Power equipment state diagnosis based on multi-source monitoring data mining

Abstract

With the construction of UHV power grid, state assessment and diagnosis of power equipment in transmission and transformation systems have increasingly become the key technology to ensure the safety and stability of power system. Currently, state diagnosis of power equipment is mostly based on one single monitoring parameter. In this paper, a comprehensive analysing method was presented based on the mining of multi-source monitoring data, offering a new thought to analyse the states and defects of power equipment. The proposed method was applied on analysing the time-sequence and correlation of multi-source online monitoring data of a defective UHV reactor. According to the analysis results, the cause of the defects were investigated. The practice shows that the analysing method proposed in this paper can be applied on the defect diagnosis of power equipment, and the multi-source monitoring data contributes to improve the diagnosis accuracy.