With the continuous improvement of living standards and the continuous increase of electricity load, the number of power transmission and transformation equipment also increases rapidly. The original maintenance mode is not enough to guarantee the safe operation of the huge power grid. This paper mainly studies the research and application of machine learning based maintenance decision optimization technology for substation equipment. Starting from the technical principles of online monitoring and condition maintenance of substation equipment, this paper has realized an intelligent monitoring and maintenance early warning system combined with deep learning model. The main functions of this system include monitoring device management, operation monitoring and comprehensive display, etc., which can effectively carry out online monitoring and state early warning of substation equipment. It greatly improves the intelligent degree of operation and management of substation equipment, saves the cost of traditional manual monitoring, and effectively prevents the economic loss caused by substation equipment failure, which has far-reaching significance for promoting the construction of smart power grid.
The internal insulation defects in gas insulated equipment leading to partial discharges will affect the insulation performance of the equipment. Among them, surface discharges are a typical type of partial discharge, which often occurs at basin-type insulators. To realize effective discharge detection and discharge degree determination, a refined discharge stage division strategy for gas-insulated creeping discharge based on multi-physical information is investigated in this study. First, an experimental platform for gas-insulated multi-physical signal detection is established, of which high frequency current transformer (HFCT), silicon photomultiplier (SiPM), ultra-high frequency (UHF) and acoustic emission (AE) sensors are applied to measurement the multi-physical energy releases in the process of creeping discharge. Then an artificial surface discharge defect is developed following the actual surface discharge of basin insulator. Subsequently, the relative optical radiation power, electromagnetic radiation power and ultrasonic radiation power produced in the process of discharge are deduced from the multi-physical information. Meanwhile, the relationships among the three energy releases are carried out for variation of different energy with the applied voltage increases. Finally, development path of multi-physical energy ratio for surface discharge is proposed to evaluate the severity of discharge.
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