Title :
Identifying Power Quality Disturbances in Real Time Using Incremental Wavelet Decomposition and Least Square Support Vector Machine
Author :
Yuan, Jinsha ; Kong, Yinghui ; Zhang, Tiefeng
Author_Institution :
Sch. of Electr. & Electron. Eng., North China Electr. Power Univ., Baoding
Abstract :
Power quality disturbances identification is the important procedure for improving power quality, and real time application has actual value. An efficient method for power quality disturbances identification is presented. Wavelet decomposition is used for extracting features of various disturbances, and least square support vector machine is used for classifying the disturbances. For real time application, sliding window and incremental algorithms for wavelet decompositions are used. This method can identify different disturbances in high accuracy and less time. Simulation experiment using several typical disturbances is finished, and the experimental results show effectiveness of proposed method.
Keywords :
fault diagnosis; least squares approximations; power engineering computing; power supply quality; support vector machines; wavelet transforms; incremental wavelet decomposition; least square support vector machine; power quality disturbances; sliding window; Feature extraction; Least squares methods; Multiresolution analysis; Neural networks; Power quality; Signal resolution; Support vector machine classification; Support vector machines; Voltage fluctuations; Wavelet analysis;
Conference_Titel :
Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-2486-3
Electronic_ISBN :
978-1-4244-2487-0
DOI :
10.1109/APPEEC.2009.4918413
