This book is the third volume of proceedings of the 1st Electrical Artificial Intelligence Conference (EAIC 2024).
Artificial intelligence and low-carbon economy are two vibrant research fields in the world today. To achieve the goal of carbon neutrality not only signifies a significant transformation in the economic growth mode and a profound adjustment of energy systems but also has equally significant implications for the global economic and social transformation. In the wave of the rapid development of digital economy, artificial intelligence has become an important driving force for promoting high-quality economic and social development. In the path to the dual carbon goals, which are the peak carbon dioxide emissions goal and the carbon neutrality goal, artificial intelligence will play an important role, especially in energy conservation and carbon reduction in the electrical field, which is worthy of in-depth exploration and research.
In order to promote the deep integration of the electrical engineering and artificial intelligence, successfully achieve the "dual carbon" goals, and promote green, low-carbon, and high-quality development, the China Electrotechnical Society and relevant units jointly held the 1st Electrical Artificial Intelligence Conference in Nanjing, China during the December 68, 2024. The conference invited well-known experts with significant influence in the fields of electrical engineering and artificial intelligence to jointly explore the application of artificial intelligence in the optimization design, fault diagnosis, intelligent control, and optimized operation of electrical equipment, promote the integration of artificial intelligence innovations and various application scenarios, and actively lead the trend of technological innovation.
Chapter
1. Global Efficiency Optimization of High-Gain IPOP System Based
on Genetic Algorithm.
Chapter
2. IGBT Open-Circuit Fault Diagnosis of MMC
Submodules Based on Tensor Data-Driven Approach.
Chapter
3. Study on the
diagnosis of industrial robot abnormalities based on SVDD.
Chapter
4. Small
defect detection of power electronic devices based on YOLO-DHGC.
Chapter
5.
Research on Parallel Multimodal Current Sharing Based on Merged Coupled
Inductance.
Chapter
6. Study on Fault Diagnosis of Power Electronic Devices
in Power Conversion System Based on Machine Learning.
Chapter
7. Power
Electronics Topology Derivation: A Technical Review and Cutting-edge
Exploration.
Ronghai Qu is a professor, of the College of Electrical and Electronic Engineering, Huazhong University of Science and Technology, China, fellow of IEEE. He received his bachelors and masters degrees from Tsinghua University, China, in 1993 and 1995, respectively. He received his doctoral degree from the University of Wisconsin, Madison, the USA, in 2002. He received the honorary titles of Distinguished Lecturer of IEEE Industry Application Society for 20192020 and the Outstanding Member Awards in 2019. His research focuses on motor design, and drive and control.
Zhengxiang Song is a professor of the College of Electrical and Electronic Engineering, Xi'an Jiaotong University, China. He obtained his bachelors, masters, and doctoral degrees from Xi'an Jiaotong University, China, in 1992, 1995, and 1999, respectively. He has served as the executive deputy director of the State Key Laboratory of Energy, China, since 2013, and the executive deputy director of the Engineering Research Center, the Ministry of Education of China, since 2018. His research interests include the theory and engineering of intelligent electrical appliances, the theory and technology of electric energy storage, electromagnetic protection, and equipment detection and fault diagnosis.
Zhiming Ding is a professor of the Institute of Software, Chinese Academy of Sciences. He obtained his bachelors degree from Wuhan University, China, in 1989, his masters degree from Beijing University of Technology, China, in 1996, and his doctoral degree from the Institute of Computing, Chinese Academy of Sciences in 2002. He serves as the director of the Center of Space-Time Data Management and Date Science. He is also the chair of the society transportation sector of the IEEE Intelligent Transportation System Society. His research interests include database and knowledge base systems, real-time processing and intelligent analysis of the big data in spatiotemporal awareness, Internet of Things and mobile data management, disaster emergency big data management, etc.
Gang Mu is a professor of Northeast Electric Power University, China. He received his bachelors and masters degrees from Northeast Electric Power University, China in 1982 and 1984, respectively. He received his doctoral degree from Tsinghua University, China, in 1991. He serves as the fellow of the Chinese Society of Electrical Engineering. He was granted two second prizes of the National Science and Technology Progress Awards of China. His research interests focus on safe operation and control of the new generation power system and large-scale renewable energy development and networking technology.
Rui Xiong is a professor of Beijing Institute of Technology, China. He also serves as a guest professor at the Massachusetts Institute of Technology, the USA, an adjunct professor at Swinburne University of Technology, and an IET Fellow. He has hosted an Outstanding Youth Fund Project by the National Natural Science Foundation of China. He has engaged in fundamental theoretical and engineering application research on power systems, power battery systems, energy storage systems, big data, and artificial intelligence for electric transport vehicles.
Li Han is a professor of Institute of Electrical Engineering (IEE), Chinese Academy of Sciences. He received his bachelors and masters degrees from Lanzhou University, China, in 1992 and 1995, respectively. He received his doctoral degree from Tsinghua University, China, in 2000. He serves as the director of the research sector of micro-nano processing technology of IEE.
