Advances in Renewable Energy and Sustainable Environment: Select Proceedings of NCRESE 2019 [1st ed.] 9789811553127, 9789811553134

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Lecture Notes in Electrical Engineering 667

Lillie Dewan Ramesh C. Bansal Ujjwal Kumar Kalla   Editors

Advances in Renewable Energy and Sustainable Environment Select Proceedings of NCRESE 2019

Lecture Notes in Electrical Engineering Volume 667

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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Lillie Dewan Ramesh C. Bansal Ujjwal Kumar Kalla •

•

Editors

Advances in Renewable Energy and Sustainable Environment Select Proceedings of NCRESE 2019

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Editors Lillie Dewan School of Renewable Energy and Efficiency National Institute of Technology Kurukshetra Kurukshetra, India

Ramesh C. Bansal Department of Electrical and Computer Engineering University of Sharjah Sharjah, United Arab Emirates

Ujjwal Kumar Kalla Department of Electrical Engineering Government Engineering College Bikaner Bikaner, Rajasthan, India

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-15-5312-7 ISBN 978-981-15-5313-4 (eBook) https://doi.org/10.1007/978-981-15-5313-4 © Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Committees

Patrons Dr. Renu Phulia, IAS (Director, DNRE and HAREDA) Dr. Satish Kumar (Director, NIT Kurukshetra)

Co-patrons Prof. Lillie Dewan (Coordinator, SREE and Professor, EED, NIT Kurukshetra) Sh. O. D. Sharma (Project Director, DNRE/HAREDA) Sh. P. K. Nautiyal (Scientific Engineer A, DNRE/HAREDA)

Conveners Dr. Shelly Vadhera (Associate Professor, EED, NIT Kurukshetra) Mr. Sukhchain Singh (Project Manager, DNRE/HAREDA)

Organizing Secretary Dr. Dr. Dr. Dr.

Gulshan Sachdeva (Assistant Professor, MED, NIT Kurukshetra) Avadhesh Yadav (Assistant Professor, MED, NIT Kurukshetra) Shashi Bhushan Singh (Assistant Professor, EED, NIT Kurukshetra) Rahul Sharma (Assistant Professor, EED, NIT Kurukshetra)

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Organizing Committee Dr. Amit Kumar (Faculty, SREE, NIT Kurukshetra) Mr. Gaurav Sharma (Faculty, SREE, NIT Kurukshetra) Ms. Amandeep Kaur (Faculty, SREE, NIT Kurukshetra)

Committees

Preface

National Conference on Renewable Energy and Sustainable Environment (NCRESE-2019) was organized by School of Renewable Energy and Efficiency, NIT Kurukshetra, in association with Bureau of Energy Efficiency, Government of India (GOI); Ministry of Power, Department of New and Renewable Energy, Haryana; and Haryana Renewable Energy Development Agency, Panchkula, during August 30–31, 2019. The aim of this conference was to bring together leading academic scientists, researchers, scholars and students across the globe to exchange and share their experiences and research results and development about all new technologies in the field of renewable energy to help the environment professionals to harness the full potential. The whole idea of the conference is to exchange thoughts and ideas in the concerned fields and transform those in real time to solve the problems. The conference was a rare opportunity for all individuals of the environment community to know about the latest technologies and strategies. The research papers for the conference were invited based on original work in the broad areas of renewable energy and harvesting technologies, alternate energy resources, wind energy, solar energy, smart grids, electric vehicle, energy storage, solar thermal and bio-energy technologies. All of these submissions went through a rigorous peer-review process commensurate with their tracks. Together, the papers presented here represent a set of high-quality contributions to the literature on agile research and experience addressing a wide range of contemporary topics. In all, the accepted 43 research papers were reviewed by three members of the Program Committee and presented in front of committee of three members in their respective tracks. Materials from all of the sessions are available on the conference Web site at www.ncrese2019.com. NCRESE-2019 attendees were also treated to a number of high-profile keynote speakers.

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It was a mammoth effort to review these and bring them together into a coherent program. We would like to thank everyone who contributed to this effort including paper authors, session presenters, track chairs, Program Committee members, volunteers, sponsors and keynote speakers. Without their support, the event would not have been as successful. Kurukshetra, India Sharjah, United Arab Emirates Bikaner, India August 2019

Lillie Dewan Ramesh C. Bansal Ujjwal Kumar Kalla

Contents

Voltage Stability and Performance Analysis of the Multi Machine System Using UPFC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akshay Kumar Dwivedi and Shelly Vadhera Harmonic Analysis at Variable Load . . . . . . . . . . . . . . . . . . . . . . . . . . . Vijit Srivastava and P. N. Gupta Voltage and Transient Stability Enhancement in Power System Using Unified Power Flow Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . Jaswant Singh Bhati and Shelly Vadhera Probabilistic Approach for Load Flow Analysis of Radial Distribution System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anuj Banshwar, Mohit Pathak, Bharat Bhushan Sharma, Naveen Kumar Sharma, and Sumit Kumar

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Harmonic Issues in Non-conventional Energy Supply and Its Remedy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vijit Srivastava and P. N. Gupta

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Artificial Neural Network-Based Source Identification Producing Harmonic Pollution in the Electric Network . . . . . . . . . . . . . . . . . . . . . Ankit Tayal, Lillie Dewan, and J. S. Lather

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Impacts of Distributed Generation on Distribution System Based on the Backward and Forward Sweep Method . . . . . . . . . . . . . . Indubhushan Kumar and Sandeep Gupta

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Cost Analysis of Multi-rotor Wind Turbines . . . . . . . . . . . . . . . . . . . . . Navjot Singh Sandhu and Saurabh Chanana

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Annualized Cost of Wind Power Generation . . . . . . . . . . . . . . . . . . . . . Sahil Bajaj and K. S. Sandhu

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Contents

A New Technique for Wind Turbine Power Curve Incomplete Bin Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bhukya Ramdas, M. Saravanan, J. C. David Solomon, and K. Balaraman

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Grid Integration of Wind Energy Conversion System with Variable Speed Analysis Using Simulink Model . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Ravneet Kaur, Deepika Bhalla, and Naveen Kumar Sharma A Comparison of Performance of DPC Technique for DFIG with Using PI and PID Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Sachhidanand Veer Savarkar and Shashi Bhushan Singh A Case Study on 24-h Simulation of V2G System . . . . . . . . . . . . . . . . . 131 Robin Chola and Shashi Bhushan Singh A Review on Bidirectional DC-DC Converters for V2G and G2V Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Robin Chola and Shashi Bhushan Singh Grid Integrated with Wind Turbine System on Fault Analysis Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Ravneet Kaur, Deepika Bhalla, and Naveen Kumar Sharma Performance Improvement of Wind Turbine Induction Generator Using Neural Network Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Sanjay Dewangan and Shelly Vadhera Solar Resource Assessment and Potential in Indian Context . . . . . . . . . 173 Vinod Nandal, Raj Kumar, and S. K. Singh Implementation of Hill Climb Search Algorithm Based Maximum Power Point Tracking in Wind Energy Conversion Systems . . . . . . . . . 191 Biaklian Tonsing, Shelly Vadhera, and Atma Ram Gupta Analytical Review of Solar Cell as Globalized Approach . . . . . . . . . . . . 201 Srishtee Chaudhary and Rajesh Mehra Active Battery-Balancing Using Ćuk Converter . . . . . . . . . . . . . . . . . . . 211 Nikhil Sarode and M. Divya Analysis of Series-Connected PV Cells Using gEDA and ngSPICE . . . . 221 Ritesh Kumar and Shelly Vadhera An Experimental Study on a Portable SPV-Integrated Forced Convective Solar Dryer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Debajit Misra A Review on Energy-Efficient and Sustainable Urban Buildings . . . . . . 245 Bishnu Kant Shukla, Harun Or Rashid, Anit Raj Bhowmik, and Pushpendra Kumar Sharma

Contents

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Role of Optimization Algorithms in Enhancing the Performance of Photovoltaic Thermal (PV/T) Systems . . . . . . . . . . . . . . . . . . . . . . . . 253 Sumit Kumar, Sourav Diwania, Anwar S. Siddiqui, and Sanjay Agrawal Selection of the Best Material for Coating on Solar Cell and Optical Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 Sanjay Kumar and Lillie Dewan Microcontroller-based PID Temperature Controller Using Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Bharat Bhushan Sharma, Anuj Banshwar, Mohit Pathak, Naveen Kumar Sharma, and Aman Joshi Optimal Energy Dispatch and Techno-economic Analysis of Islanded Microgrid with Hybrid Energy Sources . . . . . . . . . . . . . . . . . . . . . . . . . 285 V. V. S. N. Murty and Ashwani Kumar Performance Analysis of Carrier Modulation Based H-Bridge Inverter for Hybrid Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Mukh Raj Yadav and Navdeep Singh Optimal Energy Management in Hybrid Microgrid with Battery Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Masood Rizvi, Bhanu Pratap, and Shashi Bhushan Singh Optimal Placement of DG with Battery Energy Storage Using CPLS and Combined Dispatch Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Bharat Singh and Ashwani Kumar Sharma Comparison Among Conventional and Adaptive Gain Scheduling PID Controllers for Stabilizing an Inverted Pendulum System . . . . . . . . . . . 327 Vikram Chopra, Sunil K. Singla, and Lillie Dewan A Review on Protection Devices and Protection Techniques for DC Micro-grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Bholeshwar and R. S. Bhatia Positive and Negative Vibe Classifier by Converting Two-Dimensional İmage Space into One-Dimensional Audio Space Using Statistical Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 Kushal Sharma and Sandeep Gupta Coupled Field Method for Analyzing Short-Circuit Electromagnetic Forces in Power Transformers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Karan Bali and Deepika Bhalla Thermal Separation in 2D Vortex Tube for a Different Tube Length and Cold Mass Flow Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Ashish Kumar Gupt, Deepak Kumar, and M. K. Paswan

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GA and PSO Based Optimization for Benchmark Thermal System Using Wavelet-Based MRPID Controller . . . . . . . . . . . . . . . . . . . . . . . . 385 Abhas Kanungo, Monika Mittal, and Lillie Dewan The Stablization of Soil Using Treated Bagasse Ash . . . . . . . . . . . . . . . 397 Manish Chaudhary, Mohit Rathore, Manoj Bhatt, and Deepak Verma Status of Water Quality in Various Ponds and Lakes in India . . . . . . . 417 Narender Singh and S. K. Patidar Water Quality Assessment of Brahma Sarover, Sannihit Sarover and Saraswati Tirth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 Narender Singh and S. K. Patidar

About the Editors

Dr. Lillie Dewan is currently a professor at the Department of Electrical Engineering, National Institute of Technology Kurukshetra, Haryana, India. She obtained her Bachelor’s in Electrical Engineering from Punjab University in 1983, Master’s in Control System in 1987, and Ph.D. in Electrical Engineering from Kurukshetra University in 2001. She has more than 35 years of teaching and research experience. She has published papers in 61 journals and 74 conferences. She has also supervised 14 doctoral and 40 postgraduate students, and she has completed two MHRD sponsored projects. She is a life member of ISTE, SSI, and member of IEEE. Prof. Ramesh C. Bansal has more than 25 years of diversified experience of research, scholarship of teaching and learning, accreditation, industrial, and academic leadership in several countries. Currently, he is a Professor in the Department of Electrical and Computer Engineering at University of Sharjah. Previously he was Professor and Group Head (Power) in the ECE Department at University of Pretoria (UP), South Africa. Prior to his appointment at UP, he was employed by the University of Queensland, Australia; University of the South Pacific, Fiji; BITS Pilani, India; and Civil Construction Wing, All India Radio. Prof. Bansal has significant experience of collaborating with industry and government organizations. He has made a significant contribution to the development and delivery of BS and ME programmes for Utilities. He has extensive experience in the design and delivery of CPD programmes for professional engineers. He has carried out research and consultancy and attracted significant funding from Industry and Government Organisations. Prof. Bansal has published over 300 journal articles, presented papers at conferences, books, and chapters in books. He has Google citations of over 10000 and an h-index of 44. He has supervised 22 PhD, 4 Post Docs. His diversified research interests are in the areas of Renewable Energy (Wind, PV, DG, Micro Grid) and Smart Grid. Professor Bansal is an editor of several highly-regarded journals,

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IET-RPG and IEEE Systems Journals. He is a Fellow and Chartered Engineer IET-UK, Fellow Engineers Australia, Fellow Institution of Engineers (India), and Senior Member of IEEE-USA. Dr. Ujjwal Kumar Kalla is currently an assistant professor and head, department of electrical engineering, Government Engineering College Bikaner, Rajasthan, India. He obtained his Master’s of Technology in Power Electronics, Machines and Drives in 2010 and Ph.D. in Electrical Engineering in Power Electronics in 2015 from Indian Institute of Technology Delhi, India. He has more than 16 years of teaching and research experience. He has published 35 research articles in reputed journals and conferences. He is a senior member of IEEE, USA, fellow of Institution of Engineers India (IEI), life member of ISTE and fellow of Institution of Electronics & Telecommunication Engineers.

Voltage Stability and Performance Analysis of the Multi Machine System Using UPFC Akshay Kumar Dwivedi and Shelly Vadhera

Abstract The multimachine system is one of the most common systems present in the power system network. System expansion is necessary due to ever-increasing load, and thus, the analysis of the response of multimachine becomes important too. UPFC is the most common FACTS device used in power system in current scenario as it can control all parameters of the power at same time. IEEE 14 bus test system with three generators has been taken for the analysis, and also voltage stability is calculated with the help of different indices to find out suitable position of UPFC to be implemented in the system. All the simulation is carried out in the PSAT software [1]. The loss with and without UPFC is calculated. Keywords Voltage stability · Multimachine · FACTS · UPFC · PSAT

1 Introduction UPFC can be considered as the most upgraded version of the FACTS devices. It is also one of the most important FACTS devices as it can control all the parameters of the network simultaneously (Fig. 1). UPFC incorporates two VSCs, operated with DC storage capacitance also so called DC link. These arrangements operate as a perfect AC-to-AC converter within which the active power flows independently in any direction between the two converters, and each converter is capable of generating reactive power on its own terminal independently. Obviously, there can be no reactive power flow through the UPFC DC link. UPFC is so called the addition of SSSC and STATCOM mutually joined with common DC voltage link[2]. A. K. Dwivedi (B) · S. Vadhera Department of Electrical Engineering, National Institute of Technology Kurukshetra, Kurukshetra, Haryana 136119, India e-mail: [email protected] S. Vadhera e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 L. Dewan et al. (eds.), Advances in Renewable Energy and Sustainable Environment, Lecture Notes in Electrical Engineering 667, https://doi.org/10.1007/978-981-15-5313-4_1

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A. K. Dwivedi and S. Vadhera

Fig. 1 Basic UPFC scheme

The above diagram explains the typical concept of useful power flow control functions and series voltage injection. The introduction of the typical voltage phasor V pq to the pre-existing bus voltage, V 0 , at an angle that can be changed from 0° to 360° is illustrated in Fig. 2a. Effects on voltage regulation when V pq = V 0 , induced in phase with V 0 , is given in Fig. 2b. A summation of series compensation and voltage regulation is represented in Fig. 2c, and V pq represents the addition of a voltage controlling component V 0 , and the line current leads the voltage component

Fig. 2 UPFC with different modes of operation

Voltage Stability and Performance Analysis …

3

V c that provides the series compensation by 90°. During the phase-shifting process represented in Fig. 2d, the voltage V pq generated by the UPFC is the addition of phaseshifting voltage component V a and voltage-regulating element V 0 . According to the need of the system, UPFC controller can select one, two or all three variables as its control mechanism.

2 Modelling of UPFC To perform the steady-state analysis at fundamental frequency, the UPFC is represented by an equivalent circuit consisting of two voltage sources. The equivalent circuit of UPFC is shown in Fig. 3. The fundamental Fourier series component of switched voltage waveform is represented by the synchronous voltage sources at AC converter terminals of the UPFC. The UPFC voltage sources are: E vr = Vvr (cos δvr + j sin δvr )

(1)

E cr = Vcr (cos δcr + j sin δcr )

(2)

  where Vvr and δvr represent the controllable magnitude Vvr ,min ≤ Vvr ≤ Vvr ,max and phase angle (0 ≤ δvr ≤ 2π ) of voltage source that works as shunt converter, respectively. The magnitude of voltage Vcr and phase angle δcr having the limits

Fig. 3 Circuit diagram of UPFC

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A. K. Dwivedi and S. Vadhera



 Vcr ,min ≤ Vcr ≤ Vcr ,max and (0 ≤ δcr ≤ 2π ), respectively, represents the series converter variable. Power flow is determined by the phase angle of series-injected voltage. If both δcr and θcr (nodal voltage angle) are in phase, then UPFC regulates the terminal voltage, whereas when δcr becomes quadrature w.r.t. θm , it acts like a phase shifter and regulates the reactive power flow [3]. In case δcr becomes quadrature with line current, then it behaves like a variable series compensator and controls the flow of real power at any value of δcr . UPFC can work as variable series compensator, voltage regulator and phase shifter simultaneously. Power flow amount is controlled by determining the magnitude of series-injected voltage [4, 5]. On the basis of equivalent circuit diagram Fig. 3 and Eqs. (3) and (4), the real and reactive powers at bus k are formulated as [3]: Pk = Vk2 G kk + Vk Vm [G km cos(θk − θm ) + Bkm sin(θk − θm )] + Vk Vcr [G km cos(θk − δcr ) + Bkm sin(θk − θcr )] + Vk Vvr [G vr cos(θk − δvr ) + Bvr sin(θk − θvr )]

(3)

Q k = −Vk2 Bkk + Vk Vm [G km sin(θk − θm ) − Bkm cos(θk − θm )] + Vk Vcr [G km sin(θk − δcr ) − Bkm cos(θk − θcr )] + Vk Vcr [G vr sin(θk − δcr ) − Bvr cos(θk − θcr )]

(4)

At bus m: Pm = Vm2 G mm + Vk Vm [G km cos(θm − θk ) + Bmm sin(θm − θk )] + Vm Vcr [G mm cos(θm − δcr ) + Bkm sin(θm − θcr )] Q m = −Vm2 Bmm + Vk Vm [G km sin(θm − θk ) − Bkm cos(θm − θk )] + Vm Vcr [G mm sin(θm − δcr ) − Bmm cos(θm − θcr )]

(5)

(6)

At series converter: Pcr = Vcr2 G mm + Vk Vcr [G km cos(δcr − θk ) + Bkm sin(δcr − θk )] + Vm Vcr [G mm cos(δcr − θm ) + Bmm sin(δcr − θm )]

(7)

Q cr = −Vcr2 Bmm + Vk Vcr [G km sin(δcr − θk ) − Bkm cos(δcr − θk )] + Vm Vcr [G mm sin(δcr − θm ) − Bmm cos(δcr − θm )]

(8)

At shunt converter Pvr = −Vvr2 G vr + Vk Vvr [G vr cos(δvr − θk ) + Bvr sin(δvr − θk )]

(9)

Q vr = Vvr2 Bmm + Vk Vvr [G vr sin(δvr − θk ) − Bvr cos(δvr − θk )]

(10)

Voltage Stability and Performance Analysis …

5

Power equations of UPFC in the linearized form are formulated with those of AC network. It is applied when UPFC controls the following parameters [6]: • Shunt converter voltage magnitude • Flow of real power from bus k to m • Bus m is taken as PQ bus, and reactive power is injected at bus m System of equations in linearized form is given below: ⎡

⎤ Pk ⎢ P ⎥ m ⎥ ⎢ ⎢ Q ⎥ ⎢ k ⎥ ⎢ ⎥ ⎢ Q m ⎥ ⎢ ⎥ ⎢ Pkm ⎥ ⎢ ⎥ ⎣ Q km ⎦ Pbb ⎡ ∂ Pk ⎢ ⎢ ⎢ ⎢ ⎢ =⎢ ⎢ ⎢ ⎢ ⎢ ⎣

∂ Pk ∂ Pk |V | ∂θk ∂θm ∂|Vvr | vr ∂ Pm ∂ Pm 0 ∂θk ∂θm ∂ Qk ∂ Qk ∂ Qk |V | ∂θk ∂θm ∂|Vvr | vr ∂ Qm ∂ Qm 0 ∂θk ∂θm ∂ Pkm ∂ Pkm 0 ∂θk ∂θm ∂ Q km ∂ Q km 0 ∂θk ∂θm ∂ Pbb ∂ Pbb ∂ Pbb |V | ∂θk ∂θm ∂|Vvr | vr

∂ Pk |V | ∂|Vm | m ∂ Pm |V | ∂|Vm | m ∂ Qk |V | ∂|Vm | m ∂ Qm |V | ∂|Vm | m ∂ Pkm |V | ∂|Vm | m ∂ Q km |V | ∂|Vm | m ∂ Pbb |V | ∂|Vm | m

∂ Pk ∂δcr ∂ Pm ∂δcr ∂ Qk ∂δcr ∂ Qm ∂δcr ∂ Pkm ∂δcr ∂ Q km ∂δcr ∂ Pbb ∂δcr

∂ Pk |V | ∂|Vcr | cr ∂ Pm |V | ∂|Vcr | cr ∂ Qk |V | ∂|Vcr | cr ∂ Qm |V | ∂|Vcr | cr ∂ Pkm |V | ∂|Vcr | cr ∂ Q km |V | ∂|Vcr | cr ∂ Pbb |V | ∂|Vcr | cr

∂ Pk ∂δvr

0 ∂ Qk ∂δvr

0 0 0 ∂ Pbb ∂δvr

⎤⎡

⎤ θk ⎥⎢ ⎥⎢ θm ⎥ ⎥⎢ |Vvr | ⎥ ⎥⎢ |Vvr | ⎥ ⎥⎢ |Vm | ⎥ ⎥ ⎥⎢ ⎥⎢ |Vm | ⎥ ⎥⎢ δ ⎥ cr ⎥ ⎥⎢ ⎥⎣ |Vcr | ⎥ ⎦ |Vcr | ⎦ δvr

(11)

3 Simulation Results 3.1 IEEE 14 Bus Test System An IEEE 14 bus system is taken for this simulation. It has two generator buses, a slack bus, 11 load buses and 14 transmission lines. Base MVA and voltage are taken as 100 MVA and 69 kV, respectively. Test system is shown in Fig. 4.

3.2 P–V Curves Change in bus voltage w.r.t. loading factor (λ) is calculated for the test system. The whole study has been carried out in PSAT software. It is observed that line (9–14) shows most insecurity during the analysis when L-index and FVSI are used. P–V curve of the three lower most buses without UPFC is given in Fig. 5, while Fig. 6

6

Fig. 4 IEEE 14 bus test system Fig. 5 Three lower most V without UPFC

Fig. 6 Three lower most V with UPFC

A. K. Dwivedi and S. Vadhera

Voltage Stability and Performance Analysis …

7

represents the lowest three buses when UPFC is implemented in IEEE 14 bus test system (Tables 1 and 2).

3.3 Line Losses Line losses of the most severe lines (9–14) and (13–14) have been calculated with and without UPFC and given in Table 4.

3.4 Discussion In Figs. 5 and 6, it can be observed that after implementation of UPFC at bus 14, the P–V curve limit extends to greater value. Table 3 shows the change in the index with and without UPFC as the load is varied at steps of 5%. Table 4 gives the active as well as the reactive power loss of the line (9–14) and (13–14) with and without UPFC, and it is clearly visible that losses decrease in greater extent when UPFC is used in the system.

4 Conclusion It can be observed from above simulation results that when UPFC is implemented in the system, then active as well as the reactive power losses of the system decrease up to 50% level, and the stability index for the system is also improved. P–V curve for the system shows that with UPFC, system has greater stability for load variation. The paper confirms that UPFC is a better FACTS device that enables one to control the various parameters of the power system network simultaneously. Moreover, PSAT enables user to smoothly conduct the steady state and transient analysis of power system. Hence, paper is the pioneer one which conducts the performance analysis and voltage stability of the power system using a UPFC in PSAT software.

1

1.06

1.06

Bus no.

Voltage without UPFC

Voltage with UPFC

1.045

1.045

2

1.01

0.996

3

Table 1 Bus voltage without and with UPFC 4 1.009

1.001

5 1.014

1.01

6 1.07

1.07

7 1.034

1.029

8 1.09

1.09

9 1

1.001

10 1.0018

1.0019

11 1.0306

1.0301

12 1.0439

1.0423

13 1.0344

1.0289

14 0.9851

0.9639

8 A. K. Dwivedi and S. Vadhera

Voltage Stability and Performance Analysis …

9

Table 2 Line index Line

L-index

FVSI

14–9

0.116751

0.115201

14–13

0.223402

0.223322

13–12

0.026021

0.053321

13–6

0.1139092

0.113090

12–6

0.064941

0.0649211

10–9

0.0098220

0.0096617

10–11

0.094714

0.0947024

4–5

0.051780

0.0517801

4–2

0.043972

0.0439010

4–3

0.157253

0.150704

5–2

0.055584

0.055528

5–1

0.089680

0.089704

2–3

0.0109251

0.0109071

1–2

0.089042

0.088902

Table 3 Load variation versus FVSI Change in load

FVSI (13–14) without UPFC

FVSI (9–14) without UPFC

FVSI (13–14) with UPFC

FVSI (9–14) with UPFC

0

0.2234

0.11675

0.23075

0.109433

5

0.2322004

0.1248

0.230576

0.120011

10

0.241173

0.133091

0.24074

0.13062

15

0.2501994

0.14136

0.2457

0.14127

20

0.25929

0.1497

0.25078

0.1491

25

0.268393

0.1581

0.2524

0.1555

30

0.2775965

0.16656

0.26094

0.1604

35

0.28686

0.17508

0.26601

0.1690

40

0.296214

0.18366

0.27111

0.17768

Table 4 Line losses Losses

Losses without any FACTS device

Losses at line (9–14) with UPFC

Losses at line (13–14) with UPFC

Real losses

0.30192

0.18449

0.19767

Reactive losses

0.94172

0.43292

0.45828

10

A. K. Dwivedi and S. Vadhera

References 1. Power System Analysis Toolbox Quick Reference Manual for PSAT version 2.1.2, 26 June 2008 2. Seifi A, Gholami S, Shabanpour A (2010) Power flow study and comparison of FACTS: Series (SSSC), Shunt (STATCOM), and Shunt-Series (UPFC). Pacific J Sci Technol 11(1) 3. Abu-Siada A, Karunar C (2012) Improvement of transmission line power transfer capability, case study. Electr Electron Eng Int J (EEEIJ) 1(1) 4. Kazemi A, Naghshbandy AH (2006) A hybrid approach in study of UPFC effects on the transient stability of multi machine power systems. Int J Electr Eng 88(2):125–131 5. Chennapragada B, Kotamarti VK, Sankar SB, Haranath PV (2003) Power system operation and control using FACT devices. In: 17th international conference on electricity distribution, Barcelona, 12–15 May 2003 6. Chakraborty K, Majumdar S, Chattopadhyay PK, Nandi C (2011) Active line flow control of power system network with FACTS devices of choice using soft computing technique. Int J Comput Appl 25(9) 7. Babu VN, Sivanagaraju S (2012) A new approach for optimal power flow solution based on two step initialization with multi-line FACTS device. Int J Electr Eng Inf 4(1)

Harmonic Analysis at Variable Load Vijit Srivastava and P. N. Gupta

Abstract In this paper, harmonics due to variable load conditions can be discussed with mitigation technique for a three-phase power supply and compare the harmonics of source current caused by nonlinear variable loads and analyzed all the resultant signals through wavelet transform by using Haar and Daubechies function. Results through Matlab/Simulink simulation show that Total Harmonic Distortion (THD) in source current is varied according to the varying load. These results are also verified by Wavelet Function. “Wavelet Based Method” is also very effective for showing the result of varying loads due to harmonics in nonconventional energy resources like current harmonics in the wind energy system. Keywords APF · Variable loads · Harmonic analysis · Simulation results · Wavelet signal analysis

1 Introduction Day by day the demand of the energy (electricity) will be increases but the resources are limited so it is very difficult to make a balance between demand and supply ratio for electricity. Each country will also try to reduce the effect of greenhouse so they will no longer support traditional methods of energy generation like from coal, oil or natural gas etc. Making clean and pollution free environment switch to nonconventional energy resources like solar energy, wind energy, biomass, etc. but the using of nonconventional method facing a lot of problems like voltage regulation, power quality issues, stability etc. Power quality is the major issue in distribution system because of using non linear loads in the system. Nonlinear loads injected harmonics into the system and give rise to nonsinusoidal voltages and currents [1]. Due to the V. Srivastava (B) · P. N. Gupta J.K. Institute, University of Allahabad, Allahabad, India e-mail: [email protected] P. N. Gupta e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 L. Dewan et al. (eds.), Advances in Renewable Energy and Sustainable Environment, Lecture Notes in Electrical Engineering 667, https://doi.org/10.1007/978-981-15-5313-4_2

11

12

V. Srivastava and P. N. Gupta

variation in load, supply current is also varying according to varying load when load decreases supply current also decreases and when it increases supply current also increases. Variable load with active power filters are modeled for harmonic analysis and also to reduce the total harmonic distortion due to nonlinear loads. The performances of active power filters have been determined by operating on three-phase three-wire system [2–6]. Resultant signals are also analyzed by wavelet transform method by using Haar and Daubechies functions with 2D analysis [7].

2 Proposed Technique 2.1 Active Power Filter In electrical power supply system harmonics is one of the biggest issues. Harmonics are developed in the system due to the nonlinear load and it polluted the supply current. It is the integral multiple of fundamental frequency which causes the distorted waveform results are additional power loss, heating effect and also reduces the life of the component, extra burden on the wires etc. Hence harmonics elimination is required for smooth conduction of electricity in the supply system mainly in distribution and transmission system. So the proposed technique is active power filter is used for harmonic reduction this is one of the simple and best methods. Principle of active power filter is to inject harmonic currents equal in magnitude but opposite in phase to those harmonics that are present in the system due to non linear load. The characteristics of nonlinear load would be changed with APF. Applications of active power filters have become more popular and have attracted great attention as compare to other methods of mitigation technique. It removes the drawback of passive power filters, like component aging and resonant problems [8–11].

2.2 Variable Load Now, when we are using the varying load condition with APF with different time duration THD should also vary according to the given time duration which was reduced by APF circuit and shows that supply current is varied according to the varying load [12] (Fig. 1). Control strategies of APF

Harmonic Analysis at Variable Load I(supply)

13 I(load)

Varying non linear load

AC

I(injected)

Z

Power Conditioner

Energy storage

Fig. 1 Block diagram of Shunt APF

I(load) = I(supply) + I(injected) I(injected) = I(harmonics) + I(reactive part) I(load) = I(real) + I(reactive part) + I(Harmonics) I(real) = I(load) − I(harmonics) − I(reactive part) = I(supply)

3 Matlab Model/Simulation Result Discussions 3.1 Active Power Filter with Variable Load Now, when we are using varying load in one simulation for 1.5 sec of total time duration in which circuit carry full load for 0–0.5 sec time duration and circuit carry 50% of full load for 0.6–1 sec time duration and circuit carry 1.5 times of full load for 1.1 to 1.5 sec time duration (Fig. 2). In the above condition, supply current is also varying according to varying load when load decreases supply current also decreases and when it increases supply current also increases. All 1000 samples correspond to lower frequency of the analysis, carry relevant information. While disturbances occur at instant 500, 730 of samples. This shows good time and poor frequency resolution (Table 1). THD in the supply current will be decreasing when load increases and it will increase when load decreases.

14

V. Srivastava and P. N. Gupta

(a)

(b) 30

20

Amplitude

10

0

-10

-20

-30 0

0.5

1

1.5

Time

(c) Fig. 2 Matlab lab model of active power filter with variable load system. a Circuit model of APF with varying load. b Simulation waveform APF with varying load. c Single-phase simulation waveform of APF with varying load. d Wavelet-based analysis of APF with varying load by using Haar function. e Wavelet-based analysis of APF with varying load by using 2db (Daubechies) and 4 level decomposition

Harmonic Analysis at Variable Load

15

(d)

(e) Fig. 2 (continued)

16 Table 1 THD comparison with variable load

V. Srivastava and P. N. Gupta Time instant

Percentage of THD

0.1

4.41

0.3

4.28

0.5

7.74

0.7

8.13

0.9

8.10

1.1

2.96

1.3

2.96

1.4

2.96

4 Conclusion According to the result analysis, when considering the varying load condition it shows that variations in load show corresponding variation in supply current, supply current increases when load is increased and it will decrease when load decreases and the THD of supply current will be decreases when load is increases and it will be increases when load is decreases. According to the result analysis, when considering the wavelet approach it gives the better result for analysis of the signal. This control algorithm performance can be evaluated under varying load condition. Discrete wavelet analysis provides sufficient information both for analysis and synthesis of the original signal. It also provides the information of that point where disturbances occur.

References 1. Newman MJ, Zmood DN, Holmes DG (2002) Stationary frame harmonic reference generation for active filter systems. IEEE Trans Ind Appl 38(6), 1591–1599 2. Wang F, Duarte J, Hendrix M (2011) Grid-interfacing converter systems with enhanced voltage quality for microgrid application; concept and implementation. IEEE Trans Power Electron 26(12):3501–3513 3. de Araujo Ribeiro R, de Azevedo C, de Sousa R (2012) A robust adaptive control strategy of active power filters for power-factor correction, harmonic compensation, and balancing of nonlinear loads. IEEE Trans Power Electron 27(2):718–730 4. Wei X (2010) Study on digital pi control of current loop in active power filter. In: Proceedings of 2010 International Conference on Electrical and Control Engineering, June 2010, pp 4287–4290 5. Cheng P-T, Bhattacharya S, Divan D (2001) Operations of the dominant harmonic active filter (DHAF) under realistic utility conditions. IEEE Trans Ind Appl 37(4), 1037–1044 6. Odavic M, Biagini V, Zanchetta P, Sumner M, Degano M (2011) Onesample- period-ahead predictive current control for high-performance active shunt power filters, Power Electronics. IET 4(4):414–423 7. Soman KP, Ramachandran KI (2004) Insight into wavelets-from theory to practice 8. Balbo N, Penzo R, Sella D, Malesani L, Mattavelli P, Zuccato A (1994) Simplified hybrid active filters for harmonic compensation in low voltage industrial application. In: Proceedings of 1994 international conference on harmonics in power systems, pp 263–269

Harmonic Analysis at Variable Load

17

9. Rastogi M, Mohan N, Edris AA (1995) Hybrid-active filtering of harmonic currents in power systems. IEEE Trans Power Deliv 10:1994–2000 10. Fujita H, Yamasaki T, Akagi H (2000) A hybrid active filters for damping of harmonic resonance in industrial power system. IEEE Trans Power Electron 15:215–222 11. Satyanarayana G, Lakshmi Ganesh K, Narendra Kumar C, Vijaya Krishna M (2013) A critical evaluation of power quality features using Hybrid Multi-Filter Conditioner topology. In: 2013 international conference on green computing, communication and conservation of energy (ICGCE), pp 731–736, 12–14 Dec 2013 12. Gonzatti RB, Ferreira SC, Silva CH, Silva LEB, Lambert-Torres G, Fernandez Silva LG (2012) PLL-less control strategy applied to hybrid active series power filter. In: 10th IEEE/IAS international conference on industry applications (INDUSCON 2012), Fortaleza, Brazil

Voltage and Transient Stability Enhancement in Power System Using Unified Power Flow Controller Jaswant Singh Bhati and Shelly Vadhera

Abstract The power system is getting more and more complex by the use of multimachine systems. With multi-machine systems in use, the need for voltage and transient stability arises. In this paper, standard test system, having two machines and four buses is taken into account to show the better performance of the efficacious flexible alternating current transmission system (FACTS) device named as unified power flow controller (UPFC) in enhancing the voltage and transient stability in a power system network. Shunt branch of UPFC help in improving the voltage stability by exchanging the reactive power with the system whereas a series branch of UPFC help in improving transient stability by injecting variable voltage and exchanging real power with the system. Various types of symmetrical and unsymmetrical faults are created in the standard system and its behavior is observed. The test system is simulated using MATLAB/Simulink. Keywords FACTS · UPFC · Voltage stability · Transient stability · Faults

1 Introduction Unified power flow controller (UPFC) is a voltage source based flexible a.c. transmission system (FACTS) device. It has two converters one in the shunt and other in the series, hence it is called a shunt-series connected FACTS device and is used here for the purpose to improve the voltage and transient stability of the system. The effect of UPFC on transient stability investigation of a longitudinal system is exhibited in [1]. In this paper, a mathematical model is developed to study the relationship between the UPFC and transmission system. The impact of UPFC on a longitudinal system for the enhancement of its transient stability is analyzed in [2]. A mathematical model is developed to describe the interdependence of longitudinal J. S. Bhati (B) · S. Vadhera Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana 136119, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 L. Dewan et al. (eds.), Advances in Renewable Energy and Sustainable Environment, Lecture Notes in Electrical Engineering 667, https://doi.org/10.1007/978-981-15-5313-4_3

19

20

J. S. Bhati and S. Vadhera

system parameters and operating condition with UPFC. Reference [3] discusses transient power flow in UPFC experimentally as well as theoretically. It reveals that the outflow of active power from a series device of UPFC is stored as line inductance in transient state. In order to enhance the transient and voltage stability, a coordinated excitation controller and UPFC is designed with a one-machine, one-load power system in [4]. To improve the stability, a model of UPFC for reference identification of series part based on state variables is designed in [5]. Here, the UPFC is connected to an infinite bus and as such control of voltage magnitude is neglected. By harmonizing the active and reactive power, a massive reformation in the first swing transient stability is obtained. An H∞ scheme is applied to a single-neuron radial based function neural network in order to design the control strategy of UPFC [6]. This type of controller of UPFC provides improvement in transient stability to an expanded boundary of operating conditions. Reference [7] describes the dynamic regulation model (DRM) for VSC based on FACTS controller. A comparison of DFIG and DDPMSG is done on their construction, operating principle and modeling in [8]. The result shows the effectiveness of UPFC on the stability of DDPMSG when it is installed. Reference [9] exhibits a cascaded control arrangement to stabilize the a.c. current of the transmission line with MMC-UPFC which is based on voltage limit control. With suitable transformer connections for MMC-UPFC, the sequence currents except positive sequence are quelled with the help of resultant current loops. The results show the effectiveness of UPFC in maximizing the controllable region during unbalanced grid situation. In this work, UPFC is implemented in test system and its effect on the system is observed in different fault conditions. It has improved the voltage as well as transient stability of the system in all the cases. The organization of this paper is done in the following manner that MATLAB/Simulink model description is given in Sect. 2 whereas simulation results related to voltage, active power, reactive power with and without UPFC along with fault clearing time are presented in Sect. 3, Sect. 4 includes the conclusion of the work done.

2 MATLAB/Simulink Model Description 2.1 Two Machine Four Bus System Without UPFC The system has two generating stations, four buses, two transformers, four threephase loads, four different transmission lines (i.e., 280, 150, 150, and 50 km) and one three-phase fault is taken for the study of the voltage and transient stability of the test system network. This model (as shown in Fig. 1) is simulated without installing UPFC and results are recorded in both graphical and tabular formats.

Voltage and Transient Stability Enhancement in Power System Using …

21

B1

Generating Station 1

Two Winding Transformer

Line 2-1 150 KM

Three Phase Load

B4

B2

Line 1 280 KM

Line 3 50 KM Generating Station 2

Three Phase Fault

Three Phase Load

Line 2-2 150 KM

B3

Two Winding Transformer Three Phase Load

Three Phase Load

Fig. 1 Model representation of two machine four bus system B1

Generating Station 1

Two Winding Transformer

Line 2-1 150 KM

Three Phase Load

B2

B4

Line 1 280 KM

Line 3 50 KM Generating Station 2

Three Phase Fault

UPFC

Three Phase Load

Line 2-2 150 KM

B3

Two Winding Transformer Three Phase Load

Three Phase Load

Fig. 2 Model representation of two machine four bus system with UPFC

2.2 Two Machine Four Bus System with UPFC In this model (Fig. 2) one FACTS device named UPFC is installed at the line between bus B1 and B2 near to bus B2 to improve the voltage and transient stability and to reduce the fault clearing time of the system. This model also contains the same equipment used in Fig. 1 except for UPFC.

2.3 Different Types of Faults The faults in power systems can be classified into two broad categories: 1. Symmetrical Faults 2. Unsymmetrical Faults

22

J. S. Bhati and S. Vadhera

Symmetrical faults are also called balanced faults. When all the three phases get simultaneously short-circuited then this type of fault is considered as a symmetrical fault. The probability of occurrence of this type of fault in the system is very less (i.e., 2–5%) but the severity of these faults is very high and they can damage the system even though it is in a balanced condition. Examples of symmetrical faults are three-phase and three-phase to ground fault. Unsymmetrical faults are also called as unbalanced faults. Both open circuit faults and short circuit faults come under the category of unsymmetrical faults. The probability of occurrence of these faults is given in such a manner: line to ground fault (85%), double line fault (8%) and double line to ground fault (3–5%). Both LG and LL faults are less severe compared to LLG fault which has more severity. In this paper, both symmetrical and unsymmetrical faults are studied and performed on the test system. The effect of faults on this system are observed and UPFC performs its function very efficiently by reducing the fault clearing time and by improving the voltage profile of the system.

3 Simulation Results 3.1 System Without UPFC The simulation is run for 10 s without UPFC. Different types of faults (i.e., symmetrical and unsymmetrical faults) are considered. Types of faults considered here are line to ground (LG) fault, double line (LL) fault, double line to ground (LLG) fault, three-phase (LLL) fault and three-phase to ground (LLLG) fault. Here the prime focus is on the voltage profile, active and reactive power of the test system.

3.2 System with UPFC Similarly, the simulation is run for 10 s with UPFC by creating all types of faults. The UPFC is placed near to bus B2. The voltage at bus B2 is captured to show the effect of UPFC for voltage profile improvement. The same parameters are calculated and analyzed as discussed in Sect. 3.1. The simulation results are captured in Tables 1 and 2 and in Figs. 3, 4, 5, 6, 7 and 8. Table 1 tabulates the data of voltage, active and reactive power with and without UPFC. Figures 3, 4, 5, 6 and 7 shows the graphical representation of voltage at bus B2 with and without UPFC. Table 2 tabulates the fault clearing time for all the types of faults considered here with and without UPFC whereas, Fig. 8 presents the comparison of fault clearing time with and without UPFC.

Reactive power (MVAR)

652.4

−133.2

−27

−114.4

−95.7

989.9

563.3

−251.2

−121.3

−32.5

−39.33

B3

B4

B1

B2

B3

B4

1004

597.8

1363

1.015

663.9

1.015

B4

1.015

B2

1.002

B3

1.069

1

1337

1.007

B2

Voltage (per unit)

With UPFC

B1

1.007

B1

Parameters

Active power (MW)

LG

Without UPFC

Bus No.

Type of fault

−39.37

−32.63

−121.4

−251.4

563.4

990.1

664

1338

1.015

1.002

1.007

1.007

Without UPFC

LL

−95.67

−114.4

−26.99

−133

652.4

1004

597.9

1363

1.016

1.015

1.069

1

With UPFC

Table 1 Effect of faults on voltage, active and reactive power of different buses

−39.37

−32.62

−121.4

−251.4

563.4

990.1

664

1338

1.015

1.002

1.007

1.007

Without UPFC

LLG

−95.75

−114.4

−26.99

−133

652.4

1004

579.9

1363

1.016

1.015

1.069

1

With UPFC

−39.4

−32.61

−121.4

−251.3

563.3

990.1

663.9

1337

1.015

1.002

1.007

1.007

Without UPFC

LLL

−95.75

−114.4

−26.99

−133

652.4

1004

597.9

1363

1.016

1.015

1.069

1

With UPFC

−39.35

−32.63

−121.4

−251.4

563.3

990.1

663.9

1337

1.015

1.002

1.007

1.007

Without UPFC

LLLG

−95.7

−114.4

−27

133.2

652.3

1004

597.8

1363

1.015

1.015

1.069

1

With UPFC

Voltage and Transient Stability Enhancement in Power System Using … 23

24 Table 2 Effect of UPFC on fault clearing time

J. S. Bhati and S. Vadhera Type of fault

Case

Fault clearing time (s)

LG

Without UPFC

6.68

With UPFC

5.20

Without UPFC

6.92

LL

With UPFC

5.81

LLG

Without UPFC

5.98

With UPFC

4.75

LLL

Without UPFC

5.52

With UPFC

4.98

Without UPFC

5.02

With UPFC

4.01

LLLG

Fig. 3 Voltage improvement at bus 2 with LG fault

Fig. 4 Voltage improvement at bus 2 with LL fault

Voltage and Transient Stability Enhancement in Power System Using …

Fig. 5 Voltage improvement at bus 2 with LLG fault

Fig. 6 Voltage improvement at bus 2 with LLL fault

Fig. 7 Voltage improvement at bus 2 with LLLG fault

25

J. S. Bhati and S. Vadhera Fault Clearing Time (Seconds)

26 8 7 6 5 4 3 2 1 0

With UPFC Without UPFC

LG

LL

LLG Type of Fault

LLL

LLLG

Fig. 8 Fault clearing time for different faults with and without UPFC

3.3 Fault Clearing Time Time taken by the system between fault initiation and fault clearance is considered as fault clearing time of that system. Two machine four bus system had been simulated two times once without UPFC and next time with UPFC. It is found that if there is any fault in the given system then it takes usually more time to clear the fault when there is no UPFC installed. But when UPFC is introduced in the system it’s fault clearing time gets reduced significantly. All the symmetrical faults (i.e., LLL fault and LLLG fault) and unsymmetrical faults (i.e., LG fault, LL fault and LLG fault) are introduced in the test system. In both the cases, i.e., with and without UPFC the effect on the system have been observed and it is found that using UPFC one can reduce the fault clearing time in the system thereby improving the transient stability of the system.

4 Conclusion Voltage and transient stability play a crucial role in the power system are achieved using UPFC. It has reduced fault clearing time of LG fault by 22.15% which is the highest time reduction among all the faults and for LLL fault it reduced fault clearing time by 9.78% which is the smallest reduction among all the faults. The contribution of this paper is enlisted below: 1. Simulation of two machine four bus system with and without UPFC is performed 2. Analysis of the effect of all symmetrical and unsymmetrical faults for the test system under consideration 3. Graphical and numerical results for voltage, active power and reactive power are recorded 4. Fault clearing time for a different type of faults are calculated

Voltage and Transient Stability Enhancement in Power System Using …

27

Simulation results show the contribution of UPFC in enhancing the voltage and transient stability and reducing the fault clearing time for various type of faults.

References 1. Mihalic R, Zunko P, Povh D (1996) Improvement of transient stability using unified power flow controller. IEEE Trans Power Del 11(1):485–492 2. Kang YL, Shresta GB, Lie TT (2001) Application of an NLPID controller on a UPFC to improve transient stability of a power system. IEE proceedings—generation, transmission and distribution, vol 148, no. 6, pp 523–529 3. Fujita H, Watanabe Y, Akagi H (2001) Transient analysis of a unified power flow controller and its application to design of the DC-link capacitor. IEEE Trans Power Electron 16(5), 735–740 4. Chen H, Wang Y, Zhou R (2001) Transient and voltage stability enhancement via coordinated excitation and UPFC control. IEE proceedings—generation, transmission and distribution, vol 148, no. 3, pp 201–208 5. Gholipour E, Saadate S (2005) Improving of transient stability of power systems using UPFC. IEEE Trans Power Del 20(2):1677–1682 6. Mishra S (2006) Neural-network-based adaptive UPFC for improving transient stability performance of power system. IEEE Trans Neural Netw 17(2), 461–470 7. Jiang X, Chow JH, Edris A, Fardanesh B, Uzunovic E (2010) Transfer path stability enhancement by voltage-sourced converter-based FACTS controllers. IEEE Trans Power Del 25(2), 1019– 1025 8. Liu H, Li X, Qin G, Hao S (2017) Stability of grid connected system of two types of wind turbines with UPFC. J Eng 2017(13), 2178–2183 9. Hao Q, Man J, Gao F, Guan M (2018) Voltage limit control of modular multilevel converter based unified power flow controller under unbalanced grid conditions. IEEE Trans Power Del 33(3):1319–1327

Probabilistic Approach for Load Flow Analysis of Radial Distribution System Anuj Banshwar, Mohit Pathak, Bharat Bhushan Sharma, Naveen Kumar Sharma, and Sumit Kumar

Abstract This paper presents a probabilistic approach for load flow method for the radial distribution systems (RDS). The uncertainties existing in the line data and the load data have been taken into consideration. The proposed power flow method will be useful for planning purposes of radial distribution systems where the uncertainty is there in the assumed data. The sensitivity index and voltage stability index are also calculated in order to find the most sensitive node. Keywords Radial distribution system · Probabilistic load flow · Direct load flow · Bus injected to branch current · Branch current bus voltage · Voltage stability index

1 Introduction Power flow (or load flow) analysis is absolutely an important and fundamental tool in power system planning. This method is used to determine the operating state of any system, to compare different planning schemes, and to provide the preliminary state for other advanced applications. The growing integration of distributed generations (DGs) in transmission as well as distribution systems inevitably results in growing amount of uncertainties to the power system operative states. Therefore, adopting a A. Banshwar (B) Government Polytechnic Puranpur, Pilibhit, Uttar Pradesh, India e-mail: [email protected] M. Pathak Noida Institute of Engineering & Technology, Greater Noida, Uttar Pradesh, India B. B. Sharma School of Automation, Banasthali Vidyapith, Rajasthan, India N. K. Sharma IKG Punjab Technical University, Main Campus, Punjab, India S. Kumar Punjab State Power Corporation Limited, Punjab, India © Springer Nature Singapore Pte Ltd. 2021 L. Dewan et al. (eds.), Advances in Renewable Energy and Sustainable Environment, Lecture Notes in Electrical Engineering 667, https://doi.org/10.1007/978-981-15-5313-4_4

29

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A. Banshwar et al.

probabilistic framework is vital to ensure accurate and precise power flow analysis [1]. Actually, the proposal of probabilistic framework can be traced back to later 1970s; the aim was to incorporate uncertainties into the load flow problem [2, 3]. Thereafter, many efforts were made. The probabilistic load flow (PLF) requires inputs with probability density function (PDF) or cumulative density function (CDF) to obtain states of the system and power flows in terms of these density factors so that the uncertainties in the system can be included. The PLF can be solved numerically, i.e. using a Monte Carlo (MC) methods, or analytically or a combination of them. There are various load flow algorithms which are proposed for the analysis of transmission systems as well as for radial distribution system (RDS). However, most of the proposed algorithms are based on the consideration of fixed input parameters which are used to obtain the solution of RDS. But in actual practice, there are various uncertainties which are existing in the input parameters and need to be considered in order to find efficient solution; otherwise, the results obtained are only single-point solutions and may not depict the real picture of the system operating conditions. The probabilistic approach is used so that the uncertainties are being taken into account. A simple load flow method which used direct approach has been proposed by Teng in [4]. Das [5] developed bus injected to branch current (BIBC) and branch current bus voltage (BCBV) matrices followed by a simple matrix multiplication in order to make the solution possible by using the topology of the network and Kirchhoff’s laws. All the above methods provided the load flow solution without considering the uncertainties into account. These uncertainties need to be accounted for otherwise the results obtained are merely a snapshot of the actual operating conditions. In [6], various probabilistic load flow (PLF) techniques have been reviewed and some improvements which are made in the PLF techniques are presented along with the applications and the various extensions of the PLF algorithm. In [7], the various uncertainties have been taken into consideration by PLF analysis and a new method was presented by which the accuracy has been increased due to the multi-linear simulation. In [8], the load uncertainty can be modelled through a simple Gaussian distribution function which has been linearized at multiple points so as to obtain the various intervals. The algorithm proposed in this paper will take into consideration the data uncertainties using probabilistic approach.

2 Load Flow Calculation of RDS Consider a seven bus test radial distribution system as shown in Fig. 1. By applying KCL in Fig. 1, the branch currents are expressed as B1 = I1 + I2 + I3 + I4 + I5 + I6 + I7

(1.1)

Probabilistic Approach for Load Flow Analysis of Radial Distribution System

31

Fig. 1 A 7 bus test radial distribution system

B2 = I3 + I4 + I5 + I6 + I7

(1.2)

B3 = I4 + I5

(1.3)

B4 = I5

(1.4)

B5 = I6 + I7

(1.5)

B6 = I7

(1.6)

In the general form [B] = [BIBC][I ]

(2)

From Fig. 1, the following relations are obtained: V2 = V1 − B1 Z 12

(3.1)

V2 = V1 − B1 Z 12

(3.2)

V3 = V2 − B2 Z 23

(3.3)

V4 = V3 − B3 Z 34

(3.4)

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V5 = V4 − B4 Z 45

(3.5)

V6 = V3 − B5 Z 36

(3.6)

V7 = V6 − B6 Z 67

(3.7)

After manipulations, the above voltage equations can be written as V3 = V1 − B1 Z 12 − B2 Z 23

(4.1)

V4 = V1 − B1 Z 12 − B2 Z 23 − B3 Z 34

(4.2)

V5 = V1 − B1 Z 12 − B2 Z 23 − B3 Z 34 − B4 Z 45

(4.3)

V6 = V1 − B1 Z 12 − B2 Z 23 − B5 Z 36

(4.4)

V7 = V1 − B1 Z 12 − B2 Z 23 − B5 Z 36 − B6 Z 67

(4.5)

In matrix form ⎡ ⎤ ⎡ ⎤ ⎡ V2 Z 12 V1 ⎢V ⎥ ⎢V ⎥ ⎢Z ⎢ 1 ⎥ ⎢ 3 ⎥ ⎢ 12 ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ V1 ⎥ ⎢ V4 ⎥ ⎢ Z 12 ⎢ ⎥=⎢ ⎥=⎢ ⎢ V1 ⎥ ⎢ V5 ⎥ ⎢ Z 12 ⎢ ⎥ ⎢ ⎥ ⎢ ⎣ V1 ⎦ ⎣ V6 ⎦ ⎣ Z 12 V1 V7 Z 12

0 Z 23 Z 23 Z 23 Z 23 Z 23

0 0 Z 34 Z 34 0 0

0 0 0 Z 45 0 0

0 0 0 Z 36 Z 36 0

⎤⎡ ⎤ B1 0 ⎢B ⎥ 0 ⎥ ⎥⎢ 2 ⎥ ⎥⎢ ⎥ 0 ⎥⎢ B3 ⎥ ⎥⎢ ⎥ 0 ⎥⎢ B4 ⎥ ⎥⎢ ⎥ 0 ⎦⎣ B5 ⎦ B6 Z 67

(5)

In general form [V ] = [BCBV][B]

(6)

Now, distribution load flow (DLF) matrix is obtained as follows: [V ] = [BCBV][BIBC][I ]

(7)

[V ] = [DLF][I ]

(8)

or

The step-by-step algorithm is given below:

Probabilistic Approach for Load Flow Analysis of Radial Distribution System

33

1. The BIBC matrix is obtained by coping the entries of the ith column and then reset the entry corresponding to the mth row and jthcolumn as 1. Repeat this procedure so that all the line sections are included in Eq. (2) matrix. 2. The BCBV matrix is obtained by coping the entries in the ith row to the jth row and then reset the entry corresponding to jth row and mth column with the line impedance (Zth). Repeat this procedure so that all the line sections are included in Eq. (6) matrix. 3. Calculate load current at each bus 

 m Pi + j Q i (9) Ii = Vim 4. The elements of the V are computed using the equation   V m+1 = [DLF] I m

(10)

where [DLF] = [BCBV][BIBC] 5. Update the bus voltages at all buses 

  V m+1 = V O + V m+1

(11)

6. The above procedure is repeated till the following condition is satisfied tol > (12.66∗0.05)

(12)

3 System Data Modelling Uncertainty The uncertainty in the line data is accounted by the changes in the resistance and reactance values, and the uncertainty in the load data is accounted by the changes in active and reactive power values. Therefore, these uncertainties need to be modelled and the probabilistic approach is used for accounting these uncertainties. It has been assumed that the uncertainty is modelled with the help of the equation given below: f yj =



2  yj − μ 1 √ exp − 2σ 2 σ 2π

(13)

The three cases have been discussed here, and the corresponding results are obtained: 1 For varying line data and constant load data.

34

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Fig. 2 Actual voltage profile of the system without consideration of uncertainty

Fig. 3 Voltage profiles for varying line data and constant load data

2 For varying load data and constant line data. 3 For both varying line data and load data. The load flow calculations give us the voltage magnitudes at various buses for different cases which are also shown in Figs. 2, 3, 4 and 5. Here it has been assumed that the uncertainty existing in the data varies 5% around the mean value. The voltages at various buses are plotted for different cases so that the effect of uncertainties in the data has been analysed.

4 Sensitivity Analysis and VSI The sensitivity is an important factor to determine the stability of the system under consideration. Therefore, it is very important to find the most sensitive node of the system so that stability of the system is maintained. The sensitivity analysis is done

Probabilistic Approach for Load Flow Analysis of Radial Distribution System

35

Fig. 4 Voltage profiles for varying load data and constant line data

Fig. 5 Voltage profiles when both the line data and load data are varying

to see how the reactive power changes with change in voltage which is given by SVQ =

dQ r (Vs − 2Vr cos δ) = dVr X cos δ − R sin δ

(14)

A simple assumption is made here because the angles are so small that their effect may be neglected without any loss of accuracy; therefore, the above expression reduces to SVQ =

dQ r (Vs − 2Vr ) = dVr X−R

(15)

The results obtained after ignoring the effect of angles are almost equivalent to that obtained when the effect of angles has been taken into consideration. The VSI also helps us to find out the sensitive nodes of the system. The VSI helps the operating personnel to know the stability of the system nodes so that one can bring the voltage

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Fig. 6 Reactive power–voltage sensitivity curve

level within the prescribed range. Therefore, VSI(i) =

V (i) RP(i) + XQ(i) − 2 V (i)

(16)

If the value of sensitivity at a particular node is less, it means that node is the more sensitive as compared to other nodes of the system. The sensitivity values at various nodes are shown in Fig. 5, and it has been noted that node 32 is the most sensitive node; nodes 11, 15, 17, 20, and 26 are also tending towards instability. The voltage stability index has also been calculated, and the results are plotted as shown in Fig. 6. It has been noted that node 32 is the most sensitive node. Therefore, it has been concluded that both the sensitivity calculations and voltage stability index calculations have revealed that node number 32 is the most sensitive node of the radial distribution system.

5 Conclusion The load flow analysis has been carried out on an IEEE 33 bus radial distribution system. Three cases are considered in which the uncertainties in the input data have also been taken into consideration. The voltage profile for different cases has been drawn. The sensitivity analysis and voltage stability index calculations are also done in order to find out the most sensitive node of the system.

Probabilistic Approach for Load Flow Analysis of Radial Distribution System

37

References 1. Sheng H, Wang X (2019) Probabilistic power flow calculation using non-intrusive low-rank approximation method. IEEE Trans Power Syst 34:3014–3025 2. Borkowska B (1974) Probabilistic load flow. IEEE Trans. Power Apparatus Syst 93:752–759 3. Allan RN, Borkowska B, Grigg CH (1974) Probabilistic analysis of power flows. Proc Inst Electr Eng 121:1551–1556 4. Teng JH (2003) A direct approach for distribution system load flow solutions. IEEE Trans Power Syst 18:882–887 5. Das B (2006) Consideration of input parameter uncertainties in load flow solution of three-phase unbalanced radial distribution system. IEEE Trans Power Syst 21:1088–1095 6. Chen P, Chen Z, Jensen BK (2008) Probabilistic load flow: a review. In: Proceedings: third international conference on electric utility deregulation and restructuring and power technologies (DRPT-2008), pp 1586–1591 7. Silva AML, Arienti VL (1990) Probabilistic load flow by a multilinear simulation algorithm. Proc Inst Electr Eng 137:276–282 8. Chaturvedi A, Prasad K, Ranjan R (2006) Use of interval arithmetic to incorporate the uncertainty of load demand for radial distribution system analysis. IEEE Trans Power Syst 21:1019–1021

Harmonic Issues in Non-conventional Energy Supply and Its Remedy Vijit Srivastava and P. N. Gupta

Abstract Day by day the demand for electricity will be increase because of increasing population and industrial use. This greatly increases the demand for electrical utility. Conventional resources are not enough for balancing the demand and supply ratio at the present time. Now it is time to switch to non-conventional sources so that balancing the above ratio. With the effect of harmonics in sources and increasing loads, the power quality of the system is degraded; this is the major issue of non-conventional resources like in wind energy. In this paper, various harmonic mitigation techniques have been proposed for a three-phase power supply like PPF which greatly reduces harmonics but suffers from the resonance and reactive power issues; APF, according to the simulation result of this circuit, is better with the PPF, and HAPF gives the best results as compared to other technique of harmonic mitigation. Keywords PPF · APF · HAPF · Harmonic analysis · Simulation results

1 Introduction For making balance the demand and supply ratio of electricity people has moved towards the non-conventional source of energy like solar energy, wind energy, hydropower etc. Available conventional sources of electricity are not sufficient concerns about demand and supply of world energy needs and also not concern about global warming so it is time to move towards the non-conventional sources of energy but the use of renewable energy sources facing the biggest problem of power quality issues like harmonics. For making clean and pollution-free environment, switch to non-conventional energy resources; but the use of non-conventional V. Srivastava (B) · P. N. Gupta J.K. Institute, University of Allahabad, Allahabad, India e-mail: [email protected] P. N. Gupta e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 L. Dewan et al. (eds.), Advances in Renewable Energy and Sustainable Environment, Lecture Notes in Electrical Engineering 667, https://doi.org/10.1007/978-981-15-5313-4_5

39

40 Fig. 1 Block diagram of PPF

V. Srivastava and P. N. Gupta

Source

Non Linear Load

method is facing a lot of problems like voltage regulation, power quality issues, and stability. Power quality is the major issue in the distribution system because of using nonlinear loads, power electronics devices, inverter in the system [1]. Nonlinear loads injected harmonics into the system and give rise to non-sinusoidal voltages and currents. Due to the variation in load, supply current is also varying according to varying load when load decreases, supply current also decreases and when it increases, supply current also increases, and it affects the entire distribution system like additional loss, overheating and also reduce the life of the system [2]. In this paper to overcome of this problem of power quality issues, the proposed different harmonic mitigation techniques like PPF, APF, HAPF are discussed along with their simulated results and also concern the THD [3, 4].

2 Technique Used 2.1 Passive Power Filter Passive power filter is the combination of series RLC which are connected in parallel with the load. This circuit is very attractive because of its simplicity and cost-effective. The main feature of this circuit has an ability to compensate reactive power and eliminate harmonics, but the circuit is suffering from the resonance, instability, mistuning. Parallel combination of PPF with the power system is used [5, 6] (Fig. 1).

2.2 Active Power Filter In electrical power supply system, harmonics is one of the biggest issues. Harmonics is developed in the system due to the nonlinear load and it polluted the supply

Harmonic Issues in Non-conventional Energy Supply and Its Remedy I(supply)

41

I(load)

Varying non linear load

AC

I(injected)

Z

Power Conditioner

Energy storage

Fig. 2 Block diagram of Shunt APF

current. It is the integral multiple of fundamental frequency which causes the distorted waveform, and the results are additional power loss, heating effect and also reduces the life of the component and extra burden on the wires. Hence, harmonics elimination is required for smooth conduction of electricity in the supply system mainly in distribution and transmission system. So the proposed technique is active power filter which is used for harmonic reduction; this is one of the simple and best methods. The principle of active power filter is to inject harmonic currents which equal in magnitude but opposite in phase to those harmonics that are present in the system due to nonlinear load. The characteristics of nonlinear load would be changed with APF. Applications of active power filters have become more popular and have attracted great attention as compared to other methods of mitigation technique. It removes the drawback of passive power filters like component aging and resonant problems [7, 8] (Fig. 2). Control strategies of APF I(load) = I(supply) + I(injected) I(injected) = I(harmonics) + I(reactive part) I(load) = I(real) + I(reactive part) + I(Harmonics) I(real) = I(load) − I(harmonics) − I(reactive part) = I(supply)

42 Fig. 3 Block diagram of HAPF

V. Srivastava and P. N. Gupta

Non Linear Load

Source

Shunt Active Filter

2.3 Hybrid Active Power Filter Hybrid active power filter is the combination of PPF and APF so it has advantages of both circuits; it greatly reduces harmonics as well as resonance issues. This circuit is mostly used because of its simplicity and cost-effective [9, 10] (Fig. 3).

3 MATLAB/Simulation Result Discussion To verify the results discuss different technique for harmonic mitigation, their circuit and their simulated resultant waveform

3.1 Power Supply Power supply consists of three-phase supply feeding a nonlinear load which is injected 31.82% THD in first phase, 31.59% THD in second phase, and 31.86% THD in third phase at instant of 0.1 s in supply (Fig. 4).

3.2 Passive Power Filter This circuit greatly reduces the harmonics from the supply current 2.77% THD in first phase, 2.79% THD in second phase, 2.85% THD in third phase, but the circuit is suffered because of resonance problem (Fig. 5).

Harmonic Issues in Non-conventional Energy Supply and Its Remedy

43

(a) 200

150

100

Amplitude

50

0

-50

-100

-150

-200 0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Time

(b)

(c) Fig. 4 MATLAB model of supply and load system. a Circuit model of power supply. b Simulation waveform of three-phase supply with harmonics. c THD graph of first-phase signal

44

V. Srivastava and P. N. Gupta 300

200

Amplitude

100

0

-100

-200

-300 0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Time

(a)

(b) Figure 5. a Simulation waveform of three-phase supply with the reduced harmonics. b THD graph of first-phase signal of PPF

3.3 Active Power Filter This technique greatly reduces harmonics as well as resonance problem; it has 2.77% THD in first phase, 2.79% THD in second phase, and 2.85% THD in third phase (Fig. 6).

Harmonic Issues in Non-conventional Energy Supply and Its Remedy

45

20

15

10

Amplitude

5

0

-5

-10

-15

-20 0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Time

(a)

(b) Fig. 6 a Simulation waveform of I abc . b THD graph of first-phase signal of APF

0.45

0.5

46

V. Srivastava and P. N. Gupta

3.4 Hybrid Active Power Filter This circuit greatly reduces harmonics as well as resonance problem; it has 0.74% THD in first phase of supply, 0.83% THD in second phase of supply, and 0.96% THD in third phase of supply (Fig. 7). 200

150

100

Amplitude

50

0

-50

-100

-150

-200 0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Time

(a)

(b) Fig. 7 a Simulation waveform of I abc . b THD graph of first phase signal of HAPF

0.45

0.5

Harmonic Issues in Non-conventional Energy Supply and Its Remedy

47

4 Conclusion According to the result analysis, PPF is suitable for its simplicity, cost-effective and greatly reduces harmonics, but circuit suffers from the resonance; APF gives a better result as compared to the PPF circuit for harmonics reduction as well as for resonance problem; HAPF gives the best result as compared to APF or PPF; this circuit greatly reduces harmonics as well as resonance problem and improves the power quality by removing the polluted supply.

References 1. Balbo N, Penzo R, Sella D, Malesani L, Mattavelli P, Zuccato A (1994) Simplified hybrid active filters for harmonic compensation in low voltage industrial application. In: Proceedings of 1994 International Conference on Harmonics in Power Systems, pp 263–269 2. Valcárcel M, Mayordomo JG (1993) Harmonic power flow for unbalanced systems. IEEE Trans Power Deliv 8(4):2052–2059 3. Wang F, Duarte J, Hendrix M (2011) Grid-interfacing converter systems with enhanced voltage quality for microgrid application; concept and implementation. IEEE Trans Power Electron 26(12):3501–3513 4. Prabhakar N, Mishra M (2010) Dynamic hysteresis current control to minimize switching for three-phase four-leg VSI topology to compensate nonlinear load. IEEE Trans. Power Electron. 25(8):1935–1942 5. Jou HL, Wu JC, Wu KD (2001) Parallel operation of passive power filter and hybrid power filter for harmonic suppression. In: IEE Proceedings—Generations, Transmission and Distribution, vol 148, pp 8–14 (2001) 6. Smith BC, Arrillaga J, Wood AR, Watson NR (1998) A review of iterative harmonic analysis for AC-DC power systems. IEEE Trans Power Deliv 13(1):180–185 7. Xia D, Heydt GT (1982) Harmonic power flow studies, Part I—Formulation and solution, Part II—Implementation and practical application. IEEE Trans Power Apparatus Syst, PAS101:1257–1270 (1982) 8. Xu W, Jose JR, Dommel HW (1991) A multiphase harmonic load flow solution technique’. IEEE Trans Power Syst PS-6:174–182 9. Rastogi M, Mohan N, Edris AA (1995) Hybrid-active filtering of harmonic currents in power systems. IEEE Trans Power Deliv 10:1994–2000 10. Fujita H, Yamasaki T, Akagi H (2000) A hybrid active filters for damping of harmonic resonance in industrial power system. IEEE Trans. Power Electron 15:215–222

Artificial Neural Network-Based Source Identification Producing Harmonic Pollution in the Electric Network Ankit Tayal, Lillie Dewan, and J. S. Lather

Abstract Massive use of power electronic devices which are nonlinear and mostly unbalanced in nature has influenced the power quality (PQ) in electric distribution networks. They not only create significant harmonic pollution in the electric power system but also degrade the system along the grid. Prediction of the sources feeding the electrical system with polluted disturbances is a pivotal point in the estimation of the power quality. This paper proposes an fast and accurate approach based on artificial neural network (ANN). Unique identification of various types of devices along with distinct harmonic signatures is achieved via ANN utilizing feature extraction through input current waveform. To achieve feature extraction, multilayer perceptron with certain parameters is constructed and trained through backpropagation algorithm with performance compared and evaluated. The ANN incorporating MLP architecture with supervised learning is simulated in MATLAB. The results validate the ability of the proposed architecture for efficient classification of device signature for harmonic contributions with reasonably fast response. Keywords Electric power quality · Artificial neural network · Harmonic signature · Total harmonic distortion · Harmonic source identification · Smart grid

1 Introduction Measurement and identification of harmonic sources require specialized instrumentation. The complete measurement of harmonic data is useful for location and source identification. The shunt capacitors are not considered towards harmonic source recognition as it helps apriori elimination of redirected harmonic flow [1]. Electric power engineers and researchers have worked upon different aspects of harmonics and inter-harmonics to measure, monitor, control, or even mitigate it. Owing to their inherent nonlinear mapping characteristics, the artifical neural networks find their applications for power system problems incorporating nonlinear A. Tayal (B) · L. Dewan · J. S. Lather National Insititute of Technology Kurukshetra, Kurukshetra, Haryana 136119, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 L. Dewan et al. (eds.), Advances in Renewable Energy and Sustainable Environment, Lecture Notes in Electrical Engineering 667, https://doi.org/10.1007/978-981-15-5313-4_6

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loads and with few harmonic measuring instruments [2]. Cascade correction networks (CCN) can detect multiple harmonic sources in the power system if the current injection-based power flow is provided by distributed control system [3]. Online tracking and monitoring of multiple harmonic sources can be achieved by current measurement at the incoming supply point present in any electrical installation [4]. Application of data mining process can differentiate between linear and nonlinear loads. At the same time, it can identify various types of harmonic sources present in residential setup [5]. State estimation method provides a vital insight about type of harmonic sources on the basis of actual harmonic measurements necessary for all other harmonic-related studies [6]. Location of harmonic sources can be detected by using state estimation technique with least-square estimators with the help of observability analysis method [7]. Adaptive wavelet neural network (AWNN) produces quick estimation of the dominant harmonics, whereas identifying these via rotational invariance technique contributed it to manage time-varying signals with greater reliability [8]. Harmonic distortion analysis on each side of PCC shall be vital in the future studies and estimation to have an estimate of contributions of each utility or customer for possible contamination via harmonic distortions [9, 10]. Industrial and residential loads behave like harmonic voltage sources. The experimental computation made at both sides of installation of capacitor bank validates the concept that distortions in the currents and voltages are sensitive to the amount of shunt compensation utilized [11]. A hardware model was designed to develop various datasets from different harmonic sources measuring from Fluke-41 power quality analyser. Estimation of harmonics by a particular source and identification of harmonic sources employed in an electric setup was employed using pattern matching and estimation abilities of ANN. Harmonic sources condition the applied voltage on the basis of their unique impedance and characteristic; hence, the current data generated is called “harmonic signature.” The identification of harmonic signature data using ANN architectures with multiple layers was developed. Once trained, this model could identify the devices on the basis of the current harmonics of the sources. This paper is systematically structured in the following parts: Part 2 addresses the hardware implementation for extracting the harmonic signatures from various sources. It also explains the MLP-based NN models and feature extraction using NNs. Part 3 discusses simulation results and provides conclusion and future work.

2 Hardware Implementation and Signature Identification Model In this paper, neural network was applied for harmonic source identification based upon the unique signature of different sources. Different harmonic loads condition the applied voltage based upon their nonlinear impedance characteristic, and

Artificial Neural Network-Based Source Identification …

51

Fig. 1 Overall hardware implementation scheme

hence, current flowing through them contains unique harmonic signature. The periodic components of harmonics especially in current waveforms inhibit rich features for identifying harmonic signatures. The overall hardware implementation is shown in Fig. 1. The complete process was implemented in the following steps.

2.1 Data Acquisition and Computing A laboratory-based hardware implementation was set up using four commonly used devices. First, the data was measured for individual device. Since all the four subcomponents were commonly arranged in parallel, hence datasets with different combination of devices were computed using algebraic combination of the data of such sub-components. Fluke 41 power quality analyzer was utilized for capturing the waveforms for major incoming sources in the laboratory as shown in Fig. 2. With four devices available, the total combinations of different devices inducing harmonics may vary from 0 to 16 (24 = 16) different states, each representing different arrangments of components/devices switched on or off, i.e., from no source to all the four sources generating harmonics in the circuit. The employed power quality Fig. 2 Experimental Schematics for Setup installation

52

A. Tayal et al.

Table 1 Experimental data as obtained from harmonic analyzer

Title

Amps

ID

1

Type

Harmonics

X Scale

48.95

X Resolution

0

X Size

50

X Unit

Hz

X Label

Frequency

Y Scale

0

Y Resolution

0

Y Size

0

Y Unit

A

Y Label

RMS

0

0

48.95

0

97.9

139

146.85

168

195.8

−171

244.75

−15

293.7

−155

342.65

166

391.6

−106

440.55

7

489.5

167

538.45

−149

587.4

−86

636.35

48

ss685.3

−63

734.25

105

analyzer computes the harmonic components through FFT in the current waveform considering both amplitude and phase (Tables 1 and 2). In the experiments, the harmonics in the current waveforms appeared in the appreciable amounts from the fundamental components to upto fifteenth harmonic level. Therefore, here only the odd harmonics were considered for proceeding further recognition process taking the consolidated effect of amplitude as well as phase as per Eq. (1):  x m = I(

 k+1 2

) cos (

k+1 2

) , for k = 1, 3, 5, 7, 9, 11, 13, 15; m =

k+1 2

 (1)

Artificial Neural Network-Based Source Identification … Table 2 Modified dataset considering only odd harmonics as even harmonics does not contribute much in total output of the systems

Title

53 Amps

ID

1

Type

Harmonics

X Scale

48.95

X Resolution

0

X Size

50

X Unit

Hz

X Label

Frequency

Y Scale

0

Y Resolution

0

Y Size

0

YUnit

A

Y Label

RMS

48.95

0

146.85

168

244.75

−15

342.65

166

440.55

7

538.45

− 149

636.35

48

734.25

105

where x m is the mth input, I m represents the magnitude component, whereas m accounts for the phase angle of the mth odd current harmonic, respectively (Table 3). For a complete dataset taking into account of all possible combinations of all the devices, i.e., no source, possible combination of two or three sources or all four sources generating harmonics, different datasets were taken into consideration from time to time. Since the data had to incorporate in neural network under no source condition, rather than putting 0 in all the rows, authors had to use 0.00001 so as to put some value near equal to 0 but not zero; otherwise, neural networks provide ambiguous results (Table 4).

2.2 Characteristics of Harmonic Signatures During the experiment, all the devices were connected in parallel; hence, no interference was measured while the data acquisition took place from other devices. Also, the wiring resistances of the devices connected on the source were assumed to be negligibly small; hence, source voltage was least affected by these four loads. Figure 3 represents one dataset of different signatures for particular harmonic for

54

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Table 3 Final dataset for individual source

Title

Amps

ID

1

Type

Harmonics

X Scale

48.95

X Resolution

0

X Size

50

X Unit

Hz

X Label

Frequency

Y Scale

0

Y Resolution

0

Y Size

0

YUnit

A

Y Label

RMS

48.95

0

0

146.85

168

126.1732

244.75

45

−9.41274

342.65

166

−133.588

440.55

7

−3.91038

538.45

−149

−127.033

636.35

48

23,31,076

734.25

105

−93.8851

the components installed in laboratory arrangement extracted by the clamper circuit using Fluke 41 power quality analyser. The drive panel showed the highest THD value because other electronic equipments draw arbitrary amount of current from the panel’s power supply when connected through the drive panel. On the other hand, resistive load such as the fluorescent light shows lower THD value as compared to the other sources. Since all harmonic sources exhibit nonlinear characteristics in nature; hence, artificial neural networks are considered to be best-suited to accomplish the recognition of harmonic source.

2.2.1

Pattern Recognition Using Multilayer Perceptron Based NN

The pattern recognition process was carried out using MLP-based artificial NNs from various harmonic datasets extracted from the process as depicted in earlier steps. The configuration of MLP was selected that incorporated an input layer and output layer with intervening middle layer. The parameters, i.e., number of neurons and hidden layers, are chosen to best describe the mapping problem. The number of harmonic considered was taken as the number of input layer neurons as a natural

4.33

0.0001

0.0001

−12.25

−13.99

−6.6

0.0001

0.0001

0.0001

6.92

−12.81

18.19

239.7

−23.9

0.0001

0.926

0.0001

−301.8

34.3

−3.25

11.18

0.0001

−48.58

−21.69

−1.33

73.83

−1.483

328.38

−44.36

43.206

−15.15

−25.62

0.0001

D

0.01

21.711

0.0001

C

0.02

0.01

0.02

0.0001

B

A

No Source

−6.598

−13.99

−7.92

0.927

7.94

−47.32

6.56

0.03

A, B

0.33

−26.7

5.945

−22.97

45.48

−74.2

64.92

0.04

A, C

−7.93

59.86

−13.73

240.64

−290.7

302.8

−22.65

0.03

A, D

Table 4 Final dataset considering all possible combinations of all devices B, C

6.92

−12.8

22.53

−23.9

31.05

−70.3

22.06

0.03

B, D

−1.33

73.8

2.85

239.7

−305.1

306.7

−59.51

0.02

C, D

5.596

61.03

16.72

215.8

−267.5

279.8

−1.152

0.03

A, B, C

0.325

−26.79

10.27

−22.97

42.2

−95.9

49.77

0.05

A, B, D

−7.93

59.85

−9.41

240.64

−293.9

281.1

−37.8

0.04

A, C, D

−1.01

47.04

4.46

216.74

−256.3

254.2

20.56

0.05

B, C, D

5.59

61.03

21.04

215.81

−270.8

258.2

−16.3

0.04

A, B, C, D

−1.01

47.04

8.79

216.74

−259.6

232.5

5.411

0.06

Artificial Neural Network-Based Source Identification … 55

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A. Tayal et al.

Fig. 3 Fluke 41 power quality analyzer data for drive panel, monitor, UPS, and fluorescent light

choice. Since the problem of harmonic source device detection is not more involved, thus only one hidden layer is chosen. However, the number of neurons in the hidden layers was chosen on the basis of hit and trial approach. The simulations were carried out for the number of hidden layer neurons chosen from 4 to 60 and results with 20 hidden neurons were found to suffice, since there is always a tradeoff between the mapping accuracy and the complexity of ANN. Similar to case of input layer neurons, the number of output layer neurons was chosen to be equal to the number of harmonic source devices and thus was fixed at 4. Finally, the signum function was used at the output level so as to convert all positive values to two levels: High, i.e., +1, refers to inference that corresponding device is present and low, i.e., −1, when the same is absent.

Artificial Neural Network-Based Source Identification …

57

2.3 Traınıng and Performance Evaluatıon The complete dataset was divided into two parts: 67% of the total data was utilized for training the MLP architecture as well as validating its learnt behavior, while the rest of 33% data was utilized for prediction purpose, i.e., whether the MLP has achieved generalization and is able to correctly identify the device responsible for harmonic degradations.

3 Simulation Results and Conclusıon In this work, a unique method utilizing MLP with optimized hidden layer neurons is proposed for predicting the device responsible for harmonic degradation in considered electrical setup via study of current waveforms at point of supply. Figure 4 shows the plot of mean square of error with number of epochs using backpropagation algorithm. An error signal is the difference between the required output and ANN output. The results showed that BP algorithm exhibits great accuracy to identify the devices based on their harmonic signatures. With this research, authors have presented a novel way of offline-based harmonic source identification in an electrical setup. In future, research may be done for online signature recognition using big data and cloud computing. Research may also be focused upon automatic detection of devices using artificial intelligence and machine learning. Learning Curve 40

Mean Square Error ----->

35 30 25 20 15 10 5 0

0

50

100

150

200

250

300

350

400

No. of Epochs ----->

Fig. 4 Plot of mean square error with number of epochs using backpropagation algorithm

450

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References 1. Arrillaga J (1997) Power system harmonic analysis. Wiley, New York 2. Hartana RK, Richards GG (1990) Harmonic source monitoring and identification using neural network. IEEE Trans Power Syst 5(4):1098–1104 3. Lin W-M, Lin C-H, Tu K-P, Wu C-H (2005) Multiple harmonic source detection and equipment identification with cascade correlation network. IEEE Trans Power Syst 20(3), July 2005, pp 2166–2173 4. Srinivasan D, Ng WS, Liew AC (2006) Neural-network-based signature recognition for harmonic source identification. IEEE Trans Power Delivery 21(1):398–405 5. Fernandes RAS, da Silva IN, Oleskovicz M (2011) Data mining applied to harmonic current sources identification in residential consumers. IEEE Latin Am Trans 9(3):302–310 6. Heydt GT (1989) Identification of harmonic sources by a state estimation technique. IEEE Trans Power Deliv 4(1), 569–576 7. Saxena D, Bhaumik S, Singh SN (2014) Identification of multiple harmonic sources in power system using optimally placed voltage measurement devices. IEEE Trans Ind Electron 61(5):2483–2492 8. Jain P, Tiwari AK, Jain SK (2017) Harmonic source identification in distribution system using ESPRIT-THP method. Trans Inst Measur Control. https://doi.org/10.1177/0142331217721316 9. Safargholi F, Malekian K, Schufft W (2017a) On the dominant harmonic source identification— Part I: review of methods. IEEE Trans Power Deliv. https://doi.org/10.1109/TPWRD.2017.275 1663 10. Safargholi F, Malekian K, Schufft W (2017b) On the dominant harmonic source identification— Part II: application and interpretation of methods. IEEE Trans Power Deliv. https://doi.org/10. 1109/TPWRD.2017.2751673 11. Pomilio JA, Deckmann SM (2007) Characterization and compensation of harmonics and reactive power of residential and commercial loads. IEEE Trans Power Deliv 22(2):1049–1055

Impacts of Distributed Generation on Distribution System Based on the Backward and Forward Sweep Method Indubhushan Kumar and Sandeep Gupta

Abstract A conventionally large portion of the generation is derived from fossil fuel, i.e., coal, gas, nuclear, large hydropower plants. However, due to increased demand growth, higher T&D cost, fast depletion of fossil-fuels, heightened environmental concerns, utility generation paradigm is shifting from large central-station stations to various small scale generation units. These new technologies allow the generated electricity in small-sized plants known as Distributed Generation. The objective of this paper is to analyze the impact of DG in distribution system mainly on power losses, voltage profile. A MATLAB programming is developed based on the backward and forward sweep method using the data of 33-IEEE Bus 12.66 kV, distribution test system for optimal placement of Distributed Generation. The technique further tested on 33-IEEE bus systems to show the impacts of DG. Keywords Distributed generation · Power quality · Decentralized energy systems · Power mitigation

1 Introduction Distributed generation category can be based on the non-renewable as well as renewable energy-based technologies. Non-renewable based technologies often include reciprocating engine, combustion gas turbine, fuel cell, micro-turbine, micro combined heat and power (CHP), etc.; while renewable-based technologies include a wind turbine, solar photovoltaic systems, biomass gasification, geothermal, small hydro power, etc. As per the Electric Power Research Institute (EPRI), generation from a few kilowatts up to 50 MW can be considered as distributed generation [1, 2]. It is suggested by International Energy Agency (IEA) that a generating plant severing I. Kumar · S. Gupta (B) Department of Electrical Engineering, JECRC University, Jaipur, Rajasthan, India e-mail: [email protected] I. Kumar e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 L. Dewan et al. (eds.), Advances in Renewable Energy and Sustainable Environment, Lecture Notes in Electrical Engineering 667, https://doi.org/10.1007/978-981-15-5313-4_7

59

60 Table 1 Ranges of distributed generation technology [5]

I. Kumar and S. Gupta Types of distributed generation

Range

Micro

1 to