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