Short-term Prediction of GIS Partial Discharge Based on ARMA Model

Abstract

As a piece of key equipment in the power system, GIS will cause great losses when it fails. Partial discharge detection technology is an important technic aims to evaluate the degradation status of GIS equipment insulation. Among them, the UHF method has been widely used to detect GIS partial discharge due to its superior anti-interference performance, but the partial discharge mechanism is more complicated and is affected by many factors such as operating voltage and load. Sometimes, the fault may develop quickly. Operation and maintenance personnel cannot resolve the fault in time, and this situation can be effectively solved by predicting the monitoring data of partial discharge. Although it is difficult to predict the long-term slow variability of partial discharges of internal devices in GIS, the detection of short-term variability discharge status is relatively stable. In this paper, an auto-regressive and moving average (ARMA) model is established for the extracted feature quantities in partial discharges in GIS. It can well predict the discharge trend in the few minutes or hours before the failure, which has good application value.