Þ is calculated based on the equation; Yij0 ¼ ai þ bi  Yi ;

i 2 1; 2; . . .; n

ð5Þ

Stage 3 Fitness figuring Once the fundamental course of action is delivered, the wellness estimation of each individual is surveyed and secured for future reference. The wellness work is portrayed as the going with enunciation; Fitness ¼ MinimumðPAPRÞ

ð6Þ

Step 4 Calculating a, b, d and x We find a, b, d and x after the wellness calculation. Here, while considering the OGWO the alpha (a) is viewed as the most sensible course of action with a perspective to recreating soundly the social pecking request of wolves. Give the primary best wellness arrangements a chance to be Fa, the second-best wellness arrangements Fb and the third-best wellness arrangements Fd. Step 5 Encircling the prey The gray wolves encompassing activities to hunt for a prey can be expressed as

~ T ¼
~ A
~ Px ðuÞ  ~ PðuÞ


ð7Þ

~ Pðu þ 1Þ ¼ ~ Px ðuÞ  ~ C
~ T

ð8Þ

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The vectors ~ C and ~ A are computed by: ~ C ¼ 2~ b
~ r1  ~ b

ð9Þ

~ A ¼ 2
~ r2

ð10Þ

Step 6 Hunting Once the prey has been surrounded, the dim wolves center around chasing. The alpha (a) will manage this method. Sometimes beta (b) and delta (d) may moreover participate. There is no plan about the area of the ideal incentive if there should be an occurrence of inquiry space. Thus, to start the chasing strategy of dim wolves, the alpha (best hopeful arrangement), beta and delta have transcendent data regarding the area of the prey. So keep the underlying three best arrangements found and after that oblige the other hunt specialists to refresh their situations as indicated by the situation of the best inquiry operators.

4 Simulation Results This area contains result and exchange about PAPR reduction utilizing parallel PTS and SLM conspire with MIMO-OFDM. Both the techniques SLM and PTS have been clubbed to attain better performance. The proposed calculation utilizing MATLAB programming completed utilizing an arrangement. This region shows relevant ramifications of introduced system. The contrasted our displayed framework OGWO and GWO and AABC methods. In this segment, the mistake procured is least for our proposed technique than the current strategies. Obviously, the mistake accomplished is less for the proposed philosophies (Fig. 2).

Fig. 2 BER methodologies

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Fig. 3 SER methodologies

Fig. 4 PAPR versus CCDF methodologies

It can note from the above chart that the bit blunder proportion for the proposed system is significantly lower than existing strategies. Bit mistake proportion speaks to the yield restorative dimension, consequently the through for proposed strategy is better as far as BER (Fig. 3). It tends to be seen above assume that image blunder proportion for the proposed strategy is diminishing extensively quicker than existing strategies. Besides, the proposed strategy demonstrated preferable outcomes over existing system. The anticipated PAPR versus CCDF chart in Fig. 4. It can be noted from Fig. 4, PAPR reduction takes place in every technique but for the proposed technique PAPR takes place in short period of time. Moreover, the

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PAPR has been reduced whereas, it is more in the existing technique. The man motive of the work was to attain less PAPR as much as possible, which proves the efficiency of the technique.

5 Conclusion OFDM is an uncommonly appealing procedure in light of its range efficiency and channel power. The transmitted banner demonstrates data-related critical drawbacks MIMO–OFDM structures. The near vocation, two disparate PAPR methodologies, SLM and PTS contain the MIMO–OFDM contrive and the PAPR reducing stricture analyzed. The outcome exhibits that equally the SLM chart and PTS contrive are all the convincing to diminish PAPR in MIMO–OFDM systems. By propagation considers, shown anticipated figuring decrease PAPR common present systems. A parallel fake bumble bee settlement (P-ABC) estimation has made by Necmi Taspinar [7] in context of interest framework. PTS conspire relies upon a two-organize enhancement of the stage weighting factors for the parallel IFFT squares and the stage weighting factors for the information images before the summation errand have been created by Siming Peng [8]. However this PTS design could acquire considerable PAPR decrease execution change than customary PTS conspire still many-sided quality exists. A section based T1 ( Tone infusion) plot have been exhibited by Jun Hou [9] to diminish computational multifaceted nature and improve PAPR execution of OFDM signals. In OFDM systems, low-many-sided quality engineering for PAPR decrease in OFDM structures have actualized by Sen-Hung Wang [10]. Two fractional transmit arrangement (PTS) plans without side data(SI) have presented by Hyun-Seung. Joo [11] for lessening top to normal power proportion of orthogonal recurrence division multiplexing signals. The Euclidean separation between the given flag froup of starts and flag [12] turned by the stage balance.

References 1. S. Zid, R. Bouallegue, Low-complexity PAPR reduction schemes using SLM and PTS approaches for interleaved OFDMA. IEEE Commun. Surv. Tutor. 15(4) (2013) 2. H. Yang, A.S. Bell, A road to future broadband wireless access: MIMO-OFDM-based air interface. IEEE Commun. Mag. 43(1) (2005) 3. F. Sandoval, G. Poitau, F. Gagnon, Hybrid peak-to-average power ratio reduction techniques: review and performance comparison. IEEE Access, vol. 5 (2017) 4. S. Singh, A. Kumar, Performance analysis of adaptive clipping technique for reduction of PAPR in alamouti coded MIMO-OFDM systems. Int. Conf. Adv. Comput. Commun. ICACC (2016) 5. K. Anon, C. Turnover, B. Adobes, on the optimization of iterative clipping and filtering for PAPR reduction in OFDM systems. IEEE Access 5, 12004–12013 (2017)

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6. M.V.R. Vital, K. Rama Naidu, A novel reduced complexity optimized PTS technique for PAPR reduction in wireless OFDM systems. Egypt. Inf. J. 18(2), 123–131 (2017) 7. M.V.R. Vital, K. Rama Naidu, A novel parallel artificial bee colony algorithm and its PAPR reduction performance using SLM scheme in OFDM and MIMO-OFDM systems. IEEE Commun. Lett. 19(10) (2015) 8. S. Peng, A. Liu, K. Wang, X. Liang, PAPR reduction of multicarrier faster-than-nyquist Signals with partial transmit sequence. IEEE Access, vol. 5 (2017) 9. X. Zhao, F. Gong, J. Ge, PAPR and PICR reduction of OFDM signals with clipping noise-based tone injection scheme. IEEE Trans. Vehicular Technol. 66(1) (2017) 10. S.H. Wang, K.C. Lee, C.P. Li, Low-complexity architecture for PAPR reduction in OFDM systems with near-optimal performance. IEEE Trans. Vehicular Technol. 65(1) (2016) 11. H.S. Joo, K. Kim, J.S. No, D.J. Shin, New PTS schemes for PAPR reduction of OFDM signals without side information. IEEE Trans. Broadcast. 63(3) (2017) 12. D. Kumutha, N. Martha Piranha, Hybrid STBC-PTS with enhanced artificial bee colony algorithm for PAPR reduction in MIMO–OFDM system. J. Ambient Intell. Human. Comput., pp. 1–17 (2017)

Women Safety Device with GPS Tracking and Alerts A. Ranganadh

Abstract Nowadays, women’s safety in India has become concerning issues against women growing at an important stage. They are facing problems like kidnaping, and sexual harassment toward kids and young girls has been reached uncomfortable levels. This paper presents a women safety device based on the alerts of the global position systems and GSM module devices. This detection alert system consists of GPS, Arduino Uno microcontroller, and GSM modem. GPS tracks the exact location of where the women are facing problems and this information is given to the microcontroller. Microcontroller processes the information and sends the same thing to the user through GSM modem. GSM sends the information in the form of SMS to the predefined mobile number. When a woman is in danger position, she can press the button through which the system is activated and sends the emergency alerts. This paper presents the design and implementation of a system which gives information about the safety and security of women against danger situations like kidnaping and some other harassment. Keywords Women safety and security Global service technology


Global positioning system module


1 Introduction Women’s safety in India has become a concerning issue since 2012 with issues against women growing at animportant stage. They are facing problems like kidnaping; sexual harassment toward kids and young girls has been reached uncomfortable levels. According to NCRB in the year 2015, opinion at the rate of reported crimes across the women in India is less than 22%. Cases of crimes counter to women were promoted to 327.394, as recorded in 2015 [1]. Current statistics show a decrease of 3.1% compared to 2014. The highest conviction rate that has ever been reported was under the Immoral Traffic Prevention Act (49%) [2]. It was A. Ranganadh (&) Department of EEE, Guru Nanak Institute of Technology, Hyderabad, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_74

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followed by the Protection of Women from Domestic Violence Act (47.8%). Abetment to suicide and cruelty by husband and his relatives was reported to have the lowest conviction rate. According to the above figure, crime against women has increased every year. From 2012 to 2013, the cases increased from 2,44,270 to 3,09,546. Often the work of race, religion, political, cultural, and tearing promotes peace. It should be noted that the safety of all hands down in the women is properly respected. The body of the brave men and women as a helping hand at that time was a relief. The best way to reduce your chances of being the victim of violent crime (robbery, sexual assault, rape, domestic violence) is to identify and to apply resources to help you out of dangerous situations. The best way to minimize your chances of becoming a victim of violent crime (robbery, sexual assault, rape, domestic violence) is to identify and call on resources to help you out of dangerous situations. Although several were originally developed for students to reduce the risk of sexual assault on campus, they are suitable for all women In the light of recent outrage in Delhi which shook the nation and woke us to the safety issues for our daughters, people are gearing up in different ways to fight back. A series of new applications has been provided to women to provide surveillance systems on their phones. Here, we present an application that guarantees the protection of women. This helps in identifying resources and helping them out of dangerous situations. These reduce risks and provide support when we need it and help us to identify the place. The outline of this paper is as follows: Sect. 2 discusses the Internet of things (IoT); Sect. 3 discusses the components used and its brief description; Sect. 4 discusses the block diagram and its implementation; Sect. 5 discusses the working and results; and Sect. 6 gives the completion of paper.

2 Internet of Things The latest advance technology in IT sector nowadays is the Internet of things (IoT). As the number of internetworking provides optimal PLCs, sensors, actuators, and other consumer electronic devices, the network integrates with the teachings of the IoT and rules and provides interactions that are found between many of the configurations, and we are discussing information by means of exchange. In the year 1995, Bill Gates proposed “thing to thing” say that “IoT interconnects the thing to human and between any one of the transfer.” The IoT brings out a huge network by connecting different types of devices [3]. IOT targets three aspects: cost saving in a system, communication, and automation. It allows people to carry out their daily and routine activities using the Internet and saves time, making them more fertile [4]. Recent progress in the expansion of cyber-physical systems, the IoT Claudio computing model essentially provides a great contribution to the preparation and management views the parameters of Web pages. Nowadays, IOT is the most

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advanced, cost-less technological solution, and efficient, which encompasses the hardware and software resources, and allows remotely connected sensing devices to sense with more capabilities. And it could be monitored and controlled through existing systems, resulting in the real-time world integration with computer controllers.

3 Components and Its Description A. ARDUINO Arduino is an open-source platform. It recycled for the purpose of electronic projects. Arduino is a microcontroller-based system, and it is easily programmed by using a bunch of instructions. These Arduinos are easily coordinated with computer and other microcontroller devices, which are represented in Fig. 2. Atmel Corporation is developed microcontroller. It is placed on Arduino boards. Arduinos are generally of 8-bit, 16-bit, and 32-bit AVR-based architecture microcontrollers. There are many Arduino boards such as Arduino Uno, Arduino Mini, Arduino Mega, Arduino Nano, LillyPad. In this system, we have used Arduino UNO board (ATMEGA 328). Arduino UNO consists of 8 bit microcontroller board. The ATMEGA 328P uses serial communication with the help of serial pins Tx(0) and Rx (1). The Uno board has voltage regulators of 5 and 3.3 V which can be used for power supply to LEDs. Arduino Uno consists of 14 binary input/outputs; out of those 14 pins, six pins are used for PWM and six analog pins. It also has USB connection, power jack, and reset button. Serial pin 0 and pin 1 are used for receiving and transmitting data. Pin 2 and pin 3 are used for triggering an interrupt. Pins 3, 5, 6, 9, and 11 provide 8-bit PWM output—Arduino board can be programmed by using Arduino IDE software. By the use of receiver and transmitter pins, serial communication can be achieved. In order to get continuous glow of receiver and transmitter pins of LED, Arduino board is connected through USB cable. Arduino is the IDE serial monitor software, which allows to move the board information out. Software is needed for the library in digital serial communication pins (Fig. 1). B. GPS Global positioning system is a satellite navigation system, according to the time and place through the steps. It consists of 24 satellites around the earth [2]. It is a global navigation system which provides the geolocation and time information to the user anywhere on the earth. GPS working depends on the principle of “Trilateration.” According to this principle, four satellites are required to determine the receiver position [5]. Among these four satellites, fourth satellite is used to confirm the target location of each of three space vehicles, and other three satellites are used to point out the location (Fig. 2).

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Fig. 1 Ardunio Uno pin diagram

Fig. 2 GPS module

In this system, we are using UBLOX NEO 6M GPS module because of its small size and accurate location tracking. This unit has the latest technology of UBLOX to get the accurate positioning information of the user. NEO 6M global position system module has a built-in GPS antenna and a battery for backup power when the supply is interrupted and also for the faster GPS signal. This module has four pins, i.e. RX, TX, VCC, and GND, and serial TTL output. C. GSM GSM stands for global system for mobile communication. In Bell laboratories, GSM has developed in the year 1970. It is greatly used mobile communication in the world. It transmits data and operates at the frequency of 850, 900, 1800, and

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1900 MHz. It is most widely used mobile communication in the world. It transmits data and operates at a frequency of 850, 900, 1800, 1900 MHZ. GSM module is designed as digital system by using time division multiple access technique. At the same time to the celebration of the art of data implementation for language users assign different types of openings of 120 Mbps 64 kbps. GSM network consists of three components: 1. mobile station, 2. base station subsystem, and 3. network subsystem. Mobile station consists of transmitters and displays, and it is controlled by the SIM card. Base station subsystem acts as channel formed between the mobile system and network subsystem. Network subsystem provides connection to mobile stations [5]. It uses a SIM card which operates over a large range of control network subscribed by operator. GSM modem is associated with the computer through USB port or serial communication connection. In this system, we have used SIM 800A module. SIM 800A supports quad-band of 850/900/1800/1900 MHz and transmits SMS, data with less power utilization. SIM 800A modem consists of GSM chip and an RS 232 interface which makes easy connection to laptop/computer through USB. GSM works on 9600 baud rate which is the default rate. In order to make LED glow, continuous supply is given by GSM. In order to connect GSM to the network, LED should glow each and every 3 s. By the use of the AT commands, we can send and receive SMS (Fig. 3). D. LCD LCD stands for liquid crystal display. It consists of 16 pins if the backlight is built in the module. In this system, we are using 16  2 LCD Module. A 16  2 LCD has two registers, i.e., command and data registers [5]. Register select is pre-owned for the purpose of switching from one register to another. RS = 0 is also recycled for command register and RS = 1 for data register (Fig. 4).

Fig. 3 GSM module

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Fig. 4 LCD display

4 Block Diagram and Its Implementation From Fig. 5, the hardware is associated with smartphone through IoT. The device communicates with smartphone by using Arduino IDE software. The software is programmed in such a way that the GPS tracks the coordinates so that we can track it easily. The required data message is sent to family members and nearest police station using IoT [4]. A. INTERFACING GPS WITH ARDUINO GPS has four pins, namely RX, TX, VCC, and GND. Here, TX of GPS is linked to D10 pin of Arduino. By using “Software serial library,” we establish serial

Fig. 5 Block diagram of hardware implementation

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communication on D10, D11 Pins and made them TX, RX and we have left RX open [1]. We have pin 0 and pin 1 for serial communication as default, but by using software serial library we can have serial communication on any of the digital pins of Arduino. Here, GPS is powered up by 12 V supply [3]. B. INTERFACING GSM WITH ARDUINO In GSM module, we use only four pins for interfacing with Arduino [6], i.e., TX, RX, VCC, and GND. TX and RX of GSM modem are joined with receiver and transmitter of the Arduino. GSM is powered up by giving 12 V supply. C. INTERFACING LCD WITH ARDUINO For the interfacing of LCD with Arduino, we need a breadboard. LCD has 16 pins. In those pins, we can use 12 pins only. LCD data pins D4–D7 are linked with Arduino D5–D2 pins. Command pins, reset and enable of LCD, are banded together with D2 and D3 pins of Arduino, and RW is coupled to ground. VEE pin of LCD is combined with 10 K potentiometer. Voltage divider is used for the adjustment of contrast of LCD.

5 Working and Results In this paper, Arduino plays a vital role in regulating the process [5]. When the push key is pressed the system gets activated. Then, the GPS module tracks the location information in the form of latitude and longitude from the satellites. This information is given to the Arduino. Arduino processes this information and sends this information to GSM module [2]. GSM module sends the information to the user in the form of SMS to predefined mobile number. Before Execution After interfacing all components with Arduino, the corresponding system is shown in Fig. 6. Fig. 6 Before execution

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After Execution If any person facing problem regarding harassments, the LCD display will show the longitudinal and latitudinal values of the corresponding person location. GSM module will send the message to the registered mobile number, and then, the user will know about the exact location where women are facing problems. Those corresponding figures are shown in Figs. 7 and 8.

Fig. 7 Exact location of where women are facing problem

Fig. 8 Message information

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6 Conclusion This article ensures the proper safety of women, thereby providing a safety environment. As reported by the survey, many women are facing problems such as eve-teasing, robbery, and harassment. This device can play an important role by providing safe environment to women against all situations which are mentioned above. By implementing this idea in real-life situation, we may solve the harassment against women to a certain extent.

References 1. 2. 3. 4. 5. 6. 7.

P.M. Tuan, MQTT client library of ESP8266 module B. Benchoff, An SDK for the ESP8266 Wi-Fi chip. Hackaday S. Sinha, Introduction to internet of things Geeksforgeeks-IOT Vowstar, NodeMCU Devkit. GitHub. NodeMCU team. Retrieved 2 Apr 2015 Zeroday, A Lua Based Firmware for WIFI-SOC ESP8266. GitHub. Retrieved 2 Apr 2015 A. Kumar, A. Sharma, Internet of Life (IOL) (2015)

A Smart Machine for Fitness Care Scrutinizing Technique—A Review N. Pooranam, M. Diwakaran, A. Archana, S. Agalya, A. Anindhitha and E. GokulaPriya

Abstract In the modern era, monitoring a person’s health is a too high process. To manage and maintain the entire document related to a particular person becomes tough. To reduce this difficulty, some of the recent IoT technology plays a major role. In this paper, a review is made on person’s healthcare system. The data maintained in hospital or clinic should be examined by their relatives and nearby inhabitants. To reduce human achieve some intelligent method are generated to maintain and relocate data throughout Raspberry pi, RFID and other component are used to build the effective automated system using IOT sensors, controllers. A novel machine can help a person in usual intake of medicine and other treatment. A hardware and software system is built with low-cost effective components. IoT works on the cloud to store, retrieve, and process data from sensors and controllers. Cloud infrastructures reduce the cost of resource maintenance and resource utilization process. An effective method is generated to process each individual data in a secure way. Keywords IoT


RFID
Raspberry Pi
Cloud infrastructure

1 Introduction The inter-component device can be connected through network objects which can collect and exchange information which is nowadays termed as IoT. The information is now available in the collective devices which can send data and receive the information from the surroundings. Here, in this paper, IoT is used in health monitoring system such as (1) rural health care, (2) heart disease monitoring system, (3) diabetes, and (4) health monitoring system for soldiers. Development of technology was useful in various categories of different fields [1]. All these N. Pooranam (&)
M. Diwakaran
A. Archana
S. Agalya
A. Anindhitha
E. GokulaPriya Sri Krishna College of Engineering and Technology, Coimbatore, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_75

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technologies are based on Internet of Things (IoT). One of the specific areas is the physical condition examining method that can examine the patient’s health using sensors. These sensors examine the patient’s physical condition and give report by measuring patient’s blood pressure, glucose level (too high or too low), and energy efficiency (high or low) [2].

2 Rural Health Care 2.1

Health Care Monitoring System

The development of health monitoring system is more useful in rural areas where the hospitals are not available as like cities. In this proposed system, the instrument is made in which one end acts as sensors which collect the patient’s details and send the information to the doctors available in cities so that the doctors check the report and prescribe the medicines via SMS to the patient’s caretaker or to relatives or to nearby people [3]. This technology is also very useful for the pregnant ladies living in remote areas; the monitoring device gives report of blood pressure and blood rate of the women, and also the blood rate and movements of fetal were monitored just to control the women’s health condition. This would be highly useful for them during their child birth (Fig. 1).

Fig. 1 Flowchart of medicine remainder

A Smart Machine for Fitness Care Scrutinizing Technique—A Review

2.2

809

Solution to Chronical Disease

In this article, the author inflates more recent developments in deep learning. The article consists of the algorithm, applications, and the history of deep learning research. Deep learning a class of machine learning techniques includes three classes: generative, discriminative, and hybrid. The author selected some of the applications such as neural networks, information processing including audio/ speech, image/vision, language representation, natural language processing (NLP), and data recovery. Each application has a special feature that is illustrated with its architecture, and classes are been classified directly for the visible data. In this application, signal processing areas defined by the matrix with the two axes of signal and processing analyze the scheme using the deep learning algorithm. The main advantage in deep learning is to scale the CPU clustering by means of very high training data.

3 Heart Disease 3.1

Heart Disease Monitoring System

Currently, heart disease is the most important reason for deaths. In China, more than 1,500,000 deaths are due to heart diseases each year. People with heart diseases stay at home when they feel sick. Mostly, the patient will not feel anything until they feel the pain so the pain gets increased and they might die due to the late process of diagnosis. Since there are some solutions that can help in curing of diseases which can also help in improving the process of reducing the death rate that helps in providing health care and physical status [4, 5] of the patient is evaluated and examined by the doctors and the patients are treated as their genuine instance through some conditions.

3.2

Data Acquisition Part

• With the help of Internet connective devices and techniques, it is easy for evaluating through some methods, and the human will do things by their own matters. When acquired, the information is transferred and received through physical components with less cost. The machine evaluates the patient sign through some environmental indicators and balance models and transfers the information in different modes which include blood pressure, ECG, Spo2, etc., and strain for transmission, resource model.

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• The architecture, inter-component device, can be connected through network objects which can collect and exchange information which is nowadays termed as IoT which is going to evaluate the heart disease by evaluating each parts of the body and divide them into layer by layer. The way an architecture works is based on the evaluation process and acquires knowledge from different modeling techniques, each part will give different model inputs and outputs; the transmission of information is based on the two main processes [6]. The one is based on different features which evaluate differently. Another is based on the sampling frequency for each layer. • There are four operating models for evaluating the machine, or the devices with some real-world input can be sequential and process the information generated by the device which is connected by the modeling different transmission processes on the patients.

3.3

Architecture Based on Heart Disease

• The investigation made a study on the health issues and divides the several inputs into different forms such as evaluating the different process and techniques to which the information is exactly based on the patient’s data from the devices connected in the network pattern. The other investigators considered the machine with wide area of data control and such area like hospital environment. • Each of their aims was to evaluating the information and maintaining the different models with different modes that can be elaborated in such a way that it does not be a distinct [7]. With this aspect, the evaluating model will definitely measure the data on recent topics. • In the signs of patient’s information, the evaluated data can be less or more, but still some different processes can be made when it affects the system by only paying attention to one definite sign, such as heart rate, ECG, or blood pressure. • The non-phycological features provide different background information of patients which may help the distant investigation or assist context-based servicers. • The architecture of the inter-component device can be connected through network objects which can collect and exchange information which is nowadays termed as IoT which is going to evaluate the heart disease by evaluating each part of the body and divide them into layer by layer. • The kind of architecture which the evaluating system is clear and flexible enough for the monitoring system.

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4 Diabetes 4.1

A Metabolic Disorder—Diabetes

Diabetes is one of the most common diseases which affects the parts and produce some useful information. It is a metabolic disorder and leads to heart disease, stroke, kidney disease, and blindness. About 350 million of people suffered from diabetes globally. It will lead to death when there is no proper health care. The glucose level, blood pressure, and temperature should be monitored every day. Common diabetic test is done completely on patient’s condition by testing the parts level by level. However, there are different techniques for different persons who go to hospital and test the diabetes condition. With the help of sensors, the glucose level, temperature, and pressure of the patient are recorded [8]. The sensors are connected to an Arduino Nano Microcontroller to convert the analog signal to digital signal. The patient’s data are transmitted to Raspberry Pi by UART USB and then the data are stored in cloud storage. The data stored in cloud can be monitored by both the doctors and patient’s relatives [9]. SENDER SIDE See Fig. 2. RECEIVER SIDE See Fig. 3.

Fig. 2 Data acquisition from the sensor

Fig. 3 Data retrieving from the cloud storage

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Glucose Monitoring Sensor

Blood glucose-level monitoring is an important part of diabetes care, and inter-component device can be connected through network objects which can collect and exchange information which is nowadays termed as IoT and is based on continuous glucose monitor (CGM) which is an existing model, which is used to track the glucose level automatically. The CGM system is made of sensors, transmitters, and monitor. The sensor is placed on the skin, and so it is in contact with the interstitial fluid between the cells [9]. The sensor measures the glucose level in the fluid and the transmitter transmits the analog value from the sensor to the Arduino [10]. The Arduino is then converted into the digital value and stored in the cloud storage. The transmitter then wirelessly sends the status to the monitor. The patient can also view their glucose level in the monitor (Fig. 4).

4.3

Pressure Monitoring Sensor

Diabetes and high blood pressure are closely related disease. High blood pressure makes diabetes more dangerous. The device placed on the human is sensed and detect some systolic diastolic force and convert into a analog electrical sign whose size which depends on pressure applied [11]. The several detectors include sensor such as LCD which displays systolic and diastolic pressure. The patient can also view their blood pressure level in the LCD display (Fig. 5).

Fig. 4 Glucose sensor which measures the glucose level from the interstitial fluid

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Fig. 5 Diagram of pressure sensor

4.4

Temperature Monitoring Sensor

Body temperature can have a profound impact on diabetes patients. The temperature of the patients should be monitored continuously [12]. The measurement of body temperature is done by the mistor-type sensor. The common negative temperature coefficient (NTC) is used to measure the body temperature.

5 Health Monitoring System for Soldiers 5.1

Biosensor System

Warfare becomes most important thing for nation’s security and protection. Soldiers play the vital roles in army of the nation. There are many instruments and biosensor systems for the safety of soldiers. Thus, the sensor systems consist of physiological monitoring, transmission module, processing capabilities, etc. [13]. This paper is about to give the health status of the soldiers in battlefield in order to rescue in emergency situations and to path and the position of the military officials who uses GPS unit and other sensor devices which is connected to the network models. It reduces some process of searching and modeling each method by some efforts of information model [14].

5.2

Advanced Medical Care Monitoring System

The soldiers were integrated with advanced medical care monitoring and GPS, data communication in order to transmit data to the control unit and also the co-military

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persons. The mechanism should be less weighted with needed power source. The location of the soldiers is monitored by control unit to direct the warrior right path is warfield or in special mission [15]. Biomedical sensors were integrated into the jacket of soldiers with entire mobility property. This structure connects all the base stations where data are collected for future prediction. Methods

Usage

Advantages

Disadvantage

Continuous glucose monitoring system

To measure the glucose level in the blood

CGM used to track the glucose level automatically and track historical data

Pressure monitoring system Temperature monitoring system Soldier health monitoring system

To measure the blood pressure To measure the body temperature To monitor a human soldier in conflict

The sensor can take multiple readings over an extended period of time Temperature measurement becomes more stable to use

The reading from the sensor is not accurate because the sensor measures the glucose level from the interstitial fluid and not from the blood stream The sensor is not always 100% accurate

It will reduce/minimize the time, search, recue effort of control unit when soldier in Iill/emergency

Limited temperature range

The integrated components can make the jacket weight/ heavy

References 1. Rane, D.B. et al., Soldier tracking and health indication system using arm processor. Int. J. Emerg. Trends Sci. Technol. 2(05) (2015) 2. E. Madhyan, K. Mahesh, A unique health care monitoring system using sensors and zigbee technology. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(6), 183–189 (2014) 3. K.A.M. Zeinab, S.A.A. Elmustafa, Internet of things applications, challenges and related future technologies. World Scientif. News 2(67), 126–148 (2017) 4. L. Khuon, et al, Contrasting blood pressure measurement approaches in a freshman engineering design project (2012) 5. S. Biruntha, S. Dinesh, K.S. Ganesh, V. Priyadharsini, Artificial neural networks and their appliance to civil engineering—a review. Int. J. Pure Appl. Math. (2018) 6. R. Archana, S. Indira, Soldier monitoring and health indication system. Int. J. Sci. Res. (IJSR) ISSN (Online), 2319–7064 (2013) 7. M. Parida, et al., Application of RFID technology for in-house drug management system. in 2012 15th International Conference on Network-Based Information Systems (NBiS), IEEE (2012) 8. H.B. Lim, et al, A soldier health monitoring system for military applications. in 2010 International Conference on Body Sensor Networks (BSN). IEEE (2010) 9. T.N. Gia, et al., IoT-based continuous glucose monitoring system: a feasibility study. Proc. Comput. Sci. 109, 327–334 (2017) 10. K. Devipriya, D. Prabha, B. Sophia, B.P.U. Ivy, A survey on deep learning based recommender system using sentiment analysis. Int. J. Pure Appl. Math. 119(12)

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11. A. Abdelgawad, K. Yelamarthi, A. Khattab, IoT-based health monitoring system for active and assisted living. in International Conference on Smart Objects and Technologies for Social Good. Springer, Cham (2016) 12. G. Muhammad, et al., Smart health solution integrating IoT and cloud: a case study of voice pathology monitoring. IEEE Commun. Mag. 55(1), 69–73 (2017) 13. A. Murakami, et al, A continuous glucose monitoring system in critical cardiac patients in the intensive care unit. in Computers in Cardiology, 2006. IEEE (2006) 14. D.A. Clifton et al., A large-scale clinical validation of an integrated monitoring system in the emergency department. IEEE J. Biomed. Health Inf. 17(4), 835–842 (2013) 15. H. Azath, P. Amudhavalli, S. Rajalakshmi, M. Marikannan, A novel regression neural network based optimized algorithm for software development cost and effort estimation. J. Web Eng. 17(6), 3095–3125 (2018) 16. A. Sawand, et al., Multidisciplinary approaches to achieving efficient and trustworthy eHealth monitoring systems. in 2014 IEEE/CIC International Conference on Communications in China (ICCC), IEEE (2014) 17. L. Ilkko, J. Karppinen, UbiPILL a medicine dose controller of ubiquitous home environment. in UBICOMM’09. Third International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, IEEE (2009) 18. S.T. Hamida, et al., Towards efficient and secure in-home wearable insomnia monitoring and diagnosis system. in 13th IEEE International Conference on BioInformatics and BioEngineering. IEEE (2013)

Configuring MPLS Cloud Providers with Virtual Private Network M. L. S. N. S. Lakshmi and Naga Venkata Sai Sudheer Bandaru

Abstract In the fast-growing business world, networking has a vital role to play. The organizations are spread all over the world. The connectivity between these organizations has become somewhat typical with old traditional ways. To overcome them a new technology called multiprotocol label switching (MPLS) is being used. It has become very popular industry solutions. MPLS at present has more advantages than any other technologies like ATM, FR, Ethernet and SONET. VPN additionally takes part in security. Therefore, this MPLS network with VPN gives a good network connection. In this project, MPLS-based VPN is implemented in a collaborative environment. Three regional offices of an organization are connected with the central Web site through MPLS-based ISP’s network. The connectivity among the sites is established and further resolutions are made on the basis of MPLS labels instead of IP addresses. Moreover, it is also observable in that MPLS does not need any other tunneling protocol unlike traditional VPN’s. It builds tunnels based on labels. Keywords MPLS software


VPN
IPv4 address
VRF
Routing protocols
GNS3

1 Introduction Networking is a process of creating and connecting different systems under a network. In networking, the important aspect is to know about IP and routing. From these, one can learn about protocols for routing. The basic routing protocols are static routing and dynamic routing. Static routing is a bit complex method to connect the hosts under it. But by dynamic routing, it became easier. In dynamic

M. L. S. N. S.Lakshmi (&)
N. V. S. S. Bandaru Department of Electronics and Communication Engineering, QIS College of Engineering and Technology, Ongole, Andhra Pradesh, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_76

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routing, there are three types of routings namely RIP, OSPF, EIGRP. All these three are used in different circumstances. Each protocol has its advantages over the other. MPLS [1] is label-based fast data transfer technology, which is reliable in large distance communications. MPLS contains ingress and egress routers, which are responsible to convert the packet data into labels and labels into packets. Every time packet passes through the ingress router it converts packets into labels, while egress again converts them into packets and deliver to destination. VPN [2] refers to virtual private network. VPN [3] is a client-server architecture. It securely connects using public infrastructure. VPN allows computers on networks to connect securely over the Internet. VRF [4] is a technology which allows different instances of routing tables. VRF’s [5] are like VLAN’s for the routers. OSPF refers to Open Shortest Path First and EIGRP refers to Enhanced Interior Gateway Routing Protocol. OSPF [6] is a bit slower and heavier than EIGRP. EIGRP is lighter and faster due to its feasible successor. It contains backup and standby. OSPF contains equal load sharing while EIGRP has unequal sharing of loads. BGP [7] refers to Border Gateway Protocol it is an important protocol in MPLS. As BGP [8] is path-vector protocol it creates loop-free paths and transfers information to routers. As there are many network protocols to connect devices across the globe, like SONET, ATM, FR, etc. There are many disadvantages regarding these protocols. To overcome these disadvantages, they have come up with a technology called multiprotocol label switching (MPLS).MPLS with VPN [9] is to consider the customers’ security. In MPLS [10] network, each locations router with their location’s ISP. In MPLS, routing is done based on the labels therefore it is called as label switching; it makes faster to transfer data. The routers present in between the MPLS [11] topology is responsible for switching the labels used to route packets. It is called MPLS cloud because an ISP provider can easily provide connectivity option between two or more offices for an organization using MPLS backbone.

2 Basic Topology The basic topology of MPLS cloud providers with VPN contains a minimum five routers. Two of the routers act as headquarters and branch of a corporate organization. Another two of the routers are service provider1 router and service provider2 router, these are ingress router and egress router. The final router is a service provider router. This is topology is as shown in below Fig. 1. Here router1 indicates the headquarters router from which the data to be transferred. Next, router2 indicates branch office routers which receive data from headquarters routers. Routers3&4 are the provider edge routers which are ingress and egress routers. Router5 is service provider router. Ingress and egress routers with this service provider act as the backbone of MPLS network.

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Router5

Router3

Router1

Router4

Router2

Fig. 1 MPLS cloud connectivity

3 Implementation of MPLS Cloud Network This network is achieved by performing the following steps: (i) (ii) (iii) (iv) (v) (vi)

Configuring IPv4 addresses to all the routers. Configuring IGP inside SP core. Configuring MPLS LDP inside the SP core. Configuring VRF, RD and route target. Configure VPNV4 through BGP between both the SP1 and SP2 routers. Configuring routing between CE routers, Static/default, RIPv2, OSPF, EIGRP. (vii) Redistributing the routes in SP routers.

3.1

Configuring IPv4 Addresses to All the Routers

First, to continue with any routing protocol IP addresses should be configured on every router along with loopback ID. The IP address should be assigned on interfaces along with interface numbers. After configuring the IP’s, routers state should be changed to up. The command used to configure IPv4 address is:

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#configure terminal #int l0 #ip address 3.3.3.3 255.255.255.255 #interface f0/0 #ip address 192.168.23.3 255.255.255.255.0

3.2

Configuring IGP Inside0 SP Core

In Interior Gateway Protocol (IGP), the basic protocols are RIP, OSPF, EIGRP. In this context, OSPF protocol is used for Internet service provider routers. As OSPF is used for large and hierarchical networks one can choose it for ISP routers. The command for OSPF configuration is: #router ospf 1 #network 192.168.23.0 0.0.0.255 area 0

3.3

Configuring MPLS LDP Inside the SP Core

LDP means Label Distribution Protocol. LDP is used to transfer labels between routers. For every router MPLS LDP is configured using this command: #mpls ldp autoconfig This command will automatically enable LDP for all OSPF router interfaces. To verify if the ISP routers relate to each other, try to ping the router with loopback address of one end with the router of another end. To ping the routers, one must exit from configuration mode. The commands used to ping the routers is #ping 2.2.2.2 To check the connections and routes: #show ip int brief Or #show ip route

3.4

Configuring VRF, RD and Route Target

Next our target is to configure VRFs [12], RD (route distinguisher) and route target to SP1 and SP3 routers. Name should be assigned to the VRF. This can be done by

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#ip vrf CUSTOMER #rd 100:1 #route-target both 1:100 VRF is assigned to the router using their interface numbers and IP addresses. VRFs state should be changed to up. #int f0/1 #ip vrf forwarding CUSTOMER #ip address 192.168.12.2 225.255.255.0 #no shut

3.5

Configure VPNv4 Peering Between Both the SP1 and SP2 Routers

Next step is to configure the VPNv4 to the SP1 and SP3 routers because these routers are provider edge routers. To configure VPN [13], Border Gateway Protocol (BGP) [14] protocol is to be configured to the routers. These are configured using the following commands: #router bgp 234 #neighbor 2.2.2.2 remote-as 234 #neighbor 2.2.2.2 update-source loopback 0 #address-family vpnv4 #neighbor 2.2.2.2 activate

3.6

Configuring Routing Between CE Routers, Static/ Default, RIPv2, OSPF, EIGRP

Now configure the routing protocols for CE routers. Here, EIGRP is configured to connect these routers. This EIGRP will establish neighborly relation between CE routers. #router eigrp 1 #network 192.168.12.0 #network 2.2.2.2 0.0.0.0 #no auto-summary Next, use address-family ipv4 command to tell the router which its neighbor is. This is done in SP routers.

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#router eigrp 1 #address-family ipv4 vrf CUSTOMER #network 192.168.12.0 #no auto-summary

3.7

Redistributing the Routes in SP Routers

Finally, this has come to an end. Now routes should be distributed. This redistribution establishes the closed path for required network. #router bgp 234 #address-family ipv4 vrf CUSTOMER #redistribute eigrp 1 #router eigrp 1 #address-family ipv4 vrf CUSTOMER #redistribute bgp 234 metric 11111 Here, the metric command determines the best path to network destination.

4 Verification of Network These are the commands will tell you how far you are correctly configured your network. SP1#show ip route The output must include all routes in your network which you are connected. Example: This my network route Fig. 2. SP1#show ip route vrf CUSTOMER This command shows the routing table of routes connected.

Fig. 2 Network route

Configuring MPLS Cloud Providers with Virtual Private Network

Fig. 3 VRF routing table

SP1#show ip bgp vpnv4 all

Fig. 4 BGP configuration with VPN

SP1#show ip eigrp vrf CUSTOMER neighbors

Fig. 5 EIGRP VRF neighbors

HQ#show ip route

Fig. 6 Network configurations in headquarters routers

BRANCH#show ip route

Fig. 7 Network configurations in branch routers

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HQ#ping 5.5.5.5

Fig. 8 Pinging branch from headquarters

BRANCH#ping 1.1.1.1

Fig. 9 Pinging headquarters from branch

5 Advantages • • • •

Quality of service. Scalable. Service level agreement. Enhanced bandwidth.

6 Conclusion In the fast-growing world, the business organizations growth is frequent. The requirement of communication between headquarters and branch is essential. To establish such communications MPLS cloud providers with VPN are used. This provides the fastest way of data transfer by forwarding labels to egress router from ingress router. The MPLS backbone network transfers the label based on traffic on the route. At the ingress router labels are formed, and at the egress router labels will be removed and transferred in the form of normal packets. One can also migrate from FR to MPLS. MPLS is more reliable [15, 16] than Frame Relay and ATM. Through this WAN technology interconnection between site offices is easy and one can access data like voice and video. This network has enhanced bandwidth which is a major advantage compared to older protocols. But this type of bandwidth costs higher. To overcome this there is an upgrading technology known as SD-WAN technology. As MPLS technology is used by 84% of companies, it is the best way of connecting companies.

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Traffic engineering is an important application of MPLS. Using these mpls cloud providers with vpn one can route packets faster than traditional processes. The future scope of these MPLS cloud providers with VPN is continued using IPV6 addresses. Due to shortage of IPV4 addresses, IPV6 addresses came into extent, up to now IPV6 is used for basic routing protocols like dynamic and static. Tunneling process is used between IPV4 address assigned devices to communicate with IPV6 address devices. In that area, one can also implement this technology to achieve data transferring to IPV6 addressed devices.

References 1. I. Azher, M. Aurengzeb, K. Masood, Virtual private network implementation over multiprotocol label switching. 2005 Student Conference on Engineering Sciences and Technology (2005). https://doi.org/10.1109/sconest.2005.4382902 2. B. Daugherty, C. Metz. Multiprotocol label switching and IP. Part I. MPLS VPNs over IP tunnels. IEEE Internet Comput. 9(3), 6 June 2005. https://doi.org/10.1109/mic.2005.61 3. O.J. Salcedo Parra, G.L. Rubio, L. Castellanos, MPLS/VPN/BGP networks evaluation techniques. 2012 Workshop on Engineering Applications (2012). https://doi.org/10.1109/wea. 2012.6220066 4. J.A. Pico, J.O. Fajardo, A. Munoz, A. Ferro, MPLS-VRF Integration: forwarding capabilities of BGP/MPLS IP VPN in GNU/Linux. 2008 International Conference on Optical Network Design and Modeling (2008). https://doi.org/10.1109/ondm.2008.4578412 5. S. Mehraban, K.B. Vora, D. Upadhyay, Deploy Multi-Protocol Label Switching (MPLS) Using Virtual Routing and Forwarding (VRF). 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI) (2018). https://doi.org/10.1109/icoei.2018.8553949 6. C. Metz, Multiprotocol label switching and IP. Part 2. Multicast virtual private networks. IEEE Internet Comput. 10(1), 76–81 (2006). https://doi.org/10.1109/mic.2006.14 7. C.T. Chou, Traffic engineering for MPLS-based virtual private networks. Proceedings. Eleventh International Conference on Computer Communications and Networks (n.d.). https://doi.org/10.1109/icccn.2002.1043054 8. J.O. Fajardo, J.A. Pic, A. Mu, New tunneling capabilities for BGP/MPLS IP VPN in GNU/ Linux. Seventh International Conference on Networking (icn 2008) (2008). https://doi.org/10. 1109/icn.2008.105 9. A. Bahnasse, M. Talea, A. Badri, F.E. Louhab, New smart platform for automating MPLS virtual private network simulation. 2018 International Conference on Advanced Communication Technologies and Networking (CommNet) (2018). https://doi.org/10.1109/ commnet.2018.8360268 10. T.M. Almandhari, F.A. Shiginah, A performance study framework for Multi-protocol Label Switching (MPLS) Networks. 2015 IEEE 8th GCC Conference & Exhibition (2015). https:// doi.org/10.1109/ieeegcc.2015.7060069 11. C. Jacquenet, G. Bourdon, M. Boucadair, Automated production of BGP/MPLS-Based VPN networks. Service Automation and Dynamic Provisioning Techniques in IP/MPLS Environments (2008), pp. 211–225. https://doi.org/10.1002/9780470035146.ch10 12. J.A. Pico, J.O. Fajardo, A. Munoz, A. Ferro, MPLS-VRF integration: forwarding capabilities of BGP/MPLS IP VPN in GNU/Linux. International Conference on Optical Network Design and Modeling, 2008. ONDM 2008, 12–14 March 2008, pp. 1–6. https://doi.org/10.1109/ ondm.2008.4578412 13. E. Rosen, Y. Rekhter, BGP/MPLS VPNs, Request for Comments 2547, Internet Engineering Task Force (IETF) (1999)

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14. P. Marques et al., Constrained Route Distribution for Border Gateway Protocol/MultiProtocol Label Switching (BGP/MPLS) Internet Protocol (IP) Virtual Private Networks (VPNs), RFC 4684 15. G. Liu, X. Lin. MPLS performance evaluation in backbone network. 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333) (n.d.). https://doi.org/10.1109/icc.2002.997036 16. Y. Qiu, H. Zhu, Y. Zhou, J. Gu, A Research of MPLS-based network fault recovery. 2010 Third International Conference on Intelligent Networks and Intelligent Systems (2010). https://doi.org/10.1109/icinis.2010.120

A Prototype Development of Digirail-Ticket Verification and Seat Allocation S. Gobhinath, S. Karthikeyan, A. Guru Prakash, B. Balamurugan and N. Gokul

Abstract Indian Railway is the world largest transport system which is currently facing a lot of human-related errors and problems. This paper tries to address the problems associated with manual system of ticket verification and seat allocation. This investigation presents an automated biometric system for use in Indian Railway for ticket verification, which verifies passenger’s ticket through the biometric input (fingerprint). This minimizes the work of ticket collector in rail transportation. As in India, Aadhaar card is mandatory for all, it used as a tool for the project. The module installed in each compartment gets the passenger’s fingerprint and authentication is done using the information linked with Aadhaar id. Additionally, using GSM, information regarding berth confirmation will be immediately sent to the reservation against cancellation (RAC) passengers through SMS. By digitalizing the ticket verifying process, human errors can be highly reduced and verification process time can be minimized. And also, RAC passengers do not need to wait for travelling ticket examiner (TTE) approval. Keywords Fingerprint


Rail transport system
Biometric
GSM
RAC

1 Introduction Before dwelling on the current project, a handsome knowledge about the prevailing system of railway booking is essential right from allotment of ticket with berth number to reservation against cancellation to the role of travelling ticket, this section deals conservatively. It will help to understand the problem tackled by the Indian Railway and the solution this project provides for the impediment [1]. Passengers with an established reservation are allotted berths at the time of booking, S. Gobhinath (&)
A. Guru Prakash
B. Balamurugan
N. Gokul Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India S. Karthikeyan Department of Electrical and Electronics Engineering, SKI, Coimbatore, Tamil Nadu, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_77

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and berth numbers are specified on the ticket, except the case of first-class AC and first-class coaches. The compartment/cabin/coupe numbers for first class and first ACC are allotted the time preparation of chart. [2].

2 Hardware Details (i) Fingerprint Sensor The growth of biometrics system nowadays has been phenomenal around the world and its application ranges from safety to privacy and confidentiality. So, there is made rush among the business organizations to grasp this technology to its fullest magnitude [3]. This technology boasts of its salient features such as immune to fraud and eliminates security problems by identifying the unique person. Another advantage of biometric technology is cannot be breached by fraudsters and cheaters as it is tightly bound to the individual. Using the fingerprint as security and identification process, fingerprint technology becomes foolproof system and it is becoming synonymous with biometric systems [4]. Moreover, fingerprints are distinctive and permanent even if they temporarily cut. For criminal investigation, the department relies ultimately on fingerprinting. Using a scanner, the inked impression of fingerprint can capture can take instantly and quickly (Figs. 1 and 2). (ii) GSM Module Global system for mobile module is used to communicate among a personal computer and a global system of mobile, global packet radio service system. Global system for mobile module communicates is an architecture uses for mobile communication at worldwide (Fig. 3).

Fig. 1 Pixel image of human fingerprint of the participant name called Mr. R. Guruprasath

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Fig. 2 Fingerprint sensor module

Fig. 3 GSM module

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3 Proposed System—Block Diagram Reservation is done using Aadhaar or any proof linked with Aadhaar. Roping in UIDAI, a database of fingerprint of every person of India who is going to board the train is easy to develop [5]. The fingerprint of person booking ticket in Indian Railway is taken from the server by using Aadhaar card number, and a database of the particular train is created. Any cancellation of ticket should be done before 4 h from the departure time. A fingerprint sensor module(R-308) is placed in every compartment of the train. The passenger should check in to the train by placing his finger in the module [6]. If the fingerprint matches with the database of the particular train, the module will authorize him to claim his seat/berth. When the module fails to identify a reserved passenger due to technical or other problems, a “HELP” button pressed will send a message to the TTE for verifying the passenger reservation ticket manually. LCD will display the authentication message as “verification successful,” if the fingerprint matches with the UIDAI information else the LCD displays “not verified” then the user has to press help button so that he will be aided by the TTE’s HELP personally (Fig. 4). The HELP button placed integral with every module will send message to the TTE about the hitches and the exact location of the help required (compartment number) using GSM module (SIM 900a) [5, 6]. Finally, if the passenger does not show up or forgets to keep the fingerprint for verification to claim his/her berth within 10 min after departure of train will be alerted through a SMS. After that 10 min, the GSM module will send a berth confirmation to the first RAC passenger (priority-based).

Fig. 4 Proposed system—block diagram

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4 Conclusion By employing this application in Indian Railway for ticket verification, the processing time taken for ticket verifying and seat allocation will be reduced and seat allocation for RAC is made faster than usual. This project aims at controlling fraudulence and improving transparency, errorless ticket verification and seat allocation process. As all the fields in world employing technology for the betterment of the people, this Digirail application will be a huge success for India because we have the world’s largest railway transport system. So, the work of the travelling ticket examiner (TTE) will be minimized on the whole and his role can be defined for the betterment of the railway. Acknowledgements Ethics approval—The authors express their sincere thanks to the Management of Sri Krishna College of Engineering and Technology, Coimbatore, for providing the necessary facilities for the completion of this paper. The author’s state that this study conforms to the ethical standards contributes to human welfare by ensuring a research process that combines highest integrity and safety of human research participants.

References 1. S. Patil, S. Desurkar, Ticket Checker Application for Train QR Code-NCACIT-2016 2. A. Alam, A.M. Ansari, Next generation E-ticketing system. Int. J. Emerging Res. Manag. Technol. 2(12), 22787–29359 (2013) 3. A.I. Wuryandari, S.M. Nasution, Prototype of train application using NFC technology on android device. IEEE International Conference on Engineering and Technology, vol. 6. issue no. 7 (2016) 4. S. Gobhinath, V. Aparna, R. Azhagunacchiya, An automatic driver drowsiness alter system by using GSM. IEEE Explore ISCO Conference Publication (2017), pp. 125–128 5. S. Gobhinath, K. Muthukumar, S. Poorani, Hand features for ISL using combined DWT-DCT, local binary pattern. Int. J. Eng. Tech. (UAE) 7(2), 316–320 (2018, April) 6. S. Gobhinath, P. Gowthami, N. Guna Sundari, Internet of things (IOT) based energy meter. Int. Res. J. Eng. Technol. (IRJET) 3(4), 1266–1269 (2016) 7. S. Shaikh, M. Potghan, T. Shaikh, R. Suryawanshi, Urban railway ticketing application. Int. J. Adv. Comput. Sci. Softw. Eng. 4(1), 130–132 (2016) 8. S. Gobhinath, A. Ram, G. Vijayaraj, S. Lokesh, MPPT based photovoltaic charging system. Int. J. Eng. Manag. Res. (IJEMR) 6(2), 139–143 (2016, April) 9. S.I. Karthick, Velmurugan, Android s railway ticket with GPS as ticket checker. IEEE 1–4 (2016) 10. C. Sonkar, M. Swarup, A QR code processing for transparent and dynamic seat allocation in Indian Railway (2017)

Performance Analysis of Thyroid Tumor Detection and Segmentation Using PCA-Based Random Classification Method B. Shankarlal and P. D. Sathya

Abstract In this paper, the tumor regions in source thyroid image are detected and segmented using machine learning approach. The noises in the source thyroid image are detected and removed using mean filter, and then, dual-tree complex wavelet (DTCW) transform is applied on the noise-reduced image for obtaining the coefficients. The features are computed from these transformed coefficients of transform, and then, principal component analysis (PCA) is used to select the optimum set of features. The principal component features are now classified by random forest (RF) approach. Keywords Thyroid


Tumor
Transform
Features
Classifications

1 Introduction The neck in the human body has a gland which is having butterfly-shaped size and responsible for many functionalities of organ in human body. The abnormality in thyroid gland leads to the formation of hypothyroid or hyperthyroid based on its instability. If this abnormal level is crucial, then it damages the internal architecture of each cell in thyroid gland and forms the thyroid cancer. The thyroid cancer has two stages as initial and severe. During initial stage of this cancer, it does not show any noticeable symptoms on human body. Hence, it cannot be detected at the initial stage of this cancer in present method. During the severe stage of this cancer, it shows many symptoms on body, especially growing the gland in very large manner, vomiting and severe pain in neck region. The thyroid cancer can be categorized into papillary cancer, follicular cancer and medullary thyroid cancer. The papillary thyroid cancer starts from papillary regions of the thyroid gland, and it is one of the common forms of cancer in thyroid. It mainly affects the person aging between 30 and 60. Follicular thyroid cancer starts from the follicular cells in the thyroid gland,

B. Shankarlal (&)
P. D. Sathya Department of ECE, Annamalai University, Annamalai Nagar 608002, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_78

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Fig. 1 Thyroid cancer at severe stage

and it mainly affects the old age persons. It causes severe pain in neck region. The medullary thyroid cancer is the genetic disorder in thyroid gland and leads to the death in severe case. The severe case thyroid gland (as depicted in Fig. 1) is detected using scanning techniques in present scenario, and it is removed through surgery process. If this gland is removed from the human body, then the thyroid hormone instability is occurred which degrades the immunity system of the human body. Hence, the person whose thyroid gland is removed after surgery is advised to take the hormone medicine throughout his life period. If this cancer is found in its initial stage, then it can be curable by taking proper medications. Hence, there is a requirement for detecting this cancer cells at an earlier stage. This paper develops an efficient methodology to detect this cancer region in thyroid images.

2 Literature Survey Xia et al. [1] designed a novel technique and developed a machine vision-based mathematical model for classifying the pixels in thyroid image. The authors used robust training algorithm for differentiating the malignant pixel from the benign pixels in thyroid image. The authors reached 89.6% of classification rate by implementing their proposed thyroid cancer detection methodology on a large set of ultrasound thyroid images. Sokouti et al. [2] constructed a linear approach for detecting and diagnosing the papillary thyroid cancer. The authors applied discrete wavelet transform (DWT) for the decomposition of thyroid image, and then, each decomposed sub-band is diagnosed at various level set methods. The authors reached 89.6% of classification rate by implementing their proposed thyroid cancer detection methodology on a large set of ultrasound thyroid images.

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A multi-classifier system was used to distinguish between benign and malignant thyroid nodules. It used a combination of classifiers like support vector machine (SVM), Bayesian, K-nearest neighbor, probabilistic neural networks and linear least squares minimum distance. Multi-classifier systems are useful when the combined performance of the classifiers exceeds their best individual performances [3]. Devi et al. [4] used neural network classification methodology on the ultra sound thyroid images for the detection and classifications of tumor regions. The authors used radial neural networks and feed-forward neural networks for the abnormal pattern classifications. The authors achieved 87.8% of sensitivity, 92.1% of specificity and 98.1% of tumor region segmentation accuracy. Sokouti et al. [5] developed thyroid tumor detection framework for segmenting the abnormal tumor regions in ultrasound thyroid images. The authors used feed-forward back propagation neural network classification algorithm for differentiating the abnormal thyroid images from normal thyroid images in this method. The authors achieved 72% of sensitivity, 82.1% of specificity and 96.6% of tumor region segmentation accuracy.

3 Proposed Methodology In this paper, the tumor regions in source thyroid image are detected and segmented using random forest (RF) classification method. The noises in the source thyroid image are detected and removed using mean filter, and then, dual-tree complex wavelet (DTCW) transform is applied on the noise-reduced image for obtaining the coefficients. The features are computed from these transformed coefficients of transform, and then, principal component analysis (PCA) is used to select the optimum set of features. These optimum features are then classified using random forest (RF) classifier. This classifier classifies the source thyroid image into either tumor or non-tumor images. Then, morphological elements are applied on the classified abnormal thyroid image to segment the tumor regions. Figure 2 shows the proposed thyroid tumor detection flow using classification method.

3.1

Noise Reduction

The noises in the thyroid image are detected and removed using mean filter. This filter smoothes the pixels in the thyroid image and produces the noise-reduced image. In this method, a 3 * 3 filter is applied on the source thyroid image, and the mean value of the center pixel is computed with respect to its surrounding pixels in 3 * 3 windows. This will reduce the noise content in the pixels of the source thyroid image. Then, the window is moved from top position to the bottom position and applies the same procedure. Figure 3 shows the noise-reduced thyroid image.

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Fig. 2 Proposed thyroid tumor detection flow using classification method

Fig. 3 Noise-reduced thyroid image

3.2

Dual-Tree Complex Wavelet Transform

The spatial domain pixels in the noise removed thyroid image are converted into frequency and time domain using DTCWT. In this paper, this transform decomposes the noise-reduced thyroid image into number of sub-bands. These sub-bands

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Fig. 4 Four-level DTCWT architecture

are classified into approximate and detailed sub-bands. The approximate sub-band is again decomposed into approximate and details sub-bands at level 2. This procedure is repeated till level 4. This paper uses four-level DTCWT for decomposing the noise-reduced thyroid image into approximate and detailed sub-band. Figure 4 shows the architecture of the DTCWT at four levels. Each level of this architecture contains low- and high-frequency filters and the noise-reduced thyroid image is passed through these set of low- and high-frequency filters at each level. Each level of this architecture produces the coefficients.

3.3

Feature Extraction

The features are computed from the decomposed coefficients of the dual-tree transform. In this paper, gray level co-occurrence matrix (GLCM) features and local binary pattern (LBP) features are extracted from the set of decomposed coefficients. LBP feature computation process is illustrated in following section. In this computing procedure of LBP feature in thyroid image, a 3 * 3 sub-mask window is placed over the image. The intensity of the center pixel is compared by its other nearby pixels in the same mask window region. Each pixel in thyroid image has its own feature value which ranges either low or high. The low value indicates by black and high value indices by white. The size of the extracted feature image is equal to the size of the source thyroid image. Hence, the feature pixel count correlates every pixel point in source thyroid image. The extracted LBP image features are shown in Fig. 5.

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Fig. 5 Extracted LBP image

3.4

GLCM Features

The gray level co-occurrence matrix (GLCM) method is a way of extracting second-order statistical texture features considering the spatial relationship of pixels. GLCM features are computed with respect to different angle aspects and four distances (1, 2, 3, 4). Four statistical measures such as contrast, correlation, energy, entropy are computed based on GLCM, as explained in Eqs. (1)–(4). Contrast ¼

X

Energy ¼ Entropy ¼  Correlation ¼

X

X

ji  jj2 pði; jÞ

X



pði; jÞ2

ð2Þ

pði; jÞ½log2 pði; jÞ

ði  liÞðj  ljÞ

ð1Þ

pði; jÞ ½ri:rj

ð3Þ ð4Þ

where ‘i’ and ‘j’ indicate the rows and columns in GLCM matrix, respectively. The termp (i, j) depicts the values in GLCM matrix with respect to the matrix index ‘i’ and ‘j.’ The mean of the GLCM matrix is noted as ‘µ,’ and its variance is noted as ‘r.’

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PCA

In this paper, PCA is used to select the best features from the set of computed features in thyroid images. This algorithm can be explained in the following steps. Step 1: Calculate the mean and standard deviation of each extracted features in the mammogram image. Step 2: Subtract the sample mean from each observation, then dividing by the sample standard deviation. Step 3: Determine the coefficients from derived principal feature set and also find its corresponding Eigen factors. Step 4: Find the variance of each principal features which is corresponding to its Eigen factors. Step 5: The maximum variance in data results in maximum information content which is required for better classification. These optimized set of extracted features are further trained and classified using random forest classification algorithm.

3.6

Classifications and Segmentation

In this paper, random forest (RF) classification algorithm is applied on the computed set of features for classifying the source thyroid image into either tumor free or tumor-affected thyroid image. This RF classifier can be operated in two distinct modes, as training and testing. The training mode of this RF classifier trains the features which are computed from the normal and abnormal thyroid image. Then, the testing mode of this RF classifier classifies the extracted set of features from the source thyroid image into either tumor or non-tumor image based on the trained feature set. Further, the morphological elements are used in this paper to detect and segment the cancer regions in the classified abnormal thyroid image. This paper uses morphological opening and closing elements in order to segment the cancer regions in the classified thyroid image. Figure 6a shows the source thyroid image, and Fig. 6b shows the tumor region segmented image.

4 Results and Discussions The performance of the proposed thyroid tumor segmentation system is analyzed in terms of the following parameters as described in the following equations.

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Fig. 6 a Source thyroid image. b Tumor region segmented image

Sensitivity ðSeÞ ¼ TP/ðTP þ FNÞ

ð5Þ

Specificity ðSpÞ ¼ TN/ðTN þ FPÞ

ð6Þ

Accuracy ðAccÞ ¼ ðTP þ TNÞ=ðTP þ FN þ TN þ FPÞ

ð7Þ

where TP is true positive, TN is true negative, FP is false positive, and FN is false negative. The number of correctly identified cancer pixels is noted as TP, and the number of correctly identified non-cancer pixels is noted as TN. The number of wrongly identified cancer pixels is noted as FP, and the number of wrongly identified non-cancer pixels is noted as FN. The performance evaluation parameters sensitivity, specificity and accuracy are computed over the set of colon images available in the dataset and illustrated in Table 1. The performance of the proposed thyroid tumor segmentation methodology is computed with respect to the ground truth images obtained from expert radiologist. Table 2 shows the performance comparisons of the proposed thyroid cancer detection method with conventional methods.

Table 1 Performance analysis

Parameters

Experimental results (%)

Sensitivity Specificity Accuracy

98.7 99.1 98.9

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Table 2 Performance comparisons Methodology

Sensitivity (%)

Specificity (%)

Accuracy (%)

Proposed work Sokouti et al. [2] Singh and Jindal [6] Xia et al. [1]

98.7 80.0 81.8 87.6

99.1 99.7 84.3 97.1

98.9 98.1 83.1 96.6

5 Conclusions This paper uses DTCWT for transforming the pixels belonging to spatial domain into multi-class pixels in thyroid image for further processing. The features from the thyroid images are used to differentiate the normal thyroid images from abnormal tumor-affected thyroid images. In this paper, GLCM along with LBP features are extracted for the tumor detection process. In this paper, RF classifier can be used as classification algorithm. This classification algorithm differentiates the ultrasound thyroid image belonging to abnormal pattern set from the ultrasound thyroid image belonging to normal pattern set. Acknowledgements The images used in this paper are obtained from open access dataset http:// wilmingtonendo.com/image-gallery/.

References 1. J. Xia, H. Chen, Q. Li, M. Zhou, L. Chen, Z. Cai, Y. Fang, H. Zhou, Ultrasound-based differentiation of malignant and benign thyroid Nodules: an extreme learning machine approach. Comput. Methods and programs in biomedicine (2017). https://doi.org/10.1016/j. cmpb.2017.06.005 2. M. Sokouti, M. Sokouti, B. Sokouti, Computer aided diagnosis of thyroid cancer using image processing techniques. Int. J. Comput. Sci. Netw. Secur. 18(4) (2018) 3. V.A.S. Vaz, Diagnosis of hypo and hyperthyroid using MLPN network. Int. J. Innovative Res. Sci. Eng. Technol. 3(7), 14314–14323 (2014) 4. M.A. Devi, S. Ravi, J. Vaishnavi, S. Punitha, classification of cervical cancer using artificial neural networks. Procedia Comput. Sci. (2016). https://doi.org/10.1016/j.procs.2016.06.105 5. B. Sokouti, S. Haghipour, A.D. Tabrizi, A framework for diagnosing cervical cancer disease based on feed forward MLP neural network and ThinPrep histopathological cell image features. Neural Comput. Appl. (2014). https://doi.org/10.1007/s00521-012-1220-y 6. N. Singh, A. Jindal, A segmentation method and classification of diagnosis for thyroid nodules. IOSR J. Comput. Eng. 1(6), 22–27 (2012) 7. http://wilmingtonendo.com/image-gallery/

Factors Influencing the Success of Recommendations in E-Commerce K. Srihari, K. Moorthi and S. Karthik

Abstract Nowadays, in e-commerce, item recommendation to the buyers is one of the key promotional activities followed by all the e-commerce companies. While the users navigating the site, the recommendation systems help them to pick the items that they recently interested or that they recently viewed that are currently discounted. This article narrates various factors that are influencing the factors for the development of business in e-commerce using data analytics. Our analysis is performed based on log data of Flipkart; it is one of the large e-commerce sites in India. From our analysis, it shows that before selecting an item for recommendation, we have to consider various factors in parallel. The factors include as follows: item should match with the customers’ shopping interest in the previous session, discount information for the product, and recent popular items. Keywords Recommendations


Data analytics
E-commerce
Factors

1 Introduction Due to increase in the usage of the Internet, there has been an increase in the online shopping. So many companies started their business in online through various e-commerce portals. Nowadays, people are shopping their products through online based on comparing the same product in different e-commerce sites or different sellers on the same site. If the customer willing to buy a product first, he or she K. Srihari (&) Department of Computer Science and Engineering, SNS College of Engineering, Coimbatore, Tamil Nadu, India K. Moorthi Department of Computer Science and Engineering, Jansons Institute of Technology, Coimbatore, Tamil Nadu, India S. Karthik Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, Tamil Nadu, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_79

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compares the price of the product, check the offers given to the product and the usefulness of the product. So we can track the customer’s interest based on these factors. Automated personalized item recommendation helps customers to discover additional items based on their interest [1]. E-commerce business today operates in a dynamic and competitive environment. E-commerce companies compete with other e-commerce companies by offering discounts to their product cost and giving better qualities [2]. Due to the advancement of technologies, data science sellers need to adapt business intelligence based on customer needs through data analytics [3]. It is more receptive to take better decisions in the competitive market world. It is necessary to understand the knowledge about the factors that influence user’s decision to buy their product [4]. Also, they want to know what factors that influence the satisfaction and customer spending in e-commerce [5].

2 Related Work Many e-commerce Web sites provide extra recommendations to their customers while searching items in the site. These online automated recommendation systems will provide additional benefits for both customers and sellers. Scholz et al. [6] proposed a new recommender system to overcome the issues of changing preference and cold start problem with the existing recommender system. The new recommender system is called multi-attribute value theory (MAVT); the main idea behind this is configuration process which allows customers to learn about the attributes naturally. The result of using this method gives more accuracy compared to existing recommendation methods. Jiao et al. [7] tell how to create optimized product recommendation system in e-commerce using customer trade behavior information. To analyze user behavior, the authors used fuzzy theory, the customer comments are transformed into fuzzy quantitative data; then, two types of indexing method is applied—first includes product quality, service quality, and users’ feeling, and second index is based on sub index of first one, that is product size, color, packaging, logistics, and return policy. From their findings, the accuracy of recommendation is improved. Victor et al. [8] developed a hybrid approach for e-commerce recommendation —they proposed two methods: First one is item-item which groups the products’ information like goods service and activities based on similar ratings by the users. The principle behind item-item method is that if user gives good ratings to item X, which has high rating given by other users those who gave good ratings to item X. It eliminates cold start problem in recommendation. The second method is user-user, which employs ruling the comparison among users by constructing matrix based on user activities. The items liked by one user are recommended to other users those who have similar interest. Chen [9] developed a text matching algorithm for providing recommendation, and also search keywords were applied to determine the items based on the

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keyword similar items proposed. The functions used here are webpage extraction: getting enough information about users, feature analysis: clustering the data, behavior record, and user interest modeling followed by recommendation. He et al. [10] studied the challenges in various existing recommendations systems and its future research directions. From their analysis, the challenges found are cold start problem, diversity, novelty, and level of control. The authors also proposed a dynamic revelation structure for human–recommender interaction; it integrates suggestion system with visualization for improving accuracy of recommendation system.

3 Factors Influencing the Success of Recommendations The main idea of our analysis is to better understanding of the factors that influence our recommendations to the customers who were successful. In this section, we summarize the key insights that we obtained through our analysis.

3.1

The Effectiveness of Recommending Already Viewed Products

From the analysis, it is observed that around 15–20% of the recommendations were done using the items that are viewed by the user in previous sessions at least once. Usually, customers searched some products which they want or interested to buy. Due to some reasons like time constraint, financial situations, technical difficulties, etc., they postpone the shopping. Such products are identified, and the same has been recommended to the customers on the front page when they come online next time. The attractive part here is nearly half of the recommendations become successful based on this method. That is approximately 50% of the items led to purchase by the customers not to the new visitors. Actually, we do not know whether the customer bought the items because of recommendations, anyhow it helps the customers by navigating into the products that they have shown interest already.

3.2

The Role of Discounts

The next factor influencing recommendation is discount. The recommendation is given based on the products that are in discount. While doing online shopping, the first thing comes into e-commerce users’ mind is the cost of the product. Nowadays, people are having very much awareness in comparing the cost of the same product

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in various e-commerce sites. The recommendation is only useful for already visited customers not for first-time visitors. To overcome that and to give item recommendations to new visitors, we can use these discounts. For both new and already visited customers, the information about the discounted products is shown on the front page. It attracts the customers to see the products that are in discount and led to purchase.

3.3

The Effect of Limited Period Offer

The limited period offer is extended version of discount. Here the product is in discount in terms of cost, free delivery, combo offer, buy this get this free offer, etc. The main aim of limited period offer is to make customers to finish the order soon that is the offer validity is for a few hours only. The customers are attracted by providing the offers and showing the countdown in which the offer period will get over. This can change the customers’ mindset to finish the order in time, i.e., before ending of the offer period. From the analysis, it clearly shows around 25% of the customers are finished their order before ending the offer period.

3.4

The Effect of Considering Popularity of the Products

The next factor causal to the achievement of proposal is popularity. Here the products which are more popular are identified and recommended to customers. It is one of the safe strategies in the recommendation. As the product was already popular among the customers, it can be easy to convert the customers’ mindset to finish the purchase. The products which are bought by maximum customers and have good feedback are identified as popular products. The reason to consider feedback is to prove the quality of the products. Compared to limited period offer, the products recommended under popularity increased the sales. Customers easily get trust on the popular products because it was bought and used by many customers.

3.5

The Effect of Introducing Current Trends

The last factor contributing to the success of recommendation system is recommending the products which are new to the market based on current trends. In fashion e-commerce, day-to-day new style and trend becomes popular. Products in latest trend are recommended to customers. It makes the customers feel proud that they are updated with the current fashion. Also, customers like to purchase newly available products in market for showing their uniqueness.

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4 Conclusion This paper aims to improve the business process of e-commerce companies by providing better recommendations. Form our study, the factors contributing successful recommendation are the effectiveness of recommending already viewed products, the role of discounts, the effect of limited period offer, the effect of considering popularity of the products, and the effect of introducing current trends. From the study, we concluded that while providing recommendations, the e-commerce companies should consider the above-said factors for successful recommendation and improving business. Also, our recommendations give a clear idea to the customers and the manufacturers for effective decision making and to face the competitive environment by keeping track of the customer behavior and making them to be a frequent customer and giving ideas for promotional marketing.

References 1. D. Jannach, M. Ludewig, L. Lerche, Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. User Model. User-Adap. Inter. 27, 351– 392 (2017) 2. A.Y.L. Chong, E. Ch’ng, M.J. Liu, B. Li, Predicting consumer product demands via big data: the roles of online promotional marketing and online reviews. Int. J. Prod. Res. 55, 5142– 5156 (2017) 3. K. Moorthi, K. Srihari, S. Karthik, A survey on impact of big data in E-Commerce. Int. J. Pure Appl. Math. 116, 183–188 (2017) 4. T. Escobar-Rodrıguez, R. Bonson-Fernández, Analyzing online purchase intention in Spain: fashion e-commerce. Inf. Syst. E-Bus Manage. 15, 599–622 (2017) 5. T.M. Nisara, G. Prabhakar, What factors determine e-satisfaction and consumer spending in e-commerce retailing? J. Retail. Consum. Serv. 39, 135–144 (2017) 6. M. Scholz, V. Dorner, G. Schryen, A. Benlian, A configuration-based recommender system for supporting e-commerce decisions. Eur. J. Oper. Res. 259, 205–215 (2017) 7. M.H. Jiao, X.F. Chen, Z.H. Su, X. Chen, Research on personalized recommendation optimization of E-commerce system based on customer trade behaviour data, in 2016 28th Chinese Control and Decision Conference (2016) 8. V.N. Zakharov, S.A. Philippov, Clustering of goods and user profiles for personalizing in E-commerce recommender systems based on real implicit data, in Data Analytics and Management in Data Intensive Domains: XVIII International Conference, pp. 178–191, 2017 9. H. Chen, Personalized recommendation system of ecommerce based on big data analysis. J. Interdisc. Math. 21, 1243–1247 (2018) 10. C. He, D. Parra, K. Verbert, Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst. Appl. 56, 9–27 (2016)

Implementation of Alexa-Based Intelligent Voice Response System for Smart Campus K. Srihari, V. Sakthivel, G. Venkata Koti Reddy, S. Subhasree, P. Sankavi and E. Udayakumar

Abstract Retrieving information from a source is one of the hassle works to do in this world. Right from the manual intervention to computer world, information retrieval is known for its trouble factors. Conveying rightful information to the rightful person may be a guideline to face consequences. Though the information retrieval was changed digitally, it also requires a manual intervention to take look at that information. Our idea was to retrieve information of students through virtual voice assistant where it requires zero manual intervention except for asking a query. There are various virtual voice assistants in the market like Google Assistant, Cortana, Alexa, and Siri. In this, the Alexa can be chosen for a specific reason as it works with voice processing cloud, and it has scalable database. Amazon Developer helps the developers to customize the Alexa device by creating a new skill sets according to the need. Alexa works with three different devices, and they are Amazon’s Echo Dot, Echo, and Echo Plus. The skill set that is developed is compatible in all three devices, and Echo Dot is compact and portable for testing. Therefore, users can ask Alexa for specific information, and Alexa would reply them at ease. The following paper consists of development of skill set for the Alexa device that would retrieve information about the students if it is properly fed in its database. Keywords Alexa


Cloud computing
AVS
Echo Dot
Skill set

K. Srihari (&)
G. V. K. Reddy Department of CSE, SNS College of Engineering, Coimbatore, India V. Sakthivel
S. Subhasree
P. Sankavi Department of CSE, KPR Institute of Engineering and Technology, Coimbatore, India E. Udayakumar Department of ECE, Kalaignar Karunanidhi Institute of Technology, Coimbatore, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_80

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1 Introduction The Alexa is the application program that runs on the Amazon Echo devices such as Echo, Echo Dot, Echo Plus, and the recently launched Amazon Echo 2. Alexa works as an Intelligent Personal Voice Assistant (IPVA) that has the capability to perform various tasks using voice commands. It can perform tasks such as control audio, play music, book food online, book tickets online, control smart homes, deliver daily news, and weather updates. As mentioned above, its applications are limitless. The Alexa is a software program that can be modified and developed to meet various human needs. The device itself recognizes human voice efficiently and adapts to various persons interacting with it. Also, since the voice processing is done in cloud servers, the applications of the Echo devices are not restricted to a particular field. This project is concerned with the application of the Alexa device in every field. The details of the students are maintained in spreadsheet. Amazon’s Alexa can be the solution with its cloud database and NLP capabilities.

2 Motivation Digital Voice Assistants (DVAs) or Personal Voice Assistants (PVAs) are becoming popular in today’s smart modern world. People everywhere started using these devices in their lifestyle. DVAs nowadays control smart homes and offices, which ease the work done by humans. These devices work based on Natural Language Processing (NLP) algorithms. This project is inspired by getting wrong information about a student in campus and on the motivation to conserve human resource. In campus, multiple copies of the same document for each individual may be maintained, which increases both the physical storage space and the paper resources. It is also a very time-consuming process to search and acquire the patient details from the physical repository. To overcome these difficulties, Alexa Voice Service (AVS) provides a solution that involves the application of voice commands that simplify the task of storing and retrieving the data. There is most of the time a long queue, and sometimes, there is a process that involves human resource to book an appointment at the reception. Alexa device can be placed at the reception to ease the task of getting information about a student. The parents can either give voice commands or type their ID into a keypad to book appointments stating their ID and the doctor name that they prefer to consult.

3 Background: Amazon Alexa In this section, types of devices used in Alexa and voice user interface model are discussed.

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Alexa Devices

Alexa and its types will have Echo, Echo Plus, and Echo Dot. The commands are given to Alexa by the AVS. The AVS will have voice service support using cloud. First, the device called Echo was implemented for the voice service. The modes of Alexa will have listening and wakeup modes. Second, it listens the user commands and gives response only if the user told “Alexa.” After that, it goes to the listen mode. The Alexa will have 360% listening beam and responds to the user within the short time. The Echo Dot is a new device with inbuilt audio speaker. The Echo and Echo Dot will be connected through the normal power. In this paper, the Echo Dot is used for the implementation.

3.2

Service Model

AVS accepts the commands received from Echo Dot. Figure 1 represents the service model of Echo Dot. The device can be regulated by using the voice, and the based on the skill set in the device, it responds to the user. The device can be connected using Wi-Fi network. The commands are stored in the cloud, and responses will be sent based on the skills in the cloud. Alexa regulates the valid commands only for the processing of user query. The command is sent to the cloud for processing.

Fig. 1 Service model

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4 Modules 4.1

User Module

The user module consists of an Alexa device of user’s choice (Echo or Echo Dot), in which the user interacts with the AVS through voice commands. The response or feedback is delivered to the user through the Alexa device or a display. The user can perform various tasks such as schedule appointments, ask query about a student, and can also view the student history in that particular institution. Furthermore, from the view of the experts, they can view the behavioral history of each student individually, so that they can get accurate description of the student’s character and diagnose efficiently. The faculty end access point is similar to that of the student’s, i.e., either a voice response or video response using a monitor. Another interesting use of the Alexa device is to monitor the status of the fee balance of the student. The Alexa device can be used to keep track of the personal biodata administered to the admitted students. The administrator can just simply make an entry about the updated status of the students. Now referring to the proposed model as shown in Fig. 3, the user gives a voice command; this command is translated by the AVS using Lambda cloud processing to an AI machine-readable format. The machine-readable format is processed using AVS cloud service, and the result of the query is displayed as either voice or visual.

4.2

Administrator Module

The admin module or the backend module is the core of the Alexa device. The AVS is the heart of the Alexa system. This is where the developer codes the intents and the responses to the user queries in the Amazon Web Developer console, provided by AWS to build Alexa skills. The front end of the Alexa or the interface consists of the set of static dynamic commands to respond to various queries by the end user. The backend or the database connectivity is given to the Alexa device to store the details of the students. The database can be made by the managing person via a database console, or the inputs can be obtained dynamically from the users by the Q&A session to fill in the values in predefined fields. For example, admin can register every student details to an institution by stating his personal details via voice commands when the Echo device asks for each detail such as first name, last name, and DOB. The database used here is DynamoDB, and the JSON script is used to transfer information between the device and the database (Fig. 2).

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Fig. 2 Proposed model

The information is parsed using the JSON script from the database with respect to the user’s query, or the information obtained during the user registration process is entered into the respective fields in the database using the GET and POST methods in the JSON script. The DynamoDB is an unstructured database; this helps us to store various details in the different formats such as images of the patients, text, videos of surgeries, and prescription images in JPEG format.

5 Result Analysis and Study The Alexa skill set developed in this project has been tested successfully, and the following results have been obtained. As shown in Fig. 3, the Alexa skill sets were earlier developed for various applications such as entertainment, news and weather updates, controlling certain applications in smart phones, and for a variety of purposes. But, its implementation in the campus management sector is very limited and has only a few applications. This project intends to provide a major boost for the campus management sector in the Amazon Alexa’s skill sets. The impact of this project can be seen in Fig. 4. Further when compared to previous healthcare response systems developed, the features included and the performance outcome of this project has increased by a tremendous amount. The performance improvement is depicted in the graphs shown in Fig. 5 respectively.

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Entertainment

11% 9% 42%

News ApplicaƟon Control

17%

Campus Management Others

21% Fig. 3 Alexa skill development ratio (before)

Entertainment

9% 24% 14%

35%

News ApplicaƟon Control Campus Management

18% Others

Fig. 4 Alexa voice process mechanism

12 10 8 6 4 2 0

ExisƟng Model Proposed Model

Fig. 5 Existing performance versus proposed performance

6 Conclusion and Future Work Voice assistants are used for the easy processing, and manual typing work is avoided. The response system works only for the smart campuses. Because in this work, the skills are created for the smart campuses and the student information can

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be retrieved spontaneously through voice. The AI-related algorithms are used for retrieving the user commands. The parents can retrieve their ward information by asking the query using Alexa. The live assistance is provided to all the users, and response time is faster in the voice user interface. In the future work, deep learning algorithms may be used for faster response, and the emotions of the students can be identified.

Synthesis of Visual Attention-Based Robotic System and Its Present Utilization in Engineering Sridhar Prattipati, Vasimalla Ashok and N. Praneeth

Abstract In the field of robotics, individual visual attention is used to illustrate the current huge interest of robots on the vision-guided side. This functionality is commonly called Vision Guided Robotics (VGR). It is a technology that is developing at a fast pace and underlines the high costs of production and the difficulties that countries face in order to reduce labor and save production. The vision guide is applied in dynamic areas such as a medical robot, industrial robot, agricultural robot, mobile robot, telebot, and service robot, and the list goes on and on. In this article, we illustrate the approach to the design of a robotic system, focusing on the camera vision. Tasks are as follows: (1) development of a visual attention-based robotic system, (2) preparation of a vision-based system, and (3) preparation of a hardware application. We have developed and implemented a robotic system that recognizes different colors and follows the assigned paths.







Keywords Machine visualisation Global machine vision market Vision guided robot Movement of robot Matlab DC motor Hardware Research report on vision guided robots
















1 Introduction In the field of robotics, the current view of robots can be shown on one side [1]. This functionality is usually called robotic powered by vision (VGR). Research reports that the machine market in the world market considers the market that describes the scope of the project. According to the report “The Mechanism and Market Robotics (2010–2015),” the global mechanical vision system and segmental total market are S. Prattipati EEE Department, GNIT, Hyderabad, India V. Ashok ECE Department, URCE, Guntur, India N. Praneeth (&) EEE Department, ABIT, Khammam, India © Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7_81

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likely to reach $15.3 billion by 2015 [1]. The total market for income, cameras and smartphones will be around 27.3%. It is expected that from 2010 to 2015, the market for mechanical extension systems and companies will grow at a speed of 9.3%. The market has improved rapidly for machine visualization systems and companies to meet growing demand for traditional and non-traditional applications [2]. Mechanical extension systems have recently become effective with the development of innovative interface and smartphone capabilities and the ability to present them. As a result of recent advancements, problems of tasks decreased and costs and process strength increased. In this way, the use of machine vision technology has been increased for a broader application [3, 4].

2 Our Design and Implementation From the earlier chapters, we have seen the different perspectives in the designing of the vision-guided robots [2]. Based on that knowledge, we have designed and implemented a vision-guided robot, which recognizes the red, blue, and green colors and takes the direction as forward, right, and back, respectively [5]. In this section, we will discuss the designing of robot and detailed explanation of each and every component and their functions by considering the neighboring components of the particular component. First of all, let us see the overview of the implemented design which gives information about the components and signal flow [2, 6] (Fig. 1).

Fig. 1 Block diagram representing various components and the signal flow

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CONFONT NEED

REQUIREMENTS PROPOSE CONCEPT

REDESIGN

DESIGN SYSTEM ANALYZE SYSTEM

REFINE DESIGN

FABRICATE PROTOTYPE TEST PROTOTYPE PRODUCTION PLANNING PRODUCTION

END

Fig. 2 Sequential design process

3 Explanation of Each Part and Its Output See Fig. 2. Camera We used an iBall Face2Face C8.0 camera with interpolar-based 8.0 mega pixel static image resolution, 4 mega pixel video resolution, and 5G lenses with wide angle provision provides smooth video and clarity. And its specifications are given below [7]. Software Tool MATLAB The information gathered by the camera is fed to PC. MATLAB program is used to detect which color is having highest proportionality in the image grabbed by the camera [8]. In the image captured by the camera, if the proportion of red is greater than the blue and green, then red color wins and the signal “R” is fed to the microcontroller through MAX232 IC. In the similar manner, when the green color is having the higher proportion when compared to the others, then the signal “G” is the output and when the blue signal is having highest proportion, then the signal “B” will be

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the output. In that manner, in order to find out the highest proportionate color or winning color, the robot takes direction accordingly [9]. The program for our implementation is given in the program sheet which is on page number. Whenever these three colors are in same proportion, then no signal will be fed to the microcontroller, and then the robot will be in STOP position [10]. RS232 The RS-232 signal is an address (activated or deactivated), depending on whether the DEE OR the DEE signal [11]. MAX232 MAX232 is an IC that switches the RS-232 serial port signals into the correct indications for use in a digital logic circuit compatible with TLL. The MAX232 is a controller/receiver and generally converts the signals, such as TX, RX, RTS, and CTS, and provides RS-232 voltage level output (approximately ± 7.5 V) [11]. Microcontroller We use the AT89S52, a low-power, high-performance 8-bit CMOS microcontroller with 8 bytes of programmable flash memory in the system. This integrated circuit is manufactured using ethelle’s non-memory diversity high-density technology and is having compatibility with the standard Indscore 80C51 input set and the peanut [12]. The Flash on-chip program is supported by the memory reprogramming system or by a conventional nanolateral memory program. The AT89S52 is a high-performance microcontroller that provides economical and high-resolution solutions for many integrated control applications [13]. DC Motor Industrial Use: Drills, lathes, shapers, spinning and weaving machines, boring mills, electric traction, brands, air compressors, elevators, vacuum cleaners, sewing machines, presses, shears, hair drier, and reciprocating machines [13]. In our project, we used two small DC motors. They receive signals from L293D and acts accordingly. The below section explains crystal clearly about mechanism of L293D and how the ROBOT is taking diversions [12]. L293D L293D is a two-motor controller so that we can integrate two DC motors with a single microchip which can be controlled in the direction of clock, and clock and can be a controlled control motor [9]. You can use all four inputs/outputs to connect four depot motors. L293D has 600 MW output and a circle output current 1.2 per channel. In addition, the safety of the reverse voltage eff circuit has been included in the circuit. Output voltage (VCC2) has a wide range of 4.5–36 V, making L293D a good choice for a DC motor [11]. Using two motors, we can run our robots in any direction. This robot management method is called “differential drive” [14] (Figs. 3 and 4). Our total equipment are robot connected to the laptop and a camera of laptop which recognizes the red color, and the motor takes right turn. The experiment is done with blue and green colors also.

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Fig. 3 H-bridge circuit diagram

Reverse Side Of Robot

Left DC Motor

Right DC Motor

Castor Wheel

RS232

Fig. 4 Hardware, its parts, and our testing images

4 Conclusion From unmanned aerial vehicles (UAV) to toy, laparoscopic surgery to the industrial sewing machine, addition of vision guidance to the systems increases the reliability, efficiency, and flexibility of the particular system. Vision-guided robotics extremely increases the performance of the system, highly increases flexibility, and reduces manual work efficiency and reliability. From the Research Report on Vision-Guided Robotics, [8] gives us the information on how the vision-guided technology is having its great impact on the era of technology in the upcoming

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years. As the cost of production of vision-guided systems is decreased drastically, it can be suited for all the classes of industries or in any field of working [15]. Security and traffic are the two application areas with high growth potential. The applications such as traffic flow monitoring, automatic number plate recognition, traffic surveillance, and other related areas are witnessing more utilization and integration of machine vision systems. In the recent economic turmoil, badly hit application areas such as semiconductors and automotive are witnessing less demand, while infrastructure industries and medical are witnessing steady growth. Therefore, undoubtedly, we can proclaim that vision-guided robotics is the technology which is going to be ruling the whole world.

References 1. A.C. Sanderson, L.E. Weiss, (1983), in Adaptive visual servo control of robots, Robot Vision, (IFS Pub. Ltd., Bedford, UK, 1983) 2. L.E. Weiss, A.C. Sanderson, C.P. Neuman, Dynamic sensor-based control of robots with visual feedback. IEEE J. Robot. Autom. RA-3(5), 404–417 (1987) 3. P.Y. Coulon, M. Nougaret, in Use of a TV camera system in closed-loop position control of mechanisms, Robot Vision, (IFS Pub. Ltd., Bedford, UK, 1983) 4. M. Driels, M. Huang, R. Liscano, K. Michael, The use of visual feedback for the acquisition of pseudorandomly oriented parts. J. Rob. Syst. 1(2), 195–204 (1984) 5. B. Skaar, W.H. Brockman, R. Hanson, Camera-space manipulation. Int. J. Robot. Res. 6(4), 20–32 (1987) 6. P.K. Khosla, C.P. Neuman, F.B. Prinz, An algorithm for seam tracking applications. Int. J. Robot. Res. 4(1), 27–41 (1985) 7. W.F. Clocksin, J.S.E. Bromley, P.G. Davey, A.R. Vidler, C.G. Morgan, An implementation of model-based visual feedback for robot arc welding of thin sheet steel. Int. J. Robot. Res. 4(1), 13–26 (1985) 8. R.P. Paul, Robot Manipulators: mathematics programming and control (MIT Press, MA, Cambridge, 1981) 9. K.A. Dzialo, R.J. Schalkoff, Control implications in tracking moving objects using time-varying perspective-projective imagery. IEEE Trans. Indust. Electron. IE-33(3), 247–253 (1986) 10. Y. Shirai, H. Inoue, Guiding a robot by visual feedback in assembly tasks. Pattern Recogn. 5, 99–108 (1973) 11. J. Mochizuki, M. Takahashi, S. Hata, Unpositioned workpieces handling robot with visual and force sensors. IEEE Trans. Indus. Electron. IE-34(1), 1–4 (1987) 12. M. Kabuka, E. McVey, P. Shironoshita, An adaptive approach to video tracking. IEEE J. Robot. Autom. 4(2), 228–236 (1988) 13. A.G. Makhlin, Stability and sensitivity of servo vision systems, in Proc. 5th Int. Conf. on Robot Vision and Sensory Controls (1985) 14. V. Hayward, R.P. Paul, Robot manipulator control under Unix RCCL: a robot control ‘C’ library. Int. J. Robot. Res. 5(4), 94–111 (1986) 15. R.P. Paul, H. Zhang, Robot motion trajectory specification and generation, in Robotics Research (1985)

Author Index

A Agalya, S., 807 Ajay Sai Kiran, P., 625 Ajith, Athira, 13 Anandraj, P., 269, 281 Anil Kumar, S., 417 Anindhitha, A., 807 Aravind, K.V.G., 255 Archana, A., 807 Arul Praveen, T., 281 Arumugam, Prakash, 381 Aruna Kumari, K., 787 Arvin Tony, A., 321 Ashok Kumar, L., 35 Ashok, V., 449 Ashok, Vasimalla, 857 Ashraf, Syed Aqeel, 349 Ashwin, R., 601 B Balakishan, P., 235 Balamurugan, B., 827 Bandaru, Naga Venkata Sai Sudheer, 817 Bhagya sree, V., 777 Bhanu, C.V.K., 339 Bogaraj, T., 515 Bomma, Rama Rao, 515 Boobalan, S., 601, 769 Boppa, Madhu, 417 C Chalamalla, Srinivas Reddy, 215 Chander, R., 195 Chandragupta Mauryan, K.S., 65 Chandrika, Basagonda, 503

Chatterjee, Sayanti, 693 Chenchireddy, Kalagotla, 35 Chiranjeevi, M., 145 D Das, Mrutyunjay, 579 Deepak, Karanam, 203 Diggavi, Krishna Chaitanya, 569 Diwakaran, M., 807 G Gadgune, S.Y., 463 Ganapathi, A., 405 Ganesh, G., 473 Gauthami, R., 543 Gobhinath, S., 321, 601, 827 GokulaPriya, E., 807 Gokul, N., 827 Gunalan, K., 639 Guru Prakash, A., 827 H Harshavardhan Reddy, C.V., 357 I Ilango, K., 13, 661 Ilango, Karuppasamy, 543 Ilangovan, S.A., 543 Indragandhi, V., 331 J Jayakumar, J., 515 Jayalaxmi, A., 3 Jayanthy, S., 609 Jaya Sree, K., 203

© Springer Nature Singapore Pte Ltd. 2020 H. S. Saini et al. (eds.), Innovations in Electrical and Electronics Engineering, Lecture Notes in Electrical Engineering 626, https://doi.org/10.1007/978-981-15-2256-7

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864 Jegathesan, V., 35 Jeylani, A. Maideen Abdhulkader, 91 K Kalaivani, M., 739 Kanagavel, Rameshkumar, 331 Kanakaraj, J., 91 Kangale, Shubhangi, 683 Karthikeyan, S., 827 Karthik, S., 843 Kashoob, Mohammed, 349 Kavya Santhoshi, B., 561 Khatun, Koyelia, 45 Kiranmayee, V., 161 Kishore, Muppalla N.R., 439 Kishore, P. V., 81, 291 Kota, Venkata Reddy, 103 Krishnamoorthi, Santhi, 759 Krishnaveni, V., 715 Kumar, Ajay, 181 Kumar, Velakurthi Mahesh, 55 L Lakshmi, K., 769 Lakshmi, M. L. S. N. S., 751, 817 Laxmi, Ch., 579 Likhitha Reddy, M., 255 Loganathan, N., 321 Loveswara Rao, B., 625 M Madhankumar, S., 269, 281 Madhusudan, Ravilla, 473 Madian, Nirmala, 759 Mahaboob Subhani, A., 91 Mahesh, Thati, 673 Maheswari, K., 255 Malini, T., 321 Mallareddy, Ch., 27 Malleswa Rao, A.N., 255 Manitha, P. V., 13 Manjunathachari, K., 723 Mannam, Praveen, 617 Meshach, Jan, 601 Mohana Sundaram, K., 117, 561, 639 Moorthi, K., 843 Mummadi, Teja Sree, 367 Muralidhar Goud, K., 705 N Naga Jyothi, M., 339 Nagi Reddy, B., 587 Nair, Vineeth V., 543 Nanda, Haresh, 215

Author Index Narasimha Rao, Mucherla, 311 Naresh Kumar, A., 673 Naveen Kumar, D., 81, 291 Niveditha, N., 65 P Padmavathi, S. Venkata, 3 Pakkiraiah, B., 569 Palisetti, Anusha, 103 Pandi, V. Ravikumar, 661 Partha Saradhi Reddy, P., 203 Parthasaradhy, P., 723 Pavan Kumar, Chillappagiri, 311 Pooranam, N., 807 Potdar, Ashwini V., 27 Prabhu, V., 729 Prakash, J., 639 Prakash, Vodapalli, 311 Praneeth, N., 449, 857 Prasanna, T.S., 181 Prattipati, Sridhar, 857 Praveen, T. Arul, 269 Praveen Kumar, T., 235 Preeti, 429 Priyadarshini, M.S., 225 Pulluri, Harish, 417, 429 Punidha, R., 729 R Rafi, Shaik, 243 Rajagopal, Dhanasekaran, 759 Rajasree, S.R., 661 Rajendran, Anand, 381 Rajesh, S., 269 Rajkumar, K., 601 Rama Prasad Reddy, M., 203 Ramesh Babu, V., 405 Rami Reddy, Ch., 587 Ramya, CH., 339, 349 Ranganadh, A., 145, 797 Ranga Purushotham, G., 395 Ranjan Mohanty, Pratap, 357 Ranjithkumar, G., 739 Rathore, Akshay Kumar, 45 Raut Mrunmayi, N., 683 Ravindra, M., 449 Raviprabhakaran, Vijay, 367 Reddy, Chandrasekhar, 751 Reddy, G. Venkata Koti, 849 Rekha, R., 587 S Sahu, Sarat Kumar, 3 Saibabu, Ch., 395

Author Index Sai Priya, B., 429 Sai Varun, N.R., 653 Sajan, Ch., 235 Sakthidhasan, K., 117 Sakthivel, S., 729 Sakthivel, V., 849 Sakthivel, V.P., 485 Sambasiva Rao, B., 473 Sampathkumar, B., 683 Sankavi, P., 849 Sarala, B., 777 Saravanakumar, R., 55 Sathish, Aswin, 543 Sathya, P. D., 485, 833 Satyanarayana, S., 395 Selvanayakam, A., 739 Shaikh, Nurul Hasan, 349 Shankarlal, B., 833 Sharath Kumar, A., 161 Shiva Kumar, P., 673 Shraddha, Patange, 429 Singh, Kuldip, 579 Singh, R.P., 617 Sirisha, Simhadri Lakshmi, 243 Sophia, S., 609 Sree Hari, S., 777 Sreelekshmi, R.S., 543 Srihari, K., 843, 849 Sri Hari, T., 417, 429 Srikanth Goud, B., 587 Srikanth, Ravipati, 243 Srinivasa Rao, R., 449 Srinivasa Rao, V., 449 Sri Rama Krishna, K., 787 Sriram, Cholleti, 439 Srisailam, C., 705 Subhasree, S., 849 Sujatha, B.C., 173, 503 Sujatha, S., 543

865 Suman, M., 485 Sumithra, M., 173 Suresh, D., 195 Suresh Babu, G., 653 Suryanarayana, C.H.V., 349 Sushama, M., 225 Swain, Nibedita, 129 Swathy, S., 65 T Thirukkuralkani, K.N., 769 Thula, Manidhar, 569 U Udayakumar, E., 715, 849 V Vamsi Narasimha, L., 473 Varadarajan, Anirudh, 269, 281 Varishnee, A.C., 739 Veeramani, C., 255 Veerlapati, Ramaiah, 305 Venkateswara Rao, Ch., 349 Vignesh, T., 269, 281 Vineeth, V.V., 609 Vinnakoti, Sudheer, 103 Vinoth Kumar, K., 55 Vipin Krishna, M., 255 Vishnu Soureesh, K., 543 Vodapalli, Prakash, 305 Vyshnavi, M., 429 Y Yadav, Poonam M., 463 Z Zaheer Ahamed, M., 777