Overview
- Introduces credible and efficient modeling technologies for wind turbines and power data
- Studies both the model based algorithms and data driven algorithms
- Provides valuable guidance for holistic power system fault diagnosis
Part of the book series: Engineering Applications of Computational Methods (EACM, volume 9)
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About this book
This book focuses on the performance optimization of fault diagnosis methods for power systems including both model-driven ones, such as the linear parameter varying algorithm, and data-driven ones, such as random matrix theory. Studies on fault diagnosis of power systems have long been the focus of electrical engineers and scientists. Pursuing a holistic approach to improve the accuracy and efficiency of existing methods, the underlying concepts toward several algorithms are introduced and then further applied in various situations for fault diagnosis of power systems in this book. The primary audience for the book would be the scholars and graduate students whose research topics including the control theory, applied mathematics, fault detection, and so on.
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Keywords
Table of contents (7 chapters)
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Front Matter
Authors and Affiliations
About the authors
Dr. Dinghui Wu received the Ph.D. degree in Control Science and Engineering with Jiangnan University and now is a Visiting Fellow with the School of Computer and electronic engineering, University of Denver, the US. His current research interests include energy optimization control technology, fault diagnosis of power systems, and edge calculation. Since Nov. 2019, Dr. Wu has been in School of Internet of Things Engineering, Jiangnan University, Wuxi, China, as a Professor.
Ms. Juan Zhang received the master's degree in Electrical Engineering with Jiangnan University, China, in 2021. She began her doctoral program with Jiangnan University, China, in 2021. Her current research interests include fault diagnosis of power systems and random matrix theory.
Mr. Junyan Fan received master's degree in mechatronics engineering with Jiangsu Ocean University, China, in 2021. He began his doctoral program with Jiangnan University, China,in 2021. His current research interests include energy prediction and energy optimization.
Ms. Dandan Tang received the bachelor's degree in Electrical Engineering with Jiangnan University, China，in 2020. She began her master’s program with Jiangnan University, China, in 2020. Her current research interests include distributed fault diagnosis of deep learning and federated learning.
Bibliographic Information
Book Title: Performance Optimization of Fault Diagnosis Methods for Power Systems
Authors: Dinghui Wu, Juan Zhang, Junyan Fan, Dandan Tang
Series Title: Engineering Applications of Computational Methods
DOI: https://doi.org/10.1007/978-981-19-4578-6
Publisher: Springer Singapore
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023
Hardcover ISBN: 978-981-19-4577-9Published: 19 September 2022
Softcover ISBN: 978-981-19-4580-9Published: 20 September 2023
eBook ISBN: 978-981-19-4578-6Published: 18 September 2022
Series ISSN: 2662-3366
Series E-ISSN: 2662-3374
Edition Number: 1
Number of Pages: XIII, 127
Number of Illustrations: 17 b/w illustrations, 44 illustrations in colour
Topics: Control, Robotics, Mechatronics, Energy Systems, Optimization, Control and Systems Theory, Power Electronics, Electrical Machines and Networks, Renewable and Green Energy