Abstract: In order to detect short duration power quality disturbance correctly，a new classification method of short duration power quality disturbance based on K-L transformation and multiclass support vector machine is proposed in this paper.Original feature space is constructed using discrete wavelet transformation.Pattern recognition feature space is distilled using K-L transformation.A multiclass support vector machine suitable for short duration power quality disturbance is designed.The simulation results show that the classification correctness can be improved using K-L transformation.The classification result using multiclass support vector machine is better than BP neural classifier.

摘要： 为了准确检测短时电能质量扰动问题，提出了一种基于K-L变换和支持向量机多值分类器的短时电能质量扰动分类方法。采用离散小波变换获得信号在不同分解尺度下的能量分布作为原始特征空间；运用K-L变换进行模式识别特征空间的提取；设计了适用于短时电能质量扰动的支持向量机多值分类器。实验结果表明，对原始能量特征进行K-L变换后，可以提高分类准确率；支持向量机多值分类器的分类结果优于BP神经网络。