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PCA Fault Feature Extraction in Complex Electric Power SystemsZHANG, Y.   , WANG, Z. , ZHANG, J. , MA, J. |
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Author keywords
complexity, fault feature extraction, principal components analysis, PCA, phasor measurement unit, PMU, electric power system
References keywords
power(11), electric(7), analysis(6), systems(5), system(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2010-08-31
Volume 10, Issue 3, Year 2010, On page(s): 102 - 107
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.03017
Web of Science Accession Number: 000281805600017
SCOPUS ID: 77956623447
Abstract
Electric power system is one of the most complex artificial systems in the world. The complexity is determined by its characteristics about constitution, configuration, operation, organization, etc. The fault in electric power system cannot be completely avoided. When electric power system operates from normal state to failure or abnormal, its electric quantities (current, voltage and angles, etc.) may change significantly. Our researches indicate that the variable with the biggest coefficient in principal component usually corresponds to the fault. Therefore, utilizing real-time measurements of phasor measurement unit, based on principal components analysis technology, we have extracted successfully the distinct features of fault component. Of course, because of the complexity of different types of faults in electric power system, there still exists enormous problems need a close and intensive study. |
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[1] Fault Monitoring Based on the VLSW-MADF Test and DLPPCA for Multimodal Processes, Wang, Shu, Wang, Yicheng, Tong, Jiarong, Chang, Yuqing, Sensors, ISSN 1424-8220, Issue 2, Volume 23, 2023.
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[3] A Data-Driven Approach for Estimating the Power Generation of Invisible Solar Sites, Shaker, Hamid, Zareipour, Hamidreza, Wood, David, IEEE Transactions on Smart Grid, ISSN 1949-3053, Issue 5, Volume 7, 2016.
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[6] Fault localization in electrical power systems: A pattern recognition approach, Zhang, Ya-Gang, Wang, Zeng-Ping, Zhang, Jin-Fang, Ma, Jing, International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, Issue 3, Volume 33, 2011.
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[12] Dimensionality reduction and early event detection using online synchrophasor data, Yang Chen, , Le Xie, , Kumar, P. R., 2013 IEEE Power & Energy Society General Meeting, ISBN 978-1-4799-1303-9, 2013.
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[13] Bayesian fault detection based on WAMS/PMU measurement system, Yagang Zhang, , Zengping Wang, , Jinfang Zhang,, 2012 IEEE Power and Energy Society General Meeting, ISBN 978-1-4673-2729-9, 2012.
Digital Object Identifier: 10.1109/PESGM.2012.6345199 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
