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 terrestrialbased or non-terrestrial-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 capaabilities of the respective feature extractor coupled ANN Clustering Algorithm sets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.