Estimation of Metal Oxide Surge Arresters' Useful Life Based on Time Series Forecasts

Abstract

Estimating equipment useful life has been a challenge over the years. In this work, a methodology based on the application of a combined time series forecast model composed of the adaptive neuro-fuzzy inference system (ANFIS) and support vector machine for regression (SVR) is proposed to be used in the determination of metal oxide surge arresters’ (MOSAs’) useful life. For this purpose, field measurements were performed in order to build a time series database composed of values of the leakage current third harmonic component, considering that this component is one of the indicators of the MOSAs’ degradation level most used in arresters’ monitoring. Subsequently, the forecasting models (ANFIS and SVR) were implemented. Then a combined model was proposed using the obtained results of those models. The performance of each implemented models was evaluated using three types of errors: MSE, MAE, and MAPE. After of the models’ evaluation, the useful life estimation of the MOSA was carried out using the proposed model. Considering the obtained results, the forecasts using the combined model were more accurate than those provided by the models based on ANFIS and SVR. Thus, the proposed model was used to estimate the MOSA's useful life.