Paper Title:
Fault Diagnosis of Power Quality
  Abstract

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
Chapter
Chapter 4: Fault Diagnose and Electronmechanical Control
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
Authors
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Price
$32.00
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