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. Key words:Power system fault analysis, Abrupt change detection, Adaptive Whitening filter, Wavelet Transform 2 IntroductionDetection of abrupt changes in the signal characteristics has a significant role to play in failure detection and isolation (FDI) systems; one such domains, power system fault analysis is the focus of this paper. In this paper, we propose the use of adaptive whitening filter and wavelet transform, particularly the dyadic-orthonormal wavelet transform for estimating the time-instants of the abrupt changes in the power system fault signals recorded during the disturbances in the electrical power transmission network of South Africa.In this paper, adaptive whitening filter based on the adjusted Fourier filter [1] is used to pre-filter the original fault signal. Wavelet transform is used to transform the pre-filtered fault signal into the finer wavelet scales, followed by a progressive search for the largest wavelet coefficients on that scale [2]. Large wavelet coefficients that are co-located in time across different scales provide estimates of the changes in the signal parameter. The change time-instants can be estimated by the time-instants when the wavelet coefficients exceed a given threshold (which is equal to the 'universal threshold' of Donoho and Johnstone [3] to a first order of approximation).The remainder of this paper is organized as follows. In section-2, power system fault analysis as application domain is discussed. In section-3, adaptive whitening filter is reviewed. Wavelet transform is reviewed in section-4. Section-5 discusses the pre-filtering operation using the adaptive whitening filter and the signal decomposition using the wavelet transform. Utilization of the threshold method for segmentation is explained in section-6. Practical application results are presented in section-7, and conclusions are given in section-8. Power System Fault AnalysisWe consider the power system fault analysis as our application domain, focusing on the The purpose of this study is to augment...

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SUMMARYOn-line diagnostics of induction machine faults such as broken rotor bars and shorted stator windings can be accomplished by analysing the anomalies of machine stator current. Defective rotor bars result in twice slip frequency sidebands around the fundamental frequency in the stator current, while stator winding short circuits cause changes in stator current amplitude and the occurrence of negative sequence current. This paper presents an induction machine model based on the Coupled Circuit Approach to simulate both the rotor and stator faults in an induction machine and to test fault diagnostic techniques. This model is realized in Matlab/Simulink and its construction in Simulink is explained with sufficient details. Simulation results of both the rotor and stator fault conditions are presented. A novel high-resolution spectral analysis approach and a technique based on Discrete Fourier Transform (DFT) for the detection of broken rotor bars are tested using the simulated data. The results confirm that the high-resolution method overcomes drawbacks of DFT such as the requirement for long data windows.