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 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.

Autorentext
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). 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.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.

Inhalt

1. Introduction 2. Metaheuristic Optimization Techniques 3. Artificial Intelligence Techniques 4. Advanced Signal Processing Techniques for Feature Extraction 5. Improved Optimal PMU Placement Formulation for Power System Observability 6. Transmission Line Parameter and System Thevenin Equivalent Identification 7. Fault Diagnosis in Two-Terminal Power Transmission Lines 8. Fault Diagnosis in Three-Terminal Power Transmission Lines 9. Fault Diagnosis in Series Compensated Power Transmission Lines 10. Intelligent Fault Diagnosis Technique for Distribution Grid 11. Smart Grid Fault Diagnosis under Load and Renewable Energy Uncertainty 12. Utility Practices on Power System Fault Diagnosis 13. Appendix