Abstract：In the process of power quality disturbances identification, the wavelet decomposition level usually lack theoretical basis when using wavelet transform to extract energy difference distribution features and training samples for support vector machine (SVM) are usually in one condition of signal-noise ratio (SNR). For the above two questions, the wavelet decomposion level is decided by signal sampling rate when using wavelet doing multi-resolution analysis, which reduces the calculation time and the number of characteristic dimension, then the extracted energy distribution features are used as the input vector of SVM to train a SVM based classifier; Uniform SNR distribution is employed for training samples and enforces the generalization ability of SVM. The simulation results indicate that this improved method can accurately classify 6 types of PQ disturbances and the accuracy can still reach 95.20% even the SNR is 20dB.
陈华丰, 乔磊, 柳双林. 基于小波变换和支持向量机的电能质量扰动识别[J]. 电气技术, 2013, 14(02): 14-18.
Chen Huafeng, Qiao lei, Liu Shuanglin. Power Quality Disturbances Identification Using Decision Tree and Support Vector Machine. Electrical Engineering, 2013, 14(02): 14-18.
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2013, Vol. 14 
