Power System Fault Diagnosis
A Wide Area Measurement Based Intelligent Approach
- 1st Edition - January 14, 2022
- Imprint: Elsevier
- Authors: Md Shafiullah, M. A. Abido, A. H. Al-Mohammed
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 8 8 4 2 9 - 7
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 8 8 4 3 0 - 3
Power System Fault Diagnosis: A Wide Area Measurement Based Intelligent Approach is a comprehensive overview of the growing interests in efficient diagnosis of power system faults… Read more
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Request a sales quotePower System Fault Diagnosis: A Wide Area Measurement Based Intelligent Approach is a comprehensive overview of the growing interests in efficient diagnosis of power system faults to reduce outage duration and revenue losses by expediting the restoration process.
This book illustrates intelligent fault diagnosis schemes for power system networks, at both transmission and distribution levels, using data acquired from phasor measurement units. It presents the power grid modeling, fault modeling, feature extraction processes, and various fault diagnosis techniques, including artificial intelligence techniques, in steps. The book also incorporates uncertainty associated with line parameters, fault information (resistance and inception angle), load demand, renewable energy generation, and measurement noises.
This book illustrates intelligent fault diagnosis schemes for power system networks, at both transmission and distribution levels, using data acquired from phasor measurement units. It presents the power grid modeling, fault modeling, feature extraction processes, and various fault diagnosis techniques, including artificial intelligence techniques, in steps. The book also incorporates uncertainty associated with line parameters, fault information (resistance and inception angle), load demand, renewable energy generation, and measurement noises.
- Provides step-by-step modeling of power system networks (distribution and transmission) and faults in MATLAB/SIMULINK and real-time digital simulator (RTDS) platforms
- Presents feature extraction processes using advanced signal processing techniques (discrete wavelet and Stockwell transforms) and an easy-to-understand optimal feature selection method
- Illustrates comprehensive results in the graphical and tabular formats that can be easily reproduced by beginners
- Highlights various utility practices for fault location in transmission networks, distribution systems, and underground cables.
Postgraduate students of electrical power and energy systems. Professional researchers and academic practitioners. Industrial (power system) engineers. Power system and renewable energy research centers. Electric power utility companies. Professional and technical societies (IEEE, IET, and CIGRE)
- Cover Image
- Title Page
- Copyright
- Table of Contents
- About the authors
- Acknowledgments
- Chapter 1 Introduction
- Abstract
- 1.1 Introduction
- 1.2 Electric power system fault diagnosis importance
- 1.3 Electric power system fault diagnosis techniques
- 1.4 Wide area measurement system and phasor measurement units
- 1.5 Book organization
- 1.6 Summary
- References
- Chapter 2 Metaheuristic optimization techniques
- Abstract
- 2.1 Introduction
- 2.2 Classical optimization techniques
- 2.3 Metaheuristic techniques
- 2.4 Summary
- References
- Chapter 3 Artificial intelligence techniques
- Abstract
- 3.1 Introduction
- 3.2 Artificial intelligence techniques
- 3.3 Hybrid, ensemble, and other artificial intelligence techniques
- 3.4 Summary
- References
- Chapter 4 Advanced signal processing techniques for feature extraction
- Abstract
- 4.1 Introduction
- 4.2 Signal processing techniques
- 4.3 Wavelet transform
- 4.4 Feature extraction illustration
- 4.5 Summary
- References
- Chapter 5 Improved optimal phasor measurement unit placement formulation for power system observability
- Abstract
- 5.1 Introduction
- 5.2 Optimal phasor measurement unit placement formulation for power system observability
- 5.3 Network observability and measurement redundancy illustration
- 5.4 Transmission network observability
- 5.5 Distribution network observability
- 5.6 Summary
- References
- Chapter 6 Transmission line parameter and system Thevenin equivalent identification
- Abstract
- 6.1 Introduction
- 6.2 Transmission line parameter identification
- 6.3 Thevenin equivalent identification
- 6.4 Simulation results
- 6.5 Summary
- References
- Chapter 7 Fault diagnosis in two-terminal power transmission lines
- Abstract
- 7.1 Introduction
- 7.2 Fault location algorithms
- 7.3 Simulation results
- 7.4 Summary
- References
- Chapter 8 Fault diagnosis in three-terminal power transmission lines
- Abstract
- 8.1 Introduction
- 8.2 Parameter estimation of a three-terminal line
- 8.3 Adaptive fault location algorithm for three-terminal transmission line
- 8.4 Simulation results
- 8.5 Summary
- References
- Chapter 9 Fault diagnosis in series compensated power transmission lines
- Abstract
- 9.1 Introduction
- 9.2 Series capacitor locations
- 9.3 Series capacitor schemes
- 9.4 Phasor measurement unit-based parameter calculation of series-compensated line
- 9.5 Fault location algorithm description
- 9.6 Simulation results
- 9.7 Summary
- References
- Chapter 10 Intelligent fault diagnosis technique for distribution grid
- Abstract
- 10.1 Introduction
- 10.2 Four-node test distribution feeder modeling
- 10.3 Intelligent fault diagnosis approach
- 10.4 Fault diagnosis results
- 10.5 Optimized machine learning tools for fault location
- 10.6 Fault diagnosis under unbalanced loading condition
- 10.7 Summary
- References
- Chapter 11 Smart grid fault diagnosis under load and renewable energy uncertainty
- Abstract
- 11.1 Introduction
- 11.2 IEEE 13-node test distribution feeder modeling
- 11.3 Load and renewable energy uncertainty modeling
- 11.4 Fault modeling and feature extraction
- 11.5 Fault diagnosis results and discussions
- 11.6 Developed intelligent fault diagnosis scheme validation
- 11.7 Summary
- References
- Chapter 12 Utility practices on fault location
- Abstract
- 12.1 Introduction
- 12.2 Fault location methods
- 12.3 Local/device fault location solutions
- 12.4 Commercially available fault location solutions
- 12.5 Fault location detection on tapped transmission lines
- 12.6 Overview of fault location in distribution systems
- 12.7 Distribution management system-based fault location
- 12.8 Advanced fault location approaches in distribution systems
- 12.9 Examples of utility implementations
- 12.10 Artificial intelligence deployment for fault location application
- 12.11 Underground cable fault location
- 12.12 Summary
- References
- Appendices
- A.1 Software and hardware tools
- A.2 Statisctical quantities
- A.3 IEEE 13-node test distribution feeder data
- References
- Index
- Edition: 1
- Published: January 14, 2022
- Imprint: Elsevier
- No. of pages: 428
- Language: English
- Paperback ISBN: 9780323884297
- eBook ISBN: 9780323884303
MS
Md Shafiullah
Dr. Md Shafiullah is currently working as a faculty member in the Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) at King Fahd University of Petroleum & Minerals (KFUPM). He received a Ph.D. in Electrical Engineering (Electrical Power & Energy Systems) from KFUPM in 2018. Prior to that, he received the B.Sc. and M.Sc. degrees in Electrical & Electronic Engineering (EEE) from Bangladesh University of Engineering & Technology (BUET) in 2009 and 2013, respectively. He demonstrated his research contributions in 70+ scientific articles (peer-reviewed journals, international conference proceedings, and book chapters). His research interest includes power system fault diagnosis, grid integration of renewable energy resources, power system stability and quality analysis, and machine learning techniques. He received the best research paper awards in two different IEEE flagship conferences (ICEEICT 2014 in Bangladesh and CAIDA 2021 in Saudi Arabia).
Affiliations and expertise
King Fahd University of Petroleum and Minerals, Saudi ArabiaMA
M. A. Abido
Dr. M. A. Abido received his B.Sc. and M.Sc. degrees in Electrical Engineering (EE) from Menoufiya University, Egypt, in 1985 and 1989, respectively, and Ph.D. from King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, in 1997. He is currently serving at the EE Department of KFUPM as University Distinguished Professor. He’s also a Senior Researcher at K∙A∙CARE Energy Research & Innovation Center and Interdisciplinary Research Center for Renewable Energy and Power Systems, Dhahran, Saudi Arabia. His research interests are power system control and renewable energy resources integration. Dr. Abido is the recipient of KFUPM Excellence in Research Award, 2002, 2007, and 2012, KFUPM Best Project Award, 2007 and 2010, First Prize Paper Award of the IEEE Industry Applications Society, 2003, Abdel-Hamid Shoman Prize, 2005, Almarai Prize for Scientific Innovation 2017-2018, Saudi Arabia, 2018, and Khalifa Award for Higher Education 2017-2018, Abu Dhabi, UAE, 2018.
Affiliations and expertise
King Fahd University of Petroleum and Minerals, Saudi ArabiaAA
A. H. Al-Mohammed
Dr. Ali H. Al-Mohammed received his B.Sc. degree (Honors with first class) in electrical engineering from King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia, in 1994 and the M.Sc. and Ph.D. degrees from the same university in 1999 and 2013, respectively. Dr. Al-Mohammed has been serving the Saudi Electricity Company (SEC) for more than 27 years in engineering, design, and management of various HV and EHV transmission projects, including substations, overhead transmission lines, underground cables, and smart grid projects. His research interests include power system planning, fault location, asset optimization, substation engineering, phasor measurement units (PMU) applications, and power system protection.
Affiliations and expertise
Manager, E&D-EOA, National Grid SA, a subsidiary of the Saudi Electricity Company, Dammam, Saudi ArabiaRead Power System Fault Diagnosis on ScienceDirect