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Performance Optimization of Fault Diagnosis Methods for Power Systems de Dinghui Wu

Performance Optimization of Fault Diagnosis Methods for Power Systems: Engineering Applications of Computational Methods, cartea 9

Autor Dinghui Wu, Juan Zhang, Junyan Fan, Dandan Tang
en Limba Engleză Paperback – 20 sep 2023
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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Specificații

ISBN-13: 9789811945809
ISBN-10: 9811945802
Pagini: 127
Ilustrații: XIII, 127 p. 61 illus., 44 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.21 kg
Ediția:1st ed. 2023
Editura: Springer Nature Singapore
Colecția Springer
Seria Engineering Applications of Computational Methods

Locul publicării:Singapore, Singapore

Cuprins

Introduction.- Fault Diagnosis of Variable Pitch for Wind Turbine Based on Multi-innovation Forgetting Gradient Identification Algorithm.- Active Fault-tolerant Linear Parameter Varying Control for the Pitch Actuator of Wind Turbines.- Fault Estimation and Fault-tolerant Control of Wind Turbines Using the SDW-LSI Algorithm.- A New Fault Diagnosis Approach for the Pitch System of Wind Turbines.

Notă biografică

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.

Textul de pe ultima copertă

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.

Caracteristici

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