Multivariate Time Series Modeling for Long Term Partial Discharge Measurements in Medium Voltage XLPE Cables

Abstract

A multivariate time series analysis was performed for a system of several PD response variables, i.e. average charge, number of discharge pulses, average charge current, and largest repetitive discharge magnitude over the data acquisition period. Experimental lifelong PD data obtained from cable samples subjected to accelerated degradation was used to study the dynamic trends and relationships among those aforementioned response variables. Stochastically formulated cointegrated variables recognized by those tests can be combined to form new stationary variables to estimate the parameters for the Vector Auto Regression (VAR) and Vector-Error Correction (VEC) models. The validity of both models was evaluated by generating Monte Carlo and Minimum Mean Squared Error (MMSE) simulated forecasts. True observed data and forecasted data mean values lie within the 95th percentile confidence interval responses which demonstrates the soundness and accuracy of both models. A life-predicting model based on the cointegrating relations between the multiple response variables, correlated with experimentally evaluated time-to-breakdown values, can be used to set an emergent alarming trigger and as a step towards establishing long-term continuous monitoring of partial discharge activity.