Title :
Power Quality Disturbances Classification using Wavelet and Support Vector Machines
Author :
Gao, Peisheng ; Wu, Weilin
Author_Institution :
Dept. of Electr. Eng., Zhejiang Univ., Hangzhou
Abstract :
Based on wavelet multiresolution analysis (MRA) and support vector machines (SVMs), a classification method for power quality disturbances in electrical power system is presented. After multiresolution signal decomposition of power quality disturbances, characteristic vectors can be obtained. Short time power transform (STPT) is also used to supplement the characteristic vectors from MRA. Support vector machines are used to classify these characteristic vectors of power quality disturbances, and the performance of SVMs is compared with that of artificial neural network (ANN)
Keywords :
neural nets; pattern classification; power supply quality; power system analysis computing; power system faults; support vector machines; wavelet transforms; artificial neural network; electrical power system; multiresolution signal decomposition; power quality disturbances classification; short time power transform; support vector machines; wavelet multiresolution analysis; Artificial neural networks; Continuous wavelet transforms; Discrete wavelet transforms; Multiresolution analysis; Performance analysis; Power quality; Signal resolution; Support vector machine classification; Support vector machines; Wavelet analysis; multiresolution; power quality; power transform; short time; support vector machines;
Conference_Titel :
Intelligent Systems Design and Applications, 2006. ISDA '06. Sixth International Conference on
Conference_Location :
Jinan
Print_ISBN :
0-7695-2528-8
DOI :
10.1109/ISDA.2006.217
