Transient Power Quality Disturbances Identification and Classification Using Wavelet and Support Vector Machines


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Based on wavelet transform and support vector machines, a method of recognition and classification of transient power quality disturbance is presented. Using wavelet transform time-frequency localization characteristics, according to the principle of modulus maxima, realize the automatic detection positioning. After multi-resolution signal decomposition of PQ disturbances, multi-scale information in frequency domain and time domain of the signal can be extracted as the characteristic vectors. After choose and optimization of the eigenvectors based on the method of F-score, support vector machines are used to classify these eigenvectors of power quality disturbances. Effectiveness of the proposed method is verified through Matlab simulation.



Advanced Materials Research (Volumes 433-440)

Edited by:

Cai Suo Zhang




W. S. Sun et al., "Transient Power Quality Disturbances Identification and Classification Using Wavelet and Support Vector Machines", Advanced Materials Research, Vols. 433-440, pp. 1071-1077, 2012

Online since:

January 2012




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