Performance Optimization of Fault Diagnosis Methods for Power Systems

Dinghui Wu

ISBN 10: 9811945772 / ISBN 13: 9789811945779

Edité par Springer, Springer, 2022

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9789811945779

Détails bibliographiques

• Titre: Performance Optimization of Fault Diagnosis Methods for Power Systems
• Éditeur: Springer, Springer
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 9811945772
• ISBN à 13 chiffres: 9789811945779
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 241 L x 160 H x 14 P (en millimètres) 241 millimètres de largeur par 160 millimètres de hauteur par 14 millimètres de profondeur
• Poids: 389 grammes

A propos de ce titre

Synopsis

- Chapter 1 Introduction.
- Chapter 2 Fault Diagnosis of Variable Pitch for Wind Turbine Based on Multi-innovation Forgetting Gradient Identification Algorithm.
- Chapter 3 Active Fault-tolerant Linear Parameter Varying Control for the Pitch Actuator of Wind Turbines.
- Chapter 4 Fault Estimation and Fault-tolerant Control of Wind Turbines Using the SDW-LSI Algorithm.
- Chapter 5 A New Fault Diagnosis Approach for the Pitch System of Wind Turbines.
- Chapter 6 A dual-threshold state analysis and fault location method for power system based on random matrix theory.
- Chapter 7 Analysis of grid operation state based on improved MESCM algorithm.
- Chapter 8 Joint Weighted Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions.
- Chapter 9 ANS-net: anti-noise Siamese network for bearing fault diagnosis with a few data.
- Chapter 10 Fault Diagnosis of Rolling Bearing Based on Edge Calculation.

À propos de l?auteur

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.