About the Book
This book is the fourth 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 6th~8th December, 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.
Table of Contents:
Chapter 1. Improved Integrated Energy Systems Multi-Energy Load Deep Learning Joint Prediction Method Based On CEEMDAN.- Chapter 2. An Evaluation Method for Micro-Energy Networks Participating in the Electricity-Hydrogen Market Based on Cloud Model.- Chapter 3. Robust Optimisation Strategy For Distribution Of Integrated Energy Systems Considering Multiple Stakeholders.- Chapter 4. New Energy Power System Security and Stability Assessment Based on Apirori and Dynamic Weighted Cloud Model.- Chapter 5. A Review of Cluster Electric Vehicle Charging Scheduling Based on Multi-agent Deep Reinforcement Learning.- Chapter 6. Multi-Objective Optimization of Integrated Energy System Considering Double Uncertainty Of source and Load.- Chapter 7. False Data Injection Method Design for Power Sensors Based on Robust Principal Component Analysis.- Chapter 8. Key Technology of Joint Analysis of Cross-Modal Data for Integrated Service of Railroad Passenger Stations.- Chapter 9. A Two-step Soft Open Point Location And Capacity Determination Method Based On Power Flow Betweenness.- Chapter 10. Research on Intelligent Prediction Model of Ultra Short term Photovoltaic Power Generation Based on W-DA BiLSTM.- Chapter 11. Fault detection method of transmission sections based on GRU deep network.- Chapter 12. Construction and application of a grey prediction model based on periodical aggregation and periodical component factor.- Chapter 13. Research status and intelligent application of renewable energy hydrogen production and hydrogenation integrated station.- Chapter 14. Intelligent carbon emission accounting method based on deep learning algorithm.- Chapter 15. Remaining life prediction of motor bearing based on fusion degradation indicator.- Chapter 16. TD3 Deep Reinforcement Learning-Based Improved Sensorless MRAS Control Strategy for Multi-Electric Aircraft PMSM.- Chapter 17. Diagnosis of Interturn Short Circuit Faults in Switched Reluctance Machines Based on Parameter Optimized VMD and CNN-BiLST.- Chapter 18. YOLOv9-based Detection Method for Pyrotechnic Operations and Protective Equipment.- Chapter 19. Verification Method for Arc Suppression Coil Tracking Compensation Performance.- Chapter 20. Lithium battery SOC estimation based on BiLSTM MHSA.- Chapter 21. Neural Network-Based Adaptive Sliding Mode Control for Wheel Slip Ratio Control System.- Chapter 22. Comprehensive Decision-Making of Large-scale Rooftop Photovoltaic Access to Power Supply-guaranteed Microgrid.- Chapter 23. Trajectory planning of digital ray detection system for welding seam of double robot rocket tank based on MATLAB.- Chapter 24. Controllable Image Editing for Insulator Defect Generation and Detection.- Chapter 25. Topology Optimization of Offshore Wind Farm Collection System Based on Priority Queue Esau- Williams Algorith.- Chapter 26. Tabular image content reconstruction model for two- branch network design. Chapter 27. Volume Measurement Technology for Irregular Shaped Ice Cover Based on Multi-view 3D Reconstruction.
About the Author :
Ronghai Qu is Professor at the College of Electrical and Electronic Engineering, Huazhong University of Science and Technology, China, Fellow of IEEE. He received his bachelor’s and master’s degrees from Tsinghua University, China in 1993 and 1995, respectively. He received his doctoral degree from the University of Wisconsin, Madison, USA, in 2002. He received the honorary titles of Distinguished Lecturer of IEEE Industry Application Society for 2019~2020, and the Outstanding Member Awards in 2019. His research focuses on motor design, and drive and control.
Zhengxiang Song is Professor at the College of Electrical and Electronic Engineering, Xi'an Jiaotong University, China. He obtained his bachelor’s, master’s, and doctoral degrees from Xi'an Jiaotong University, China in 1992, 1995, and 1999, respectively. He has served as Executive Deputy Director of the State Key Laboratory of Energy, China since 2013, and 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 Professor at the Institute of Software, Chinese Academy of Sciences. He obtained his bachelor’s degree from Wuhan University, China in 1989, his master’s 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 Director of the Center of Space-Time Data Management and Date Science. He is also 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 Professor at Northeast Electric Power University, China. He received his bachelor’s and master’s 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 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 Professor at Beijing Institute of Technology, China. He also serves as Guest Professor at the Massachusetts Institute of Technology, the USA, Adjunct Professor at Swinburne University of Technology, and 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 Professor at Institute of Electrical Engineering (IEE), Chinese Academy of Sciences. He received his bachelor’s and master’s degrees from Lanzhou University, China in 1992 and 1995, respectively. He received his doctoral degree from Tsinghua University, China in 2000. He serves as Director of the research sector of micro-nano processing technology of IEE.
