Open AccessResearch on Multi-level Cooperative Detection of Power Grid Dispatching Fault Based on Artificial Intelligence Technology
Author(s) -
Jianzhong Dou,
Zhicheng Liu,
Wei Xiong,
Hongzhong Chen,
Yifei Wu,
Tao Sun
Publication year - 2021
Publication title -
distributed generation and alternative energy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.19
H-Index - 12
eISSN - 2156-3306
pISSN - 2156-6550
DOI - 10.13052/dgaej2156-3306.3545
Subject(s) - fault detection and isolation , computer science , scheduling (production processes) , artificial neural network , real time computing , grid , power grid , fault (geology) , data mining , artificial intelligence , power (physics) , engineering , operations management , physics , geometry , mathematics , quantum mechanics , seismology , actuator , geology
The traditional power grid dispatching fault detection method has low detection efficiency and accuracy due to the lack of uncertainty in modeling. Aiming at the above problems, a multi-level cooperative fault detection method based on artificial intelligence technology is studied. After the preliminary processing of the dispatching data, the multilevel fault detection architecture is established. BP neural network is used to realize the multi-level cooperative detection of scheduling faults in the multi-level detection architecture. Through simulation experiment, it is proved that the failure rate and false detection rate of the proposed method are far lower than those of traditional methods, and the method has high stability and advantages.