Abrupt Change Detection in Power System Fault Analysis using Adaptive Whitening Filter and Wavelet Transform

TL;DR

This paper introduces a method combining adaptive whitening filters and wavelet transforms to accurately detect and segment abrupt changes in power system signals during faults, enhancing fault analysis in electrical networks.

Contribution

It presents a novel approach integrating adaptive whitening filtering with wavelet-based multiresolution analysis for precise fault change detection in power systems.

Findings

Effective detection of change points in power signals

Improved segmentation of fault events

Application demonstrated on South African power network data

Abstract

This paper describes the application of the adaptive whitening filter and the wavelet transform 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 time-instants 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. The key idea is to decompose the fault signals, de-noised using the adaptive whitening filter, 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 into the event-specific sections for further signal processing and analysis. This paper presents application on the recorded signals in the power transmission network of South Africa.