Performance Optimization of Fault Diagnosis Methods for Power Systems

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.

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

Autorentext

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 USA. 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, Jiangnan University, as a professor. 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. Mr. Shenxin Lu received bachelor's degree in Electrical Engineering with Luoyang Institute of Science and Technology, China, in 2020. He took a successive postgraduate and doctoral programs of study at Jiangnan University, China, in 2022. His current research interests include energy scheduling and deep learning. Ms. Jing Wang obtained a master's degree in power system and automation from Wuhan University, China, in 2002. Her main research directions include metallurgical industry energy management informatization, low-carbon energy-saving technology, energy process control automation, multi-media energy scheduling optimization, process industry smart manufacturing, etc. Mr. Yong Zhu received his bachelor's degree in engineering from Huaqiao University, China, in 2019. He began his master program with Jiangnan University, China, in 2020. His current research interests are energy prediction. Mr. Hongtao Hu received his bachelor's degree in engineering from Huainan Normal University, China, in 2019.He began his master 's degree at Jiangnan University, China, in 2020. His current research interests are energy optimal scheduling of iron and steel enterprises under multiple working conditions.

Inhalt
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.