DocumentCode :
2598385
Title :
Study on the classification method of power quality disturbances based on generalized S-transform and DMT SVMs classifier
Author :
Wang, Jing ; Shen, Yueyue ; Weng, Guoqing
Author_Institution :
Coll. of Inf. Eng., Zhejiang Univ. of Technol., Hang Zhou, China
fYear :
2009
fDate :
6-7 April 2009
Firstpage :
1
Lastpage :
4
Abstract :
A new method based on Generalized S-transform (GST) time-frequency analysis and decision-making tree support vector machines (DMT SVMs) classifier for identification of power quality disturbances (PQDs) is presented. Firstly, GST is introduced to analyze the typical PQDs, including the inter-harmonics, where a set of useful characteristics are extracted. Then 50 disturbance training samples are employed to construct the characteristics sets which are applied to train a multi-lay SVMs classifier. Finally, 500 testing PQDs samples are identified using the SVMs classifier, in which N kinds of PQDs are classified by N-1 turns. Results show that the proposed method could detect and classify the PQDs effectively. The classifier has an excellent performance on training speed and correct ratio.
Keywords :
decision making; power supply quality; power system harmonics; support vector machines; decision-making tree support vector machines; generalized S-transform; power quality disturbances; time-frequency analysis; Artificial neural networks; Classification tree analysis; Convergence; Decision making; Fault diagnosis; Filters; OFDM modulation; Power quality; Signal analysis; Time frequency analysis; Generalized S-transform; disturbance identification; power quality; the decision-making tree SVMs;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Sustainable Power Generation and Supply, 2009. SUPERGEN '09. International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4934-7
Type :
conf
DOI :
10.1109/SUPERGEN.2009.5347969
Filename :
5347969
Link To Document :
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