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Wavelet transform based fuzzy inference system for power quality classification

dc.contributor.authorTiwari A.K.; Shukla K.K.
dc.date.accessioned2025-05-24T09:58:14Z
dc.description.abstractThe paper presents a hybrid scheme using a Discrete Wavelet Transform and a Fuzzy Expert System for feature extraction and classification. The signal under test (electrical current or voltage for Power Quality study) is processed through a DWT decomposition block to generate the feature extraction curve. The DWT Level and Energy information from the feature extraction curve is then passed through a diagnostic module that computes the truth-value of the signal combination and determines the class to which the signal belongs. The proposed scheme is much simpler and powerful than currently available PQ classification schemes. © Springer-Verlag Berlin Heidelberg 2002.
dc.identifier.doihttps://doi.org/10.1007/3-540-45631-7_21
dc.identifier.urihttp://172.23.0.11:4000/handle/123456789/23078
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.titleWavelet transform based fuzzy inference system for power quality classification

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