Open AccessStudy of Fault Diagnosis Distribution Network Based on Rough Set and Artificial Intelligence
Author(s) -
Hui Zhou,
Zhong Wang,
Chunqing Shi,
Chaoying Liu,
ShiQin Zhao,
Ninghuan Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1754/1/012204
Subject(s) - rough set , fault (geology) , decision table , data mining , computer science , set (abstract data type) , reliability engineering , artificial intelligence , engineering , seismology , programming language , geology
The current real-time data collected by the power grid includes remote measurement, remote signaling, and other fault factors. The research in this paper is based on the fault diagnosis technology of rough set theory and self-learning theory, taking fault influencing factors as conditional attributes, fault type as decision-making attribute, and generating rough set rule table through self-learning reduction through a large number of fault history records, the influence of meteorological factors, environmental factors, equipment factors and other factors on the probability of equipment failure, which can realize the route Risk level prediction and fault location. Case analysis shows that the distribution network diagnosis technology based on rough set plays an extremely important role in predicting the risk level of each section of the line and positioning after a fault occurs.