Abrupt change detection in power system fault analysis using wavelet transform

Abstract

This paper describes the application of the wavelets used to detect the abrupt changes in the signals recorded during disturbances in the electrical power network in South Africa. Main focus has been to estimate exactly the timeinstants of the changes in the signal model parameters during the pre-fault condition and following events like initiation of fault, circuit-breaker opening, auto-reclosure of the circuit-breakers using the wavelet transform, particularly the dyadicorthonormal wavelet transform. The key idea is to decompose the fault signals into effective detailed and smoothed version using the multiresolution signal decomposition technique based on discrete wavelet transform. Then we apply the threshold method on the decomposed signals to estimate the change time-instants, segmenting the fault signals. After segmenting the fault signal precisely into the 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 i.e., segmentation of the fault signal into event-specific sections using the wavelet transform and threshold method. This paper presents application on recorded signals in the transmission network of South Africa.