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Partition Fault Diagnosis of Power Grids Based on Improved PNN and GRA
Author(s) -
Zhang Qian,
Ma Wenhao,
Li Guoli,
Xie Min,
Shao Qingzhu
Publication year - 2021
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.23268
Subject(s) - fault (geology) , grid , partition (number theory) , artificial neural network , power grid , computer science , probabilistic logic , probabilistic neural network , power (physics) , artificial intelligence , mathematics , time delay neural network , physics , geometry , combinatorics , quantum mechanics , seismology , geology
With the increase of energy demand, the scale of power grid is expanding, and the difficulty of power grid fault diagnosis is increasing. Aiming at the problem of large power grid fault diagnosis, a method of partition fault diagnosis based on improved Probabilistic neural network (PNN) and gray relational analysis (GRA) integral is proposed. Firstly, the large power grid divided into small areas for fault diagnosis through power grid partition, which reduces the difficulty of fault diagnosis. Then the PNN diagnosis module is established by the PNN optimized by GA‐CPSO for diagnosing the power grid fault. Finally, the faults in the overlapping area are reanalyzed by the GRA method, in order to realize the accurate fault diagnosis of the whole power grid. The feasibility and effectiveness of the method are analyzed by two cases. The diagnosis results show that the method can effectively identify the faults in the nonoverlapping area and the overlapping area, and has strong fault tolerance and high diagnosis accuracy. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.