Advancement in Power Transformer Infrastructure and Digital Protection (Studies in Infrastructure and Control) [1st ed. 2023] 9819938694, 9789819938698

Citation preview

Studies in Infrastructure and Control

Nilesh Chothani Maulik Raichura Dharmesh Patel

Advancement in Power Transformer Infrastructure and Digital Protection

Studies in Infrastructure and Control Series Editors Dipankar Deb, Department of Electrical Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad, Gujarat, India Akshya Swain, Department of Electrical, Computer & Software Engineering, University of Auckland, Auckland, New Zealand Alexandra Grancharova, Department of Industrial Automation, University of Chemical Technology and Metallurgy, Sofia, Bulgaria

The book series aims to publish top-quality state-of-the-art textbooks, research monographs, edited volumes and selected conference proceedings related to infrastructure, innovation, control, and related fields. Additionally, established and emerging applications related to applied areas like smart cities, internet of things, machine learning, artificial intelligence, etc., are developed and utilized in an effort to demonstrate recent innovations in infrastructure and the possible implications of control theory therein. The study also includes areas like transportation infrastructure, building infrastructure management and seismic vibration control, and also spans a gamut of areas from renewable energy infrastructure like solar parks, wind farms, biomass power plants and related technologies, to the associated policies and related innovations and control methodologies involved.

Nilesh Chothani · Maulik Raichura · Dharmesh Patel

Advancement in Power Transformer Infrastructure and Digital Protection

Nilesh Chothani Department of Electrical Engineering School of Energy Technology Pandit Deendayal Energy University Gandhinagar, Gujarat, India

Maulik Raichura Department of Electrical Engineering Faculty of Engineering and Technology Monark University Ahmedabad, Gujarat, India

Dharmesh Patel Department of Electrical Engineering Government Engineering College Bharuch, Gujarat, India

ISSN 2730-6453 ISSN 2730-6461 (electronic) Studies in Infrastructure and Control ISBN 978-981-99-3869-8 ISBN 978-981-99-3870-4 (eBook) https://doi.org/10.1007/978-981-99-3870-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The book is based on research of the electrical device known as the transformer. Various working conditions of the transformer and its response during various events which are encountered by the device during its operation are elaborated. Moreover, certain fundamental transformer defensive schemes are explained. Advanced techniques that exist and are currently implemented in the real field are also explained along with its research gaps. Newer technological advancements based on AI and other techniques are included in the book. The result analysis along with illustrations that are validated on software as well as on hardware platforms is elaborated to prove the efficacy of the proposed methodologies. This book will prove a one-point resource for readers who are willing to perform further research on the transformer infrastructure and protection. The entire book is organized into ten chapters as follows: Chapter 1 outlines the power system infrastructure, transformer infrastructure, and role of large power transformers in the power system web. Various transformer connections, recent developments in terms of design technology as well as its testing, explanation of various important parts of the device; smart transformers, and advanced transformer infrastructure with its benefits are described here. Chapter 2 presents an overview of the transformer basics, its various parts and terminologies used in power systems, its response during various stimuli, fundamental protective devices used, its response during a variety of connections, extraction of various defensive techniques with its classification, etc. Chapter 3 elaborates in detail about the inrush condition which needs special attention to prevent false actuations of protective techniques during such instances. Various magnetizing inrush condition’s generation, its mitigation, and detection are detailed along with past research philosophies. Chapter 4 discusses about CTs’ saturation conditions that are generally observed during high flow of current during faulty and inrush cases. Moreover, in this chapter, CTs’ design constraints as well as effects of various events on this component and its measures are elaborated with suitable illustrations of software and hardware v

vi

Preface

approaches. Chapter 5 contains the excessive fluxing condition of the transformer that is usually observed during the change in voltage (v)-to-frequency (f) ratio of the device. Causes of over-fluxing, its effects on the defensive system, and its treatment using certain innovative logic are discussed in this chapter. Chapter 6 includes Total Harmonic Distortion (THD)-level-based protective scheme to prevent damages during various abnormal conditions. The scheme is purposely explained along with its implementation on software and hardware platforms. The result discussion of this scheme proves its accuracy to validate its capabilities. Chapter 7 demonstrates adaptive methodologies that are proposed by various researchers to prevent false actions of defensive techniques during temporary anomalies. This adaptive technique could be applied in connection to the existing defensive scheme to enhance the competence of the scheme and consequently increases the dependability of the device. Chapter 8 comprises a classifier technique based on CNN and XGBoost technique. This technique collects numerous data of transformer operation and proves a novel classification to properly filter out the encountered abnormality. This scheme can learn from the gathered data and improve in real-time operation. Chapter 9 describes a sequential component-based defensive technique to discriminate inrush, normal, and internal as well as external faulty cases. Chapter 10 elaborates on the Kalman Filter (KF)-based defensive scheme and also includes recent development taken place to upgrade this filter such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). At the end, details of the simulation and hardware parameters are given in an appendix. Literatures used during the preparation of the book are outlined in the reference section. Gandhinagar, India Ahmedabad, India Bharuch, India

Nilesh Chothani Maulik Raichura Dharmesh Patel

Acknowledgments

Authors, firstly, express their sincere gratitude and soulful respect to Almighty for providing such potential that helps in publishing this knowledgeful work. Nobody has been more important to us in the pursuit of this book project than the members of our family. We would like to thank our family members for their moral support, motivation, and guidance to complete this book. Authors extend their special tanks to Dr. Dipankar Deb, Book Series Editor of Studies in Infrastructure and Control, Springer Nature and Dr. Aninda Bose, Executive Editor, Springer Nature for their continuous guidance and encouragement. Special thanks to the Springer Nature publication and associated press for the care they have given during the preparation and production of this book. Authors express sincere thanks to Pandit Deendayal Energy University—Gandhinagar, Monark University—Ahmedabad, and Government Engineering College— Bharuch for providing constant support in the execution of the work presented in this book. Moreover, the authors are also grateful to the staff members of these institutes for their continuous support. Authors would like to thank all of those who have supported directly or indirectly from all aspects towards the completion of this book project. Nilesh Chothani Maulik Raichura Dharmesh Patel

vii

Key Features of the Book

The book is fundamentally prepared in accordance to the power system and transformer infrastructure. It is entirely arranged in such a way that the readers get a complete idea about the power transformer working patterns during on-load, off-load, various inrush conditions, a variety of faulty conditions as well as during unforeseen situations also. It also includes protection history, current, and upcoming development, and a comprehensive analysis of different transformer protection schemes. Conventional protection schemes of power transformer may operate in anomalies such as during different types of inrush conditions, CTs may saturate during faulty cases, and high-resistance internal fault conditions. The existing defensive schemes encounter various false actuations during certain events. These deficiencies can be overcome by thorough research work, which is detailed in the book. The content of this book assists protection engineers and potential researchers for solutions of many complications during system failures. Moreover, the identification of inrush and fault as well as various anomalies is filtered out using analytical- and classification-dependent methods. This book is substantially useful to researchers and those persons who are working in the field of Power System Protection. The book includes comprehensive philosophy of different protection schemes as well as monitoring techniques used for the transformer. It also narrates the inrush research in transformers and their detection. The researcher working on a power transformer can find innovative algorithms and novel schemes for transformer and related equipment protection. Further, they can design innovative protection schemes by referring to the content of chapters and specifically the hardware validation of the developed technique. This book covers . Detailed infrastructure of the power transformer and its operation in the power grid. . No-load operation, full-load operation, and overload operation of the transformer. . Various phenomena such as different types of inrush, turn-to-turn faults, and internal faults take place inside the transformer during its operation.

ix

x

Key Features of the Book

. Mitigation of inrush current and excessive fluxing conditions in power transformer. . Detailed protective schemes of the power transformer. . Validation of the protective schemes on software and hardware prototypes. . Result analyses of various transformer protective schemes. . Comparison of the presented transformer protective schemes with existing competitive protective schemes. . Protection of power transformer in the presence of CT saturation, excessive fluxing conditions, and high-resistance faults.

Contents

1

Transformer Infrastructure for Power Grid . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Role of Large Power Transformers in the Electric Grid . . . . . . . . 1.3 Power System Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Three-Phase Transformer Interconnections . . . . . . . . . . . . . . . . . . . 1.5 Transformer Technology Development . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Design Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Testing of Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 On-Load Tap Changer (OLTC) of Transformer . . . . . . . . . . . . . . . 1.6.1 Where to Employ the OLTC on Transformer . . . . . . . . . . 1.6.2 Classification of OLTC Based on Its Construction . . . . . 1.6.3 Advantages of OLTCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.4 Disadvantages of OLTCs . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Dissolved Gas Analysis for Transformer Monitoring and Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.1 How Gases Generated in Transformer . . . . . . . . . . . . . . . 1.7.2 Identification of Faults by Gas Analysis . . . . . . . . . . . . . . 1.7.3 Methods for DGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7.4 Advantages of Performing DGA . . . . . . . . . . . . . . . . . . . . 1.8 Condition Monitoring of the Transformer . . . . . . . . . . . . . . . . . . . . 1.8.1 Working Condition Monitoring . . . . . . . . . . . . . . . . . . . . . 1.8.2 Emergency Condition Monitoring . . . . . . . . . . . . . . . . . . . 1.9 Real-Time Operation and Protection of Power Transformer . . . . . 1.10 Smart Transformer for Smart Grid Operation . . . . . . . . . . . . . . . . . 1.11 Advanced Transformer Infrastructure (ATI)—Various Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.12 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 2 2 3 4 6 6 8 9 10 10 12 13 14 15 16 18 18 19 19 19 21 22 23 25 25

xi

xii

2

3

Contents

An Overview of the Protection of Power Transformers . . . . . . . . . . . . 2.1 Protection Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Unit and Non-unit Protection . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Primary and Backup Protection . . . . . . . . . . . . . . . . . . . . . 2.2 Problem Statements and Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Investigation Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Different Faults/Abnormalities Observed in Transformer . . . . . . . 2.5.1 Internal Fault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Sources of Internal Fault in Transformer . . . . . . . . . . . . . 2.6 External Fault for the Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Abnormalities in the Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 Different Transformer Protective Schemes Used in Field . . . . . . . 2.8.1 Overcurrent (OC) Protection . . . . . . . . . . . . . . . . . . . . . . . 2.8.2 OC (Overcurrent) Protection with Harmonic Restrain Unit (HRU) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8.3 REF (Restricted Earth Fault) Protective Scheme . . . . . . . 2.8.4 Unit-Type Protection of Transformer (Differential Protection) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9 General Magnetizing Inrush Phenomenon . . . . . . . . . . . . . . . . . . . . 2.10 Over-Fluxing Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.11 Inter-Turn Fault Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.12 Non-electrical Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.12.1 Thermal Relay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.12.2 Temperature-Based OTI and WTI Relays . . . . . . . . . . . . . 2.12.3 Buchholz Relay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.12.4 Pressure Relays (PRs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.13 Generalized Protections Applied to Transformer . . . . . . . . . . . . . . 2.14 Adverse Effect of Single Phasing on Three-Phase Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.14.1 Basic Magnetic Circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.14.2 Observation and Confirmation of the Theoretical Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.14.3 Remarks of Single Phasing Supply to Three-Phase Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.15 Different Research Techniques Used in Transformer Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.16 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.17 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27 28 29 29 30 32 33 34 34 35 35 36 37 37

Introduction to Magnetic Inrush of Power Transformer . . . . . . . . . . . 3.1 Basic of Magnetic Inrush . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Various Classifier Techniques to Identify Inrush States . . . . . . . . .

71 71 74

38 38 39 43 44 45 46 46 46 47 48 49 51 51 54 56 57 62 65 66

Contents

xiii

3.2.1

Discriminative Technique Depending on Harmonics Content (Which Contains DC Offset) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Electrical Quantity’s Wave Pattern-Based Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Discriminative and Decomposing Schemes . . . . . . . . . . . 3.2.4 Morphological-Based Analysis . . . . . . . . . . . . . . . . . . . . . 3.2.5 Power Utilization-Dependent Techniques . . . . . . . . . . . . . 3.2.6 Flux-Based Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.7 Methodology for Mitigation of Level of Inrush Current . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 The Proposed Technique for Inrush Stimuli Discrimination . . . . . 3.4 System Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Anticipated Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Obtained Results Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Magnetic Inrush Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Interior Type of Fault Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9 Interior Type of Fault Followed by Inrush Case . . . . . . . . . . . . . . . 3.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.11 Question and Answer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Current Transformer Infrastructure and Its Application to Power System Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Basic of Current Transformer (CT) . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Design Consideration of Current Transformer . . . . . . . . . . . . . . . . 4.2.1 Over-Sizing Factors of CT . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Diminishing the Effects of CT Saturation . . . . . . . . . . . . . . . . . . . . 4.3.1 Time-to-Saturation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Required Caution in CT Optimal Choice . . . . . . . . . . . . . 4.4 Consequences of CT Saturation on Protective Relays . . . . . . . . . . 4.4.1 Impact of CT Saturation on Electromechanical Relays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Impact of CT Saturation on Static/Digital Relays . . . . . . 4.4.3 Influence of CT Saturation on Differential Relays . . . . . 4.5 Important Points to Select CTs for Protective Schemes . . . . . . . . . 4.6 System Diagram and Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Effect of Parameter Variations on CT Performance . . . . . . . . . . . . 4.7.1 Consideration of Core Over-Sizing Factors at FIA = 0.515 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7.2 Effect of DC Component . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7.3 CT Secondary Burden Effect on Saturation . . . . . . . . . . . 4.7.4 CT Saturation Effect Under the Influence of the Remnant Flux Density . . . . . . . . . . . . . . . . . . . . . . .

74 76 77 78 80 81 82 82 86 86 87 88 89 90 91 92 94 95 101 102 105 109 110 110 112 112 112 113 113 113 114 115 115 116 117 117

xiv

Contents

4.7.5 Effect of FIA Variation on CT . . . . . . . . . . . . . . . . . . . . . . CT Saturation Analysis in Laboratory Prototype . . . . . . . . . . . . . . Detection of Saturation of CT in Unit-Type Protection of Power Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9.1 Simulation Modeling of Power System . . . . . . . . . . . . . . . 4.9.2 Projected Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.10 Result Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.10.1 Internal Fault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.10.2 External Fault Without CT Saturation . . . . . . . . . . . . . . . . 4.10.3 External Fault with CT Saturation . . . . . . . . . . . . . . . . . . . 4.11 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 4.9

5

Impact of Transitory Excessive Fluxing Condition on Power Transformer Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Modeling of System Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Problem Declaration and Algorithm Suggestion . . . . . . . . . . . . . . 5.4 Investigation of the Obtained Results . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Performance Evaluation of the Projected Scheme for the Period of Normal State/Exterior Fault State of the Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Performance Evaluation of the Projected Scheme While Insider Fault State of the Transformer . . . . . . . . . . 5.4.3 Performance Evaluation of the Projected Scheme for Excessive Fluxing State of the Considered Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Elaboration of Hardware Arrangement and Result Conversation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Current Wave Pattern While Interior Fault Case of Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Current Wave Pattern While Exterior Fault Case of the Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.3 Current Wave Pattern While Continuous and Temporary Excessive Fluxing State of the Considered Transformer . . . . . . . . . . . . . . . . . . . . . . 5.6 Advantages of the Presented Scheme Over the Conventional Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8 Question and Answer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

118 119 120 122 122 124 125 126 126 128 129 130 135 136 138 139 141

141 142

143 145 146 146

147 149 149 150 154

Contents

6

7

Total Harmonic Distortion-Based Improved Transformer Protective Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Modeling of Power Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Presented Technique for Inrush and Fault Discrimination . . . . . . . 6.4 The Outcome of the Proposed Technique . . . . . . . . . . . . . . . . . . . . 6.4.1 Initial Inrush . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Internal Fault Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Energization of Transformer in Existence of Faulty Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.4 Fault Case While CT Saturates . . . . . . . . . . . . . . . . . . . . . . 6.5 Hardware Test Arrangement for Different Result Investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 Preliminary Inrush Situation . . . . . . . . . . . . . . . . . . . . . . . . 6.5.2 Sympathetic Type of Inrush Condition . . . . . . . . . . . . . . . 6.5.3 Recovery Type of Inrush Condition . . . . . . . . . . . . . . . . . . 6.5.4 Exterior Fault Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.5 Exterior Fault with CT Saturation Cases . . . . . . . . . . . . . . 6.5.6 Interior Fault Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.7 Interior Fault While CTs Saturates . . . . . . . . . . . . . . . . . . 6.5.8 No-load Current with Its Harmonics . . . . . . . . . . . . . . . . . 6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Question and Answer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adaptive Biased Differential Protection Considering Over-Fluxing and CT Saturation Conditions . . . . . . . . . . . . . . . . . . . . . 7.1 The Preamble of Idea Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Problem Declaration and System Diagram Descriptions . . . . . . . . 7.3 Projected Algorithm for Adaptive Transformer Differential Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Modified Full Cycle DFT (MFCDFT) Algorithm for Phasor Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Setting of Biased Percentage Differential Relaying Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.3 Detection of Magnetizing Inrush in Transformer . . . . . . 7.3.4 Adaptation in Basic Pickup Setting . . . . . . . . . . . . . . . . . . 7.3.5 V avg /f Transformer Protection or Transformer Over-Fluxing Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.6 Current Transformer Saturation Detection . . . . . . . . . . . . 7.4 Various Result Exploration with Argument . . . . . . . . . . . . . . . . . . . 7.4.1 Normal Load, Overloading, and External Fault State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Transformer Inrush Detection . . . . . . . . . . . . . . . . . . . . . . .

xv

159 159 161 162 165 165 166 167 168 170 171 172 174 175 176 177 178 179 180 181 183 187 188 190 192 192 194 195 196 197 198 200 200 200

xvi

Contents

7.4.3

A Fault Within the Internal Premises of the Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.4 External Fault with CT Saturation Condition . . . . . . . . . . 7.4.5 Discrimination of Over-Fluxing in Transformer Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.6 Inception of Internal Fault in the Existence of Over-Fluxing Situation . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Laboratory Setup for Hardware Test Results . . . . . . . . . . . . . . . . . . 7.5.1 The Inrush of Transformer on Hardware . . . . . . . . . . . . . 7.5.2 Normal Load, Overloading, and External Fault Situation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.3 Internal Fault Situation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.4 Over-Fluxing Situation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.5 Saturation of CT During External Fault . . . . . . . . . . . . . . 7.5.6 Very Severe External Fault in the Existence of Over-Fluxing Condition . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 Question and Answer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

9

201 202 203 204 205 208 208 209 210 211 212 213 214 224 226

Convolution Neural Network and XGBoost-Based Fault Identification in Power Transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Brief Introduction About the Work . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Combined CNN-XGBoost Technique . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Convolutional Neural Network (CNN) . . . . . . . . . . . . . . . 8.2.2 Extreme Gradient Boosting (XGBoost) . . . . . . . . . . . . . . 8.3 Power System Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Training and Testing Data Generation . . . . . . . . . . . . . . . . 8.4 Algorithm of the Proposed XGBoost Scheme . . . . . . . . . . . . . . . . . 8.4.1 Parameter Setting in Algorithm . . . . . . . . . . . . . . . . . . . . . 8.5 Result in Discussion on Fault Classification . . . . . . . . . . . . . . . . . . 8.6 Hardware Setup for Various Result Analyses . . . . . . . . . . . . . . . . . 8.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.8 Questions and Answers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

231 231 233 233 236 238 239 244 244 245 246 248 249 258 259

Sequential Component-Based Improvement in Percentage Biased Differential Protection of a Power Transformer . . . . . . . . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Projected Transformer Differential Protection Performance . . . . . 9.2.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 System Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Results Exploration with Discussion . . . . . . . . . . . . . . . . . . . . . . . .

263 263 265 265 267 268 270

Contents

9.4.1 Internal and External Fault Conditions . . . . . . . . . . . . . . . 9.4.2 High Resistance Internal Faults (HRIFs) . . . . . . . . . . . . . 9.4.3 CTs Saturation Conditions . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.4 Magnetizing Inrush State . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.5 Effect During Sudden Load Variation . . . . . . . . . . . . . . . . 9.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Current Direction Comparison-Based Transformer Protection Using Kalman Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Basics of Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.1 Extended Kalman Filter (EKF) . . . . . . . . . . . . . . . . . . . . . 10.1.2 Unscented Kalman Filter (UKF) . . . . . . . . . . . . . . . . . . . . 10.2 Work Done so Far on Transformer Protection . . . . . . . . . . . . . . . . . 10.3 Anticipated Methodology for Protection . . . . . . . . . . . . . . . . . . . . . 10.4 Kalman Filtering Application for Phasor Computation with Its Advantage Over DFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Modeling of Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Examination of Outcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6.1 Magnetic Inrush . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6.2 Internal Fault (L-L-G) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6.3 External Fault (LL-G) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7 External Fault with CT Saturation (LL-G External Fault with CT Saturation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xvii

270 273 276 277 278 279 280 280 285 286 287 289 290 292 296 298 299 299 302 303 305 306 306 307

About the Authors

Dr. Nilesh Chothani is an Assistant Professor in the Department of Electrical Engineering at Pandit Deendayal Energy University, Gandhinagar. He received a B.E. degree from Saurashtra University, Rajkot, Gujarat in 2001 and M.E. degree in power systems from Sardar Patel University, Vallabh Vidyanagar in 2004. He received his Ph.D. degree in Electrical Engineering from Sardar Patel University in 2013. He has 17 years of teaching experience. His research interests include Power system Analysis, Power System Protection, Power System Simulation and Modeling, Artificial Intelligence, Integration of Renewable Energy Sources to the Grid, and Smart Grid. He has developed state of an art power system protection laboratory including real-time operation of digital/numerical relaying scheme. He has published 33 peerreviewed refereed international journal papers, 7 national journal papers, and 26 IEEE international conference papers. He has also published 4 books in the field of power system protection and switchgear. He received the ‘TECH GURU’ Award from Gujarat Technological University (GTU) in the year 2021 for outstanding contribution to the Engineering field. He has also completed a research grant funded by the Department of Science and Technology, New Delhi, India. He is also a lifetime member of ISTE and IE (India). Dr. Maulik Raichura is an Assistant Professor and Head in the Electrical Engineering Department at Monark University, Ahmedabad. He was a Research Scholar in the Department of Electrical Engineering at A. D. Patel Institute of Technology, Gujarat, India under SERB (DST-New Delhi) project ref. no. EMR/2016/006041. He received his B.E. degree from A. D. Patel Institute of Technology, New Vallabh Vidhyanagar, Gujarat, India in 2014 and Master’s degree in power systems from the Shantilal Shah Engineering College, Bhavnagar, Gujarat, India in 2016. He received Ph.D. degree in Electrical Engineering from Gujarat Technological University, Gujarat, India in 2021. His area of research is power transformer protection. He has published 17 research articles and 1 book with an international publisher. Dr. Dharmesh Patel is an Assistant Professor in the Department of Electrical Engineering at Government Engineering College, Bharuch, Gujarat, India. He received xix

xx

About the Authors

a B.E. degree from North Gujarat University, Patan, Gujarat in 1999 and Master’s degree in power systems from the Sardar Patel University, Vallabh Vidhyanagar, Gujarat, India in 2002. He received Ph.D. degree in Electrical Engineering from Sardar Vallabhbhai National Institute of Technology, Surat, India in 2019. His field of research includes the protection of power transformers and distribution networks. He has published a few research articles in various international journals. He has published 2 international books on power system protection. He is a potential reviewer of refereed international Journals.

Abbreviations

AAF ADC AM ANN ATI BC CB CNN CRGO CRNGO CT CTC CTI CVS DAQ DC DERs DFT DG DGA DLNN DSO DWT EHV EII EILI EKF ELM EMTP EVs EWT

Anti-aliasing Filter Analog-to-Digital Converter Amorphous Steel Artificial Neural Network Advanced Transformer Infrastructure Bayesian Classifier Circuit Breaker Convolutional Neural Network Cold-Rolled Grain-Oriented Cold-Rolled Non-Grain-Oriented Current Transformer Continuously Transposed Conduction Coordination Time Interval Controlled Voltage Source Data Acquisition Direct Current Distributed Energy Resources Discrete Fourier Transform Distributed Generation Dissolved Gas Analyzer Deep Learning Neural Network Digital Storage Oscilloscope Discrete Wavelet Transform Extreme High Voltage Equivalent Instantaneous Inductance Equivalent Instantaneous Leakage Inductance Extended Kalman Filter Extreme Learning Machine Electromagnetic Transient Program Electric Vehicles Empirical Wavelet Transform xxi

xxii

FC FCDFT FCF FDI FFT FIA FL GA GIC GNA GOES HDF HE-ELM HIF HRIFs HRU HT HV HVAC IDMT IEEE IoT JA KF KPV LDF LPT LSC LSE LT LV LVRT MATLAB MCT MCU MDFT MFCDFT MG MM NLTC OC OF OLTC OSOWOG OTI

Abbreviations

False Classified Full Cycle Discrete Fourier Transform Flux Condition Flag Foreign Direct Investment Fast Fourier Transform Fault Initiation Angle Fault location Genetic Algorithm Geomagnetic-Induced Current Geometric Neutral Axis Grain-Oriented Electrical Steel High-pass Decomposition Filters Hierarchical Ensemble of Extreme Learning Machine High Impedance Fault High-Resistance Internal Faults Harmonic Restrain Unit High Tension High Voltage High-Voltage Alternating Current Inverse Definite Minimum Time Institute of Electrical and Electronics Engineers Internet of Things Jiles–Atherton Kalman Filter Knee Point Voltage Low-pass Decomposition Filter Large Power Transformers Least-Squares Curve Least-Square Error Low Tension Low Voltage Low-Voltage Ride Through Matrix Laboratory Minimum Coordination Time Micro-Controller Unit Modified Discrete Fourier Transform Modified Full Cycle Discrete Fourier Transform Morphological Gradient Mathematical Morphological No-Load Tap Changer Overcurrent Over-fluxing On-Load Tap Changer One Sun, One World, One Grid Oil Temperature Indicator

Abbreviations

PAR PD PNN PRs PSCAD PSO PT REF ReLU RMS ROM RTD RTDS RVM SC SCs Sg/oPR SgPR SI SoPR SPI SVM TC TCL TGM THD UF UHV UKF UT WPT WT WTI XGBoost

xxiii

Phase Angle Regulators Partial Discharge Probabilistic Neural Network Pressure Relays Power System Computer-Aided Design Particle Swarm Optimization Potential Transformer Restricted Earth Fault Rectified Linear Unit Root Mean Square Read-Only Memory Resistance Temperature Detector Real-Time Digital Simulator Relevance Vector Machine Signal Conditioning Sequence Components Sudden gas/oil Pressure Relay Sudden gas Pressure Relay Source Impedance Sudden oil Pressure Relay Serial Peripheral Interface Support Vector Machine True Classified Transient Current Limiter Time Gradient Margin Total Harmonic Distortion Under-frequency Ultra-High Voltage Unscented Kalman Filter Unscented Transform Wavelet Packet Transform Wavelet Transform Winding Temperature Indicator Extreme Gradient Boosting

List of Figures

Fig. 1.1 Fig. 1.2 Fig. 1.3 Fig. 1.4 Fig. 1.5 Fig. 1.6 Fig. 1.7 Fig. 1.8 Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 2.5 Fig. 2.6 Fig. 2.7 Fig. 2.8 Fig. 2.9 Fig. 2.10 Fig. 2.11 Fig. 2.12 Fig. 2.13 Fig. 2.14 Fig. 2.15 Fig. 2.16 Fig. 2.17

Interconnection of a large power transformer . . . . . . . . . . . . . . . Different transformer configurations used in practice . . . . . . . . Principle winding arrangement of a regulating transformer in Y-D connection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Loss of system load with single contact switching . . . . . . . . . . . Basic switching principle “Make (2) Before Break (1)” using transition impedances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . OLTC connection to main transformer tank . . . . . . . . . . . . . . . . Gas generation chart for transformer oil . . . . . . . . . . . . . . . . . . . Generalized block for condition monitoring of transformer . . . Classification of protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classification of protective relays . . . . . . . . . . . . . . . . . . . . . . . . Fundamental arrangement of transformer protection with its significant fault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scheme of OC protection for small rating transformer . . . . . . . Elementary overcurrent relay with harmonic restrain unit . . . . . Scheme of REF protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Differential protection based on circulating current . . . . . . . . . . Percentage-biased differential protection of transformer . . . . . . Classic dual slope percentage-biased characteristics . . . . . . . . . Two-stage characteristic. a Basic characteristic, b, c modified adaptive version . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Three-winding transformer protection . . . . . . . . . . . . . . . . . . . . . Effect of flux linkages and core characteristic on magnetizing inrush . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transformer inter-turn fault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thermal relay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WTI and OTI with alarm unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . Schematic of Buchholz relay location with its extravagant sight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Generalized protections applied to power transformer . . . . . . . .

4 5 11 12 13 14 16 20 29 31 33 37 38 39 40 41 42 42 43 44 46 47 48 49 50 xxv

xxvi

Fig. 2.18 Fig. 2.19 Fig. 2.20 Fig. 2.21 Fig. 2.22 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 3.5 Fig. 3.6 Fig. 3.7 Fig. 3.8 Fig. 3.9

Fig. 3.10

Fig. 3.11

Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 4.4 Fig. 4.5 Fig. 4.6 Fig. 4.7 Fig. 4.8 Fig. 4.9 Fig. 4.10 Fig. 4.11

List of Figures

Transformer core with length as dimension . . . . . . . . . . . . . . . . Equivalent magnetic circuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Two-phase supply (R and Y) given to a three-phase transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Percentage differential protections for Y-∆ power transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Percentage differential protection for Y-∆ power transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Core feature inrush and flux linking representation . . . . . . . . . . Harmonic- and DC offset-dependent discriminative technique’s sample diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Waveform’s shape-based analysis . . . . . . . . . . . . . . . . . . . . . . . . Discriminative- and decomposing-based block diagram . . . . . . Mathematical morphology-based analysis . . . . . . . . . . . . . . . . . Power consumption-based analysis . . . . . . . . . . . . . . . . . . . . . . . Circuit diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Schematic flow of the presented technique . . . . . . . . . . . . . . . . . Wave pattern during inrush case for a each side currents of device, b power usage in transformer, c arctan of ∆, and d average value of obtained angle θ . . . . . . . . . . . . . . . . . . . Wave pattern during interior type of fault. a Both sides current magnitude of the device, b power consumption of both the components active and reactive, c ∆ arctan, and d θ avg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wave patterns of inrush case which is followed by interior type of fault. a Both sides current data of the device, b power consumption of both components of the considered device, c arctan of ∆, d θ avg . . . . . . . . . . . . . . Equivalent simplified circuit of CT . . . . . . . . . . . . . . . . . . . . . . . CT saturation curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Consideration in CT selection . . . . . . . . . . . . . . . . . . . . . . . . . . . Power system diagram CT parameter changes . . . . . . . . . . . . . . Fault current versus time with change in R and X (at inception angle 0.515) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DC component in fault current for R = 1 Ω and L = 0.1 H at FIA = 0.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fault current versus time at FIA = 0.5, R = 10 Ω and L = 0.1 H . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CT saturation effect under influence of different remnant at FIA = 0.5, R = 1 Ω, L = 0.1 H, and burden = 0.5 Ω . . . . . . Effect fault inception angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CT primary and secondary currents recorded in DSO in laboratory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . System diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51 53 55 63 64 72 75 76 78 79 80 86 88

89

90

91 106 109 114 115 116 117 118 119 120 121 122

List of Figures

Fig. 4.12 Fig. 4.13 Fig. 4.14 Fig. 4.15 Fig. 4.16 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4

Fig. 5.5 Fig. 5.6

Fig. 5.7 Fig. 5.8 Fig. 5.9 Fig. 5.10 Fig. 5.11 Fig. 5.12 Fig. 5.13 Fig. 5.14 Fig. 5.15 Fig. 6.1 Fig. 6.2 Fig. 6.3 Fig. 6.4

Fig. 6.5 Fig. 6.6

xxvii

Projected algorithm to detect CT saturation in transformer protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Internal faults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . External fault without CT saturation . . . . . . . . . . . . . . . . . . . . . . External faults with mild CT saturation . . . . . . . . . . . . . . . . . . . . External fault with severe CT saturation . . . . . . . . . . . . . . . . . . . Indian power structure grid schematic line diagram . . . . . . . . . . Projected algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I diff /I bias trajectory for the period of normal condition/ external fault condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transformer higher voltage side and lower voltage side currents (CT secondary) waveform for normal state (till 0.2 s) and exterior fault state (from 0.2 to 0.35 s time frame) with DC component of faulty current . . . . . . . . . . . . . . . I diff /I bias trajectory while interior faulty condition . . . . . . . . . . . Higher voltage level and the lower voltage level of the current waveform during insider faulty state of the transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I diff /I bias curve while excessive fluxing state . . . . . . . . . . . . . . . . Higher voltage side and lower voltage side current wave patterns during excessive flux state of the transformer . . . . . . . . Front sight of hardware prototype for experiment . . . . . . . . . . . Back side of a hardware prototype model for experimental purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Current waveform of DSO during an interior faulty case of the transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DSO current wave pattern while exterior fault case of the considered transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . DSO current wave pattern while continuous excessive flux case of the considered transformer . . . . . . . . . . . . . . . . . . . . DSO wave pattern while a temporary excessive fluxing case of the dedicated transformer . . . . . . . . . . . . . . . . . . . . . . . . . I diff /I bias curve for excessive flux test case . . . . . . . . . . . . . . . . . . Replicated system taken into consideration . . . . . . . . . . . . . . . . . Flowchart of the proposed scheme . . . . . . . . . . . . . . . . . . . . . . . . a Inrush condition current wave pattern. b Individual phase THD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a Wave pattern of current in case of B-G interior fault contains DC contents. b Wave pattern of current in B-G interior fault case excluding DC contents. c All 6 phases THD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wave pattern of current for inrush case for pre-fault condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wave pattern of current quantity while CT saturates through interior faulty case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

124 125 126 127 128 138 140 141

142 143

143 144 144 145 145 146 147 147 148 148 162 165 166

168 169 170

xxviii

Fig. 6.7 Fig. 6.8 Fig. 6.9 Fig. 6.10 Fig. 6.11 Fig. 6.12 Fig. 6.13 Fig. 6.14 Fig. 6.15 Fig. 7.1 Fig. 7.2 Fig. 7.3 Fig. 7.4 Fig. 7.5 Fig. 7.6 Fig. 7.7 Fig. 7.8 Fig. 7.9 Fig. 7.10 Fig. 7.11 Fig. 7.12 Fig. 7.13 Fig. 7.14 Fig. 7.15 Fig. 7.16 Fig. 8.1 Fig. 8.2 Fig. 8.3 Fig. 8.4 Fig. 8.5 Fig. 8.6

Fig. 8.7 Fig. 8.8 Fig. 8.9

List of Figures

Prepared hardware model in laboratory . . . . . . . . . . . . . . . . . . . . Waveform and Harmonic during Inrush . . . . . . . . . . . . . . . . . . . Sympathetic inrush wave pattern and its harmonic . . . . . . . . . . . Recovery inrush current wave pattern and its harmonics . . . . . . Exterior fault case and amount of harmonics . . . . . . . . . . . . . . . Exterior fault with CT saturation case fault case, current and harmonics wave pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interior fault case, current and harmonics wave pattern . . . . . . . Current under interior fault condition while CT saturates and its harmonic wave pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . No-load current with its harmonics . . . . . . . . . . . . . . . . . . . . . . . System diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed adaptive protection algorithm . . . . . . . . . . . . . . . . . . . Normal/external fault condition . . . . . . . . . . . . . . . . . . . . . . . . . . Inrush condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Internal fault condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . External fault with CT saturation condition . . . . . . . . . . . . . . . . Discrimination of over-fluxing in transformer protection . . . . . Over-fluxing state followed by internal fault . . . . . . . . . . . . . . . . Hardware model a forward-facing panel vision, b rear panel view . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inrush current waveform on 50 kVA hardware setup . . . . . . . . . External fault condition in transformer with hardware setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Internal fault condition in hardware setup . . . . . . . . . . . . . . . . . . Over-fluxing condition in hardware setup . . . . . . . . . . . . . . . . . . CT Saturation state under external fault in hardware setup . . . . Over-fluxing state railed by CT saturation with external fault in hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phasor estimation for fault current using DFT and MFCDFT (L-g fault applied at 0.1 s, i.e., 400 sample) . . . . Representation of combined CNN-XGBoost technique . . . . . . . Portion of the Indian power system for the study . . . . . . . . . . . . Illusion for understanding different types of transformer internal fault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Algorithm of the suggested CNN-XGBoostScheme . . . . . . . . . Power and control circuit of hardware setup . . . . . . . . . . . . . . . . Scaled CT secondary current for a inrush situation, b In-zone fault, c out-of-zone fault, d saturated CT during outside fault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a Three-phase transformer, b tapping on winding of primary and secondary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variable resisters of transmission line . . . . . . . . . . . . . . . . . . . . . Variable inductors of transmission line . . . . . . . . . . . . . . . . . . . .

172 173 174 175 176 177 178 179 180 191 193 200 201 202 203 204 205 206 208 209 210 211 212 213 218 234 238 241 245 247

248 251 252 253

List of Figures

Fig. 8.10 Fig. 8.11 Fig. 8.12 Fig. 8.13 Fig. 8.14 Fig. 8.15 Fig. 9.1 Fig. 9.2 Fig. 9.3 Fig. 9.4

Fig. 9.5

Fig. 9.6

Fig. 9.7

Fig. 9.8

Fig. 9.9 Fig. 10.1 Fig. 10.2 Fig. 10.3 Fig. 10.4 Fig. 10.5 Fig. 10.6 Fig. 10.7 Fig. 10.8 Fig. 10.9 Fig. 10.10

xxix

Waveforms captured for inrush and various fault cases during hardware validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Line-ground fault on the primary winding of the transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Line to line fault on primary winding of the transformer . . . . . . Triple line fault on the primary winding of the transformer . . . . Line-ground external fault with mild CT saturation . . . . . . . . . . Multi-run block and setting of variables . . . . . . . . . . . . . . . . . . . Phasors of symmetrical components of fault current . . . . . . . . . Transformer fault case identification algorithm . . . . . . . . . . . . . Model of power system for simulation . . . . . . . . . . . . . . . . . . . . LL-G internal fault, a current waveform of primary and secondary sides of a transformer, b phasor angle comparison for SCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L-L external fault, a current waveform of primary and secondary side of a transformer, b phasor angle comparison for SCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L-G high resistance internal fault, a current waveform of primary and secondary side of a transformer, b phasor angle comparison for SCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CT saturation condition, a current waveform of primary and secondary side of a transformer, b phasor angle comparison for SCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a Magnetizing inrush current waveform, b value comparison of fundamental and second harmonic component . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a Current waveforms during 10% overload, b phasor angle comparison for SCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Principle of Kalman filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basic block diagram of Kalman filter . . . . . . . . . . . . . . . . . . . . . The principle of the UT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a External fault on line-2, b external fault on line-1, c internal fault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed transformer protection algorithm . . . . . . . . . . . . . . . . . System diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Magnetizing inrush a primary and secondary inrush current waveform, b primary and secondary current phasor . . . Internal fault a primary and secondary current waveform, b primary and secondary current phasor . . . . . . . . . . . . . . . . . . . External fault a primary and secondary current waveform, b primary and secondary current phasor . . . . . . . . . . . . . . . . . . . CT saturation in external fault a primary and secondary current waveform, b primary and secondary current phasor . . .

254 255 255 256 256 257 267 269 270

273

274

275

277

278 279 286 287 290 294 295 299 300 303 304 305

List of Tables

Table 1.1 Table 1.2 Table 1.3 Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 2.7 Table 2.8 Table 2.9 Table 2.10 Table 2.11 Table 2.12 Table 2.13 Table 3.1 Table 4.1 Table 6.1 Table 6.2 Table 6.3

Discharge of energy and thermal effect in transformer . . . . . . . Fault identification based on gases concentration in oil . . . . . . . A list of various types of conditions in power transformer . . . . Coordination time intervals for relays . . . . . . . . . . . . . . . . . . . . . Different reasons for the transformer failure . . . . . . . . . . . . . . . Earth fault protection cases for power transformer . . . . . . . . . . CT connections for differential protection . . . . . . . . . . . . . . . . . Secondary winding voltage for supply from phases R and Y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of voltages in normal operating conditions (DY connection) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of voltages for supplying R and Y phases only (DY connection) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Secondary connection in case of various possibilities of one phase of primary disconnection . . . . . . . . . . . . . . . . . . . . Voltages of the secondary windings for supply from phases R and Y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparisons of voltages in normal operating conditions (YY connection) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of voltages for supplying R and Y phases only (YY connection) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Secondary connections in case of various possibilities of one phase of primary disconnection . . . . . . . . . . . . . . . . . . . . Different research techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparative analysis of various inrush detection schemes for the transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Remarks based on codes of CT . . . . . . . . . . . . . . . . . . . . . . . . . . THD percentage for initial inrush case . . . . . . . . . . . . . . . . . . . . THD percentages for interior fault case . . . . . . . . . . . . . . . . . . . THD percentage of inrush during interior fault cases . . . . . . . .

17 18 22 30 35 39 41 53 54 54 55 56 56 56 57 58 83 103 167 169 170

xxxi

xxxii

Table 6.4 Table 7.1 Table 8.1 Table 8.2 Table 8.3 Table 8.4 Table 8.5 Table 8.6 Table 8.7 Table 9.1 Table 9.2 Table 9.3 Table 9.4 Table 9.5 Table 10.1

List of Tables

THD percentage during CTs saturation while there exists an interior fault . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Withstand the capability of the V /Ffor a classic transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Generation of data for different inrush conditions . . . . . . . . . . . Creation of training and testing cases for internal faults . . . . . . Generation of training and testing cases for faults outside the transformer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data preparation for simultaneous fault and over-fluxing situation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Altogether data collected and separated for training and testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Outcome of the suggested scheme for various conditions . . . . . Sample feature vector for the testing process . . . . . . . . . . . . . . . Considered values of numerous fault and system parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phasor angle values (in degree) of SCs during internal faults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phasor angle values (in degree) of SCs during external faults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Percentage restrained current (in Ampere) for various internal and external faults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phasor angle values (in degree) of SCs within various abnormalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Algorithm operations for various test conditions . . . . . . . . . . . .

171 198 239 240 242 243 244 246 257 271 271 272 272 276 301

Chapter 1

Transformer Infrastructure for Power Grid

Abstract Electricity infrastructure is the arrangement made by a human being with the use of available resources to generate power, transfer it from one place to another, conversion and dispense electrical power at various locations. The parameters and ratings of individual national, state, and regional grids are diverse, but they follow the same ideology and protocol for the functioning of important infrastructure. The amount of electricity produced by green energy sources is increasing day by day in contrast to major electricity being generated by conventional sources. Apart from the local generation and utilization, the majority of electricity generated by the centralized plant is transported using large power transformers and complex transmission infrastructure. Power transformer stands in electricity infrastructure to safely and consistently transport energy to consumers over a long distance. Investment in dedicated transformer infrastructure is essential to tackle some unforeseen situations of weather, storms, and internal failure of the system as well as to recover the flexibility of the grid. However, the conditions of existing transformers are unpleasant due to the aging effect and overloading stress. Thus, flexible power transformers are the need for a future smarter grid to deal with the mixture of conventional and bidirectional power flow from renewable energy sources. Moreover, the advanced control, monitoring, and stand-alone protection of such transformers increase the efficiency of the entire power/smart grid. This chapter deals with the role of large power transformers in electrical infrastructure with possible interconnections and operations. The advanced technology development, design aspect, testing of a transformer, voltage regulation by OLTC, fault analysis with DGA, and condition monitoring are discussed in this chapter. Moreover, a smart/flexible transformer is facilitated with the adoption of the change in voltage and variation in impedance in the event of a malfunction of the power grid. They are self-controllable with the use of smart sensors and can change the load pattern as well as monitor their operation with the use of artificial intelligence and IoT techniques.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Chothani et al., Advancement in Power Transformer Infrastructure and Digital Protection, Studies in Infrastructure and Control, https://doi.org/10.1007/978-981-99-3870-4_1

1

2

1 Transformer Infrastructure for Power Grid

1.1 Introduction The transformer infrastructure plays an imperative role in sustaining the reliable power supply which is nowadays in high demand by consumers. Nowadays, all human beings are highly dependent on the power supply. Out of 365 days in a year, at any time the supply outage during real-time operation in the various sectors/fields is highly undesired by the consumers at any cost. The usage of power is increasing day by day and also will undoubtedly increase in the upcoming future as the largest load of transportation is going to switch to the existing grid system. All the countries in the world are shifting towards switching their transportation system from either goods or public or either private transport loads empowered by electric energy to prevent the hazardous consequences of using fossil fuel and to make use of clean energy. This becomes an opportunity for the electrical engineering community to lead society for the betterment of tomorrow. However, in this process, the health of the transformer is a major concern in smoothly developing and operating the whole power system network. Hence, the transformer infrastructure should be built in such a way that it could tackle all the unforeseen conditions that take place not only short but also from a far-future perspective [1–3]. Although the development of transformer infrastructure is not so easy task, as electrical engineers, we must build the system in such a way that it could sustain in every possible unforeseen aspect. Major challenges that could face are changes in unforecast load, i.e., development and penetration of DERs (distributed energy resources) [4], power theft, development of metro cities, and many more like or unlike situations. The infrastructure should be developed in such a way that it can be adaptable to tackle changes in near- or far-future conditions (mostly increasing load conditions) and easy to install. It has a convenient and sound protective system that could easily identify all the major and minor faults that are taking place inside the protective zone of the transformer. Moreover, it should be set up in such a kind that can be easily installed in all the geometric and landing conditions having minimum necessary criteria to install, i.e., it could be installed everywhere, etc. The details of such transformer infrastructure are discussed in this chapter.

1.2 Role of Large Power Transformers in the Electric Grid As discussed in the above section, electrical energy demand is increasing day by day due to many factors. Because of this increasing demand, the power rating of the instruments or machines that are in use in the existing power system network will also definitely increase. With the help of study and ongoing development in technology and using material science, the power rating of these instruments can be extended but simultaneously the physical size of these machines or instruments is also increasing due to an increase in demand for energy. The power transformer can be considered the heart of the power system network which pumps power to and from every point of

1.3 Power System Infrastructure

3

the entire power system network or grid. Generally, the power rating of transformers having more than 100 MVA ratings is considered Large Power Transformers (LPTs). The large power transformers are the first step to start flowing (pumping) the power in the entire power system network or grid. After receiving power from the large power transformer, transmission lines connect that power with the substations which are placed at specific spaces to process and distribute that received power. In the substations, that power is going through the process of stepping down the voltage and it is further connected to the distribution system. From that distribution system, the power is then distributed to various distribution points where a small amount of voltage or the desired amount of voltage is distributed to the consumer end where the power is consumed. In this whole process of power transportation from the generating end to the consumer end, if the first step fails, i.e., one large transformer breakdown then the entire portion may get affected. This situation consequently interrupts the power supply to the remaining system till the consumer end. As the cost of replacement or repairing that transformer is too high, the preferable condition is to prevent its failure from any undesired conditions that are taking place during the operation of a large transformer. A one-stop solution for this problem is to have a highly sound protective scheme that can prevent the undesired conditions that are taking place during the transformer operation. Hence, it is mandatory to take utmost care of these large transformers to smoothly operate the entire power system network or grid. Certainly, other power system components are equally important as that of transformers to smoothly operate the grid but the origin point is more important to get an uninterrupted power supply.

1.3 Power System Infrastructure Power system infrastructure, the word itself suggests that the entire power system should be emphasized. The power system infrastructure can be considered a bridge between the electric energy sources to the end consumers. In this term, all the electrical components such as types of generators, types of transformers, entire transmission line network, protection and switchgear instruments, the peripheral equipment connected in and out of the substations, communication channels, distribution lines, underground cables, energy meters, and measuring devices are included. The electrical grid is considered the largest network or web developed by humans. It includes not only the field of electrical engineering but it covers mechanical engineering, civil engineering as well as communication engineering also. The infrastructure is developed by the contribution of all the engineering wings and then the final product is evolved. Indian Prime Minister Shri Narendra Modi proposed the One Sun, One World, One Grid (OSOWOG) initiative in the first assembly in October 2018. Such initiatives have been taken and would take place soon to make the grid globally connected. The global connectivity of the grid has its advantages and one of them is the possibility of optimum utilization of generated power with forecasted load demand. Countries

4

1 Transformer Infrastructure for Power Grid

Fig. 1.1 Interconnection of a large power transformer

can trade on the power energy based on their requirement and per capita generation capacity. In connection with advantage, the major concern is to manage it with minimum complexity along with monitoring it to avoid trust issues of measurement of supplied power and received power. Also, to develop such a kind of global grid connectivity, grid infrastructure plays an important role because without installing a transmission medium between the countries it is currently impossible to transmit power across the globe. With the advances in all aspects of engineering, this grid should also be modernized by having smart devices and equipment connected to it. Figure 1.1 shows the interconnection of a large power transformer as part of the power grid infrastructure.

1.4 Three-Phase Transformer Interconnections Three-phase transformers are formed by connecting three single-phase windings in different patterns to get the desired electrical output. So, the type of interconnection to be made certainly depends on the desired electrical parameter output. The types of interconnections are mesh or delta connection, star connection, or zigzag connection. These connections can be made in two ways and the primary winding and secondary winding configurations can also be possible in two ways, hence by considering the total of it, 12 types of connections are formed. These 12 types of interconnections are shown in Fig. 1.2. The connections are categorized into four categories as per the phase displacement or phase difference on both sides of the transformer. Moreover, here, 0 denotes a 0degree phase shift, 6 denotes a 180-degree phase shift, 1 denotes a −30° phase shift, and 11 denotes a +30° phase shift.

1.4 Three-Phase Transformer Interconnections

Fig. 1.2 Different transformer configurations used in practice

5

6

1 Transformer Infrastructure for Power Grid

Category 1: Zero phase difference (Yy0, Dd0, Dz0). Category 2: 180° phase difference (Yy6, Dd6, Dz6). Category 3: 30° lag phase difference (Dy1, Yd1, Yz1). Category 4: 30° lead phase difference (Dy11, Yd11, Yz11). Here, Y/y indicates star connection for primary and secondary windings, respectively, D/d indicates delta connection for primary and secondary windings, respectively, and z indicates zigzag connection. Hence, depending on the desired output, the application, and the location of the transformer service, the winding interconnections are made for three-phase transformers.

1.5 Transformer Technology Development The important role of the transformer in the power system grid is already discussed in the above sections. This section narrates the recent developments that are taking place or are about to take place in terms of the construction, design, and usage aspects of the transformers. Formerly, the transformer is structured or constructed by the way the raw materials are supplied by various vendors, and after that with properly following the due process on the material (considering the guidelines from the standards), large-scale sellers assemble the entire unit. Also, the testing engineers (either appointed internally or through a third party) test the transformers from every angle along with the consideration of electrical parameter aspects in all the possible events. The market-leading distributors like TBEA [5]; Hitachi [6]; ABB [7, 8]; Schneider Electric [9]; Siemens [10, 11]; GE [12]; etc. Provide guidelines for transformer manufacture/assembly and after-sales quality service, short- or longterm maintenance contracts, and many more market-required services. In a report, it is stated that most of the transformers that are currently in use are of age 25– 40 years and also most of them are running in overloaded conditions [13]. Hence, transformers are nowadays built with higher quality standards and consider the life span of a device also in a continuous overload condition.

1.5.1 Design Technology In the design process, there are majorly four parts that are considered, they are listed as coil or winding, a section of core, assembling of the coil on the core section, final touching or finishing, and the testing process (the testing part will be separately covered in next subsection). Majorly affected factors during the construction of transformers are the supply chain of raw materials like core material, winding coil material as well as insulating

1.5 Transformer Technology Development

7

materials (both solid and liquid oil). The availability of these materials can predict the accessibility and price of the final product. The preparation of the special type core, winding, and insulating material takes time and hence the availability of these materials at a prescribed period is always a question mark. Not only these major component’s availability is important, but simultaneously availability of transformer bushings and tap changers of the transformers also plays an equally vital role in final product preparation, especially in unforeseen situations like the COVID-19 pandemic. Moreover, the increasing demand for the manufacturing of electrical vehicles also becomes a hindrance while providing material for transformer manufacturing because electrical vehicles also use more or less the same material components that are used in the transformer manufacturing process. The transformer now is designed by generally having usual material copper coils (cylindrical-type winding disks’ shape in case of three-phase transformer) and CRGO (cold-rolled grain-oriented) material core. Nowadays, the transformer is constructed with the help of a special type of copper wire material which is known as CTC (continuously transposed conduction) to lower the losses that occur during transformer operation and lower the chances of forming hot spots that are taking place often during its operation. The use of CTC is increasing because it provides compressed winding that can fit in less space and has improved short-circuit capability. In between the windings, spacers that are made from high-quality pressboard material are also inserted to facilitate cooling vents. The coils are placed with pressed clamping and then it will be placed in an oven for drying purposes. This will improvise the short-circuit withstand capability of the transformer. The transformer core is now made up of a material that contains high permeability. The high permeability feature enables the maximum flow of flux from one end of the transformer to another end, i.e., consequently it reduces the content of leakage flux and hand in hand improves linkage flux amount. A newer technology to manufacture core is known as domain-refined GOES (grain-oriented electrical steel) material. The material GOES is made by cutting it to a geometrical shape by using precise systemcontrolled equipment for optimum cutting edges that can provide a minimum air gap between the joints of two sequential core parts. After that, the material is annealed to ensure minimal loss generation. Post to that, the core is then laminated to minimize losses that are taken place due to eddy currents, and hence it can also reduce the chances of magnetic short circuit. This core is enabled with an earthing point to make it shockproof equipment. More on this, some pressboard and Kraft papers are also furnished to provide insulation and mechanical strength to the equipment. The outer tanks are manufactured from low-carbon mild steel sheet materials. All the other peripherals are then fitted to the tank and the complete assembly is prepared. After this, insulating transformer oil is poured into the transformer tank and then the terminal connections through bushings are fixed (obviously after ensuring no leakage of insulation oil). After all these preparations, the prepared consignment is kept left for inspection and testing purposes.

8

1 Transformer Infrastructure for Power Grid

1.5.2 Testing of Transformer Transformer testing is equally important as equivalent to its manufacturing part. If the manufactured transformer is not performed according to its standards or design specification or operational desirous, then it can cause malfunctioning of all the equipment or instruments that are connected to it. Hence, before commissioning the transformer, it needs the transformer’s fitness certificate which is generated after testing the prepared device by competent authorities or by the seller itself. One thing here is noteworthy; only the transformer’s fitness certificate is not ample enough because it provides data about the transformer when it is operated during the testing phase only. The transformer needs continuous monitoring after commissioning while it is in operation because anomaly conditions [14] can arise at any time without assigning any prior notice and those conditions may not be experienced during the testing phase of the transformer. Though as per the standards, transformer testing is necessary to certify that it is working properly during normal operative conditions and fulfills all the required criteria that are desired while preparing the transformer. Sometimes transformers are designed and assembled as per the requirement of the consumers/buyers. Several examples of consumer requirements are loading and overloading capability, dielectric withstand capacity, extendable operating performance, etc. The transformer testing procedure fulfills the internal quality assessment process as per the company’s policy. Although the product has been manufactured by adhering to the company’s specified criteria and as per the Electricity Standards which the company followed and also depends on the customer’s requirements. A variety of transformer tests are carried out like rise in temperature tests to check the loading capability, dielectric tests are performed to validate the integrity of the transformer when it is subjected to high dielectric stresses and chances of overvoltage during normal operation, losses during no-load and loading conditions, impedance measurements while short-circuiting condition, etc. to validate the performance of the product during its operation. Different standards have different types of test lists, but most probably the following tests are carried out on the power transformer of substantial capacity. . Routine tests . Type or design tests . Special or other tests Firstly, as and when the transformer arrives from the manufacturer, it must be verified that the received transformer is dry (the transformer should not contain insulating oil while it is in transportation). Also, it should not contain any dents or no damage should be occurred at all and the internal connections are perfect. Moreover, the ratio of the transformer, polarity marking, winding configuration, and percentage impedance should be written on the nameplate. It also needs to be checked and ensured that the insulating elements between windings, between core and windings, and between cores to the tank are in their original condition. The size of the transformer, the class of the insulation, and kVA ratings are the major elements

1.6 On-Load Tap Changer (OLTC) of Transformer

9

that indicate how much attention is required to put the transformer in operation. Moreover, it indicates the type and how many auxiliary devices it needs. All the tests performed on a transformer while erection must be inspected by the test engineer to ensure that the transformer tests are satisfactorily completed and now it can be energized. Some of the tests need a specialist person to perform them on the dedicated device and they should be strictly performed under the supervision of specialists. The below-mentioned checks are in general to be done: . . . . . . . . . . . . . . . . . . . . . . . . .

Verification of nameplate data with required specifications Power Meggering Auxiliary elements and connection checks Requirement of lightning arrestors Hand Meggering device Temperature devices CT (current transformer) tests Winding temperature indicator (WTI) and thermal image Bushing power factoring done Remote temperature indication device Transformer power factoring done Auxiliary power requirement Voltage ratio test Automatic transfer switch placed or not Polarity test Cooling system working or not Transformer-turns ratio test Bushing potential measurement Tap changers properly working Auxiliary-equipment protection requirements and alarms and indicators Short-circuit impedance test Overall loading condition Zero sequence test Trip checks done Winding resistance measurement

1.6 On-Load Tap Changer (OLTC) of Transformer The transformer is the key part of the power network. It constantly remains on duty to transmit power in the power system network. To transmit the power, the major role of a transformer is to step up or step down the voltage level and/or current. To perform this procedure, it is required to regulate the voltage level with its pre-set value frequently. A tap changer mechanism is a very essential component for the transformer to regulate the voltage level. It works on the loaded condition of the transformer. It means there is no need to turn off the transformer while changing the tapping of the device and it doesn’t interrupt the power continuity. For around

10

1 Transformer Infrastructure for Power Grid

100 years, the transformer is equipped with a tap changer for tap changing as well as phase shifting by varying the voltage ratio purpose. By providing the taps on the primary or the secondary winding of the transformer, the number of turns can be chosen with the help of the tap-changing mechanism. This tap-changing mechanism can be of two types such as No-Load Tap Changer (NLTC) and On-Load Tap Changer (OLTC). The on-load tap changer is employed on the transformer where very frequent interruptions occur in the power system because of the tap-changing process. So without interrupting the power supply one can change the turn ratios of the transformer windings. In usual practice, OLTC is designed for 33 numbers of taps. Out of which, the middle tap is of rated voltage value, subsequent with 16 taps the output voltage obtained is above the rated voltage and the remaining 16 taps are generating voltage below the rated one.

1.6.1 Where to Employ the OLTC on Transformer The OLTC can be employed at the ending point of the winding or the center point of the winding or may be at the neutrality point. There are some advantages to placing the OLTC at various locations on the transformer winding such as . By placing the OLTC at the end of the phase winding, the rating of the insulator for bushing is reduced . By locating OLTC at the center of the phase winding, the required insulation between various parts of the device is also reduced

1.6.2 Classification of OLTC Based on Its Construction The OLTC can be designed in two ways such as (i) center tap reactor-type OLTC and (ii) center tap resistor-type OLTC. Construction of OLTC The OLTC consists of a center tap reactor type or resistor type along with voltage switch V1 on the HV side. On the other LV side diverter switch S is employed. Selector switches named S1 , S2 , S3 , and S4 and tapings T1 , T2 , T3 , and T4 are placed in a separate oil-filled chamber to reduce arcing formation while changing the tapings. Nowadays, the OLTC can be operated manually as well as remotely. For manual control, a separate handle is provided. Whereas, for remote OLTC operation, a controlled motor with a gear mechanism is provided on the body of the transformer.

1.6 On-Load Tap Changer (OLTC) of Transformer

11

Working of OLTC To regulate the output voltage of the transformer, one has to change the transformer turn ratio. The OLTC performs this function by adding or subtracting the turns from either the primary or secondary windings of the transformer. Figure 1.3 shows the arrangement of a three-phase regulating transformer in which OLTC is installed at the HV winding of a star–delta connected device. Under the energized condition if we change the taps of the winding, there may be chances of momentary system load loss as shown in Fig. 1.4. Therefore OLTCs are designed on the basic concept of “make before break contact” as can be seen from Fig. 1.5.

Fig. 1.3 Principle winding arrangement of a regulating transformer in Y-D connection

12

1 Transformer Infrastructure for Power Grid

Fig. 1.4 Loss of system load with single contact switching

For the transition of tap, a resistor or reactor can be used to bridge among the adjacent taps to transfer the load without interruption of supply. At the same moment, it will also restrict the amount of circulating current (I C ) for the duration when both taps are in use. In reactor-type OLTCs, the reactor is so designed for continuous loading. Figure 1.6 shows the connection of OLTC with the power transformer for on-load voltage regulation. Tap-changing mechanism is generally located on HV winding due to the following reasons: 1. HV winding currents are lower in magnitude. 2. The dimensions that are required for tap changer contacts, their leads, etc. are smaller. 3. As the HV winding is wrapped above the LV windings, it is easier to fetch out the tapping connections.

1.6.3 Advantages of OLTCs The following are the advantages of OLTCs: . The voltage ratio can be modified without interrupting the transformer operation . Provides voltage regulation . OLTCs increase efficiency and productivity by providing uninterrupted supply

1.6 On-Load Tap Changer (OLTC) of Transformer

13

Fig. 1.5 Basic switching principle “Make (2) Before Break (1)” using transition impedances

. According to the load requirements, the voltage levels can be varied and the flow of reactive power can also be controlled

1.6.4 Disadvantages of OLTCs The following are the disadvantages: . The cost of the transformer is increased . Required regular maintenance . The reliability of the supply is decreased

14

1 Transformer Infrastructure for Power Grid

Fig. 1.6 OLTC connection to main transformer tank

1.7 Dissolved Gas Analysis for Transformer Monitoring and Protection In the power system network, the transformer continuously operates nearby full-load conditions. Due to overloading operations, the insulating material of the transformer encounters continuous electrical as well as thermal stresses. This results in arcing, corona discharge, sparking, and overheating effects which further lead to incipient faults inside the transformer. During these kinds of stresses, the insulating material releases a certain types of gases. In the power transformer fault detection procedure, to examine the pending or occurrence of faults, Dissolved Gas Analysis (DGA) is

1.7 Dissolved Gas Analysis for Transformer Monitoring and Protection

15

used for many years. It is observed that the generation of such kind of gases inside the device is an admirable indicator of faults [15].

1.7.1 How Gases Generated in Transformer Due to continuous loading conditions, the transformer releases hydrocarbon gases in its oil. These gases create potential problems on the transformer windings. Due to the aging effect also, a transformer may release gases. Hence, it is to be identified the pace of normal gassing versus pace of excessive gassing. Generally, gas formation in the transformer varies with the loading condition, specific design, types of insulating material used, cooling methods, etc. Here, the gassing pace is used to identify the abnormal behavior and to forecast the inception of faults inside the transformer. In the transformer tank, gases such as hydrogen (H), methane (CH4 ), acetylene (C2 H2 ), ethylene (C2 H4 ), and ethane (C2 H6 ) can be identified using a gas analyzer. As the operating temperature of the transformer increases, these gases begin to form at a particular temperature. Here, these gases also self-dissolve naturally in the transformer oil. The amount of gases generated is based on the temperature rise and types of fault that takes place inside the device. The gases in the transformer Figure 1.7 demonstrates the formation of gas in transformer oil at a particular temperature. Approximately at 150 °C, hydrogen and methane gases started to generate, and hydrogen gas formation continues as temperature increased. The ethane gas formation generally starts at about 250 °C as well as ethylene gas also starts formation at nearly 350 °C temperature. If the temperature further increases and reaches maximum points, methane, ethane, and ethylene gas production might decrease. The acetylene type of gas may be produced at around 500–700 °C. Due to internal arcing, a large amount of acetylene gas would be produced at above 700 °C temperature. The methane gas formation may exceed that of hydrogen gas formation between 200 and 300 °C temperature and beyond 275 °C the ethane gas formation rate could surpass the formation of that of methane gas. Moreover, above 450 °C to 800 °C, hydrogen gas formation may increase and it exceeds other gas formation. Due to the increase of temperature around 100 °C, thermal decomposition of cellulose materials of the device may be initiated. At the same time, it generates carbon monoxide (CO), carbon dioxide (CO2 ), hydrogen (H), methane (CH), and oxygen (O) kinds of gases. It is to be noted that the most economical operating temperature of a transformer is around 90 °C or below.

16

1 Transformer Infrastructure for Power Grid

Fig. 1.7 Gas generation chart for transformer oil

1.7.2 Identification of Faults by Gas Analysis Proper identification of faults may be achieved by proper detection of the formation of any gases along with their increment pace, their concentrations in the oil, rate of changes, or by identification of a ratio that might be above the predefined standard value. By reviewing a variety of faulty transformers and with the help of DGA methods, the state of the device can be categorized concerning fault cases as shown in Table 1.1. (i) Partial Discharge (PD) It looks like a corona effect and can create “X-Wax” on paper insulation. Sometimes, it also generates an arc that leads to puncture in paper insulation.

1.7 Dissolved Gas Analysis for Transformer Monitoring and Protection Table 1.1 Discharge of energy and thermal effect in transformer

Abbreviations

17

Descriptions

PD

Partial discharges

D1

Discharges of low energy

D2

Discharges of high energy

T1

Thermal fault, t < 300 °C

T2

Thermal fault, 300 °C < t < 700 °C

T3

Thermal fault, t > 700 °C

(ii) Discharges of Low Energy (D1 ) It can be identified by observing bulky carbonized punctures in paper insulation—pinholes or carbonization of the paper surface or may diffuse Carbone particles in the transformer oil. (iii) Discharges of High Energy (D2 ) It can be noted from inspecting insulating paper. This category involves large destruction of windings and carbonization of paper or metal fusion. It may diffuse extensive carbon particles in the transformer oil or may lead to the tripping of the switch gear due to a large fault current draw. (iv) Thermal fault (T1 ) At around 300 °C, it may take place in the paper and/or in oil and because of this the paper insulation color may become "Brownish". (v) Thermal fault (T2 ) Above 300 °C and below 700 °C, this kind of fault is observed in oil and/or paper insulation due to which paper seems completely carbonized. (vi) Thermal fault (T2 ) It is observed at around 700 °C in oil and/or paper insulation and finds strong evidence of carbonization of the oil as well as metal particle diffusion (below 1000 °C). Fault Identification Chart Potential faults such as partial discharge, sustained arcing, and overheating produce different gases at different levels of temperature. The kind of fault and its severity can be identified using gas concentration and composition as shown in Table 1.2. Here, it is to be noted that multiple gases may be generated during different anomalies that take place inside the transformer tank. Hence, the diagnostic approach is necessary to identify the multiple gases.

18

1 Transformer Infrastructure for Power Grid

Table 1.2 Fault identification based on gases concentration in oil Indication/faults

H2

Cellulose aging Mineral oil decomposition

CO

CO2

*

*

*

CH4

C2 H2

C2 H4

C2 H6

*

*

*

H2 O *

*

*

*

*

Leaks in oil expansion systems, gaskets, welds, etc.

O2

*

Thermal faults-cellulose

*

Thermal faults in oil @ 150 –300 °C

*

*

*

Thermal faults in oil @ 300–700 °C

*

*

*

* Trace

*

Trace

*

*

*

Thermal faults in oil 700 °C

*

*

*

Partial discharge

*

*

Trace

Arcing

*

*

*

*

*Indicates presence of gas in oil

1.7.3 Methods for DGA To analyze the dissolved gases, there are certain techniques like (a) (b) (c) (d) (e) (f)

Principal gas technique Dornenburg fraction technique Rogers ratio technique IEC fraction technique Duval triangle-based technique CIGRE technique Nowadays, artificial intelligence-based DGA system is also utilized in the field.

1.7.4 Advantages of Performing DGA (i) (ii) (iii) (iv)

It can warn the site personnel before faults take place With help of DGA, the condition of units can be checked One can prepare a schedule for transformer maintenance By analyzing the DGA one can monitor the unit potential overload condition of the device

1.8 Condition Monitoring of the Transformer

19

1.8 Condition Monitoring of the Transformer The testing part of the transformer is already discussed at the time of commission. But after the commissioning of a transformer, it should be monitored continuously to prevent hazardous consequent damages occurring to the transformer, its vicinity area, and connected devices, while it is in operation. The transformer condition should be continuously monitored, and accordingly necessary actions should be taken in case of predefined limits of the transformer parameter breaches.

1.8.1 Working Condition Monitoring The transformer when connected online should run in healthy condition. The health of the transformer should be continuously monitored as discussed in [16]. With the help of a variety of meters/sensors, the health of the transformer can be monitored. Certain exceptional cases may occur while continuously monitoring the health of the transformer like various types of inrush conditions, over-fluxing conditions (or momentary over-fluxing conditions), overloading conditions, heating of oil and solid insulation, etc. These conditions need attention, as the device can withstand them but beyond a certain predefined level, the equipment should be isolated from the system. So, some kind of adaptive system that changes its tripping criteria by sensing the severity of the disturbance may be adopted to prevent false isolation of this huge device in case of momentary disturbances occurring during the transformer’s operation. Keeping up with the trending developments in power systems due to several benefits offered by smart grids and technology nowadays, it is required to change the criteria of protective schemes by adding a self-healing feature. To improve the overall monitoring and protection of a transformer, it is necessary to analyze all the working parameters. Considering its self-importance and complexity due to nonlinear magnetizing core characteristics with different voltage levels, transformer protection proves its significance. The online or working condition monitoring scheme should continuously monitor the health of the transformer along with providing necessary protection whenever it is required as shown in Fig. 1.8.

1.8.2 Emergency Condition Monitoring In line with continuous monitoring performed on the dedicated device, certain instances may arise suddenly which can harm the device under supervision. At that time, it needs immediate or emergency actions. Though the transformers are meant such that they can withstand unforeseen situations easily for short time but not for a prolonged time duration, and hence needs to be isolated immediately from the system

20

1 Transformer Infrastructure for Power Grid

Fig. 1.8 Generalized block for condition monitoring of transformer

under such instances. On the other hand, certain situations arise for a shorter duration of time, and after that, they can easily be settled down. During such stimulus, no actions are required from the relay side. Such types of situations need to be identified by the dedicated protection or condition monitoring system accurately. Otherwise, there may be chances of false or malfunctioning of the protection or monitoring schemes which is highly undesirable. Most of the time, the protective schemes are designed based on the discrimination of inside faults with outside abnormalities such as inrush and fault under current transformer (CT) saturation conditions [17, 18]. To detect anomalous situations such as an over-fluxing event, a separate over-fluxing protection relay is in use for transformer protection. However, during conditions like momentary over-fluxing, this type of protective scheme mal-operates. To avoid such incidents, a sound protection technique that modifies its defined criteria by accurately identifying the type of undesirable condition that arises during transformer operation is mentioned in [19]. One algorithm has been proposed in that article which filters out every aspect of the transformer parameter and then recognizes the type of disturbances occurring inside the transformer and acts accordingly.

1.9 Real-Time Operation and Protection of Power Transformer

21

1.9 Real-Time Operation and Protection of Power Transformer The transformer has to tackle all the situations that arise in the load that reflects on the transformer itself. All these situations may cause unstability in transformer’s prefixed parameter boundaries and may lead to the isolation of the transformer. Asset management and protection are the best concepts for the prolongation of transformer lifespan as well as for increased grid reliability. Due to the increased usage of Distributed Energy Resources (DERs), the transformer should handle all the disturbances that arise from the load end. It should work safely and healthily to ensure an uninterrupted and reliable power supply. In real-time operations, the engineer should ensure that the employed transformer protection scheme should tackle all the disturbances whether it is mild or severe conditions, and should take necessary steps whenever required [2]. Many researchers have published many research articles to identify fault conditions that arise during transformer operation. However, these protection or detection schemes have their imitations to confine for special abnormal cases only. These suggested protective techniques cannot go beyond their area of fault and hence they cannot tackle any never seen situations or either it mal-operates. There are certainly identified faults or disturbances in which transformer protective schemes should act (here act means it should either send trip signals or should remain silent). A list of various types of conditions or a combination of these conditions a transformer encounters during its operation are listed in Table 1.3. Taking all these conditions and also the unforeseen situations, protective techniques should be designed which take care of the transformer’s health in a real-time scenario. The good health of the transformer can provide a reliable and uninterrupted power supply to the entire grid and consumers. In addition to these, an electrical engineer must also focus on future load expansion and increasing usage of DERs.

22 Table 1.3 A list of various types of conditions in power transformer

1 Transformer Infrastructure for Power Grid

Inrush conditions

Over-fluxing condition

• Amount of residual flux a transformer core already carries • Magnetic or initial inrush • Recovery inrush • Sympathetic inrush

• Momentary over-fluxing condition • Prolonged over-fluxing condition

Internal fault conditions

External faults

• • • • • • • •

• • • • •

Turn-to-turn faults Inter-winding faults Intra-winding faults L-G faults LL-G faults LLL faults LLL-G faults High-resistance internal faults

L-G faults LL-G faults LLL faults LLL-G faults High-resistance external faults

Internal fault with CT saturation

External fault with CT saturation

• Mild CT saturation • Moderate CT saturation • Severe CT saturation

• Mild CT saturation • Moderate CT saturation • Severe CT saturation

Other considerations Cross-country faults • Overload conditions • Location of the fault occurrence • Value of fault initiation angle • Degree of load angle • Value of source impedance • Equalized magnetic balancing of the windings • The severity of winding temperature • Aging of the transformer and its insulations • Winding defragmentation • Insulation oil level, its rating and its degradation in various environmental conditions • Amount and types of harmonics generated during the changes in loading conditions

1.10 Smart Transformer for Smart Grid Operation Nowadays, it’s an era of the revolution of the electrical power system. The power system network is becoming smarter day by day due to the addition of the latest technologies and smarter equipment. The grid system should be smart enough to optimally utilize the power with the increasing usage of various DERs. A variety of study techniques are available on smart grid operation and control. So, for a complete revolution of the power system network, the heart of the power system

1.11 Advanced Transformer Infrastructure (ATI)—Various Benefits

23

network, which is a transformer, should also be smart enough to be connected with the smart grids. Recently, a newer version of the transformer was introduced which is named Flexible Transformer [20]. The flexible transformer word is derived from the ability of the transformer to balance the scale between the demand and supply of electrical energy. Bidirectional power flow in the smart grid consequently adds challenges to maintaining this balance. Western countries are developing a new type of “flexible transformer” that helps to protect the grid from various stimuli such as power line breakdowns or weather changes. The newly designed flexible transformers can also help during severe outages and are also capable of promptly restoring power. Also, it helps in adding more DERs online. As per the recent report [20], the flexible transformer undergoes a testing stage and is recently installed in Columbia, Mississippi, at a site operated by Cooperate Energy. In that report, it is stated that this smart transformer is having much flexibility to install wherever you want and can be tuned without fulfilling the required criteria, unlike a traditional transformer. The traditional transformer replacement criteria are so strict that it needs the same type of transformer or a twin type of transformer. This requires a lot of space and a lot of costs to reserve the same type of transformer. It added that a flexible transformer has a knob, by which one can change the required amount of impedance at the time of commissioning of the transformer. This feature of changing impedance will also facilitate changing DERs outputs and also helps in making the grid stable whenever requires. The smart transformers are ahead of this feature, it can change the required impedance automatically. They can sense the changing load and manage their parameters automatically. Moreover, they have facilities to control their operation by human fingertips with the help of IoT (Internet of Things). The smart transformers come with lots of features by having a variety of sensors on them. This will lead the whole power system network or grid to the next level of control and stability. Consequently, it will lead to increased reliability of the entire power system.

1.11 Advanced Transformer Infrastructure (ATI)—Various Benefits Power grid modernization is nowadays in need of faster system control and integration of DERs with the existing grid. A continuous, reliable, quality power supply substantially influences an entire country’s economic growth. The existing grid is not designed by the way it runs currently without a proper design of reversible power flow or increased load demand. Moreover, the transformers currently running are at the peak of their average predicted life span. It also needs more attention for a special universal design that can be easily adaptable, easy to manufacture, cost-effective, and easy to operate and handle. The whole thing needs personal utmost attention for rearranging it neatly by considering future demand. It is now time to refurbish the whole

24

1 Transformer Infrastructure for Power Grid

power system network due to several challenging tasks (listed below) encountered in the power system operation. 1. A huge headache of power outages reduction. 2. Improved power restoration process. 3. Prevention of transformer failures and their disturbance identification with 100% accuracy to prevent mal-operation of protective devices. 4. Compliant with Distributed Energy Resources (DERs). 5. Compatible with the increased demand for Electric Vehicles’ (EVs) charging points. 6. Accurate monitoring to prevent unauthorized entry of load in the power system and increased power system security. 7. Implementation of sound artificial intelligence-based techniques for power system operations/forecasting/budgeting preparation, and real-time purposes. 8. Lower the risk of damages occurring to the power system and its connected system. 9. IoT-based smartness integration of the entire power system network. The role of the Advanced Transformer Infrastructure (ATI) is to accurately capture and store the necessary data for the real-time operation of the grid. The captured data are undoubtedly trustable and can be utilized for future planning and improvement purposes. The ATI can facilitate observing the real-time data for the load end directly and can send the necessary actions whenever needed so no formation of any blind spot remains unnoticeable by the operator. ATI can certainly benefit from creating intra-grid insight, i.e., whenever any sudden change in the predefined limit of load, then the data will be immediately sent to the operator so that they can plan its supply accordingly. Currently, the consumers are not reporting to the operators for increasing load demand such as EV charging, installation of large-rating loads like AC, induction furnace, and inductive loads which become a hindrance while forecasting the load demand. This may result in transformer overloading, and consequently overheating of the device. On the other hand, rapid demand for the installation of DERs also creates challenges while handling the grid effectively. The stakeholders are constantly creating pressure on the operators to increase the reliability of the supply, control operational costs, accommodating more DERs, prevention of power theft, improved energy efficiency, fulfill the requirements of EV charging point demand, improve voltage stability and regulations, etc. along with increasing the revenue of the company. Adoption of ATI will be beneficial in all the concerns mentioned above. The benefits of ATI are listed below. ATI can 1. lower the unnoticeable events that take place in the system; 2. reduce the troubles in power system operation and can resolve certain problems on its own; 3. higher the generated revenue by sensing meter conditions (also meter errors) and providing a constant uninterrupted power supply;

References

25

4. lower the damages that occurred to the power system and its connected peripherals; 5. facilitate in preparing demand forecast capability and provide insights into the development; 6. improve grid visibility and hence can reduce the power theft problems in suspicious areas; 7. reduce the requirement of monitoring the grid by humans and results in the cost-cutting of resources required for monitoring and inspection of the installed grid; 8. be compatible to add DERs in the system easily and facilitate improved control over balancing in supply and demand of the power at any point in time; 9. improve distribution grid automation and reduced distribution complexity; 10. lower the risks of damages occurring to the power system devices and hence lower the risk of uncertain power quality at the consumer end; 11. cause an overall increase in power system network or grid efficiency.

1.12 Conclusion Finally, it can be concluded that the transformer and power system infrastructure contains utmost importance in today’s extensively increasing electricity demand and technological development. In this chapter, certain aspects of transformer infrastructure like the Role of Large Power Transformers in the Electric Grid, ThreePhase Transformer Interconnections, and Transformer Technology Development are discussed in detail. Moreover, the role of the on-load tap changer for voltage stability in a grid and DGA analysis for condition monitoring of the transformer are explained. Also, real-time operation and protection of power transformers, smart transformer for smart grid operation, and advanced transformer infrastructure (ATI) with its various benefits are covered. This chapter reveals the importance of transformers in power systems, their protection needs, recent development in transformer defensive techniques, future needs, and ongoing development.

References 1. Patel D, Chothani N (2020) Introduction to power transformer protection. In: Digital protective schemes for power transformer. Springer, Singapore, pp 1–31 2. Chothani N, Patel D, Raichura M (2019) Transformer protection with sequence components and digital filters, 1st edn. LAP LAMBERT Academic Publishing, Latvia 3. Bhalja B, Maheshwari RP, Chothani NG (2017) Protection and switchgear, 2nd edn. Oxford University Press, New Delhi 4. Shah H, Chakravorty J, Chothani NG (2023) Protection challenges and mitigation techniques of power grid integrated to renewable energy sources: a review. Energy Sour Part A Recover Util Environ Eff 45(2):4195–4210. https://doi.org/10.1080/15567036.2023.2203111

26

1 Transformer Infrastructure for Power Grid

5. TBEA-Shenyang-Transformer-Products. Reliability & maintainability manual of TBEA Shenyang transformer products. Capacitor-Transformer. https://www.scribd.com/document/ 52460937/Reliability-Maintainability-Manual 6. Solutions HT. Transformers. Hitachi T&D Solutions, Inc. https://www.hitachi-tds.com/pro ducts/Transformers.html 7. ABB. ABB distribution transformer guide—DOKUMEN.TIPS. https://dokumen.tips/docume nts/abb-distribution-transformer-guide-56265245987d0.html 8. ABB. ABB transformers power transformers—The largest installed base worldwide. ABB Management Services Ltd. Transformers 9. Schneider M. Power transformer. Schneider Electric India. https://www.se.com/in/en/productsubcategory/3620 10. Siemens. Transformers. Portfolio. Siemens Energy Global. https://www.siemens-energy.com/ global/en/offerings/power-transmission/portfolio/transformers.html 11. Siemens. Power transformers. Transformers. Siemens Energy Global. https://www.siemensenergy.com/global/en/offerings/power-transmission/portfolio/transformers/power-transform ers.html 12. Solutions GG. Power transformers and reactors. Worldwide Contact Center, GE Grid Solutions. www.GEGridSolutions.com/contact 13. GRID2020. Advanced transformer infrastructure (ATI)TM . GRID2020-Inc.html.https://www. globenewswire.com/en/news-release/2019/07/16/1883478/32165/en/Advanced-TransformerInfrastructure 14. Patel D, Chothani N (2020) Real-time monitoring and adaptive protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore pp 173–190. 15. Roncero-Clemente C, Roanes-Lozano E (2018) A multi-criteria computer package for power transformer fault detection and diagnosis. Appl Math Comput 319:153–164. https://doi.org/ 10.1016/j.amc.2017.02.024 16. Chothani NG, Raichura MB, Patel DD, Mistry KD (2019) Real-time monitoring protection of power transformer to enhance smart grid reliability. Electr Control Commun Eng 15(2):104– 112. https://doi.org/10.1109/EPEC.2018.8598427 17. Raichura MB, Chothani NG, Patel DD (2020) Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique. IET Sci Meas Technol 14(1). https://doi.org/10.1049/iet-smt.2019.0102 18. Raichura M, Chothani N, Patel D (2021) Efficient CNN-XGBoost technique for classification of power transformer internal faults against various abnormal conditions. IET Gener Transm Distrib 15(5):972–985. https://doi.org/10.1049/gtd2.12073 19. Raichura M, Chothani N, Patel D (2020) Development of an adaptive differential protection scheme for transformer during current transformer saturation and over-fluxing condition. Int Trans Electr Energy Syst 31:1–19. https://doi.org/10.1002/2050-7038.12751 20. GE. Special power: ‘flexible transformer’ could become the grid’s new superhero. GE News. GE. https://www.ge.com/news/reports/special-power-flexible-transformer-could-become-thegrids-new-superhero

Chapter 2

An Overview of the Protection of Power Transformers

Abstract A transformer is the heart of the entire power system and power is the heartbeat of the entire nation for the growth of the manufacturing, production, and industrial aspects. All terms are directly concerned with the Direct Foreign Investment (FDI) of the nation. Mostly, all types of businesses depend on the reliability and continuity of electricity. So, the “without power no business” slogan proves its usefulness. Power system protection is very crucial and complex due to having huge numbers of nodes. The contingency of various tactics is involved in the system and if it is done randomly then the mis-operation of the protective schemes occurs. Nowadays, the deregulation of the power network involves the malfunctioning of several system parameters. As far as a concern with the reliability of the power network, it is directly apprehension to the growth of the nation. As a key component in the power system, the protection of the power transformer has remarkable importance. Power transformer designing has many complexities like nonlinearity of its core, higher power rating, different voltage and current ratios, different phase angles in primary and secondary, and connection class. Having numerous complexity and different operating characteristics of power transformers, protection also becomes multifarious. Also, unwanted tripping of power transformer generates issues not only for consumer or industry but it gives an effect for the ecosystem of society, economy, political scenarios, and the entire nation. Different power transformer failure analysis is carried out by focusing on the failure of protective schemes (Rajurkar et al. in 16th National power systems conference, pp 180–185 [1]). Even, in the international market, different failure analyses with recent trends and involvement of further scope for the protection are analyzed in depth (Binder in Transformers-magazine, vol 1, no 1, pp 30–33 [2]).

Device Number 24 49 50

V /f Relay Thermal Relay Inst. O/C Relay

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Chothani et al., Advancement in Power Transformer Infrastructure and Digital Protection, Studies in Infrastructure and Control, https://doi.org/10.1007/978-981-99-3870-4_2

27

28

50N 51 51G 51NB 52 59 67 67G 87 87G

2 An Overview of the Protection of Power Transformers

Inst. Neutral O/C Relay Inverse Time O/C Relay Grounded O/C Relay O/C Backup Relay (Neutral) Circuit Breaker Over-voltage (OV) Relay Directional O/C Relay Directional Ground O/C Relay Differential Protection Relay Ground Differential Protection Relay

2.1 Protection Basics With an increase in power demand, generation needs to be increased which increases the fault MVA and short-circuit current of the system. In normal conditions, when Distributed Generation (DG) sources are not inserted, power flow is only from the main grid to the microgrid to the consumer. The inclusion of DGs not only reverses the flow of power but also increases the fault level of the system. Faults or other abnormal conditions may occur at any time in the power system. The high current associated with a fault or severe abnormal conditions may damage the system as well as equipment if proper protection is not provided. It is very much important to provide sensitive and reliable protection to the system for any type of faulty condition. If a fault occurs in any part of the system, it is the duty of an element called “Relay” to detect the fault and send a tripping signal to the circuit breaker to remove the faulty part from the system and keep the healthy part of the system in running condition. This must happen in a short time as possible. The function of the protection relay is to sense and not to predict the fault. So, it will operate only after a fault occurs in the system. With recent technologies, the operating time of a relay is just 4–5 cycles. All protection schemes include relay and switchgear devices like a circuit breaker, isolator, and earthing switch (wherever required). Whenever the transformer needs to be put under maintenance in normal operating conditions, then the operating sequence of these switchgear devices would be circuit breaker open–isolator open–earthing switch close. Once the maintenance is over and equipment is required to reconnect, then the operating sequence of switchgear devices would be earthing switch open– isolator close–circuit breaker close. It is also important to choose the correct rating of this protection and switchgear element carefully. Protection schemes for any system can be classified into two ways, viz., unit and non-unit protection and primary and backup protection. Detailed classifications are shown in Fig. 2.1.

2.1 Protection Basics

29

Fig. 2.1 Classification of protection

2.1.1 Unit and Non-unit Protection When a fault occurs within its zone, the protection scheme which will operate is known as the unit protection scheme. Unit protection is mainly used for equipment like generators, transformers, induction motors, and busbars so it can also be called equipment protection. Differential protection is an example of unit protection that is used for all the mentioned equipment. If a protection scheme is applied by grading sequential relays, then it is called non-unit protection. Overcurrent protection is an example of non-unit protection that is used for distribution feeders and in the case of a transmission line. Distance protection used for the transmission line is also an example of non-unit protection.

2.1.2 Primary and Backup Protection In a particular zone, whenever a fault occurs, isolation of faulty parts of the system and keeping the healthy part of the system alive is done by a primary protective relay or primary protection. Primary protection is considered to be the first line of defense. If because of any reason, the primary relay fails to operate or to isolate the faulty part, backup protection will operate as a secondary line of defense. Reasons for the failure of primary protection could be a failure of a relay, circuit breaker, instrument transformer, etc. It is very much important for any protection scheme to operate the primary relay first as the operation of the backup relay will affect a larger part of the system. Primary relays are considered to be instantaneous or time-delayed, but backup relays are always time-delayed. If a time delay is not provided, then the primary and backup relays may operate together. To restrict simultaneous operation, Coordination Time Interval (CTI), or Time Gradient Margin (TGM), or Minimum Coordination Time (MCT) is necessary between the primary and backup relay. According to IEEE standard 242-2001 [5], coordination time interval is considered for different relays as given in Table 2.1.

30

2 An Overview of the Protection of Power Transformers

Table 2.1 Coordination time intervals for relays Type of relay

Coordination time interval (CTI)a, b

Induction disk overcurrent relays (considering disk overtravel component)

0.3–0.4 s

Static overcurrent relays (eliminating disk overtravel component) 0.2–0.3 s Numerical overcurrent relay (eliminating disk overtravel component)

0.2–0.3 s

a Field

Calibration further reduces CTI by 0.05 s is the addition of circuit breaker opening time, relay over travel, relay tolerance, and setting errors

b CTI

Generally, the protection and switchgear components associated with primary protection are not associated with backup protection. There are two types of backup protection, viz., remote backup and local backup. Backup protection situated at the adjacent substation to backup complete primary protection scheme is known as remote backup. It will operate after a time delay if the primary relay fails to trip. Remote backup is considered to be the cheapest, simplest, most desirable, and widely used backup protection in the system. Again, local backup protection is subdivided to relay backup and breaker backup in the power system. In relay backup, an additional relaying scheme is used to trip the same set of circuit breakers in case the primary relay fails to operate. It is suggested to use relay backup where it is not possible to have a remote backup. Breaker backup is inevitable for the busbar where several incoming lines and outgoing feeders are connected to it. In this case, all the circuit breakers connected to the busbar would operate together. In breaker backup protection, a time delay is considered for its operation and it will operate if the designated breaker is not opened in specified time duration. Many protective relays are used in a system that depends on • • • •

Function Technology Characteristics Operating quantities Detailed classification of relays is shown in Fig. 2.2 based on said criteria.

2.2 Problem Statements and Basics In the entire power grid, the transformer is the basis for connecting the whole power grid and plays an important role in power transportation and distribution. A power transformer is the core component of the substation and the key component of a power network. The role of the transformer in the power system is to transmit generated

2.2 Problem Statements and Basics

31

Fig. 2.2 Classification of protective relays

electric energy to the user side. If the transformer fails, it will cause a huge loss of revenue and property to society and people. Different problems associated with transformer protections are enlarged here: • Philosophy and basic considerations for the magnetizing inrush of the transformer with its hazardous effects are the main issues. The detailed mathematical formulation for the magnetizing inrush with an effect on the power system was explained in 1944 by Brownlee [3] and Blume et al. [4]. Various effects on power systems under magnetizing inrush of distribution transformers are elaborated by Holcomb [5]. Magnetizing effects of different inrush like main (initial) inrush, recovery inrush, and sympathetic inrush are also enlarged by Warrington [6]. Nowadays Cold-Rolled Grain-Oriented (CRGO) steel material is consumed to manufacture transformer cores and its saturated maximum flux density is 2.0 T. However, dayby-day improvement is going in core saturation characteristics in the terms of VA/ kg and watt/kg. Magnetizing inrush is generated during the switching of the power transformer at no-load condition. This inrush depends on the breaker switching angle, flux density, and core residual flux. Among them, residual magnetism plays an important role. • The fast and reliable protective scheme in the power system is applied as per the importance of the equipment. Transformer is a key important portion of the power system just like the heart in the human system. Due to its complexity

32

2 An Overview of the Protection of Power Transformers

and nonlinear core characteristics, many issues are generated for the CT and PT to acquire reliable sensing of the parameter. Magnetizing inrush conditions cause the malfunctioning of the percentage bias differential protection of power transformers. Nonlinearity, core saturation of the power transformer, and current transformer may cause the relay to mal-operate. • Due to the core nonlinearity, harmonics are also generated and under resonant conditions maximum current passes through the circuit. It means fault and overload are not the only reasons for drawing maximum current through circuits. • Saturation of the current transformer is a key issue in the unit protection of the transformer as CT secondary current is not able to track the actual value of the primary side current. Detection and compensation of saturated current of CT generate more complications in different protective schemes. • Thus, over-fluxing (OF) condition, magnetizing inrush, and core saturation of CT create major issues during a unit protection or percentage bias differential protection applied to the power transformer. In the HVAC transmission system of greater than 132 kV, the power transformer protection faces many problems of sensitivity, discrimination, reliability, and stability due to system complexity. All these issues may create the mal-operations of the protective schemes [7].

2.3 Investigation Targets The targets of this investigation are incorporated as follows: • Discriminate inrush conditions from the internal fault. • Proper identification of internal faults and abnormalities like CT saturation conditions. • Development of an advanced algorithm for the transformer protection system. • Operate the algorithm with a minimum time of operation under all internal fault conditions. • Improve conventional protective schemes with a minimum computational burden. • Build an adaptive transformer protection algorithm considering various CT saturation conditions. This study activity aims to improve a unique relaying scheme for the transformer considering all the said abnormalities. The features like reducing the complexity in data acquisition, signal analysis, less computational burden, and fast operation are accounted for while developing the relaying scheme.

2.4 Introduction

33

2.4 Introduction Connections of the numbers of transmission lines, generators, and distribution lines with distributed generations are causing complexity in fault analysis and calculations. Providing an accurate, reliable, and economical protective scheme is also affected due to this reason. Many problems are associated with the deployment of such protective schemes with all considerations. Normally, 10% of transformer faults appeared in the power system as per the fault statistics [8]. Detailed specifications of a transformer are required to decide the protective elements like unit-type protection or else. Detailed specifications such as kVA rating, percentage impedance, reactance/resistance, types of earthing with detailed parameters, installation type, insulation, cooling, types of a conservator, KPV of the core as well as fault level of the transformer are required to develop a stand-alone protective scheme. Moreover, the selection of switchgear equipment like CT, PT, CB, lead length of CT/PT, lead resistance, burden, relay type, required sensitivity, and selectivity of a protective scheme in the power system is also important [9]. Basic requirements and information are also provided as per the international standard by IEEE guidelines of power transformer protection [10]. Protections applied to the transformer are cauterized as (1) electrical protection and (2) non-electrical protection. Figure 2.3 narrates the sub-classification of the above-mentioned protection schemes. Normally, transformers are divided into three classes based on kVA rating (1) small transformer < 500kVA rating, (2) medium transformer >500kVA and 5MVA rating. It has been observed that the transformers mostly undergo fault due to the failure of insulation of winding or tap changer [11].

Fig. 2.3 Fundamental arrangement of transformer protection with its significant fault

34

2 An Overview of the Protection of Power Transformers

2.5 Different Faults/Abnormalities Observed in Transformer Different abnormalities and faults that appeared in a transformer are explained in the below section with its detailed analysis [6]. Normally, there are two types of faults available in the power system: (1) short-circuit fault and (2) open-circuit fault. Detailed short-circuit faults are explained here with abnormalities. Open-circuit fault analysis with practical aspects is explained afterwards.

2.5.1 Internal Fault There are two categories of internal fault in transformers (1) major fault and (2) minor fault. Normally, a major fault is recognized as a serious active fault in the system. There is a possibility of winding insulation damage and a risk of fire, sometimes an explosion of an entire transformer. Due to the internal fault in the transformer, a large portion of the power system may remain in a dark zone for a longer time. (a) Major faults Due to this type of fault, extensive mechanical and electromagnetic forces are acting on the internal parts of the equipment like winding, core, and insulation. These forces cause severe damage to the equipment and also it is possible to affect the entire power system. Different faults are possible such as phase-to-phase, winding-to-winding, and bushing faults on HV or LV side. Based on the failure of insulation, a ground short circuit may happen in the transformer. If there is tertiary winding (three winding), the probability of transformer failure is increased. (b) Minor faults On the occurrence of minor faults, the effect of the equipment damage is lesser or slower compared to major faults. Sometimes, these types of minor fault conditions are not possible to discriminate by conventional sensing devices. Generally, minor faults arise due to bad electrical connections, failure of coolant, unbalanced load, overloading, and fault on the core section of the transformer. Under these circumstances, very low or small arcing is generated in transformer oil and raises the overheating inside the transformer tank. As per the severity of the fault, the major fault must be discriminated from the other abnormalities and the faulty part must be disconnected as fast as possible from the other healthy part. On the other hand, minor faults are slowly developing inside the transformer tank. Though in its starting condition, the minor fault’s effect is less, however, there is a possibility of conversation from minor to major fault after some time. So, minor faults can’t be ignored if they persist for a longer time. Thus, a protective scheme must be capable to discriminate against any kind of fault from healthy conditions within a short time to improve the reliability of the power system.

2.6 External Fault for the Transformer

35

2.5.2 Sources of Internal Fault in Transformer There are many reasons for the transformer failure [12] as described in Table 2.2.

2.6 External Fault for the Transformer In a unit type of protection scheme, any fault occurring outside the zone of the transformer is considered an external fault. The zone of protection in a transformer is decided by the location of current-sensing devices that provide current quantity to relaying coils. Such external faults may occur on the instrument transformer, on the wire connected to the transformer from the busbar, on the busbar itself, and a transmission line. The reason for the external fault may be the malfunctioning of equipment or the failure of insulation. During heavy external faults, massive forces Table 2.2 Different reasons for the transformer failure S. No.

Failure type

Reasons

1

Winding breakdown

Insulation weakening or failure Defects under manufacturing Due to overheating beyond its thermal limit Due to excess voltage surge Mechanical stress and vibrations

2

Terminal board and OLTC failure

Improper assembly, designing issue Damaged during carrying or transport Heavy vibration

3

Failures of bushing

Aging effect Furious effect Animal/severe hunt Due to contagion and destruction

4

OLTC failure

Mechanical malfunctioning Contacts issues Extreme vibrations Insulating oil contagion Inappropriate assemblage and elevated pressure

5

Miscellaneous failures

Failure of CT bushing and core insulation Oil leakage Existence of strange substances in tank and oil Transport damages

36

2 An Overview of the Protection of Power Transformers

are acting on the transformer winding and core. So, these types of situations are also required to discriminate from the internal fault within the transformer.

2.7 Abnormalities in the Transformer Power system events not belonging to any external or internal fault are considered abnormalities like overloading, different inrush conditions, over-fluxing, overvoltage or under-voltage, and frequency violation [11]. Moreover, a saturation of the CT core specifically during a heavy external fault (current greater than 20 times the normal operating range) may malfunction the unit protective scheme. These abnormal conditions may lead to some issues in the power system if they persist for a longer time. If the load on the transformer is increased by more than 10 percent, then the condition is considered an overloading condition. Due to the prolonged overload conditions, aging effect, stresses on winding, overheating, and weakening of insulation may cause everlasting damage to the apparatuses. Overloading conditions are detected by the thermal relay in the transformer protection and give an alarm to the operator in order to take the required precautions. In a power system, two types of overvoltages are generated: (1) short-term overvoltage means transient overvoltages and (2) long-term overvoltages. During the disconnection of a huge load from the transformer, the system voltage may rise. Due to this overvoltage phenomenon, the core laminations and winding insulations may get damaged in the transformer. A major difference between load demand and generations may cause the Under Frequency (UF) in the system (i.e., load is too high and generation is very less). This situation may impose flux deviation in the core of the transformer. In both, the above condition of either overvoltage or under-frequency, V /f protection called an over-fluxing protection is activated to protect the transformer. Different inrush conditions like initial inrush, recovery inrush, and sympathetic inrush are generated in the transformer. Initial inrush is caused during the transformer activation under no-load condition and its severity depends on the circuit breaker switching instant and residual flux present in the core. On the other hand, when the side-by-parallel transformer is energized, the working transformer observes the effect of a rise in current is known as a sympathetic inrush for that particular transformer. The effect of sympathetic inrush is lesser than the initial inrush but it is accountable in terms of the protective system’s sensitivity. During the removal of the fault, whatever current rises is called recovery inrush. Among all inrush conditions, initial inrush is very effective compared to sympathetic and recovery inrush. The amplitude of the initial inrush current depends on the core material, switching inception angle, and remnant flux present in the core at that instant. Normally, for ferromagnetic material, these inrush values are around 6 to 8 times the rated current of the transformer for a shorter time. This current is approximately the same as the fault current of the system as the rating of the transformer is very higher. However, it is not considered

2.8 Different Transformer Protective Schemes Used in Field

37

a fault because they are transient and die out within a short time. Thus, such inrush conditions must be separated from the internal fault of the transformer. Subsequent sections describe the protective scheme for different abnormal conditions and fault conditions.

2.8 Different Transformer Protective Schemes Used in Field Stability and reliability are the main requirements for the smooth operation of the entire power system. With the additional consideration of price and significance of the equipment like power transformer, it is required to provide proper protection on this unit. Different protective schemes are elaborated here as per the rating and importance of the equipment.

2.8.1 Overcurrent (OC) Protection As far as a concern with the protection of large power transformers, overcurrent protection is not preferred as the foremost protection. OC protection is used as a backup protection for the unit type of protection. However, OC protection is used as the main protection in small transformers with inverse and extreme inverse characteristics. The instantaneous characteristic of OC relay is used for the extreme kind of fault in small rating power transformers. More than 400% of the rated current is considered as the setting of an instantaneous relay. On the other hand, inverse time over the current relay setting is done with the coordination of other OC relays in the power system. Figure 2.4 shows the overcurrent protection applied to one of the windings of the transformer. Fig. 2.4 Scheme of OC protection for small rating transformer

38

2 An Overview of the Protection of Power Transformers

Fig. 2.5 Elementary overcurrent relay with harmonic restrain unit

2.8.2 OC (Overcurrent) Protection with Harmonic Restrain Unit (HRU) Harmonics are generated in the power system due to the nonlinear conditions. Nonlinearity is normally generated on the power transformer due to the core saturation. So, to discriminate the inrush conditions against the fault, it is necessary to use a harmonic restrain unit (HRU) with overcurrent (OC) protection. Figure 2.5 shows the relay coil connection in the power system and DC control circuit for OC, HRU, and IT [13].

2.8.3 REF (Restricted Earth Fault) Protective Scheme Unit-type protection is very costlier and it is not economic for the small-size transformer. So, for the small-size transformer, such protective schemes are used to discriminate the inrush conditions and avoid mal-operations during momentary core saturation conditions of a transformer. REF scheme is usually adopted for the star-connected transformer winding which has neutral grounded. As far as cost consideration, this scheme is used for mediumrating transformers. REF is used as the main protective scheme and OC protection is used as a backup. The important feature of this scheme is that it operates under internal ground fault only, like unit-type protection. However, it protects particular winding only. Generalized connections are as per Fig. 2.6. If the proper ratio of the CTs is selected then under heavy external fault, the REF relay wouldn’t operate as unit-type scheme [6]. Different cases when REF protection of the transformer is involved are given in Table 2.3.

2.8 Different Transformer Protective Schemes Used in Field

39

Fig. 2.6 Scheme of REF protection

Table 2.3 Earth fault protection cases for power transformer Power transformer winding configurationa

Protection provideda Primary

Primary

Secondary

Delta

Star with neutral solidly grounded or Residual overcurrent grounded through impedance

Restricted earth fault (high impedance)

Delta

Star with neutral solidly grounded or Earth fault protection grounded through impedance through earthing transformer

Restricted earth fault (high impedance)

Delta

Star with neutral unearthed

Residual overcurrent

a Vice

Residual overcurrent

Secondary

versa winding configuration and protection can be considered

2.8.4 Unit-Type Protection of Transformer (Differential Protection) Differential protection (unit-type protection) scheme is the most efficient protective scheme in the entire protection of the power system to distinguish internal faults from all other external faults/abnormalities. REF and differential protections belong to the unit-type protection of the transformer. Also, the differential protection is subdivided as (1) circulating current differential and (2) percentage-biased differential protection. Both protective schemes are elaborated as follows.

40

2 An Overview of the Protection of Power Transformers

Fig. 2.7 Differential protection based on circulating current

(a) Differential protection based on circulating current Mostly, differential protection is called sensitive protection for internal disturbances. Also, it is considered the simplest form of circulating current differential protection. A generalized connection for the circulating differential protection is shown in Fig. 2.7 with connections of CTs and relays. CTs and relays are connected via pilot cable from the field to the relay panel. CT secondary is connected in such a manner across the differential relay coil to balance the current from both sides of the transformer. Due to the higher cost of the unit-type protection, it’s applicable for a higher rating and important pieces of equipment. Normally, having more than a 10 MVA rating of the power transformer, differential protection is used as the main protection. The current difference in the primary and secondary winding, different phase shifting for both windings, different inrush conditions, over-fluxing, and CT saturation conditions may affect the operation of circulating current differential protection. Due to the said reason, the transformer differential relay is not of much sense as that of the bus bar and generator differential protection. Unlike other equipment of the power system, transformer differential protections are more complicated due to having multi-winding, zigzag connections of the winding, OLTC, phase angle regulators (PAR) or voltage regulators as well as three single-phase transformers used to develop one three-phase transformer. Table 2.4 shows the type of CT connection preferable for avoiding this issue with the transformer having different winding configurations. (b) Percentage-biased differential protection schemes Due to magnetizing current, mismatch in CT ratio and tap-changing facilities, simple differential protection fails to provide an accurate and reliable unit type of protection for the transformer. Percentage bias differential protection provides perfect solutions for different phase shifting, different connections, and different lead lengths of CT even though it also considers the secondary burden of the CT. Connections for the said protection are elaborated in Fig. 2.8. However, CT saturations during heavy external fault and high-resistance internal fault may mal-operate the percentage-biased differential protection. Conventionally, single-stage and two-stage percentage-biased differential protection is used to protect a transformer.

2.8 Different Transformer Protective Schemes Used in Field

41

Table 2.4 CT connections for differential protection Power transformer winding configuration

Current transformer connection provided on both sides of the power transformer

Primary side (HV/LV)

Secondary side (HV/ LV)

The primary side of the transformer

The secondary side of the transformer

Yb

∆a

∆

Y

∆

Y

Y

∆

Yc

Yb

∆

∆

∆

∆

Y

Y

a With

or without earthing transformer b With or without tertiary winding and with neutral earthed c With neutral earthed

Fig. 2.8 Percentage-biased differential protection of transformer

As per the percentage-biased differential characteristic, single slope characteristics are preferred for the medium range of the transformer and two-stage characteristics are preferred for the higher range of the transformer. Figure 2.9 shows two-stage characteristics of the differential relaying scheme [14]. Basic settings for the slope M1 and M2 range from 0.3 to 0.7 and 0.5 to 0.75, sequentially. A simple single-stage biased differential characteristic is shown in Fig. 2.10a. A modified version of this characteristic as per the adaptive algorithm by shifting either basic or biased settings as per the need of protection is shown in Fig. 2.10b, c. Said differential protection improves the system reliability with increasing system stability against abnormal conditions. If adaptive criteria are added then it includes a self-checking facility within the same unit. It provides highly reliable performance by reducing the burden on CT and higher flexibility concerning conventional relays. Nowadays, numerical relay provides better performance with memories and multifunctional features [15]. Proper selection of parameters like CT ratio and its KPV also

42 Fig. 2.9 Classic dual slope percentage-biased characteristics

Fig. 2.10 Two-stage characteristic. a Basic characteristic, b, c modified adaptive version

2 An Overview of the Protection of Power Transformers

2.9 General Magnetizing Inrush Phenomenon

43

Fig. 2.11 Three-winding transformer protection

reduces the issue of CT saturation [16]. The differential protection of three winding transformers is displayed in Fig. 2.11.

2.9 General Magnetizing Inrush Phenomenon The maximum value of core flux is defined as [8] ∅ = ∅r + ∅m cos θ + ∅m cos(ωt + θ )

(2.1)

From Eq. 2.1, it is concluded that the flux of the transformer core deepens on the magnetic property of the core and ∅r , ∅m, and θ. Here, ∅r = Residual flux, ∅m = Maximum flux, θ = Switching instant angle. From Eq. 2.1, it is recognized that when switching angle θ is 0° and residual flux ∅r is equal to maximum flux ∅m , the total amplitude of flux in the transformer core is 3 ∅m at ωt = π radians. To establish such massive demand of the flux, the primary winding of the transformer draws a huge current called magnetizing current. The waveform of the magnetizing current is non-sinusoidal and contains a large harmonic component with greater magnitude such as a fault current. A basic concept of the magnetizing inrush is quietly elaborated in Fig. 2.12. Flux linkages and the

44

2 An Overview of the Protection of Power Transformers

Fig. 2.12 Effect of flux linkages and core characteristic on magnetizing inrush

BH curve are directly linked with the generation of the magnetizing current. Heavy inrush current is drawn when flux linkages increase more than the level of core saturation (BH curve). Mostly, second harmonic components are superimposed during the inrush conditions. Many researchers have proposed methods to discriminate the inrush conditions in the transformer.

2.10 Over-Fluxing Condition Relation among the applied voltages, flux generated, number of turns, and system frequency are mostly considered in the generalized emf equation of the transformer as per the below equation: V = 4.44 ∗ ∅m ∗ f ∗ N Here, V = Voltage (RMS value),

(2.2)

2.11 Inter-Turn Fault Protection

45

∅m = Maximum flux, f = Frequency, N = Number of winding turns. So, flux is rewritten as ∅m =

V 4.44 ∗ f ∗ N

(2.3)

Normally, in an infinite bus bar system, frequency is constant. The numbers of turns in the winding of a transformer are also constant. Referring to Eq. 2.3, the rate of change of flux in the core is directly proportional to the system voltage or terminal voltage. At the time of an overvoltage situation, the core flux is also increased to cope with that voltage in the system. As per the core design consideration, the transformer core working area is belonging up to the knee region of the BH curve. Beyond the knee point region, it belongs to the saturated region so heavy magnetizing currents will be drawn. Usually, V /f protections (over-fluxing protection) are implemented on both sides of the winding (HV winding and LV winding) of a transformer. Different techniques are utilized by the protection society as per suitability like harmonic restrain, harmonic blocking, harmonic sharing, wave shape recognitions, etc. During the over-fluxing conditions, the 5th and 7th harmonics are superimposed on the fundamental component of the current signal. Due to higher voltage, the core is getting saturated and it generates excessive heat with higher static force. When a transformer is directly connected to a generator, there is a possibility for occurrence of frequency and voltage violations at the transformer terminal mostly under starting conditions of the generator. During the over-frequency condition, mostly odd harmonics are interfering as 3rd harmonics are 26%, the fifth harmonics are 11%, seventh harmonics are 4%, and so on.

2.11 Inter-Turn Fault Protection Internal faults are also known as turn-to-turn faults. During an inter-turn fault, a low number of turns of the same winding are involved in a short circuit but it tends to increase fault severity in the transformer over time. So, it is necessary to discriminate inter-turn fault as fast as possible. Inter-turn fault is elaborated in Fig. 2.13. A minor inter-turn fault may rise and be extended into the major fault in the entire winding or transformer. Based on the terminal current, inter-turn fault detection by the differential relay is difficult because the primary and secondary currents are almost equal in magnitude [8]. Due to the inter-turn fault, there are also possibilities of generation of hotspots on the winding, insulation failure of the winding, and insulating oil heating. Buchholz relay can detect inter-turn fault because oil decomposes into gases. Based on the rate of rise of oil pressure, the pressure relay gives maximum sensitivity towards this type of fault conditions [13].

46

2 An Overview of the Protection of Power Transformers

Fig. 2.13 Transformer inter-turn fault

2.12 Non-electrical Protection Incipient faults and minor faults are decomposing the transformer oil and generating gas inside the transformer chamber. To protect against these types of faults, gasactuated or oil-pressure-based relays are normally used in the transformer. Various relays based on these schemes are explained as follows.

2.12.1 Thermal Relay A thermal relay is used to protect the transformer during overload conditions. Normally, a nickel- and steel-alloyed bimetallic strip is used in the thermal relay along with the heating element. This relay protects the equipment based on the generation of heating effects during overload conditions. The general arrangement of the thermal relay is shown in Fig. 2.14. This relay is used for the protection of small-capacity transformers.

2.12.2 Temperature-Based OTI and WTI Relays The provision of temperature-based protection with an alarm is necessary for medium and large-rating power transformers. Under the small internal panic of the transformer, it gives an alarm to alert the operator about the internal disturbance. To measure the temperature of winding and transformer oil, a thermocouple or Resistance Temperature Detector (RTD) is used. Figure 2.15 shows the position of the Oil temperature Indicator (OTI) and Winding Temperature Indicator (WTI). RTD is placed inside the transformer tank, to measure the temperature of the oil at the top level. The measured temperature is indicated in the meter available on the body of the transformer tank. The current measured by a small CT inserted into the bushing

2.12 Non-electrical Protection

47

Fig. 2.14 Thermal relay

is utilized for winding temperature measurement through the sensing bulb. When the measured temperature exceeds the predefined value then either it gives an alarm signal or a trip signal depending on the operating condition of the transformer.

2.12.3 Buchholz Relay Buchholz relay is mainly used to detect the gas generation in the transformer during oil decomposition. Based on the gas generation rate and gas quantity, the device either gives an alarm or generates a trip signal. The gas generation rate is directly proportional to the severity of internal disturbances in the transformer main tank. Buchholz relay is placed in between the transformer main tank and oil conservator as per Fig. 2.16. For proper and reliable operation of this relay, the pipe of the conservator tank is adjusted in an inclined position (5°). Under small disturbances, the rate of gas decomposition is lesser. These gases are accumulated in an upper portion of the Buchholz relay and if gas quantity is higher than a certain value, float “F” (Fig. 2.16) is moved and completes the alarming circuit. Under any major disturbance, the rate of gas decomposition is very larger. At that time, the trip circuit float is deflected, and the closing of the trip circuit will isolate the transformer. Setting for the alarm and trip signal is decided as per the size, working, and importance of the transformer. Normally, at a 50 cm3 /kW/s rate of speed, an oil gas surge is generated under the heavy arc. Switches with mercury contacts are attached with floats and are placed in the Buchholz relay with an inclination of 150 for the correct operation of the relay in the event of an incipient fault.

48

2 An Overview of the Protection of Power Transformers

Fig. 2.15 WTI and OTI with alarm unit

2.12.4 Pressure Relays (PRs) Pressure relays (PRs) are subdivided into four categories: (1) sudden oil pressure relay (SoPR), (2) sudden gas/oil pressure relay (Sg/oPR), (3) sudden gas pressure relay (SgPR), and (4) static pressure relay. Those relays operated with a rate of rise of pressure of oil and gas are called pressure relays. They measure the pressure difference with normal and abnormal conditions [17]. Normally, these types of relays are adopted for more than 5 MVA transformer ratings [18]. In such transformer, the conservator tank is not provided, PRs are more suitable. PRs are installed in the transformer tank at the upper portion. Normally, SgPRs are provided on the top of the transformer tank with a 20–50 g/cm2 /s operating range of pressure [11].

2.13 Generalized Protections Applied to Transformer

49

Fig. 2.16 Schematic of Buchholz relay location with its extravagant sight

2.13 Generalized Protections Applied to Transformer Ahead of the 132 kV system voltage, fault clearance requires a higher speed operation to maintain the system stability and minimize the damage to the system equipment [7]. Mostly, a percentage bias differential relay is used to differentiate the internal fault from all other abnormalities and external faults. As backup protection of this differential protection, REF and OC relays are used. Different temperature detectors like WTI and OTI are placed to measure temperature and provide protection too. Moreover, gas-operated non-electrical Buchholz relays and sudden pressure relays are used to detect an incipient fault inside the transformer. Control and electrical wiring of such protective schemes are elaborated with a concern of CT/PT and CB in Fig. 2.17.

50

2 An Overview of the Protection of Power Transformers

Fig. 2.17 Generalized protections applied to power transformer

2.14 Adverse Effect of Single Phasing on Three-Phase Transformer

51

2.14 Adverse Effect of Single Phasing on Three-Phase Transformer An open-circuit fault takes place when one phase out of three phases is accidentally disconnected from the transformer connection. Open-circuit fault generates the worst effect on the transformer under loading conditions. Consider a distribution transformer of 11 kV/440 V with a DY11 connection with the nominal secondary phase-neutral voltage of 230 V. When one phase opens on the primary side, the secondary has one phase with 230 V and the remaining two-phase voltage lies in the range of 100–120 V, i.e., approximately half of the nominal phase voltage. Here, the distribution transformer connection of the DY11 connection is examined. The results are compared with measurements taken in a laboratory environment.

2.14.1 Basic Magnetic Circuit Induced emf Generated voltages are always in proposition to the fluxes available in a magnetic circuit (core) of a transformer. A simplified transformer core is designed as shown in Fig. 2.18 with three-phase magnetic flux linkages. An examination of the magnetic circuit would reveal that the reluctance offered by the flux of the central limb is less than the reluctance offered by the outer two fluxes produced inside the limbs. Consequently, the exciting current for the phase winding on the central limb is less than it is for the outer two-phase winding. However, the exciting current is so small that does not have any material effect on the operation of a three-phase transformer. According to the magnetic circuit Ohm’s law: mmf = flux × S

Fig. 2.18 Transformer core with length as dimension

(2.4)

52

2 An Overview of the Protection of Power Transformers

mmf = ϕ × S

(2.5)

where mmf = ampere-turns (magnetomotive force), F = magnetic flux, S = core reluctance. The reluctance of the path established, S=

L μA

(2.6)

where L = magnetic path length, μ = permeability, A = core cross-sectional area (normally area remains the same throughout the core). Induced voltage = induced emf, e = N ω ϕ

(2.7)

where e = voltage, N = number of turns, ω = angular frequency, F = magnetic flux. The equivalent magnetic circuit is shown in Fig. 2.19. The following equations can be written for magnetic flux generation at a particular instant of time: ϕ R = ϕY + ϕB

(2.8)

ϕB SB = ϕY SY

(2.9)

ϕR = ϕY + ϕB From which, ϕ Y = ϕR

SB SB + SY

(2.10)

ϕ B = ϕR

SY SY + SB

(2.11)

Here, the flux and hence the flux density are dependent on the reluctance of the magnetic circuit path. This indicates that the flux is dependent on the magnetic

2.14 Adverse Effect of Single Phasing on Three-Phase Transformer

53

Fig. 2.19 Equivalent magnetic circuit

permeability of the material used for the core, core area, and length of the limb. First, two factors will not cause a change in the value of the reluctance as they are always fixed. However, for practical purposes, one can take the length of the limb for all three phases as almost equal, and the effect of length can be ignored. Hence, the reluctance offered by each limb can be considered equal. Thus, Eqs. 2.10 and 2.11 will reduce to ϕY =

ϕR 2

(2.12)

ϕB =

ϕR 2

(2.13)

The nominal flux FR and hence the flux density BR are directly proportional to the applied voltage V R, thus, nominal secondary voltage (V 2 ) will be induced in R-phase. The flux densities in the remaining two phases (BY and BB ) depend on the applied voltages V Y and V B . A sum of both the voltages V Y and V B equals V 2 (nominal voltage). Relations of the voltages are as per Table 2.5. Table 2.5 Secondary winding voltage for supply from phases R and Y

Phase voltage of secondary winding

Voltage supply

V ry = V r + Vy

Vr = V2

V y = V 2 /2 V b = V 2 /2

P-P voltages

Vr * Vy * Vb

V yb = V y – Vb V br = − (V r + V b)

54

2 An Overview of the Protection of Power Transformers

2.14.2 Observation and Confirmation of the Theoretical Approach To observe the effect of single-phase disconnection from three phases, a transformer having a rating of 5 kVA, 440/230 V is considered. Both configurations such as star–star and delta–star connections of the transformer are validated and tested in a laboratory. Results are explained here with a category of delta and star connection of the transformer. (a) Delta–Star Transformer Connections As an example, a three-phase transformer of 5 kVA, 440/220 V has been used for the DY11 connection. Secondary winding voltage calculation has been done for a twophase supply given to primary winding. Tables 2.6 and 2.7 show the comparative calculation for healthy three-phase and two-phase supplies, respectively. From Tables 2.6 and 2.7, the observed values and measured values are close in comparison. Because of load fluctuations and unbalanced loading on the system, dissimilarity is observed between measured and calculated voltages. Also, from Table 2.7, it is found that by supplying only two phases of a three-phase transformer, normal operating phase voltages are found across one phase and two phases are practically giving half of the normal operating phase voltage. With one phase open on the primary side, two line voltages (V RY and V BR ) are still healthy but not equal to the nominal line voltage. Table 2.8 shows how the connection should be made to get the correct nominal voltage in case one of the phases on the primary side becomes open. (b) Star–Star Transformer Connection Figure 2.20 shows only R- and Y-phase supply given to the 440/220 V, YY0 transformer configuration. Table 2.6 Comparison of voltages in normal operating conditions (DY connection) Primary voltage (in volts)

Calculated secondary voltage

Measured secondary voltage

V ph (in volts)

V L (in volts)

V ph (in volts)

V L (in volts)

V RY = 443.6

V r = 128

V ry = 221.8

V r = 125.89

V ry = 219.6

V YB = 440.2

V y = 127

V yb = 220.1

V y = 125.16

V yb = 218.75

V BR = 442.7

V b = 127.8

V br = 221.35

V b = 126.1

V br = 218.95

Table 2.7 Comparison of voltages for supplying R and Y phases only (DY connection) Primary voltage (in volts) V RY = 444.2

Calculated secondary voltage

Measured secondary voltage

V ph (in volts)

V L (in volts)

V ph (in volts)

V L (in volts)

V r = 128.3

V ry = 222.1

V r = 129

V ry = 223.4

V YB = 254.7

V y = 73.52

V yb = 127.35

V y = 73.87

V yb = 128.54

V BR = 255.3

V b = 73.7

V br = 127.65

V b = 74.42

V br = 128.94

2.14 Adverse Effect of Single Phasing on Three-Phase Transformer

55

Table 2.8 Secondary connection in case of various possibilities of one phase of primary disconnection Primary phase disconnected

Secondary phase voltage

Connection of the load on secondary between phases

B

r—Full voltage

r and n

y and b—Half voltage y and b R

y—Full voltage

y and n

b and r—Half voltage r and b Y

b—Full voltage

b and n

r and y—Half voltage r and y

Fig. 2.20 Two-phase supply (R and Y) given to a three-phase transformer

From the equivalent magnetic circuit, the following equations can be written: ϕR SR + ϕY SY = 0

(2.14)

ϕR = −ϕY

(2.15)

ϕB = 0

(2.16)

The nominal flux in phases R and Y is proportional to their nominal phase voltages. Hence, the flux densities BR and BY are proportional to the voltages V R and V Y , respectively. This results in the nominal secondary phase voltage V 2 for a threephase supply. The flux density BB is zero, and hence V B is zero. The vector sum of V R and V Y is equal to the nominal phase–phase (P-P) voltage as shown in Table 2.9. Observation and confirmation of the theoretical approach The effect of the two-phase supply on the YY connection is carried out and the voltages of the secondary windings have been estimated. Tables 2.10 and 2.11 show the subsequent calculations for normal operation and single phasing condition, respectively.

56

2 An Overview of the Protection of Power Transformers

Table 2.9 Voltages of the secondary windings for supply from phases R and Y Phase voltage of secondary winding

Voltage supply

Vr = V2

P–P voltages V ry = V r + V y

Vy = V2

V yb = V y

Vr * Vy * Vb

V br = −V r

Vb = 0

Table 2.10 Comparisons of voltages in normal operating conditions (YY connection) Primary voltage

Calculated secondary voltage

Measured secondary voltage

V L (in volts)

V ph (in volts)

V ph (in volts)

V L (in volts)

V L (in volts)

V RY = 442.4

V r = 127.71

V ry = 221.2

V r = 128.23

V ry = 223.54

V YB = 441.25

V y = 127.38

V yb = 220.625

V y = 127.56

V yb = 221.71

V BR = 442.3

V b = 127.68

V br = 221.15

V b = 128.65

V br = 220.97

Table 2.11 Comparison of voltages for supplying R and Y phases only (YY connection) Primary voltage

Calculated secondary voltage

Measured secondary voltage

V L (in volts)

V ph (in volts)

V L (in volts)

V ph (in volts)

V L (in volts)

V RY = 441.3

V r = 127.40

V ry = 220.65

V r = 126.21

V ry = 221.47

V YB = 221.6

V y = 126.97

V yb = 126.80

V y = 127.08

V yb = 126.02

V BR = 222.4

V b = 0.0

V br = 126.20

V b = 0.0

V br = 125.57

It is to be noted from Table 2.11 that by supplying only two phases, keeping the third phase open for a three-phase star–star-connected transformer, normal operating voltages are found across one line and other line voltages are giving half of the nominal operating voltages. One phase is giving zero value; however, in the other two remaining phases, half of the total secondary voltages are found. Table 2.12 shows how the connection should be made to get the correct nominal voltage in case one of the phases on the primary side becomes open for a star–star-connected three-phase transformer.

2.14.3 Remarks of Single Phasing Supply to Three-Phase Transformer By considering the above exercise, the magnitude of the secondary phase voltage and line voltages can be determined theoretically. For verification of the theoretical calculation, measurement in a laboratory environment has been also carried out. It has been observed that both values, theoretical and practical, are in close comparison. Forgoing analysis has shown that in the case of a fuse blown in a distribution transformer, still it is possible to supply load through two phases (reduced

2.15 Different Research Techniques Used in Transformer Protection

57

Table 2.12 Secondary connections in case of various possibilities of one phase of primary disconnection Primary phase disconnected B

Secondary phase voltage

Connection of the load on secondary between phases

y and r—Half voltage r and y b—Zero voltage

R

b and y—Half voltage y and b r—Zero voltage

Y

r and b—Half voltage b and r y—Zero voltage

voltage) of a three-phase transformer. Further extension is possible by connecting the load directly across the phases having reduced voltage rather than connecting them between phase and neutral. In the case of delta–star transformer connection, nominal secondary phase voltage is found in one phase, while two healthy secondary phase voltages are found in a star–star connection of three-phase transformer.

2.15 Different Research Techniques Used in Transformer Protection There are many research articles available based on different schemes mentioned in Table 2.13 with its advantage and disadvantage. The effect of CT saturation needs to be accounted for while developing any protective schemes [19, 20]. Same as for designing the transformer protection schemes, it is also necessary to learn about the different core saturation of the transformer [21, 22]. Some protective schemes are based on sequential components [23] and various other techniques [24] are elaborated with the significant outcome.

58

2 An Overview of the Protection of Power Transformers

Table 2.13 Different research techniques S. No.

Protective schemes base

Refs

Advantages

Limitations

1

Adaptive digital diff. protection

[25–30]

. Reliable as per consideration of parameter . No further setting required

. Sometimes it may generate complexity as per the higher parameter consideration in the algorithm . Equipment costs may increase . Sometimes not economic

2

FFT/DFT/All filters

[31–35]

. It is less affected by the inconsistency of sampling

. DC decaying components and noise may be present

3

Snubber circuits

[36, 37]

. Absorbed transient of the power system . Keep disturbance frequency low

. Very much careful study is required regarding harmonic generations . More concentration is required to design the circuit

4

AI (fuzzy/ANN)

[38–41]

. Widely used for the . Less accuracy power system as compared to classifier master frameworks for and regression protection techniques . Possibility to change the modeling of a framework as per requirement

5

WAVELET

[42–44]

. Higher accuracy due to higher sampling frequency

. Very high decomposing frequency. So sometimes it does generate complexity . The computational time for analysis of waveform is increased (continued)

2.15 Different Research Techniques Used in Transformer Protection

59

Table 2.13 (continued) S. No.

Protective schemes base

Refs

Advantages

Limitations

6

SVM/HE-ELM/ RVM/DLNN

[45–50]

. These methods are AI-based classifier modus operandi . Due to its higher accuracy level for the classification, so-called research is high shield by researchers. Sometimes it is also called regression techniques like RVM, DLNN, etc . In techniques such as DLNN, separate feature extractors are not required . Addition and modification are possible with a minor change in the programming without sacrificing the accuracy level . It is also possible to use optimization techniques to optimize its parameter automatically as per the requirement

. Simply reveal extreme details to raise convolution . No guarantee concerning the taken details and relation with each essential section . Competent disintegration is producing disparagement in the practice . The collection of actual training data for the real power system is a very hectic course

7

DGA

[51, 52]

. Wide range of fault measurements based on different gas generation . Highest accuracy with alarming . Small size . Simple and robust construction . Working in high temperatures is possible

. A long time required to analysis . Frequently calibration is required . Maintenance cost is high . Interferences of gas affected on accuracy

(continued)

60

2 An Overview of the Protection of Power Transformers

Table 2.13 (continued) S. No.

Protective schemes base

Refs

Advantages

Limitations

8

Wave shape properties

[53, 54]

. Every abnormal . Lower sample rates condition has its own distort the accuracy unique and different and higher sample rate waveform signature generate complexity. concerning normal So, it is very important conditions. So, it is to select the perfect easy to capture the decomposing different abnormalities frequency for signal with internal fault extraction . The nonlinearity of the conditions equipment, magnitude, . Sometimes additional phasor angle, and equipment or feature instrumental error give requirements in the considerable effects on present relaying the protective schemes scheme are required to recognition of this technique like PTs or feature extractors, etc.

9

Specific sound waves

[55, 56]

. Specific protection . Low cost

. Generate complexity . May mal-operate during unwanted surrounding noise . The level of the sound may be differed as per the transformer design

10

Increments of Flux linkages

[57]

. Easy to develop the scheme

. Due to the nonlinearity of the core possibility of the flux linkages in transformer protection . Accurate flux measuring required

11

Mathematical morphology

[58, 59]

. Schemes give accurate . Mathematical and confirmed modeling for the operation if proper nonlinearity of the tactics are core is difficult . Designing equipment implemented with modeling in approval morphological terms for the effect of nonlinearity with the presence of eddy current and hysteresis current with behavior is almost extremely difficult (continued)

2.15 Different Research Techniques Used in Transformer Protection

61

Table 2.13 (continued) S. No.

Protective schemes base

Refs

Advantages

Limitations

12

THD

[60, 61]

. It is easy to implement . Harmonics are because only THD is generated due to any requisite to investigate core nonlinear . Most of all relay is conditions now assembled with . Same as other these features, so it is techniques sampling easy to implement in selection is required an existing system for major considerations . Saturation of the core may result in an error

13

Sequence component-based scheme

[23, 32, 62]

. The scheme is fast and . The method requires sensitive additional equipment . Reliable for a single . Higher carefulness is event required as per its sensitivity . Face many nontraditional issues

14

Phasor angle and magnitude

[31, 63, 64]

. Reliable . Accurate measurement and measuring . The basic scheme used equipment required in all types of relay

15

Condition monitoring with protection

[27, 30, 33, 41]

. Full equipment conditions are analyzed . May improve the health and life of a transformer

. Additional equipment are required . The cost of the protective scheme may be increased

16

Derivative-based technique

[65]

. Reliable and accurate

. Increase the complexity of analysis and computation to the algorithm as per the increase in the derivations

62

2 An Overview of the Protection of Power Transformers

2.16 Examples Example 1 If a 3-ϕ, 10 MVA, 33/11 kV, ∆-Y-connected power transformer is protected by a differential protection scheme, what is the CT ratio of both the CTs on the primary as well as secondary side of the transformer? Solution Line current on LT side of transformer ILT = √

10 ∗ 106 3 ∗ 11, 000

Line current on HT side of transformer IHT = √

= 524.86 A

10 ∗ 106 3 ∗ 66, 000

= 87.47 A

The standard secondary current for both the CTs is considered as 5 A. Selected CT ratio for CT on LT side =

600 A 5

Selected CT ratio for CT on HT side =

100 A 5

Example 2 A 3-ϕ, Y-∆-connected power transformer rated 30 MVA, 11/66 kV needs to be protected by biased differential protection. The phase voltage of the HT side is lagging LT side phase voltage by 30°. Develop complete differential protection for the transformer. What will be the CT ratio on the 66 kV side if the CT ratio of CT on the 11 kV side is 2000/5 A? Solution: (See Fig. 2.21 for beginner’s perceptive) LT Side (11 kV) Line current on LT side of transformer ILT = √ CT ratio for CT on LT side =

30 ∗ 106 3 ∗ 11, 000

= 1575 A

2000 A = 400 5

CT secondary current for CT on LT side =

1575 A = 3.93 A 400

11 kV side transformer winding is star connected. So, CTs will √be delta connected. CT current flowing in the pilot wire connected with the relay is 3 ∗ 3.93 = 6.82 A.

2.16 Examples

63

Fig. 2.21 Percentage differential protections for Y-∆ power transformer

HT Side (66 kV) 30 ∗ 106 Line current on HT side of transformer IHT = √ = 262 A 3 ∗ 66, 000 66 kV side transformer winding is delta connected. So, CTs will be star connected. CT current flowing in the pilot wire connected with the relay is 6.82 A. CT secondary phase and line currents are the same. CT Ratio =

262 = 38.5 A ∼ = 40 6.82

The secondary current is selected as 5 A. So, the primary current will be 40 * 5 = 200 A. Hence, the CT ratio on the 66 kV side is 200/5 A. Example 3 For 50 MVA, 11/132 kV, Y-∆-connected power transformer, develop differential protection considering 25% overload and CTs rating on 11 sides as 3000/ 5 A and on 132 kV side as 300/5 A. Solution: (See Fig. 2.22 for beginner’s perceptive) Note: 11 kV side transformer is Y connected so the CT connection will be in ∆. 132 kV side transformer is ∆ connected so the CT connection will be in Y. Step-1: Full load line current 50 ∗ 106 Full load line current on LT side of transformer IFLLT = √ = 2624.31 A 3 ∗ 11, 000

64

2 An Overview of the Protection of Power Transformers

Fig. 2.22 Percentage differential protection for Y-∆ power transformer

50 ∗ 106 Full load line current on HT side of transformer IFLHT = √ = 218.69 A 3 ∗ 132, 000

Step-2: Full load line current considering 25% overload Full load line current on LT side considering 25% overload = 2624.31 ∗ 1.25 = 3280.38 A

Full load line current on HT side considering 25% overload = 218.69 1.25 = 273.36 A

Step-3: CT ration Transformer LT side CT ratio =

3000 = 600 5

Transformer HT side CT ratio =

300 = 60 5

Step-4: CT secondary current Transformer LT side CT secondary current =

2624.31 = 4.3738 A 600

Transformer HT side CT secondary current =

218.69 = 3.6448 A 60

2.17 Conclusion

65

Step-5: Current in pilot wire connected with CTs Current in pilot wire connected with CT √ (∆) secondary on LT side = 3 ∗ 4.3738 = 7.5756 A Current in pilot wire connected with CT (Y) secondary on HT side = 3.6448 A Step-6: Turn ratio of interposing CT connected with CT secondary on LT side of the transformer (interposing CT not required on HT side of the transformer). Turn ratio of interposing CT =

7.5756 = 2.0784 3.6448

Step-7: Current on secondary of interposing CT connected with CT secondary on LT side of the transformer. Current on secondary of interposing CT =

7.5756 = 3.64449 A 2.0784

Considering a slope of 30%, the spill current which is required to actuate the relay is ( = 0.3 *

3.6449 + 3.6448 2

) = 1.1 A

The differential current is 3.6449 − 3.6448 = 0.0001 which is less than the spill current required to actuate the relay. Hence, the relay will not operate and keep the protection scheme stable.

2.17 Conclusion The reliability and stability of the national grid can be achieved by an uninterrupted power supply to all the developing sectors. Power transformer infrastructure plays an important role in the power system grid. This chapter discusses the design aspect of the transformer and an overview of various protective schemes used for the power transformer. First energization of a transformer may lead to a heavy inrush current and subsequent phenomena are also observed during the operation of the transformer in the power system. When a transformer is working under loading conditions, different abnormalities and faults may arise due to power system disturbance. Various internal faults along with their causes, external faults, and abnormalities are broadly discussed in this chapter. Unit-type differential protection along with modification in the percentage-biased characteristic has been elaborated in detail. Overloading, incipient faults, over-fluxing, and decomposition of oil due to heating may create

66

2 An Overview of the Protection of Power Transformers

unwanted effects on the working of the transformer. Application of Buchholz relay, OTI, WTI, and V/F relay is appended along with associated abnormalities in the transformer. Moreover, short-circuit faults and open-circuit faults are explained with practical exposure in the laboratory. This chapter also focuses on various research techniques recently used in the field for transformer protection along with their advantages and disadvantages. Some numerical exposure is also given for the most widely used differential protection to the power transformer. Subsequent chapters of this book include dedicated protection techniques used for small-to-large-capacity power transformer.

References 1. Rajurkar SS, Nandapurkar JG, Kulkarni A (2010) Analysis of power transformer failure in Transmission utilities. In: 16th National power systems conference, pp 180–185 2. Binder W. Trends in power transformer failure analysis. In: Transformers-magazine, vol 1, no 1, pp 30–33 3. Brownlee WR (1944) Transformer magnetizing inrush currents and influence on system operation. Trans Am Inst Electr Eng 63(6):423–500. https://doi.org/10.1109/T-AIEE.1944.505 8951 4. Blume LF, Camilli G, Farnham SB, Peterson HA (1944) Transformer magnetizing inrush currents and influence on system operation. Trans Am Inst Electr Eng 63(6):366–375. https:// doi.org/10.1109/T-AIEE.1944.5058946 5. Holcomb JE (1961) Distribution transformer magnetizing inrush current. Trans Am Inst Electr Eng Part III Power Appar Syst 80(3):697–702. https://doi.org/10.1109/AIEEPAS.1961.450 1117 6. Van Warrington ARC (1962) Protective relays—Their theory and practice, vol 1, 1st ed. Chapman and Hall, London 7. Rushton J (1995) In: Mewes KG (rev) Power system protection, 2nd edn. The Institutes of Electrical Engineers, London 8. Paithankar YG, Bhide SR (2010) Fundamentals of power system protection. PHI Learning Pvt. Ltd., New Delhi 9. Rao S (2008) Switchgear and protection, 1st edn. PHI Learning Pvt. Ltd., New Delhi 10. IEEE guide for protecting power transformers (revision of IEEE Std. C37.91-2000). IEEE Power Engineering Society Sponsored by the Power System Relaying Committee, New York. https://doi.org/10.1109/IEEESTD.2008.4534870 11. Anderson PM (1999), Power system protection, 1st edn. Wiley, IEEE Press, New York, pp 673–709 12. Industrial IIAS, Committee CPS (1986) IEEE recommended practice for protection and coordination of industrial and commercial power systems: approved September 19, 1985, reaffirmed June 27, 1991 IEEE Standards Board: Approved February 28, 1986, reaffirmed December 9, 1991, American National St. IEEE 13. Elmore WA (2003) Protective relaying theory and applications, 2nd edn. Marcel Dekker, New York. https://doi.org/10.1109/MPER.1996.539057 14. Wu QH, Lu Z, Ji TY (2009) Protective relaying of power systems using mathematical morphology, vol 1. London, New York. https://doi.org/10.1007/978-1-84882-499-7 15. Soman SA. Power system protection learning resources. NPTEL, Web Course, IIT, Bombay 16. Blackburn JL, Domin TJ (2015) Protective relaying: principles and applications, 3rd edn. CRC Press, Tailor and Francies Group, New York, London 17. Ram B (2011) Power system protection and switchgear. Tata McGraw-Hill Education, New Delhi

References

67

18. Bhalja B, Maheshwari RP, Chothani NG (2017) Protection and switchgear, 2nd edn. Oxford University Press, New Delhi 19. Patel D, Chothani N (2023) Evaluation of various dynamics on current transformer saturation with a model study on power system protection. In: Recent advances in power systems, pp 121–136 20. Babaria SJ, Patel DD, Chothani NG, Chaini PK, Patel RM, Joshi SR (2022) Influence of system parameters on current transformer saturation in power system. In: 2022 IEEE 2nd international conference on sustainable energy and future electric transportation (SeFeT), 2022, pp 1–6. https://doi.org/10.1109/SeFeT55524.2022.9909425 21. Raichura M, Chothani N, Patel D (2021) Review of methodologies used for detection of magnetising inrush and fault conditions in power transformer. IET Energy Syst Integr 3:109–129. https://doi.org/10.1049/esi2.12012 22. Raichura M, Chothani N, Patel D, Sharma J (2020) Methodologies for the detection of magnetizing inrush and fault condition in power transformer. In: 2020 IEEE international conference on computing, power and communication technologies (GUCON), Oct 2020, pp 146–151. https://doi.org/10.1109/GUCON48875.2020.9231065 23. Chothani N, Patel D, Raichura M (2019) Transformer protection with sequence components and digital filters, 1st edn. LAP LAMBERT Academic Publishing, Latvia 24. Patel D, Chothani N (2020) Digital protective schemes for power transformer, 1st edn. Springer, Singapore. https://books.google.co.in/books?id=tjaSzQEACAAJ 25. Patel DD, Chothani N, Mistry KD, Tailor D (2018) Adaptive algorithm for distribution transformer protection to improve smart grid stability. Int J Emerg Electr Power Syst 19(5):1–14. https://doi.org/10.1515/ijeeps-2018-0022 26. Raichura M, Chothani N, Patel D (2020) Development of an adaptive differential protection scheme for transformer during current transformer saturation and over-fluxing condition. Int Trans Electr Energy Syst 31:1–19. https://doi.org/10.1002/2050-7038.12751 27. Patel D, Chothani N (2020) Real-time monitoring and adaptive protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 173–190. https:/ /doi.org/10.1007/978-981-15-6763-6_7 28. Patel D, Chothani N (2020) Adaptive digital differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore. pp 83–106. https://doi. org/10.1007/978-981-15-6763-6_4 29. Raichura M, Chothani N, Patel D (2020) Improved transformer differential protection by adaptive pickup setting during momentary over-fluxing condition. Adv Electr Electron Eng 18(3). https://doi.org/10.15598/aeee.v18i3.3833 30. Raichura M, Chothani NG, Patel D (2019) Real-time monitoring & adaptive protection of power transformer to enhance smart grid reliability. Electr Control Commun Eng 15(2):104–112. https://doi.org/10.2478/ecce-2019-0014 31. Patel D, Chothani N, Mistry K (2018) Discrimination of inrush, internal, and external fault in power transformer using phasor angle comparison and biased differential principle. Electr Power Components Syst 46(7):788–801. https://doi.org/10.1080/15325008.2018.1509915 32. Patel DD, Chothani NG, Mistry KD (2015) Sequence component of currents based differential protection of power transformer. In: 12th IEEE international conference electronics, energy, environment, communication, computer, control: (E3–C3) INDICON 2015, pp 1–6. https:// doi.org/10.1109/INDICON.2015.7443855 33. Chothani NG, Raichura MB, Patel DD, Mistry KD (2018) Real-time monitoring protection of power transformer to enhance smart grid reliability. In: 2018 IEEE electrical power and energy conference (EPEC), Oct 2018, pp 1–6. https://doi.org/10.1109/EPEC.2018.8598427 34. Patel DD, Mistry KD, Chothani NG (2016) Digital differential protection of power transformer using DFT algorithm with CT saturation consideration. In: 2016 National power systems conference (NPSC), Dec 2016, pp 1–6. https://doi.org/10.1109/NPSC.2016.7858854 35. Mistry DDPKD, Chothani NG (2017) Transformer inrush/internal fault identification based on average angle of second order derivative of current. In: Asia-Pacific power and energy engineering conference, APPEEC, pp 1–6. https://doi.org/10.1109/APPEEC.2017.8309017

68

2 An Overview of the Protection of Power Transformers

36. Sutherland PE, Valdes ME, Fox GH (2016) Snubber design for transformer protection. IEEE Trans Ind Appl 52(1):692–700. https://doi.org/10.1109/TIA.2015.2473137 37. Sutherland PE (2012) Analysis of integral snubber circuit design for transformers in urban high rise office building. In: 48th IEEE industrial commercial power systems conference, May 2012, pp 1–17. https://doi.org/10.1109/ICPS.2012.6229607 38. Barbosa D, Coury DV, Oleskovicz M (2012) New approach for power transformer protection based on intelligent hybrid systems. IET Gener Transm Distrib 6(10):1009–1018. https://doi. org/10.1049/iet-gtd.2011.0711 39. Rahmati A, Sanaye-Pasand M (2015) Protection of power transformer using multi criteria decision-making. Int J Electr Power Energy Syst 68:294–303. https://doi.org/10.1016/j.ijepes. 2014.12.073 40. Balaga H, Gupta N, Vishwakarma DN (2015) GA trained parallel hidden layered ANN based differential protection of three phase power transformer. Int J Electr Power Energy Syst 67:286– 297. https://doi.org/10.1016/j.ijepes.2014.11.028 41. Moravej Z, Sanaye-Pasand M (2004) A novel approach for protection and condition monitoring of power transformer using MRBFNN. Electr Power Components Syst 32(5):491–503. https:/ /doi.org/10.1080/15325000490224229 42. Valsan SP, Swarup KS (2008) Wavelet based transformer protection using high frequency power directional signals. Electr Power Syst Res 78(4):547–558. https://doi.org/10.1016/j.epsr.2007. 04.008 43. Valsan SP, Swarup KS (2008) Protective relaying for power transformers using field programmable gate array. IET Electr Power Appl 2(2):135–143. https://doi.org/10.1049/ietepa:20070355 44. Davarpanah M, Sanaye-Pasand M, Iravani R (2013) Performance enhancement of the transformer restricted earth fault relay. IEEE Trans Power Deliv 28(1):467–474. https://doi.org/10. 1109/TPWRD.2012.2208204 45. Dharmesh Patel NC. Relevance vector machine based transformer protection. In: Digital protective schemes for power transformer, 1st edn. Springer, Singapore, pp 107–131 46. Patel D, Chothani NG, Mistry KD, Raichura M (2018) Design and development of fault classification algorithm based on relevance vector machine for power transformer design and development of fault classification algorithm based on relevance vector machine for power transformer. IET Electr Power Appl 12(4):557–565. https://doi.org/10.1049/iet-epa.2017.0562 47. Raichura MB, Chothani NG, Patel DD (2020) Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique. IET Sci Meas Technol 14(1). https://doi.org/10.1049/iet-smt.2019.0102 48. Raichura M, Chothani N, Patel D (2021) Efficient CNN-XGBoost technique for classification of power transformer internal faults against various abnormal conditions. IET Gener Transm Distrib 15(5):972–985. https://doi.org/10.1049/gtd2.12073 49. Chothani NG, Patel DD, Mistry KD (2017) Support vector machine based classification of current transformer saturation phenomenon. J Green Eng River Publ 7:25–42. https://doi.org/ 10.13052/jge1904-4720.7122 50. Patel D, Chothani N (2020) HE-ELM technique based transformer protection. In: Digital protective schemes for power transformer. Springer, Singapore, pp 133–172. https://doi.org/ 10.1007/978-981-15-6763-6_6 51. Al-Janabi S, Rawat S, Patel A, Al-Shourbaji I (2015) Design and evaluation of a hybrid system for detection and prediction of faults in electrical transformers. Int J Electr Power Energy Syst 67:324–335. https://doi.org/10.1016/j.ijepes.2014.12.005 52. de Faria H, Costa JGS, Olivas JLM (2015) A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis. Renew Sustain Energy Rev 46:201–209. https://doi.org/10.1016/j.rser.2015.02.052 53. Hooshyar A, Afsharnia S, Sanaye-Pasand M, Ebrahimi BM (2010) A new algorithm to identify magnetizing inrush conditions based on instantaneous frequency of differential power signal. IEEE Trans Power Deliv 25(4):2223–2233. https://doi.org/10.1109/TPWRD.2010.2040844

References

69

54. Hooshyar A, Sanaye-Pasand M (2015) Waveshape recognition technique to detect current transformer saturation. IET Gener Transm Distrib 9(12):1430–1438. https://doi.org/10.1049/ iet-gtd.2014.1147 55. Dukic G, Cukaric A (2014) New algorithm for detecting power transformer faults based on M-robust estimation of sound signals. IET Gener Transm Distrib 8(6):1117–1126. https://doi. org/10.1049/iet-gtd.2012.0492 56. Zawieska WM (2007) Power transformer as a source of noise. Int J Occup Saf Ergon 13(4):381– 389. https://doi.org/10.1080/10803548.2007.11105095 57. Kang YC, Lee BE, Zheng TY, Kim YH, Crossley PA (2010) Protection, faulted phase and winding identification for the three-winding transformer using the increments of flux linkages. IET Gener Transm Distrib 4(9):1060–1068. https://doi.org/10.1049/iet-gtd.2010.0094 58. Li Y, Tian X, Li X (2014) Hybrid algorithm for traction transformer differential protection based on intrinsic mode function energy entropy and correlation dimension. IET Gener Transm Distrib 8(7):1274–1283. https://doi.org/10.1049/iet-gtd.2012.0653 59. Oliveira LMR, Cardoso AJM, Cruz SMA (2011) Power transformers winding fault diagnosis by the on-load exciting current Extended Park’s Vector Approach. Electr Power Syst Res 81(6):1206–1214. https://doi.org/10.1016/j.epsr.2011.01.009 60. Raichura M, Chothani N, Patel D, Mistry K (2021) Total Harmonic Distortion (THD) based discrimination of normal, inrush and fault conditions in power transformer. Renew Energy Focus 36:43–55. https://doi.org/10.1016/j.ref.2020.12.001 61. Raichura MB, Chothani NG, Patel DD, Mistry KD (2019) Identification of inrush and fault conditions in power transformer using harmonic distortion computation. In: 2019 IEEE 1st international conference on energy, systems and information processing (ICESIP), July 2019, pp 1–6. https://doi.org/10.1109/ICESIP46348.2019.8938308 62. Patel DD, Mistry KD, Chotrhani NG (2015) A novel approach to transformer differential protection using sequence component based algorithm. J CPRI 11(3):517–528 63. Patel D, Mistry KD, Raichura MB, Chothani N (2018) Three state Kalman filter based directional protection of power transformer. In: 20th National power systems conference (NPSC), pp 1–6. https://doi.org/10.1109/NPSC.2018.8771716 64. Patel D, Chothani N (2020) Phasor angle based differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 51–81. https:// doi.org/10.1007/978-981-15-6763-6_3 65. Patel D, Chothani N (2020) CT saturation detection and compensation algorithm. In: Digital protective schemes for power transformer. Springer, Singapore, pp 33–49. https://doi.org/10. 1007/978-981-15-6763-6_2

Chapter 3

Introduction to Magnetic Inrush of Power Transformer

Abstract A trustworthy and devoted defensive system for a transformer is a prime necessity in power structure because of an extremely pricey and dependable apparatus. Nonlinearity feature of this device offers puzzling characteristics during inrush as well as CTs’ saturation states. Being an electrical-magnetic machine, it is mostly inducted by a heavy rush of current known as inrush. The behavior of the core becomes a significant case which may be an obstruction to discriminating the faults. Here, in this chapter, the impact of the inrush condition, precedent study performed to distinguish inrush from fault state, and a distinct technique for identification of it are explained. Around 50 research articles, a variety of books along with research theory are meticulously referred to evaluate the existing classifier methods. A relative investigation is performed to propose to filter out the pros and cons of different methodologies. Additionally, the average derivative angle of differential currentbased resolution is presented to pick out the inrush case from faults and abnormalities. A detailed software simulation is performed to check the feasibility of the suggested scheme. The obtained outcome demonstrates the impact of inrush as well as the identification of faults. It is to be observed from the results that the scheme is capable of discriminating inrush and all kinds of faulty conditions within the considered transformer.

3.1 Basic of Magnetic Inrush Just as a heart of a power system structure, the safety of the transformer is a sweltering matter of HVAC infrastructure. The power system of 132kV and above needs a sound defensive system having the swift speed to issue signal to isolate the device to boost the stability of the entire system [1]. The inherent nonlinear nature of the transformer core may cause a magnetic inrush current that may pose false measurements. Brownlee and Blume described magnetic inrush as having probable impacts on power networks [2, 3]. Without load condition, the transformer excitation procedure generates around 6–7 times more amount of current than the normal value of current. That lasts till certain cycles and is treated as an inrush type of current. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Chothani et al., Advancement in Power Transformer Infrastructure and Digital Protection, Studies in Infrastructure and Control, https://doi.org/10.1007/978-981-99-3870-4_3

71

72

3 Introduction to Magnetic Inrush of Power Transformer

Toroidal-type transformer (needs less copper material that is analogous to the usual transformer) creates more inrush current in multiples of 10. Generally, it is difficult to avoid inrush that creates false actions to a defensive system. Residual flux acts as a major component while the core saturates. The constant flux link scheme [4] describes that the amount of core flux produce numerous complexity that can be seen from Fig. 3.1. Though core remanent polarity plays a vital part to induce inrush. Figure 3.1 represents core diffusion arising near the “knee” state. During this condition, the improved harmonic content exists because of the harsh fall of winding inductance. In this case, a small impedance is visible in the unit protective scheme, inrush condition is distressed badly [5]. About large-rated transformers, the level of winding inductance measured is relatively larger (X to R fraction). Hence, inrush is induced in a grid for a few cycles, consequently, the structure may unbalance. To formulate distribution-type transformers depending on utmost inrush, the design

Fig. 3.1 Core feature inrush and flux linking representation

3.1 Basic of Magnetic Inrush

73

considerations are chosen such as to minimize the loss and increase the efficacy [6]. Constraints such as voltage, current, and flux are in principle to devise the transformers and their defensive system. The peak value of realistic voltage can be formulated as Vm sin(ω t + θ ) = i m Rp + Np

dφm dt

(3.1)

where V m is the peak value of voltage, ω known as angular velocity, im indicates magnetizing current, Rp denotes primary side resistance, φ m for instantaneous flux, and N p indicates the number of primary side turns. The value of prompt flux can be given as ϕm = (ϕp max . cos θ ± ϕresi. ) e

−Rp Lp

t

− φp max . cos(ω t + θ )

(3.2)

where φ pmax = crest value of primary side flux, θ is the angle of circuit breaker switched on, and φ resi is the remnant flux. During the saturation phase of the core, the winding inductance modifies radically. Because of this, the circuit order constant also varies. In the beginning, the diminishing rate is higher due to the mutual effect of fluxes. Magnetizing inrush (preliminary inrush), recovery inrush, and sympathetic inrush are the major types of inrush conditions of transformers [7]. Although in case of no connection with load, residual flux presents all the time in lower quantity. Hence, at the time of subsequent excitation, a huge inrush will be created (the amount depends on switching time). The amount of remnant flux can be predicted from its hysteresis loop [8]. A major reason for the sympathetic condition is because of null load condition of a device, it encounters the prime inrush of itself but it parallelly affects a nearby transformer that is known as sympathetic inrush. For sympathetic inrush conditions, flux linkage will be altered in another transformer. Depending on substation dynamics, features of sympathetic inrush are defensible [9]. Moreover, the level of diffusion is eventually drawn by opposite polarity [10]. Another type of situation is known as the recovery type of inrush which is not more injurious as magnetic inrush and sympathetic inrush. Though it creates an undesirable effect on power structure performance. Usually, during momentary fault recovery outside of the transformer protective region, refurbishment of the voltage level is noticed. The abrupt revival of voltage level may induce an recovery inrush condition. Generally, inrush involves DC contents, disturbances, and harmonics. 2nd harmonic, typically 20–50 percent of the basic component is observed in incipient inrush state. Moreover, the current waveforms during the inrush state are sharper than the normal conditions. The deviation in the shape of the differential current waveform at the time of inrush compared to the normal and faulty condition will be utilized for discrimination purposes. Hence, recognition of the inrush state depending

74

3 Introduction to Magnetic Inrush of Power Transformer

on the tangent of the second derivative of differential current has been presented in this chapter. Moreover, the induced inrush is because of the excitation of the transformer in zero-load condition, voltage revival post elimination of the exterior fault, and probably during attribute divergence of faulted generators. This condition may direct the defensive technique to falsely actuate and send isolation commands. In this case, researchers developed various inrush detection techniques that are elaborated on in the following sections along with their pros and cons. Inrush while zero-load excitation case needs extraordinary notice. For estimation and metering of magnetic current, certain difficulties are encountered for different models [11]. An appropriate depiction of diffusion characteristics of the core, its nonlinearity, and its simulation are the main concerns while inrush finding. Still, each core as well as copper losses depend on frequency, the illustration of that losses and evaluation of the constraints of the model are complex. Simultaneous magneticelectrical connection and data compilation to confirm models are almost complex while inrush states. Development of a prototype considering each of the mentioned provisions is a key requirement for engineers and crucial aim is to design a technique to truly recognize inrush and/or to discover sound harmonics minimization method. To recall the research performed in history, certain former schemes are elaborated in this chapter that proposed for identification of inrush states of the transformer.

3.2 Various Classifier Techniques to Identify Inrush States From the various research articles and their arrangements, the classifier techniques are majorly categorized into six categories along with their pros and cons with a sample diagram.

3.2.1 Discriminative Technique Depending on Harmonics Content (Which Contains DC Offset) The interior type of faulty conditions is further categorized into two clusters: (i) key faults and (ii) negligible faults. Based on the harshness of the fault, it may have a probability of blast or possibly can harm the windings and considering the worst case it can entirely burst the tank of the device. It is because of electromagnetic and/ or physical stress that is built inside the device. It may direct towards the breakdown of expensive equipment and interruption in supply. From Fig. 3.2, a sample block diagram is displayed pleasantly by covering almost all basic harmonics (including DC offset). Further investigation is carried out by data collection based on electrical quantities that are measured from the higher voltage level side as well as the lower voltage level side of the considered transformer which

3.2 Various Classifier Techniques to Identify Inrush States

75

Fig. 3.2 Harmonic- and DC offset-dependent discriminative technique’s sample diagram

is placed before ADC. The data collection is performed with a certain frequency amounted signals by elimination of unwanted signals. The collected analog-type data are converted to digital form using ADC. Post to this process, the data is then transferred to the harmonic or DC offset recognition block. In this case, the data are researched depending on the level of harmonics as well as the DC offset present. Some additional blocks are used to pause the process till the signals are perfectly identified. Using the juncture dot, the collected data is then exported and passed to a processor. Afterwards, the signal is then differentiated from a prefixed threshold magnitude. A comparator behaves as a level identifier and issues “1” for tripping or “0” for obstructing the signal. One can see from Fig. 3.2, a part could be chosen according to the necessity for this generalized system. During core magnetic diffusion, it may be directed towards the nonlinear characteristic of the B versus H relation and can generate higher level harmonics. Usually, harmonics and DC offset are already present in the inrush current, depending on the incipient instance and core behavior. Mostly, it is believed that the amount of second harmonic content is dominant which may be 50% of the basic current [12]. Depending on remnant and flux dispersal while switching on the instant of an inductive load, a second harmonic-based set point is decisive [13] to discriminate inrush case. Using a variety of techniques like percentage average blocking, cross blocking, per-phase, and harmonic-dependent methodologies, this condition is distinguished efficiently. A 2nd harmonic content-dependent inrush investigation [14] is an obsolete method because of certain loopholes which may be falsely activated during inrush or faulty cases. Hence, second-order derivation of difference current [15] is involved. Interior as well as exterior fault classification can be done using the angle level between the higher voltage level and lower voltage side currents of the device. These schemes [14, 15] are authenticated by DFT-based technique given CTs’ diffusion as well as its remedies [16]. Moreover, second-order harmonic-dependent whole transformer protective schemes are deployed along with adaptive changing in percentage bias feature respective to the severity of CTs diffusion [17, 18]. A combined secondorder harmonic and phasor angle disparity of sequential components is described in [19]. The majority classification scheme uses FFT, DFT, or MFCDFT analyzers. Though, because of development, Kalman filter-based modus operandi is proven

76

3 Introduction to Magnetic Inrush of Power Transformer

efficient compared to Fourier transform-based schemes. An upgraded edition of the Kalman filter-based scheme such as three-state Kalman filter is elaborated [20]. Pros of the technique: (1) This type of discriminative scheme is not dependent on the sampling rate. (2) Only the amount of harmonic and percentage THD is needed for classification purposes.

3.2.2 Electrical Quantity’s Wave Pattern-Based Techniques In this section, techniques depend on wave pattern singularity aspect, sinusoidal closeness aspect, self-co-relation, enhanced co-relation aspect, current ascent, wave pattern and its size sensational techniques are involved. From Fig. 3.3, basic provisions are explained depending on a simple illustration of altered waveform response practices in protective schemes. From the primary and secondary sides of the transformer, the required electrical parameters are captured for the selected quantity block in terms of voltage or current. These selected factors are processed under AAF (anti-aliasing filter), SC (signal conditioning), and ADC (analog-to-digital converter) units. An AAF unit filters the collected waveform and conditions it through an SC unit after that the wave is reformed in digital type using ADC. The signal is then explicated depending on certain conditions such as voltage wave slope, quantity degree disparity, normal derivation, wave shape, and its size, similarity as well as the pace of magnitude change. Using this info, the signal is then differentiated from prefixed limits. The wave is passed from the processor post-differentiation to decide to issue a tripping or restraining indication. The features of inrush current waveform are not similar to faulty current. Usually, the captured faulty current includes sinusoidal-type decay current signal alike inrush case, which has crest and decays after a certain time interruption. Different currents

Fig. 3.3 Waveform’s shape-based analysis

3.2 Various Classifier Techniques to Identify Inrush States

77

which are slope dependent are recognized [21], though a tough job is to select a limit for every instant. Quantity degree disparity-dependent inrush retention scheme [22] excluded the impact of Fault Initiation Angle (FIA), sympathetic type of inrush current, and recovery-based inrush current. The amount of voltage on each side of the device while inrush persists is observed as about equal, using this fact, inrush can be discriminated among other disturbances [23, 24]. All other turbulences such as interior as well as exterior fault cases having or not having CTs diffusion are authenticated using adaptive changing percentage-biased feature dependent on the extent of CTs diffusion. Using characteristics of the non-diffusion region of the transformer core [25], interior fault, as well as inrush states, generates standardized difference current by just a quadratic one-cycle sample. Positive and negative sequence component-based and quantity angle disparity-based detection [19, 26, 27] and phasor position-dependent enhanced differential-type defensive techniques also exist. Moreover, statistics of higher level-dependent inrush detection schemes proved efficient results [28]. Though it includes difficulty while executing the method in the real-time scenario. Fractions of V and I data to prefixed limits-dependent unit-type defensive and inrush recognition are elaborated in [29]. Yet, to observe inrush condition, a Least-Squares Curve (LSC)-based scheme [30] utilized having a good amount of accurateness. A foremost drawback of this type of scheme is having inadequate sample pace, a limited amount of bandwidth, having phase response of nonlinear type, and instrumentation defects.

3.2.3 Discriminative and Decomposing Schemes Usually, during decomposing schemes, it uses transformation techniques such as WPT (wavelet packet transform), WT (wavelet transform), and S-transform. While discriminative-type techniques generally use artificially intelligent-based techniques [31]. It can be seen from Fig. 3.4, one generalized block diagram of discriminative as well as decomposer methodologies is illustrated. In such techniques, parameters such as frequency, current, voltage, power factor, or contents of dissolved gases are given as input. These data are then repaired and compiled using ADC slab. Post-compilation of the data will be investigated depending on purposeful behavior using discriminative and decomposing schemes. Using a prefixed limit, a logical comparator generates an answer in digital form to issue an isolation command or not. Discrete wavelet transform depends on RMS magnitude of a range of frequencies and is utilized in [32] for discrimination of inrush or interior fault conditions. Though the most important shortcoming is considered numerical complication and complexity to acquire the data. Power spectrum intensity-dependent investigation to classify faulty or inrush conditions by Low-Pass Decomposition Filter (LDF) and High-Pass Decomposition Filter (HDF) is utilized in [33]. Moreover, Empirical Wavelet Transform (EWT) [34], an upgraded version of wavelet packet transform,

78

3 Introduction to Magnetic Inrush of Power Transformer

Fig. 3.4 Discriminative- and decomposing-based block diagram

is practiced for discrimination between inrush or interior fault cases of the device. Additionally, the resultant textures are utilized for tutoring Support Vector-based Machine (SVM). Inrush recognizant by the WPT technique is elaborated [35] but the criterion to choose fundamental wavelets is a foremost concern. With the help of the severity of the inrush current, WT-dependent sympathetic inrush recognizant technique is described in [36]. Concerning the complex nature of the decomposing scheme, AI-based discriminative schemes are preferred by researchers nowadays. Artificial Neural Network (ANN) along with Bayesian classifier (BC) [37] is utilized to protect the transformer for discrimination of numerous anomalies such as inrush as well as CT diffusion. A combined Genetic Algorithm (GA) and ANN is proposed [38] to properly classify anomalies and fault cases. Adaptively modifiable principle using ANN- and PSObased scheme is detailed in [39], but the time of operation for the recommended settings is not estimated. Currently, RVM (relevance vector machine) [40, 41] and HE-ELM (hierarchical ensemble extreme learning machine) [42, 43], and combined CNN and XG-Boost-based [44] schemes are described duly bearing in mind every system as well as fault constraints to improve discriminative efficacy. Though the presented scheme achieved higher discriminative efficacy but capturing run-time training data is a main concern. Drawbacks: (1) The data required will be huge in amount to accurately train the system. (2) The trained system encounters unforeseen situations and does not give a guarantee of reliable results. (3) Real-time data collection is a serious concern from run-time scenarios.

3.2.4 Morphological-Based Analysis In this category, morphology, numerical solution type, and grill factor-based schemes are included. In the article [45], Mathematical Morphological (MM)-based analytic methodology is elaborated in detail to exaggerate numerous illustrations. Figure 3.5 demonstrates a general block arrangement of this type of technique. Initially, the

3.2 Various Classifier Techniques to Identify Inrush States

79

Fig. 3.5 Mathematical morphology-based analysis

model is developed after which certain test conditions are then authorized. SC, AAF, as well as ADC blocks are utilized to collect the required data using SC and ADC. Analysis of the collected signal will be done based on singularity, state-space formulation, backward and forward differential value, eigenvalue, eigenvector, fractal analysis, or higher order statistics. After that, the analyzed data are then compared with the predefined threshold. A comparator compares the derived quantities with a set threshold value. The result of the comparator is then used for decision-making purposes. Even though for sympathetic inrush, MM-based top hat, and bottom hat are defined with threshold conditions for both flux linkages [46]. Morphological Gradient (MG)-based detection strategy is very effectively elaborated for various inrush cases in an article [47]. Though varied data such as changing load condition, operational time, and fault initiation instance are not involved. Mathematic representation by including numerous parameters of the device is detailed in [48]. Modeled inrush using backward differentiation equation [49] is utilized by statespace formation. Though mathematical oscillatory trouble encounters in due course. Still, deployment seems difficult. Fractal study-dependent inrush identification technique of difference current is performed in [50]. Yet, distortions during CTs diffusion, during exterior and interior faulty conditions are not examined. Limitations: Usually simulation of the transformer during inrush conditions is hard as it offers various constraints to configure dispersal performance. Nonlinear characteristic is problematical to characterize in morphological form.

80

3 Introduction to Magnetic Inrush of Power Transformer

3.2.5 Power Utilization-Dependent Techniques Power consumed-dependent techniques are generalized as shown in Fig. 3.6. Frequency–time response scheme, two-terminal network-dependent scheme, active and reactive power consumption-based technique, Equivalent Instantaneous Leakage Inductance (EILI)-based exploration, etc. are included in this classification. Initially, from each face of the transformer electrical parameters are estimated by instrument transformers such as CTs and PTs. These data are exported to the subsequent slab at which AAF filters the necessary data, and SC unit conditions the gathered data as well as ADC slab converts the analog type of data to digital terms. Power estimation blocks compute necessary quantity in form of active and reactive power. A reference threshold and the captured data are then compared in a comparator; afterwards, the dedicated processor generates a trip or restraining signal. Usually, zero-load losses consist of hysteresis and eddy current losses which are calculated below: Pn0 = Ph + Psi-eddy + Pex-eddy

(3.3)

The flux if linked evenly along the core limb, the connection among average magnetization, flux, and voltage is   1 d∅ d∅ dM = ∞ dt 2Ld dt dt  B(t)∞∅(t)∞ v(t)dt

(3.4) (3.5)

According to [51, 52], cumulative power usage with each of the three elements is given as P0 = W (Mmax ) f +

√ π2 2 σ d (Mmax f )2 + 8 σ G SV0 (Mmax f )3/2 6

Fig. 3.6 Power consumption-based analysis

(3.6)

3.2 Various Classifier Techniques to Identify Inrush States

where Psi-eddy =

σ d2 dB 12 dt

and Pex-eddy =

n 0 V0 2

/

1+

So, as oer Eq. 3.12, Ph ∞ f , Psi-eddy ∞ f 2 and Pex-eddy ∞ f 1.5

81 4σ G S dM n 20 V0 dt

 −1

(3.7)

During the energization of the device, usually, the consumption of fruitful power is around zero. On the other side, the power consumption is higher while exciting the device, having an interior fault [53]. However, the amount of active power required by the device is huge and it remains for a certain number of cycles [54]. Post to that the requirement of power is decreased and depicts the real state of the device which means consumption of fruitful power is reduced and reactive power demand is increased. This may increase the discrimination time and delays the protection process. Distinguishing inrush conditions depending on the value of instantaneous frequency of difference power are detailed for precise discrimination within a quarter of a cycle [55]. A comparison of this technique with already existing techniques such as neural networks, wavelet transform, power consumed, and second-order harmonic-based technique are also presented. Though a variety of inrush conditions along with the existence of an interior type of fault also need to be validated. Instantaneous inductance-based discrimination technique is explained well in [56]. The methodology utilizes electrical parameters that add more cost for the placement of instrument transformers. Power-dependent time-based investigation is included to distinguish inrush cases of transformers [57]. The severity of current and change in inductance (L)-dependent examination to sense inrush cases is described in [58]. Though certain cases like the variety of inrushes with various FIAs and additional parameter variations are not verified. The impact of load power factor investigation [59] provides results on the scale and for the duration of magnetic inrush condition. Because of the sympathetic inrush situation, disturbances in actual inrush current due to nearby transformers may extensively increase.

3.2.6 Flux-Based Methodologies Various uncertainties during electromagnetic stress can be used for the identification of transformer anomalies such as inrush and core over-fluxing. Presently, different computational-based techniques such as radical electromagnetic forces [60] are a key factor that can be considered as a measure to distinguish inrush cases of the device under consideration [61]. Logical, mathematical, and practical aspects-based analysis can detect inrush situations. Though better technical knowledge and adequate skills are mandatory which increases its complications. Additionally, CT diffusion cases were not considered. In the case of inrush conditions, the surplus flow of radial as well as longitudinal stresses may pose distortion in the device’s structure [62]. The ionosphere-based flow of current is concerned with Geomagnetic-Induced Current

82

3 Introduction to Magnetic Inrush of Power Transformer

(GIC) because of the core diffusion of the transformer [63]. Excessive flux cases are also authenticated [64, 65] by the inclusion of inrush situations.

3.2.7 Methodology for Mitigation of Level of Inrush Current Key factors that contribute to the severity of inrush setup are mainly impacted by breaker switching instance [66], amount of residual flux and its polarity, winding resistance, source impedance, winding inductance, core geometry, and its characteristics with flux-sustaining capability [67]. Currently, the increasing use of renewable converters may also diminish the magnetic inrush level. Low-Voltage Ride Through (LVRT) assets [68] and Transient Current Limiter (TCL)-based techniques [69] are available to mitigate abrupt changes in current waveforms. Also, using controlled switching [70], the level of inrush can be restricted. Though suitable accurate steps with determination are needed in applying these schemes. Amplitude-dependent inrush restraint schemes [71] are also existing. A comparative analysis (Table 3.1) is prepared with conceptual aspects of different techniques for study and developments in the field of inrush detection and/or control. This enables ease of understanding along with the pros and cons of the work done to date by numerous respected researchers.

3.3 The Proposed Technique for Inrush Stimuli Discrimination At the time of primary voltage approach zero instances and with the equal polarity of the core residual flux, a huge amount of inrush current is fetched by the transformer. In the case of inrush states of the device, the magnetic intensity is close to the “knee” point of the hysteresis loop. The maximum magnitude of the functional voltage is given by Vm sin(𝛡 t + θ ) = i mag. Rpri. + Npri.

dφ dt

(3.8)

Here, V m is denoted for the crest magnitude of a given voltage, ω indicates the amount of angular velocity, imag denoted for magnetizing current, Rpri. = primary resistance, φ = instantaneous value of flux, N pri . = primary turns. φ = (φpri. max . cos θ ± φresi. ) e

−Rpri. L pri.

t

− φpri. max . cos(𝛡 t + θ ) (2)

(3.9)

where φ pri.max . = crest magnitude of flux related to primary side, θ = fault initiation instance, φ resi . = remnant flux.

3.3 The Proposed Technique for Inrush Stimuli Discrimination

83

Equation 3.9 denotes the flux reaction which includes steady-state AC, as well as DC, offset component. The current wave pattern will be entirely offset within a few cycles. L/R (value of time constant) is not consistent because of the saturations of a core which ultimately modifies the value of L. Hence, about this cause, initial envelopes of the inrush wave diminish swiftly. As the device is made off-load, the presence of flux remains through winding as the dipoles of the materials are still aligned to a particular direction. The leftover amount of flux after unloading the device is known as remnant flux. During subsequent energization of the device, because of the presence of this remnant flux, an inrush event is observed. It is approximately 6–7 times higher compared to normal current. It is analyzed by various researchers that the second-ordered content is dominant in such cases. This type of situation often falsely actuates the differential defensive system. Hence, a sound discriminative technique is presented here which can effectively categorize inrush condition and fault condition. Table 3.1 Comparative analysis of various inrush detection schemes for the transformer S. No

Scheme class

Pros

Cons/research gaps

Reference no.

1

Discriminative technique depending on harmonics content (that contains DC offset)

(1) Sampling divergence may not affect the system performance (2) Only an amount of percentage THD is needed for classification purposes

(1) During the conditions such as CT diffusion, there exist probabilities for the harmonics and DC offset. Hence, it may falsely actuate the system (2) Load types such as nonlinear loads and frequent switching of loads can inject harmonics into the system

[12–23]

2

Electrical quantity’s wave pattern-based techniques

(1) This method uses a wave pattern of electrical quantities and proved efficient (2) The only wave pattern is required in this type of methodology and hence does not require additional instrumental devices

(1) Due to the [24–34] distortion in wave pattern by any means the system may sense it as fault and possibly can falsely operate the defensive system (2) When inrush and faulty conditions follow the same type of wave pattern it may be difficult to discriminate (continued)

84

3 Introduction to Magnetic Inrush of Power Transformer

Table 3.1 (continued) S. No

Scheme class

Pros

3

Discriminative and (1) Currently, this class of decomposing schemes technology is widely utilized as it uses artificial intelligence (2) This category offers more accurate results as it can learn from the training data and can respond during unforeseen situations also (3) The scheme once fully installed does not require any further intervention and can be operated remotely (4) The technique is adaptable to make necessary changes whenever required with slight modifications

(1) Training and testing [35–48] datasets are huge in amount and hence required more space for storage (2) This type of technique requires specially trained personnel to implement the system (3) Frequent up-gradation in the technology required (4) Historical data must be accurate to accurately train the entire system (5) This category of technique needs more peripheral devices which adds to the cost of the system and if any device or sensing element breaks down then the entire system may break out

4

Morphological-based analysis

(1) Usually, simulation [49–56] of a transformer during inrush conditions is hard as it offers various constraints to configure diffusion performance. Nonlinear characteristic is problematical to characterize in morphological form (2) The mathematical model changes as the type of transformer varies

(1) This type of category is robust and hence can provide accurate data that are required

Cons/research gaps

Reference no.

(continued)

3.3 The Proposed Technique for Inrush Stimuli Discrimination

85

Table 3.1 (continued) S. No

Scheme class

Pros

Cons/research gaps

Reference no.

5

Power utilization-dependent techniques

(1) This category offers advantages such as it works only on one electrical parameter and can efficiently discriminate the type of anomaly that occurred in the considered device

(1) Only limited types [57–65] of anomalies can be detected by this category of the method (2) No detailed investigation is possible with this kind of technique

6

Flux-based methodologies

(1) The major pros of this category of the defensive system are that it can sense the change in linkage flux and based on that it can also detect the incipient type of fault condition which is hard to detect by other techniques

(1) This kind of [66–73] technique purely depends on one parameter which is flux and for measurement of such kind of constraint it required wrapping of search coils which adds extra costs to the system (2) Highly skilled personnels are required for the wrapping of such coils (3) Existing devices cannot be covered with this scheme or if implemented then it required a complete shutdown of the system and which may pose an interruption in supply for a long duration of time

7

Mitigation of level of inrush current techniques

(1) Inrush generation is the chief factor to develop a variety of defensive systems to discriminate between faulty conditions and inrush conditions. In this type of technique, inrush generation is focused and tried to minimize it which may facilitate avoiding discrimination procedures

(1) This category of [74–79] technique required special peripheral instruments which need to be accurate and hence it highly depends on these instruments (2) Also, during the conditions such as inrush with already existing fault conditions, this kind of scheme may falsely operate and can harm the whole power system

86

3 Introduction to Magnetic Inrush of Power Transformer

Fig. 3.7 Circuit diagram

3.4 System Modeling A typical visual of 400/230 kV, 3-phase, 50 Hz, 350 MVA, YY-connected power transformer is available in Fig. 3.7. The left side of it is linked to generating station and opposite side is linked to the grid/load. This visual is simulated in the PSCAD™ platform [19]. Certain test cases such as preliminary inrush, fault followed by inrush, and interior type of fault are created on this simulation. Inrush and fault conditions are generated by an operating breaker and fault block, respectively, at various switching angles of applied voltage. The current signal is sampled at a sampling frequency of 4 kHz and sampled data are migrated to MATLAB for validation of the proposed algorithm.

3.5 Anticipated Algorithm CT secondary side’s currents I 1 and I 2 of both sides of the transformer are collected. Depending on acquired data, the difference current (I D ) is projected using Eq. 3.10, ID = I1 − I2

(3.10)

This difference in current is the major factor in sense of initiation of any anomalies during transformer operation. Therefore, when the value of the projected ID is higher compared to the prefixed limit, the algorithm transmits subsequent cycle data to distinguish the inrush or interior type of fault case. The presented scheme operates on a floating window concept. The length of the floating window is considered here as one cycle. Therefore, it constantly monitors the occurrence of any anomaly. It has been researched that double derivative (∆) of projected difference current ID gives fruitful data of the fetched wave pattern. The double derivative (∆) of the projected difference current would be utilized to discriminate inrush or interior faulty stimulus. In addition to this, it is advised that if the tangent of a double derivative of difference current is derived it would be effective to discriminate inrush or interior faulty situation. Equations 3.11–3.13 explain how to estimate the average angle (θ avg ) that is useful in this technique.

3.6 Obtained Results Discussion

87

 ∆=

d2 ID dt 2

 (3.11)

θ = arctan (∆) Degree

θavg

1 = m i − ni

(3.12)

ni θ (t) dt

(3.13)

mi

[mi , ni ] indicates the time intervals considered for deriving the average of θ. A schematic flow of the presented technique is employed in the MATLAB platform as shown in Fig. 3.8. Here, the MATLAB platform is used to authenticate the obtained results using the programming of the prepared algorithm in m-code. As discussed earlier, the floating window concept of one-cycle length is constantly examined for any anomaly that occur during the transformer operation. In case an anomaly is detected then the presented scheme exports one subsequent cycle of data to the defensive system. From various cases, it is visualized that θ avg magnitude is estimated to be around 1–4° during interior fault cases and it is estimated to be greater than 4° every time for inrush cases. Moreover, during interior faulty conditions, it is probable to have an uniform type of wave pattern, and in such cases θ avg is estimated nearly zero. However, during the worst type of interior faulty wave pattern, it may be estimated till 4°. So, the prefixed threshold of 4° is decided considering the CTs’ ratio mismatch and decreasing DC offset. This logic is also checked for a variety of transformer connections and for that examination satisfactory results are obtained. However, it is good to note that for a certain type of connection threshold limit needs to be changed (here, 4° is considered for the star–star type of transformer connection).

3.6 Obtained Results Discussion The presented technique is compared with the power consumption-dependent technique [53], so the wave pattern of the proposed and reactive power-based scheme is included in the results. Few limitations are conquered in the presented scheme by deploying a novel double derivative-based scheme. By using the tangent of an average angle, interior fault and inrush cases can be classified during one cycle of post-anomaly. The anticipated technique is also applicable and validated for another type of transformer connection.

88

3 Introduction to Magnetic Inrush of Power Transformer

Fig. 3.8 Schematic flow of the presented technique

3.7 Magnetic Inrush Case As can be seen that the inrush is applied at 0.2 time instant by energizing the device while keeping the secondary side of the device open. Section of Fig. 3.9a depicts the wave pattern of inrush at the primary side of the device when the secondary side opens. Further, Fig. 3.9b depicts power requirement rapidly varies during transformer switching-on time of active and reactive components. The red-colored line indicates that the consumption of reactive power is lower at the instant of switching compared to active power [53]. From Fig. 3.9c, the amount of arc tangent (∆) of the

3.8 Interior Type of Fault Case

89

Fig. 3.9 Wave pattern during inrush case for a each side currents of device, b power usage in transformer, c arctan of ∆, and d average value of obtained angle θ

double derivative of difference current and its average value can be seen. Figure 3.9d indicates the average amount of angle (θ avg ) during the inrush case is significantly more to 7.8°. This value is more compared to the limit settled in the logic; hence, this kind of phenomenon is understood as an inrush case.

3.8 Interior Type of Fault Case In the prepared software model, the interior type of fault case is employed at 0.2 instant of time on R-phase at the primary connection of the transformer as can be seen in Fig. 3.10a. On the other hand, the opposite side of the device is connected to the load but it shows zero reading because of the presence of an interior type of fault. The consumption of reactive-type power is more significant than that of the active power because of the faulty path having a high amount of inductive part compared to the resistive one. It is observed from Fig. 3.10b that the rate of flux linkage changes is decreased and consequently decreases the usage of the reactive power component. In such cases, only power-dependent protective schemes [53] may falsely actuate. Hence, to

90

3 Introduction to Magnetic Inrush of Power Transformer

Fig. 3.10 Wave pattern during interior type of fault. a Both sides current magnitude of the device, b power consumption of both the components active and reactive, c ∆ arctan, and d θ avg

conquer this, the presented scheme uses a double derivative of the difference current and it is tangent for distinguishing between interior faults effectively from the inrush cases in a minimal instance of time. Figure 3.10c, d depicts the magnitude of θ avg = 0.24° that remains beneath the prefixed limit.

3.9 Interior Type of Fault Followed by Inrush Case In this kind of situation, the device is switched on at 0.2 instants of time to apply the effect of inrush afterwards at 0.3 instants of time, an interior fault is employed on the R-phase of high voltage winding of the device through a reasonable amount of fault resistance as can be seen from Fig. 3.11. The wave pattern of both sides of current can be seen from Fig. 3.5a for the said situation. Power consumption of both the active and reactive components for both these cases is illustrated in Fig. 3.11b. For such cases, the power consumption-based scheme [53] may falsely operate at 0.3 instant of time because of a mismatch of active and reactive power. Figure 3.11c, d illustrates the arctangent of ∆ and θ avg . The magnitude of θ avg is estimated as 7.8 for 0.2–0.3 s and after 0.3 s it abruptly decreases to 0.24°. Hence, the interior type

3.10 Conclusion

91

Fig. 3.11 Wave patterns of inrush case which is followed by interior type of fault. a Both sides current data of the device, b power consumption of both components of the considered device, c arctan of ∆, d θ avg

of fault that is employed at 0.3 instant of time during inrush condition can be simply identified and tripping command would be generated.

3.10 Conclusion In this chapter, a review of various inrush discriminative techniques is described in detail for power transformers. Various methods based on analytical computation, harmonics, signal decomposition, classifiers, and artificial intelligence are broadly discussed and compared for the inrush phenomenon in the transformer. The presented technique is based on the average magnitude estimated for the arc tangent of the double derivative of the difference current. A software model of certain situations is performed in PSCAD™ and the developed logic is authenticated in the MATLAB platform. Numerous conditions such as preliminary inrush, interior type of transformer fault, as well as a faulty condition during inrush states are modeled to verify the competency of the presented technique. Also, a comparative analysis is presented for the proposed technique with a power consumption-dependent scheme. It is then

92

3 Introduction to Magnetic Inrush of Power Transformer

finally proved from the obtained results that the presented novel scheme gives satisfactory results to discriminate inrush from interior fault conditions with minimal time duration.

3.11 Question and Answer Question-1: Discuss the modern technology used in transformer core manufacturing. Answer: The material used in the transformer core and its saturation effect on it is described below. CRGO (high-grade material offers less saturation) Cold-Rolled Grain-Oriented (CRGO) and Cold-Rolled Non-Grain-Oriented (CRNGO) materials are utilized for stampings of the cores. CRNGO has a breadth of more than 0.50 mm compared to 0.27 mm of CRGO material. So, the amount of losses in CRNGO is four times higher. CRNGO is a processed steel material based on iron–silicon alloys having a changeable amount of silicon. It is majorly applied in appliances such as ballasts, motors, generators, alternators, small transformers, and various other electromagnetic devices. Whereas CRGO-type material is in use for distribution transformers as well as power transformers because of having low loss feature. CRGO material is obtainable in certain grades (usually known as grades M3, M4, M5, and M6). Amorphous Steel (AM) is one of the best options for manufacturing magnetic limbs in transformers. The amorphous cores have a few skinny metallic tapes for the reduction of hysteresis loss and eddy current loss. The reduction in this noload loss is mainly due to high permeability (randomization of grain and magnetic domain structure of material). Thus, amorphous material provides a slim hysteresis curve in comparison to the conventional CRGO material. Though the amorphous material is widely used in distribution transformer due to high-temperature withstand capability, but AM having a lower saturation point compared to CRGO. Due to the early saturation of AM core at lower flux density, larger coils and tank size are required for the same size of CRGO material. Question-2: Explain the effect of core saturation on the performance of the transformer. Answer: When a transformer is turned off, the magnetic current chases the hysteresis curve, but the flux density value faces a nonzero magnitude. During a particular residual

3.11 Question and Answer

93

flux density, the maximum amount of inrush current is drawn during the switching on of the device at the voltage zero. The amount of magnetic flux of an inductive circuit may not vary rapidly, the amount of flux very soon post closer to the switch (i.e., at t = 0+ ) should be the same as the flux just before closing to the switch (at t = 0− ). So the density of flux rather than initiating from a negative maximum will now initiate and attain the crest positive value while directing the core to a diffusion state [4]. As the given voltage wave is sinusoidal, the post-forming flux also flows sinusoidal wave pattern and hence the fetched current is forming a peaky type of curve. A similar outcome is estimated using Eq. 3.14: V p sin(ωt + θ ) = i 0 R1 + N1

d∅m dt

(3.14)

where V p = peak value of the applied voltage, Θ = angle at which voltage is switched on, i0 = instantaneous value of magnetizing current, ∅m = instantaneous value of flux at any time t, R1 = primary winding resistance, N 1 = primary winding turns. Here, the solution is derived using the earlier conditions that at t = 0, ∅m = ±∅r ,

−R1 t ∅m = ∅mp cos θ ± ∅r e L 1 − ∅mp cos(ωt + θ )

(3.15)

For θ = 0 and residual flux +∅r . It can be observed from Eq. 3.15, flux (flux density) contains a transient DC offset, which decreases at a pace identified by the fraction of resistance to the inductance of primary side winding (R1 /L 1 ), and a steady-state AC content ∅mp cos(ωt + θ ). It can be observed that the current wave pattern is entirely offset for early certain cycles with decaying in nature after alternate half cycles. So, the generated inrush is extremely asymmetrical and contains second-ordered harmonic content that is utilized in various defensive systems [72]. Looking at the above discussion it is concluded that the core-forming technique must be such as core diffusion/saturation and generation of noise must be minimal. That might be obtained using amorphous/CRGO-based core material, overlap joints/ step lap joints at corners, and proper selection of the core.

94

3 Introduction to Magnetic Inrush of Power Transformer

Question-3: Does the accuracy of the presented method depend on the sampling rate? Answer: The selection of sampling rate is important here from a speed and accuracy point of view. Here, a 4 kHz sampling rate is selected. The lower sampling frequency (less than 1 kHz) will decrease the efficacy of the presented scheme, as it is not giving ample data. If a higher sampling frequency (>8 kHz) is chosen then the execution time will be large and also impose complexity in algorithm buildup. Hence, the sampling rate should be chosen wisely considering Nyquist’s theorem and system dynamics. Question-4: How the switching angle of the applied voltage is considered in this study? Answer: For the sake of understanding the “breaker switching angle” or “fault inception angle”, consider 50 Hz one cycle of voltage waveform which has the time of 0–0.02 s (20 ms) or in other words has a degree of 0 to 360. Hence, if we apply fault at 0.01 s (half time of one cycle), i.e., it is applied at 180°. If we apply fault at 0.005 s (quarter time of one cycle), i.e., the fault inception angle is 90°. In this connection, if the fault is applied at 0.2 s, i.e., it is applied at 0°. Subsequently, if the fault is applied at 0.2025 s, i.e., it is applied at 45°. This way the breaker switching angle and fault initiation instance are considered around 0 to 360° of applied voltage.

Appendices Appendix 1

S. No.

Parameter

Values

Three-phase voltage source-1 1.

Normal power

100 MVA

2.

Phase-to-phase RMS voltage

400 kV

3.

Frequency

50 Hz

4.

Phase angle

0°

5.

Positive sequence impedance

0.5 Ω with 85°

6.

Zero sequence impedance

1 Ω with 85° (continued)

References

95

(continued) S. No.

Parameter

Values

Three-phase transformer parameter 1.

Normal power

100 MVA

2.

Frequency

50

3.

Leakage reactance (changed)

0.1 pu to 0.0001pu

4.

Magnetizing current

0.4%

5.

Voltage (primary/secondary)

400/220 kV

6.

Saturation placed on winding

First (primary)

7.

Knee voltage

1.25 pu

CTp = 150/5 amp and CTs = 300/5 amp

Appendix 2 Transformer data

:

2 KVA, 220/110 V, 1-phase, 50-Hz, %Z = 12

CT data

:

Primary side: 10/5 amps, 15 VA, 5p10 and for secondary side 20/ 5 amps, 15 VA, 5p10

Load

:

Lamp load, 25 A

Source data primary side

:

1-phase, 0–300 V, 50-Hz, variable supply from the electricity board

Source data secondary side

:

1-phase, 0–150 V, 50-Hz, variable supply from AC variac, electricity board

References 1. Rushton J (1995) In: Mewes KG (rev) Power system protection, 2nd edn. The Institutes of Electrical Engineers, London 2. Brownlee WR (1944) Transformer magnetizing inrush currents and influence on system operation. Trans Am Inst Electr Eng 63(6):423–500. https://doi.org/10.1109/T-AIEE.1944.505 8951 3. Blume LF, Camilli G, Farnham SB, Peterson HA (1944) Transformer magnetizing inrush currents and influence on system operation. Trans Am Inst Electr Eng 63(6):366–375. https:// doi.org/10.1109/T-AIEE.1944.5058946 4. Kulkarni SV, Khaparde SA (2004) Transformer engineering: design and practice. Taylor & Francis 5. Dashti H, Davarpanah M, Sanaye-Pasand M, Lesani H (2016) Discriminating transformer large inrush currents from fault currents. Int J Electr Power Energy Syst 75:74–82. https://doi.org/ 10.1016/j.ijepes.2015.08.025

96

3 Introduction to Magnetic Inrush of Power Transformer

6. Sobrinho AM, Camacho JR, Malagoli JA, Mamede ACF (2016) Analysis of the maximum inrush current in the otimal design of a single phase transformer. IEEE Lat Am Trans 14(12):4706–4713. https://doi.org/10.1109/TLA.2016.7817001 7. Elmore WA (2003) Protective relaying theory and applications, 2nd edn. Marcel Dekker, New York, Basel 8. Wang Y, Liu Z, Chen H (2017) Research on residual flux prediction of the transformer. IEEE Trans Magn 53(6):1–4. https://doi.org/10.1109/TMAG.2017.2664886 9. Peng F, Gao H, Liu Y (2018) Transformer sympathetic inrush characteristics and identification based on substation-area information. IEEE Trans Power Deliv 33(1):218–228. https://doi.org/ 10.1109/TPWRD.2017.2730854 10. Rudez U, Mihalic R (2016) Sympathetic inrush current phenomenon with loaded transformers. Electr. Power Syst. Res. 138:3–10. https://doi.org/10.1016/j.epsr.2015.12.011 11. Patel D, Chothani N (2020) Digital protective schemes for power transformer, 1st edn. Springer, Singapore 12. van Warrington ARC (2012) Protective relays: their theory and practice, vol 1. Springer 13. Hamilton R (2013) Analysis of transformer inrush current and comparison of harmonic restraint methods in transformer protection. IEEE Trans Ind Appl 49(4):1890–1899. https://doi.org/10. 1109/TIA.2013.2257155 14. Patel DD, Mistry KD, Chothani NG (2016) Digital differential protection of power transformer using DFT algorithm with CT saturation consideration. In: 2016 national power systems conference (NPSC), Dec 2016, pp 1–6. https://doi.org/10.1109/NPSC.2016.7858854 15. Patel D, Chothani N, Mistry K (2018) Discrimination of inrush, internal, and external fault in power transformer using phasor angle comparison and biased differential principle. Electr Power Components Syst 46(7):788–801. https://doi.org/10.1080/15325008.2018.1509915 16. Patel D, Chothani N (2020) CT saturation detection and compensation algorithm. In: Digital protective schemes for power transformer. Springer, Singapore, pp 33–49 17. Patel D, Chothani N (2020) Adaptive digital differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 83–106 18. Patel DD, Chothani N, Mistry KD, Tailor D (2018) Adaptive algorithm for distribution transformer protection to improve smart grid stability. Int J Emerg Electr Power Syst 19(5):1–14. https://doi.org/10.1515/ijeeps-2018-0022 19. Patel DD, Mistry KD, Chothani NG (2015) A novel approach to transformer differential protection using sequence component based algorithm. J. CPRI 11(3):517–528 20. Patel D, Mistry KD, Raichura MB, Chothani N (2018) Three state Kalman filter based directional protection of power transformer. In: 20th National power systems conference (NPSC), pp 1–6. https://doi.org/10.1109/NPSC.2018.8771716 21. Alencar RJN, Bezerra UH, Ferreira AMD (2014) A method to identify inrush currents in power transformers protection based on the differential current gradient. Electr Power Syst Res 111:78–84. https://doi.org/10.1016/j.epsr.2014.02.009 22. Hosny A, Sood VK (2014) Transformer differential protection with phase angle difference based inrush restraint. Electr Power Syst Res 115:57–64. https://doi.org/10.1016/j.epsr.2014. 03.027 23. Patel D, Chothani N (2020) Real-time monitoring and adaptive protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 173–190 24. Chothani NG, Raichura MB, Patel DD, Mistry KD (2019) Real-time monitoring protection of power transformer to enhance smart grid reliability. Electr Control Commun Eng 15(2):104– 112. https://doi.org/10.1109/EPEC.2018.8598427 25. Sahebi A, Samet H (2017) Efficient method for discrimination between inrush current and internal faults in power transformers based on the non-saturation zone. IET Gener Transm Distrib 11(6):1486–1493. https://doi.org/10.1049/iet-gtd.2016.1086 26. Patel DD, Chothani NG, Mistry KD (2015) Sequence component of currents based differential protection of power transformer. In: 12th IEEE international conference electronics, energy, environment, communication, computer, control: (E3–C3), INDICON, pp 1–6. https://doi.org/ 10.1109/INDICON.2015.7443855

References

97

27. Patel D, Chothani N (2020) Phasor angle based differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 51–81 28. Zhang LL, Wu QH, Ji TY, Zhang AQ (2017) Identification of inrush currents in power transformers based on higher-order statistics. Electr Power Syst Res 146(Supplement C):161–169. https://doi.org/10.1016/j.epsr.2017.01.029 29. Ali E, Helal A, Desouki H, Shebl K, Abdelkader S, Malik OP (2018) Power transformer differential protection using current and voltage ratios. Electr Power Syst Res 154:140–150. https://doi.org/10.1016/j.epsr.2017.08.026 30. Ahmadi M, Samet H, Ghanbari T (2015) Discrimination of internal fault from magnetising inrush current in power transformers based on sine-wave least-squares curve fitting method. IET Sci Meas Technol 9(1):73–84. https://doi.org/10.1049/iet-smt.2014.0012 31. Chothani NG, Patel DD, Mistry KD (2017) Support vector machine based classification of current transformer saturation phenomenon. J. Green Eng. River Publ. 7:25–42. https://doi. org/10.13052/jge1904-4720.7122 32. de Alencar RJN, Ferreira AMD (2016) Transformer inrush currents and internal faults identification in power transformers using wavelet energy gradient. J Control Autom Electr Syst 27(3):339–348. https://doi.org/10.1007/s40313-016-0236-4 33. Youssef OAS (2003) A wavelet-based technique for discrimination between faults and magnetizing inrush currents in transformers. IEEE Trans Power Deliv 18(1):170–176. https://doi.org/ 10.1109/TPWRD.2002.803797 34. Maya P, Roopasree K, Soman KP (2015) Discrimination of internal fault current and inrush current in a power transformer using empirical wavelet transform. Procedia Technol 21:514– 519. https://doi.org/10.1016/j.protcy.2015.10.038 35. Abbas MF, Zhiyuan L, Zhiguo H, Guanjun Z (2016) Inrush current discrimination in power transformer differential protection using wavelet packet transform based technique. In: 2016 IEEE PES Asia-Pacific power and energy engineering conference (APPEEC), Oct 2016, pp 944–948. https://doi.org/10.1109/APPEEC.2016.7779634 36. Marques JP, Junior JC, De Morais AP (2018) New methodology for identification of sympathetic inrush for a power transformer using wavelet transform, vol 16, no 4, pp 1158–1163 37. Yazdani-Asrami M, Taghipour-Gorjikolaie M, Razavi SM, Gholamian SA (2015) A novel intelligent protection system for power transformers considering possible electrical faults, inrush current, CT saturation and over-excitation. Int J Electr Power Energy Syst 64:1129–1140. https://doi.org/10.1016/j.ijepes.2014.08.008 38. Balaga H, Gupta N, Vishwakarma DN (2015) GA trained parallel hidden layered ANN based differential protection of three phase power transformer. Int J Electr Power Energy Syst 67:286– 297. https://doi.org/10.1016/j.ijepes.2014.11.028 39. Naghizadeh RA, Vahidi B, Hosseinian SH (2016) An adaptive approach for simulation of inrush current in three-phase transformers considering hysteresis effects. Electr Power Components Syst 44(6):673–682. https://doi.org/10.1080/15325008.2015.1122102 40. Dharmesh Patel NC (2020) Relevance vector machine based transformer protection. In: Digital protective schemes for power transformer, 1st edn. Springer, Singapore, pp 107–131 41. Patel D, Chothani NG, Mistry KD, Raichura M (2018) Design and development of fault classification algorithm based on relevance vector machine for power transformer design and development of fault classification algorithm based on relevance vector machine for power transformer. IET Electr Power Appl 12(4):557–565. https://doi.org/10.1049/iet-epa.2017.0562 42. Patel D, Chothani N (2020) HE-ELM technique based transformer protection. In: Digital protective schemes for power transformer. Springer, Singapore, pp 133–172 43. Raichura MB, Chothani NG, Patel DD (2020) Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique. IET Sci Meas Technol 14(1):111–121. https://doi.org/10.1049/iet-smt.2019.0102 44. Raichura M, Chothani N, Patel D (2021) Efficient CNN-XGBoost technique for classification of power transformer internal faults against various abnormal conditions. IET Gener Transm Distrib 15(5):972–985. https://doi.org/10.1049/gtd2.12073

98

3 Introduction to Magnetic Inrush of Power Transformer

45. Lin X, Ma J, Tian Q, Weng H (2013) Electromagnetic transient analysis and novell protective relaying techniques for power transformers 46. Zhang AQ, Ji TY, Li MS, Wu QH, Zhang LL (2018) An identification method based on mathematical morphology for sympathetic inrush. IEEE transactions on power delivery 47. Wu Q, Ji T, Li M, Wu W (2016) Using mathematical morphology to discriminate between internal fault and inrush current of transformers. IET Gener Transm Distrib 10(1):73–80. https:/ /doi.org/10.1049/iet-gtd.2015.0216 48. Ning G, Aiyuan W, Jie W, Haitao L, Zhong R (2013) Analysis and reduction of magnetizing inrush current for switch-on unloaded transformer. In: 2013 2nd international symposium on instrumentation and measurement, sensor network and automation (IMSNA), Dec 2013, pp 1022–1026. https://doi.org/10.1109/IMSNA.2013.6743455 49. Toki´c A, Milardi´c V, Ugleši´c I, Jukan A (2015) Simulation of three-phase transformer inrush currents by using backward and numerical differentiation formulae. Electr Power Syst Res 127 50. Hong C, Haifeng L, Hua L, Jiran Z, Haiguo T, Zhidan Z (2017) Waveform complexity analysis of differential current signal to detect magnetizing inrush in power transformer. In: 2017 9th International conference on measuring technology and mechatronics automation (ICMTMA), Jan 2017, pp 120–123. https://doi.org/10.1109/ICMTMA.2017.0037 51. Bertotti G (1998) Hysteresis in magnetism : for physicists, materials scientists, and engineers. Hysteresis Magn xii–xvii. https://doi.org/10.1016/B978-012093270-2/50049-0 52. Chiesa N, Mork BA, Hoidalen HK (2010) Transformer model for inrush current calculations: simulations, measurements and sensitivity analysis. IEEE Trans Power Deliv 25(4):2599–2608. https://doi.org/10.1109/TPWRD.2010.2045518 53. Yabe K (1997) Power differential method for discrimination between fault and magnetizing inrush current in transformers. IEEE Trans Power Deliv 12(3):1109–1118. https://doi.org/10. 1109/61.636909 54. Mistry DDPKD, Chothani NG (2017) Transformer inrush/internal fault identification based on average angle of second order derivative of current. In: Asia-Pacific power and energy engineering conference, APPEEC, pp 1–6. https://doi.org/10.1109/APPEEC.2017.8309017 55. Hooshyar A, Afsharnia S, Sanaye-Pasand M, Ebrahimi BM (2010) A new algorithm to identify magnetizing inrush conditions based on instantaneous frequency of differential power signal. IEEE Trans Power Deliv 25(4):2223–2233. https://doi.org/10.1109/TPWRD.2010.2040844 56. Abniki H, Monsef H, Khajavi P, Dashti H (2010) A novel inductance-based technique for discrimination of internal faults from magnetizing inrush currents in power transformers. In: 2010 Modern electric power systems, Sept 2010, pp 1–6 57. Hooshyar A, Sanaye-Pasand M, Afsharnia S, Davarpanah M, Ebrahimi BM (2012) Timedomain analysis of differential power signal to detect magnetizing inrush in power transformers. IEEE Trans Power Deliv 27(3):1394–1404. https://doi.org/10.1109/TPWRD.2012.2197869 58. Cazacu E, Petrescu L (2014) Magnetising inrush current of low-voltage iron core three phase power reactors. In: 2014 16th International conference on harmonics and quality of power (ICHQP), May 2014, pp 843–847. https://doi.org/10.1109/ICHQP.2014.6842874 59. Moradi A, Madani SM, Sadeghi R (2016) Impact of load power factor on sympathetic inrush current. In: 2016 24th Iranian conference on electrical engineering (ICEE), May 2016, pp 1416–1421. https://doi.org/10.1109/IranianCEE.2016.7585743 60. Tang WH, Wu QH (2011) Condition monitoring and assessment of power transformers using computational intelligence, vol 58, 1st edn. Springer, London, New York 61. Guimarães R, Delaiba AC, Oliveira JC, Saraiva E, Rosentino AJJP (2013) Electromechanical forces in transformers caused by inrush currents: an analytical, numerical and experimental approach. J Control Autom Electr Syst 24(6):863–872. https://doi.org/10.1007/s40313-0130068-4 62. Bagheri S, Moravej Z, Gharehpetian GB (2018) Classification and discrimination among winding mechanical defects, internal and external electrical faults, and inrush current of transformer. IEEE Trans Ind Inf 63. Ramírez-Niño J, Haro-Hernández C, Rodriguez-Rodriguez JH, Mijarez R (2016) Core saturation effects of geomagnetic induced currents in power transformers. J Appl Res Technol 14(2):87–92. https://doi.org/10.1016/j.jart.2016.04.003

References

99

64. Raichura M, Chothani N, Patel D (2020) Improved transformer differential protection by adaptive pickup setting during momentary over-fluxing condition. Adv Electr Electron Eng 18(3). https://doi.org/10.15598/aeee.v18i3.3833 65. Raichura M, Chothani N, Patel D (2020) Development of an adaptive differential protection scheme for transformer during current transformer saturation and over-fluxing condition. Int Trans Electr Energy Syst 31:1–19. https://doi.org/10.1002/2050-7038.12751 66. Raichura M, Chothani N, Patel D, Sharma J (2020) Methodologies for the detection of magnetizing inrush and fault condition in power transformer. In: 2020 IEEE international conference on computing, power and communication technologies (GUCON), Oct 2020, pp 146–151. https://doi.org/10.1109/GUCON48875.2020.9231065 67. Patel D, Chothani N (2020) Introduction to power transformer protection. In: Digital protective schemes for power transformer. Springer, Singapore, pp 1–31 68. Chen HC, Wu PH, Cheng PT (2016) A transformer inrush reduction technique for low-voltage ride-through operation of renewable converters. IEEE Trans Ind Appl 52(3):2467–2474. https:/ /doi.org/10.1109/TIA.2016.2533497 69. Amiri P, Akhbari M (2017) Transient current limiter for suppressing transformer inrush, motor starting and fault currents in power system. IET Electr Power Appl 11(3):423–433. https://doi. org/10.1049/iet-epa.2016.0190 70. Cano-González R, Bachiller-Soler A, Rosendo-Macías JA, Álvarez-Cordero G (2017) Controlled switching strategies for transformer inrush current reduction: a comparative study. Electr Power Syst Res 145:12–18. https://doi.org/10.1016/j.epsr.2016.11.018 71. Balamurugan Saravaran AR (2017) Inrush blocking scheme in transformer differential protection. Energy Procedia, 117:1165–1171 72. Van Warrington ARC (1968) In: van Warrington ARC (ed) Protective relays: their theory and practice, 2nd edn. Chapman & Hall, London 73. Chothani NG, Raichura MB, Patel DD, Mistry KD (2018) Real-time monitoring protection of power transformer to enhance smart grid reliability. In: 2018 IEEE Electrical Power and Energy Conference (EPEC), Oct. 2018, pp. 1–6. https://doi.org/10.1109/EPEC.2018.8598427 74. Raichura M, Chothani N, Patel D, Mistry K (2021) Total Harmonic Distortion (THD) based discrimination of normal, inrush and fault conditions in power transformer. Renew Energy Focus 36, 43–55. https://doi.org/10.1016/j.ref.2020.12.001 75. Raichura MB, Chothani NG, Patel DD, Mistry KD (2019) Identification of inrush and fault conditions in power transformer using harmonic distortion computation. In: 2019 IEEE 1st International Conference on Energy, Systems and Information Processing (ICESIP), Jul. 2019, pp. 1–6. https://doi.org/10.1109/ICESIP46348.2019.8938308 76. Chothani N, Patel D, Raichura M (2019) Transformer protection with sequence components and digital filters, 1st ed. Latvia, European Union: LAP LAMBERT Academic Publishing, 2019 77. Patel D, Chothani N (2023) Evaluation of various dynamics on current transformer saturation with a model study on power system protection. In: Recent advances in Power Systems, pp. 121– 136 78. Raichura M, Chothani N, Patel D (2021) Review of methodologies used for detection of magnetising inrush and fault conditions in power transformer. IET Energy Syst. Integr 3,109–129. https://doi.org/10.1049/esi2.12012 79. IEEE Guide for Protecting Power Transformers (Revision of IEEE Std C37.91-2000). New York, USA: IEEE Power Engineering Society Sponsored by the Power System Relaying Committee, 2008

Chapter 4

Current Transformer Infrastructure and Its Application to Power System Protection

Abstract Progressively increasing demand for load forces the entire power system to uplift the infrastructure in terms of capacity and size. Moreover, the level of current increases multifold in EHV and UHV AC systems during peak load and severe fault conditions. Recording of this high value of current is necessary for the power system control and protection. Hence, current transformers (CTs) are inserted in every segment of the power system to scale down the actual current to a reasonable limit. However, due to the nonlinear core characteristic of the CTs, different parameters interfere in the measurement of the proper current during abnormal conditions like heavy fault, heavy burden, etc. Particularly in the unit type of protection, the selections of proper CTs are essential for developing an efficient protective system. Mismatch in parameters like CT ratio, Fault inception Angle (FIA), CT saturation, secondary burden, ratio and phasor error, higher retentivity, and presence of heavy remnant flux in the core gives major effects on measurement and analysis of the current signal in the protective scheme. All the said issues become a cause for the misoperation of the protective scheme. Various types of CT saturation effects occur on different types of relays like electromagnetic, static, or digital relays based on their protective arrangements. Sometimes, it is not possible to predict the outcome of the CT saturation effect on the relay protective algorithm. So, it is compulsory to have all basic fundamental knowledge regarding the possibilities of discrepancy and its probable solution on the protective schemes. Based on this knowledge, system engineers may be able to search for the solution to a problem in the protection field. Phase shifting and magnitude difference play an important role in measuring quantity. Different quantities of dissimilar frequencies cause the generation of distorted current signals. There is also non-uniformity generated in primary and secondary signals of the CT under core saturation, i.e., entering the core characteristics into a nonlinear region. This book chapter involves different parameters’ effects on CT saturation, its effect on software algorithms, and hardware analysis. The simulation is modeled using PSCADTM software for a wide range of data generation. In the last portion, how to detect CT saturation based on the saturation index in transformer protection has been elaborated pleasantly.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Chothani et al., Advancement in Power Transformer Infrastructure and Digital Protection, Studies in Infrastructure and Control, https://doi.org/10.1007/978-981-99-3870-4_4

101

102

4 Current Transformer Infrastructure and Its Application to Power System …

4.1 Basic of Current Transformer (CT) A Current Transformer (CT) is a current sensing tool in the power system. Due to its prime importance for sensing the current, it is most important to know the basics of CT. There are so many points required to design and selection of CT as per the requirement of the protective system. Accuracy, class of protection, burden, bushing type, thermal rating, Geometric induced current (GICs), knee point voltage, turns ratio, rated primary/secondary current, remanence, saturation factor/voltage, residual flux density, and time to saturate are crucial factors of CT to be accounted while designing the different protective scheme. Furthermore, types of core design with some basic knowledge are also required to propose a particular protective scheme. Sometimes, these parameters are getting violation under heavy fault or any heavy abnormalities. Also in the power system, the fault inception angle plays a major role in the characteristics of the current. All the above-said parameters are affected under the fault conditions and they are supposed to create any nuisance for sensing the real current signal of the power system. In this study, various power system effects on CT performance are included with its effect on protective schemes. For the realization of CT parameter deviation, PSCADTM software is considered with changing various system constraints. When the CT core enters its saturation zone, protective schemes get influenced and misbehave from its designed path. Different types of constraints and internal/external parameter variations are included in this chapter with suggested techniques to detect the CT saturation in power transformer protection. The effect of linear and nonlinear characteristics produces unusual behavior during various faults imparted at varying switching angles. Under normal conditions of the power system, measuring and protective element demands proper information on voltage/current signals and works satisfactorily as per the suggested algorithm. However, the power system ranges in terms of kA and kV, measuring system captures the signal in ampere and voltage with lower ranges. So, to scale down this range of parameters, CTs and PTs are used. Protective and measuring elements are electrically isolated on the same core of the instrument transformers. Based on the signal sensed by these instrument transformers, the accuracy of relaying algorithm is validated for that, the CT must provide very less resistance to the primary current as it is connected in series with the line. Similarly, the secondary CT must be connected to a low-burden relaying scheme. After due consideration of all precautions for designing CT, some abnormalities may arise due to unavoidable situations in the operation of CT. Core saturation is directly affected by the selection of core material and its parameters. Different types of ferromagnetic core materials like iron, silicon steel, CRGO, amorphous, and nanocrystalline are used in designing the CT core. Different parameters like saturation index, magnetostriction effect, resistivity, retentivity, the thickness of the stamping, and laminations are also taken into account. Moreover, temperatures like crystallization T x (°C) and curie temperatures T c (°C) are also considered while selecting the material for the CT core.

4.1 Basic of Current Transformer (CT)

103

In an HVAC system to sense the voltage and current within a certain limit and for further analysis, the magnitude is drastically reduced from its original value with the help of CTs and PTs. They are electrically isolated but magnetically coupled means they provide minimum burden. CT saturation is the prime issue in the power system due to the heavy fault current and nonlinear characteristics of the CT core. Some standards are provided by IEEE to classify the protective and measuring CTs [1]. As per IEEE guidelines and accuracy level, CTs are classified as C, K, T, H, and L types. Further, detailed categorization of CT is given here in Table 4.1. Time to saturate depends on many factors like the degree of fault current (fault inception angle (FIA)), the magnitude of fault current, the presence of remnant flux in CT core, saturation voltage, turns ratio, winding resistance, Ohmic burden, and secondary impedance, etc. Many researchers have developed a different algorithm to detect CT saturation in the power system with a different foundation. Wave shape-based signal analysis with a mathematical derivative scheme is designed against CT saturation in the power system [2]. DC decaying component plays a major role during the CT saturation and fault [3]; however, only simulation is carried out, and hardware implementation is remain left. The window length of the waveform varies during abnormal conditions and faults, based on these tactics, an effective protective scheme is developed [4]. The effect of CT saturation on transmission line protection becomes an enlightening issue [5]. In this scheme, adaptive fuzzy logic technique-based validation is taken place and proves its effectiveness of 100%. However, test results are validated with very smaller data. IEC standard-based CT saturation calculation and its effect with a generalized view is elaborated effectively [6] with a basic idea. Based on the point of zero crossing distance, CT saturation is validated through EMTP software and Table 4.1 Remarks based on codes of CT S. No.

Code/types of CTs

Remarks

1

C

• The leakage flux is very low • The performance of CT is directly proportional to its excitation characteristics

2

K

• Mostly, the “K”-type characteristic of CT is the same as the “C” type. However, a larger cross section is required compared to “C”-type CT • KPV >70% of the V s (Here, KPV = Knee point Voltage and V s = CT secondary Voltage)

3

T

• High ratio error so it must be tested • High flux leakage effect in the core

4

H

• 2.5 and 10% are two accuracy modules renowned in the specification of 10L200, 2.5H400, etc. Here, 10 and 2.2 (first number) are the accuracy classes and 200 and 400 are secondary voltages • CTs are designed based on 5–20 times the burden for the normal current • Old designed and manufactured (>year before 1954)

5

L

• Designed for normal operation with 20 times rated current • Old designed and manufactured (>year before 1954)

104

4 Current Transformer Infrastructure and Its Application to Power System …

certain formulation [7]. Support Vector Machine (SVM) is a classifier technique to classify the healthy and unhealthy conditions of a system. The SVM technique is realized on a large number of data sets of training and testing to classify the CT saturation conditions with higher accuracy [8]. However, implementation of the proposed schemes on hardware is not exercised. Waveform phasor and its magnitude-based data analysis using the DFT algorithm is also suggested for CT saturation detection in the protective scheme [9–11]. A certain level of current signal derivation gives numerous information regarding signal distortion. Based on this information, CT saturation is discriminated. Fourth-level [12] and second-level [13, 22] derivatives are used to drive the CT saturation conditions from the power system with hardware implementation. An improved version of the DFT algorithm (Modified Full Cycle DFT) gives the best results against noise [14]. Three-state Kalman filter is validated with improved transformer percentage biases differential protection [15]. During the core saturation of the current transformer, THD is also generated. Based on these estimated THD values, saturation conditions of the CT are discriminated in transformer protection [16, 17]. Adaptive percentage-biased characteristics based on CT saturation conditions are adopted with linear and nonlinear load conditions [18–20]. During the transient region operation of CT, its dynamic parameters, coefficient, and time to saturate the CT are analyzed properly [21]. However, it is also observed that system parameters give a significant effect on the dynamics of CT behavior. Analysis of the nonlinear differential equations provides one scope for the CT saturation assessment [22]. This technique elaborates DC offset and CT core saturation, but the effect of remnant flux and over-fluxing conditions remain unclear. Dynamic behavior of the CT saturation based on current magnitude and phasor calculation with predefine index between two current samples give proper suggestions regarding the time to saturate the CT [23]. However, phasor value is also affected by system harmonics and noise, so it must be accounted for in the index considerations. A Controlled Voltage Source (CVS) is connected with relaying system to provide compensation against the saturation region of the CT and it balances the generated voltage across the burden of the CT [24]. In another way, it requires more concentration on FIA and DC decaying components of the system. Least Square Error (LSE) and Artificial Neural Network (ANN)-based detection of CT saturation along with reconstruction of the distorted signal proposed in [25]. LSE and ANN-based techniques provided a minor variation of the system parameters and noise. It is also possible to suppress the CT saturation with Flux Condition Flag (FCF) technique same as the compensation [26, 27]. CT saturation is detected by Sound to Noise Ratio (SNR) under presence of the noise [28]. CT saturation is explained based on the loss coordination of the partial differential with its time interval coordination effectively [29]. Numerous PSCADTM -based simulation results are captured after developing a controlling algorithm based on current derivation techniques to archive the saturation condition of CT in the power system [30]. Wave shape monitoring and recognition are also effective analyses of the distorted secondary signal under CT saturation [31]. Kalman filtering techniques are also the most popular for decomposing the signal; based on these techniques, partial saturations are also recognized sharply [32]. Condition monitoring of the power transformer and fault analysis also includes the impact of the CT saturation

4.2 Design Consideration of Current Transformer

105

on percentage bias differential relay with some special cases [33–35]. The alone LSE technique is also implemented to compensate for the CT saturation in the power system [36]. Even now a day to achieve the highest accuracy level, many classifier and regression techniques [37–40] are used to discriminate the external abnormalities from the internal fault conditions. All these techniques utilize numerous data and are tuned to perform with the highest fault classification accuracy. However, for actual testing, data collection from the real field of the power system is a major issue. Due to major issues involved through the saturation of CT in unit-type protection of the equipment, it is required to properly identify the level of CT saturation for the protective schemes in the power system. Having several parameters of the CT, the concerned power system and associated metering and protection schemes incline to operate in abnormal conditions. The effects of such dynamic parameters of CT on the power system are elaborated here in this chapter. The first session of this chapter provides basic details of the parameters violations of the power system and inherent parameters of CT encountered. The same effect is also validated in a laboratory environment on actual CT connected to a prototype power system network. A case study of transformer protection with the effect of CT saturation and its detection based on a derivation of the saturation index is elaborated in the last part of the chapter. All the said unwanted conditions are discriminated properly using DFT analysis and suggested saturation index-based algorithm [47].

4.2 Design Consideration of Current Transformer Current transformers (CTs) are essential to drop the current rating of the power system up to a measurable value. Ratings of the HVAC system are very large concerning the current ratio of the instrument transformers used for protective purposes. Many turns and ampere ratings differ drastically to patch up the secondary current requirement of the CT. Due to the said reason designing the core of the CT become more complicated and require dense concentration for HVAC. Rather than the core magnetizing property of CT, parameters like a secondary burden, fault inception time, remnant flux, and magnitude of the fault current major impact on the CT core saturation. Figure 4.1 depicts the equivalent circuit of CT with all the parameters very well labeled. The instrument Transformer core becomes saturated with certain conditions only; otherwise, it always operates in the linear region of the characteristic. It means, CT secondary always gives a replica of its primary winding unless it is saturated. Different current distribution is shown as per the Fig. 4.1, and the equation terms as IM + ILOSS = IE & IST = IS + IE  IP =

 N2 N2 = CT Turns Ratio (IS + IE ), Where N1 N1

106

4 Current Transformer Infrastructure and Its Application to Power System …

Fig. 4.1 Equivalent simplified circuit of CT

VB = VS − (IS ∗ RS ) VS = IS (RS + Z S ) Various means are elaborated to examine the CT saturation factor as discussed earlier. However, as per the above analysis of the circuit diagram, it is observed that the secondary terminal voltages are directly proportional to the burden. When the low burden is connected, the saturation current is also reduced, and vice versa. The amount of burden to be connected to secondary CT will be decided based on the accuracy class of CT. One of the ways to define a saturation factor is by taking a ratio of secondary current to maximum voltage or excitation to knee voltage. Saturation factors under normal conditions and during high secondary burden are given as IF × Z B 100 × Z C

(4.1)

IF × (Z B + RS ) 100 × (Z B + RS )

(4.2)

SF = SF' =

Comparison of both equations with ratio analysis is given as SF ZC (Z B + RS ) = × SF' ZB (Z C + RS )

(4.3)

4.2 Design Consideration of Current Transformer

107

It is predefined that Eq. 4.3 depends on Z B, Z C, and internal resistance of CT. Rating of the fault current varies as per the types and parameters of the fault. However, the designing of the CT core is done by accounting for possible severe fault current ratings. When the CT core undergoes saturation, secondary winding are getting a distorted waveform for the primary winding waveform. During saturation, AC and DC both fluxes are settled as φAC and φDC sequentially in the secondary of the CT. φDC causes the decaying DC component in the system and it is a function of the time constant of the secondary circuit. The primary of the CT is connected in series with the line of the power system, so the primary winding time constant is not a variable component. The secondary winding time constant is derived based on secondary burden and leakage impedance. The burden of the CT secondary winding, the value of the primary current (normally >20 times its rated current), remnant core flux, and primary current asymmetry impose a major impact on the saturation of CT secondary. The remnant flux in the core is zero when CT is energized the first time, that is, at t = 0, remnant flux = 0; thus, V2 = N2 1 φ(t) − φ(0) = N2

dφ dt

(4.4)

t v2 dt 0

 R I0  −t = τ 1−e τ N2  L I0  −t 1−e τ = N2  L I0  −t 1−e τ φ(t) = φ(0) + N2  L I0  −t 1−e τ = N2

(4.5)

It is proved that the flux setup exponentially increases with its peak value as L I0 as t →∝ N2 L Vm Vm max = = = φdc |Z | N |Z |

φdmax c =

(4.6)

Equation (4.6) represents flux as unidirectional, induced by sinusoidal AC voltage. The flux setup in a practical CT core during transient conditions is a combination of AC and DC flux.

108

4 Current Transformer Infrastructure and Its Application to Power System …

Replace dtd by j ω, to define AC induced flux, and to define phase comparison between V 2 and φ ac as φ=

V2 j ωN2

(4.7)

If, V2 (t) = Vm sin(ωt + φ), then φac =

 Vm π sin ωt + φ − ωN2 2

(4.8)

Maximum flux setup is defined as max φac =

Vm ωN2

If Vm = R2 I0max . Therefore, max φac =

R2 I0max ωN2

Peak flux is defined as max max φac + φdc =

Vm L I max + 0 ωN2 N2

(4.9)

In practice, during flux passing through the saturation region of the magnetizing curve (B–H curve, beyond knee point “K”), the CT secondary core gets saturated as shown in Fig. 4.2. The knee point in CT is defined as per the non-gapped CT or gapped CT in the core as per the abscissa angle with its tangent. It is getting 45-degree and 30-degree variations with the abscissa tangent sequentially for the defining knee point. A second reason for saturation is based on open-circuited or high burden voltage, which raise to 50% when 10% of excitation current is increased, this point is considered as the knee point of these CTs. Thus, CT operates in a nonlinear region of its characteristic beyond point “K” as shown in Fig. 4.2a, and not gives an actual replica of the primary winding on the secondary side. So, the unit type of protection (so-called very sensitive for the two signal analysis) may mal-operate during severe fault appears in the outer region of its premises. Due to these reasons, it is very necessary to identify the CT saturation in the power system with proper reason. After CT saturation perception, further steps are taken such as suppression, compensation, elimination, or discrimination in the power system with various bases. To make further analysis, now it is necessary to evaluate the effect of various parameters effects on CT saturation.

4.2 Design Consideration of Current Transformer

109

Fig. 4.2 CT saturation curve

4.2.1 Over-Sizing Factors of CT As discussed earlier, there is a combined effect of the AC and DC flux on the CT under saturation conditions. This means the CT core section design is based on the max max (φac + φdc ) flux densities during its operation in the power system. Moreover, a core section is designed as per the knee point factor of the CT characteristic (Fig. 4.2)

110

4 Current Transformer Infrastructure and Its Application to Power System …

and this knee point is decided by the fault magnitude in the primary and burden in the secondary winding. The core over-sizing is decided on the flux set up by AC and DC components as given max max + φdc ) (φac max φac φ max Core-over sizing factor = 1 + dc max φac  L I0 N 2  =1+ R I0 ωN2 ωL =1+ R X =1+ R

Core-over sizing factor =

(4.10)

Here, the X/R value is defined as per the size and rating of the power system. For the LV and MV rating of the power system, the X/R ratio is lower than 10, whereas, for EHC and UHV systems, it is in the range of 10–20. So, it is suggested to design the core to sustain flux at 20 times the normal current rating from economic and technical points of view. Moreover, the high X/R ratio of a system involves DC components in fault current and makes the signal very asymmetrical and last for a longer time.

4.3 Diminishing the Effects of CT Saturation Normally, CT selection is differentiated as per the requirement of protective purpose or measurement. The class of accuracy for both these CTs are different and have different KPVs of operation [41]. Also, the study of the CT steady-state performance with the help of a secondary excitation curve is based on phasor angle error and magnitude ratio error.

4.3.1 Time-to-Saturation The time of CT saturation is measured just after the inception of the fault in the power system. Different dynamic parameters of the CT are getting affected during the saturation. Based on the comparative analysis of these parameters and different techniques, the time to saturate is estimated precisely. In case the fault current is higher than 20 times the normal CT rating, core saturation occurs. FIA, remnant flux, secondary burden, knee point, and turns ratio, are also make an effect on CT saturation. In a nutshell, all these dynamic parameters such as FIA, remnant flux,

4.3 Diminishing the Effects of CT Saturation

111

secondary burden, CT ratio, fault current amplitude, waveform asymmetry, knee point, etc., may impact the time to saturate CT. (a) Avoid CT saturation by measuring ct secondary voltage Saturation voltage (CT secondary voltage) can carry the burden without distortion and is defined as V X > IS ∗ Z S

(4.11)

where I S = Primary current/Turns ratio and Z S = Total secondary burden (RS + X L + Z B ). (b) Saturation voltage setting under effect of the DC decaying component Again, the voltage should not be greater than the suggested KPV range when DC decaying components are appeared as an effect of the X/R ratio, as per Eq. 4.12.  X VX > I S ∗ Z S 1 + R

(4.12)

where X = Primary system reactance and R = Resistance at fault point. Under the inductive burden equation reformed as Eq. 4.13, 



V X > IS ∗ Z S 1 +

X RS + RB ∗ R ZS

 (4.13)

(c) Considering remnant flux effect If the CT saturates at the time of every energization, then the effect of the remnant flux is considered as per Eq. 4.14: ⎡ VX > ⎣

 IS ∗ Z S 1 + XR ∗

RS +RB ZS

1 − ∅r

 ⎤ ⎦

(4.14)

where ∅r = Remnant flux. Including the above three analyses, time-to-saturate is defined as per Eq. 4.15: 

KS − 1 TS = −T1 In 1 −  X  R

where T S = Time-to-saturate, In = Natural Log Function,

 (4.15)

112

4 Current Transformer Infrastructure and Its Application to Power System …

T 1 = Primary System Time constant, X = Reactance up to Fault, ω = 2π f (where f = System Frequency), R = Resistance up to Fault, K S = Saturation factor = (V X /V S ) = (Saturation Voltage/I S (RS + RB ).

4.3.2 Required Caution in CT Optimal Choice The selection of CT directly affects the quality and reliability of the protective schemes. • Rating of the CT must be accorded with maximum load current. For example, if the maximum load current is 1400 A, then 1500:1 or 1500:5 Amp CT is suitable. However, it is not favored to provide a 1000:5 or 1000:1 Amp current rating. • Rated fault current up to 20 times the rated secondary current is allowed. It is very difficult to provide a higher rating as per the economic consideration of designing the CT. For example, if a 500:5 Amp CT rating is considered then it is preferred to have a fault current of 20 times the primary current rating. It means 10,000 Amps fault current is safer to reproduce perfect CT secondary current as per primary fault current. • CT voltage rating must be matched with the knee point saturation voltage. This means the operating secondary voltage must be lower than the KPV of saturation characteristic. Saturation voltage (V X ) > I S * (RS + X L + Z B ) as per Eqs. 4.11– 4.14.

4.4 Consequences of CT Saturation on Protective Relays In a power system, different types of relay constructions and schemes are employed for the protection of grid components.

4.4.1 Impact of CT Saturation on Electromechanical Relays Nowadays, in the era of digitalization of the protective scheme, electromagnetic types of relaying schemes are not preferred in the smart power system. However, they are still used in distribution and low-voltage systems. The operation of these types of relays is directly proportional to the RMS current flowing through them. The inductive coil of the electromagnetic relay offers a high burden on CT secondary and leads to saturation. On the other hand, during the saturation phenomena of the current

4.5 Important Points to Select CTs for Protective Schemes

113

transformer, generated current frequency differs from the fundamental frequency. Due to the frequency difference, a phase shift is generated between the fundamental and saturated current frequency signals. Due to the said phase shifting, a generated circulating current may produce a torque on the disc of the relay. Due to this torque, relay may be given mal-operation in protective schemes.

4.4.2 Impact of CT Saturation on Static/Digital Relays Static and digital relays are very sensitive relays. Normally, in a power system, the captured signal is first converted from analog to digital form by applying different filtrations processes. Average values are estimated from the derived signals. Thus, the response of the relay depends on the methodology of the average current estimation and processor used in the relay.

4.4.3 Influence of CT Saturation on Differential Relays As a unit-type protection of any equipment, the differential relay is used as a very sensitive and reliable protection scheme. As far as accuracy is concerned, the differential relay gives a most reliable operation than other relaying schemes. The basic purpose of this relay is to operate under internal faults and remain stable against the external fault and all other outside abnormalities in the power system. In a differential relay, CT saturation depends on the burden on the secondary winding as well as the level of fault current. Conditions of the CT saturations become serious when the fault current is higher than 20 times the rated primary current. There is a possibility to set the percentage bias characteristics of the differential relay as per the requirement of the protective. However, sometimes, differential relay may mis-operate during severe CT saturation.

4.5 Important Points to Select CTs for Protective Schemes Figure 4.3 shows the application of CT for different protective schemes with some important considerations.

114

4 Current Transformer Infrastructure and Its Application to Power System …

Fig. 4.3 Consideration in CT selection

4.6 System Diagram and Parameters To capture the effect of variation of parameters in the power system, PSCAD™ simulation is developed as per Fig. 4.4. Here, RG , X G and R1 , and X 1 are the resistance, and reactance parameters of the generator and line sequentially. One end of the transmission line is connected to a generator through a bus and another end of the transmission line is connected to the load bus. The connected current transformer

4.7 Effect of Parameter Variations on CT Performance

115

Fig. 4.4 Power system diagram CT parameter changes

(CT) in the transmission line observes the system behavior and captured the signal continuously send to the relaying system. Under fault conditions, the relay senses the fault with the help of a CT waveform and gives a trip signal to the CB. Three phase load with an appropriate rating is connected to the second end of the transmission line via the bus bar. Parametric values of the considered power system are elaborated in Appendix 1.

4.7 Effect of Parameter Variations on CT Performance Mostly, all the related parameters are varied in the analysis-related CT saturation effect. Normally, under the CT saturation condition, secondary gives reduced current, and as a result, a relay may mal-operate. A wide range of parameters is considered for testing and confirming analysis. Subsequent sections describe the performance of CT in varying system situations.

4.7.1 Consideration of Core Over-Sizing Factors at FIA = 0.515 As discussed earlier, the core over-sizing depends on the X and R as per the equation: Core-over sizing factor = 1 +

max φdc max φac

 L I0 N 2  =1+ R I0 ωN2 X =1+ R ωL =1+ R

(4.16)

116

4 Current Transformer Infrastructure and Its Application to Power System …

Fig. 4.5 Fault current versus time with change in R and X (at inception angle 0.515)

The effect of different values of X, and R is observed on the fault current magnitude. Figure 4.5a–c shows the outcome of such parameter variation. It is to be observed that the current waveform doesn’t contain DC decaying component when max max = φdc ; so, the effect of a fault is applied at 0.515 (FIA). At this movement, φac DC decaying components is not visible in the results. However, under the effect of a heavy range of X and R, a little bit of DC decaying components appear in the fault current.

4.7.2 Effect of DC Component Mainly, inception angle and line parameters are responsible for the insertion of DC component g in the current waveform. At FIA = 0.5 with the parameters rating R

4.7 Effect of Parameter Variations on CT Performance

117

Fig. 4.6 DC component in fault current for R = 1 Ω and L = 0.1 H at FIA = 0.5

= 0.1 Ω and L = 0.1 H, the effect of the DC decaying component on current is illustrated in Fig. 4.6. To observe the consequence of the different FIA, compare the waveform of Fig. 4.5b (with FIA = 0.515) and Fig. 4.6 (with FIA = 0.5). If the involvement of this DC component is more, CT tends to saturate because of the DC flux setup in the CT core. However, due to the low burden on the secondary side of CT, the saturation effect is negligible in Fig. 4.6.

4.7.3 CT Secondary Burden Effect on Saturation The effect of different burden values is already explained to set the knee point voltage with Eqs. 4.11–4.15. In this simulation, only the effect of a resistive burden on the CT secondary is accounted for. Normally, 0.5 Ω resistance is considered a normal burden on the CT secondary side. Having an additional 10 Ω heavy burden (deliberately inserted) on the CT secondary, the effect of the CT saturation appeared after 1.5 cycles. The effect of CT saturation can easily be visible on the waveform in Fig. 4.7. With a further increase in burden resistance (beyond 10 Ω), the level of saturation is increased and it is set up early. This higher nonlinear burden is due to the lead length of the pilot wire, relay coil impedance, or loose connection on the secondary side of CT.

4.7.4 CT Saturation Effect Under the Influence of the Remnant Flux Density Remnant flux is the flux that remains present in the core of the current transformer since the last de-energize time. Four different values of remnant flux density are validated with 0.5, 0.9, 1.0, and 2.0 T. The effect of those remnant fluxes is depicted in Fig. 4.8 (A–D). It is perceived that the effect of remnant flux density on CT

118

4 Current Transformer Infrastructure and Its Application to Power System …

Fig. 4.7 Fault current versus time at FIA = 0.5, R = 10 Ω and L = 0.1 H

saturation depends on its value as well as the time at which the CT is re-energized. If the remnant flux density is slighter, then saturation of CT occurs after a longer time, and time to saturation reduces with an increase in the remnant flux density. As shown in Fig. 4.8, the saturation of the core of the CT occurs nearly at1.25 cycles (for 0.5 T), around 0.60 cycles (for 0.9 T), approximately at 0.35 cycles (for 1.0 T), and lastly at 0.05 cycle (for 2.0 T) after the inception of fault.

4.7.5 Effect of FIA Variation on CT DC decaying components are transient and do not influence much on the power system components. However, it gives the worst impact on sensitive protective schemes which are operated within a very short duration of time. With the variation in FIA, varying degrees of DC decaying components are involved in the fault currents. Fault current in the power system is either sinusoidal (AC component only) or has the effect of AC and DC components (non-sinusoidal) as presented in Eq. 4.17. 

I(t)

Vm Vm − = sin(ωt + ∅ − θ ) − sin(ωt + ∅ − θ )e |Z | |Z |

t−t0 t



(4.17)

At t = t0 , it is derived as I0 = −

Vm sin(ωt + ∅ − θ ) |Z |

Based on Eq. 4.17, currents depend on FIA, applied voltage angle, and power system impedance and angle. With the help of the said equation, one can say that the effect of DC offset is totally zero at t 0 = 0 and ∅ = θ . However, with the variation in these values (FIA and system angle ∅), the positive and negative effect of DC offset is observed in fault current as shown in Fig. 4.9.

4.8 CT Saturation Analysis in Laboratory Prototype

119

Fig. 4.8 CT saturation effect under influence of different remnant at FIA = 0.5, R = 1 Ω, L = 0.1 H, and burden = 0.5 Ω

4.8 CT Saturation Analysis in Laboratory Prototype Similar to PSCAD™-based simulation analysis, a prototype model is settled in the laboratory environment to watch the effect of CT saturation. Under normal loading conditions of the line, CT primary and secondary waveforms are represented in Fig. 4.10a. Waveforms are obtained here on high-resolution DSO. Figure 4.10b shows current signals during the heavy CT saturation without DC decaying component. Heavy CT saturations are generated in the laboratory by inserting 10 Ω external resistance (variable) in the secondary circuit of the CT. The third window, Fig. 4.10c demonstrates the effect of the presence of inductive load without DC decaying component with light CT saturation conditions. It is clear from Fig. 4.10c that the phasor difference between primary and secondary is due to the inductive load. Moreover, heavy CT saturations with positive and negative DC decaying components are carried out with changing FIA as shown in Fig. 4.10d, e, sequentially.

120

4 Current Transformer Infrastructure and Its Application to Power System …

Fig. 4.9 Effect fault inception angle

4.9 Detection of Saturation of CT in Unit-Type Protection of Power Transformer Generally, large power transformers are protected by unit-type differential protection schemes. If the fault is inside the transformer, then the relay operates successfully. Sometimes, the outside heavy fault pushes the circulating current to flow in the differential relay coil and unnecessarily isolates the transformer from the system. The reason behind this circulating differential current under heavy external fault is the saturation of one of the CTs [42]. To avoid unwanted isolation of the power transformer, it is required to upgrade the protective scheme for discrimination of the internal fault with outside abnormalities of the power system. For that, a unique technique is elaborated for defining the CT saturation and then it is implemented in the power transformer protection. Sliding window-based Modified Full Cycle DFT (MFCDFT) is used with its first-order form. Different cases of transformer protections are successfully validated and analyzed on PSCAD™ software. The proposed algorithm proves its effectiveness to isolate the transformer during internal faults and remain stable during heavy external faults with CT saturation.

4.9 Detection of Saturation of CT in Unit-Type Protection of Power …

Fig. 4.10 CT primary and secondary currents recorded in DSO in laboratory

121

122

4 Current Transformer Infrastructure and Its Application to Power System …

Fig. 4.11 System diagram

4.9.1 Simulation Modeling of Power System To validate the saturation detection algorithm, a part of the power system is developed in PSCAD™ [43] simulation as per Fig. 4.11. A unit of a power transformer having 350MVA value with 400/220 kV voltage rating arranged as a part of the said power system. One side of the considered transformer is connected to a 400 kV transmission line and the second one is connected to a 220 kV line bus. The 220 kV bus is connected with an infinite bus and load (P + jQ). CTP and CTS are the current sensing devices connected to a power system. Captured current signals are analyzed using the MFCDFT algorithm in MATLAB software. Detailed parameters are provided in Appendix 2. Different test cases are conducted to analyze the proposed CT saturation detection and transformer protection algorithm. Different internal and external fault conditions are generated with the variation in different parameters like FIA, fault resistance (Rf ), and location of fault with the help of the fault block of the PSCAD™ software. It is required to consider a proper CT ratio to neutralize the circulating current under normal conditions of the power system for the unit type of transformer protection [44].

4.9.2 Projected Approach Here, the projected approaches are to detect the CT saturation effect and to discriminate the internal and external faults of the power transformer. The secondary side of the CT is unable to read the primary signal properly under the saturation of the core. Normally, initial flux after switching the device which causes inrush current in the system [45]. Same as CT saturation is also a major reason for mal-operation of relaying schemes

4.9 Detection of Saturation of CT in Unit-Type Protection of Power …

123

Primary current in the CT is given as Eq. 4.18 [46, 47]: i primary (t) = C ∗ e−∝t + D sin φt Amp

(4.18)

where C is the constant of the exponential part, D is the constant of the sinusoidal part, α is the decaying DC coefficient, and. φ is Fault Inception Angle (FIA). Special effects of the decaying DC components are as given below:  √ i primary (t) =

  2Vrms  sin(𝛡 t + φ) − sin(φ)e−tτ |R + j X |

Where τ =

(4.19)

R R =  X  , 𝛡 = 2π f, L ω

α = Decaying Coefficient, φ = Fault Inception angle CT Secondary current is derived by applying DFT: ISecondary(n) = X e

nt Ts

+ Ye

nt Tp



2π n−α−β − Z sin N

 (4.20)

where T is the sampling interval, N is the number of samples/cycles, α is FIA, (X, Y, Z) are constant parameters, and β = angle introduces by parameters of CT secondary. tan β = ωTs ∴ β = tan−1 (ωTs ) where Ts depends on total impedance in the secondary circuit of CT. Mostly, CT saturation occurs if saturation voltage (secondary voltage) is greater than knee point voltage. And, the knee point voltage is defined here as per Eqs. 4.11– 4.14. The burden on the secondary is the main reason for the CT situation. Figure 4.12 demonstrates the algorithm for the detection of CT saturation based on the MFCDFT technique. Currents from CTP and CTS are captured in the form of sampled values with a 4 kHz sampling frequency. Collected current samples move ahead in the MFCDFT method to detect the level of CT saturation. The degree of saturation (DN ) is defined as per Eq. 4.21:

124

4 Current Transformer Infrastructure and Its Application to Power System …

Fig. 4.12 Projected algorithm to detect CT saturation in transformer protection

Degree of Saturation(DN ) = 1 −

Saturated Current(ISaturated ) 100%Non-Saturated Current(Inon Saturated ) (4.21)

To define different saturation conditions like mild, medium, and heavy CT saturation, a pre-defined threshold is decided as (DNth ). Based on the I d and I bias currents, internal and external fault currents are separated. Under internal fault, I d is always greater than I bias current and under external fault, I bias is greater than the I d . However, under external fault with CT saturation conditions, sometimes, I d becomes greater than I bias current. To identify the situation of CT saturation, the derived degree of saturation (DN ) is compared with the threshold value (DNth ). If the saturation index or the degree of saturation is higher than the DNth value, CT saturation is declared. Thus, the internal and external faults with or without saturation are segregated and, accordingly, the trip signal will be generated from the relay.

4.10 Result Analysis A 350MVA, YY connected transformer with a 400/220 kV voltage rating on the primary and secondary sides sequentially connected in a part of the power system. For this transformer, CTs of 1/800 and1/1405 turn’s ratio are selected for the primary

4.10 Result Analysis

125

and secondary sides sequentially. Turns ratios are selected based on the minimum circulating currents in the differential relay coils. For the sake of easiness, all faults are applied at 0.2 s and last up to 0.3 s. The developed system is tested with internal faults and external faults with and without CT saturation conditions.

4.10.1 Internal Fault When an internal fault is created in the circuit at 0.2 s as shown in Fig. 4.13, the fault current waveform of the power transformer’s primary and secondary sides follow a phase relation [45], which means around zero phasor difference among them. The second window shows that the differential current magnitude is very higher than the bias current as per Fig. 4.13. The proposed scheme is validated with various test cases with different parameters changing like Rf and FIA. During all internal fault conditions, the trip signal is successfully issued to circuit breakers as shown in the last window of Fig. 4.13.

Fig. 4.13 Internal faults

126

4 Current Transformer Infrastructure and Its Application to Power System …

Fig. 4.14 External fault without CT saturation

4.10.2 External Fault Without CT Saturation All results under external faults without CT saturation are reported in Fig. 4.14. During an external fault, primary and secondary waveforms are almost 180-degree phase-shifted due to the YY connection of the transformer. Biased current is higher than the differential current and no saturation is detected. Due to all above-said conditions, the algorithm doesn’t issue the trip signal to the CB. Numerous external fault conditions are generated with different fault locations on the bus and transmission lines to validate the algorithm. The algorithm proves its effectiveness under external fault and remains stable.

4.10.3 External Fault with CT Saturation This is the most alleged case under which the unit-type protective scheme may maloperate. As discussed earlier, CT might be saturated under the effect of heavy fault current (greater than 20 times the rated primary current) or due to the larger burden

4.10 Result Analysis

127

Fig. 4.15 External faults with mild CT saturation

on the secondary. To validate the proposed algorithm, two test cases are created with (1) mild and (2) heavy CT saturation. Validations of both conditions are elaborated as per Figs. 4.15 and 4.16. It is to be noted that the first window of both these Figures (Figs. 4.15 and Fig. 4.16) shows the comparison of actual saturated signals with standard non-saturated signals (they are not the primary and secondary current signals) during external faults. Low CT Saturation With the insertion of the burden on the secondary side, CT gets saturated if DN is higher than DNth , i.e., more than 10% settled value, these situations are considered here as the saturation condition as per the algorithm (as per Fig. 4.12)., external faults are created on both 220 and 400 kV sides of the transformer with different lengths of the line. Under the light CT saturation conditions, after two cycles CT gets saturated as shown in the first window of Fig. 4.15. Thus, Id becomes greater than I bias , and at that time, algorithm again checks the DN > DNth conditions before giving the final tripping signal. If the above condition is satisfied, then the trip signal is not released by the relay as shown in the last window of Fig. 4.15.

128

4 Current Transformer Infrastructure and Its Application to Power System …

Fig. 4.16 External fault with severe CT saturation

Heavy CT Saturation Heavy CT saturation gives an effect within the first cycle after the occurrence of external fault as shown in Fig. 4.16. This type of CT saturation can be achieved in PSCAD™ software by setting/connecting a heavy burden on the CT secondary. At around 0.237 s, the impact of CT saturation during external fault is detected as per the second and third windows of Fig. 4.16. However, as per the algorithm logic, the relay blocks the trip signal because of an external fault with heavy CT saturation.

4.11 Conclusion The current Transformer (CT) is an integral part of the power system without whom measurement and protection are impossible. The selection of the right current transformer may solve many issues in terms of measurement, control, and operation of the entire power grid. Thus, knowledge of the detailed specifications of CT is essential for protection and maintenance engineers. Some of the specifications of protective CT are rated current, rated voltage, current ratio, core size, accuracy class, CT burden, primary and secondary impedances, and knee point voltage (KPV). Faults

Appendices

129

may happen in power systems due to the stressed condition of the apparatus and the weakening/failing of insulations at a particular node. The severity of the fault and system complexity may disturb or confuse the decision power of relaying scheme. Current juice is transferred to such a protective relaying scheme through the CT. Thus, it is essential to study the design and behavior of CT in real-time operation of the power system from no-load to full load to faulty condition. This chapter deals with the phenomenon of CT saturation and its detection at the time of minor to major faults. Different parameter variations have been exercised for recording the saturation effect of CT in software simulation as well as in hardware setup. It has been observed that the saturation of CT occurs when the fault current goes 20 times beyond the rated current or secondary voltage across the KPV on excitation characteristic. An algorithm is developed in a MATLAB environment to detect the saturation condition of CT based on the index derived by comparing saturated and non-saturated standard current signals. To validate the suggested technique, a case study of differential protection is performed on a power transformer. A part of the power system is designed in PSCAD software with a power transformer having CTs located on both primary and secondary sides. In this study, various internal and external faults are created to check the action of a differential relay in the presence of CT saturation. It is to be noted that the proposed algorithm successfully detects the saturation of CT in the event of minor to major faults inside and outside of the transformer. The differential relay perfectly operates during all kinds of internal faults. On the other hand, the relay may mal operate in case of heavy faults outside the transformer with CT saturation. However, the CT saturation detection technique proposed here helps to block the tripping signal from a differential relay in the event of an external fault with mild to heavy saturation of CT. The results obtained in this study confirm the feasibility of the proposed CT saturation detection scheme.

Appendices Appendix 1 PSCAD™ Simulation Details: Current Transformer Data 1. Primary/secondary turns

1/1000

2. Secondary resistance

0.5 Ω

3. Secondary inductance

0.8e–3 [H]

4. Area path length

2.601e–3 [m*m]

5. Remnant flux

0–2 T

Transmission Line Data 1. Resistance

5Ω

130

4 Current Transformer Infrastructure and Its Application to Power System …

2. Inductance

64 mH

Source Data 1. Voltage and Frequency

110 kV and 50 Hz

Hardware Details: 1. Primary/secondary turns

5/1 Amp

2. Secondary resistance

0.02 Ω

3. Secondary inductance

0.04e−3 [H]

4. Source Data (Voltage and Frequency)

230 V and 50Hz

Appendix 2 Source Data: 350 MVA, 400 kV, 50 Hz, Three-phase Voltage Source (Having +Ve sequence impedance and Zero sequence impedance are 1 and 2 Ω sequentially). Transformer Data: 350 MVA, 400/220 kV, 50 Hz, Three-phase YY connected, (with 0.4% Magnetizing current, 0.1 pu Leakage Reactance, and 1.25 pu Knee Voltage). CT Data (Jiles Atherton) CT ratio (primary and Secondary) are 800/1 and 1405/1 sequentially. Transmission Line: R, X L , and X C are 0.162 × 10–5 Ω/m, 0.124 × 10–2 Ω/m, and 374.34 Mohm/m sequentially. Load: P and Q are 250 MW and 50 MVAR sequentially.

References 1. Committee PSR (1981) ANSI/IEEE C57.13.1-198X guide for field testing of relaying current transformers. IEEE Trans Power Appar Syst. https://doi.org/10.1109/TPAS.1981.316949 2. Hooshyar A, Sana-Yepasand M, El-Saadany EF (2013) CT saturation detection based on waveshape properties of current difference functions. IEEE Trans Power Deliv 28(4):2254–2263. https://doi.org/10.1109/TPWRD.2013.2266799

References

131

3. Hooshyar A, Sanaye-Pasand M (2012) Accurate measurement of fault currents contaminated with decaying DC offset and CT saturation. IEEE Trans Power Deliv 27(2):773–783. https:// doi.org/10.1109/TPWRD.2011.2176965 4. Hooshyar A, Sanaye-Pasand M (2011) CT saturation detection based on waveform analysis using a variable-length window. IEEE Trans Power Deliv 26(3):2040–2050. https://doi.org/10. 1109/TPWRD.2011.2142404 5. Solak K, Rebizant W, Klimek A (2012) Fuzzy adaptive transmission-line differential relay immune to CT saturation. IEEE Trans Power Deliv 27(2):766–772. https://doi.org/10.1109/ TPWRD.2011.2179815 6. Bertrand P, Mendik M, Hazel T, Tantin P (2012) CT saturation calculations: IEC standards and nonconventional instrument transformers. IEEE Ind Appl Mag 18(1):12–20. https://doi. org/10.1109/MIAS.2011.943098 7. dos Santos EM, Cardoso G, Farias PE, de Morais AP (2013) CT Saturation detection based on the distance between consecutive points in the plans formed by the secondary current samples and their difference-functions. IEEE Trans Power Deliv 28(1):29–37. https://doi.org/10.1109/ TPWRD.2012.2220382 8. Chothani NG, Patel DD, Mistry KD (2017) Support vector machine based classification of current transformer saturation phenomenon. J Green Eng River Publ 7:25–42. https://doi.org/ 10.13052/jge1904-4720.7122 9. Patel DD, Chothani NG, Mistry KD (2015) Sequence component of currents based differential protection of power transformer. In: 12th IEEE international conference electronics, energy, environment, communication, computer, control: (E3–C3) INDICON 2015, pp 1–6. https:// doi.org/10.1109/INDICON.2015.7443855 10. Patel D, Chothani N, Mistry K (2018) Discrimination of inrush, internal, and external fault in power transformer using phasor angle comparison and biased differential principle. Electr Power Components Syst 46(7):788–801. https://doi.org/10.1080/15325008.2018.1509915 11. Patel D, Chothani N (2020) Phasor angle based differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 51–81 12. Patel D, Chothani N (2020) CT saturation detection and compensation algorithm. In: Digital protective schemes for power transformer. Springer, Singapore, pp 33–49 13. Patel DD, Mistry KD, Chothani NG (2015) A novel approach to transformer differential protection using sequence component based algorithm. J CPRI 11(3):517–528 14. Chothani N, Patel D, Raichura M (2019) Transformer protection with sequence components and digital filters, 1st edn. LAP LAMBERT Academic Publishing, Latvia 15. Patel D, Mistry KD, Raichura MB, Chothani N (2018) Three state Kalman filter based directional protection of power transformer. In: 20th National power systems conference (NPSC), pp 1–6. https://doi.org/10.1109/NPSC.2018.8771716 16. Raichura M, Chothani N, Patel D, Mistry K (2021) Total Harmonic Distortion (THD) based discrimination of normal, inrush and fault conditions in power transformer. Renew Energy Focus 36:43–55. https://doi.org/10.1016/j.ref.2020.12.001 17. Raichura MB, Chothani NG, Patel DD, Mistry KD (2019) Identification of inrush and fault conditions in power transformer using harmonic distortion computation. In: 2019 IEEE 1st international conference on energy, systems and information processing (ICESIP), July 2019, pp 1–6. https://doi.org/10.1109/ICESIP46348.2019.8938308 18. Patel DD, Chothani N, Mistry KD, Tailor D (2018) Adaptive algorithm for distribution transformer protection to improve smart grid stability. Int J Emerg Electr Power Syst 19(5):1–14. https://doi.org/10.1515/ijeeps-2018-0022 19. Raichura M, Chothani N, Patel D (2020) Development of an adaptive differential protection scheme for transformer during current transformer saturation and over-fluxing condition. Int Trans Electr Energy Syst 31:1–19. https://doi.org/10.1002/2050-7038.12751 20. Patel D, Chothani N (2020) Adaptive digital differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 83–106 21. Kuzhekov SL, Degtyarev AA, Vorob’ev VS, Moskalenko VV (2017) Determination of the time-to-saturation of current transformers in short-circuit transient regimes. Power Technol Eng 51(2):234–239. https://doi.org/10.1007/s10749-017-0816-x

132

4 Current Transformer Infrastructure and Its Application to Power System …

22. Vakhnina VV, Kuznetsov VN, Shapovalov VA, Samolina OV (2017) Modeling the saturation processes of a power-transformer core under simultaneous direct and alternating current passing through the winding. Russ Electr Eng 88(4):223–228. https://doi.org/10.3103/S10683712170 40137 23. Biswal S, Biswal M (2018) Algorithm for CT saturation detection with the presence of noise. In: 2018 4th international conference on electrical energy systems (ICEES), Feb 2018, 248–251. https://doi.org/10.1109/ICEES.2018.8443196 24. Hajipour E, Vakilian M, Sanaye-Pasand M (2017) Current-transformer saturation prevention using a controlled voltage-source compensator. IEEE Trans Power Deliv 32(2):1039–1048. https://doi.org/10.1109/TPWRD.2016.2580585 25. Haghjoo F, Pak MH (2016) Compensation of CT distorted secondary current waveform in online conditions. IEEE Trans Power Deliv 31(2):711–720. https://doi.org/10.1109/TPWRD. 2015.2448634 26. Davarpanah M, Sanaye-Pasand M, Iravani R (2013) A saturation suppression approach for the current transformer-part i: fundamental concepts and design. IEEE Trans Power Deliv 28(3):1928–1935. https://doi.org/10.1109/TPWRD.2013.2253496 27. Schettino BM, Duque CA, Silveira PM, Ribeiro PF, Cerqueira AS (2014) A new method of current-transformer saturation detection in the presence of noise. IEEE Trans Power Deliv 29(4):1760–1767. https://doi.org/10.1109/TPWRD.2013.2294079 28. Davarpanah M, Sanaye-Pasand M, Iravani R (2013) A saturation suppression approach for the current transformer—Part II: performance evaluation. IEEE Trans Power Deliv 28(3):1936– 1943. https://doi.org/10.1109/TPWRD.2013.2253497 29. Smith T, Hunt R (2013) Current transformer saturation effects on coordinating time interval. IEEE Trans Ind Appl 49(2):825–831. https://doi.org/10.1109/TIA.2013.2243397 30. Hooshyar A, Sanaye-Pasand M, Davarpanah M (2012) Development of a new derivative-based algorithm to detect current transformer saturation. IET Gener Transm Distrib 6(3):207–217. https://doi.org/10.1049/iet-gtd.2011.0476 31. Hooshyar A, Sanaye-Pasand M (2015) Waveshape recognition technique to detect current transformer saturation. IET Gener Transm Distrib 9(12):1430–1438. https://doi.org/10.1049/ iet-gtd.2014.1147 32. Esmail EM, Elkalashy NI, Kawady TA, Taalab AI, Lehtonen M (2015) Detection of partial saturation and waveform compensation of current transformers. IEEE Trans Power Deliv 30(3):1620–1622. https://doi.org/10.1109/TPWRD.2014.2361032 33. Chothani NG, Raichura MB, Patel DD, Mistry KD (2018) Real-time monitoring protection of power transformer to enhance smart grid reliability. In: 2018 IEEE electrical power and energy conference (EPEC), Oct 2018, pp 1–6. https://doi.org/10.1109/EPEC.2018.8598427 34. Chothani NG, Raichura MB, Patel DD, Mistry KD (2019) Real-time monitoring protection of power transformer to enhance smart grid reliability. Electr Control Commun Eng 15(2):104– 112. https://doi.org/10.1109/EPEC.2018.8598427 35. Patel D, Chothani N (2020) Real-time monitoring and adaptive protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 173–190 36. Ajaei FB, Sanaye-Pasand M, Davarpanah M, Rezaei-Zare A, Iravani R (2011) Compensation of the current-transformer saturation effects for digital relays. IEEE Trans Power Deliv 26(4):2531–2540. https://doi.org/10.1109/TPWRD.2011.2161622 37. Patel D, Chothani N (2020) Relevance vector machine based transformer protection. In: Digital protective schemes for power transformer. Springer, Singapore, pp 107–131 38. Patel D, Chothani NG, Mistry KD, Raichura M (2018) Design and development of fault classification algorithm based on relevance vector machine for power transformer. IET Electr Power Appl 12(4):557–565. https://doi.org/10.1049/iet-epa.2017.0562 39. Raichura MB, Chothani NG, Patel DD (2020) Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique. IET Sci Meas Technol 14(1). https://doi.org/10.1049/iet-smt.2019.0102 40. Raichura M, Chothani N, Patel D (2021) Efficient CNN-XGBoost technique for classification of power transformer internal faults against various abnormal conditions. IET Gener Transm Distrib 15(5):972–985. https://doi.org/10.1049/gtd2.12073

References

133

41. Guide for the application of current transformers used for protective relaying purposes, Sponsor. In: IEEE/ANSI C, IEEE Standard C37.110-2007, IEEE power system relaying committe, vol 37, pp 110–2000 42. Bhalja B, Maheshwari RP, Chothani NG (2017) Protection and switchgear, 2nd edn. Oxford University Press, New Delhi 43. PSCAD Research Center (2005) EMTDC-transient analysis for PSCAD power system simulation. Winnipeg 44. Pan J, Vu K, Hu Y (2004) An efficient compensation algorithm for current transformer saturation effects. IEEE Trans Power Deliv 19(4):1623–1628. https://doi.org/10.1109/TPWRD. 2004.835273 45. Paithankar YG, Bhide SR (2010) Fundamentals of power system protection. PHI Learning Pvt. Ltd., New Delhi 46. Wu QH, Lu Z, Ji TY (2009) Protective relaying of power systems using mathematical morphology, 1st edn. Springer, London, New York 47. Patel DD, Mistry KD, Chothani NG (2016) Digital differential protection of power transformer using DFT algorithm with CT saturation consideration. In: 2016 National power systems conference (NPSC), Dec 2016, pp 1–6. https://doi.org/10.1109/NPSC.2016.7858854

Chapter 5

Impact of Transitory Excessive Fluxing Condition on Power Transformer Protection

Abstract The unit protection method is universally accepted as the chief protective scheme for transformers. In conditions such as transitory over-fluxing, the universally accepted unit type of protection may mal-operate. Higher voltage level side and lower voltage level side currents become unequal in the transformer, because of core saturation. This chapter depicts the recognition of such conditions compared to the condition like interior fault, because of the fifth harmonic content of difference current. By identifying the amount of fifth harmonic w.r.t. the primary constituent of the difference current, the operational setting of the percentage bias feature is customized to evade false actions. When this type of circumstance is recognized, the presented algorithm triggers time delayed V /f relaying scheme to save the equipment. Here, the projected scheme is authorized on the PSCADTM platform, and post to that these collected data are exported to a MATLAB-based program, and then it is used for additional corroboration. The projected scheme is certified by testing various test circumstances like normal loading conditions, exterior fault, interior fault, as well as excessive fluxing in the core. One can say that the presented algorithm truthfully spots the interior fault condition inside of the transformer or can obstruct the triggering of relay operation under momentary over-fluxing (temporary excessive fluxing) circumstances. More on these, to evaluate the proficiency of the projected scheme, an experimental evaluation is checked on a one-phase transformer. Data received from diverse test conditions are recorded using DSO, the captured data then transfer to the computer to export it to the algorithm to check its proficiency. After examining the entire outcome, it can be clearly stated that the projected algorithm could never prevent the transformer separation during the occurrence of temporary excessive fluxing.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Chothani et al., Advancement in Power Transformer Infrastructure and Digital Protection, Studies in Infrastructure and Control, https://doi.org/10.1007/978-981-99-3870-4_5

135

136

5 Impact of Transitory Excessive Fluxing Condition on Power …

5.1 Introduction The transformer can be considered a crucial component of the power grid. Nonlinear intrinsic features of the transformer device’s core could originate an excessive fluxing state inside the transformer. Moreover, an alteration in voltage to frequency ratio (V / f ) and succeeding excessive flux may result due to various phenomena like abrupt load drop, generator running on lower frequency because of insufficient mechanical energy, tripping of a heavy burdened transmission line, indecent shunt compensator shut down, etc. There exists a variety of committed protections, e.g., V /f relay available to prevent the device from dangerous effects caused because of the mentioned situations earlier. Though, the brutality of excessive flux conditions may violate the equality between the major and minor face amount of currents of considered transformer and spurious action of unit-type protective method may outcome. Hence, there is a need for an optimistic defensive scheme that makes the characteristic adaptive to tackle the temporary excessive flux circumstances. In normal cases, the over/excessive-fluxing type of relay (i.e., V /f relay) is set in between the range of 1.1 and 1.3 pu [1] along with modifiable time response. Ahead of this, a mixture of inter-harmonics and voltage notches may provoke inside the considered arrangement when the susceptible load is removed from the transformer. Electrical separation of the transformer is not competent enough to eliminate those voltages distend wholly [2]. Xiangning Lin et al. talk about this in their article [3], on page number 3 that “the V-by-f security system issues excursion indication if this relation (V by f ) go beyond 1.1 pu”. They mentioned that these kinds of circumstances may occur gradually and it is well thoughtout as a type of initial fault condition. Moreover, it is written that in case the excess flux condition is spotted, the considered transformer is supposed to be removed from the system. Though, if this condition is identified as temporary the instantaneous tripping action is undesired. As per IEEE standards, excessive flux amounting to 25% higher or up to 1.25 pu is permissible [4]. As per flux locus [5], a rise in the amount of flux in transformers is also a major useful constraint that is used to spot the winding and/or the faulty phase [6]. On the other side, there is no need for hysteresis data and its analysis. The core characteristics of the transformer are also recommended as an enhanced protective technique [7]. The flux linkage ratio to both sides of the transformer is considered a significant factor for the transformer protective case. A relative algorithm can provide transformer protection as mentioned in [8]. In the same way, fluxional current can protect the transformer [9]. One sole protective plan for transformer protection is recommended [10] considering CT saturation, excessive voltage as well as excessive fluxing circumstances to evade faulty action of protection relay along with stability point of view. Nevertheless, such a technique also falsely activates along with flux-based shelter because of a lack of defensive measures. In connection with this, excess current and excessive fluxing conditions are dealt with in numerical-based Inverse Definite Minimum Time (IDMT) relay design [11]. This IDMT-based relay although restrict spurious action alongside instantaneous

5.1 Introduction

137

trip, may cause a delay in core protection also. A narrative scheme based on ultrasaturation discovery is presented by Bahram Noshad and fellow authors [12]. This proposed scheme notice ultra-saturation using Discrete Wavelet Transform (DWT), to keep it restrained. Yet results for the cases like extra high saturation and its persistence for longer duration are not elucidated. Neha Bhatt and fellow authors [13] presented the effects of the excessive fluxing case of the transformers and prepared a comparison of various methodologies for its identification. Evaluation of the amount of current in a saturated transformer is proposed in [14]. Where extended, the Kalman filter, as well as Gauss–Newton methods, is used for the current evaluation. Authors used various hypotheses which may not always be likely to presume. DFT is normally used for protection scheme [15–17]. Three-state Kalman filter performs better than DFT algorithm [18]. CT saturation detection techniques [19, 20] and inrush conditions [21, 22] identification are also verified with the changes in various parameters of CT and power system. The use of harmonics is advisable to categorize inrush and fault phases with suitable analysis [23–25]. In the context of second harmonics, magnetic inrush condition recognition is possible [26, 27]. Cases like generation of harmonics may also arise during geomagnetic disorder and, hence, transformer observation and its investigation can be done [28]. Abbas Ketabi and fellow authors [29] studied the effect of transformer switching conditions. Also, the period of overvoltages during various stimuli can be forecasted. Harmonic congestion and command modus operandi for transformer unit protection is projected in [30]. In transformer protection, various phasor comparison based schemes [31– 33], sequential component based methods [34, 35] and derivative of differential current based techniques [36] are also authenticated bt different researchers. Different classifiers [37], regression [38–41], and decomposition techniques [42, 43] are also used for internal fault classification with all other abnormal conditions in transformer protections. The self-adaptive protective technique is described in [43–45], along with slope changing aptitude of percentage biased feature. On the other hand, the excessive fluxing criterion remains still intact in terms of the protection framework. Regarding excessive over-fluxing conditions, many of the researchers in this field have presented their research on the V /f conditional scheme [46, 47] to take care of the transformer. Maheshwari and Verma [48] and their fellow authors acknowledged excessive excitation cases revealed by the use of the fifth harmonic content-based methodology. On the other hand, in the planned scheme they only mentioned the relay control features. All over this discussion, in this chapter, an adaptive alteration of the basic differential setting is incorporated to put off false action of the considered transformer protective method during provisional excessive fluxing conditions. This technique commences with very less difficulty and curtails estimated burden with a positive outcome. The presented method is capable enough to avert abrupt action of the temporary excessive fluxing conditions. More on this, V /f safeguard is also triggered simultaneously to handle this situation in a permanent excessive fluxing state with no effect on the act of the deployed differential-based relay. It operates concurrently

138

5 Impact of Transitory Excessive Fluxing Condition on Power …

percentage-biased differential-based protection post-detection of excessive fluxing state.

5.2 Modeling of System Diagram Figure 5.1 illustrates a segment of the Indian power grid network with a YY-linked transformer, rating 400 by 220 kV, 100 MVA, three-phase (three transformers of single phase connection), with a frequency rating of 50 Hz. The considered portion of the grid system is imitated in PSCAD. Moreover, to certify the projected scheme, algorithm coding is programmed in MATLAB software. Higher voltage terminal of considered transformer winding is linked to the generator. On the other hand, the lower voltage-level terminal of the considered device is coupled with the load as well as with 220 kV and 80 km line. Numerous assessment cases of normal load conditions at lower voltage side, exterior faults conditions, and transformer interior faults conditions are imparted by altering electrical parameters. Furthermore, excessive fluxing instances are produced by slowly changing primary voltage with the help of a slider as shown in Fig. 5.1.

0 .0

5 0 .0

R

Timed Breaker Logic Closed@t0

3-Phase Power Transformer 100MVA, 400/220kV

V

F RL

RL

RL

Ph AR

a

A

b

c

RR A

d f

B

Ib2s

Ib1p

B RL

Timed Breaker Logic RR Closed@t0

T TLine1

Ia2s

Ia1p Vp

a

C

B

c

RL

B C

C b

RL

A

T TLine2

d

RL

C

f Vp

Main : Controls 400kv voltage

Ic2s

Ic1p a

T TLine3

c

500

b

d

kV

Timed Breaker BRK Logic Open@t0

f

440

500

A B FAULTS C A->G

Fig. 5.1 Indian power structure grid schematic line diagram

Timed Fault Logic

BRK P+jQ

RL

5.3 Problem Declaration and Algorithm Suggestion

139

5.3 Problem Declaration and Algorithm Suggestion In a variety of operational conditions, the considered transformer core may saturate, which can lead to an excessive fluxing condition in the transformer. Additionally, it may arise from amplified winding voltages. Accordingly, the relevant current amount may also amplify to some degree. The growth in the amount of current will become a standstill because of the saturation phase of the considered transformer core. Conversely, the corresponding other side of the current may not replicate the higher voltage-level winding side current, because of the saturation state of the considered transformer core. Therefore, at the relay terminal, the current equivalency is not observed. The resultant difference in current which is not due to the interior fault case may pose triggering of the differential-based relay. Figure 5.2 represents the projected plan of an algorithm to defend against the false triggering of the considered transformer differential-based relay during an excessive flux situation. The algorithm starts by fetching currents of both the terminals of the considered transformer through CTs. Using Modified Discrete Fourier Transform (MDFT), the difference current [i.e., I diff as per Eq. (5.1)], the biased current [i.e., I bias as per Eq. (5.2)], and first and fifth harmonic components of difference current are estimated [49]. Moving ahead, to recognize any abnormal condition in the transformer, differential current, as well as restraining current, is calculated (I diff > K 1 times I bias ), where K 1 indicates a gradient of the differential-based relay and which is considered as 30% [50]). In case the given criterion is fulfilled, a supplementary examination will be carried out. Contrarily, in case this criterion is not fulfilled, then it is interpreted as a normal or exterior type of fault condition; hence, the developed technique will be redirected towards its initial control. In case the difference current (that means I diff ) is larger than K 1 multiplied by biased/restraining current (that means I bias ), the projected scheme additionally does more filtration to find if the case is of excessive fluxing or actual insider type of fault. According to study [30], in case the fifth harmonic content of the difference current remains within the limit of 25% of the first harmonic content of the difference current, the fluxing state is tolerable, i.e., the considered transformer can survive in such fluxing state. Therefore, it can be said that if the fraction of the fifth harmonic content of different currents to the first harmonic content of different currents is estimated to be up to 25%, then the situation is pretended as the condition of an insider transformer fault state, and instantly a tripping signal should be conveyed. On the opposite side, if this ratio of the fifth harmonic content of the difference current to the first harmonic content of the difference current is estimated beyond 25%, then it is pretended as an excessive fluxing condition. In that case, the fundamental differential-based relay setting must be adaptively changed (i.e., shifted up) to stop the false operation of the differential-based relay. Moreover, the prefixed biased setting (slope) should not be changed as it is depending on the CT saturation state. In connection with this, the V /f shield must be triggered together to shelter the core of the transformer from harmful impacts of excessive flux conditions. Below

140

5 Impact of Transitory Excessive Fluxing Condition on Power …

Fig. 5.2 Projected algorithm

computations are obtained with the help of MDFT to authorize the projected proposal in MATLAB. Idiff = Ip − Is

(5.1)

Ip + Is 2

(5.2)

Ibias =

I p and I s sequentially indicate higher voltage level and lower voltage level winding currents of the considered transformer, respectively. The amendment of basic pickup of differential-based protective scheme can be performed out as given below: The fraction of the fifth harmonic content of the difference current by the first harmonic content of the difference current is indicated by S m : Sm =

5th Harmonic of Id Fundamental component of Id

(5.3)

5.4 Investigation of the Obtained Results

∆I = (Sm − Th )Id0

141

(5.4)

where ∆I is the amount of alteration needed in basic pickup because of excessive flux Th (25%), Th is the fixed threshold, and Id0 is the fundamental value of a current set. So, the latest fundamental value of current triggering is as follows: Id0new = Id0 + ∆I

(5.5)

The newer magnitude estimated by Eq. (5.5) is a needed fundamental current setting that is required for the differential-based relay. From Eq. (5.5), the parameter tuning of differential-based relay can obligatory moved during elevated fifth harmonic and avert relay false operations as of the excessive fluxing state.

5.4 Investigation of the Obtained Results 5.4.1 Performance Evaluation of the Projected Scheme for the Period of Normal State/Exterior Fault State of the Transformer For the period of normal or in case of exterior fault state of the transformer, an equal amount of current flows through the transformer’s upper voltage side and lesser voltage side windings. Thus, the amount of difference current is approximately zero which is observed in the given Fig. 5.3. It is observed from Fig. 5.3 that for the period of nominal condition and during the exterior fault state, the I diff /I bias curve (red-colored line in Fig. 5.3) resides under

Fig. 5.3 I diff /I bias trajectory for the period of normal condition/external fault condition

142

5 Impact of Transitory Excessive Fluxing Condition on Power …

Fig. 5.4 Transformer higher voltage side and lower voltage side currents (CT secondary) waveform for normal state (till 0.2 s) and exterior fault state (from 0.2 to 0.35 s time frame) with DC component of faulty current

the stated limit which means I d0 and slope K 1 . Hence, additional consideration does not need in such cases. Figure 5.4 demonstrate higher voltage and lower voltage level side current waveforms of CTs secondary for the attended transformer. The exterior fault is beginning at 0.2 s, as depicted in Fig. 5.4, and the extent of both higher and lower side current amounts enhances after the initiation of exterior faulty conditions. Though, the augment in the amount of higher and lower voltage side currents is equivalent and cancels out each other at relay terminals. Thus, the difference current remains approximately zero. Thus, while exterior fault as well as during ordinary states, the transformer defensive system remains stable.

5.4.2 Performance Evaluation of the Projected Scheme While Insider Fault State of the Transformer It is to be visible from Fig. 5.5 that, in the interior fault case, I diff /I bias curve (red line in Fig. 5.5) straight away traverses the definite limits and penetrates the operating zone. These situations trigger the differential-based relay to activate circuit breakers. Though it is a deserving situation for an electrical engineer, however, according to the developed algorithm (from Fig. 5.2) of the anticipated methodology, the state of excessive flux is determined according to Eq. (5.3). Ahead of this, if the excess flux is lower than the definite threshold limit (which is 25%), so, the relay would settle as an interior fault state and productively send a trip indication. Contrarily, as per Eq. 5.3, if excessive flux goes beyond the prefixed threshold, it activates the voltage by frequency defense of the transformer. Simultaneously, it prevents the differentialbased relay’s forged operation by modifying the characteristic as discussed in the next section.

5.4 Investigation of the Obtained Results

143

Fig. 5.5 I diff /I bias trajectory while interior faulty condition

Fig. 5.6 Higher voltage level and the lower voltage level of the current waveform during insider faulty state of the transformer

5.4.3 Performance Evaluation of the Projected Scheme for Excessive Fluxing State of the Considered Transformer When the transformer faces an excessive fluxing state because of enhance in voltage level or shrink in the system frequency, the projected algorithm prevents abrupt false operation of the differential-based relay by adaptive changing in the fundamental triggering setting. It is to be noted from Fig. 5.7 that, when the excess flux condition is determined as per Eq. (5.3), the presented scheme adaptively alters the fundamental triggering tuning of the differential-based relay according to Eq. (5.5). For the period of excessive flux state, I diff /I bias curve (line with red color in Fig. 5.7) traverses the older fundamental triggering setting (blue colored line “AB” at I do in Fig. 5.7). This accompanies the false action of differential based relay that is not a desirable condition. So, during this situation, the presented method adaptively modifies the fundamental trigger setting of the differential relay (indicated as spotted blue-colored line A’B’ as

144

5 Impact of Transitory Excessive Fluxing Condition on Power …

Fig. 5.7 I diff /I bias curve while excessive fluxing state

indicated by I donew in Fig. 5.7) and restricts false action of the relay. Supplementary to this, it concurrently triggers the V /f -based defensive scheme to tackle prolonged flux conditions. Figure 5.8 confirms the higher voltage side and lower voltage side current wave patterns of the dedicated transformer, while the excessive fluxing case is employed on the higher voltage side. Excessive flux is imparted at 0.1 s by implementing an abrupt hike on the higher voltage side. As a consequence, the amount of higher voltage side current and lower voltage side current may be unequal as shown in Fig. 5.8. This kind of event looks like an insider faulty case by the normal differential protective system of the transformer. The case is not an insider fault and it can be tolerable to some extent for a few moments. The harshness of the excessive flux is calculated and necessary measures would be taken.

Fig. 5.8 Higher voltage side and lower voltage side current wave patterns during excessive flux state of the transformer

5.5 Elaboration of Hardware Arrangement and Result Conversation

145

5.5 Elaboration of Hardware Arrangement and Result Conversation One-phase 1000 VA capacity, 220/110 V rating transformer, with multiple tapping on both sides, is taken into consideration. This prototype model is prepared in a laboratory which is seen in Figs. 5.9 and 5.10. Diverse controlling as well as gauging instruments such as voltage variac, circuit breakers, digital current gauge, Digital Storage Oscilloscope (DSO), as well as current sensing instruments, etc., are equipped to compute and obtain defined parameters from multiple tapping of a transformer.

Fig. 5.9 Front sight of hardware prototype for experiment

Multi Tapping Transformer

Fig. 5.10 Back side of a hardware prototype model for experimental purpose

146

5 Impact of Transitory Excessive Fluxing Condition on Power …

Fig. 5.11 Current waveform of DSO during an interior faulty case of the transformer

Numerous test conditions are performed on the considered hardware prototype and the captured waveforms of DSO are shown in the subsequent parts.

5.5.1 Current Wave Pattern While Interior Fault Case of Transformer The insider fault condition is imposed with a substantial applied voltage by operating switch S2 located on the lower voltage terminal of the transformer (Fig. 5.9). Interior anomalies are imposed among two subsequent current sensing devices at the tapping of transformer windings. It is noted that the current wave patterns that are sent to DSO remain in phase throughout the interior fault state. These data are saved in the form of *.CSV files and then exported and compiled to the computer memory. Afterward, the accumulated sampled data are then used to verify the capability of the anticipated algorithm prepared in MATLAB.

5.5.2 Current Wave Pattern While Exterior Fault Case of the Transformer The exterior faulty condition is imitated with the help of a considerable resistance by switching S1 as can be seen from Fig. 5.9. The exterior faults are performed outside the zone of the area of the current sensor. One can see from Fig. 5.12 that the captured current wave patterns are of the same value and they are out of the phase to each other during the application of the exterior fault case. During this condition, the projected scheme remains inoperative.

5.5 Elaboration of Hardware Arrangement and Result Conversation

147

Fig. 5.12 DSO current wave pattern while exterior fault case of the considered transformer

Fig. 5.13 DSO current wave pattern while continuous excessive flux case of the considered transformer

5.5.3 Current Wave Pattern While Continuous and Temporary Excessive Fluxing State of the Considered Transformer From Figs. 5.14 and 5.15, it can be seen that the current wave signatures while continuous excessive flux state employed on the practical prototype. As discussed, an excess excitation situation can be observed while alerting the V /f quantity. The alteration in the magnitude of voltage alters the respective current amount of the considered transformer windings.

148

5 Impact of Transitory Excessive Fluxing Condition on Power …

Fig. 5.14 DSO wave pattern while a temporary excessive fluxing case of the dedicated transformer

Fig. 5.15 I diff /I bias curve for excessive flux test case

With changes in the supplied voltage value at the higher voltage level side through single-phase variac, relevant side current (blue colored trajectory of Fig. 5.15) also changes. The rise in the number of current breaches equivalence between the higher voltage level side and lower voltage level side current. This situation leads to the false action of a differential-based protective scheme of the transformer. Certainly, the V /f shield takes utmost care of the state of excess flux present for a prolonged amount of cycles. Nevertheless, if the situation of excess flux condition is temporary as dictated by Fig. 5.15, the devoted V /f shield delays its action and, for that time frame, an existing differential relay could cut off the transformer connection. On the other hand, the presented scheme correctly distinguishes between these kinds of temporary conditions and tunes the relay constraints adaptively.

5.7 Conclusion

149

In connection to this, the presented defensive scheme uses the amount of the fifth harmonic level of difference current to classify the concentration of excess flux case. In this event, the presented scheme automatically modifies the basic pickup setting to stop the isolation of the transformer. In this viewpoint, a hardware model for the data acquirement objective is used and then the collected data are transferred to the MATLAB platform to authorize the presented algorithm. The obtained results of the hardware model for an excessive fluxing state are also retrieved from the MATLAB platform and are shown in Fig. 5.15. One can see the I diff /I bias curve during the excess flux state of the transformer for data gathering of the hardware model. From the observation of Fig. 5.15, it would be said that the curve traverses through the old basic trigger setting (I d0 ) of the relay characteristic. However, the customized trigger setting (I d0new ) saves the false action of the differential relay for the moment of such excessive fluxing conditions.

5.6 Advantages of the Presented Scheme Over the Conventional Scheme 1. The estimation of the fifth harmonic content is quicker compared to the V /f calculation. 2. Adaptive change of fundamental trigger setting can facilitate relay accuracy. 3. It avoids urgent actions of conventional differential relays for the cases like temporary excess flux conditions and can trigger a time-relying V /f -based protection scheme.

5.7 Conclusion Individual differential protective method of the power transformer develops trustworthiness, constancy, and safety of the whole power system grid. Excessive fluxing of the transformer is most likely happening because of “excess voltage” or “underneath frequency”. This kind of state may affect the disparity of each side current of the device and result in differences in current which may be directed towards false actions of the protective system. The conventional unit-type protective scheme needs improvement to preserve the grid constancy and load stability. Large amounts of fifth harmonics content are generated during excessive fluxing and this actuality is utilized for the anticipated scheme to alleviate the conventional relay against unwanted tripping nature. The dedicated scheme constantly determines the amount of fraction of the fifth harmonic to the first content of different currents to adaptively change the fundamental settings of the differential relay. A chief benefit of the presented scheme is the avoidance of instant actions of the differential relay only while sensing excessive fluxing conditions and stimulating activates the time-relying V /f protection. With due consideration of various parameters, a portion of the power system

150

5 Impact of Transitory Excessive Fluxing Condition on Power …

network is replicated in the PSCAD™ platform and the presented algorithm is authorized in M-code (which is MATLAB) platform. The normal state, excessive flux case, exterior faults, as well as interior fault conditions are simulated and authenticated on a presented algorithm. More on this, it is also checked for a single-phase-type transformer by preparing one hardware model for numerous fault cases and excessive fluxing states. From the results, it can say that the presented method functions in a 47 ms period for the cases of insider fault and in 50 ms to trigger the V /f protection to defend against isolation. Hence, it provides an effectual resolution to avert the false actions of the protective scheme and perfectly differentiates between excess-fluxing phenomenon in contrast to faulty conditions of the power transformer.

5.8 Question and Answer Question-1: How does fifth harmonic analysis differ from conventional second harmonic analysis in transformer protection? Answer: The harmonic analysis for transformer protection is utilized for a long. Though, the harmonic analysis is not novel still it is completely emerging in the transient behavior of power systems, specifically for transformer and rotating machine protection. A harmonic restrain relay is used for transformer protection but it detects only inrush conditions by measuring the amount of second-order harmonic dilution in the difference current [30]. The fifth-order harmonic component of difference current-based analysis is rarely reported in past literature, particularly along with differential protection. Hence, here, fifth harmonic-based analysis is utilized for the detection of the over-fluxing condition and based on it the differential relay prevents false action of the defensive system during temporary excess flux situations. Moreover, the main protection of the transformer is guarded by a biased differential relay itself, and hence no major modification is required in the existing transformer protection scheme. Also, the basic pickup setting will be adaptively changed up to a certain level based on the intensity of the over-fluxing condition if identified. Question-2: Is the proposed algorithm valid for different transformer connections? What is the impact of nonlinear load? Answer: The presented scheme is tested with a unique transformer connection that is the YY system. However, for different transformer connections, the proposed algorithm is also applicable. As the fifth analysis has nothing to do with transformer configuration.

5.8 Question and Answer

151

Initially, the thought was to use odd-number harmonics to identify the intensity of over-fluxing. However, the third harmonic in the three currents is co-phase that is they have the same phase. The third harmonic component flows around the closed loop of the delta. Hence, cancel out in the line if the winding is DELTA-connected winding. Triplen harmonics such as 3rd, 9th, and 15th are purely zero sequences. Contradictory, the harmonics of an order of 5th, 11th, and 17th are purely negative sequences. Moreover, the fifth harmonics have different phases that are not canceled out in line whether the winding is DELTA or STAR. Hence, by considering these situations, it is decided to prefer the fifth harmonic over all other harmonics. Now, coming to the next question of the impact of nonlinear load, it is to be said that in most cases, the differential relay along with over-fluxing protection is provided for high-capacity power transformers. They are not used for low-capacity distribution transformers where these nonlinear loads are connected. A nonlinear load such as rectifiers, variable-speed drives, and electronic devices in turn generates harmonics and can draw distorted current from the source like distribution transformers. The winding connectivity of the device has considerable prints of the propagation of triplen order harmonics (3rd, 9th, 15th …) from one-phase nonlinear loading states. However, if the nonlinear load is linked on the lower voltage level side of the transformer, the level of the fifth harmonic will be within the given limit (25%) set in the algorithm. Also, when such nonlinear load increases in magnitude, the fundamental component also rises in proposition to the fifth harmonic. Hence, a nonlinear load cannot affect the operation of the relay if it is used for the protection of the subtransmission system transformer. Hence, it cannot make a significant impact on power transformers or high-capacity transformers used in power plants and substations. Question-3: What is the effect of CT transient behavior on the relay setting? Answer: During the over-fluxing condition, only the fundamental triggering configuration of the differential-based relay characteristic (area AA’BB’ of Fig. 5.7) will be affected. While during CT transient behavior, generally biased differential characteristics of the relay (line BB’C of Fig. 5.7) will be affected. A simple differential protection scheme along with V /f protection will falsely operate the relaying scheme during temporary excessive fluxing cases. The excessive fluxing cases can be detected well before CTs get into saturation state. Moreover, CT transient effects have already been considered in Chap. 4 of this book. Here, in this chapter, the work is carried out only for preventing unwanted transformer tripping during the momentary over-fluxing condition.

152

5 Impact of Transitory Excessive Fluxing Condition on Power …

Question-4: Why a residual flux of the core is not considered to avoid maloperation due to over-fluxing? Answer: The residual flux mainly affects the inrush period of the transformer. The amount of residual flux and its polarity present in the devices affects the nature and crest value of the inrush current waveform at the time of energization. The main properties of magnetizing inrush current are the non-sinusoidal character of inrush and existence of a considerable amount of second-order harmonics. The over-fluxing condition may be raised during inrush conditions also. However, the magnetizing inrush condition is predominant due to the initial flux setup in the core of the transformer and has a high second harmonic component. Now, during transformer energization, if the overfluxing condition takes place along with remnant flux then the proposed scheme will surely identify it depending on the level of the fifth harmonic components. However, during a normal loading operation period (other than initial switching), the residual flux is not that much important. Hence, the proposed scheme for the detection of the over-fluxing condition is not much exaggerated by residual flux. Question-5: During the interior fault of a transformer, the differential relay first confirms the over-fluxing condition ( < 25%) and then issues a trip signal to CB. How this delay time to calculate I d0new affects differential protection performance during a fault? Answer: One thing here is worth noting that the consolidated reaction time of this technique involves first, data sampling time (Simulation/DSO); second, data processing time; third, computational time of the algorithm in PC (plus intentional delay provided to particular protection function); and at last, the time required to issue a trip/ block signal. Considering these, the consolidated time needed to execute the logic is estimated as described below. Reaction Time The time step required for one complete cycle of MDFT is 250 µs (0.25 ms) at 4 kHz sampling frequency. Looking at the practical test scenario, when the logic executes its first screening, it will take 20 ms processing time of taking 80 samples. A USB 3.0 slot is utilized in case of complete serial data transfer to and fro DSO and system. The Baud pace configured here is 57.6 KBPS [51] which is well suited for the transmission of data at an optimum pace throughout the USB slot. Sampled data is then buffered in the prefixed work ledger of the *.bin extension and it is then transferred constantly to a dedicated algorithm by performing the *.m file in the MATLAB platform. Therefore, the sampling time frame with data processing is taken around 20 ms. Consequently, for the next consecutive screening, due to

5.8 Question and Answer

153

a floating window, the next data of the sample is collected and sampled during the algorithm execution time of the previous cycle and the older sample will be discarded simultaneously. The performance space in the form of clock frequency is 3 GHz of CPU. The dimension of a developed program of the presented MDFT-based technique is 3.5 KB in an interior type of fault case. The reason may be described from the flow of the presented algorithm. In this case, if the internal fault is detected by not fulfilling the condition of the fraction of fifth-ordered harmonic to basic, it means I diff > T H of the algorithm, it will not calculate I donew (no time delay for I d0new estimation is added) and immediately issue trip signal to isolate the transformer. Hence, the size and execution time of the algorithm is lower than in the case of an over-fluxing condition. The algorithm execution time can be found by writing the “TIC” command at the start and the “TOC” command at the end of the MATLAB program. Hence, with the help of these commands in MATLAB programming, the minimum time to perform MDFT is observed at 25.9 ms excluding intended stoppage. Here, it is supposed that the issuance of the tripping command and transmission time required for the residual signal conditioner slab is approximately 1 ms. Therefore, the consolidated reaction time for the initial screening is around 20 ms (for the first whole cycle capturing and sampling) + 25.9 ms (this includes algorithm execution and decision) + 1 ms (this includes issuing trip signal and propagation delay time) = 46.9 ms. On the opposite side, before the subsequent screening, the sampled data are involuntarily transferred to the prefixed work ledger of the system, while the algorithm is executing and making a final decision. Hence, the time needed to execute MDFTbased algorithm for consequent screening would be calculated as 25.9 ms + 1 ms ≈ 26.9 ms. Hence, during an internal fault condition, all the above-mentioned steps will be executed in the dedicated PC step by step, and after confirming, it as an interior fault type, the protection system generates a tripping command. Hence, the execution time required during issuing a tripping command during an interior fault case is ≈26.9 ms and the same is ≈46.9 ms during the first scan as the algorithm has to go through the entire flow step by step. Question-6: Current-based harmonic contents are considered in this work why voltage-based harmonics not considered? Answer: Generally, transformer protection is performed with the help of current-based quantity. In a real power system, today also the protection of the power transformer is mainly provided by differential protection. The differential protection uses the current quantity and, hence, a feature of the current signature has been used during the over-fluxing condition to measure it. The momentary overvoltage is applied to

154

5 Impact of Transitory Excessive Fluxing Condition on Power …

create the temporary over-fluxing condition in the considered system. One can use voltage-based harmonics (after analyzing voltage signatures during various conditions that arise in transformer operation) to detect over-flux. However, the voltagebased harmonics include more cost as it requires separate Potential Transformers (PTs) at both ends of the considered transformer. Moreover, the complexity and run time of the protection algorithm may increase due to the addition of voltage signals analysis in the execution process. Hence, the use of current harmonics is a better option instead of voltage-based harmonics. Question-7: What is the upper limit for shifting of basic pickup setting during over-fluxing? Answer: As per the proposed algorithm, the modified basic pickup setting purely depends on the fraction of fifth-ordered harmonic content to first-ordered content of different currents, and hence depending on the level/severity of the fifth ordered harmonic component, the fundamental pickup setting will be modified proportionally. In general, the fifth harmonic component remains within its range (approximately within 20–60% of fundamental [52] during over-fluxing as well as during normal conditions of the transformer. Hence, the proposed adaptive change in basic pickup (I d0new ) can shift a maximum of up to 40% of the original pickup (I d0 ) during over-fluxing.

References 1. Bhalja B, Maheshwari RP, Chothani NG (2017) Protection and switchgear, 2nd edn. Oxford University Press, New Delhi, India 2. Masoum MAS, Fuchs EF (2015) Power quality in power systems and electrical machines, 2nd edn. Academic Press, Boston 3. Lin X, Ma J, Tian Q, Weng H (2013) Electromagnetic transient analysis and novell protective relaying techniques for power transformers 4. IEEE guide for protective relay applications to power transformers (2000). IEEE Std C37.912000 (Revision of IEEE Std C37.91-1985), pp 1–85. https://doi.org/10.1109/IEEESTD.2000. 91943 5. Zhao X, Chai J, Su P (2003) Identification of magnetizing inrush currents of power transformers based on features of flux locus. In: Sixth international conference on electrical machines and systems, 2003. ICEMS 2003, Nov 2003, vol 1, pp 317–320 6. Kang YC, Lee BE, Zheng TY, Kim YH, Crossley PA (2010) Protection, faulted phase and winding identification for the three-winding transformer using the increments of flux linkages. IET Gener Transm Distrib 4(9):1060–1068. https://doi.org/10.1049/iet-gtd.2010.0094 7. Xu Y, Wang ZP, Yang QX (2004) Study on the improved transformer protection method based on the magnetic flux characteristics. In: Eighth IEE international conference on developments in power system protection, Amsterdam, Netherlands, pp 360–363. https://doi.org/10.1049/cp: 20040137 8. Kang YC, Kang SH Crossley P Lee SJ, Lee BE (2003) A transformer protective relaying algorithm using the ratio of the flux linkages. In: 2003 IEEE Bologna PowerTech—Conference proceedings, vol 2, pp 996–1001. https://doi.org/10.1109/PTC.2003.1304682

References

155

9. Xu Y, Wang Z, Liu Q, Yang Q (2003) A new method for transformer protection based on the ratio of voltage and fluxional differential current. In: IPEC 2003—6th International power engineering conference, pp 641–645. https://doi.org/10.1109/tdc.2003.1335245 10. Al-Fakhri B (2002) A unique transformer protection which eliminates over-voltage, overfluxing, and CT saturation protection. In: IEEE/PES transmission and distribution conference and exhibition, Oct 2002, vol 1, pp 142–147. https://doi.org/10.1109/TDC.2002.1178274 11. Devipriya P, Sneha T, Shanmugapriya P (2018) Design of numerical relay for over flux and over current protection in transformers. In: 7th IEEE international conference on computation of power, energy, information and communication ICCPEIC 2018, pp 153–158. https://doi. org/10.1109/ICCPEIC.2018.8525225 12. Noshad B (2019) A new algorithm for three-phase power transformer differential protection considering effect of ultra-saturation phenomenon based on discrete wavelet transform. Int J Emerg Electr Power Syst 20(1). https://doi.org/10.1515/ijeeps-2017-0233 13. Bhatt N, Kaur S, Tayal N (2016) Causes and effects of overfluxing in transformers and comparison of various techniques for its detection. In: IJCA proceedings on international conference on advances in emerging technology, pp 17–22 14. Chen KW, Glad ST (1991) Estimation of the primary current in a saturated transformer. In: 1991 Proceedings of the 30th IEEE conference on decision and control, vol 3, pp 2363–2365. https://doi.org/10.1109/CDC.1991.261872 15. Patel DD, Mistry KD, Chothani NG (2016) Digital differential protection of power transformer using DFT algorithm with CT saturation consideration. In: 2016 National power systems conference (NPSC), Dec 2016, pp 1–6. https://doi.org/10.1109/NPSC.2016.7858854 16. Mistry DDPKD, Chothani NG (2017) Transformer inrush/internal fault identification based on average angle of second order derivative of current. In: Asia-Pacific power and energy engineering conference, APPEEC, pp 1–6. https://doi.org/10.1109/APPEEC.2017.8309017 17. Patel D, Chothani N (2020) Real-time monitoring and adaptive protection of power transformer. In: Digital protective schemes for power transformer. Springer Singapore, pp 173–190 18. Patel D, Mistry KD, Raichura MB, Chothani N (2018) Three state Kalman filter based directional protection of power transformer. In: 20th national power systems conference (NPSC), pp 1–6. https://doi.org/10.1109/NPSC.2018.8771716 19. Babaria SJ, Patel DD, Chothani NG, Chaini PK, Patel RM, Joshi SR (2022) Influence of system parameters on current transformer saturation in power system. In: 2022 IEEE 2nd international conference on sustainable energy and future electric transportation (SeFeT), pp 1–6. https:// doi.org/10.1109/SeFeT55524.2022.9909425 20. Patel D, Chothani N (2023) Evaluation of various dynamics on current transformer saturation with a model study on power system protection. In: Recent advances in power systems, pp 121–136 21. Raichura M, Chothani N, Patel D, Sharma J (2020) Methodologies for the detection of magnetizing inrush and fault condition in power transformer. In: 2020 IEEE international conference on computing, power and communication technologies (GUCON), Oct 2020, pp 146–151. https://doi.org/10.1109/GUCON48875.2020.9231065 22. Raichura M, Chothani N, Patel D (2021) Review of methodologies used for detection of magnetising inrush and fault conditions in power transformer. IET Energy Syst Integr 3:109–129. https://doi.org/10.1049/esi2.12012 23. Madzikanda E, Negnevitsky M (2012) A practical look at harmonics in power transformer differential protection. In: 2012 IEEE international conference on power system technology (POWERCON), Oct 2012, pp 1–6. https://doi.org/10.1109/PowerCon.2012.6401274 24. Raichura MB, Chothani NG, Patel DD, Mistry KD (2019) Identification of inrush and fault conditions in power transformer using harmonic distortion computation. In: 2019 IEEE 1st international conference on energy, systems and information processing (ICESIP), July 2019, pp 1–6. https://doi.org/10.1109/ICESIP46348.2019.8938308 25. Raichura M, Chothani N, Patel D, Mistry K (2021) Total Harmonic Distortion (THD) based discrimination of normal, inrush and fault conditions in power transformer. Renew Energy Focus 36:43–55. https://doi.org/10.1016/j.ref.2020.12.001

156

5 Impact of Transitory Excessive Fluxing Condition on Power …

26. Hamilton R (2013) Analysis of transformer inrush current and comparison of harmonic restraint methods in transformer protection. IEEE Trans Ind Appl 49(4):1890–1899. https://doi.org/10. 1109/TIA.2013.2257155 27. Patel DD, Chothani N, Mistry KD, Tailor D (2018) Adaptive algorithm for distribution transformer protection to improve smart grid stability. Int J Emerg Electr Power Syst 19(5):1–14. https://doi.org/10.1515/ijeeps-2018-0022 28. Bernabeu EE (2015) Single-phase transformer harmonics produced during geomagnetic disturbances: theory, modeling, and monitoring. IEEE Trans Power Deliv 30(3):1323–1330. https:// doi.org/10.1109/TPWRD.2014.2371927 29. Sadeghkhani I, Ketabi A, Feuillet R (2013) Study of transformer switching overvoltages during power system restoration using delta-bar-delta and directed random search algorithms. Int J Emerg Electr Power Syst 13(3). https://doi.org/10.1515/1553-779X.2996 30. Ken Behrendt CL, Fischer N (2011) Considerations for using harmonic blocking and harmonic restraint techniques on transformer differential relays. SEL J Reliab Power 2(3):1971–1980 31. Patel D, Chothani N (2020) Phasor angle based differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 51–81 32. Patel D, Chothani N, Mistry K (2018) Discrimination of inrush, internal, and external fault in power transformer using phasor angle comparison and biased differential principle. Elect. Power Components Syst 46(7):788–801. https://doi.org/10.1080/15325008.2018.1509915 33. Patel DD, Mistry KD, MChothani NG (2015) A novel approach to transformer differential protection using sequence component based algorithm. J CPRI 11(3):517–528 34. Chothani N, Patel D, Raichura M (2019) Transformer protection with sequence components and digital filters, 1st edn. LAP LAMBERT Academic Publishing, Latvia 35. Patel DD, Chothani NG, Mistry KD (2015) Sequence component of currents based differential protection of power transformer. In: 12th IEEE international conference electronics, energy, environment, communication, computer, control: (E3-C3), INDICON 2015, pp 1–6. https:// doi.org/10.1109/INDICON.2015.7443855 36. Patel D, Chothani N (2020) CT saturation detection and compensation algorithm. In: Digital protective schemes for power transformer. Springer, Singapore, pp 33–49 37. Chothani NG, Patel DD, Mistry KD (2017) Support vector machine based classification of current transformer saturation phenomenon. J Green Eng River Publ 7:25–42. https://doi.org/ 10.13052/jge1904-4720.7122 38. Dharmesh Patel NC. Relevance vector machine based transformer protection. In: Digital protective schemes for power transformer, 1st edn. Springer, Singapore, pp 107–131 39. Patel D, Chothani NG, Mistry KD, Raichura M (2018) Design and development of fault classification algorithm based on relevance vector machine for power transformer design and development of fault classification algorithm based on relevance vector machine for power transformer. IET Electr Power Appl 12(4):557–565. https://doi.org/10.1049/iet-epa.2017.0562 40. Raichura MB, Chothani NG, Patel DD (2020) Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique. IET Sci Meas Technol 14(1). https://doi.org/10.1049/iet-smt.2019.0102 41. Raichura M, Chothani N, Patel D (2021) Efficient CNN-XGBoost technique for classification of power transformer internal faults against various abnormal conditions. IET Gener Transm Distrib 15(5):972–985. https://doi.org/10.1049/gtd2.12073 42. Chothani NG, Raichura MB, Patel DD, Mistry KD (2018) Real-time monitoring protection of power transformer to enhance smart grid reliability. In: 2018 IEEE electrical power and energy conference (EPEC), Oct 2018, pp 1–6. https://doi.org/10.1109/EPEC.2018.8598427 43. Raichura M, Chothani NG, Patel D (2019) Real-time monitoring & adaptive protection of power transformer to enhance smart grid reliability. Electr Control Commun Eng 15(2):104–112. https://doi.org/10.2478/ecce-2019-0014 44. Tan Q, Liu P, Miao S, Zhang W, Zhou L (2013) Self-adaptive transformer differential protection. IET Gener Transm Distrib 7(1):61–68. https://doi.org/10.1049/iet-gtd.2011.0739 45. Patel D, Chothani N (2020) Adaptive digital differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 83–106

References

157

46. Nimkar PR, Arora TG, Bangde PS, Narnaware PJ (2018) Over-flux protection of the transformer. In: 2018 International conference on smart electric drives and power system (ICSEDPS), June 2018, pp 171–176. https://doi.org/10.1109/ICSEDPS.2018.8536055 47. Raichura M, Chothani N, Patel D (2020) Development of an adaptive differential protection scheme for transformer during current transformer saturation and over-fluxing condition. Int Trans Electr Energy Syst 31:1–19. https://doi.org/10.1002/2050-7038.12751 48. Maheshwari RPP, Verma HKK (1997) Adaptive digital differential relay with overexcitation and inrush restraint. Electr Power Syst Res 41(3):175–184. https://doi.org/10.1016/S0378-779 6(96)01153-4 49. Chothani NG (2016) Development and testing of a new modified discrete Fourier transformbased algorithm for the protection of synchronous generator. Electr Power Components Syst 44(14):1564–1575. https://doi.org/10.1080/15325008.2016.1181688 50. IEEE guide for protecting power transformers (revision of IEEE Std. C37.91-2000). IEEE Power Engineering Society Sponsored by the Power System Relaying Committee, New York 51. Keysight InfiniiVision 3000 X-series oscilloscopes-user’s guide. http://literature.cdn.keysight. com/litweb/pdf/75019-97073.pdf 52. ABB (2006). Protection application handbood—ABB BA THS/BU transmission systems and substations. C/o ABB Switchg. AB, SE-721 58 Västerås Sweden, no 6, p 356

Chapter 6

Total Harmonic Distortion-Based Improved Transformer Protective Scheme

Abstract The nonlinear inherent nature of the transformer core escorts current and/ or voltage wave deformation in instances of disturbance and/or fault. Harmonics involvement in current relies on the material of the transformer core, type of load condition, and anomalous states. Different odd and even Harmonics levels can be used for the discrimination of the normal state, inrush state and faulty state of the transformer. Through Fast Fourier Transformation (FFT) investigation, the Total Harmonic Distortion (THD) of an acquired quantity’s wave pattern for a cycle is estimated. From the study, it is extracted that amount of THD remains higher in the instance of inrush because of early flux built in the core. On the contrary, for the faulty states, %THD will be noted lower for the reason of symmetric wave pattern. The amount of %THD beneath the prefixed level can be considered as a usual state and higher THD levels are treated as an inrush state. In case, THD falls inside the prefixed limits, it is pretended a faulty state. In this chapter, the legitimacy of this discriminative technique is checked by creating a variety of tests such as preliminary inrush, interior fault, toggling the dedicated transformer during faulty states, and CT saturation while persistent of the fault. A hardware experiment establishes the faithfulness of this discriminative technique for transformer fortification. This technique is trouble-free as well as proficient to resolve the difficult problem of cataloging of inrush state or faulty state of the transformer.

6.1 Introduction A defensive scheme should operate along with proper consideration during anomalous states. In a transformer, undulation in frequency level is because of harmonics, transients, magnetic field and variation in system conditions. System disorder may affect wave pattern alteration and sequentially pose circulation of useless harmonics into the entire system. Due to the existing nonlinear uniqueness of the core, a transformer can be considered a key source of harmonics in different operating conditions. From the study, it is found that the relations between power system dynamics, as well as core nonlinearity could help to discriminate various situations of a transformer. The © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Chothani et al., Advancement in Power Transformer Infrastructure and Digital Protection, Studies in Infrastructure and Control, https://doi.org/10.1007/978-981-99-3870-4_6

159

160

6 Total Harmonic Distortion-Based Improved Transformer Protective …

foremost factors that are exploited to acquire a harmonical model of a transformer are core characteristics, leakage reactance, core-loss component, and winding resistance [1]. The existence of the DC factor of the AC structure augments temperature and hence reduces the lifetime of the considered apparatus. The effect of current harmonics involves excessive heating, false actions of a relay, overloading, capacitor overstressing and skin effect also. The impact of harmonics in the power structure may falsely act the control devices, telephonic interference, resonance, supplementary losses/noise, etc. The same type of consequences caused by voltage-based harmonics are noise, increased voltage alteration, and induction devices protection and operation. Nonlinearity of the transformer core and electronics loads is the major cause for the creation of harmonics in the grid system. The impacts of the THDs on power quality with modeling, power transfer as well as mitigation techniques are explained [2]. As the case is of losses, transformers possess an 8% loss of overall production and around 60–80% from the consolidated system power losses due to distributiontype devices [3]. The level of losses can be estimated by counting of THDs, the loss aspect along with depreciation of these transformers beneath harmonics that can augment operational cost [4]. For instance, in variation harmonics, the health of the device is abridged [5]. The wave pattern may unclear as the reason for harmonics, which impacts defensive structures such as overcurrent protections, induction motor protection, and transformer protection [6]. Article [7] discussed the inrush resistance scheme to protect the transformer using Mathematical Morphology (MM), though it is hard to extend the morphologicalbased system for the nonlinear central part of the transformer. Most of the time second harmonic part is utilized to detect inrush, but under firm situations of interior fault accompanied by CT saturation, second harmonic content is dominant. Hence, the schemes using second harmonic quantity could false-operate during exterior fault along with profound CT saturation. Xiangning Lin and fellow authors [8] intricate second harmonic using a control system-based RTDS simulator. The combined harmonic control scheme amid wave profile detection till the fifth harmonic is discussed [9]. The outcome of the measured harmonics for the current differencebased defense of the transformer by experimental approach is performed by Madzik [10]. Aspects distressing inrush and harmonic control technique throughout inrush event amid second harmonic is elaborated in [11]. Someway, diverse test cases such as disparity in percentage winding, fault resistance, and fault inception angle location where not covered with clarity. Harmonics could be also generated because of geomagnetic turbulences [12] as well as for this period second harmonic provides sensitive patterns. Magnetic pseudo-characteristic-based core replication is elaborated [13] to spot inrush and fault cases by Fast Fourier Transform (FFT). Though the competency of this technique is smaller amount compared to classifier techniques such as Relevance Vector Machine (RVM) [14, 15], HE-ELM [16, 17], SVM [18], and DLNN [19]. Still, THD dependent proposal provides a better resolution for inrush identification. For a few instances, the seventh harmonic contents may also be higher compared to the third and fifth harmonic quantity that depends on the saturation cases and deviation of system parameters for disturbances.

6.2 Modeling of Power Structure

161

Additionally, voltage, as well as current fraction-dependent transformer protective schemes, are convoluted to distinguish inrush and CT saturation cases [20]. Moreover, phasor angle examination-based sequential components are utilized [16, 21, 22] to develop transformer-biased unit protection. An improved version of this technique is utilized considering constructive sequence angle as well as destructive sequence magnitude [17]. Fundamental of CT saturation elaborated as a dynamic effect with a real case study added with various parameter changes in the power system and its protective schemes [23, 24]. Effect of CT saturation detection and adaptively shift the percentage-biased characteristic based on the saturation factor of the CT with prototype under laboratory environment validated sharply [25–28]. Usually, researchers extensively focus to develop a novel technique to shelter the transformer efficiently for all kinds of abnormal cases. For that the scheme must be such as; it should separate the device into the smallest amount of interior fault, and avoid false trips in the disturbances such as inrush cases (i.e., recovery, sympathetic and initial [29, 30]) and exterior faults. Categorization of inrush and faults should be a principal focus that must be attended correctly [31]. The likelihood of wrong operation of relay is possible in the cases like inrush due to lack of suitable categorization format as well because of the relationship among inrush and interior faulty quantities wave pattern. Such trouble is attended in this study and accomplished a sound discrimination system that can efficiently catalog normal, faulty, and inrush cases. Also, the presented methodology is authorized through Power System Computer Aided Design (PSCAD) application [32] and utilizes a prototype also. Obtained results confirm the effectiveness of this scheme suggested in this chapter.

6.2 Modeling of Power Structure Figure 6.1 contains a 100 MVA, 3-φ transformer rated, 400/230-kV, having a frequency of 50 Hz, Yn-Yn type of connection is attached to the generator from one side and the load from another side. The considered Generator is of 100 MVA capacity, 400-kV, 50 Hz. In this case, 3-single phase transformers are considered using encoding in the FORTRAN platform, for the device’s interior winding fault simulation. For accurate replication of the magnetic behavior of Current Transformers (CTs), the JA (Jiles-Atherton) model of CT [33], accessible in the library of PSCAD rated with 2000/2 A and burden of 25 VA is considered. The structured system is actuated on software and gathered data are afterward analyzed. CTs on each side are used to fetch the current quantity from the considered system and the obtained data are transferred to Fast Fourier Transform (FFT) for extracting and segregating data of available harmonics. In this case, Harmonics till the level of seventh order are considered for proper THD evaluation. After this step, the outcome from FFT is provided to the Harmonics distortion block. It is capable to estimate the percentage of THD for a selected period and also able to provide an amount of a particular harmonic.

162

6 Total Harmonic Distortion-Based Improved Transformer Protective …

Fig. 6.1 Replicated system taken into consideration

Here, a vast range of cases are experimented however; a few are displayed in the section of result analysis. During the primary inrush test state, 14 cases are created considering a variation of system constraints that are detailed in the result analysis segment. In the case of interior-type faults, 8 test cases are considered. Additionally, switching ON of the transformer (inrush case) having already faulted situation is also considered to authenticate the capability of the scheme. Approximately 12 cases of inrush in the existence of interior fault circumstances are created. One extraordinary case of CT saturation during the existence of interior fault is also analyzed for 10 testing conditions.

6.3 Presented Technique for Inrush and Fault Discrimination Here, a sound classifier scheme to distinguish inrush and fault conditions is presented. With the help of a vast amount of literature review and replication on software, it is established that the percentage harmonic can be considered as a key feature for effective classification between inrush and faulty states of the transformer. An entire current that pumps in the power structure can be given as the sum of the fundamental AC component, DC component as well as certain harmonics that is

6.3 Presented Technique for Inrush and Fault Discrimination

163

formulated mathematically as below X (t) = X (DC) +

n ∑

h X rms cos(hωt + αh )

(6.1)

h=1

X denotes voltage/current quantity, h indicates the order of harmonic, ω denotes basic frequency, and α shows phase transfer. Root Means Square (RMS) includes squared values for all the harmonics and addition of harmonics products. [ ( (1) )2 ( (2) )2 ( (3) )2 ( (h) )2 [1/2 X rms = X dc + X rms + X rms + X rms + · · · + X rms

(6.2)

In general, THD can be given as [ ( ) | N (h) 2 |∑ X rms | THD X = X (1) h=2

(6.3)

Here, h is the order of Harmonic, N is considered as the highest harmonic included, X rms is to be understood as the magnitude of system quantity, and X (1) is the basic constituent of system quantity (fundamental component). The highest amount of RMS value of harmonics for a transformer [34] is given as pu Imax

[ | | =|

1 + PEC 1 + K h ∑∞X

2

h=1

Ih2

(PEC )

(6.4)

PEC is taken as the amount of eddy current losses, K h is harmonic constant, X h is harmonic current. More on this, the Harmonic constant can be formulated as Kh =

∑∞

2 2 h=1 Ih h 2 IR

(6.5)

I R is considered as the R.M.S. value of current for a specific instant. A transformer is a device that can be energized from any of the sides (either a higher voltage-level side or from a lower voltage-level side). So, the scaled value of currents from CTs located on both sides has been considered for %THD calculation. Harmonics till the seventh order are taken into consideration to calculate THD to improve the sensibility of the protective technique. It is possible to select lower or upper order of harmonics but that may eventually limit the sensibility or may raise the computational burden sequentially. One floating window constantly fetches CT’s secondary data for one cycle. Fast Fourier Transform (FFT) is utilized here for

164

6 Total Harmonic Distortion-Based Improved Transformer Protective …

analysis purposes to fetch the required harmonics of the current. 50 Hz frequency amounted to FFT is synchronized for evaluation of harmonic values till seventh order. The THD of the fetched waveform is afterward calculated by the mean of the harmonic analyzer feature. A triggering point of 5–30% is considered for THD to categorize between the usual state, faulty state and inrush state [35]. This threshold is chosen from the cited literature and also depends on the widespread analysis of a variety of load conditions, diverse fault conditions and inrush conditions that are validated on the software platform. The selected range depicts that if the amount of estimated THD remains beneath the lower limit (i.e., 5%), it is the case of a normal state as during the normal state the wave pattern is entirely a frequency subset of a basic wave pattern. The lower trigger of 5% is chosen due to some already present amount of harmonics in the supply. On the other side, if the upper trigger (i.e., 30%) traversed, it is interpreted as the wave pattern undulated sinusoidal which means it is mostly distorted in nature and that is the condition for the inrush state. Further, the calculated THD for a particular cycle is assessed to check if it falls between 5 and 30% as if that stipulation is satisfied, the case is of fault state. It is not advisable to choose less than 30% because, during CT saturation states, the 30% limit may be violated which may falsely actuate the system. Similarly for the upper limit (i.e., 30%), the %THD should not be chosen above this limit because, during exterior fault case along with CT saturation state, it may falsely actuate the system. Figure 6.2 illustrate the flow diagram of the presented technique. During the execution process of this algorithm, data from the real field are collected; for this case, current quantity samples of the entire three phases, of each side of the transformer are obtained constantly. Hence, post-collection of one particular cycle of the current wave pattern, the data is availed for analysis of harmonics. After the collection of harmonics data till the seventh order, the proposed system checks the percentage THD aspect concerning fundamental harmonics. In case the calculated %THD of either phase goes beyond 30%, it is treated as the condition of an inrush state and the algorithm is then redirected back to capture the next data. Along with this, in case the calculated % THD remains lower than 5%, which can be interpreted as the case of the normal state and the system will be directed to capture the next data. In case, both the conditions mentioned are not fulfilled, in that case, apparently, the %THD falls within 5–30% which is the state of the faulty situation. For this case, the scheme interprets it as a faulty state and further moves to classify whether it is an interior fault or exterior fault with the use of existing techniques such as differential protection (preferable), AI method, machine learning, and classifier techniques.

6.4 The Outcome of the Proposed Technique

165

Fig. 6.2 Flowchart of the proposed scheme

6.4 The Outcome of the Proposed Technique Numerous tests are performed to authorize the anticipated harmonic contents dependent classifier method to classify the normal state, inrush state, or faulty state of the transformer. For the sake of simplification, the interior fault case is considered only along with the inrush state in the result investigation section.

6.4.1 Initial Inrush Figure 6.3 undoubtedly demonstrates that through the preliminary inrush case, the current wave pattern is vastly hazy which ultimately creates a higher value of THD percentage. Table 6.1 demonstrates the percentage THD of each of the three phases

166

6 Total Harmonic Distortion-Based Improved Transformer Protective …

Fig. 6.3 a Inrush condition current wave pattern. b Individual phase THD

of various air-cored reactance along with changing instances (i.e., degree) of circuit breaker (CB) closer for the period of preliminary inrush cases. Table 6.1 depicts that percentage of THD remains more than 30% all-time.

6.4.2 Internal Fault Condition The current wave pattern in case of interior fault included DC content as well as without DC content can be seen in Fig. 6.4. Moreover, the amount of percentage THD for the fifth instant of Table 6.2 can be seen in Fig. 6.4c. For the modeled transformer, interior fault cases during various fault instances, various fault perspectives, as well as various percentages of primary side transformer winding concerned, are modeled. The percentage THD of the analogous phase is

6.4 The Outcome of the Proposed Technique

167

Table 6.1 THD percentage for initial inrush case Preliminary Inrush condition S. No.

Reactance of air cored (pu)

1

0.0001

Closing time of circuit breaker in degrees

%THD for phase A

%THD for phase B

%THD for phase C

0

31.44

52.17

73.14

45

39.92

46.26

90.36

3

90

40.42

48.8

98.76

4

180

56.81

94.3

73.2

0

63.32

46.75

78.7

6

45

64.83

48.2

81.0

7

90

65.36

50.02

83.3

8

135

68.34

88.27

64.23

180

2

5

0.001

9

72.8

66.34

91.1

0

75.67

77.8

82.5

45

78.24

79.54

86.4

12

90

80.14

79.84

88.2

13

135

81.14

80.01

90.2

14

180

83.45

81.74

92.1

10 11

0.1

calculated and data for a variety of conditions are available in Table 6.2. Numerous test conditions are created arbitrarily and it is observed that for an interior fault of each kind, the amount of percentage THD falls in the range that is predefined in the algorithm, i.e., more than 5% and lower than 30%.

6.4.3 Energization of Transformer in Existence of Faulty Condition The current wave pattern for the instance of inrush while fault persists in transformer pre-start condition can be seen in Fig. 6.5. This special type of condition is considered to prove the efficacy of the presented technique for the effective identification of inrush cases while the presence of a fault in the transformer means faults already exist and the transformer is switched ON. In this case, different values of residual flux are considered during various circuit breaker closer periods and for various types of faults. The THD of a particular phase that is involved in the fault case is calibrated and noted in Table 6.3. It is visible from Table 6.3 that the elaborated scheme can truly recognize pre-fault cases while switching the transformer.

168

6 Total Harmonic Distortion-Based Improved Transformer Protective …

Fig. 6.4 a Wave pattern of current in case of B-G interior fault contains DC contents. b Wave pattern of current in B-G interior fault case excluding DC contents. c All 6 phases THD

6.4.4 Fault Case While CT Saturates Primary and secondary side current wave patterns while CTs saturates during an interior fault can be seen in Fig. 6.6. CTs could be saturated in the event of a severe fault; hence there may possibility of false action of the elaborated technique. Therefore,

6.4 The Outcome of the Proposed Technique

169

Table 6.2 THD percentages for interior fault case Interior fault case S. No.

Fault position in terms of percentage winding

Fault initiation angle (FIA) in degree

Category of fault

THD percentage of included phases

1

25

45

AG

A = 17.593

2

30

90

ABG

A = 13.41, B = 18.601

3

35

135

ABC

A = 15.60, B = 16.80, C = 45.50

4

50

180

AG

A = 9.89

5

50

270

BG

B = 12.165

6

75

45

ABG

A = 20.48, B = 19.914

7

80

90

ABC

A = 17.54, B = 19.87, C = 15.48

8

90

135

AG

A = 19.489

Fig. 6.5 Wave pattern of current for inrush case for pre-fault condition

to verify authenticity, various testing conditions of CT saturation are involved. The test conditions with changing various fault parameters are taken and the measured percentage THD for the respective phase is noted in Table 6.4. It can be noted from Table 6.4 that during the CT saturation period, the level of percentage THD is not going beyond the preset upper trigger (30%) and can correctly detect these kinds of cases also.

170

6 Total Harmonic Distortion-Based Improved Transformer Protective …

Table 6.3 THD percentage of inrush during interior fault cases Inrush cases during interior fault cases S. No.

Amount of residual flux (in pu)

1

0.5

0.5

60

6

60

9

A = 15.47, B = 15.28 ABG

60

A = 11.34, B = 13.92 A = 10.21, B = 15.26 A = 13.89, B = 22.17

180 0.5

A = 9.95, B = 9.10 A = 9.25, B = 7.81

100

8

A = 12.06 A = 14.76

AB

180 0.5

Percent THD of included phase A = 9.19

100

5

10

AG

180

3

7

60

Type of fault

100

2 4

Closing time of circuit breaker in degrees

ABC

A = 10.65, B = 9.85, C = 11.53

11

100

A = 13.57, B = 15.87, C = 14.97

12

180

A = 12.60, B = 10.52, C = 13.58

Fig. 6.6 Wave pattern of current quantity while CT saturates through interior faulty case

6.5 Hardware Test Arrangement for Different Result Investigation A hardware test model developed for result analysis is depicted in Fig. 6.7. 2-kVA, 220/110 V rated two single-phase multi-tapped transformers are connected in parallel to create numerous inrush and fault cases. For simplicity only a single transformer is displayed in Fig. 6.7. One lamp-type load having a 15 A burden is applied via

6.5 Hardware Test Arrangement for Different Result Investigation

171

Table 6.4 THD percentage during CTs saturation while there exists an interior fault CTs saturation while there exists an interior fault S. No.

Value of resistance as CT burden (Ω)

Fault incipient angle (FIA)

Category of fault

THD percentage of involved phase(s)

1

5

0

AG

A = 21.14

2

15

45

AB

A = 27.54, B = 23.45

3

5

90

AG

A = 22.45

4

5

135

ABG

A = 25.47, B = 20.14

5

10

180

AG

A = 24.48

6

10

270

AB

A = 26.78

7

5

0

AB

A = 19.48, B = 21.45

8

10

45

ABC

A = 19.71, B = 22.48, C = 26.23

9

10

90

AG

A = 22.75

10

15

135

AB

A = 23.789, B = 28.789

switch S4 and contactors are considered as an analogy of circuit breakers of the line. Diverse exterior, as well as interior faults are created through-fault switches (i.e., S1 and S2 for an interior type of fault and switch S3 for an exterior type of fault). Moreover, 12 A, 18 Ω resisters (i.e., R1 and R2 ) are used to simulate faults as displayed in Fig. 6.7. The CTs of suitable capacity are attached at each side of the principal transformer to measure and investigate the current wave pattern. IEEE advisory [36] directed to model the prepared hardware prototype. Subsequent subsections introduce quantitative faults as well as inrush states that are created in a laboratory.

6.5.1 Preliminary Inrush Situation Preliminary inrush is set up when there is null load switching of the transformer takes place and it relates to the amount of core saturation. The level of core saturation relies on switching instant hence, the magnitude of the inrush current and its maximum values for positive and negative cycles are identified according to the Fault Inception Angle (FIA). In this case, two distinct unidirectional magnetizing inrush current cases as positive side and negative side are tested on hardware setup as shown in Fig. 6.8a, b. For these cases, second harmonic contents are estimated more than 50% as observed in Fig. 6.8c. Even though the estimated percentage of THD is also higher than 30%

172

6 Total Harmonic Distortion-Based Improved Transformer Protective …

Fig. 6.7 Prepared hardware model in laboratory

(i.e., 86.2%). Traditional protective schemes use standalone second harmonic-based content for inrush identification purposes. Researchers have tried to minimize the quantity of inrush current that is usually produced at the instant of switching the transformer by revolute the raw material of the core, controlling incipient instant, etc. Other than initial inrush there are certain types of inrushes also taking place in the device, such as sympathetic inrush and last but not least recovery inrush cases which are elaborated on in the succeeding subsections.

6.5.2 Sympathetic Type of Inrush Condition Sympathetic inrush case generally occurs during parallel connection of transformers. If the side-by transformer is switched ON during a no-load situation, that transformer itself encounters an initial inrush and another parallel-connected transformer will observe a sympathetic type of inrush condition. DC content of a closed proximity transformer may direct saturate the core of a principal transformer. The waveforms of the Sympathetic type of inrush case collected from the hardware-based prototype are given in Fig. 6.9a. For this case, the harmonic spectrum can be visible in Fig. 6.9b. Figure 6.9b demonstrates second-order harmonic level and percentage THD amount (i.e., 56.6%), which is more than the permissible level.

6.5 Hardware Test Arrangement for Different Result Investigation

Fig. 6.8 Waveform and Harmonic during Inrush

173

174

6 Total Harmonic Distortion-Based Improved Transformer Protective …

Fig. 6.9 Sympathetic inrush wave pattern and its harmonic

6.5.3 Recovery Type of Inrush Condition The cases such as disturbances that occur in the power system such as an abrupt change in voltages pose a stressful situation for the operation of the transformer. The abrupt change in voltage levels may encounter due to fault conditions, temporary trips, voltage changes, auto reclosing, etc. However, the impact is not too dominant such as preliminary inrush though it may impact the transformer operation. This condition of the generation of inrush during voltage recovery is known as the recovery inrush state of the transformer. The impact of recovery inrush may be dominant in case the fault clearing contains all three phases and in proximity to a transformer. The

6.5 Hardware Test Arrangement for Different Result Investigation

175

Fig. 6.10 Recovery inrush current wave pattern and its harmonics

wave pattern of recovery inrush condition that takes place during a fault clearance in the vicinity of the transformer is displayed in Fig. 6.10a. For the same, a Harmonic spectrum can be seen in Fig. 6.10b.

6.5.4 Exterior Fault Cases As and when the transformer encounters an exterior fault condition, it fetches an additional amount of current compared to the normal rated amount. A fault condition is known as an unwanted pathway of the current that has less amount of resistance and for which the amount of current fetched from the source becomes higher. This type of condition leads to elevated pressure on the components of the device as it is not formulated for such high amount of quantity. This is more understandable

176

6 Total Harmonic Distortion-Based Improved Transformer Protective …

Fig. 6.11 Exterior fault case and amount of harmonics

from Fig. 6.11a. As and when the exterior fault encounters, the amount of current is elevated rapidly. More on this, the amount of harmonics is minimal, which is visible in Fig. 6.11b. The cause for a minimal amount of harmonics is symmetricity in the wave pattern at the time of the exterior fault.

6.5.5 Exterior Fault with CT Saturation Cases Under the circumstances of CT saturation when external fault occurs in the system, the CT secondary waveforms are distorted and result slightly higher harmonics

6.5 Hardware Test Arrangement for Different Result Investigation

177

Fig. 6.12 Exterior fault with CT saturation case fault case, current and harmonics wave pattern

compared to no-saturation period. The waveforms of this faulty case are elaborated in Fig. 6.12a and its harmonic generation with its spectrum is viewed in Fig. 6.12b.

6.5.6 Interior Fault Case The interior fault condition’s current wave pattern is displayed in Fig. 6.13a. During interior fault cases the amount of current is elevated which origins electrical and

178

6 Total Harmonic Distortion-Based Improved Transformer Protective …

Fig. 6.13 Interior fault case, current and harmonics wave pattern

mechanical pressure on the windings as well as on the insulation material of the device. Hence, to limit this type of harm to the transformer and its consequences, it is required to take the necessary steps as soon as probable. Figure 6.13b shows harmonic spectrum while the transformer encounters the interior fault case. It is to be noted that the harmonic value is within the preset threshold limit as per the algorithm.

6.5.7 Interior Fault While CTs Saturates For interior fault cases, the amount of current required from the source is elevated which may lead to saturation of CTs. The saturation of CT depreciates the current wave pattern which can’t perfectly replicate the current flows through the system. However, the currently applied protective method depends on a differential-based

6.5 Hardware Test Arrangement for Different Result Investigation

179

Fig. 6.14 Current under interior fault condition while CT saturates and its harmonic wave pattern

principle that may recognize the presence of interior fault before CT’s saturation. This protective technique separates the device instantly, hence preventing damage to the system. Acquired current signal and harmonic pattern recorded during interior fault applied on hardware setup are shown in Fig. 6.14a, b.

6.5.8 No-load Current with Its Harmonics After energization of transformer, inrush current persists for few cycles depending on the system condition. Once the inrush phase dies out, the transformer takes no-load magnetizing current from source just to compensate the no-load loss in the core and

180

6 Total Harmonic Distortion-Based Improved Transformer Protective …

Fig. 6.15 No-load current with its harmonics

negligible copper loss in the energized winding. The waveform of such magnetizing current along with harmonic analysis recorded in the laboratory is illustrated in Fig. 6.15.

6.6 Conclusion In this chapter, an algorithm for discrimination of transformer inrush type of current and interior fault along with numerous anomalies is presented. For the transformer inrush case, the percentage of THD of the current is observed higher (i.e., >30%). Further, for the duration of interior/exterior fault cases, the percentage of THD stays in a defined range (i.e., 5% < %THD < 30%). On the other hand, with less distortion and normal condition of a transformer, the same THD limit will be maintained lower than 5%. The preset value of THD in percentage is decided based on literature and experience of experimental results. Authentication in the PSCAD platform is also carried out for the anticipated scheme. The percentage THD of each phase of the considered device is attained using Fast Fourier Transformation. A variety of test

6.7 Question and Answer

181

conditions such as preliminary inrush case, interior fault case along with DC contents and its counterpart, inrush in case of already existence of fault inside the transformer and saturated CTs during interior fault have been authenticated. Moreover, to test the viability of this scheme in real time scenario, a hardware model is prepared and validated. For all the cases of interior fault state, the percentage THD of the involved phase remains between the prefixed range and during all other situations percentage THD breaches the prefixed range. Thus, the proposed scheme proves its capability to recognize between inrush and interior fault cases during different turbulences. A combined percentage THD-based technique and differential-based scheme can give complete protection to the power transformer. Hence, it is well suggested to apply this proposed technique in connection to the adaptive differential-based technique for unit-type protection in power systems.

6.7 Question and Answer Question-1: How this study approach can be extended and applied to any similar cases? Answer: The transformer is occasionally subjected to an internal fault and magnetizing inrush conditions. These two situations sometimes mal-operate the existing conventional protection scheme. It is obligatory to isolate the transformer in the event of a fault, hence it should be properly distinguished from inrush condition. As mentioned in the chapter, the inrush and fault conditions can be discriminated against with the help of the estimation of THD. Moreover, the authenticity of the proposed methodology is proved by successfully performing the hardware validation in the proposed work. The captured results demonstrate that the presented tactic is capable enough for implementation in a real field. However, one can extend the proposed methodology to discriminate against other disturbances based on some unique ideology. One can extend it for harmonic analysis and power quality estimation too. Moreover, the presented study on THD estimation can be extended for the detection of iron core saturation, over-fluxing condition as well as the nature of the incipient fault in the transformer. Furthermore, this scheme is limited to only for transformer, the extended skill can be utilized for the protection, control, and monitoring of various other apparatus of a power system.

182

6 Total Harmonic Distortion-Based Improved Transformer Protective …

Question-2: How to eliminate the noise from the signal when experimenting on the real transformer? Answer: As discussed in the chapter, the Fast Fourier Transform (FFT) is used for the extraction of various quantities from the real-time signal of the current wave. The FFT itself has the inherent feature to filter out the noise signal using a low-pass first-order Butterworth filter and hence proper measurement can be achieved. The FFT sorts out the unwanted distortions and provides only the desired basic quantities having a fundamental frequency, hence it can be observed that the presented approach is well suitable during noisy environment also. Additionally, precaution limits are fixed with the cut-off frequency of the filter for accurate harmonic detection. Question-3: What should be prepared to implement this method in a real network? Answer: As per the flowchart, the estimation of harmonics up to 7th order mod and computation of THD using the FFT algorithm remains unchanged. Yet the limit set for discrimination of inrush and fault conditions also remains unchanged. However, the proposed scheme can be implemented in utility electric companies with slight modifications as required in the data capturing. In this research work, the scheme has been validated in a laboratory prototype which is a replica of a real electric utility system. Certain precautions should be taken while erecting the presented scheme in real time. Moreover, the hardware utilized in setting up the anticipated technique, data acquiring and other peripheral defensive schemes as employed by the device may influence the establishment of the relay at the site. The time of operation relies on various factors concerning the presented technique: 1. 2. 3. 4. 5. 6.

Methodology of data collection from each side of the transformer Data sampling method and required time for sampling Data exporting time from the model to the system Estimation of harmonic levels for all phases THD calculation by FFT/DFT or Modified FFT algorithm Execution of the algorithm on a controller (DSP/FPGA)

Involving all these factors, the utmost response time or time of operation required for the presented technique will be within the prescribed limit (approximately 30– 50 ms).

References

183

Question-4: How is the outcome of the laboratory test effective when extended to the real situation? Answer: Here, the purpose of laboratory validation is satisfied thus, the scheme remains certainly effective if it is extended to real situations. Only the real field has wider parameters and they may affect the calculation of THD during transient phenomena. This is because in a laboratory, the type of load which is linked with the secondary of the transformer is resistive and the fault is merely through a high resistance. But, in the real field, the power system parameters and load are variable from zero loads to maximum loads to faulty conditions. So, to a certain extent, the results may differ from the simulation/laboratory test to that of the real scenario. Also, the margin set for detection of the inrush and faulty condition can be varied while implementing the proposed scheme in a real situation.

References 1. Fuchs EF, Masoum MAS (eds) (2008) Chapter 1—Introduction to power quality. In: Power quality in power systems and electrical machines. Academic Press, Burlington, pp 1–54. https:/ /doi.org/10.1016/B978-012369536-9.50002-4 2. Kalair A, Abas N, Kalair AR, Saleem Z, Khan N (2017) Review of harmonic analysis, modeling and mitigation techniques. Renew Sustain Energy Rev 78(March 2016):1152–1187. https://doi. org/10.1016/j.rser.2017.04.121 3. Pierce LW (1995) Transformer design and application considerations for nonsinusoidal load currents. In: 1995 IEEE cement industry technical conference. 37th conference record, pp 35–47. https://doi.org/10.1109/CITCON.1995.514242 4. Taher MA, Kamel S, Ali ZM (2016) K-Factor and transformer losses calculations under harmonics. In: 2016 eighteenth international middle east power systems conference (MEPCON), pp 753–758. https://doi.org/10.1109/MEPCON.2016.7836978 5. Fuchs EF, Masoum MAS (eds) (2008) Chapter 6—Lifetime reduction of transformers and induction machines. In: Power quality in power systems and electrical machines. Academic Press, Burlington, pp 227–260. https://doi.org/10.1016/B978-012369536-9.50007-3 6. Elmore WA, Kramer CA, Zocholl SE (1993) Effect of waveform distortion on protective relays. IEEE Trans Ind Appl 29(2):404–411. https://doi.org/10.1109/28.216551 7. Saravanan B, Rathinam A (2017) Inrush blocking scheme in transformer differential protection. Energy Procedia 117:1165–1171. https://doi.org/10.1016/j.egypro.2017.05.242 8. Lu J, Lin X et al (2015) Abnormal operation behavior analysis and countermeasures on the differential protection of converter transformer. Int J Electr Power Energy Syst 64:516–525. https://doi.org/10.1016/j.ijepes.2014.07.048 9. Guzman A, Zocholl S, Benmouyal G, Altuve HJ (2002) A current-based solution for transformer differential protection part II: relay description and evaluation. IEEE Power Eng Rev 22(7):60–68. https://doi.org/10.1109/MPER.2002.4312417 10. Madzikanda E, Negnevitsky M (2012) A practical look at harmonics in power transformer differential protection. In: 2012 IEEE international conference on power system technology (POWERCON), pp 1–6. https://doi.org/10.1109/PowerCon.2012.6401274

184

6 Total Harmonic Distortion-Based Improved Transformer Protective …

11. Hamilton R (2013) Analysis of transformer inrush current and comparison of harmonic restraint methods in transformer protection. IEEE Trans Ind Appl 49(4):1890–1899. https://doi.org/10. 1109/TIA.2013.2257155 12. Bernabeu EE (2015) Single-phase transformer harmonics produced during geomagnetic disturbances: theory, modeling, and monitoring. IEEE Trans Power Deliv 30(3):1323–1330. https:// doi.org/10.1109/TPWRD.2014.2371927 13. Khalkhali B, Sadeh J (2015) Transformer differential protection by online core modeling and orthogonal polynomials. IEEE Trans Power Deliv 30(5):2146–2153. https://doi.org/10.1109/ TPWRD.2014.2380452 14. Patel D, Chothani NG, Mistry KD, Raichura M (2018) Design and development of fault classification algorithm based on relevance vector machine for power transformer. IET Electr Power Appl 12(4):557–565. https://doi.org/10.1049/iet-epa.2017.0562 15. Dharmesh Patel NC. Relevance vector machine based transformer protection. In: Digital protective schemes for power transformer, 1st edn. Springer, Singapore, pp 107–131 16. Raichura MB, Chothani NG, Patel DD (2020) Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique. IET Sci Meas Technol 14(1). https://doi.org/10.1049/iet-smt.2019.0102 17. Patel D, Chothani N (2020) HE-ELM technique based transformer protection. In: Digital protective schemes for power transformer. Springer, Singapore, pp 133–172. https://doi.org/ 10.1007/978-981-15-6763-6_6 18. Chothani NG, Patel DD, Mistry KD (2017) Support vector machine based classification of current transformer saturation phenomenon. J Green Eng River Publ 7:25–42. https://doi.org/ 10.13052/jge1904-4720.7122 19. Raichura M, Chothani N, Patel D (2021) Efficient CNN-XGBoost technique for classification of power transformer internal faults against various abnormal conditions. IET Gener Transm Distrib 15(5):972–985. https://doi.org/10.1049/gtd2.12073 20. Ali E, Helal A, Desouki H, Shebl K, Abdelkader S, Malik OP (2018) Power transformer differential protection using current and voltage ratios. Electr Power Syst Res 154:140–150. https://doi.org/10.1016/j.epsr.2017.08.026 21. Patel D, Chothani N, Mistry K (2018) Discrimination of inrush, internal, and external fault in power transformer using phasor angle comparison and biased differential principle. Electr Power Compon Syst 46(7):788–801. https://doi.org/10.1080/15325008.2018.1509915 22. Patel D, Mistry KD, Raichura MB, Chothani N (2018) Three state Kalman filter based directional protection of power transformer. In: 20th national power systems conference (NPSC), pp 1–6. https://doi.org/10.1109/NPSC.2018.8771716 23. Patel D, Chothani N (2023) Evaluation of various dynamics on current transformer saturation with a model study on power system protection. In: Recent Advances in Power Systems, pp. 121–136 24. Babaria SJ, Patel DD, Chothani NG, Chaini PK, Patel RM, Joshi SR (2022) Influence of system parameters on current transformer saturation in power system. In: 2022 IEEE 2nd international conference on sustainable energy and future electric transportation (SeFeT), pp 1–6. https:// doi.org/10.1109/SeFeT55524.2022.9909425 25. Patel D, Chothani N (2020) Adaptive digital differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 83–106. https://doi. org/10.1007/978-981-15-6763-6_4 26. Patel DD, Chothani N, Mistry KD, Tailor D (2018) Adaptive algorithm for distribution transformer protection to improve smart grid stability. Int J Emerg Electr Power Syst 19(5):1–14. https://doi.org/10.1515/ijeeps-2018-0022 27. Raichura M, Chothani N, Patel D (2020) Development of an adaptive differential protection scheme for transformer during current transformer saturation and over-fluxing condition. Int Trans Electr Energy Syst 31:1–19. https://doi.org/10.1002/2050-7038.12751 28. Patel D, Chothani N (2020) Real-time monitoring and adaptive protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 173–190. https:/ /doi.org/10.1007/978-981-15-6763-6_7

References

185

29. Patel DD, Mistry KD, Chothani NG (2017) Transformer inrush/internal fault identification based on average angle of second order derivative of current. In: Asia-Pacific power and energy engineering conference. APPEEC, pp 1–6. https://doi.org/10.1109/APPEEC.2017.8309017 30. Raichura M, Chothani N, Patel D, Sharma J (2020) Methodologies for the detection of magnetizing inrush and fault condition in power transformer. In: 2020 IEEE international conference on computing, power and communication technologies (GUCON), pp 146–151. doi: https:// doi.org/10.1109/GUCON48875.2020.9231065 31. Patel D, Chothani N (2020) CT saturation detection and compensation algorithm. In: Digital protective schemes for power transformer. Springer, Singapore, pp 33–49. https://doi.org/10. 1007/978-981-15-6763-6_2 32. PSCAD Research Center (2005) EMTDC-transient analysis for PSCAD power system simulation. Winnipeg, MB, Canada 33. Annakkage UD, McLaren PG, Dirks E, Jayasinghe RP, Parker AD (2000) A current transformer model based on the Jiles-Atherton theory of ferromagnetic hysteresis. IEEE Trans Power Deliv 15(1):57–61. https://doi.org/10.1109/61.847229 34. Masoum MAS, Fuchs EF (2015) Chapter 2—harmonic models of transformers. In: Masoum MAS, Fuchs EF (eds) Power quality in power systems and electrical machines, 2nd edn. Academic Press, Boston, pp 105–205. https://doi.org/10.1016/B978-0-12-800782-2.00002-6 35. IEEE Std 519-2014 (2014) IEEE recommended practice and requirements for harmonic control in electric power systems. IEEE Std 519-2014 (Revision of IEEE Std 519–1992). pp 1–29. https://doi.org/10.1109/IEEESTD.2014.6826459 36. IEEE (2008) IEEE guide for protecting power transformers (revision of IEEE Std C37.912000). In: IEEE power engineering society sponsored by the Power System Relaying Committee. New York, USA. https://doi.org/10.1109/IEEESTD.2008.4534870

Chapter 7

Adaptive Biased Differential Protection Considering Over-Fluxing and CT Saturation Conditions

Abstract Modern-day unit-type transformer protection required a special performance that can adapt to any situation carefully with flexibility in all functions. It should cover all the abnormalities and discrimination ability against small internal faults with caution. Many possibilities of inter-turn fault, CT saturation under fault conditions, core saturation of transformer, magnetizing inrush, etc., are considered individually or with a different combination. This chapter exemplifies all the abovesaid conditions with combinations under unit-type protection. A smart relay is developed with the multifunctioning condition to operate under faulty conditions in the internal area of the device. An algorithm is validated in PSCAD™ software and a laboratory environment. Detection of inrush in transformer core to avoid malfunctioning in unit-type protection, itself a very crucial condition. The second derivative of differential current is elaborated here to evaluate the inrush current successfully. CT saturation conditions also cause severely misguided tools in protection. Here, in this chapter, adaptive algorithm is developed based on the saturation index. If saturation is detected in the CT, then percentage-biased characteristics shifted adaptively and avoid malfunctioning under such abnormality. Sometimes over-fluxing conditions are generated due to the amplification of system parameters. Times for overfluxing are normally momentary or very small under starting of the transformer for a few cycles. Mostly, fifth and seventh harmonics are predominant under over-fluxing conditions. So, based on the fifth and seventh harmonic levels, detection techniques are also incorporated into the proposed algorithm to overcome this abnormality. So, here in this chapter inrush, CT saturation, and over-fluxing conditions are incorporated with discrimination of internal fault and other abnormal conditions to avoid the unnecessary operation of percentage-biased differential protection of the power transformer. A trip signal is generated as per the suggested algorithm when an internal fault or severe abnormality arises in a transformer. The considered power system diagram is simulated in PSCAD™ software. Voltage and current data are captured through the CT & PT of PSCAD™ and analyzed by the Modified Full Cycle DFT (MFCDFT) algorithm in MATLAB software. The hardware setup is developed in the laboratory environment considering the physical three-phase transformer with tap facilities on both windings. Various abnormal and fault conditions are generated and real-time testing has been performed on a developed algorithm. The suggested © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Chothani et al., Advancement in Power Transformer Infrastructure and Digital Protection, Studies in Infrastructure and Control, https://doi.org/10.1007/978-981-99-3870-4_7

187

188

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

algorithm runs successfully on software and hardware with result analysis and proved its acceptability in a real field of power systems.

7.1 The Preamble of Idea Generation As the pumping of power from one circuit to another circuit without changing the frequency in the power system, the transformer plays an important role like a heart in the human body. Due to its vital position, eminence, and prominence, transformer protection proves sufficient importance. However, due to the design for various ranges and variations of voltage, and current in primary and secondary circuits with nonlinear core conditions, it is a very crucial situation to develop a proper unit type of protection scheme for the transformer. Abnormalities like transformer core saturations, overfluxing conditions, CT core saturations, and high-resistance faults create very difficult to develop an individual unique protective scheme. Many researchers are involving the work in the direction of particular problems. One of the major considerations in terms of mathematical analysis or mathematical morphology [1] of the transformer parameters. It is an important analysis for the development of simulation and fulfilling other practical aspects like actual field tests. Based on data on magnetizing current [2] and wave profile exploration or recognition methodology [3] many scholars have developed different arrangements of protective schemes. For analyzing various nonlinear conditions, Lissajous configuration is utilized [4], and Runge–Kutta techniques [5, 6] are also implemented for scrutiny of nonlinearity. However, in these investigations, cases of momentary over-fluxing are not incorporated. An artificial intelligence technique can be utilized for the different protective schemes due to its higher accuracy for fault detection and classification. Different classifiers, decomposers, and regression techniques are used in the research field to improve the accuracy of the discrimination of particular conditions. Regression techniques like Relevance Vector Machine (RVM) [7, 8] and Hierarchical Ensemble Extreme Learning Machine Technique (HE-ELM) [9, 10] are used to discriminate the various abnormality for the transformer unit type of protection. A combination of different techniques like hyperbolic S-transform with probabilistic neural network [11] and the combination of Markov model with S-transform [12] are also available. As per the above articles, most of the classifier techniques are capable to discriminate the transformer abnormalities. However, the training phase of these classifier schemes has not considered some technical cases or issues to be fully implemented in a real field. Yet, most of the cases regarding overfluxing and CT saturation in power transformers are not available in those studies of classification. Under any abnormality, voltage and current signals always remain present originally or in the form of sequential components. Many researchers have used the presence of those components or combinations of the component for the evaluation of protection schemes. Sequential component based as well as magnitude- and phasorbased various schemes were presented in the past to identify the transient condition of

7.1 The Preamble of Idea Generation

189

power systems. The exploitation of negative/positive/zero sequence components was elaborated very nicely for a unit type of protection of transformer in [13–15]. Still, issues of ungrounded and grounded conditions of the power system cause different appearances under changed fault conditions. As of now, second harmonic analyses are normally utilized for the discrimination of the inrush condition in the transformer. Yet, possibilities of harmonic generations under various abnormalities like displacement of Geometric Neutral Axis (GNA) [16], a saturation of CT, and over-fluxing may mal operate those schemes which belong to the second harmonic contents. Other electrical parameters like winding impedance, inductance, and reactance-based schemes are also elaborated by various investigators. Moreover, equivalent values of said parameters are used to prove better opportunities concerning second harmonic schemes for the detection of inrush-like Equivalent Instantaneous Inductance (EII) technique [17]. In an actual field of transformer protection, over-fluxing protection [18, 19] is utilized as a separate protection scheme known as V/f protection. As said earlier, sequential components are continually present in that situation and with the help of that component over-fluxing can also detect accurately [20]. Yet, leakage flux is also occurring during the over-fluxing phenomena. So, incremental flux linkages [21] techniques are also introduced by a researcher to scrutinize the over-fluxing, inrush, etc. Under rate of rise or change of voltage-based [22] schemes are introduced for the high-frequency transient discrimination, which can develop or cause over-fluxing situations. The basic parameters are current and voltage in protective schemes. Based on the ratio of these basic parameters [23, 24], protective schemes are represented very sharp manner; however, in those articles, over-fluxing conditions are not incorporated. So, it is an imaginary portion of over-fluxing and there may be possibilities of mal-operation under the same situations. This means the threshold condition set may be smashed under over-fluxing conditions and trip signals generated under abnormal conditions. Nowadays, many adaptive protective schemes [25–29] are introduced with incorporating different fault conditions and protective criteria. Most of the said research regarding adaptive protection incorporates shifting of percentage-biased characteristics under some situations with few constraints. Mostly, all methods are including CT saturation condition-based shifting of characteristics with finding different techniques for CT saturation index detection. The accuracy of the CT saturation detection may affect the alteration of biased differential characteristics and sometimes leads to the wrong decision of relay. Moreover, over-fluxing conditions violate the basic parameter setting for adaptive protections like preliminary data or basic no-load threshold parameters of restraining and differential settings. It means pickup settings for differential and restraining are not properly mentioned due to the lack of consideration of the over-fluxing situation. Adaptive two-stage multi-region percentagebiased differential protection based on weighted factor [25] presented with various CT saturation and inrush situations. Harmonic blockings are also used under adaptive transformer unit-type protection. Different CT saturation and its effect-based shifting

190

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

of percentage-biased characteristics with harmonic blocking functions are demonstrated in [26–30]. These techniques are properly validated through proper results discussion; however, some conditions and combinations of the fault and abnormal conditions are not validated like over-fluxing with inrush or fault conditions. Many schemes based on total harmonic detection (THD) and phasor angle of different quantities are presented for discrimination of various abnormal conditions with internal fault for transformer protection [31–34]. The THD and phasor computation are done with the use of FFT or DFT algorithms in the above techniques. In this chapter, all the said issues mentioned in the above literature are incorporated with the unique feature because each one has different controversial signaling conditions. Combinations of all above said issues/abnormalities are incorporated into a single solution. A combination of an over-fluxing phenomenon with CT saturation and separate inrush evaluation techniques is introduced here. A complete solution is provided with a distinctive algorithm with result approval on software and hardware laboratory prototype model. The laboratory-type model incorporates various test conditions with a combination of different abnormalities like inrush followed by fault, inrush followed by over-fluxing, CT saturation, etc. It means all the conditions and combinations of abnormalities and different fault conditions are analyzed in this proposed study work. Some of the important results are discussed here as per the flow of the algorithm. MFCDFT is utilized for the analyses of the signaling data and coding is performed on MATLAB software. The essential part of this work includes validation of the proposed algorithm on hardware within a stipulated time limit with proper discrimination of different transient and abnormal conditions. The main advantage of the MFCDFT algorithm is that it uses very little space for storing memories in the calculation and further accurate validation with all mathematical steps and including propagation delay time. A novel scheme comprises second-order derivative-based inrush detection, harmonics-based over-fluxing detection, and adaptive shifting of both stages of percentage-biased characteristics (dual-slope characteristic) under CT saturation and over-fluxing conditions.

7.2 Problem Declaration and System Diagram Descriptions An Indian power system diagram is elaborated in Fig. 7.1. A star–star configuration, three-phase power transformer with a 400 kV primary side and 220 kV secondary side having a 100 MVA rating is connected as shown in Fig. 7.1. A 400 kV, 100 km transmission line connected with the consideration of the Thevenin equivalent AC synchronous generator source having a capacity of 100 MW power generation with 0.85 power factor. On the secondary side of a transformer, a 220 kV, 60 km highvoltage transmission line is connected with an infinite bus (grid). The power system with the given data is simulated in PSCAD™ software for the validation of various conditions. Test data are captured in PSCAD™ software and then after data are migrated to the storage space of computers. Later, the MATLAB coding utilizes

7.2 Problem Declaration and System Diagram Descriptions

191

Fig. 7.1 System diagram

these captured data for the execution of the MFCDFT algorithm. There are very salient features of the MFCDFT algorithm for avoiding noise signals and providing very sharp results compared to the simple DFT algorithm. Different test conditions of external/internal faults with and without CT saturation conditions, normal loading, and overloading conditions are carried out on simulations. By changing the primary voltage and the operation of circuit breaker in switch-off conditions, different inrush and over-fluxing conditions are generated correspondingly. Transformer, transmission line, CT, and generator details are given in the Appendix with more amplification. During the transformer operations in the given power system simulation, various disturbing conditions analyze like sudden load rejection, changes in load angle, development of the Ferranti effect, over- and under-voltages, and over- and underload conditions. Due to sudden changes in load, voltage variation is occurring. This may cause the increment of transformer core flux and its saturation. Moreover, simultaneous variation in the winding current up to a certain limit is also noticed. Due to core saturation, injected primary winding current will not be on the secondary of the transformer if the transformer operated under the knee region of its saturation curve. This means the suggested circulated zero differential current conditions are violated and if the differential current is beyond the limit of the percentage-biased setting then the protective system may be malfunctioning without internal fault. During some transient operations, an over-fluxing condition is generated and it takes very few cycles to diminish this condition. The transformer must not be operated during such momentary over-fluxing conditions. So, discrimination against over-fluxing is compulsorily required. However, in routine protection, a separate time-delayed V/f protection scheme is applied. Here, in this scheme, over-fluxing conditions are incorporated with an adaptive unit-type protective scheme and the characteristics will be modified from the first stage to two stages of characteristics as per the algorithm. In another way, if over-fluxing conditions are extended with time, the proposed algorithm takes decisions appropriately and provides tripping with delay time. The same procedure is applied for all conditions like inrush, CT

192

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

saturations, and internal/external faults. An algorithm in the mode of a flowchart is represented here in the next section.

7.3 Projected Algorithm for Adaptive Transformer Differential Protection The proposed algorithm with consideration of CT saturation, over-fluixing, Inrush, external fault, and abnormal conditions with internal fault is elaborated in a flowchart manner in Fig. 7.2. CTs and PTs are used to capture waveforms of currents and voltages correspondingly according to the need of the suggested algorithm. After the data collection from the data acquirement scheme, MFCDFT [35] is used for approximation of the phasor values of derived signals. MFCDFT is executed under the sliding window concept and discretizes the current and voltage waveforms with a 4000 Hz sampling frequency (means 80 samples per cycle) [36].

7.3.1 Modified Full Cycle DFT (MFCDFT) Algorithm for Phasor Estimation The MFCDFT scheme extracts precise components of the fundamental frequency with the removal of DC components. With the use of low-pass filtering technique, noise and unwanted harmonic signals are filtered for better accuracy of the relay operation. Here, input signal of the MFCDFT algorithm is noted as f (t). Here, f s and N are the sample frequency and the number of sampling (used for the one complete cycle time duration T ) sequentially. The passage of time between two sample signals = ∆T = T /N . f (t) and kth sample signal f (k) are as under: f (t) = A0 +

N −2 

An cos(nωt + θn )

(7.1)

n=1

f (k) = A0 +

N −2  n=1

( An cos

2nkπ + θn N

) (7.2)

Equation (7.2) comprises as fr (k) = real part and f i (k) = imaginary part 2 fr (k) = N

k 

(

2r π ∗ f (r ) ∗ cos N r =k−N +1

) (7.3)

7.3 Projected Algorithm for Adaptive Transformer Differential Protection

Fig. 7.2 Proposed adaptive protection algorithm

193

194

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

f i (k) = −

(

k 

2 N

∗ f (r ) ∗ sin

r =k−N +1

2r π N

) (7.4)

When k ≥ N , fr (k) = A1 cos θ1

(7.5)

f i (k) = A1 sin θ1

(7.6)

Hence, Amplitude A1 can be defined as A1 =

/

fr2 (k) + f i2 (k)

(7.7)

Similarly, the phase angle θ1 is θ1 = tan−1

(

f i (k) fr (k)

) (7.8)

7.3.2 Setting of Biased Percentage Differential Relaying Scheme Differential current, I d will be given as per Eq. (7.9). Biased/restraining current, I r will be estimated as per Eq. (7.10). The average voltage, V avg as per Eq. (7.11) has been calculated. Id = Ip − Is

(7.9)

Ip + Is 2

(7.10)

Ir =

where I p and I s = Primary side current and Secondary side currents successively. Vavg =

Vp + Vs 2

(7.11)

where V p = Voltage on the primary winding and V s = Voltage on the secondary winding. Moreover, The transformer disturbance can be identified as

7.3 Projected Algorithm for Adaptive Transformer Differential Protection

195

Id > Id0 + K 1 . Ir where K 1 and I do are the percentage-biased differential relay slope and basic pickup setting sequentially. Same as I d and I r are differential and restraining currents, respectively. If this condition is satisfied then only further examination is requisite. Under nonverification conditions of a said algorithm, the signal is considered normal, healthy, or disturbance-free for external fault for a unit type of protection. Within normal conditions, the algorithm returns to starting position as per Fig. 7.2.

7.3.3 Detection of Magnetizing Inrush in Transformer If the differential current I d > (I do + K 1 * I r ), then the algorithm does an additional check for an existing inrush situation. As per the flow chart, an inrush state is noticed, with the average angle (θavg ) of I d for one cycle post-disturbance [37]. θ = arctan(∆)

(7.12)

( 2 ) where ∆ = ddtI2d and Id represents differential current as given in Eq. (7.9). Subsequently, θavg can be estimated as below: θavg

1 = m i − ni

(ni θ (t)dt

(7.13)

mi

[mi , ni ] = different time intervals for an average estimated value of θ. Here, the value of θavg = 4◦ is considered as a threshold for the identification of inrush. Mostly, the value of θavg will be roughly 1–4° in case of fault or normal condition. In a power system, when high asymmetricity arises like inrush, then only θavg becomes higher than 4°. For perfect sinusoidal waveform during normal operating condition, θavg is ideally zero degrees. However, under any faulty conditions or fault with CT saturation, the value of θavg varies lower than the 4° maximum. After consideration of many test conditions of CT saturation with decaying DC components with different fault ranges is confirmed. To acquire more details regarding the behavior of θavg with different faults and abnormal conditions of transformer protection are given in article [37, 38] with the comparison of reactive power intake-based inrush detection techniques. In nutshell, under a slope higher than 4° (θavg ≮ 4◦ ), the said condition is understood as an inrush. Further, when the inrush condition is detected, an algorithm returns to the preliminary data acquisition step as detailed in Fig. 7.2. Contrariwise,

196

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

for the slope less than 4° (θavg < 4◦ ) in the system, further disturbance analysis is taken place as per the algorithm. Now, for further analysis of the reason and type of the disturbance, fifth and seventh harmonic components of the same differential current will be analyzed using MFCDFT. The harmonic deviation represented here as IdH =

√

(Id5 )2 + (Id7 )2

(7.14)

The ratio of derived value IdH as in Eq. (7.14) with fundamental (Id1 ) components is derived as S h Sh =

IdH Id1

(7.15)

This ratio (S h ) is compared with the stipulated threshold (T h ) whether the disruption is due to over-fluxing or not. If the threshold value falls within 25% [39] of an acceptable limit of over-fluxing, then the situation is sustainable for the power system. Here, 25% value of the threshold is considered for the given algorithm because it is the safest boundary of the over-fluxing withstanding capacity of the transformer. A higher value of this ratio (S h ) than the predefined threshold setting will be considered a harmful condition for the power transformer and system. As per the algorithm (Fig. 7.2), this type of state conducts as the parallel progression. Two ways are remaining presented here to remove mis-operation and protection of equipment also. First, the percentage-biased differential relay preliminary characteristic is shifted as per the severity means the basic peak-up characteristic is shifted to avoid mis-operation. On the second way for the severe over-fluxing transformer protection, V/f security protection is employed with delayed timing functions. Particulars of both schemes are described as under.

7.3.4 Adaptation in Basic Pickup Setting When circulating current flow in the primary of the transformer, generated emf due to main flux is captivated supply voltage and this voltage is indicated here as e = −N

dB ∗ A dB d∅ = −N = −N A ∗ V dt dt dt

(7.16)

where ∅, B, and A are flux, flux density, and area of the core, respectively. Overall, the offered core flux density stands B = B1m sin ωt + B3m sin 3ωt + B5m sin 5ωt + B7m sin 7ωt + · · ·

(7.17)

7.3 Projected Algorithm for Adaptive Transformer Differential Protection

197

Henceforward, ignoring higher order harmonics, the emf generated up to seventh harmonics considerations only by flux density (B) is e = (ωN A ∗ B1m ∗ cos ωt) + (3ωN A ∗ B3m ∗ cos 3ωt) + (5ωN A ∗ B5m ∗ cos 5ωt) + (7ωN A ∗ B7m ∗ cos 7ωt)

(7.18)

Due to the same phase sequence of the third harmonics and way of the neutral pass out, the selected remaining odd number harmonics of the seventh and fifth sequences are major than the third harmonics. Aberration of both harmonics and representations is already given under Eq. (7.14). Moreover, the ratio for the deviation to the fundamental component is represented in Eq. (7.15). With the help of both threshold settings, as per the Eq. (7.19), the preliminary setting of percentage-biased differential currents is given as ∆Id0 = (Sh − Th ) ∗ Id0 ∗ Sf

(7.19)

Here, ∆Id0 shows the adjustment for the basic pickup setting of the differential relay characteristic in the event of any external abnormalities of a transformer. So, the revised basic setting for the percentage-biased differential relay for a next step change can be defined as Id0_new = Id0 + ∆Id0

(7.20)

Therefore, as per the harshness of the over-fluxing condition, the basic pickup setting adaptively moved to “Id0_new ” as described in Eq. 7.20.

7.3.5 Vavg /f Transformer Protection or Transformer Over-Fluxing Protection As a transformer core, silicon steel, Cold Rolled Grain Oriented (CRGO), and amorphous materials are used due to Ferro-magnetic characteristics. Due to cost and loss consideration, CRGO material is used broadly [40]. Additionally, the core material with CRGO has up to 1.8–2 Wb/m2 range of flux density handling capacity during saturation. So, the rated flux density is considered up to 1.1 p.u. Wb/m2 . If the flux density rises for a short period than the considered one due to variation in system parameters, the core is saturated with over-fluxing state. Transformer protection scheme must be stabilized during such temporary period of over-fluxing state. As per IEEE standard, Table 7.1 shows iron core withstanding capacity of overfluxing within stipulated operating time [41]. As sown in Table 7.1, the transformer core has flux sustained capacity up to 10% (1.1 pu) of nominal flux value. Thus, a transformer will not be damaged by this increment of 10% flux and need not be disconnected for a longer time. However, if the flux in the core increased by 20%

198

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

Table 7.1 Withstand the capability of the V /F for a classic transformer

ϕ = (V avg /f ) p.u

The time limit in minutes to withstand over-fluxing conditions

1.1 (10% over-fluxing)

Uninterrupted

1.2 (20% over-fluxing)

2

1.25 (25% over-fluxing)

1

1.3 (30% over-fluxing)

0.5

>1.4 (40% over-fluxing)

Instant tripping

(1.2 pu) to that of nominal values, the transformer should be switched off after 2 min to reduce the damaging effect on the core of a transformer. Likewise, when the V/ f ratio gradually increases and the flux value exceeds 40% (1.4 pu) of the nominal value, the tripping signal must be initiated instantaneously to save the transformer from additional destruction. Mostly, in the practical field, Inverse Definite Minimum Time (IDMT) characteristic-based V/f relay is used to detect and give a trip command for monitoring and protection of the transformer.

7.3.6 Current Transformer Saturation Detection The algorithm demonstrated in Fig. 7.2 shows further steps for checking out CT saturation conditions during fault. There is a very verse condition generated with CT saturation during fault conditions which may generate mis-operation of percentagebiased relay used for transformer protection. It is necessary to define the saturation index to detect the severity of CT saturation. Based on the saturation index (δn ), the level of CT saturation [27] is considered in this script. The saturation index (δn ) estimation is defined as per Eq. 7.21. δn =

[ ] δ2(n) δ3(n) 1 δ1(n) + + × 100% H 1 2 3

(7.21)

Here, H = sampling interval, δ1(n) , δ2(n), δ3(n) = first-, second-, and third-level derivatives of CT secondary current. The estimated saturation index (δn ) as per Eq. 7.21 will be compared with a threshold value. The comparison makes a clear decision regarding the CT saturation level and further action to alter the relay setting. All concerns are approved by mathematical formulations and derivations as discussed earlier. As per the level of saturation, percentage-biased characteristics are shifted adaptively in transformer protection. Mostly, during a heavy external fault, these types of saturation conditions

7.3 Projected Algorithm for Adaptive Transformer Differential Protection

199

are generated [42]. Normally, 10% of CT saturation deviation level is permissible in a power system for the initial slope of the percentage-biased differential protection. More than this level, it is possible to mal operate the protective scheme. So, it is mandatory to discriminate against this type of circumstance to bypass the malfunctioning of the relaying scheme. For this case, Id > Id0 + K 1 . Ir state must be satisfied for the generation of trip indication. Normally, the percentage slope (K 1 ) is 30% as per IEEE guidelines [41]. This 30% slope setting in biased differential relay covers CT mismatch in heavy external fault, no-load current, and additional on-load tap changing facility on a transformer. The algorithm suggested here detects a level of saturation of CT at the time of an intolerant fault situation and estimates additional slope (K s ) concerning saturation level or degree of saturation as per Eq. 7.22 [43]. Ks =

δn − 10 δn

(7.22)

The new slope of percentage-biased differential characteristic recognized based on the factor (K s ) as K 1' = K 1 + K s

(7.23)

Suppose, as per Eq. 7.21, saturation level is 15% greater than a threshold (1.0), i.e., estimated δn = 1.15. So, from Eq. 7.22, K s is considered as Ks =

0.15 1.15. − 1.0 = = 0.13 1.15 1.15

So, as per Eq. 7.23, the adaptive slope diverges as under: K 1' = K 1 + K s = 0.3 + 0.13 = 0.43 So, the new slope diverges from 30% and shifted at 43%. During both conditions like short-lived over-fluxing conditions and CT saturation conditions, basic and slope settings of characteristic are shifted as per the algorithm. At that juncture, the protection system again verifies the modified operating condition; I d > I d0 _new + K ' 1 * I r . For the moment, the modified condition is satisfied, algorithm will issue a tripping command to separate the damaged/faulty transformer from power system. The planned algorithm shows preventive steps for the mal-operation of the differential relay under numerous disorders of a power transformer.

200

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

Fig. 7.3 Normal/external fault condition

7.4 Various Result Exploration with Argument 7.4.1 Normal Load, Overloading, and External Fault State In normal load, overloading, and external fault without CT saturation state, there are balanced conditions for primary current and secondary currents. As shown in Fig. 7.3, during the said conditions differential current or circulating current at the relay terminal is approximately null (below the pickup setting of the relay). Also, the I d /I r trajectory has its place on X-axis (red line in Fig. 7.3), i.e., it remains below the defined operating zone (K 1 ). So, it can be said that unit-type protection is stable during all the above-mentioned conditions.

7.4.2 Transformer Inrush Detection As per the preliminary steps of the algorithm, inrush is detected or discriminated with the second derivative-based angle comparison of the differential current. Figure 7.4 demonstrates primary and secondary current waveforms with equivalent θavg (as per Eq. 7.13). Figure 7.4c illustrates that under inrush conditions the θavg exceeds a threshold of 4º. Even this inrush detection technique [37] is also able to detect internal fault trailed by inrush or inrush trailed by internal fault. So, under such kind

7.4 Various Result Exploration with Argument

201

Fig. 7.4 Inrush condition

of disruption, an algorithm can discriminate various inrush conditions with different fault conditions.

7.4.3 A Fault Within the Internal Premises of the Transformer As shown in Fig. 7.5, the I d /I r curve promptly crosses the well-defined restrain boundary and comes into the operating zone when an internal fault occurs in the transformer. This is the main reason for issuing the trip signal to the circuit breaker by relaying scheme. However, as per the algorithm, simultaneously over-flux condition needs to be detected as per Eq. 7.3. If any internal fault is incepted in the existence of the over-fluxing situation, the relay provides an instantaneous trip signal. On the other hand, if there is no internal fault detected under over-fluxing conditions, the relay will not issue the trip signal and provide a delay for the further extent of checking the severity of the over-fluxing conditions. In short, V/f protection is triggered. Moreover,

202

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

Fig. 7.5 Internal fault condition

this algorithm is also proficient to sense high resistance internal fault, turn-to-turn fault, and CT saturation during severe internal fault.

7.4.4 External Fault with CT Saturation Condition Figure 7.6a represents the primary and secondary current waveforms during CT saturation under external fault conditions. Figure 7.6b represents the percentage-biased characteristics as per the proposed algorithm. The emerald green line represents the normal biased characteristic set as per the normal operation of the differential relay. In the event of CT saturation detection by algorithm, the percentage-biased characteristic is shifted from an emerald green line to a light green shaded as shown in Fig. 7.6b. Fault trajectory moved upward as per the level of CT saturation and simultaneously, as per the algorithm, the percentage-biased characteristic is modified to its new position so that the relay does not generate a signal. The algorithm detects the CT saturation conditions accurately and the characteristic shifted from K 1 to K' 1 as per Eq. 7.23. The dominance of this algorithm prevents the differential relay operation against mild to harsh CT saturation conditions during external faults.

7.4 Various Result Exploration with Argument

203

Fig. 7.6 External fault with CT saturation condition

7.4.5 Discrimination of Over-Fluxing in Transformer Protection Over-fluxing conditions in the power transformer arise due to drastic variations in system voltage and frequency. Due to these changes, odd harmonics become predominant and differential protection of the power transformer may mis-operate. To overcome this problem, it is necessary to detect these abnormal conditions with timedelayed v/f protection. The proposed algorithm suggests an over-fluxing detection technique as per the criteria set in Eq. 7.15 (Sh > Th ). Figure 7.7 elaborates that when Sh > Th , over-fluxing is detected in the system. Under these conditions, preliminary basic settings are changed adaptively in the differential relay as per Eq. 7.20. When over-flux is generated in the transformer core, I d /I r trajectory is shown as a red line in Fig. 7.7b, crosses the basic pickup setting (continuous blue line (I do )). Under these circumstances, normal differential relay mal-operates. As per the proposed algorithm during over-fluxing conditions, preliminary basic settings are changed from their original positions and shifted to new positions (dotted blue line (I do_new ))

204

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

Fig. 7.7 Discrimination of over-fluxing in transformer protection

with respect to harshness of the over-fluxing. Corresponding V avg /f over-fluxing protection is also initiated for the necessary action by the IDMT relay.

7.4.6 Inception of Internal Fault in the Existence of Over-Fluxing Situation A crucial condition for the power system is considered here as a test condition, such as an over-fluxing state followed by internal fault. Figure 7.8a shows the primary and secondary current waveforms. Up to 0.2 s of fluxing is generated in the system and afterward, the internal fault is generated in the existence of over-fluxing situation. Current waveforms and I d /I r trajectory are plotted in Fig. 7.8. Here, in this algorithm, MFCDFT-based technique is used which adopts sliding window-based signal capturing and analysis. Sampling is taken with a 4 kHz sampling frequency, which means 80 samples/cycle is considered. In the event of over-fluxing, the basic current setting will be shifted maximum of up to 2 A. As per the logic set in an algorithm, if the basic pickup setting is moving ahead of 2 A, then it automatically stops at 2 A. So, for the next sampling, if the internal fault is incepted,

7.5 Laboratory Setup for Hardware Test Results

205

Fig. 7.8 Over-fluxing state followed by internal fault

then the I d /I r trajectory immediately moves in the tripping region of the relay as per Fig. 7.8b. Finally, the occurrence of internal fault during over-fluxing situations is easily detected.

7.5 Laboratory Setup for Hardware Test Results To authenticate the suggested algorithm in a laboratory environment with a hardware setup, a multi-tapping, 50 kVA transformer is taken as a test setup with 440 V primary and 220 V secondary voltage. All phases of the primary side are tapped with a 254–228–204–180–0 V/phase and same as all secondary sides are tapped with 127–114–102–90–0 V/phase. To generate the real-time effect of a transmission line on the primary and secondary sides of a transformer, variable rheostats and inductors are employed as per Fig. 7.9a. Various necessary equipment like an auxiliary relay, current transformers (CTs), potential transformers (PTs), data capturing arrangement, and finally, as a circuit breaker, three-phase contactors are placed as exposed in Fig. 7.9b. All necessary details and ratings regarding all equipment are given in the Appendix which is utilized in the laboratory prototype model.

206

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

Fig. 7.9 Hardware model a forward-facing panel vision, b rear panel view

7.5 Laboratory Setup for Hardware Test Results

207

Taking the 4 kHz sampling frequency in to account, we obtain 80 samples in one full cycle for the 50 Hz fundamental frequency of the Indian power system. The accuracy of the proposed algorithm for fault detection is affected by lower sampling frequency because of lacking proper information. Likewise, if a short window means half or quarter cycle is considered, then the information gets wider variation during the disturbance, abnormal, or any fault conditions. So, discrimination of fault is not properly justified as insufficient information. Henceforward, the sampling frequency selection requires carefulness for proper discrimination of the fault and abnormal conditions. This algorithm is processed through the CORTEX ARM4 microcontroller. For defining total reaction time, it should be a summation of delay time, data acquisition time, time taken for the process in the processor, and latency time in communication. The required time is calculated here to perform the algorithm as below. Total Required Time: • In the projected arrangement, here, MFCDFT-based algorithm takes 250 µs time step per interval. It needs 20 ms to complete one cycle of 50 Hz system frequency as signals are filtered with a 4 kHz sampling frequency. Normally, at 4 kHz frequency, 80 samples per cycle are counted. Under any abnormality of the power system within the first scan cycle, 20 ms is mandatory to declaim the 80 samples. Sampled data is stored in the controller’s buffer memory (ROM) continuously for analysis, simultaneously data is migrated constantly for algorithm analysis and computation. Normally, data processing time of the sample is considered as 20 ms. As a continuous moving window-type algorithm, it is applicable for the process of the data in the algorithm. • It is also necessary to consider the delay in data conversion and communication as per the controller’s data transfer rate. Here, 57.6 kbps is set for the data transfer rate in the CORTEX ARM4 controller. The process bus is capable to transfer data to the controller as fast as within 1 ms. • Considering 168 MHz as the speed of microcontroller (168 million instructions processed per second) and the size of the developed algorithm as 4 kB. So, the minimum time to compute this size of file and computation of all suggested parameters in the proposed algorithm is found 2 ms maximum without deliberate delay. • In the proposed validation through hardware setup, CORTEX Arm4 generates a trip signal at its output port once the internal fault is detected. It is assumed that here time taken for the trip signal generation and the delay time of the signal conditioning unit are around 2 ms. So, the total time required to complete the signal sensing trip signal includes Data Processing Time + Data Conversion and Communication time + deliberate delay time + delay time for propagation. It is 20 ms + 1 ms + 2 ms + 2 ms = around 25 ms. Moreover, continuously acquired digital data are automatically stored in the internal memory of the MCU (ARM4) before the next set of samples is acquired.

208

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

Fig. 7.10 Inrush current waveform on 50 kVA hardware setup

Hence, the required successive scan time to perform the offered algorithm will be minimized. Numerous internal faults and external faults with different abnormal conditions including inrush and over-fluxing conditions are developed under a laboratory environment for proposed algorithm validation. Some of the important hardware deployed algorithm results are explained below.

7.5.1 The Inrush of Transformer on Hardware Inrush phenomena are generated during the no-load condition in the transformer. Here, in the laboratory setup of the 50 kVA transformer, the secondary side is kept open circuited and the primary side is supplied by 440 V directly through a threephase contactor. Here, contactor is considered as a circuit breaker. For getting the different magnitude of inrush, several such tasks are taken place at different time intervals. As per the algorithm, the microcontroller discriminates inrush conditions successfully and the trip signal is not generated during any test case of inrush conditions. Waveforms captured for three phases are shown in Fig. 7.10. Due to heavy inrush, it is not possible to capture the first peak of any phase as shown in Fig. 7.10.

7.5.2 Normal Load, Overloading, and External Fault Situation For capturing the external fault on the secondary side of the transformer, at 0.25 s, external fault is applied as shown in Fig. 7.11. This means up to 0.25-s current waveforms are shown with normal load conditions as in Fig. 7.11a. This external

7.5 Laboratory Setup for Hardware Test Results

209

Fig. 7.11 External fault condition in transformer with hardware setup

fault is taken place without CT saturation condition after 0.25 s onwards, waveform shows a clear image of the effect of external fault with its magnitude. As per the algorithm, I d /I r trajectory is placed within the blocking region, a trip signal is not generated and is considered as a healthy condition. The same conditions are there for overloading conditions. The behavior of I d /I r trajectory and percentage-biased characteristics is observed as per Fig. 7.11b.

7.5.3 Internal Fault Situation Current signal waveforms for the secondary and primary sides are shown in Fig. 7.12a for an internal fault on transformer winding. At the time of internal fault in transformer, current phasors of primary winding and secondary windings are mostly in-phase with different magnitudes. The proposed scheme removes the internal fault conditions by issuing the trip signal via the CORTEX ARM4 processor. The behavior of I d /I r trajectory with internal fault is moving from the blocking zone to the operative zone instantly as shown in Fig. 7.12b. All preset values and pre-defined values are checked and processed successfully by the microcontroller which issues an immediate trip signal. Here, various types of internal faults with numerous FIA with

210

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

Fig. 7.12 Internal fault condition in hardware setup

high-resistance internal faults, and CT saturation under internal faults are also tested and authenticated on this algorithm effectively.

7.5.4 Over-Fluxing Situation These are the very uncommon cases performed on hardware setups developed in a laboratory. Mostly, during the over-fluxing condition individual, V/f protection is applied in the field of protection. In addition, in this study work, the over-fluxing condition is incorporated within an adaptive zone of percentage-biased differential relay in a more precise way. Waveforms of primary and secondary current signals are displayed in Fig. 7.13b under an over-fluxing situation. Normally, during a severe over-fluxing situation, I d /I r trajectory enters into the operative zone within the primary setting (cross basic pickup setting (I d0 ) of the differential relay) as per Fig. 7.13b. During such conditions, as per the algorithm, preliminary (basic) relay setting is moved adaptively upward momentarily as the over-fluxing conditions are also considered momentary. So, during the over-fluxing state, the basic peak-up setting shifted to (I d0_new ) from the basic pickup setting (I d0 ) as shown in Fig. 7.13b. This shifting of the basic setting of a relay is completely based on the detection and level of fifth and seventh harmonics estimated during over-fluxing situations

7.5 Laboratory Setup for Hardware Test Results

211

Fig. 7.13 Over-fluxing condition in hardware setup

(Sect. 6.3). So, finally, unwanted tripping is avoided by the projected algorithm by adaptively shifting the characteristic as per the severity of over-fluxing.

7.5.5 Saturation of CT During External Fault Different types of CT saturation conditions are considered for the testing and validation of the proposed scheme on the laboratory hardware setup. When the external fault current is exceeding the 20 times limit of the CT rating or during a higher burden on CT secondary is taken place at that time saturation of CT occurs as shown in Fig. 7.14a. Due to the variation of primary and secondary current magnitude, there is ample possibility of the mis-operation of the percentage-biased differential protection. Here, a normal percentage differential relay gives a trip signal under these conditions, but as per the suggested algorithm degree of CT saturation is detected, and based on it, slope K 1 is modified automatically. These modifications of slope avoid malfunctioning of the percentage-biased differential relay as per Fig. 7.14b.

212

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

Fig. 7.14 CT Saturation state under external fault in hardware setup

7.5.6 Very Severe External Fault in the Existence of Over-Fluxing Condition It is also an exceptional circumstance reflected here to cross-check the capability of the projected arrangement. Over-fluxing state is continuing in the system at the same time external fault with CT saturation is generated. The primary and secondary current waveforms on the hardware setup is clearly shown in Fig. 7.15a. Over-fluxing state is continued up to 0.36 s; afterward, external fault with CT saturation is incepted to check the feasibility of the proposed technique. As per Fig. 7.15b, the effectiveness of the algorithm is revealed from this very exceptional test case. The algorithm successfully detects the over-fluxing condition and as per the logic, the preliminary setting means the basic setting of the percentagebiased differential protection is shifted from its original position as per the severity of the over-fluxing. Sometime after over-fluxing state, external fault with CT saturation is appeared in the system then again as per the algorithm, K 1 is shifted from its original position by considering the saturation level of CT. Under all those abnormal states, the algorithm behaves in steady-state conditions, and a trip signal is not generated. Smooth-shifting of the basic pickup setting (I d0 ) to the new pickup setting (I d0_new ) under over-fluxing situation and shifting of K 1 from its original position to the new position K ' 1 as per the degree of the CT saturation will be done as per the logic decided

7.6 Conclusion

213

Fig. 7.15 Over-fluxing state railed by CT saturation with external fault in hardware

in the algorithm. Here, the relay shows its inoperative conditions in all above-said abnormal and faulty situations and sustains system stability.

7.6 Conclusion This chapter describes a modern advance adaptive protection scheme for the improvement of transformer unit-type protection. Said scheme properly discriminates the inrush, CT saturation, over-fluxing, and overloading conditions with internal/external fault conditions with different fault resistance. An advanced technique is also introduced to discriminate the inrush conditions based on the second derivative of the differential current. It plays a key role to discriminate the various inrush conditions for a different switching instant of a circuit breaker. Improvement of percentagebiased differential protection of power transformer is held by implementing the adaptive techniques for the various CT saturation and over-fluxing condition with validation on PSCAD™ software and also on hardware. Third-order derivative of the current signal-based scheme is distinct for CT saturation identification. Moreover, after discrimination of CT saturation, the degree of saturation is also calculated, and based on that percentage-biased characteristic is adaptively shifted. Over-fluxing conditions are also detected based on the fifth and seventh harmonics content in

214

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

the differential current. After the detection of over-fluxing, it is also added in adaptive shifting of basic characteristics for the temporary over-fluxing conditions. All above-said conditions are tested on PSCAD™ software simulation and captured data of the software is validated through the MFCDFT algorithm coded in MATLAB software. Various simulation test conditions are developed on a three-phase, 50 Hz, 440/ 220 kV, and 100 MVA transformer in PSCAD™ software. The proposed technique completely validates the various inrushes, CT saturation, and over-fluxing conditions followed by various fault conditions. Finally, it is noted that the scheme is operated under internal fault only, remaining all abnormalities are discriminated adaptively as per the steps of the algorithm. The hardware setup is carried out with matching realtime field data for the transformer and transmission line. An algorithm is projected with a CORTEX ARM4 processor as a relay prototype. Different test conditions are developed and validated on a three-phase, 50 Hz, 440/220 V, and 50 kVA transformer in a laboratory environment. It is to be noted that the total validation time for the hardware setup for the trip signal generation is within 25 ms from the initiation of fault to send a trip signal to the circuit breaker. The result derived from hardware setup and software simulation shows the efficacy of the suggested algorithm for commercial applications.

7.7 Question and Answer Question-1: How will the method respond to star–delta/delta–star/star–star or delta–delta transformers? Answer: The projected technique also competent for star–delta/delta–star/star–star or delta– delta connection. The only modification in the threshold limit with proper calculation is required for particular type of transformer connection. Mostly, all conditions are verified, and only some minor changes are required like CT ratio, the saturation level of CT as per recommendations, the core saturation level of the transformer, etc. Question-2: How the value of θavg will be followed in 1–4° for internal fault even if it is greater than 4° for the period of magnetizing inrush state? Answer: Mostly, in the second derivative, the angle of differential current without saturation will be approximately zero degrees (0°). If it deviates that means some level of saturation is penetrated in CT secondary current. Here, 4° as a cut-off is taken concerning relay sensitivity to detect the internal fault in the transformer.

7.7 Question and Answer

215

Question-3: How is PSCAD used with MATLAB? Answer: The PSCAD software is used to generate data of various fault and abnormal conditions with the help of multirun block. Post capturing data and validating with the MFCDFT algorithm which is coded with m-code in MATLAB. The captured data from PSCAD in form of csv file is stored in the internal drive of computer system and later migrated to MATLAB software. Thus, PSCAD is mainly used for power system simulation and data generation, whereas MATLAB is used for coding of the proposed algorithm that uses the data generated by PSCAD. Question-4: Is it possible to realize this replication in MATLAB? Answer: Yes, it is also possible to realize this replication in MATLAB. One can simulate the considered power system in MATLAB software too and analyze faults and abnormalities. However, the PSCAD possesses EMTDC as a solution engine. PSCAD™ is the more convenient software for power system protection analysis for data capturing and with a multi-run block at a time large numbers of data can be collected from an analysis point of view [44]. Sometimes, this type of analysis is very tedious in MATLAB software. However, after capturing data from the multi-run block of the PSCAD™ software whatever analysis remains will be performed using various m-code of the MATLAB software. However one of the major disadvantages of the PSCAD™ software is to develop controlling algorithm or block which must be implemented by FORTRAN language which is more complex than the m-coding of MATLAB. Question-5: The effect of the Fault Inception Angle (FIA) should be discussed. Answer: The total flux of the transformer is elaborated here during energization or under any transient switching conditions as per Eq. 7.24. ∅(t) = Here,

Vp (sin θ sin ωt + cos θ (1 − cos ωt) + ∅(0) ωN

(7.24)

216

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

V P = Peak amplitude of voltage, ∅(0) = Remaining magnetizing flux resent in transformer core when switching, N = total turns of transformer coils, and θ = switching angle or Fault Inception Angle (FIA). There is an effect of DC decaying components which are present under any transient condition in the power system. Offset for this DC decaying component is offered using (θ ) switching angle or Fault Inception Angle (FIA) as given in Eq. 7.24. There are two types of zero crossing which are present in AC power systems. Positive and negative zero crossings provide positive and negative offsets that go up and down sequentially. At zero-voltage inception, there is full offset in DC decaying component. This means in between two zero crossings of the waveform, DC offset is affected by the switching angle. As the switching angle rises (means for positive voltage zero crossing edge waveform), remnant flux is reduced in the current. Increasing the switching angle (starting from the positive rising edge of the voltage waveform) will decrease the amount of remnant flux of the inrush current. Similarly, when faults arise at any angle of the voltage waveform, the DC offset in current is observed. The scale of DC offset in current is determined based on FIA. Thus, it is required to remove it from the fault current for the perfect operation of any relay which is only based on the fundamental component of the current. In the proposed algorithm, the MFCDFT completely removes the decaying DC component. Hence, consistent relay operation is achieved with varying FIA for various internal and external faults. Question-6: The protection platform seems to be a personal computer and the hardware setup is using a DSO to acquire data. Is this platform economically feasible? Answer: Nowadays, most of the newly suggested technique utilizes simulation software for data generation by modeling a part of the power system network in PSCAD or ATPEMTP or ETAP. In continuous to this, the generated data is processed for validation of that scheme which is programmed in a personal computer using technical computing language (MATLAB) or general-purpose programming language (C, C++ ). On the other hand, the main objective of this study is to validate the proposed scheme in PC with the use of practical data that are generated in a laboratory environment. Moreover, in the conventional method to test the algorithm in PC using field data, the current and voltage signals from the secondary side of CT and PT (after scaling down) have been obtained through the signal conditioning unit and further proceed to DAQ (Data Acquisition) device. The data sampling has been carried out in DAQ by setting the clock frequency of the on-chip ADC. The DSO does the same function in this study just to acquire the data and sample them. Here, to record the precise data from the generator terminal (through CT/PT), high-resolution voltage and clamp-on category current sensor probes are used. The required number of samples can be obtained for a specified time duration by setting the sampling rate on DSO. In this study, sampling of logged voltage/current signal is supported out with a rate of 80

7.7 Question and Answer

217

samples/cycle and the same has been transferred to the PC through the USB device port of DSO. Question-7: What is the time step used in the algorithm? Answer: In the proposed algorithm, sampling for the fundamental input signal is taken as N = 80 samples per cycle. The power system frequency in India is f = 50 Hz, i.e., a period of T = 1/f = 0.02 s. The time step (∆t) = T /N, which is equals to 0.02/80 = 0.00025 s. Thus, the time step used in this algorithm is 250 µs. Question-8: Give the novelty of the proposed Modified Full Cycle Discrete Fourier Transform (MFCDFT) technique for transformer protection over existing FFT and DFTbased schemes with its advantages. Answer: The proposed MFCDFT algorithm utilizes phasor and magnitude calculation and grounded on those value estimations, the algorithm takes the judgment of internal fault and other abnormalities in a transformer. After validation of the algorithm if an internal fault is there in the transformer, the proposed algorithm provides a digital signal within a single module. Mostly, all abnormalities are covered within a single unit like overloading, CT saturations, over-fluxing, inrush, etc. This means the proposed techniques have cost efficiency feature with different function flexibility for different types of connection of the transformer like star–delta/delta–star/star–star or delta–delta connections in the real field applications. As per the past publication details, all the suggested schemes are based on the simulation result analysis algorithms. However, here in the proposed algorithm, all important test conditions are validated on software as well as hardware. The operation time is also under the required limits of the unit type of the protections. These all matters prove that the proposed scheme is perfect for real-time implementation. Now a day in a real field of operation, normally FFT/DFT-based schemes are utilized. Here in the algorithm, MFCDFT [45, 46] is used to estimate the phasor magnitude and angles for further analysis. The advantages of the MFCDFT scheme compared to normal FFT and DFT schemes provide a sharp smoothen operation with smaller storage data occupation. MFCDFT also gives reliable, fast, and sharp operation compared to normal DFT/FFT scheme. Moreover, certain advantages of the scheme are elaborate as under, (1) The most prominent required factor is the quick operation of the relay under internal fault conditions and remains stable under external fault and other abnormal conditions. The conventional DFT/FFT algorithms are unable to give proper estimation within time bound limit. Due to the effect of the DC decaying component & effect of the harmonics, conventional FFT/DFT-based algorithms

218

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

are unable to properly measurement the fundamental values of the system frequency. So due to this delaying and inappropriate measurement of the proper value of the fundamental frequency in power system convergence time increases. So finally decision time of the algorithm is increased. However, the proposed MFCDFT-based algorithm is capable to handle the effect of harmonics, Noise, and decaying DC components. MFCDFT scheme eliminates the harmonics and remove the effects of DC decaying components and after that, it analyzes the fundamental component. So due to this, MFCDFT scheme takes less time concerning DFT/FFT-based schemes. (2) In this work, MFCDFT algorithm-based work is elaborated which is run on the samples by samples base sliding window type analysis. As per the sliding widow analysis data rates are equal for the input and output per unit cycle. The advantage of the schemes is, they required very little data computation memory and analyzing time for real-time application. So, MFCDFT has a lesser burden on the computational system with a reduction in workload [47]. Traditional filtrations techniques like FFT/DFT-based schemes are become more complex due to the higher computational burden and effect of the noise signal and DC decaying components. Moreover, the confirmation of the superiority of the proposed MFCDFT-based algorithm on a simple FFT/DFT-based algorithm is as per shown in Figure 7.16. As per Figure 7.16, 1000 samples are taken for the validation of the superiority in the DFT and MFCDFT-based algorithm in MATLAB based m-coding. At complete 400 numbers of the sample, an internal fault is applied. Result shows the effectiveness of the MFCDFT-based algorithm compared to DFT based algorithm. A DFT-based algorithm is continuously damping around the MFCDFT-based algorithm waveform. However, the MFCDFT-based algorithm critically damped within one cycle only.

Fig. 7.16 Phasor estimation for fault current using DFT and MFCDFT (L-g fault applied at 0.1 s, i.e., 400 sample)

7.7 Question and Answer

219

Thus, the suggested MFCDFT-based digital protective relay functions are competently and steadfast. Question-9: How to obtain I d 0 and K 1 ? Answer: For percentage-biased differential relay, basic pickup setting (Id0 ) or minimum pickup setting and the percentage slope (K 1 ) of the differential characteristic. These two factors are very important factors for the percentage-biased differential protection of a transformer. It should be selected consciously to deliver proper transformer protection and to avoid unwanted mal-operation of the differential protection scheme of the transformer. IEEE published certain guidelines [41] for the selection of various parameters for protecting power transformers and facilitating the protection of engineers. According to these guidelines the values of these parameters should be chosen. According to this guideline, the minimum pickup setting (I do ) can be selected by considering certain factors. The maximum unbalance in differential current is due to the no-load current of the transformer which will be 7.5% and the maximum unbalances due to the on-load-tap changer (OLTC) is 10%. Hence, the total percentage unbalance in differential current due to said mismatch is therefore 17.5%. The least pickup setting of differential relay must be higher than this value. Therefore, a setting of 20% (0.2 times the tap setting) may be selected. The percentage slope (K 1 ) of the differential relay is used to avoid differential current arising in the events of CT ratio mismatch, CT saturation during external faults, different CT burdens, extension cable resistance nuisance, etc. For determining the percentage slope, the following factors are usually considered: 1. Error in current due to CT ratio mismatch. 2. Errors due to the auxiliary CTs if they are used. A typical value used for this purpose is 5%. 3. Error due to off-nominal taps used in transformer for voltage regulation. 4. Error due to no load current from one side of transformer. 5. Aggregated error including above factors during the full-load operation of the transformer. Generally, the percentage slope (K 1 ) would be set at 30% by considering a sufficient margin as per IEEE norms. Question-10: How can this algorithm tolerate noise? Answer: Modified Discrete Fourier Transform (MDFT) technology is utilized for the extraction of the required electrical quantities. The MDFT technology, itself has the inherent feature to filter out the noise signal using low pass first-order Butterworth filter, and

220

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

hence proper measurement can be achieved. The MDFT will filter out all the undesired noise/harmonics and extract only the required fundamental quantities and hence one can say that the proposed algorithm works well in conjunction with the noise signals. Moreover, the safety margins are used while selecting the thresholds that are used in the proposed methodology, these margins avoid undesired tripping of the digital relay. Question-11: Earlier also fifth and seventh harmonic components were used for determining over-excitation conditions, thus what is new about it? Answer: Earlier the scheme was used to detect the over-fluxing mostly based on the calculated V/f ratio of the system. This V/f scheme use inverse time characteristic to protect the transformer against over-fluxing which is time delayed. Meanwhile, this conventional scheme takes a decision, differential protection may mal-operate due to fluxing conditions. Though the harmonic analysis is not novel still it is completely emerging in the transient behavior of power systems, specifically for transformer and rotating machine protection. A harmonic restrain relay is used for transformer protection but it detects only inrush conditions by measuring the level of 2nd harmonic content of the differential current. In the existing scheme, still, only the fifth harmonic-based scheme is used to detect the over-fluxing scheme which provides redundant information about a change in flux of the transformer core. The proposed fifth and seventh harmonic components of the differential current-based analysis are rarely reported in past literatures, particularly along with differential protection for over-fluxing detection of the transformer. As per the practical observations, fifth and seventh harmonic components have been utilized for the detection of the over-fluxing condition, and based on it the differential relay prevents mal-operation of the protective scheme during momentary/tolerable over-fluxing conditions. The main problem as mentioned in the study chapter is that the transformer differential protection operates during the momentary over-fluxing condition of the transformer. After a certain period, the over-fluxing condition will get die out, and also the transformer can tolerate the momentary over-fluxing without any harm up till a certain period hence there is no need to trip a transformer for transitory over-fluxing condition. By keeping this in mind to prevent undesired tripping of the transformer differential protection scheme during momentary disturbances, the adaptive protection scheme has been proposed here. Moreover, a big advantage can be counted over here is that the main protection of the transformer is guarded by the adaptive biased differential relay itself and hence no major modification is required in the existing universally adopted transformer differential protection scheme. Also, the basic pickup setting will be adaptively changed based on the intensity of the over-fluxing condition if identified. Question-12:

7.7 Question and Answer

221

In the hardware setup how the inrush condition is initiated? Answer: Inrush is the phenomenon that is generated during the no-load condition in the transformer. For a generation of inrush condition here, in the laboratory setup of a 50 kVA transformer secondary side is kept open circuited and the primary side is injected 440 V directly through a three-phase contactor. Here contactor is considered a circuit breaker. For getting the different magnitude of inrush many such tasks are taken place. Question-13: What will be the response of the proposed system in case of switching in of some nonlinear loads such as arc furnaces? Answer: The planned configuration is not grounded on the power factor of the transformer, load, or power system network. The proposed methodology is free from the impact of power factor and hence any type of load will not affect the working of the presented scheme. Hence, if nonlinear loads such as arc furnaces are connected to the transformer, the proposed scheme will work well and provide the necessary protection to the transformer independently of the type of load. The response and effectiveness of this scheme will not vary for arc furnaces or heavy reactive or capacitive loads. Question-14: Explain the effect of short circuit levels and X/R ratios. Answer: The X/R ratio of the transformer accomplishes the percentage or per-unit impedance (%Z) of the transformer which remain the same on both sides. Thus, the effect of the short circuit level (fault MVA) during external fault remains almost equal on primary and secondary sides of the transformer. However, this is not an issue for internal faults as differential relays operate based on the difference of current on both sides irrespective of the short circuit level. Hence, in its application to the power transformer, the current differential principle is typically not affected by changing short-circuit levels, and other issues that may cause problems for non-unit protection techniques (single-ended protection or protection based on measurement from one end of the equipment/line). The short circuit level as it affects the saturation of current transformers (CTs), which can jeopardize the security of differential protection. However, the suggested scheme incorporates fast and sensitive external fault detection to secure the differential relay elements under such a CT saturation phenomenon. Therefore, the effect of short circuit level and X/R ratio is not considered in the proposed adaptive differential protection of the power transformer. Question-15: What is the effect of communication latency of 2–20 ms , Bad Data, etc?

222

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

Answer: Data Transmission Module is a critical component in the data processing system for the protection of electrical components using a microcontroller. Here, a signal conditioning circuit for data sampling, and the sampled data was transferred to the controller with W5100 as an Ethernet driver chip is used. As the W5100 working frequency is up to 100 MHz. The W5100 chip includes a fully hardwired TCP/IP stack, which could facilitate easy implementation of Serial Peripheral Interface (SPI) protocol due to which communication latency is very less in the proposed work. Communication Latency is the time the system takes for a data packet to travel from the data acquisition system to the controller and after processing the data, the trip signal is transmitted to respective isolation devices in the proposed work. Higher communication latency can result in bad data. In the proposed work, as the frequency of the Ethernet driver is very high, communication latency is very low. Data conversion and communication delay time up to the controller is also considered. This is possible with the 57.6 kbps data transfer rate of the band. Within this rate, data is transferred within 1 ms to the controller. Thus, the practical setup used in this investigation can be used for real-time implementation. Question-16: What are the data window and time delay? Can it satisfy real-time protection in practice? Answer: The description of the data window (80 samples) and time delay (25 ms) is given as follows: In this investigation, a sampling frequency of 4 kHz is used. Hence, considering the system frequency of 50 Hz, the sample window will be 80 samples/cycle. It is observed that when a low rate of sampling frequency is taken into account for the signal analysis, lesser sample frequency unpleasantly distress the precision of the algorithm. Due to the imitational information transferred under lower sampling frequency accuracy of the discrimination of the abnormality is affected directly. Same as, if the length of the data window is short (suppose a quarter or half cycle) again due to the limitation of the data information of the whole cycle discrimination of various disturbances is affected. So, due to these reasons, it is essential to take a suitable sampling rate for the decomposing of the signal. This algorithm is processed through the CORTEX ARM4 microcontroller. For defining total reaction time it should be a summation of delay time, data acquisition time, time taken for the process in the processor, and latency time in communication. The required time is calculated here to perform the algorithm as below. Total Reaction Time: • In the projected arrangement, here MFCDFT-based algorithm take 250 µs time step per interval. It needs 20 ms to complete one cycle of 50 Hz system frequency as signals are filtered with a 4 kHz sampling frequency. Normally, at 4 kHz frequency, 80 samples per cycle are counted. Under any abnormality of the power system

7.7 Question and Answer

223

within the first scan one cycle, 20 ms is mandatory to declaim the 80 samples. Sampled data is stored in the controller’s buffer memory (ROM) continuously for analysis, parallelly data is migrated constantly for algorithm analysis and computation. Normally, for data processing time of the sample is considered as 20 ms. As a continuous moving window-type algorithm, it is applicable for the process of the data in the algorithm. • It is also necessary to consider the delay in data conversion and communication as per the controller’s data transfer rate. Here, 57.6 kbps is set for the data transfer rate in the CORTEX ARM4 controller. The process bus is capable to transfer data to the controller as fast as within 1 ms. • Considering 168 MHz as the speed of microcontroller (168 million instructions processed per second) and the size of the developed algorithm as 4 kB. So, the minimum time to compute this size of file and computation of all suggested parameters in the proposed algorithm is found 2 ms maximum without deliberate delay. • In the proposed validation through hardware setup, Cortex Arm4 generates a trip signal at its output port once the internal fault is detected. It is assumed here time taken for the trip signal generation and the delay time of the signal conditioning unit is around 2 ms. So, the total time required to complete the signal sensing trip signal includes Data Processing Time + Data Conversion and Communication time + deliberate delay time + delay time for propagation. It is 20 ms + 1 ms + 2 ms + 2 ms = around 25 ms. Furthermore, sample data is automatically protected. It is noted that data are automatically saved in the controller’s default memory before the subsequent scan. Hence, the required successive scan time to perform the offered algorithm will be concentrated. Therefore, the proper selection of sampling frequency, data acquisition system, and microcontroller results in accurate and faster fault detection. This highspeed fault detection will be very much helpful for the real-time protection of any power system element.

224

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

Appendix Parameters of transformer are used for modeling of power transformer in PSCAD S. No. Equipment 1

2

3

Power transformer

Parameters

Range

Transformer MVA

100

Base operating frequency

50 Hz

Winding type

YY

Positive sequence leakage reactance

0.1 (PU)

The first winding line-to-line voltage

400 kV

Second winding line-to-line voltage

220 kV

Air core reactance

0.2 (PU)

Knee voltage

1.25 (PU)

Time to release flux clipping

0.1 (s)

Magnetizing current

0.4 (%)

Current Transformer Primary to secondary turns ratio (for 400 kV line)

2/1000

Primary to secondary turns ratio (for 220 kV line)

2/1800

Transmission line (Bergeron Model)

Secondary resistance

0.5 (Ω)

Secondary inductance

0.8e−3 (H)

Core area

2.601e−3 (m2 )

Path length

0.6377

Remnant flux density

0.0

Burden resistance

0.5 (Ω)

Burden inductance

0.8e−3 (H)

Positive sequence line resistance

0.297 * e−4 (Ω/m)

Positive sequence line inductive reactance

0.332 * e−4 (Ω/m)

Positive sequence line capacitive reactance

245 * e−4 (MΩ * m)

Zero sequence line resistance

0.162 * e−4 (Ω/m)

Zero sequence line inductive reactance

0.124 * e−4 (Ω/m)

Zero sequence line capacitive reactance 374.34 * e−4 (MΩ * m) Length of 220 kV line

60 km

Length of 400 kV line

100 km

Appendix

225

For laboratory prototype rating and parameters of power transformer: Rating of Transformer: Base System Frequency: Types of Winding: Positive Sequence Leakage Reactance: Voltage Rating: Air Core Reactance: Release Time to Flux Clipping: Magnetizing Current:

50 kVA 50 Hz YY 0.1 PU Primary side 440 V and Secondary side 220 V 0.3 PU at 1.25 PU knee point voltage 0.1 s 0.4%

B-H Curve for Transformer Core Parameters of Current Transformer (CT): Winding Type: Both Ratio: Protective Class: System Frequency: Rated Burden: Permissible Insulation level: The voltage of the Knee Point (KPV):

Tape Wound Primary side (20/1 A) and Secondary side (40/1 A) 5P20 50 Hz 25 VA 2 kV 50 V

226

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

CT Core Excitation Curve

References 1. Wu Q, Ji T, Li M, Wu W (2016) Using mathematical morphology to discriminate between internal fault and inrush current of transformers. IET Gener Transm Distrib 10(1):73–80. https:/ /doi.org/10.1049/iet-gtd.2015.0216 2. Khalkhali B, Sadeh J (2015) Transformer differential protection by online core modeling and orthogonal polynomials. IEEE Trans Power Deliv 30(5):2146–2153. https://doi.org/10.1109/ TPWRD.2014.2380452 3. Hooshyar A, Sanaye-Pasand M (2015) Waveshape recognition technique to detect current transformer saturation. IET Gener Transm Distrib 9(12):1430–1438. https://doi.org/10.1049/ iet-gtd.2014.1147 4. Yao C, Zhao Z, Mi Y, Li C, Liao Y, Qian G (2015) Improved online monitoring method for transformer winding deformations based on the Lissajous graphical analysis of voltage and current. IEEE Trans Power Deliv 30(4):1965–1973. https://doi.org/10.1109/TPWRD.2015.241 8344 5. Weng H, Lin X (2009) Studies on the unusual maloperation of transformer differential protection during the nonlinear load switch-in. IEEE Trans Power Deliv 24(4):1824–1831. https:// doi.org/10.1109/TPWRD.2008.2005655 6. Patel D, Chothani N (2020) CT saturation detection and compensation algorithm. In: Digital protective schemes for power transformer. Springer, Singapore, pp 33–49. https://doi.org/10. 1007/978-981-15-6763-6_2 7. Dharmesh Patel NC, Relevance vector machine based transformer protection. In: Digital protective schemes for power transformer, 1st edn. Springer, Singapore, pp 107–131

References

227

8. Patel D, Chothani NG, Mistry KD, Raichura M (2018) Design and development of fault classification algorithm based on relevance vector machine for power transformer design and development of fault classification algorithm based on relevance vector machine for power transformer. IET Electr Power Appl 12(4):557–565. https://doi.org/10.1049/iet-epa.2017.0562 9. Raichura MB, Chothani NG, Patel DD (2020) Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique. IET Sci Meas Technol 14(1):111–121. https://doi.org/10.1049/iet-smt.2019.0102 10. Patel D, Chothani N (2020) HE-ELM technique based transformer protection. In: Digital protective schemes for power transformer. Springer, Singapore, pp 133–172. https://doi.org/ 10.1007/978-981-15-6763-6_6 11. Moravej Z, Abdoos AA, Sanaye-Pasand M (2013) Power transformer protection scheme based on time-frequency analysis. Int Trans Electr Energy Syst 23(4):473–493. https://doi.org/10. 1002/etep.671 12. Hasheminejad S, Esmaeili S (2014) Transient actions analysis of power transformers based on S-transform and hidden Markov model. Int Trans Electr Energy Syst 24(6):826–841. https:// doi.org/10.1002/etep.1740 13. Zacharias D, Gokaraju R (2016) Prototype of a negative sequence turn-to-turn fault detection scheme for transformers. In: 2016 IEEE power and energy society general meeting (PESGM), p 1. https://doi.org/10.1109/PESGM.2016.7741232 14. Patel DD, Chothani NG, Mistry KD (2015) Sequence component of currents based differential protection of power transformer. In: 12th IEEE international conference electronics, energy, environment, communication, computer, control (E3-C3), INDICON 2015, pp 1–6. https://doi. org/10.1109/INDICON.2015.7443855 15. Patel DD, Mistry KD, Chothani NG (2015) A novel approach to transformer differential protection using sequence component based algorithm. J CPRI 11(3):517–528 16. Bernabeu EE (2015) Single-phase transformer harmonics produced during geomagnetic disturbances: theory, modeling, and monitoring. IEEE Trans Power Deliv 30(3):1323–1330. https:// doi.org/10.1109/TPWRD.2014.2371927 17. Zheng T, Chen PL, Qi Z, Terzija V (2014) A novel algorithm to avoid the maloperation of UHV voltage-regulating transformers. IEEE Trans Power Deliv 29(5):2146–2153. https://doi. org/10.1109/TPWRD.2014.2301452 18. Nimkar PR, Arora TG, Bangde PS, Narnaware PJ (2018) Over-flux protection of the transformer. In: 2018 international conference on smart electric drives and power system (ICSEDPS), pp 171–176. https://doi.org/10.1109/ICSEDPS.2018.8536055 19. Raichura M, Chothani N, Patel D (2020) Improved transformer differential protection by adaptive pickup setting during momentary over-fluxing condition. Adv Electr Electron Eng 18(3). https://doi.org/10.15598/aeee.v18i3.3833 20. Haghjoo F, Mostafaei M, Mohammadi H (2018) A new leakage flux-based technique for turnto-turn fault protection and faulty region identification in transformers. IEEE Trans Power Deliv 33(2):671–679. https://doi.org/10.1109/TPWRD.2017.2688419 21. Kang YC, Lee BE, Zheng TY, Kim YH, Crossley PA (2010) Protection, faulted phase and winding identification for the three-winding transformer using the increments of flux linkages. IET Gener Transm Distrib 4(9):1060–1068. https://doi.org/10.1049/iet-gtd.2010.0094 22. Smugala D, Piasecki W, Ostrogorska M, Florkowski M, Fulczyk M, Granhaug O (2015) Wind turbine transformers protection method against high-frequency transients. IEEE Trans Power Deliv 30(2):853–860. https://doi.org/10.1109/TPWRD.2014.2343261 23. Ali E, Helal A, Desouki H, Shebl K, Abdelkader S, Malik OP (2018) Power transformer differential protection using current and voltage ratios. Electr Power Syst Res 154:140–150. https://doi.org/10.1016/j.epsr.2017.08.026 24. Ali E, Malik OP, Abdelkader S, Helal A, Desouki H (2019) Experimental results of ratiosbased transformer differential protection scheme. Int Trans Electr Energy Syst 29(11). https:// doi.org/10.1002/2050-7038.12114 25. Dashti H, Sanaye-Pasand M (2014) Power transformer protection using a multiregion adaptive differential relay. IEEE Trans Power Deliv 29(2):777–785. https://doi.org/10.1109/TPWRD. 2013.2280023

228

7 Adaptive Biased Differential Protection Considering Over-Fluxing …

26. Stanbury M, Djekic Z (2015) The impact of current-transformer saturation on transformer differential protection. IEEE Trans Power Deliv 30(3):1278–1287. https://doi.org/10.1109/ TPWRD.2014.2372794 27. Patel DD, Chothani N, Mistry KD, Tailor D (2018) Adaptive algorithm for distribution transformer protection to improve smart grid stability. Int J Emerg Electr Power Syst 19(5):1–14. https://doi.org/10.1515/ijeeps-2018-0022 28. George SP, Ashok S (2018) Adaptive differential protection for transformers in grid-connected wind farms. Int Trans Electr Energy Syst 28(9):e2594. https://doi.org/10.1002/etep.2594 29. Patel D, Chothani N (2020) Adaptive digital differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 83–106. https://doi. org/10.1007/978-981-15-6763-6_4 30. Patel DD, Mistry KD, Chothani NG (2016) Digital differential protection of power transformer using DFT algorithm with CT saturation consideration. In: 2016 national power systems conference (NPSC), pp 1–6. https://doi.org/10.1109/NPSC.2016.7858854 31. Raichura M, Chothani N, Patel D, Mistry K (2021) Total Harmonic Distortion (THD) based discrimination of normal, inrush and fault conditions in power transformer. Renew Energy Focus 36:43–55. https://doi.org/10.1016/j.ref.2020.12.001 32. Patel D, Chothani N (2020) Phasor angle based differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 51–81. https:// doi.org/10.1007/978-981-15-6763-6_3 33. Raichura M, Chothani N, Patel D (2021) Review of methodologies used for detection of magnetising inrush and fault conditions in power transformer. IET Energy Syst Integr 3:109–129. https://doi.org/10.1049/esi2.12012 34. Raichura M, Chothani N, Patel D, Sharma J (2020) Methodologies for the detection of magnetizing inrush and fault condition in power transformer. In: 2020 IEEE international conference on computing, power and communication technologies (GUCON), pp 146–151. doi: https:// doi.org/10.1109/GUCON48875.2020.9231065 35. Chothani NG (2016) Development and testing of a new modified discrete fourier transformbased algorithm for the protection of synchronous generator. Electr Power Components Syst 44(14):1564–1575. https://doi.org/10.1080/15325008.2016.1181688 36. Shiddieqy HA, Hariadi FI, Adiono T (2018) Effect of sampling variation in accuracy for fault transmission line classification application based on convolutional neural network. Int Sympos Electron Smart Devices (ISESD) 2018:1–3. https://doi.org/10.1109/ISESD.2018.8605469 37. Patel DD, Mistry KD, Chothani NG (2017) Transformer inrush/internal fault identification based on average angle of second order derivative of current. In: Asia-Pacific power and energy engineering conference, APPEEC, pp 1–6. https://doi.org/10.1109/APPEEC.2017.8309017 38. Patel D, Chothani N, Mistry K (2018) Discrimination of inrush, internal, and external fault in power transformer using phasor angle comparison and biased differential principle. Electr Power Components Syst 46(7):788–801. https://doi.org/10.1080/15325008.2018.1509915 39. Ken Behrendt CL, Fischer N (2011) Considerations for using harmonic blocking and harmonic restraint techniques on transformer differential relays. SEL J Reliab Power 2(3):1971–1980 40. BHEL (2009) Transformers, 2nd edn. Tata McGraw-Hill Education 41. IEEE (2008) IEEE guide for protecting power transformers (Revision of IEEE Std C37.912000). IEEE Power Engineering Society Sponsored by the Power System Relaying Committee, New York, USA. https://doi.org/10.1109/IEEESTD.2008.4534870 42. Chothani NG, Bhalja BR (2014) New algorithm for current transformer saturation detection and compensation based on derivatives of secondary currents and Newton’s backward difference formulae. IET Gener Transm Distrib 8(5):841–850. https://doi.org/10.1049/iet-gtd.2013.0324 43. Raichura M, Chothani N, Patel D (2020) Development of an adaptive differential protection scheme for transformer during current transformer saturation and over-fluxing condition. Int Trans Electr Energy Syst 31:1–19. https://doi.org/10.1002/2050-7038.12751 44. PSCAD Research Center, EMTDC-Transient Analysis for PSCAD Power System Simulation. Winnipeg, MB, Canada, 2005.

References

229

45. Kang YC, Ok SH, Kang SH (2004) A CT saturation detection algorithm. IEEE Trans Power Deliv 19(1):78–85. https://doi.org/10.1109/TPWRD.2003.820200 46. Wiszniewski A, Rebizant W, Schiel L (2008) Correction of current transformer transient performance. IEEE Trans Power Deliv 23(2):624–632. https://doi.org/10.1109/TPWRD.2008. 915832 47. Jacobsen E, Lyons R (2003) The sliding DFT. IEEE Signal Process Mag 20(2):74–80. https:// doi.org/10.1109/MSP.2003.1184347

Chapter 8

Convolution Neural Network and XGBoost-Based Fault Identification in Power Transformer

Abstract The power transformer is working in strained conditions due to the complex power system structure and increasing load demand by various sectors such as industry, commercial, and agriculture. Abnormal conditions and faults may arise during the operation of the power transformer, which may lead to insecurity and instability of the power system at large. Thus, it is mandatory to identify the types and locations of faults to minimize the interruption. Here, in this chapter, the convolution neural network (CNN) with the XGBoost technique has been proposed to accurately identify the transformer faults. To authenticate the proposed methodology, a small Indian power system network is simulated in PSCAD™ software. Voltage and current data are being recorded for the analysis and validation of the proposed method. The generated data is transferred to a one-dimensional CNN for accurate feature extraction. The extracted data from the CNN has been migrated to an algorithm prepared using XGBoost in MATLAB software. To authenticate the proposed method, a huge quantity of data is created in PSCAD™ software using a multi-run facility available in it. The training and testing of the data have been carried out in a high-performance CPU using considered AI techniques. The proposed technique accurately discriminates between the internal fault in the transformer with all external faults/abnormalities. Further, to demonstrate the right fullness of the suggested protective scheme, a hardware setup is prepared with a 50 KVA, 440/ 220 V transformer in the laboratory. It has been observed that the demonstrated method provides an accurate classification of different faults, and it operates in a short time.

8.1 Brief Introduction About the Work Transformer requires special attention because it is the heart of the Power Grid Just like the heart of the human body. Thus, it is the utmost duty of a protection engineer to design an impressive protection scheme for the transformer to retain alive the power system. The protection scheme is designed in such a way that it can immediately detect the fault within the transformer and isolate it from the stability point of view © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Chothani et al., Advancement in Power Transformer Infrastructure and Digital Protection, Studies in Infrastructure and Control, https://doi.org/10.1007/978-981-99-3870-4_8

231

232

8 Convolution Neural Network and XGBoost-Based Fault Identification …

in the power grid. In the past various researchers have suggested approaches for the detection of internal faults in the transformer compared to external abnormalities. These techniques include machine learning, artificial intelligence, wavelet transform, and fast Fourier transformation. Due to the evolution of Science and Technology, artificial intelligence-based techniques are more popular nowadays for power system analysis and protection too. In past, Chothani et al. [1, 2] have suggested protective methods for transformers based on Relevance Vector Machine (RVM). Fault classier techniques such as Support Vector Machine (SVM) [3] and Probabilistic Neural Network (PNN) [4] have been compared with RVM in that paper. It is observed that the execution time of RVM is slightly higher than the required one. Moreover, the modified Extreme Learning Machine (ELM) technique has been exercised in references [5, 6]. A comparative analysis of ELM and SVM has been carried out by Wang et al. [7] and Dogaru et al. [8]. Looking at the results in terms of accuracy and time, the ELM outperforms than SVM [9]. However, ELM undergoes data overfitting and complex calculations with data attributes. Artificial Neural Network (ANN) [10] undergoes several training processes and takes a long time to reach the final decision of classification. To protect the transformer, various non-classifier-based methods have been suggested by researchers. Techniques [11–14] reveal the automatic change in relay setting taking into consideration of CT saturation. Mentioned techniques are capable to detect faults inside the transformer with varying power system conditions. Nevertheless, the detection of high resistance faults is difficult with adaptive changes in relay characteristics. Bagheri et al. [15] proposed a transformer differential scheme that can tackle the effect of core and winding defects as well as faults. Though the rise in current beyond the threshold set in differential relay may not be guaranteed for every mechanical failing of winding and core. There may be only a change in inductance and capacitance of the transformer winding in the event of mechanical. Researchers [16] have presented a condition monitoring scheme for the transformer with the use of online parameter recording. But only monitoring is not sufficient. A comprehensive protective scheme must be combined with the online condition monitoring of power transformers [17–19]. Ghanbari et al. [20] proposed a fault current limiter to restrict the fault current in the neutral circuit. Conversely, the scheme may not detect a low magnitude of current when a fault occurs very near the neutral point. After that, Dukic et al. [21] suggested the M-robust assessment method which uses high-frequency sound signals measured with the microphone. However, in the event of microphone failure as well as during external faults with similar sound waves, the suggested scheme may mal-operate. Ali Hooshyar et al. [22] demonstrated current signal asymmetry-based CT saturation detection and blocking of relay operation during external fault. Conversely, the correction and compensation of saturated CT signal need to be incorporated otherwise relay may mis-operate during cross-country faults. Abdoos and fellow authors [23] proposed CT saturation detection using the variation mode decomposition technique. Later, Chothani et al. [24] explained backward derivative formula-based CT saturation measurement and recompense.

8.2 Combined CNN-XGBoost Technique

233

Moreover, flux measurement using search coil-based inter-turn fault detection has been proposed by Mostafaei and Haghjoo [25]. However, the additional cost of the search coil and its mounting inside the transformer becomes very difficult to implement. Hamed Dashti et al. [26]suggested a scheme for the separation of high magnitude inrush current from fault in the transformer. Conversely, low-magnitude inrush current can be more precisely detected by a conventional harmonic-based scheme. Lin et al. [27] described transformer internal fault detection using the superimposed current component. Moreover, Oliveira et al. [28] proposed a flexible differential relaying scheme considering transient signals. However, the internal fault detection accuracy of the above schemes reduces during fault through a high resistance. Huge numbers of data are generated considering abnormalities such as inrush, over-fluxing, and faults with and without CT saturation. It has been found that with massive data, Convolution Neural Network (CNN) gives promising results for feature extraction from raw data of current signals. Moreover, XGBoost provides better data classification capability. In this chapter, both AI techniques are utilized for the discrimination of transformer internal faults against outside abnormalities. The chapter also exemplifies fault data creation through hardware setup and realtime validation of the proposed technique. The subsequent section illustrates the theory of the proposed scheme, system modeling, result validation, and hardware setup.

8.2 Combined CNN-XGBoost Technique The techniques presented in the past either for fault classification or inrush detection have certain sort of limitations. Thus, it is essential to develop a competent, fast, and consistent method to distinguish internal faults in the transformer from external anomalies. In this chapter, Convolutional Neural Network (CNN) is combined with an efficient classifier Extreme Gradient Boosting (XGBoost) technique [29] for transformer fault classification. The CNN is utilized for valuable feature withdrawal from captured signal and these special features are then transferred to XGBoost. In this process, the outcome received from the fully linked last layer of CNN is provided to XGBoost for classifying the internal fault in the transformer.

8.2.1 Convolutional Neural Network (CNN) If two-dimensional data sets of internal and external faults are available in huge quantities, CNN may be implemented directly as a classifier. Afrasiabi [30] presented a technique on CNN classifier for transformer fault with data conversion from 1 to 2D. In this method [30], the use of artificial data for training and testing may impair the efficacy of the classification.

234

8 Convolution Neural Network and XGBoost-Based Fault Identification …

Fig. 8.1 Representation of combined CNN-XGBoost technique

It has been examined that CNN has good feature extraction capability, Initially, LeCun [31] used the CNN technique for the recognition of handwritten text by way of image processing. The inbuilt filters of CNN help in obtaining effective features from the input data. Moreover, the weight adjustment and pooling layer of CNN reduces the required parameters and mitigates the overfitting of data, respectively. Therefore, the characteristic of CNN makes it preferable for transforming raw data into a digital feature. Figure 8.1 shows the organization of the proposed CNN. To begin with, the first layer receives raw data and is forwarded to the convolutional layer and sub-sampling layer, subsequently, the output will be received from a fully connected layer. The gradient descent algorithm with the backpropagation technique automatically adjusts required kernels in the entire CNN process. With the use of respective filters, the first layer mines initial features, and afterward last layer (known as pooling layers) pools outs the necessary feature with reduced dimensions. This process of double filtration by convolutional and pooling layers removes unclear data [32]. Transformer fault data are one-dimensional (1D), hence, 1D CNN is used for feature extraction in this work. Kiranyaz et al. [33–35] first presented 1D CNN for bio-medical data classification. 1D CNN doesn’t require preprocessing of data for training and testing like that require in ANN. Reference [36] narrated the application of 1D CNN for fault classification in the motor. The forward propagation (1D) can be written as xkl = bkl +

Nl−1 

( l−1 l−1 ) conv 1D wik , si

i=1

where xkl denotes input. bkl is depicted as the bias of kth neuron, at layer l.

(8.1)

8.2 Combined CNN-XGBoost Technique

235

l−1 wik is termed as the kernel from an ith neuron, at layer l − 1 to kth neuron at layer l. sil−1 is output at layer l − 1 for ith neuron.

The average error (E P ) can be derived at the last layer for total L layers from input to output with input matrix P. This error is estimated once the process of forward and backpropagation task is over in 1D CNN. NL ( L )2 ( p )  yi − ti E p = MSE ti [y1L , . . . , y NL L ] =

(8.2)

i=1 p

Here, E p is the average mean square error, N L represents No. of classes, ti is the object vector and [y1L , . . . , y NL L ] is the output vector. This estimated error can be further reduced by taking a derivative of EP considering the bias and weight of the neuron. The gradient descent method plays an important role to finalize the error with updated weight and bias. The derivative of error ∆lk at a kth neuron is given by ∂E l−1 ∂wik

= ∆lk yil−1 and

∂E = ∆lk ∂bkl

(8.3)

The backpropagation is estimated as  ∂ E ∂ x l+1  ∂E i l l = ∆s = = ∆l+1 k i wki l+1 l ∂skl ∂s ∂ x k i i=1 i=1 Nl+1

Nl+1

(8.4)

Delta ∆lk will be further calculated once the backpropagation is carried out. The ∆lk equation will be ∆lk =

∂ E ∂uskl ' ( l ) ( l ) ' ( l ) ∂ E ∂ ykl = f xk up ∆sk β f xk l l ∂ yk ∂ xk ∂uskl ∂ ykl

(8.5)

Here, β = (ss) − 1, ss = sub-sampling, As every element of skl is gained by averaging ss of intermittent output ykl . The ( ) ∑ delta error ( ∆skl ← ∆l+1 can be written as l ∆skl =

Nl+1 

( ( l )) convol1Dz ∆l+1 l , reverse wki

(8.6)

i=1

Here, reverse (…) will overturn the weight array, whereas convol1Dz (…) execute convolution fully in one dimension. Afterward, the bias and weights susceptibility can be set as

236

8 Convolution Neural Network and XGBoost-Based Fault Identification …

 ) ( ∂E ∂E and l = = conv1D skl , ∆l+1 ∆lk (n) l l ∂wik ∂bk n

(8.7)

Thus, weights and biases can be restructured [35] considering learning factor ε as l−1 l−1 wik (t + 1) = wik (t) − ε

bkl (t + 1) = bkl (t) − ε

∂E l−1 ∂wik

∂E ∂bkl

(8.8) (8.9)

The process of XGBoost will be followed by the outcome received from CNN.

8.2.2 Extreme Gradient Boosting (XGBoost) Guestrin et al. [37] have presented a gradient boosting ensemble machine learning technique. This method sequentially unites the results of delicate classifiers and as a result, it becomes an efficient decision-based learning technique (XGBoost). Additionally, the XGBoost technique minimizes calculation complications and the time of the algorithm execution. The numerical derivation of the XGBoost technique is further explained below. 

yi =

k 

f k (xi ), f k ∈ S

(8.10)

k=1

) { }( where S = f (x) = ωq (x) q : Rm → T, ω ∈ RT is known as space for regression and classification, whereas T denotes leaves of a meticulous tree. Further, f k has two components namely structure (q) weights (ω). For the successful classification of data, the method uses the structure (q) decision rule of the tree and weight (ω) to judge the final prediction. Here, ωq stand to achieve a score of qth leaf. The parameter f k becomes skilled by reducing the objective function: O=

 ( )  l yˆi , yi + Ω( f k ) i

(8.11)

k

where l represents the loss function during the training phase which calculates the distance between the actual (yi ) and target ( yˆi ) object. Ω is the penalty term of the tree model. 1  2 ω Ω( f ) = γ T + λ 2 j=1 j T

(8.12)

8.2 Combined CNN-XGBoost Technique

237

Overfitting of data in a training session is controlled by managing the regularization parameters. In Eq. (8.12), T denotes the number of leaves and ω represents the vector of a score on leaves. Due to the training of the model in an additive way, the boosting technique is known as the gradient tree boosting method. Equation (8.11) can be reframed considering yˆi(t) as a predictive term of ith instance, at tth iteration and adding f t in the previous predictive term: O (t) =

n 

l(yi , yˆi(t−1) + f t (xi )) + Ω( f t )

(8.13)

i=1

At this junction, the XGBoost scheme estimates Eq. (8.13) by the second-order Taylor series. The resultant outcome at “t” step will be O (t) ≃ O˜ (t) =

] n [ ( )  1 l yi , yˆi(t−1) + gi f t (xi ) + h i f t2 (xi ) + Ω( f t ) 2 i=1

(8.14)

where gi and h i represent the first and second-order gradient information on loss function, respectively. The modified version of Eq. (8.14) is given after eliminating the constants. ⎞ ⎤ ⎡⎛ ⎞ ⎛ T    1 ⎣⎝ gi ⎠ω j + ⎝ h i + λ⎠ω2j ⎦ + γ T (8.15) O˜ (t) = 2 j=1 i∈I i∈I j

j

Here, I j = {i|q(xi ) = j} at the instance of leaf j. The resultant weight ω∗j for a particular leave “j” can be written ∑ ω∗j

i∈I j

= −∑

i∈I j

gi

(8.16)

hi + λ

From Eqs. (8.15) and (8.16)

˜ O(q) =−

T 1

2

j=1

(∑ ∑

)2

i∈I j

i∈I j

gi

hi + λ

+γT

(8.17)

Equation (8.17) acts for the estimation of the optimal tree structure for classification. An algorithm [37] started with one leaf and progressively adding branches to the tree is effectively utilized. Equation (8.18) denotes the loss reduction after splitting, Osplit

[ (∑ (∑ )2 )2 (∑ )2 ] 1 i∈I L gi i∈I R gi i∈I gi ∑ +∑ −∑ = −γ 2 i∈I L h i + λ i∈I R h i + λ i∈I h i + λ

(8.18)

238

8 Convolution Neural Network and XGBoost-Based Fault Identification …

where I L and I R are the case sets of left and right nodes, respectively. XGBoost is validated here for transformer fault classification due to its progressive gradient boosting classifier which provides high accuracy and committed results. The extracted features from CNN are provided to the XGBoost classifier to distinguish transformer internal faults in contrast to external abnormalities.

8.3 Power System Network For the analysis, an Indian power system has been considered for the study as shown in Fig. 8.2. Four transformers along with loads and reactors are connected to generators of varying power and voltages level. Out of them, Y-∆ configured transformer-1 with 13.8/220 kV having a capacity of 150 MVA is deliberated for the study investigation. The considered Indian power system with 50 Hz frequency is imitated in PSCAD™ software. With the use of a multi-run facility in the PSCAD™, the required numbers of fault data are generated for the successful realization of the considered method in MATLAB. As shown in Fig. 8.2, CT1 and CT2 of rated capacity are placed on both sides of the considered transformer to record current signals. Internal faults in the transformer within the CTs location as well as external faults out of the CTs zone are created with varying parameters of the system and faults. The specification of the electrical components used in the selected power system is provided in Appendix 1.

Fig. 8.2 Portion of the Indian power system for the study

8.3 Power System Network

239

8.3.1 Training and Testing Data Generation The initial magnetizing current is set up in the transformer considering breaker switching time, remnant flux, and other source and load parameter variations. Similarly, to generate sympathetic inrush parallel transformer is energized at different instants. Moreover, the recovery inrush condition is imparted with the removal of load and isolation of fault from the system. In this way, a total of 720 cases of inrush are produced taking into account the 525 training cases and the remaining 195 for the testing purpose as depicted in Table 8.1. There are three different categories of fault possible within the transformer. They are inter-turn faults, winding-winding faults on the same phase, and primary to secondary short circuits. Table 8.2 reveals parameter variations with all categories of faults created inside the transformer. Figure 8.3 represents the situation of inter-winding, intra-windings, and inter-turn faults. Table 8.1 Generation of data for different inrush conditions Parameters Magnetizing Magnetizing inrush inrush (training case)

Sympathetic Sympathetic inrush inrush (training case)

Recovery inrush

Recovery inrush (training case)

Source Three (85%, Three impedance 100%, 115%) (SI)

Three

Three

Three

CB switching instant

Six (0°, 20°, Five (0°, 20°, Six (0°, 45°, 90°, 90°, 135°, 20°, 45°, 135°, 160°) 160°) 90°, 135°, 160°)

Six (0°, 20°, Five (0°, 20° 45°, 90°, 90°, 135°, 135°, 160°) 160°)

Three

Five (0°, 20°, 90°, 135°, 160°)

Load angle Five (0°, 3°, Five (0°, 3°, (δ) 5°, 7°, 10°) 5°, 7°, 10°)

Five (0°, 3°, Five (0°, 3°, 5°, 7°, 10°) 5°, 7°, 10°)

Five (0°, Five (0°, 3°, 5°, 7°, 3°, 5°, 7°, 10°) 10°)

Residual flux

–

–

Six (0%, 5%, 10%, 20%, 50%, 65%)

Total cases 540

Five (0%, 5%, 10%, 50%, 65%)

Out of the 90 total, training case = 375, and testing case = 165

Total cases created for inrush = 720 Out of total cases, training data = 375 + 75 + 75 = 525 Out of total cases, testing data = 165 + 15 + 15 = 195

–

Out of the 90 total, training case = 75, and testing case = 15

Out of the total, training case = 75, and testing case = 15

240

8 Convolution Neural Network and XGBoost-Based Fault Identification …

Table 8.2 Creation of training and testing cases for internal faults Parameter variations

Inter-turn fault

Training case for inter-turn fault

Winding Winding to to winding winding fault fault (Training)

Primary to secondary winding fault

Primary to a secondary winding fault (training)

Winding name

Six (three on primary, three on secondary)

Six (three on primary, three on secondary)

Three (among the windings)

Ten types of fault

Ten types of fault

Source impedance (SI)

Three (85%, 100%, 115%)

Three (85%, Three 100%, (85%, 115%) 100%, 115%)

Three (85%, Three 100%, (85%, 115%) 100%, 115%)

Three (85%, 100%, 115%)

Fault location (FL)

Six (0.2%, 0.5%, 1%, 1.5%, 3%, 5%)

Five (0.2%, 0.5%, 1%, 3%, 5%)

Seven (5%, 7.5%, 15%, 25%, 50%, 75%, 90%)

Six (5%, 7.5%, 15%, 50%, 75%, 90%)

Seven (5%, 7.5%, 15%, 25%, 50%, 75%, 90%)

Six (5%, 7.5%, 15%, 25%, 75%, 90%)

Fault Six (0°, 20°, Five (0°, inception 45°, 90°, 20°, 90°, angle (FIA) 135°, 150°) 135°, 150°)

Six (0°, 20°, 45°, 90°, 135°, 150°)

Five (0°, 20°, 90°, 135°, 150°)

Six (0°, 20°, 45°, 90°, 135°, 150°)

Five (0°, 20°, 90°, 135°, 150°)

Fault resistance (Rf )

Six (0 Ω, 5 Ω, 7 Ω, 10 Ω, 15 Ω, 20 Ω)

Five (0 Ω, Six (0 Ω, 5 Ω, 7 Ω, 5 Ω, 7 Ω, 15 Ω, 20 Ω) 10 Ω, 15 Ω, 20 Ω)

Five (0 Ω, 5 Ω, 7 Ω, 15 Ω, 20 Ω)

Five (0°, 3°, 5°, 7°, 10°)

Five (0°, 3°, 5°, 7°, 10°)

Five (0°, 3°, 5°, 7°, 10°)

–

–

Load angle Five (0°, 3°, Five (0°, 3°, (δ) 5°, 7°, 10°) 5°, 7°, 10°) Total cases

3240

Training and 11,340 testing are 2250 and 990, respectively

Three (among the windings)

Five (0°, 3°, 5°, 7°, 10°)

Training and 37,800 testing are 6750 and 4,590, respectively

Training and testing are 22,500 and 15,300, respectively

For internal fault, total cases produce = 3240 + 11,340 + 37,800 = 52,380 Training data set = 2250 + 6750 + 22,500 = 31,500 Testing data set = 990 + 4590 + 15,300 = 20,880

Looking at Table 8.2, considering all the above-mentioned scenarios, a total of 52,380 data sets are produced, out of which 31,500 and 20,880 cases are allocated for the training and testing process, respectively. Likewise, numerous data have been simulated for faults outside of the transformer. These faults are applied on low-voltage and high-voltage buses (bus-1 and bus-2 as

8.3 Power System Network

241

Fig. 8.3 Illusion for understanding different types of transformer internal fault

shown in Fig. 8.2) as well as on line extended from Bus-2. Referring to Table 8.3, overall 48,600 cases are created for faults outside of the transformer. Out of these data, around 33,750 are training cases and 14,850 are testing cases considered for the classification.

242

8 Convolution Neural Network and XGBoost-Based Fault Identification …

Table 8.3 Generation of training and testing cases for faults outside the transformer Considered parameter

Fault on the LV and HV buses

Fault on the LV and HV buses (training cases)

Fault on 220 kV line

Fault on 220 kV line (training cases)

Fault type (F type )

40 (10 types of standard fault * 2 (Bus-1 and Bus-2) *2 (Normal CT and saturated CT)

40 (10 types of standard fault * 2 (Bus-1 and Bus-2) * 2 (Normal CT and saturated CT)

Ten types of standard faults

Ten types of standard faults

Source impedance (SI)

Three (85%, 100%, 115%)

Three (85%, 100%, 115%)

Three (85%, 100%, 115%)

Three (85%, 100%, 115%)

Fault location (FL)

One

One

Five Five (2%, 5%, (2%, 5%, 15%, 30%, 50%) 15%, 30%, 50%)

Fault inception angle (FIA)

Six (0°, 25°, 45°, 60°, 100°, 150°)

Five (0°, 25°, 60°, 100°, 150°)

Six (0°, Five (0°, 25°, 25°, 45°, 60°, 100°, 150°) 60°, 100°, 150°)

Fault resistance (Rf )

Six (0 Ω, 5 Ω, 7 Ω, 10 Ω, 15 Ω, 20 Ω)

Five (0 Ω, 5 Ω, 7 Ω, 15 Ω, 20 Ω)

Six (0 Ω, Five (0 Ω, 5 Ω, 5 Ω, 7 Ω, 7 Ω, 15 Ω, 20 Ω) 10 Ω, 15 Ω, 20 Ω)

Load angle (δ)

Five (0°, 3°, 5°, 7°, 10°)

Five (0°, 3°, 5°, 7°, 10°)

Five (0°, Five (0°, 3°, 5°, 3°, 5°, 7°, 10°) 7°, 10°)

Total fault cases

21,600

Training and testing cases for Bus faults are 15,000 and 6600, respectively

27,000

Training and testing cases for Line fault are 18,750 and 8250, respectively

For external fault, total cases produce = 21,600 + 27,000 = 48,600 Training cases = 15,000 + 18,750 = 33,750 Testing cases = 6600 + 8250 = 14,850

There may be a possibility of two faults occurring at different locations at the same time on the considered power system, they are termed cross-country faults [38, 39]. Moreover, the working flux setup in the transformer depends on the voltage and frequency of the system. This voltage and frequency change is prominent during variation of system parameters and fluctuation of load and in the worst case during rejection of bulk load from the system. Table 8.4 demonstrates various abnormalities

8.3 Power System Network

243

taken into consideration for the improvement of the learning of the proposed algorithm. For these simulated abnormal conditions, as a whole 2268 cases are produced, from which 1212 cases are reserved for training purposes and the remaining 1056 cases are deliberated for validation. Therefore, by summarizing the simulation cases generated as per from above Tables for various conditions on the transformer such as inrush, faults, and outside abnormalities, a total of 103,968 cases are produced for the authentication of the CNN-XGBoost technique. Table 8.5 displays the bifurcation of total data into learning and validation data. Table 8.4 Data preparation for simultaneous fault and over-fluxing situation Parameter variation

Over-fluxing situation

Training cases of over-fluxing situation

Primary voltage variation

Five (15%, 20%, 25%, 30%, 35%)

Four (15%, 20%, 30%, – 35%)

–

Frequency (Hz)

Five (48.5, 49, 49.5, 50, 50.5)

Five (48.5, 49, 49.5, 50, 50.5)

–

–

Source switching angel

Six (0°, 25°, 45°, 60°, 100°, 150°)

Five (0°, 25°, 45°, 100°, 150°)

Cross Training case of cross country fault country fault

Residual flux Five (0%, 5%, Four (0%, 5%, 25%, 10%, 25%, 50%) 50%) Percentage of load rejection

Three (90%, 70%, 50%)

Three (90%, 70%, 50%)

Fault type (F type )

–

–

One (L-G)

One (L-G)

Location of fault

–

–

Three at different locations

Three at different locations

Resistance in – fault path (Rf )

–

Six (0 Ω, 5 Ω, 7 Ω, 10 Ω, 15 Ω, 20 Ω)

Four (0 Ω, 5 Ω, 10 Ω, 25 Ω)

Total

Training and testing cases for over-fluxing are 1200 and 1050, respectively

18

Training and testing cases for cross-country faults are 12 and 6, respectively

2250

For abnormalities, total cases = 2250 + 18 = 2268 Training cases out of total = 1200 + 12 = 1212 Testing cases out of total = 1050 + 6 = 1056

244

8 Convolution Neural Network and XGBoost-Based Fault Identification …

Table 8.5 Altogether data collected and separated for training and testing

Considered case

No. of training data

No. of testing data

Total data

Internal fault

31,500

20,880

52,380

External fault

33,750

14,850

48,600

525

195

720

1212

1056

2268

66,987

36,981

1,03,968

Inrush Miscellaneous conditions Total

8.4 Algorithm of the Proposed XGBoost Scheme Figure 8.4 represents an algorithm of the proposed CNN-XGBoost technique. At the start, necessary signals are collected from the current transformers connected on both side of transformer-1 (Fig. 8.2). After that, the transient identification algorithm [40, 41] discover the abnormal situation. If any unusual situation is detected then the collected data will be forwarded to the next step of the algorithm where sampling is done. From the sampled data, feature extraction is processed by the CNN technique as described in Sect. 8.2.1. Based on the training data separated in the previous section, the Extreme Gradient Boosting scheme generates a training model. This trained model of XGBoost is then used to classify the data collected for testing purposes. If the classifier scheme distinguishes the transient conditions as in-zone faults based on the signal pattern, then it generates a high pulse (trip signal). On the other hand, for the out-of-transformer zone fault or inrush or over-fluxing situation, the algorithm repeats the step of data collection.

8.4.1 Parameter Setting in Algorithm The convolutional layer is composed of a Max Pooling layer which is activated by a Rectified Linear Unit (ReLU). The convolutional layer of CNN extracts the major feature from the raw data of current signals, whereas the fully connected layer finally pulls out the required feature. In 1D CNN, three convolutional layers are set with [60; 40; 40] neurons. The last and fully connected two layers are composed of twenty (20) neurons. The parameters, kernel size (K) equal to 9, and sub-sampling (ss) factor valued to 4 are utilized for the acceleration of 1D CNN performance [34]. Moreover, these parameters can be regulated by optimization techniques such as grey-wolf optimizer [42] for better performance of CNN. In addition, four constraints, iterations, max delta step, gamma, max. depth [43] are needed to set in the XGBoost model. Further, to regulate the overfitting of data and achieve the best classification result, max. depth is set equal to 5. Similarly, to increase the efficiency of the classifier, gamma, and max delta are adjusted as 0 and 1, respectively.

8.5 Result in Discussion on Fault Classification

245

Fig. 8.4 Algorithm of the suggested CNN-XGBoostScheme

8.5 Result in Discussion on Fault Classification The validation of the algorithm is based on the classification accuracy of the data segregated under fault, inrush, and abnormal conditions of the transformer. The cases classified perfectly are considered True Classified (TC). On the other hand, cases recognized incorrectly are specified as False Classified (FC). The outcome of the selected 36,981 test cases in terms of classification efficacy is tabulated in Table 8.6. After successfully validating all the test cases, it has been found that the overall efficiency of data classification is 99.94%. It has been noted from Table 8.6 that the proposed scheme distinguishes transformer internal faults with 99.60% and external faults with 99.94% with and without saturation of CTs. Further, 100% classification accuracy is achieved in the case of different inrush states because of typical waveform patterns. Algorithm also provides enough accuracy for the categorization of other external abnormalities such as over-fluxing and simultaneous faults. Looking at the results obtained from the execution of the predicated scheme, it can compete with other classifier-based techniques and can be implemented for field data validation.

246

8 Convolution Neural Network and XGBoost-Based Fault Identification …

Table 8.6 Outcome of the suggested scheme for various conditions S. No.

Types of stimulus

1

Transformer internal faults

2

3

4

Transformer external faults

Inrush situations

Abnormal situations

Type of faults/ abnormalities

TC

FC

Accuracy (%)

Inter-turn faults

990

986

4

99.60

Winding to winding faults

4590

4588

2

99.96

Primary to secondary winding Faults

15,300

15,294

6

99.96

Fault on the LV and HV Buses (with and without saturation of CT)

6600

6596

4

99.94

Faults on 220 kV line

8250

8245

5

99.94

Magnetizing inrush

165

165

00

100.00

Sympathetic inrush

15

15

00

100.00

Recovery inrush

15

15

00

100.00

1050

1050

00

100.00

6

5

01

83.33

36,981

36,959

22

Over-flux condition Cross-country fault

Total data

Number of test cases

99.94

8.6 Hardware Setup for Various Result Analyses A hardware prototype is developed in a laboratory environment with three phases 440 V/220 V, 50 kVA, transformer, and peripheral measuring devices. Figure 8.5 shows a schematic diagram of a multi-tapped transformer connected with transmission line components. The other end of Line-1 on the HV side of the transformer is connected with 440 V utility supplies. Similarly, the second end of simulated line-2 on the LV side of the considered transformer is connected to a 220 V local generator. Rated three-phase loads are connected to the secondary terminals of the transformer. The specification of the transformer and parameters of transmission lines resistance and inductor are detailed in Appendix 2. Figure 8.5 also displays the designed control strategy which manages the operation of the transformer in the event of abnormalities or fault through the proposed technique in real-time operation. Various types of faults are simulated on hardware using variable rheostat of 18 Ω, 12 A inserted in the fault path. Heavy-duty contactors (CB1 and CB2) and CTs of relevant ratings are placed outside the terminals of the transformer. The developed hard setup is designed as per the directive of IEEE recommendation [44]. The current signals acquired from CT secondaries are forwarded to the Analog to Digital Converter (ADC-ADS1263)

8.6 Hardware Setup for Various Result Analyses

247

through a signal conditioning unit which reduces the level of the acquired current signals. The digitally sampled data from ADC are transferred to a high-speed CPU through a serial monitor port. CPU will execute the developed algorithm in realtime and give the decision of fault categorization. Various inrush phenomenon are simulated by closing CB1 at different instants under the no-load condition of the transformer. Moreover, with the use of fault switches, a variety of internal faults on a transformer and external faults on lines are generated and tested on the proposed algorithm. Around, 140 events are initiated including inrush, within and outside of transformer faults. Out of these 100 cases are considered for training purposes and the remaining 40 cases are selected for validation purposes. Due to the low data size of hardware-based training cases, they are mixed with the software-based training cases for the learning of CNN and model development of the proposed algorithm. The suggested XGBoost method gives fault classification accuracy of more than 99.95% for the hardware-based test data set. The current signal waveform for different events simulated on hardware setup is shown in Fig. 8.6. The promising accuracy achieved during the validation of the hardware-based data set reveals the reliability of the proposed technique during an internal fault in the transformer. Same way, the equivalent classification accuracy realized during external fault shows the stability of the proposed scheme. Hence, the suggested method can compete with the conventional

Fig. 8.5 Power and control circuit of hardware setup

248

8 Convolution Neural Network and XGBoost-Based Fault Identification …

Fig. 8.6 Scaled CT secondary current for a inrush situation, b In-zone fault, c out-of-zone fault, d saturated CT during outside fault

fault classifier scheme and can be implemented in the real field for the transformer and protection of other components of the power system.

8.7 Conclusion This chapter deals with the novel CNN-XGBoost technique which can discriminate transformer inside faults against different outside abnormalities. This combined technique is analytically explained step by step along with the execution of the algorithm. 1D CNN is deployed to fetch out required features from the raw current signals acquired from the field, whereas the XGBoost is exploited as a classifier tool to segregate data of two different classes. PSCAD™ software is used for simulating a portion of an Indian power network. By changing system parameters and imparting numerous faults on the considered network, large data have been collected to operate the suggested scheme. Inrush, transformer in-zone faults, faults outside the transformer, simultaneous faults at two different locations, and high core flux situations are considered in this study work. The extracted attributes of data from CNN are

8.8 Questions and Answers

249

given as input to XGBoost for validation. The outcome of the proposed technique is measured in terms of the percentage of classification accuracy. It is to be noted that the promising efficacy of more than 99% for fault classification is accomplished through this recommended scheme. Few real-time fault data are collected from the hardware setup built up in a laboratory. The data gathered from the hardware setup are used for the learning process and validation of the proposed technique. Accuracy of the order of 99.95% is claimed for software-simulated data and hardware-generated data. Therefore, the methodology suggested here can be capable to distinguish any kind of fault in a power transformer. Looking at the outcome of the study, the scheme can be easily implemented for real field data classification.

8.8 Questions and Answers Question-1: The hardware current waveforms contain 0.4 A peak current (only) as a transformer inrush current while the rating of the transformer is 50 kVA, 440/220 V. Justified it. Answer: Inrush current is normally 6–7 times the rated current of the transformer. But again the peak amplitude of inrush current is based on a few aspects like remnant flux present in the transformer and the voltage magnitude imparted at the instant at which the transformer is energized. Moreover, here a 10:1 ratio of current to voltage sensor probe of DSO is used on the secondary of CT having a ratio of 25/1 A. Thus, 100 A in the line or primary of the transformer is scaled down to 100/25 = 4 A by CT and again 4/10 = 0.4 A by the DSO probe. Hence, the actual current taken by the transformer at successive energization will be in the range of 100 A and higher as observed physically in the laboratory during hardware operation. Question-2: Mention the valuable details of faults generated in the hardware setup. Also include the three-phase current waveforms with detailed hardware photographs. Answer: The transformer taken here is of rating, 50 kVA, 440/220-V capacity. To simulate the transmission line just like the real system, inductors, and resisters are inserted in the circuit connected on both sides of the considered transformer. The Primary (HV) and secondary (LV) of the transformer are connected to the rated voltage of 440 and 220 V three-phase supply through the line segment, respectively. A Set of circuit breakers and Current Transformers of suitable rating are connected on both sides of the power transformer. Moreover, a variable rheostat is used to

250

8 Convolution Neural Network and XGBoost-Based Fault Identification …

simulate the faults in all three phases of a transformer, bus bars, and T-Lines. To saturate CT during in-zone and outside faults, 250 Ω variable rheostats were inserted in all six CTs. Multi-tap transformer with six tapping on both sides is utilized for the creation of various in-zone faults and turns short circuits. With the manual selection of the tap number, the percentage of winding involved in the fault is considered. Similarly, line resistors and inductors as well as fault path resistors are physically varied to simulate effective faults at different locations on line with high impedance fault (HIF). This way various L-g, LL, LL-g, and LLL faults are created on the developed hardware setup. Figures 8.7, 8.8 and 8.9 shows the physical three-phase transformer and peripheral components used in the hardware set up in a laboratory Fig. 8.10 shows the combined figure of three-phase waveforms for various test cases created during hardware validation. Furthermore, Figs. 8.11, 8.12, 8.13 and 8.14 illustrates the fault currents recorded in DSO used in hardware setup. These DSO waveforms are captured for L-g, L-L, and L-L-L internal faults as well as one L-g external fault with CT saturation. Question-3: How training and testing data are prepared as feature vectors? What is the matrix size for training and/or testing data? Answer: In this study work, 80 samples per cycle per phase with a 4 kHz sampling frequency is considered for an Indian power system having a fundamental frequency of 50 Hz. Post-fault cycle current signals are obtained from all three phases of the primary and secondary sides. Hence, for each test case, the data length is 80 (samples) × 3 (phases) × 2 (sides) = 480 samples arranged in a row of the feature vector matrix. Table 8.7 shows an empty feature vector matrix for the 36,981 test dataset used in this particular study work. Similarly, the training feature vector is created for 66, 987 cases. Therefore, the size of the one-dimensional feature vector given as an input to the XGBoost algorithm is 66,987 × 480 for training purposes and 36,981 × 480 for the testing performance. However, in a real-time application, each test case has to be utilized for testing of the CNN-XGBoost classifier and thereafter it decides whether the test case falls under in-zone fault or outside fault, or inrush condition. Question-4: How to implement the proposed algorithm in a real system? What special equipment should be provided to implement this algorithm? Answer: Implementation of the proposed algorithm is done by coding the CNN-XGBoost instruction in MATLAB or in C-language (same as ANN and SVM used in existing protection techniques). Later the m-code is migrated in the hardware-based processor

8.8 Questions and Answers

251

(a)

(b) Fig. 8.7 a Three-phase transformer, b tapping on winding of primary and secondary

through a software known as a programmer. The controller uses designated pins to upload the code into its flash memory. Later, the trained data is forwarded to a controller for the generation of a trained model which will be further used for real-time validation of the power system events. DSP or FPGA or CORTEK ARM processors are generally used for the real-time implementation of the suggested CNN-XGBoost technique.

252

8 Convolution Neural Network and XGBoost-Based Fault Identification …

Fig. 8.8 Variable resisters of transmission line

Question-5: How the PSCAD is used to simulate numerous fault signals in this work? Answer: The PSCAD™ gives reliable performance under steady state and transient phenomena in the power system. It is used in the area of power systems, protection, and load flow studies. The components modeled in PSCAD™ are similar to that of real equipment as it represents the mathematical modeling of electrical components. Manual data generation is a time-consuming process and leads to errors in faulty data. Therefore, after simulating the considered network in PSCAD™, numerous faults are automatically created in sequence as per requirement. PSCAD™ allows the designing of user components with the FORTRAN compiler. Thus, in this work, the multi-run block is used with user-design components and inbuilt library components for huge data. With this multi-run block, a range of parameters is simultaneously varied for automatic fault data creation. The multi-run block and set system variables are shown in Fig. 8.15. Types of fault, location of a fault, impedance in fault path, fault instance, and load variation are such variables set in the PSCAD™. Though this extended model is not an innovation, it facilitates a lot in the proposed scheme by systematically generating diversified fault patterns.

8.8 Questions and Answers

253

Fig. 8.9 Variable inductors of transmission line

Question-6: What is the length of the data window? Answer: Fundamental frequency = 50 Hz, Sampling Time interval (∆t) = 250 μ-s, Sampling frequency= 1/∆t= 4 kHz Hence, Samples/cycle = 80 Hence, a data window of 80 samples is considered in this work. Question-7: How accurate are the models of the current transformers (CT) in the program? Answer: Jiles-Atherton (JA) CT model of PSCAD™ is used in this model which gives identical characteristics to that of CT used in real practice [46]. The KPV and saturation characteristics are also matched with the field data for CT used in this work.

254

8 Convolution Neural Network and XGBoost-Based Fault Identification …

Fig. 8.10 Waveforms captured for inrush and various fault cases during hardware validation

Question-8: Which transformer is energized in sympathetic inrush current condition? Answer: As is seen in Fig. 8.2, a power system model has been selected for data collection and simulation purposes. Various test cases have been implemented on this power system model to generate certain data, which can be used for training as well as the testing purpose for the proposed transformer protective scheme. In this work, transformer1 (Fig. 8.2) is selected for test data generation. For sympathetic inrush condition, parallel-connected transformer-2 is switched on at different instants. Under the operating condition of transformer-1, switching transformer-2 will cause an abnormal upshot on the operation of transformer-1. This abnormal consequence on transformer1 is called the sympathetic inrush condition. Hence, the answer to this question is, transformer-2 of Fig. 8.2 is energized to realize the sympathetic inrush current condition.

8.8 Questions and Answers

Fig. 8.11 Line-ground fault on the primary winding of the transformer

Fig. 8.12 Line to line fault on primary winding of the transformer

255

256

8 Convolution Neural Network and XGBoost-Based Fault Identification …

Fig. 8.13 Triple line fault on the primary winding of the transformer

Fig. 8.14 Line-ground external fault with mild CT saturation

8.8 Questions and Answers

257

Table 8.7 Sample feature vector for the testing process Testing data

Three-phase current samples, 80 samples/cycle/phase (primary side) 80 * 3 = 240

Three-phase current samples, 80 samples/cycle/phase (secondary side) 80 * 3 = 240

1

241

2

–

–

–

240

242

–

–

–

480

Test case-1 Test case-2 Test case-3 Test case-4 || || || Test case-36981

.

. .

Multiple Run

V1 V2 V3 V4 V5

Fig. 8.15 Multi-run block and setting of variables

Question-9: Explain the impact of variations of source switching angle and residual flux in over-fluxing conditions. Answer: Source switching is momentary and it has a fast transient effect on parameter variations in the system. So, if the source is switched at a different angle, it poses the effect of DC decaying current in the system. Moreover, the transformer core has

258

8 Convolution Neural Network and XGBoost-Based Fault Identification …

enough capacity to retain the residual flux whose magnitude is always lower than the maximum working flux of the transformer. Therefore, these two parameters do not impose any effect of over-fluxing condition which may persist for a longer time in the transformer. Over-flux situations arise when voltage or frequency violates certain limits. During over-fluxing, the corresponding secondary current may not follow the primary winding current properly, because of transformer core saturation. In this condition, if remains up to some extent then it is not hazardous for the transformer but if it exceeds from certain threshold level then it may lead to heat in the core of the equipment, and in the worst condition it may damage the equipment. Hence, the over-fluxing condition of the transformer must be addressed properly. It is to be clarified that the over-fluxing condition purely depends on two factors only, i.e., change in voltage and frequency during transformer operation after energization. Hence, in this work, only two parameters for the study of the over-fluxing condition of the transformer are verified. Moreover, as far as the variation of source switching angle and residual flux is concerned, it is already considered in the parameter variation portion for the inrush condition. Hence, it is not worth considering the impact of the source switching angle and residual flux for over-fluxing conditions.

Appendices Appendix 1 Parameters of a transformer are used for modeling of power transformer in PSCAD™ S. No. Equipment 1

2

Power transformer

Current transformer

Parameters

Range

Transformer MVA

150

Base operating frequency

50 Hz

Winding type

YY

Positive sequence leakage reactance

0.1 (pu)

First winding line-to-line voltage

13.8 kV

Second winding line-to-line voltage

220 kV

Air core reactance

0.2 (pu)

Knee voltage

1.25 (pu)

Time to release flux clipping

0.1 (s)

Magnetizing current

0.4 (%)

Primary to secondary turns ratio (for 13.8 kV line)

1250/5

Primary to secondary turns ratio (for 220 kV line)

80/5

Secondary resistance

0.5 (Ω) (continued)

References

259

(continued) S. No. Equipment

3

Transmission line (Bergeron model)

Parameters

Range

Secondary inductance

0.8e−3 (H)

Core Area

2.601e−3 (m2 )

Path length

0.6377

Remnant flux density

0.0

Burden resistance

0.5 (Ω)

Burden inductance

0.8e−3 (H)

Positive sequence line resistance

0.297 * e−4 (Ω/m)

Positive sequence line inductive reactance

0.332 * e−4 (Ω/m)

Positive sequence line capacitive reactance

245 * e−4 (MΩ * m)

Zero sequence line resistance

0.162 * e−4 (Ω/m)

Zero sequence line inductive reactance

0.124 * e−4 (Ω/m)

Zero sequence line capacitive reactance 374.34 * e−4 (MΩ * m) Length of 220 kV line 4

80 km

Source data: 3-phase, 250 MW, 13.8 kV, 50 Hz

Appendix 2 Power transformer parameters for hardware setup: Transformer rating: Base operating frequency: Winding type: Positive sequence leakage reactance: Voltage rating: Air core reactance: Time to release flux clipping: Magnetizing current:

50 kVA 50 Hz YY 0.1 pu 440 V (primary)/220 V (secondary) 0.3 pu, knee point voltage: 1.25 pu 0.1 s 0.4%.

References 1. Patel D, Chothani NG, Mistry KD, Raichura M (2018) Design and development of fault classification algorithm based on relevance vector machine for power transformer. IET Electr Power Appl 12(4):557–565. https://doi.org/10.1049/iet-epa.2017.0562

260

8 Convolution Neural Network and XGBoost-Based Fault Identification …

2. Patel D, Chothani N (2020) Relevance vector machine based transformer protection. In: Digital protective schemes for power transformer. Springer, Singapore, pp 107–131. https://doi.org/ 10.1007/978-981-15-6763-6_5 3. Shah AM, Bhalja BR (2013) Discrimination between internal faults and other disturbances in transformer using the support vector machine-based protection scheme. IEEE Trans Power Deliv 28(3):1508–1515. https://doi.org/10.1109/TPWRD.2012.2227979 4. Tripathy M, Maheshwari RP, Verma HK (2010) Power transformer differential protection based on optimal probabilistic neural network. IEEE Trans Power Deliv 25(1):102–112. https://doi. org/10.1109/TPWRD.2009.2028800 5. Raichura MB, Chothani NG, Patel DD (2020) Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique. IET Sci Meas Technol 14(1):111–121. https://doi.org/10.1049/iet-smt.2019.0102 6. Patel D, Chothani N (2020) HE-ELM technique based transformer protection. In: Digital protective schemes for power transformer. Springer, Singapore, pp 133–172. https://doi.org/ 10.1007/978-981-15-6763-6_6 7. Chorowski J, Wang J, Zurada JM (2014) Review and performance comparison of SVMand ELM-based classifiers. Neurocomputing 128(Supplement C):507–516. https://doi.org/10. 1016/j.neucom.2013.08.009 8. Bucurica M, Dogaru R, Dogaru I (2015) A comparison of extreme learning machine and support vector machine classifiers. In: 2015 IEEE international conference on intelligent computer communication and processing (ICCP), pp 471–474. https://doi.org/10.1109/ICCP.2015.731 2705 9. Chothani NG, Patel DD, Mistry KD (2017) Support vector machine based classification of current transformer saturation phenomenon. J Green Eng River Publ 7:25–42. https://doi.org/ 10.13052/jge1904-4720.7122 10. Balaga H, Gupta N, Vishwakarma DN (2015) GA trained parallel hidden layered ANN based differential protection of three phase power transformer. Int J Electr Power Energy Syst 67:286– 297. https://doi.org/10.1016/j.ijepes.2014.11.028 11. Patel DD, Chothani N, Mistry KD, Tailor D (2018) Adaptive algorithm for distribution transformer protection to improve smart grid stability. Int J Emerg Electr Power Syst 19(5):1–14. https://doi.org/10.1515/ijeeps-2018-0022 12. Patel D, Chothani N (2020) Adaptive digital differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 83–106. https://doi. org/10.1007/978-981-15-6763-6_4 13. Raichura M, Chothani N, Patel D (2020) Development of an adaptive differential protection scheme for transformer during current transformer saturation and over-fluxing condition. Int Trans Electr Energy Syst 31:1–19. https://doi.org/10.1002/2050-7038.12751 14. Patel D, Chothani N (2020) CT saturation detection and compensation algorithm. In: Digital protective schemes for power transformer. Springer, Singapore, pp 33–49. https://doi.org/10. 1007/978-981-15-6763-6_2 15. Bagheri S, Moravej Z, Gharehpetian GB (2017) Effect of transformer winding mechanical defects, internal and external electrical faults and inrush currents on performance of differential protection. IET Gener Transm Distrib 11(10):2508–2520. https://doi.org/10.1049/iet-gtd.2016. 1239 16. Ballal MS, Jaiswal GC, Tutkane DR, Venikar PA, Mishra MK, Suryawanshi HM (2017) Online condition monitoring system for substation and service transformers. IET Electr Power Appl 11(7):1187–1195. https://doi.org/10.1049/iet-epa.2016.0842 17. Chothani NG, Raichura MB, Patel DD, Mistry KD (2019) Real-time monitoring protection of power transformer to enhance smart grid reliability. Electr Control Commun Eng 15(2):104– 112. https://doi.org/10.1109/EPEC.2018.8598427 18. Patel D, Chothani N (2020) Real-time monitoring and adaptive protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 173–190. https:/ /doi.org/10.1007/978-981-15-6763-6_7

References

261

19. Chothani NG, Raichura MB, Patel DD, Mistry KD (2018) Real-time monitoring protection of power transformer to enhance smart grid reliability. In: 2018 IEEE electrical power and energy conference (EPEC), pp 1–6. https://doi.org/10.1109/EPEC.2018.8598427 20. Ghanbari T, Samet H, Ghafourifard J (2016) New approach to improve sensitivity of differential and restricted earth fault protections for industrial transformers. IET Gener Transm Distrib 10(6):1486–1494. https://doi.org/10.1049/iet-gtd.2015.1343 21. Dukic G, Cukaric A (2014) New algorithm for detecting power transformer faults based on M-robust estimation of sound signals. IET Gener Transm Distrib 8(6):1117–1126. https://doi. org/10.1049/iet-gtd.2012.0492 22. Hooshyar A, Sanaye-Pasand M (2015) Waveshape recognition technique to detect current transformer saturation. IET Gener Transm Distrib 9(12):1430–1438. https://doi.org/10.1049/ iet-gtd.2014.1147 23. Abdoos AA (2016) Detection of current transformer saturation based on variational mode decomposition analysis. IET Gener Transm Distrib 10(11):2658–2669 24. Bhalja BR (2014) New algorithm for current transformer saturation detection and compensation based on derivatives of secondary currents and Newton’s backward difference formulae. IET Gener Transm Distrib 8(5):841–850 25. Mostafaei M, Haghjoo F (2016) Flux-based turn-to-turn fault protection for power transformers. IET Gener Transm Distrib 10(5):1154–1163. https://doi.org/10.1049/iet-gtd.2015.0738 26. Dashti H, Davarpanah M, Sanaye-Pasand M, Lesani H (2016) Discriminating transformer large inrush currents from fault currents. Int J Electr Power Energy Syst 75:74–82. https://doi.org/ 10.1016/j.ijepes.2015.08.025 27. Lin X, Lu J, Zhang R, Tong N, Adio OS, Li Z (2015) Internal fault fast identification criterion based on superimposed component comparison for power transformer. Int J Electr Power Energy Syst 73:491–497. https://doi.org/10.1016/j.ijepes.2015.05.023 28. Oliveira MO, Bretas AS, Ferreira GD (2014) Adaptive differential protection of three-phase power transformers based on transient signal analysis. Int J Electr Power Energy Syst 57:366– 374. https://doi.org/10.1016/j.ijepes.2013.12.013 29. Ren X, Guo H, Li S, Wang S, Li J (2017) A novel image classification method with CNNXGBoost model. In: Digital forensics and watermarking, pp 378–390 30. Afrasiabi S, Afrasiabi M, Parang B, Mohammadi M (2019) Integration of accelerated deep neural network into power transformer differential protection. IEEE Trans Ind Inform 16:865– 876. https://doi.org/10.1109/TII.2019.2929744 31. Le Cun Y et al (1990) Advances in neural information processing systems, vol 2, pp 396–404 32. Borovykh A, Bohte S, Oosterlee CW (2017) Conditional time series forecasting with convolutional neural networks. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinform) 10614:729–730 33. Kiranyaz S, Ince T, Ridha H, Gabbouj M (2015) Convolutional neural networks for patientspecific ECG classification. https://doi.org/10.1109/EMBC.2015.7318926 34. Abdeljaber O, Avci O, Kiranyaz S, Gabbouj M, Inman DJ (2017) Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J Sound Vib 388:154–170 35. Kiranyaz S, Ince T, Gabbouj M (2016) Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans Biomed Eng 63(3):664–675. https://doi.org/10. 1109/TBME.2015.2468589 36. Ince T, Kiranyaz S, Eren L, Askar M, Gabbouj M (2016) Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans Ind Electron 63(11):7067–7075. https://doi. org/10.1109/TIE.2016.2582729 37. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22Nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 785–794. https://doi.org/10.1145/2939672.2939785 38. Lin X, Weng H, Wang B (2009) Identification of cross-country fault of power transformer for fast unblocking of differential protection. IEEE Trans Power Deliv 24(3):1079–1086. https:// doi.org/10.1109/TPWRD.2009.2013663

262

8 Convolution Neural Network and XGBoost-Based Fault Identification …

39. Distribution automation handbook section 8.6 MV feeder earth-fault protection distribution automation handbook (prototype) power system protection, 8.6 MV feeder earth-fault protection 40. Wiot D (2004) A new adaptive transient monitoring scheme for detection of power system events. IEEE Trans Power Deliv 19(1):42–48. https://doi.org/10.1109/TPWRD.2003.820416 41. Mohanty SR, Pradhan AK, Routray A (2008) A cumulative sum-based fault detector for power system relaying application. IEEE Trans Power Deliv 23(1):79–86. https://doi.org/10.1109/ TPWRD.2007.911160 42. Chen X, Kopsaftopoulos F, Wu Q, Ren H, Chang FK (2019) A self-adaptive 1D convolutional neural network for flight-state identification. Sensors (Switzerland) 19(2). https://doi.org/10. 3390/s19020275 43. Song R, Chen S, Deng B, Li L (2016) EXtreme gradient boosting for identifying individual users across different digital devices. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinform) 9658:43–54. https://doi.org/10.1007/978-3-319-39937-9_4 44. IEEE (2008) IEEE guide for protecting power transformers (revision of IEEE Std C37.91– 2000). IEEE Power Engineering Society Sponsored by the Power System Relaying Committee, New York, USA. https://doi.org/10.1109/IEEESTD.2008.4534870 45. Raichura M, Chothani N, Patel D (2021) Efficient CNN-XGBoost technique for classification of power transformer internal faults against various abnormal conditions. IET Gener Transm Distrib 15(5):972–985. https://doi.org/10.1049/gtd2.12073 46. Annakkage UD, McLaren PG, Dirks E, Jayasinghe RP, Parker AD (2000) A current transformer model based on the Jiles-Atherton theory of ferromagnetic hysteresis. IEEE Trans Power Deliv 15(1):57–61. https://doi.org/10.1109/61.847229

Chapter 9

Sequential Component-Based Improvement in Percentage Biased Differential Protection of a Power Transformer

Abstract Percentage-biased differential protection may falsely actuate for the cases like CT saturation and inrush generation. Sequential components of recorded quantity may also differ during abnormal events in terms of their phasor and/or magnitude. Here, in this chapter, a combination of the phasor difference of the sequential component is added as the parallel defensive technique of the percentage-biased differential protection to improve its operation under various anomalous conditions. Various irregularities such as high resistance internal fault, CT saturation, magnetizing inrush, and variation of the load level may appear and can become the reason for the mis-operation of the percentage-biased differential protection. Here, to validate the algorithm, a Full Cycle Discrete Fourier Transform (FCDFT) technique is used to investigate the current signals. Initially, the acquired phasor current signals are transformed into equivalent sequence components. Later, the phasor angle of all three sequential components (i.e., “ +Ve”, “−Ve” and “0” sequence components) are estimated on both primary and secondary side current signals. On the occurrence of the internal fault, the phasor angle difference of like sequence components is observed very low in degree, and for external fault, it may be observed up to 180°. Simultaneously, the percentage-biased differential relaying scheme work as a parallel defensive protective algorithm. All possible test conditions are replicated in PSCAD™ software and authenticated by the MATLAB coding of the FCDFT algorithm. Numerous test cases are carried out on the developed simulation and the suggested scheme is validated successfully for all the fault categories and anomalies.

9.1 Introduction Transformers play an important role in exchanging power from one voltage-level network to another voltage-level network as per the need of power infrastructure and consumers. Thus, reliable operation and dedicated protection for this costly equipment is the foremost requirement. However, certain issues such as the nonlinear behavior of the core, mismatch in the voltage level of a transformer, mismatch in CT ratio, on-load tap changing facilities, magnetizing inrush, CT saturation, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Chothani et al., Advancement in Power Transformer Infrastructure and Digital Protection, Studies in Infrastructure and Control, https://doi.org/10.1007/978-981-99-3870-4_9

263

264

9 Sequential Component-Based Improvement in Percentage Biased …

and winding connection configurations generates more complications to implement an ideal percentage-biased differential protection with higher accuracy. In past, numerous schemes are recommended by researchers for better percentagebiased differential protections of power transformers. Some of them are developed on discrimination logic of internal fault with other conditions such as inrush current, heavy load changes, presence of decaying DC component in external fault, and the transient nature of fault current [1]. Hosny et al. [2] presented transformer differential protection with angle difference of the phasor among HV and LV side currents however, high resistance fault using sequence components remains unclassified in this case. Sometimes, DC decaying components and generated harmonics also play a major role to analyze the protective schemes [3, 4]. Based on the same techniques, Wagh et al. proposed a transformer protective scheme [5]. However, various cases have not been considered during the validation procedure. Harmonics are the major prediction constraint under any abnormal event in the power system. Harmonicsbased transformer defensive techniques are implemented by many researchers with their practical analysis [6–8]. An adaptive type of protective scheme is also explained with the consideration of the CT saturation effect in the transformer protection using shifting of percentage-biased characteristics [9, 10]. With considerations of overfluxing conditions, the shifting of basic pickup adaptive for the percentage-biased characteristics is deployed for transformer protection [11, 12]. Moreover, certain schemes are based on phasor comparison [13–15] and include simulation and hardware analysis to improve transformer protection successfully. Sequential componentbased analysis is also validated in [16], based on the magnitude and phasor computation of one-phase transformer protection. Real-time monitoring is also a burning issue and some points are included to incorporate monitoring as well as protection of the power transformer [17–19]. During the magnetizing inrush phenomenon, the power system frequency instantaneously varies drastically, based on this drastic change logic, the frequency signal of differential power is analyzed and validated to discriminate the inrush [20]. This algorithm is compatible with the inrush conditions to discriminate it within a quarter of a cycle. Harmonic blocking and restraining techniques are also useful to stabilize the unit-type protection against various abnormalities. Guzman et al. [21, 22] have explained such techniques based on harmonics which are derived from the current quantity. However, this methodology is nowadays outdated, due to false accusations in various faulty cases like CT saturation and magnetic inrush. AI-Fakhri and fellow authors have described a differential relay that depends on the restraint quantity vector difference [23]. Though only a few test cases are validated in the above scheme. Phasor angle difference and rate of differential current change based combination of the bus bar and transformer protection is defined by Narendra et al. [24]. However, they have not validated it for high resistance internal fault. Based on the direction analysis, Khan et al. [25] presented a novel approach, but the additional cost of a voltage transformer may increase the overall cost of this type of defensive scheme. Curve fitting on the sine-wave method is adopted by Mohammad Ahmadi et al. [26] on the three-phase transformer to identify magnetizing inrush and internal fault phenomena. A combination of harmonic measurement

9.2 Projected Transformer Differential Protection Performance

265

for inrush recognition and to also provide a restraining feature in transformer protection is described by Hamilton in [27]. However, different test cases are still missing in the result validation. Jettanasen et al. [28] proposed a scheme that discriminates internal faults in the transformer utilizing the spectrum comparison technique using DWT. In this scheme, LV winding ground fault efficiency is observed as 94.44%, and for internal faults, it is observed as 83.33% which is very less. Moravej et al. [29] presented a time–frequency dependent analysis of a differential current-based algorithm in comparison with DFT-based analysis. Meshal Al-Shaher and Mohamed Saied [30] analyzed the faults using the input impedance with consideration of only fault types which are taking place on transformer winding turns. Tripathy et al. [31] proposed a review paper advising the utilization of classifier techniques based on ANN and Fuzzy. However, neural network and fuzzy logic-based methods lacking with lesser accuracy compared to other classifier techniques like SVM [32], RVM [33, 34], CNN XG Boost [35], and HE-ELM [36, 37], etc. However, all the abovesaid techniques also suffer from their tedious training and testing procedure. Fani et al. [38] proposed transformer differential protection using a waveform feature monitoring scheme. This chapter presents a combined differential relaying principle and phasor angle comparison-based sound defensive technique for transformer protection. Sequential component phasor comparison is working in parallel with biased differential-based defensive technique of power transformer. Numerous test cases are validated through simulation as a virtual replication of real field scenarios. These test cases are generated in PSCAD™ software [39]. In Sect. 9.2, the problem description and possible solution with the proposed algorithm are given. Section 9.3 describes system modeling and Sect. 9.4 shows simulation results for a suggested scheme of transformer protection. The planned relaying structure impeccably works through all internal fault disorders even during high resistance internal fault. On the other hand, it remains steady in cases such as external fault, magnetizing inrush, CTs saturation and consequently can offers desired recital.

9.2 Projected Transformer Differential Protection Performance 9.2.1 Problem Description The existing schemes which are based on a computation of pre/post-fault current and voltage data, transient reactance, presence of harmonic content, and noise can work well up to a certain extent. However, they fail to protect the transformer in certain unwanted situations such as CT saturation and close-in external fault conditions. Moreover, the existing methods are based on simple differential principal which compare only current magnitude and may fail during faults having a high amount of resistance which generates the same wave pattern as observed during normal load conditions.

266

9 Sequential Component-Based Improvement in Percentage Biased …

Many schemes proposed in the past depend on a measurement of voltage and current quantity and may suffer from the elevated calculation, difficulty in performance, higher expenses, and unfortunate discrimination among internal and abnormalities like CT saturation under external faults. The solution to the aforementioned problems is presented here by proposing a new transformer fault distinguishing scheme using sequence components of current as well as its phasor comparison. The solution to the aforementioned problem is described here. The symmetrical components of the primary and secondary side current of a transformer like “+Ve”, “−Ve”, and “zero” sequence components (SCs) are computed by Eqs. (9.1), (9.2) [40]. ⎡

⎤ I0 p ⎣ I1 p ⎦ = I2 p ⎡ ⎤ I0s ⎣ I1s ⎦ = I2s

⎡

1 1 1⎣ 1 a 3 1 a2 ⎡ 1 1 1⎣ 1 a 3 1 a2

⎤ ⎤⎡ Iap 1 a 2 ⎦⎣ Ibp ⎦ a Icp ⎤⎡ ⎤ Ias 1 a 2 ⎦⎣ Ibs ⎦ a

(9.1)

(9.2)

Ics

where a = 1∠120◦ = e j2π/3 . As per Fig. 9.1a, e, the transformer is connected in an interconnected network and is represented with one line diagram for internal and external types of fault respectively. The algorithm obtains CT secondary current Ix and Iy from the primary and secondary sides of the transformer respectively. The remaining section of Fig. 9.1, i.e., sub-figures (b), (c), (d) and (f), (g), (h), illustrates the behavior of phasor angle of “+Ve” (i.e., 1), “−Ve (i.e., 2)” and “Zero” (i.e., 0) sequence components (SCs) during internal and external faults, respectively. It is to be noted that the relative phasor angle difference of “+Ve”, “−Ve”, and “0” sequence components during an internal type of fault is small and usually falls within a cone of 90°. For the Y –Y configuration, it may be near about 0°, and for ∆ − Y or Y − ∆ type of connection, it may be around 30°. Whereas in the case of an external type of fault for any configuration of a transformer, the phasor angle difference of the sequence component is greater than 90° [41]. One thing is better to note here is that if only one sequence component out of three is utilized to discriminate, the scheme may fail to recognize a particular fault type. Hence, the phasor angle of all the sequential components (i.e., +Ve, −Ve, and zero) of primary as well as secondary side currents must be utilized to distinguish internal and external faults of a transformer. It is also observed that the pure phasor angle comparison-based method is not guaranteed during certain phenomena such as magnetizing inrush and turn-to-turn type of faulty cases. Thus, for the dedicated protective scheme, a percentage-biased differential method along with a second harmonic restraint is used in conjunction with the symmetrical components (SCs) based logic.

9.2 Projected Transformer Differential Protection Performance

267

Fig. 9.1 Phasors of symmetrical components of fault current

9.2.2 Proposed Algorithm In this chapter, a new tactic to distinguish internal faults and other external situations of a power transformer have been proposed. In this method, phasor values (magnitude and phasor angle) of CT secondary current as well as harmonic components are calculated using Discrete Fourier Transform (DFT) filter. At the initial stage, the algorithm utilizes a harmonic level to sense any faulty and/or inrush condition. The calculations of symmetrical components of current from the derived actual phasor are

268

9 Sequential Component-Based Improvement in Percentage Biased …

performed to compare the phasor angle of primary and secondary current. On the base of comparative analysis of all phasor angle components of primary and secondary current, it discriminates internal fault and other situations. Figure 9.2 describes the logic of the proposed algorithm for fault identification in a transformer. A sampling frequency of 4 kHz, i.e., 80 samples per/cycle with a 50 Hz operating frequency is utilized here. Initially, the data acquisition system acquires primary and secondary current data through CTs which are located on both sides of the transformer. Whenever the fault detector unit detects any faulty condition, samples of one cycle post-fault data are given to DFT for phasor estimation. The DFT calculates phasor values and harmonics of the given current signals. As per the algorithm, each phasor angle comparison unit compares either positive (P) or negative (N), or zero (Z) sequence phasor angle difference. This unit generates an output value as 1 (i.e., tripping signal) if comparative phasor values are lying within 45° or otherwise 0 (i.e., in-operative signal for further calculation). Successively, biased percentage differential protection in conjunction with the second-harmonic restrain scheme is evaluated by an algorithm to detect magnetizing inrush and internal fault conditions. The level of second harmonic components is set to greater than 10% of the fundamental component to detect the magnetizing inrush condition [42, 43]. The threshold of the biased differential scheme depends on the normal/overload condition of the transformer which is 0.05A in this case (one can alert this threshold as per the specification of the transformer). Hence, the output contact status of the phasor angle-based fault discrimination scheme and biased differential-based scheme are connected in parallel, i.e., OR logic is used.

9.3 System Modeling To validate the above algorithm on simulation analysis, PSCAD™ software is used. Here, a 3-phase transformer block is used from the library of the power system equipment. The system is developed as per Fig. 9.3 as a portion of the integrated power system. Detailed parameters related to power system simulation are illustrated in the appendix. PSCAD software is unique software to make multiple faults and transient analyses [39]. Transformer provides a taping facility on each winding to make internal as well as inter-tern faults at various percentages of winding including terminal faults. To analyze sequence components and to measure their phasor and magnitude value, the FCDFT algorithm is used. A three-phase transformer having 350 MVA, 400/220 kV, with 50 Hz rating, developed in software and linked with generators via breakers. Some special arrangement is made on the 220 kV side generator for the variation of the load angle and bus voltage. This unique setting is used to make the effect of a sudden change of load condition. Various test cases like internal/external fault, CTs saturation under external fault, inrush conditions, overloading situations, internal faults with high resistance, etc. can be attained. Different percentages of the winding faults and inclusion of the

9.3 System Modeling

Fig. 9.2 Transformer fault case identification algorithm

269

270

9 Sequential Component-Based Improvement in Percentage Biased …

Fig. 9.3 Model of power system for simulation

resistances in the fault path are formulated for the accurate analysis of the suggested scheme.

9.4 Results Exploration with Discussion Table 9.1 shows the fault and system parameters considered and numerous data that are generated for testing of the algorithm. As per Table 9.1, a total of 1080 and 360 numbers of internal faults in transformer winding and external faults are simulated, respectively in PSCAD™ software. Yet, limited cases are represented in this result demonstration division. The results for the above-mentioned cases are shown in the next substitute section. Total 1440 test cases under various parameters changing like percentage winding coverage under an internal type of fault, different fault resistance (Rf ), fault type (10 no’s), with different FIA in connection to three types of load angle implemented on 220 kV infinite bus side.

9.4.1 Internal and External Fault Conditions According to Table 9.1, a total of 1080 test cases are considered for a different type of internal fault. The result of the phasor angle comparison of sequential components and the magnitude of differential and restraining currents are described in Tables 9.2, 9.3 and 9.4. It has been observed that under internal type fault cases, the phasor angle estimation of all sequential components is estimated as lesser than 30°. For

9.4 Results Exploration with Discussion

271

Table 9.1 Considered values of numerous fault and system parameters Fault

FL (percentage winding from Terminal)

Rf (Ω)

Fault type (F type )

FIA (deg.)

Load angle δ (deg.)

Internal fault in winding (1080)

0, 25 and 50% of winding (primary side) (3)

0, 5 and 10 Three values (3)

0°, 25°, 45° and 90° Four values (4)

External fault (360)

Not applicable

0, 10 and 20 Three values (3)

L-g (3 No.) L-L (3 No.) L-L-g (3 No.) L-L-L-g (1 No.) Ten types of fault (10)

0°, 5° and 10° Three values (3)

the precise operation of the differential protection, the safe side is considered to set a threshold of 45° for internal fault case discrimination. Whereas for external fault conditions, this difference is observed as 180° as can be seen from Table 9.2. Phasor difference is 4.5° for the LL-g type of internal fault which is lower than the set threshold (45°). Various internal fault conditions are taken into account for the testing of the algorithm and analysis of the results based on its sequential components as shown in Table 9.2. Among them, one test condition is shown here in the form of the waveform in Fig. 9.4(b). Bold numbers in Table 9.2 are for the illustration point of view in Fig. 9.4(b). It is noted that all sequential component phasor angle values are lesser than the prefixed threshold value. Surrounded by all the trial cases, LL-g internal fault condition is considered for the analysis and applied at 0.2 s. The graphical representation of the simulated internal fault at 50% of the entire winding on the primary side is shown in Fig. 9.4a. The phasor of sequence components for primary and secondary side currents are represented in Fig. 9.4b. Successively for the same fault, Table 9.3 shows the calculation of biased percentage differential current for internal as well as external fault conditions. It is to be noted that during internal fault the differential current magnitude (I 1 − I 2 ) turns out to be more compared to (I 1 + I 2 )/2. On the other hand, during an external fault, the biased value of current is observed more than the differential current. Hence, Table 9.2 Phasor angle values (in degree) of SCs during internal faults SCs

Internal faults R-g

Primary

Secondary

RY

RY-g

RYB

RYB-g

–

–

–178.7

– 178.9

− 178.9

− 60.1

–

–

–

–

174.3

174.1

–

–

Z

179.7

–

62.4

P

179.1

– 178.4

N

179.7

− 66.1

Z

− 177.6

–

56.8

P

− 179.8

175.1

174.2

N

− 177.0

− 72.3

− 66.4

272

9 Sequential Component-Based Improvement in Percentage Biased …

Table 9.3 Phasor angle values (in degree) of SCs during external faults SCs

External faults R-g

Primary

Secondary

RY

RY-g

RYB

RYB-g

Z

– 179.9

–

129.2

–

–

P

179.9

– 178.0

– 179.9

– 179.8

− 179.8

N

179.9

– 128.3

– 128.8

–

–

Z

0.060

–

− 52.1

–

–

P

– 0.049

1.640

0.078

0.104

0.104

N

– 0.022

52.65

52.4

–

–

Table 9.4 Percentage restrained current (in Ampere) for various internal and external faults Type of fault 1

L-g

Internal fault

I1 2.3

I2 0.4

I 1− I 2 1.83

(I 1 + I 2 )/2 1.38

I1 −I2 (I 1 +I2 )/2

1.319545

2

L-L

4.0

0.14

3.90

2.09

1.865361

3

LL-g

2.7

0.04

2.74

1.41

1.94123

4

LLL

5.7

0.11

5.60

2.91

1.921744

5

LLL-g

1

L-g

2.9 External fault

24.6

0.09 24.1

2.84

1.52

1.873139

0.42

24.40

0.017305

2

L-L

19.5

19.2

0.33

19.42

0.017392

3

LL-g

24.7

24.2

0.42

24.5

0.017306

4

LLL

26.4

26.0

0.45

26.26

0.017284

5

LLL-g

24.7

24.3

0.42

24.57

0.017304

the proposed scheme issues a trip signal in case of any internal fault and remains stable during external fault situations. Waveforms of the primary and secondary currents are given in Fig. 9.5a for the external type of fault conditions. The phasor of sequence components for primary and secondary currents are represented in Fig. 9.5b for external fault. Consecutively for the same fault, Table 9.3 shows the calculation of the phasor angle of the primary and secondary sides under various test conditions for all sequential components. Bold numbers in Table 9.3 are used for illustration point of view in Fig. 9.5(b). From Table 9.4, it is noted that the differential current under internal fault is higher and biased current found lesser while for an external type of fault, the relation is vice-versa. Percentage-biased differential current under internal fault is more than 1 and it is found very less for an external type of fault with different fault conditions as given in Table 9.4.

9.4 Results Exploration with Discussion

273

Fig. 9.4 LL-G internal fault, a current waveform of primary and secondary sides of a transformer, b phasor angle comparison for SCs

9.4.2 High Resistance Internal Faults (HRIFs) Test conditions for the internal fault having a high amount of resistance become a unique investigation for the validation of this scheme. By inserting certain resistive values in the fault block of the PSCAD™ software, it is possible to generate an internal fault with high resistance. Here, 20 Ω resistance is placed in the internal fault path via fault block. This type of fault is taken place covering 10% of windings from the terminal of the transformer on the primary side. The current magnitude decreases due to the insertion of a fault resistance. In this type of case, the sequential component’s phasor angle falls under the 45° as desired by the algorithm. So during the high resistance internal fault, percentage-biased and phase angle difference-based

274

9 Sequential Component-Based Improvement in Percentage Biased …

Fig. 9.5 L-L external fault, a current waveform of primary and secondary side of a transformer, b phasor angle comparison for SCs

schemes confirms that it is an internal fault and the dedicated relay gives a tripping command to the circuit breaker.

9.4 Results Exploration with Discussion

275

Figure 9.6 demonstrates the waveform of currents through HRIFs and phasor comparison. Figure 9.6a reveals that the values of fault currents during HRIF are smaller than the external and internal fault magnitude (refer Figs. 9.4a and 9.5a). Table 9.5 illustrates the calculated phasor angles for the said HRIFs condition. Thus, both the parallel path of the proposed algorithm successfully operates and issues a trip signal. Bold numbers in Table 9.5 are used to illustrate the Fig. 9.6(b).

Fig. 9.6 L-G high resistance internal fault, a current waveform of primary and secondary side of a transformer, b phasor angle comparison for SCs

276

9 Sequential Component-Based Improvement in Percentage Biased …

Table 9.5 Phasor angle values (in degree) of SCs within various abnormalities

Primary

Secondary

SCs

High resistance internal fault (L-g)

CT saturation (external fault)

Load variation (10% overload)

0

− 111.9

− 110.6

126.9

1

− 116.9

− 124.0

125.4

2

− 112.0

− 113.3

126.8

0

− 112.2

0.395

− 53.07

1

− 111.74

0.3338

− 54.65

2

− 108.9

0.3352

− 53.17

9.4.3 CTs Saturation Conditions Normal internal and external faults are easy to discriminate from the power system with the help of percentage-biased differential current. CT’s saturations may cause a major impact on the unit-type protection of the transformer. Mostly, CT saturation occurs due to the heavy flow of fault currents and external burden on the secondary CTs. Percent biased protection may falsely actuate during the CTs saturation as the bias current and differential current values are increased. So, percentage-biased current gets its peak up thrust, and the relay gets energized under these abnormal conditions. Unit-type protection must be stable for any type of external faults. CTs are deliberately saturated in PSCAD™ software by inserting the additional value of resistances in the secondary of CTs as a burden. By changing different FIAs and CTs burden, different degrees of CTs saturation with distorted current signals are obtained in PSCAD™. Here, a close-in external fault is created on the bus terminals connection of the transformer nearby the CTs (Fig. 9.3). Resistances on the secondary of the CTs are adjusted as 8 Ω value as a burden. Figure 9.7a shows the secondary waveform of the CTs with its heavy saturation effect. At 0.2 s an external fault is created and due to having a higher burden, the CTs starts saturating, which may result in a slightly lower value of I 1 − I 2 . Therefore, in such states, simply circulating differentialbased protection systems would mal-operate and escort to needless tripping of a transformer. However, in such cases, the proposed percentage-biased differential protection scheme tackles the said situation as the considered differential current (I 1 − I 2 ) stays above the restrain current (I 1 + I 2 /2). Moreover, the SCs-based phasor comparison scheme works well and maintains the phasor angle difference (around 124.3°) which is higher than the situate threshold. Figure 9.7b along with Table 9.5 exemplify that the phasor angle difference of similar SCs is higher than 45° so the relay does not operate. Bold numbers in Table 9.5 are used to illustrate Fig. 9.7(b).

9.4 Results Exploration with Discussion

277

Fig. 9.7 CT saturation condition, a current waveform of primary and secondary side of a transformer, b phasor angle comparison for SCs

9.4.4 Magnetizing Inrush State Magnetizing inrush is a condition when the transformer corresponds to a large current from the supply while the load current is either zero or of nominal magnitude. The amount of inrush current depends on the nature, magnitude, and direction of the residual magnetizing flux of the core as well as the FIA of the event. On harmonics analysis, it is found that during an inrush situation, flux consists of a significant amount of higher-order harmonics out of which second harmonics are predominant. Generally, the range of the second harmonic is in the order of 15–20% of the fundamental frequency component [43]. In the proposed method, the FCDFT algorithm computes the level of second harmonic contents and decides whether it is fault current or magnetizing inrush current. Figure 9.8a shows magnetizing inrush waveform while energizing the transformer during the no-load condition. Figure 9.8b shows the level of the second harmonics component compared to the fundamental which is elevated

278

9 Sequential Component-Based Improvement in Percentage Biased …

Fig. 9.8 a Magnetizing inrush current waveform, b value comparison of fundamental and second harmonic component

than the placed limit (10%) of the proposed system. Thus, as per the flowchart, the algorithm returns to collect the next set of sampled data for further analysis.

9.4.5 Effect During Sudden Load Variation Here, as per the system diagram, the virtual load is connected to the secondary side of the transformer. This event is simulated by changing the load angle (δ°) of the generator connected to the secondary side of the transformer. 0°, 5°, and 10° values of load variation can be implemented as per Table 9.1. When a load is getting abruptly changed on the secondary side, the system parameters are suddenly violated as per the loading condition. Active and reactive powers are changed as the load varies. These changes would be reflected by the momentary variation of quantities on the relay terminals also. Moreover, the phasor angle differences of the same sequence components are observed almost out of phase during load variation as shown in

9.5 Conclusion

279

Fig. 9.9 a Current waveforms during 10% overload, b phasor angle comparison for SCs

Fig. 9.9b. So as per the algorithm, a relay is not sending any trip signal during transient load variations. Load variation effect in terms of magnitude can be easily verified in Fig. 9.9a. Bold numbers in Table 9.5 are used to illustrate Fig. 9.9(b).

9.5 Conclusion This chapter demonstrates a novel method in detail for transformer protection combining the phasor angle difference of sequence components of currents and percentage-biased differential-based defensive scheme. To discriminate internal fault from external fault/other situations both algorithms are analyzed as a prelim requirement. At the same time, the proposed scheme presents a percentage-biased differential protection scheme including the detection of magnetizing inrush current. FCDFT algorithm is used to derive the required parameters and harmonic contents by eliminating decaying DC components and noise present in current signals. Around 1440

280

9 Sequential Component-Based Improvement in Percentage Biased …

numbers of test cases are verified by different parameter settings on PSCAD™ software to check the authenticity of the proposed scheme. The application of the proposed scheme is easy for relay programming. It is observed that the relay operates within a shorter duration as the proposed algorithm depends on minimum numerical calculation. Different test cases are implemented and validated by the software base analysis and elaborated in tabular and waveform terminology. The proposed algorithm operates under any kind of internal fault including high resistance in the fault path as well as a percentage of transformer winding. Moreover, the phasor angle comparison-based scheme tries to restrain the relay operation for any kind of external fault including CT saturation and varying loading conditions. Thus, the suggested scheme can be implemented for real field protection purposes as the practicability of mal-operation is very less.

Appendix 3-Phase voltage source-1: 1. RMS voltage: 2. Frequency (f) Hz: 3. “+Ve” sequence impedance: 4. “0” sequence impedance:

400 kV 50 Hz 1.0 Ω and 85° 2.0 Ω and 85°

3-Phase transformer considerations (YY connected): 1. RatedPower: 2. Frequency (f) Hz: 3. Leakage Reactance (XL ): 4. Magnetizing Current: 5. Primary/Secondary Voltages:

350 MVA 50 Hz 0.1 pu 4% 400/220 kV

Current transformers: 1. The primary of the CT: 1200/2 2. Secondary of the CT: 2000/2

References 1. Beaty HW, Santoso S (2015) Handbook of electric power calculations, 4th edn. McGraw-Hill Education 2. Hosny A, Sood VK (2014) Transformer differential protection with phase angle difference based inrush restraint. Electr Power Syst Res 115:57–64. https://doi.org/10.1016/j.epsr.2014. 03.027

References

281

3. Patel D, Chothani N (2023) Evaluation of various dynamics on current transformer saturation with a model study on power system protection. In: Recent advances in power systems, pp 121–136 4. Babaria SJ, Patel DD, Chothani NG, Chaini PK, Patel RM, Joshi SR (2022) Influence of system parameters on current transformer saturation in power system. In: 2022 IEEE 2nd international conference on sustainable energy and future electric transportation (SeFeT), pp 1–6. https:// doi.org/10.1109/SeFeT55524.2022.9909425 5. Wagh SR, Kumar S, Sreeram V (2013) Extraction of DC component and harmonic analysis for protection of power transformer. In: 2013 IEEE 8th conference on industrial electronics and applications (ICIEA), pp 32–37. https://doi.org/10.1109/ICIEA.2013.6566336 6. Madzikanda E, Negnevitsky M (2012) A practical look at harmonics in power transformer differential protection. In: 2012 IEEE international conference on power system technology (POWERCON), pp 1–6. https://doi.org/10.1109/PowerCon.2012.6401274 7. Raichura M, Chothani N, Patel D, Mistry K (2021) Total Harmonic Distortion (THD) based discrimination of normal, inrush and fault conditions in power transformer. Renew Energy Focus 36:43–55. https://doi.org/10.1016/j.ref.2020.12.001 8. Raichura MB, Chothani NG, Patel DD, Mistry KD (2019) Identification of inrush and fault conditions in power transformer using harmonic distortion computation. In: 2019 IEEE 1st international conference on energy, systems and information processing (ICESIP), pp 1–6. https://doi.org/10.1109/ICESIP46348.2019.8938308 9. Patel D, Chothani N (2020) Adaptive digital differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 83–106 10. Patel DD, Chothani N, Mistry KD, Tailor D (2018) Adaptive algorithm for distribution transformer protection to improve smart grid stability. Int J Emerg Electr Power Syst 19(5):1–14. https://doi.org/10.1515/ijeeps-2018-0022 11. Raichura M, Chothani N, Patel D (2020) Development of an adaptive differential protection scheme for transformer during current transformer saturation and over-fluxing condition. Int Trans Electr Energy Syst 31:1–19. https://doi.org/10.1002/2050-7038.12751 12. Raichura M, Chothani N, Patel D (2020) Improved transformer differential protection by adaptive pickup setting during momentary over-fluxing condition. Adv Electr Electron Eng 18(3). https://doi.org/10.15598/aeee.v18i3.3833 13. Patel D, Chothani N, Mistry K (2018) Discrimination of inrush, internal, and external fault in power transformer using phasor angle comparison and biased differential principle. Electr Power Compon Syst 46(7):788–801. https://doi.org/10.1080/15325008.2018.1509915 14. Patel D, Chothani N (2020) Phasor angle based differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 51–81 15. Patel D, Mistry KD, Raichura MB, Chothani N (2018) Three state Kalman filter based directional protection of power transformer. In: 20th national power systems conference (NPSC), pp 1–6. https://doi.org/10.1109/NPSC.2018.8771716 16. Patel DD, Chothani NG, Mistry KD (2015) Sequence component of currents based differential protection of power transformer. In: 12th IEEE international conference electronics, energy, environment, communication, computer, control: (E3-C3), INDICON 2015, pp 1–6. https:// doi.org/10.1109/INDICON.2015.7443855 17. Chothani NG, Raichura MB, Patel DD, Mistry KD (2018) Real-time monitoring protection of power transformer to enhance smart grid reliability. In: 2018 IEEE electrical power and energy conference (EPEC), pp 1–6. https://doi.org/10.1109/EPEC.2018.8598427 18. Patel D, Chothani N (2020) Real-time monitoring and adaptive protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 173–190 19. Raichura M, Chothani NG, Patel D (2019) Real-time monitoring & adaptive protection of power transformer to enhance smart grid reliability. Electr Control Commun Eng 15(2):104–112. https://doi.org/10.2478/ecce-2019-0014 20. Hooshyar A, Afsharnia S, Sanaye-Pasand M, Ebrahimi BM (2010) A new algorithm to identify magnetizing inrush conditions based on instantaneous frequency of differential power signal. IEEE Trans Power Deliv 25(4):2223–2233. https://doi.org/10.1109/TPWRD.2010.2040844

282

9 Sequential Component-Based Improvement in Percentage Biased …

21. Guzman A, Zochol S, Benmouyal G, Altuve H (2001) A current-based solution for transformer differential protection. Part I: problem statement. IEEE Power Eng Rev 21(8):61. https://doi. org/10.1109/MPER.2001.4311564 22. Guzman A, Zocholl S, Benmouyal G, Altuve HJ (2002) A current-based solution for transformer differential protection Part II: relay description and evaluation. IEEE Power Eng Rev 22(7):60–68. https://doi.org/10.1109/MPER.2002.4312417 23. Al-Fakhri B (2002) A unique transformer protection which eliminates over-voltage, overfluxing, and CT saturation protection. In: IEEE/PES transmission and distribution conference and exhibition, vol 1, pp 142–147. https://doi.org/10.1109/TDC.2002.1178274 24. Narendra K, Fedirchuk D, Zhang N, Midence R, Perera N, Sood V (2011) Phase angle comparison and differential rate of change methods used for differential protection of busbars and transformers. In: 2011 IEEE electrical power and energy conference, pp 134–139. https://doi. org/10.1109/EPEC.2011.6070183 25. Khan UN, Sidhu TS (2014) A phase-shifting transformer protection technique based on directional comparison approach. IEEE Trans Power Deliv 29(5):2315–2323. https://doi.org/10. 1109/TPWRD.2014.2308898 26. Ahmadi M, Samet H, Ghanbari T (2015) Discrimination of internal fault from magnetising inrush current in power transformers based on sine-wave least-squares curve fitting method. IET Sci Meas Technol 9(1):73–84. https://doi.org/10.1049/iet-smt.2014.0012 27. Hamilton R (2013) Analysis of transformer inrush current and comparison of harmonic restraint methods in transformer protection. IEEE Trans Ind Appl 49(4):1890–1899. https://doi.org/10. 1109/TIA.2013.2257155 28. Jettanasen C, Ngaopitakkul A (2012) The spectrum comparison technique of DWT for discriminating between external fault and internal fault in power transformer. J Int Counc Electr Eng 2(3):302–308. https://doi.org/10.5370/JICEE.2012.2.3.302 29. Moravej Z, Abdoos AA (2012) An improved fault detection scheme for power transformer protection. Electr Power Compon Syst 40(10):1183–1207. https://doi.org/10.1080/15325008. 2012.682251 30. Al-Shaher M, Saied M (2007) Recognition and location of transformer winding faults using the input impedance. Electr Power Compon Syst 35(7):785–802. https://doi.org/10.1080/153 25000601175157 31. Tripathy M, Maheshwari RP, Verma HK (2005) Advances in transformer protection: a review. Electr Power Compon Syst 33(11):1203–1209. https://doi.org/10.1080/15325000590951618 32. Chothani NG, Patel DD, Mistry KD (2017) Support vector machine based classification of current transformer saturation phenomenon. J Green Eng River Publ 7:25–42. https://doi.org/ 10.13052/jge1904-4720.7122 33. Patel D, Chothani NG, Mistry KD, Raichura M (2018) Design and development of fault classification algorithm based on relevance vector machine for power transformer design and development of fault classification algorithm based on relevance vector machine for power transformer. IET Electr Power Appl 12(4):557–565. https://doi.org/10.1049/iet-epa.2017.0562 34. Dharmesh Patel NC. Relevance vector machine based transformer protection. In: Digital protective schemes for power transformer, 1st edn. Springer, Singapore, pp 107–131 35. Raichura M, Chothani N, Patel D (2021) Efficient CNN-XGBoost technique for classification of power transformer internal faults against various abnormal conditions. IET Gener Transm Distrib 15(5):972–985. https://doi.org/10.1049/gtd2.12073 36. Raichura MB, Chothani NG, Patel DD (2020) Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique. IET Sci Meas Technol 14(1). https://doi.org/10.1049/iet-smt.2019.0102 37. Patel D, Chothani N (2020) HE-ELM technique based transformer protection. In: Digital protective schemes for power transformer. Springer, Singapore, pp 133–172 38. Fani B, Hamedani Golshan ME, Askarian Abyaneh H (2011) Waveform feature monitoring scheme for transformer differential protection. J Zhejiang Univ Sci C 12(2):116–123. https:// doi.org/10.1631/jzus.C1010042

References

283

39. PSCAD Research Center (2005) EMTDC-transient analysis for PSCAD power system simulation. Winnipeg, MB, Canada 40. Stevenson WD (1982) Elements of power system analysis. McGraw-Hill 41. Chothani NG, Bhalja BR (2014) Development of a new bus zone identification algorithm based on phase angle comparison using sequence components of currents. Electr Power Compon Syst 42(2):215–226. https://doi.org/10.1080/15325008.2013.846441 42. van Warrington ARC (1977) Protective relays their theory and practice, vol. 2. Springer, Berlin 43. Bhalja B, Maheshwari RP, Chothani NG (2017) Protection and switchgear, 2nd edn. Oxford University Press, New Delhi

Chapter 10

Current Direction Comparison-Based Transformer Protection Using Kalman Filtering

Abstract The complexity of the power system network increases day by day as the power demand increases rapidly. Per capita power consumption is increased in the entire nation. To create comfort in life, it’s required to attain the power system stability further to run all the electrical appliances. So, the reliability of the power is directly concerned with comfort and national growth. Power is transferred in a network by a transformer just as a pumping system. In a power system, a transformer has the highest efficiency due to its static nature. However, due to the different voltage/current and turns ratio, it is considered a very complicated device. Also, the core saturation of the transformer gives the worst effect on the unit-type protective scheme. Thus, there is a need to provide an accurate protective scheme for this device. Most of the study works have utilized Fast Fourier Transform (FFT)/Discrete Fourier Transform (DFT) algorithm to capture the required signals for protection purposes. This chapter represents three-state Kalman Filtering-based analyses involving the advantages over the FFT/DFT scheme. Different scenarios of the transformer misoperation are involved with result analysis such as magnetizing inrush, CT saturations under internal and external faults, and high resistance internal fault. All the said test cases are authenticated on PSCAD™ software. After capturing data from PSCAD™ software, they are analyzed by MATLAB programming coded with the Kalman Filtering method. The proposed method is exploited to derive the phasor angle of the current and voltage signals acquired from both the primary and secondary winding of a transformer. Here, a comparative direction of the voltage and current-based analysis is carried out to discriminate all the above-said conditions. Said algorithm gives perfect tripping command for an internal fault and stays steady against external fault and all other abnormal conditions which is a prime requirement of a unit type of protection.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Chothani et al., Advancement in Power Transformer Infrastructure and Digital Protection, Studies in Infrastructure and Control, https://doi.org/10.1007/978-981-99-3870-4_10

285

286

10 Current Direction Comparison-Based Transformer Protection Using …

10.1 Basics of Kalman Filter Researchers have tried to improve in the areas of control, monitoring, and protection of power system components. Various difficulties are faced during the estimation of nonlinear states of the power structure. To improve the exactness and presentation of a method, certain state estimation perspectives are utilized. A Kalman Filter (KF) can be considered and extensively applied linearized system for nonlinear state estimation. That gives a significant reaction in a weaker nonlinear type of system, i.e., it may represent a replica of a nonlinear system [1, 2]. Kalman Filter can be considered a basic calculation tool that uses a series of information that is obtained after a few instances that contain noise and distortions. This Filter is also identified as Linear Quadratic Estimation, a logic that is utilized to measure a series of obtained data for a particular period. They include erroneous values and arithmetical disturbances and process calculation of imprecise variables [3]. It works on rectification and prediction logic and it is widely utilized in linear as well as time-invariant systems. The prediction logic needs a factual system and filter disturbance, whereas the upgraded model needs to update the predicated magnitudes. Principle and block diagrams of such Kalman filters are involved in Figs. 10.1 and 10.2. This filter uses only early two instants of the stimulus (average and covariance). Though this is a comparatively simple state demonstration, it provides numerous major practical advantages. (1) The average, as well as the covariance of an indefinite distribution, needs the preservation of certain small and steady levels of data. However, that data is enough to sustain most types of operational manners (such as preparing a validation entrance for a search area of a system). Therefore, it can be considered a winning negotiation in computational complications and representational resilience. The entire characterization of a developing miscalculation needs the attention of boundless numerous constraints. In case, if it is possible to keep the whole pdf data, that data could not be considered fruitful (i.e., the wringing of the data may cause a stubborn issue).

Fig. 10.1 Principle of Kalman filter

10.1 Basics of Kalman Filter

287

Fig. 10.2 Basic block diagram of Kalman filter

(2) The average as well as covariance (or its square roots) may be proportional. For an instant, when an erroneous quantity contains an average value known as x and covariance which is denoted as ∑x, the average and covariance of that quantity post it gone through the linear alteration. It can be said that the average, as well as covariance states, could effectively be retained conditional to proportional and quasi-linear alterations. Analogous outcomes should not pause at nonzero instants of a distribution factor. (3) Stack of average and covariance approximation may use to formulate more qualities of distribution factors, such as considerable modes. This feature of the multimode tracking technique depending on the preservation of various average and covariance approximations involves multi-speculation tracking, Gaussian filter addition, as well as Rao–Blackwellized element technique.

10.1.1 Extended Kalman Filter (EKF) A most usual utilization type of Kalman Filtering method regards to nonlinear states can be considered as an Extended Kalman Filter (EKF). Utilizing the conjecture that each and every alteration is quasi-linear, the EKF generally tries for linearization of all the nonlinear alterations and puts Jacobian matrices in linear alterations of KF formulations. It has applications in the fields of nonlinear systems that contain zero distortions. As most of the systems are nonlinear, the Extended Kalman Filter works well compared to the Kalman Filter. EKF is a second-order training algorithm, rapid convergence is likely to occur. Additionally, as EKF does not require tuning of the parameters which considerably facilitates the convergence characteristics, it becomes easier to operate. EKF could be employed for the rejection of multi-machine divergent quantities consisting of the speed of the rotor and its angle. It displays enough suitable results for

288

10 Current Direction Comparison-Based Transformer Protection Using …

large as well as small disturbance states. Though the system state elimination f (x) as well as certain measurement formulations h(x) are nonlinear functions of state quantities. Hence, the linearization and Jacobian matrix estimations are essential. This technique alters whole nonlinear alterations and finds Jacobian matrices while the estimation progress. The EKF also has certain noteworthy disadvantages. (1) it’s complex to obtain prior data of the test cases, hence estimation covariance slips and the process clamor covariance web of EKF is hard to set [4], (2) It may encounter volatility because of alterations and distorted constraints; (3) expensive estimation of Jacobian matrices; (4) A intolerant character of its states. These limitations may lead to undesired results and divergent the filter. On the other hand, when the EKF is employed in a composite system, certain intricacies may arise. One of them is the state-transition matrix, which uses an extremely complex Jacobian matrix [5]. In this case, the alteration of each certain time rate is capable to initiate distortions and may pose deviation of filtration parameters. On the other side, higher ordered Kalman Filter in such cases is found more complicated to apply. Moreover, from the stability viewpoint of nonlinear structure, an upgraded version of KF, which is an Unscented Kalman Filter (UKF), is observed more useful for the nonlinear state of stimuli [6]. Moreover, the EKF keeps the neat and computationally effective repetitive nature of the Kalman Filter, it encounters numerous severe restrictions. (1) Linearized alterations are certainly trustworthy only when the erroneous data prolongation could be well estimated using a linear equation. If it is not satisfied, the linearized estimation may be certainly worst. Moreover, it weakens the action of the system. On the other side, it induces its estimates to deviate together. Though, it is tough to verify this supposition as it relies on the alteration, the available state approximation, and the level of severity of its covariance. The setback is finely presented in various fields like the elimination of ballistic constraints of missiles [7] and computer vision. (2) Linearized transformation can be applied strictly if the Jacobian matrix is present. But, it is not possible always. Various stimuli may inconsistent, e.g., the system sample may jump linearly, for which the constraints may change rapidly, or the sensor may revisit extremely quantized sensor estimation. Whereas others have singularities, e.g., perception shelf formulation, and the system itself may be intrinsically discrete (for example, a rule-dependent method for forecasting the elusive kind of a pilot-driven flight). (3) Estimating Jacobian matrices may be a hard and erroneous procedure. The Jacobian formulas may often create several pages for opaque algebra which should be transformed into code. Again, it may increase the possibilities of human coding mistakes which may affect the result of the concluding structure in such a way that can’t be easy to recognize and restore—specifically provided the actuality that it would be hard to identify which quality outcome to be expected. Nevertheless, the ambiguous code linked to a linear alteration may or may not be true. This indicates a severe issue for the case of concurrent

10.1 Basics of Kalman Filter

289

users who should authenticate it for the application of several high-integrity structures. In short, it can be said that the Kalman Filter may be employed for a nonlinear structure if a steady group of envisaged measures may be estimated. Those measures are obtained by analyzing a former assassination using a nonlinear alteration. The process of linearization, employed in EKF, maybe mostly identified as incomplete. However, other substantial costs will be incurred in the form of derivation and estimation difficulty. Hence, there is a strong requirement for a technique that is certainly more efficient compared to linearization; however, it should not acquire the execution and/or not bear the computational expenses of other larger order filtration techniques. Recently, a novel Unscented Kalman Filter (UKF) [8] was developed to meet these needs.

10.1.2 Unscented Kalman Filter (UKF) [8] The Unscented Kalman Filter (UKF) is most likely to overcome the limitations of the process of linearization using a more direct and precise system, therefore, converting average and covariance data. The UKF is developed using instincts that are convenient for the estimation of a possible distribution factor compared with as it is estimated a random nonlinear task or alteration. A simplified mechanism is depicted in Fig. 10.2. Certain groups of dots (known as sigma dots) were selected such as its average and covariance may be x and ∑x, respectively. A nonlinear approximation should be employed for every dot, for obtaining a set of altered dots. Statistical data of these altered dots may afterward estimate to develop a formulation of the nonlinearly altered average and covariance. Though this scheme tolerates an apparent similitude for particle filters, there exists a certain basic disparity. One of them is, the sigma dots may not be located at arbitrary locations. They must be specifically selected to inhibit various characteristics (such as having specific averages and covariance). As a consequence, larger order data for the distribution factor may be fetched using predefined, fewer numbers of dots. Another disparity is; sigma dots may be emphasized such that they are contradictory to the distribution viewpoint of sample dots of a particle filtration technique. For instance, the weights of the dots should not necessarily lie in the limit [0, 1]. Even though it’s obvious simplicity, the unscented transform (UT) technique contains numerous characteristics as per Fig. 10.3. (1) As this technique uses a definite figure of so-called sigma dots, they lend to be utilized as a “black box” filtration source. For a prototype (having certainly prescribed inputs and outcomes), a predefined practice may be utilized for the estimation of the forecasted measures as required for the provided alteration. (2) The incurred expense of this technique is of a similar level as in the case of EKF. It can be noted here that the most costly process is estimating the square

290

10 Current Direction Comparison-Based Transformer Protection Using …

Fig. 10.3 The principle of the UT

root of the given matrix and the external multiplications need to calculate the covariance of the estimated sigma dots. (3) A group of sigma dots that encrypts the average and covariance truly estimates the predicted average and covariance effectively to the second order. Hence, the projected involves the second level “bias correction” expression of a shortened second level filter, however, lacking the requirement for finding several derivatives. Hence, the UKF cannot be considered similar to the use of a fundamental difference technique to estimate Jacobian. (4) The technique may utilize discrete alterations. Sigma dots can overlap a discontinuity; hence it can be estimated the impact of a discontinuity on the altered approximate. The enhanced efficiency of the UKF can easily understand by using the polar-toCartesian alteration technique. The major benefit of UKF can be counted as it would not utilize any linearized technique to estimate state possibilities and covariance. Hence, its covariance and Kalman achieve additional precise estimation. This precise achievement, leads to enhanced state estimates, at the final stage.

10.2 Work Done so Far on Transformer Protection Just as the importance of the heart in the human body, a transformer has significance in a power system infrastructure. So, it requires making more concentration on the transformer operation and protection. Now a day, continuous monitoring and protective schemes are also implemented in the field with different objectives [9–11]. There are only 10% of the faults are appearing on the power transformer out of the entire power system network. Among the transformer fault statics, approximately 70% of faults are through the transformer winding [12].

10.2 Work Done so Far on Transformer Protection

291

Normally, the unit type of protection is applied with different inrush blocking or discrimination techniques. During the saturation phenomenon, harmonics are generated, and based on this concept CT saturation and transformer core saturations (Inrush) are discriminated [13]. It is proven by many researchers that second harmonic component is superimposed on the fundamental during the transformer inrush, and its value is larger than 20% [14, 15]. Harmonic restrain and blocking techniques sometimes gives the unfortunate result as the core saturation of the CT and transformer act similarly. Because of these reasons, it is necessary to discriminate inrush and CT saturation individually in transformer protection for reliable operation. Many causes may generate the worst conditions for the protective scheme. Out of them, inrush followed by the internal fault is tested with the second derivative of the differential current successfully [16–18]. Different internal faults, as well as external faults with the consideration of CT saturations, are also required to discriminate and appropriate action will be taken as per requirement [19–21]. Relay blocking is a desire in the event of an outside transformer fault with severe CT saturation. Harmonics may be anticipated at the time of iron core saturation due to a violation of system parameters. Many schemes are incorporated by researchers on harmonic restraining, blocking, and comparison. Cancelation or restraint of even harmonics is also incorporated into the unit-type protection scheme [22]. Mostly, odd harmonic cancelation or restrain techniques are used to avoid mis-operation under core saturation conditions. The fifth harmonic is superimposed on the fundamental current signal during the over-fluxing conditions of a power transformer [20, 23, 24]. Moreover, the unit-type scheme must be capable to discriminate between the core saturation case and the internal fault with CT saturation. Under CT saturation conditions, virtual third harmonic-based theory may mis-operation for transformer unit-type protections [25]. Voltage-to-frequency (V/f ) based technique is popular for the protection of transformers against excessive fluxing situations. A combination of this V/f and harmonic-based schemes is broadly elaborated and tested successfully in [26]. Different ratio-based perditions like DC decaying and fundamental components or second harmonic and DC decaying components are also validated [27]. However, different conditions like an internal fault with CT saturation require more focus. It is also noticeable that the noise signal gives a major impact on the protective scheme which is based on the time and frequency components [28]. Total Harmonic Distortion (THD) [29, 30] based schemes are also used in the field of transformer protection. Many classifier and regression techniques like Relevance Vector Machine (RVM) [31, 32], Hierarchical Extreme Learning Machine (HE-ELM) [33, 34], Convolution Neural Network (CNN) [35], Support Vector Machine (SVM) [36] dependent techniques are presented in the field of the power system protection to discriminate all abnormal conditions from internal fault. Probabilistic Neural Network (PNN) based protection is also suggested; however, the validation time is too high for the testing. So, trip signals are generated with a delaying process [37]. Moreover, in all the classifier and regression techniques, capturing the training data is a big issue. Comparison of the phasor angle of the positive-sequence components as well as

292

10 Current Direction Comparison-Based Transformer Protection Using …

magnitude comparison-based schemes are also revealed for transformer protection [38]. Different CT saturation detection and restraining techniques are also implemented in the research field. In the power system, the effect of various system dynamics on CT is explained pointedly [39, 40]. The degree of CT saturation and its impact on protection as well as harmonic blocking under external fault is suggested with advanced techniques [41]. The Least Square Estimation (LES) technique is implemented with FCDFT to detect the fault conditions in power transformers [42]. However, limited validations are captured. Even under Half Cycle DFT, transformer protection claims 97% accuracy among all fault detections. However, there are many possibilities of CT saturation after half-cycle passing from the inception of external faults [43]. Wavelet Transform (WT) is an advanced version of DFT; however, the decomposing frequency is far above the fundamental frequency used in the DFT algorithm. A spectrum comparison-based discrimination of internal fault is suggested through the Discrete Wavelet Transform (DWT) [44]. However, due to the higher decomposition frequency relaying schemes become more complex, and the computational burden on the relaying schemes may be increased. Further, two-state Kalman Filtering is used for transformer protection to discriminate internal faults with other abnormalities within a short duration [45]. The effectiveness of the Kalman Filtering over the DFT-based algorithm is considerable. Many Kalman Filtering techniques are available with different considerations like Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). Here, the proposed technique uses three-state Kalman Filter to measure the phasor value of the voltage and current on both sides of the transformer [46]. Here, a directional comparison-based transformer protective scheme is implemented. Different test cases of the internal fault including high resistance, CT saturations, and inrush followed by faults are simulated on the considered power system. Similarly, external faults with CT saturation and load variations are also taken into account. The proposed algorithm gives correct discrimination for the internal fault and all other abnormalities. For the validation, this chapter is further organized as, the proposed technique and flowchart of the process are elaborated in Sect. 10.3. Kalman Filtering process and its mathematical formulations are described in Sect. 10.4. The phasor measurement state model is described with its advantage over DFT in Sect. 10.5. PSCAD™ simulator-based simulations with a small section of the power system are described in Sect. 10.6 to analyze the proposed algorithm. The outcome of the anticipated scheme is enlightened in Sect. 10.6 of this chapter.

10.3 Anticipated Methodology for Protection In the proposed technique, first data are acquired from the primary and secondary sides in terms of voltage and current signals. Primary phasor angle and secondary phasor angles are derived by three-state Kalman Filtering approaches. To acquire voltage and current signal, it is required to provide appropriate CTs and PTs on both

10.3 Anticipated Methodology for Protection

293

sides of a transformer. Both primary and secondary terminal’s phasor angles are compared as described in Fig. 10.4. Proposed relaying scheme provides a trip signal only after discriminating the internal fault from other situations. Here, the current direction is considered as positive (− 90° > θ < 90°, relaying region), i.e., the direction of the current inside the transformer winding is referred to as bus voltage, then the “1” signal is generated; called a high signal. On the other way, if the direction of the current flow to the outer side of the transformer winding concerning the bus or towards the bus (− 90° < θ > 90°), it generates a “low signal”, say “0”, or called a blocking signal. Case-1: (Refer to Fig. 10.4a), During the normal condition of the power system or external fault on secondary winding, the direction of the current for the primary side generates “1” (High signal) and on the other hand of a transformer means secondary of a transformer generates “0” (Low signal). Case-2: (Refer to Fig. 10.4b), It illustrates the fault scenario similar to case-1. But, in this case, an external fault is considered here on the primary side of the transformer. So, as per the current directions shown in Fig. 10.4b, the primary side generates a “0” (Low signal), and on the other hand, a transformer means the secondary of the transformer generates a “1” (High signal). Case-3: (Refer to Fig. 10.4c), It demonstrates the internal fault or abnormal conditions within the transformer zone. Under the internal fault of the power transformer, current directions at both sides of the transformer winding are the inner side concerning the bus. So, it generates the high signal “1” from both ends. During inrush, the secondary of the transform is kept open, so the directions of the currents are not comparable. That means, it generates a low signal “0”. After considering all three cases as per the above discussion, there are two types of signals generated “0” or “1” (low or high). Direction-based transformer protective scheme is depicted in Fig. 10.5 as per the proposed flowchart. The Current Transformers (CTs) and Potential Transformers (PTs) are placed on both sides of a transformer on each winding/terminal. Secondary signals of CTs are captured for the phasor measurement using three-state Kalman Filtering process. As per the differential relaying principle, for a Delta-Star (∆ − Y ) configuration of the transformer, the connections of the CTs are in the Star-Delta (Y − ∆). One cycle data are captured from the CT and PT (Current and Voltage) continuously from the power transformer (sliding window concept). A simultaneous phasor angle calculation for one cycle of like phases is done on both the primary and secondary sides of a transformer. The primary side phasor angle is designated as “θ 1 ” and the secondary side is denoted as “θ 2 ”. Both phasors are analyzed with the help of the Kalman Filtering process as per Sects. 10.4 and 10.5. Here bus voltages are taken as the reference quantity and the phasor angle of currents is considered by these reference values. On both sides of a transformer, if the captured signal fetches the high value “1”, after comparing the estimated value with the set threshold then only with the “AND” logic of the relaying scheme trip signal is generated.

294

10 Current Direction Comparison-Based Transformer Protection Using …

Fig. 10.4 a External fault on line-2, b external fault on line-1, c internal fault

10.3 Anticipated Methodology for Protection

295

Fig. 10.5 Proposed transformer protection algorithm

Specifically for the inrush conditions, the scheme gives a special effect. On the primary side of the transformer the high signal “1” is generated for the operating region of the relay. However, on the secondary side low signal, “0” (blocking the signal) is generated. So, as per the proposed algorithm with “AND” logic, it does not generate a trip signal under this type of inrush situation and it returns to fetch the next signal. One more special case is validated with a consideration of the bolted fault on the transformer bus. Due to this type of fault, enough voltages are not available at the PT location and further to the relay coils. Due to the unavailability of the reference voltage from the PT, the whole algorithm may not work properly. Under this circumstance, the proposed scheme collects the information from the storage buffer (internal memory of the controller) for one pre-cycle data. Due to this facility of the use of the buffering data from the memory, under the certain condition when the voltage signal is unavailable, the algorithm plays a specific role and gives appropriate

296

10 Current Direction Comparison-Based Transformer Protection Using …

decision. The effectiveness of the proposed algorithm has been tested for numerous fault cases with simplicity and judgment to issue trip signals or not.

10.4 Kalman Filtering Application for Phasor Computation with Its Advantage Over DFT Kalman Filtering process proves its superiority concerning DFT, anchored in reliability, compatibility, adaptive capacity, mathematical process ability and simplicity with newer transducers. Due to said reason programming of the relaying schemes become simple and it works with a lesser burden. Thus, program execution provides better results compared to DFT [45]. The process of Kalman Filtering as a state model is X k+1 = ∅k X k + Wk

(10.1)

Here, X k = State vector (at time tk ) ∅k = Transition matrix Wk = Un-correlated vector sequence (with known covariance structure). Vabc (x, s) = AV012 (x, s) Iabc (x, s) = AI012 (x, s)

(10.2)

⎡

⎤ 1 1 1 where A = ⎣ 1 a 2 a ⎦; a = 1∠120◦ 1 a a2 Under the complex frequency domain, primary and secondary voltage and current equations are as under, ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣

VPR VPY VPB IPR IPY IPB

⎤

⎡

⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎥ = [K ]⎢ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦

VSR VSY VSB ISR ISY ISB

Here, R, Y, and B denote the phase sequence P = Primary and S = Secondary of a transformer K = Transferred resistance.

⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦

(10.3)

10.4 Kalman Filtering Application for Phasor Computation with Its …

297

Symmetrical components are thus obtained as 

V s012 I s012





U −Z T (s) = 0 U



V p012 I p012

(10.4)

Here, 0, 1, and 2 refer, to “Zero”, “+Ve” and “−Ve” sequence components successively. Inversion process as under, f (t) =

1 2π

Ω F(α + j ω)(σ (ω) exp((α + j ω)t)dω

(10.5)

−Ω

With discrete sample at 3 ∆ω , α ± j ∆ω, etc . . . 2 2  sin ωπ Ω And sigma factor σ (ω) = ωπ

s=α± j

Ω

where α = Convergence parameter ω = Step and ∆ω = Step length Ω = Range. It is also possible to analyze voltage and current signals with the help of a two-state Kalman Filter with the required details. However, due to the DC decaying component effect on current, three-state Kalman Filter is necessary. It offers a lesser burden for the mathematical morphology process and thus achieves a higher speed of operation. The process steps of Kalman filtering are as below: (1) State equation ⎡

⎤ ⎡ ⎤⎡ ⎤ ⎡ ⎤ X 1k+1 10 0 X 1k 0 ⎣ X 2k+1 ⎦ = ⎣ 0 1 0 ⎦⎣ X 2k ⎦ + ⎣ 0 ⎦ X 3k+1 X 3k 0 0 e−β∆t Wk

(10.6)

(2) Equation for measurement ⎡ ⎤  X 1k Z k = cos ω0 k∆t − sin ω0 k∆t 1 ⎣ X 2k ⎦ + Vk X 3k

(3) The initial covariance matrix

(10.7)

298

10 Current Direction Comparison-Based Transformer Protection Using …

⎡

⎤ σi2 0 0 ⎣ 0 σ2 0 ⎦ i 0 0 σi2 Here, the benefits of Kalman Filtering concerning DFT are particularized as under [46]: • Kalman Filtering process is stable against the noise signal due to its autocorrelation function. • Convergence is very fast compared to DFT for the estimation of the voltage and current phasor quantities. • Converge error is very lesser concerning DFT. There is only a 0.2% error for the half cycle to extract the post-fault signal and 1% error to extract the steady state after the full cycle, whereas DFT revealed a 5% error for a full cycle. • Computational load is lesser for analysis. • Possibility to store offline subtracted previous covariance records to reprocess data from storage memory.

10.5 Modeling of Power System In recent times, most of the digital protective schemes are validated by FFT/DFT algorithms, however, there are some limitations in comparison to the Kalman Filtering process as discussed in Sect. 10.4. Here, a novel scheme is projected based on threestate Kalman Filtering process to capture the magnitude and angle of acquired voltage and current signals. Later, the direction of the current using the phasor angle difference between these quantities is estimated on both sides of the transformer. Here, the direction is analyzed concerning the respective side bus voltage. The proposed technique and algorithm are validated on MATLAB software; however, data are generated on PSCAD™ software. A part of the power system is developed as per Fig. 10.6 to validate the scheme. A power system is developed by connecting two generators at the end of Line-1 and Line-2 interconnected by a power transformer. A power transformer has a threephase rating, ∆ − Yn connection, 100 MVA, 400/230 kV, 50 Hz. A 100 MVA rating generator connected with a 50 km transmission line (Line-1) with 400 kV, 50 Hz rating. The second generator has 100 MVA, 230 kV, 50 Hz rating connected to the transformer via bus and transmission line-2 of 30 km. At the terminal of the second generator, a three-phase load having a rating of 100 MW and 25MVAR is connected in the considered system. Detailed ratings of all equipment of the line diagram are elaborated in Appendix. Both sides of the terminal of the transformer are equipped with appropriate rating CT and PT to capture the voltage and current signals. In PSCAD™ software, to measure the perfect CT core saturation characteristic, the JA-model of the CT is used [47]. As shown in Fig. 10.6, the scaled-down value of voltage and current from

10.6 Examination of Outcome

299

Fig. 10.6 System diagram

bus PTs and the line CTs of all the phases are given to the proposed relaying scheme to validate the algorithm.

10.6 Examination of Outcome Three test cases are validated here (1) Inrush (2) Internal fault (3) External fault. Here, different sub-faults are also validated with varying system conditions like Internal/ External faults with CT saturation and with higher resistance in the fault path. Numerous fault cases such as L-g, L-L, L-L-g, and L-L-L, as well as inrush cases, are generated for validation; however, in the result, the analysis only L-L-g case along with inrush is explained in subsequent sections.

10.6.1 Magnetic Inrush Normally inrush is generated in the transformer during its no-load energization. This means under secondary open-circuited conditions, the primary transformer draws a large current at the initial switching instant. Current waveforms of the primary and secondary winding of a transformer are represented in Fig. 10.7a. Phasor differences between the primary and secondary currents are as per Fig. 10.7b. Phasor differences are calculated here concerning the bus voltage (means bus PT voltage). Table 10.1 demonstrates the obtained values of the different phasors for the various fault and abnormal conditions simulated on a system as given in Fig. 10.6. As per

300

10 Current Direction Comparison-Based Transformer Protection Using …

Fig. 10.7 Magnetizing inrush a primary and secondary inrush current waveform, b primary and secondary current phasor

appropriate of the phasor value generated signal is depicted here high as “1” and low as “0” in Table 10.1. All tests conducted on the PSCAD™ software and after capturing the signal from software data signals are validated on MATLAB software through three-state Kalman Filtering process. Table 10.1, denotes a value of the current phasor difference concerning the bus voltage for both primary and secondary windings of the transformer. It is to be noted from Table 10.1 that during inrush and any kind of external faults, the outcome of the algorithm is dissimilar on both sides of the signal analysis. That means the output status of logic blocks is “1” and/or “0”, so trip signals are not generated for these conditions. On the other hand, for any kind of internal fault, the output of log blocks is similar at least for any one phase on both sides of signal analysis. That means signals are generated simultaneously with high (“1”) status, thus algorithm initiates the trip command as shown in Table 10.1.

10.6 Examination of Outcome

301

Table 10.1 Algorithm operations for various test conditions S. No.

1.1

2.1

2.2

2.3

2.4

2.5

3.1

Various test conditions

Inrush

Internal fault (L-G)

Internal fault (L-L-G)

Internal fault (L-L-L-G)

High Resi. internal fault (L-G)

CT saturation in internal fault (L-G)

External fault (L-G)

Primary

Secondary

Final signal

High (1)/ low (0)

Θ1

High (1)/ low (0)

Θ2

Trip (1)/ block (0)

R

1

85.55

0

− 59.98

0

Y

1

− 34.84

0

− 180

B

0

− 156.0

1

59.1

R

1

− 83.19

1

− 88.29

Y

1

− 88.42

0

− 173.1

B

1

− 89.19

0

90.77

R

1

− 69.97

1

− 59.88

Y

0

140.3

0

134.8

B

0

− 142.2

0

104.8

R

1

− 84.9 1

− 59.02

Y

0

155

1

− 59.02

B

1

34.22

1

59.65

R

1

− 22.61

1

2.635

Y

0

− 142.6

0

− 117.4

B

0

96.49

0

121.7

R

1

− 83.18

1

− 87.74

Y

1

− 87.86

0

− 175.5

B

1

− 88.58

0

91.37

R

0

90.5

1

− 89.49

Y

1

− 59.57

0

− 182.8

1

1

1

1

1

0

(continued)

302

10 Current Direction Comparison-Based Transformer Protection Using …

Table 10.1 (continued) S. No.

3.2

3.3

3.4

3.5

Various test conditions

External fault (L-L-G)

External fault (L-L-L-G)

High Resi. external fault (L-G)

CT saturation in external fault (L-L-G)

Primary

Secondary

Final signal

High (1)/ low (0)

Θ1

High (1)/ low (0)

Θ2

B

0

− 90.36

1

89.55

R

0

115

1

− 60.05

Y

1

− 55.16

0

134.6

B

0

− 147.3

0

104.7

R

0

90.73

1

− 59.07

Y

1

− 29.39

0

− 179.6

B

0

− 150.6

1

59.56

R

0

153.7

1

3.667

Y

1

33.69

0

− 116.4

B

1

− 87.22

0

122.8

R

1

115.9

0

− 10.09

Y

0

− 54.35

0

135.3

B

1

− 147.1

0

105.7

Trip (1)/ block (0)

0

0

0

0

10.6.2 Internal Fault (L-L-G) In the proposed protective scheme, if the phasor difference of currents between the primary and secondary side is higher than 90°, a low “0” signal is generated, and if it is lesser than 90° then a high “1” signal is generated. If the status of the generated signal is high “1” from both sides process then the relay provides a trip signal using “AND” logic. In short, when the directions of current on primary and secondary windings become opposite to each other, the relay activates the tripping mechanism. The presented protective scheme is also applicable in an interconnected power system where the power is injected on both sides of the transformer. The Waveform of the current on the primary and secondary side of the transformer during internal fault is elaborated in Fig. 10.8a. From the first visualization, it is seen that the primary and secondary currents of the faulted phase are in-phase under

10.6 Examination of Outcome

303

Fig. 10.8 Internal fault a primary and secondary current waveform, b primary and secondary current phasor

internal fault. As per the algorithm, phasor vectors are shown in Fig. 10.8b for the internal fault. Phasor vectors of the primary and secondary sides of currents having angle differences are lesser than 90°. So, as per the suggested logic in the coding of an algorithm, the trip signal is conveyed to CB.

10.6.3 External Fault (LL-G) When an external fault appears in the power system, unit-type protection of special equipment must not be affected during this situation. When a fault occurs outer side of the zone of the relaying scheme (decides by CT locations), this fault is considered an external fault. Under the external types of fault, the direction of the fault current remains the same in the CTs located on both sides of the transformer. So, the algorithm does not satisfy the logic sets in it, and the trip command is blocked. Further, the algorithm steps return to fetch newer data samples for analysis. Result validation in

304

10 Current Direction Comparison-Based Transformer Protection Using …

terms of the current signals and phasor comparisons are depicted in Fig. 10.9 and phasor values for such external fault cases are noted in Table 10.1. LL-G fault is simulated on line-1 in the PSCAD™ software and fault current magnitudes are shown in Fig. 10.9a. Though the fault current signals are of the same magnitude but opposite to each other as shown in Fig. 10.9a. Figure 10.9b shows the phase angle of the primary and secondary currents. Mostly, all the phasor vectors are settled approximately at 180° out-of-phase difference. So, as per the suggested algorithm, a trip signal is not generated.

Fig. 10.9 External fault a primary and secondary current waveform, b primary and secondary current phasor

10.7 External Fault with CT Saturation (LL-G External Fault with CT …

305

10.7 External Fault with CT Saturation (LL-G External Fault with CT Saturation) Figure 10.10a shows the effect of the CT saturation after the first cycle from the inception of external fault on line-2. Figure 10.10b shows the phasor vectors of currents during external fault considering CT saturation. During CT saturation, the phase angle difference between opposite side currents of the transformer is slightly trimmed down. But, this difference remains above 90° and less than 180°. Thus, the algorithm senses this angle difference greater than 90° and decides to block the CB actuating command.

Fig. 10.10 CT saturation in external fault a primary and secondary current waveform, b primary and secondary current phasor

306

10 Current Direction Comparison-Based Transformer Protection Using …

10.8 Conclusion Protection of the transformer used in the interconnected power system demands a distinguished protection technique. The current signal’s directional comparisonbased technique with the implementation of three-state Kalman Filtering process is reported here in this chapter. The proposed scheme is simulated in PSCAD™ software and validated on MATLAB software. Different internal and external fault conditions with variations in system parameters are examined and the outcome is depicted in tabular form. Some of the results are shown in terms of a waveform for more clarity and vision. A worst case of CT saturation under external fault is validated to expand the vision of the algorithm. It is visible from the tabular data and figures of the result section that during internal fault conditions, the direction of the currents is opposite at both ends of the transformer. That means phasor vectors of currents of both sides fall within a cone of 90° (ideally 0°). Contradictory, for all abnormal conditions other than internal faults, the direction of faulted/normal currents at both ends is always in parallel. That means phasor vectors of currents of both sides reside above 90° and practically below 180° (ideally 180° for external fault). The proposed algorithm is also tested for worst cases of external fault with CT saturation and bolted fault on the transformer. There is a possibility of current signal dissertation in CT saturation and drastic voltage deep at the bus due to bolted fault. However, the suggested algorithm successfully operates and takes appropriate action as its operation depends on phase angle and not on the magnitude of sensing quantity. Thus, it can speedily discriminate the transformer’s internal faults against all kinds of outside abnormalities.

Appendix

Equipment

General details

Generator G1 Generator G2

100 MVA, 400 kV, 50 Hz, 30° phase “+Ve” sequence impedance = 0.2 100 MVA, 230 kV, 50 Hz, 0° phase “+Ve” sequence phase angle = 85° “0” sequence impedance = 0.05 “0” sequence phase angle = 85°

Transformer

100 MVA, 400/230 kV, 50 Hz, ∆ − Yn connected

CT data

CTp = 150/5 A and CTs = 300/5 A

Prolonged details

Leakage reactance (changed) = 0.1–0.0001 pu Magnetizing current = 0.4% Saturation placing on winding = first (primary) Knee voltage = 1.25 pu Air core reactance = 0.2 (PU) Release flux clipping (time) = 0.1 (s) (continued)

References

307

(continued) Equipment

General details

Transmission line

R = 0.162 × 10–5 Ω/m, X L = 0.124 × 10–2 Ω/m, X C = 374.34 MΩ/m

Prolonged details

Load

Active power (P) = 100 MW, Reactive power (Q) = 25 MVAR

References 1. Auger F, Hilairet M, Guerrero JM, Monmasson E, Orlowska-Kowalska T, Katsura S (2013) Industrial applications of the Kalman filter: a review. IEEE Trans Ind Electron 60(12):5458– 5471. https://doi.org/10.1109/TIE.2012.2236994 2. Kalman Filtering (1986) IEEE Control Syst Mag 6(1):46–47. https://doi.org/10.1109/MCS. 1986.1105051 3. Antonio Pérez-Ortiz J, Calera-Rubio J, Forcada ML (2004) Processing symbolic sequences by recurrent neural networks trained by kalman filter-based algorithms [Online]. Available: https:/ /www.academia.edu/download/30838389 4. Grewal MS, Andrews AP (2001) Kalman filtering: theory and practice using MATLAB California State University at Fullerton, vol 5, no 5 5. Holley R (1973) Introduction to stochastic processes. IEEE Trans Syst Man Cybern 3(5):533. https://doi.org/10.1109/TSMC.1973.4309295 6. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Fluids Eng Trans ASME 82(1):35–45. https://doi.org/10.1115/1.3662552 7. Lerro D, Bar-Shalom Y (1993) Tracking with debiased consistent converted measurements versus EKF. IEEE Trans Aerosp Electron Syst 29(3):1015–1022. https://doi.org/10.1109/7. 220948 8. Julier SJ, Uhlmann JK (2004) Unscented filtering and nonlinear estimation. Proc IEEE 92(3):401–422. https://doi.org/10.1109/JPROC.2003.823141 9. Chothani NG, Raichura MB, Patel DD, Mistry KD (2018) Real-time monitoring protection of power transformer to enhance smart grid reliability. In: 2018 IEEE electrical power and energy conference (EPEC), pp 1–6. https://doi.org/10.1109/EPEC.2018.8598427 10. Patel D, Chothani N (2020) Real-time monitoring and adaptive protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 173–190 11. Raichura M, Chothani NG, Patel D (2019) Real-time monitoring & adaptive protection of power transformer to enhance smart grid reliability. Electr Control Commun Eng 15(2):104–112. https://doi.org/10.2478/ecce-2019-0014 12. Blackburn JL, Domin TJ (2015) Protective relaying: principles and applications, 3rd edn. CRC Press, Tailor and Francies group, New York, London 13. Paithankar YG, Bhide SR (2010) Fundamentals of power system protection. PHI Learning Pvt. Ltd., New Delhi 14. Bhalja B, Maheshwari RP, Chothani NG (2017) Protection and Switchgear, 2nd edn. Oxford University Press, New Delhi 15. Patel DD, Mistry KD, Chothani NG (2015) A novel approach to transformer differential protection using sequence component based algorithm. J CPRI 11(3):517–528 16. Patel DD, Mistry KD, Chothani NG (2017) Transformer inrush/internal fault identification based on average angle of second order derivative of current. In: Asia-Pacific power and energy engineering conference, APPEEC, pp 1–6. https://doi.org/10.1109/APPEEC.2017.8309017 17. Patel D, Chothani N, Mistry K (2018) Discrimination of inrush, internal, and external fault in power transformer using phasor angle comparison and biased differential principle. Electr Power Compon Syst 46(7):788–801. https://doi.org/10.1080/15325008.2018.1509915

308

10 Current Direction Comparison-Based Transformer Protection Using …

18. Patel D, Chothani N (2020) Phasor angle based differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 51–81 19. Patel DD, Chothani N, Mistry KD, Tailor D (2018) Adaptive algorithm for distribution transformer protection to improve smart grid stability. Int J Emerg Electr Power Syst 19(5):1–14. https://doi.org/10.1515/ijeeps-2018-0022 20. Raichura M, Chothani N, Patel D (2020) Development of an adaptive differential protection scheme for transformer during current transformer saturation and over-fluxing condition. Int Trans Electr Energy Syst 31:1–19. https://doi.org/10.1002/2050-7038.12751 21. Patel D, Chothani N (2020) Adaptive digital differential protection of power transformer. In: Digital protective schemes for power transformer. Springer, Singapore, pp 83–106 22. Madzikanda E, Negnevitsky M (2012) A practical look at harmonics in power transformer differential protection. In: 2012 IEEE international conference on power system technology (POWERCON), pp 1–6. https://doi.org/10.1109/PowerCon.2012.6401274 23. Guzman A, Zocholl S, Benmouyal G, Altuve HJ (2002) A current-based solution for transformer differential protection part II: relay description and evaluation. IEEE Power Eng Rev 22(7):60–68. https://doi.org/10.1109/MPER.2002.4312417 24. Raichura M, Chothani N, Patel D (2020) Improved transformer differential protection by adaptive pickup setting during momentary over-fluxing condition. Adv Electr Electron Eng 18(3). https://doi.org/10.15598/aeee.v18i3.3833 25. Chen D, Chen W, Yin X, Zhang Z, Hu Y (2002) The analysis of operation characteristic of transformer differential protection based on virtual third harmonic theory. In: Proceedings of international conference on power system technology, vol 2, pp 720–722, https://doi.org/10. 1109/ICPST.2002.1047492 26. Mozina CJ (2006) Protection and commissioning of multifunction digital transformer relays at medium voltage industrial facilities. In: 2006 IEEE industrial and commercial power systems technical conference—conference record, pp 1–11. https://doi.org/10.1109/ICPS.2006.167 7279 27. Al-Othman AK, El-Naggar KM (2009) A new digital dynamic algorithm for detection of magnetizing inrush current in transformers. Electr Power Compon Syst 37(4):355–372. https:/ /doi.org/10.1080/15325000802548699 28. Moravej Z, Abdoos AA, Sanaye-Pasand M (2013) Power transformer protection scheme based on time-frequency analysis. Int Trans Electr Energy Syst 23(4):473–493. https://doi.org/10. 1002/etep.671 29. Raichura MB, Chothani NG, Patel DD, Mistry KD (2019) Identification of inrush and fault conditions in power transformer using harmonic distortion computation. In: 2019 IEEE 1st international conference on energy, systems and information processing (ICESIP), pp 1–6. https://doi.org/10.1109/ICESIP46348.2019.8938308 30. Raichura M, Chothani N, Patel D, Mistry K (2021) Total Harmonic Distortion (THD) based discrimination of normal, inrush and fault conditions in power transformer. Renew Energy Focus 36:43–55. https://doi.org/10.1016/j.ref.2020.12.001 31. Patel D, Chothani NG, Mistry KD, Raichura M (2018) Design and development of fault classification algorithm based on relevance vector machine for power transformer design and development of fault classification algorithm based on relevance vector machine for power transformer. IET Electr Power Appl 12(4):557–565. https://doi.org/10.1049/iet-epa.2017.0562 32. Dharmesh Patel NC, Relevance vector machine based transformer protection. In: Digital protective schemes for power transformer, 1st edn. Springer, Singapore, pp 107–131 33. Raichura MB, Chothani NG, Patel DD (2020) Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique. IET Sci Meas Technol 14(1). https://doi.org/10.1049/iet-smt.2019.0102 34. Patel D, Chothani N (2020) HE-ELM technique based transformer protection. In: Digital protective schemes for power transformer. Springer, Singapore, pp 133–172 35. Raichura M, Chothani N, Patel D (2021) Efficient CNN-XGBoost technique for classification of power transformer internal faults against various abnormal conditions. IET Gener Transm Distrib 15(5):972–985. https://doi.org/10.1049/gtd2.12073

References

309

36. Chothani NG, Patel DD, Mistry KD (2017) Support vector machine based classification of current transformer saturation phenomenon. J Green Eng River Publ 7:25–42. https://doi.org/ 10.13052/jge1904-4720.7122 37. Moravej Z, Abdoos AA, Sanaye-Pasand M (2010) A new approach based on s-transform for discrimination and classification of inrush current from internal fault currents using probabilistic neural network. Electr Power Components Syst 38(10):1194–1210. https://doi.org/10. 1080/15325001003649476 38. Patel DD, Chothani NG, Mistry KD (2015) Sequence component of currents based differential protection of power transformer. In: 12th IEEE international conference electronics, energy, environment, communication, computer, control (E3-C3), INDICON 2015, pp 1–6. https://doi. org/10.1109/INDICON.2015.7443855 39. Patel D, Chothani N (2023) Evaluation of various dynamics on current transformer saturation with a model study on power system protection. In: Recent advances in power systems, pp 121–136 40. Babaria SJ, Patel DD, Chothani NG, Chaini PK, Patel RM, Joshi SR (2022) Influence of system parameters on current transformer saturation in power system. In: 2022 IEEE 2nd international conference on sustainable energy and future electric transportation (SeFeT), pp 1–6. https:// doi.org/10.1109/SeFeT55524.2022.9909425 41. Stanbury M, Djekic Z (2015) The impact of current transformer saturation on transformer differential protection. IEEE Trans Power Deliv. https://doi.org/10.1109/TPWRD.2014.237 2794 42. Abniki H, Sanaye-Pasand M (2014) A novel technique for internal fault detection of power transformers based on moving windows. Int Trans Electr Energy Syst 24(9):1263–1278. https:/ /doi.org/10.1002/etep.1777 43. Khalkhali B, Sadeh J (2015) Transformer differential protection by online core modeling and orthogonal polynomials. IEEE Trans Power Deliv 30(5):2146–2153. https://doi.org/10.1109/ TPWRD.2014.2380452 44. Jettanasen C, Ngaopitakkul A (2012) The spectrum comparison technique of DWT for discriminating between external fault and internal fault in power transformer. J Int Counc Electr Eng 2(3):302–308. https://doi.org/10.5370/JICEE.2012.2.3.302 45. Girgis AA, Hart DG, Chang WB (1991) An adaptive scheme for digital protection of power transformers. In: [Proceedings] conference papers 1991 power industry computer application conference, pp 2–9, https://doi.org/10.1109/PICA.1991.160643 46. Girgis AA, Brown RG (1981) Application of Kalman filtering in computer relaying. In: Engineering News-Record, Ph.D. Thesis, IOWA State University, Ann Arbor, MI 48106, pp 1–218. https://doi.org/10.1109/mper.1981.5511703 47. PSCAD Research Center (2005) EMTDC-transient analysis for PSCAD power system simulation. Winnipeg, MB, Canada