On September 25-26, 2021, the 40th anniversary of the establishment of the China Electrotechnical Society and the 16th Annual Conference of the China Electrotechnical Society were held in Beijing Convention Center. Professor Zhang Jun of Wuhan University was invited to give a special report on "Equipment Health Monitoring: Intelligent Pre-diagnosis and Maintenance Based on Situation" at the special conference of the annual conference "New Energy Power System and Equipment". Professor Jun Zhang's report is hereby shared with all readers with a view to promoting academic exchanges and technological progress in this field. This issue will successively push some expert reports of the conference, please continue to pay attention to the readers.
<h1 class="pgc-h-arrow-right" data-track="2" > expert profile</h1>
Zhang Jun, a national high-level imported talent, is a professor and doctoral supervisor at the School of Electrical Engineering and Automation of Wuhan University. He received his Ph.D. from Arizona State University and taught at the Department of Electrical and Computer Engineering at the University of Denver and was a tenured professor.
He is currently the deputy secretary-general of the Chinese Society of Automation, a member of the Academic Committee of the Faculty of Engineering of Wuhan University, the vice chairman of the IEEE Radio Frequency Identification Council (COUNCIL ON RFID), a member of the editorial board of the Chinese Journal of Automation (Chinese and English edition), and a member of the editorial board of IEEE TRANSACTIONS ONCOMPUTATIONAL SOCIAL SYSTEMS. His research interests include the theoretical methods and applications of big data, artificial intelligence, information technology and blockchain in power systems. In 2019, he was awarded the "Yang Jiaqi Science and Technology Award" jointly issued by the Chinese Society of Automation and the Chinese Society of Astronautics.
<h1 class="pgc-h-arrow-right" data-track="5" > excerpt from the report</h1>
Through the property mining and equipment operation and maintenance research of power transformers of heterogeneous systems, the application research on improving the level of power asset management has been effectively improved, and the following results have been achieved: 1) A multi-source heterogeneous data fusion system for power transformers has been constructed. It realizes the effective sharing of data between different systems, providing solid data support for the all-round and multi-dimensional data mining process, and 2) introducing the concept of composite knowledge and constructing a transformer state evaluation model based on composite knowledge. Transformer status assessment based on historical O&M data is realized, which provides strong support for transformer maintenance decisions, and 3) Based on transformer status evaluation results, an optimization framework for transformer O&M strategy is constructed. Considering the remaining life and economic value, a customized operation and maintenance strategy scheme is formulated for the transformer in operation.
In the process of state assessment and equipment operation and maintenance optimization through historical operation and maintenance data, some of the content research is not in-depth enough, and further refinement is needed: 1) In the process of using the health index for residual life assessment, the mapping of equipment operation and maintenance status and health index and the correction of health index need to be further refined on the basis of previous research; 2) When evaluating the value of transformers, its real power supply cost is difficult to quantify, and the calculation in this paper lacks authenticity testing, and the subsequent value assessment system needs to be improved 3) The structure of the currently constructed transformer state assessment model is relatively simple, and the degree of data mining and model accuracy also have room for improvement.
