基于最小二乘支持向量机的电能质量扰动分类方法

提出一种基于最小二乘支持向量机和小波包分解的电能质量扰动分类方法。对正常电压和几种常见电能质量扰动(电压骤升、电压骤降、电压中断、暂态脉冲、暂态振荡、谐波和电压闪变)进行小波包分解，提取各终节点小波包系数的标准偏差作为特征向量；然后，用自适应优化算法对最小二乘支持向量机进行优化；最后，利用基于优化参数和最小输出编码的最小二乘支持向量机进行分类和识别。与BP神经网络分类方法相比，该方法能克服训练时间较长、容易陷入局部最小等问题，具有较快的训练速度和较高的分类准确率，在样本数较小时仍取得较好的效果。仿真实验验证了该方法对扰动分类的有效性。

Classification of Power Quality Disturbances Based on LS-SVM

A new method classifying power quality disturbances based on least square support vector machine (LS-SVM) and wavelet packet decomposition is presented. Normal voltage and several power quality disturbances (voltage swell, voltage sag, voltage interruption, transient disturbance, transient oscillation, harmonics and voltage flicker) are decomposed by wavelet packet, and the standard deviation of the wavelet packet coefficients of each end node are extracted as eigenvectors. Adaptive optimizing algorithm is used to optimize LS-SVM. The disturbances are classified using LS-SVM based on optimized parameters and minimum output coding. Compared with the classification of BP neural network, the method is able to overcome the shortage of long training time, easy to fall in local minimum and has more fast training speed and higher classification accuracy percentage. It also performs well when the samples are fewer. The simulation verifies its validity to classify power quality disturbances.