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Discussion on Key Technology and Operation & Maintenance of Intelligent Power Equipment

School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China


At present, the power grid introduces an agent based state monitoring management mode for some transmission equipment. However, this mode has a safety risk of single point failure and relies heavily on the high reliability of communication, which is not suitable for power distribution equipment widely distributed. That is, the power equipment currently still only performs functions without ‘intelligence’, which cannot realize intelligent perception of state, and does not meet the requirements of Energy Internet. To this end, the concept of ‘intelligent power equipment’ is elaborated, and its intelligent functions are comprehensively detailed, including self-sensing, self-alarming, self-state analysis, assessment of state of health, and self-protection. This equipment management mode is based on the intelligent perception, state analysis, and health management of the power equipment to operate autonomously and become an independent unit that can intelligently interact with the external environment. It realizes the efficient and comprehensive perception of power equipment, and adapts to the requirements of intelligent perception and flexible operation & maintenance of the power grid. In addition, the key technologies supporting this mode are studied, including sensing technologies, fault prediction algorithms, fault diagnosis algorithms, equipment degradation models, health assessment methods, communication methods and transmission protocols. Finally, the operation & maintenance management of the intelligent power equipment is discussed.



This work is supported by National Natural Science Foundation of China (No. 51807063).

Get Citation
[1]ZHAO Shice, ZHAO Hongshan, SHOU Peiyao. Discussion on Key Technology and Operation & Maintenance of Intelligent Power Equipment[J]. Automation of Electric Power Systems,2020,44(20):1-10. DOI:10.7500/AEPS20200303004
  • Received:March 03,2020
  • Revised:May 27,2020
  • Adopted:
  • Online: October 16,2020
  • Published: