The detection of abrupt changes using recursive identification for power system fault analysis

Abstract

This paper describes the application of the recursive parameter estimation technique used to detect the abrupt changes in the signals recorded during disturbances in the power network of South Africa. The recursive identification technique uses M parallel Kalman filters. Main focus has been to estimate the time-instants of the changes in the signal model parameters during the pre-fault condition and following the events like initiation of fault, circuit-breaker opening, auto-reclosure of the circuit-breakers and the like. After segmenting the fault signal precisely into these event-specific sections, further signal processing and analysis can be performed on these segments, leading to automated fault recognition and analysis. In the scope of this paper, we focus on the first task, that is, segmenting the fault signal into event-specific sections using the recursive identification technique.