Fault Diagnosis of Power Quality

Abstract:

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The proposed method in this paper indicates two issues, selection of discriminative features and classifies event classes with minimum error. Wavelets features (WF) of power quality (PQ) events are extracted using wavelet transform (WT) and fuzzy classifiers classify events using these features. The captured signals are often corrupted by noise; the non-linear and non-stationary behaviors of PQ events make the detection and classification tasks more cumbersome. Performance comparison of the proposed method is made with three other fuzzy classifiers using different wavelets and superiority is verified. In the proposed approach of event classification, fuzzy product aggregation reasoning rule based method has been used.

Info:

Periodical:

Edited by:

Zhixiang Hou

Pages:

870-873

DOI:

10.4028/www.scientific.net/AMM.128-129.870

Citation:

Y. L. Ou "Fault Diagnosis of Power Quality", Applied Mechanics and Materials, Vols. 128-129, pp. 870-873, 2012

Online since:

October 2011

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Price:

$35.00

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