Comparison of feature extractors on DC power system faults for improving ANN fault diagnosis accuracy

Abstract

The power system operator's need for a reliable power delivery system calls for a real-time or near-real-time AI-based fault diagnosis tool. These needs are universal, whether they be for terrestrial-based or nonterrestrial-based power delivery systems, namely the NASA Space Station Alpha (Alpha). In this paper, we present a comparison of feature extractors suitable to the training and consultation phases for a fault diagnosis tool based on a two-stage ANN clustering algorithm. One of the prime concerns in selecting an appropriate feature extractor is to provide the ANN with enough significant details in the pattern set so that the highest degree of accuracy in the ANN's performance can be obtained. Candidate feature extractors include time domain analysis, frequency-domain analysis using the fast Fourier transform and the Hartley transform, and wavelet domain analysis using the wavelet transform. Simulated fault studies on a small system are performed and results presented to illustrate the performance capabilities of the respective feature extractor coupled ANN clustering algorithm sets.