Study of Fault Diagnosis Distribution Network Based on Rough Set and Artificial Intelligence

Autor:	Shi Chunqing, Ninghuan Zhang, Zhou Hui, Liu Chaoying, Zhong Wang, Zhao Shiqin
Rok vydání:	2021
Předmět:	History Risk level Distribution networks Computer science Fault (power engineering) computer.software_genre Computer Science Applications Education Reduction (complexity) Line (geometry) Table (database) Rough set Data mining computer Case analysis
Zdroj:	Journal of Physics: Conference Series. 1754:012204
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1754/1/012204
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::210e3928f0ea93d671262881da1aaf06 https://doi.org/10.1088/1742-6596/1754/1/012204 Zobrazit plný text záznamu