DocumentCode :
2659313
Title :
Power quality detection and discrimination in distributed power system based on wavelet transform
Author :
Peilin, Pang ; Guangbin, Ding
Author_Institution :
Hebei Univ. of Eng., Handan
fYear :
2008
fDate :
16-18 July 2008
Firstpage :
635
Lastpage :
638
Abstract :
A novel approach for the power quality (PQ) disturbances classification based on the wavelet transform and self-organizing learning array system is proposed. Wavelet network is utilized to extract feature vectors for various PQ disturbances and the wavelet transform can accurately localizes the characteristics of a signal both in the time and frequency domains. These feature vectors then are applied to the system for training and disturbance pattern classification. By comparing with a classic neural network, it is concluded that the proposed system has better data driven learning and local interconnections performance. The research results between the proposed method and the other existing method are discussed and the proposed method can provide accurate classification results. On the basis of hypothesis test of the averages, it is shown that corresponding to different wavelets selection, there is no statistically significant difference in performance of PQ disturbances classification and the relationship between the wavelet decomposition level and classification performance is discussed. The simulation results demonstrate the proposed method gives a new way for identification and classification of dynamic power quality disturbances.
Keywords :
feature extraction; frequency-domain analysis; learning (artificial intelligence); power supply quality; power system analysis computing; power system faults; self-organising feature maps; signal classification; signal detection; statistical testing; time-domain analysis; wavelet transforms; distributed power system; feature vector extraction; frequency domain analysis; hypothesis testing; power quality detection; power quality discrimination; power quality disturbance classification; self-organizing learning array system; time domain analysis; wavelet decomposition; wavelet network; wavelet transform; Learning systems; Neurons; Power quality; Power system measurements; Power systems; Testing; Voltage-controlled oscillators; Wavelet analysis; Wavelet domain; Wavelet transforms; Classification performance; Power quality disturbance; Self-organizing learning system; Wavelet transform;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location :
Kunming
Print_ISBN :
978-7-900719-70-6
Electronic_ISBN :
978-7-900719-70-6
Type :
conf
DOI :
10.1109/CHICC.2008.4605103
Filename :
4605103
Link To Document :
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