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• W2138439887 startingPage "2132" @default.
• W2138439887 abstract "The wavelet transform coefficients (WTCs) contain plenty of information needed for transient signal identification of power quality (PQ) events. However, once the power signals under investigation are corrupted by noises, the performance of the wavelet transform (WT) on detecting and recognizing PQ events would be greatly degraded. At the mean time, adopting the WTCs directly has the drawbacks of taking a longer time and much memory for the recognition system. To solve the problem of noises riding on power signals and to effectively reduce the number of features representing power transient signals, a noise-suppression scheme of noise-riding signals and an energy spectrum of the WTCs in different scales calculated by the Parseval's theorem are presented in this paper. The neuro-fuzzy classification system is then used for fuzzy rule construction and signal recognition. The success rates of recognizing PQ events from noise-riding signals have proven to be feasible in power system applications." @default.
• W2138439887 created "2016-06-24" @default.
• W2138439887 creator A5046413348 @default.
• W2138439887 creator A5051204343 @default.
• W2138439887 date "2009-10-01" @default.
• W2138439887 modified "2023-10-10" @default.
• W2138439887 title "Recognizing Noise-Influenced Power Quality Events With Integrated Feature Extraction and Neuro-Fuzzy Network" @default.
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• W2138439887 doi "https://doi.org/10.1109/tpwrd.2009.2016789" @default.
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