Open AccessFailure Rate Model of Electric Equipment Based on Meteorological Environment
Author(s) -
Chen Song,
Yan Zhao,
Kaijin Xue,
LI Yu-cai
Publication year - 2020
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 - 10.1088/1742-6596/1626/1/012054
Subject(s) - reliability engineering , failure rate , reliability (semiconductor) , cluster analysis , fuzzy logic , fault (geology) , condition based maintenance , computer science , electrical equipment , state (computer science) , fuzzy clustering , sequence (biology) , power (physics) , engineering , artificial intelligence , electrical engineering , physics , algorithm , quantum mechanics , seismology , biology , genetics , geology
With the development and progress of equipment status monitoring and fault diagnosis technology, the status maintenance based on equipment status evaluation is gradually popularized and applied. By monitoring the quantity of equipment state and diagnosing the equipment state, a reasonable method of equipment state evaluation can be used to determine whether the equipment needs maintenance or not. This paper proposes a method of power equipment failure rate model based on meteorological environment. This method firstly uses the fuzzy c-means algorithm(FCM) to conduct fuzzy clustering of meteorological environment sequences in the historical sample data and forecast meteorological sequences in the maintenance period. Then, on the basis of clustering analysis, the grey relational degree analysis is carried out on the same kind of meteorological feature sequence, so as to obtain the power equipment failure probability with meteorological conditions in the maintenance period. The results show that the proposed method can more accurately calculate the failure probability of the equipment, and it is of practical significance to reduce the influence of maintenance on the reliability of power grid operation.