Performance Optimization of Fault Diagnosis Methods for Power Systems

1. Auflage 2023
ISBN: 978-981-19-4577-9
Verlag: Springer

Buch, Englisch, 127 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 389 g

Reihe: Engineering Applications of Computational Methods

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.

Zielgruppe

Research

Autoren/Hrsg.

Wu, Dinghui

Zhang, Juan

Fan, Junyan

Tang, Dandan

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Energie- & Versorgungswirtschaft Energiewirtschaft: Alternative & Erneuerbare Energien
• Technische Wissenschaften Energietechnik | Elektrotechnik Alternative und erneuerbare Energien
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Technische Wissenschaften Energietechnik | Elektrotechnik Energietechnik & Elektrotechnik
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung

Inhaltsverzeichnis

- 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.

Über Autor(innen)

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.