TY - JOUR
T1 - Recognizing noise-influenced power quality events with integrated feature extraction and neuro-fuzzy network
AU - Liao, Chiung Chou
AU - Yang, Hong Tzer
N1 - Funding Information:
Manuscript received May 27, 2008; revised November 24, 2008. First published July 28, 2009; current version published September 23, 2009. This work was supported in part by Taiwan National Science Council under Grant 96-2221-E-231-026. Paper no. TPWRD-00385-2008. C. C. Liao is with the Department of Electronic Engineering, Ching Yun University, Jung-Li, Taoyuan 320, Taiwan, R.O.C. (e-mail: [email protected]). H. T. Yang is with the Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan, R.O.C. (e-mail: [email protected]. tw). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TPWRD.2009.2016789
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
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U2 - 10.1109/TPWRD.2009.2016789
DO - 10.1109/TPWRD.2009.2016789
M3 - Article
AN - SCOPUS:70350261511
SN - 0885-8977
VL - 24
SP - 2132
EP - 2141
JO - IEEE Transactions on Power Delivery
JF - IEEE Transactions on Power Delivery
IS - 4
ER -