针对局部放电在线检测中的局部放电信号模式识别，在对局部放电信号进行去噪预处理的基础上，对去噪后的局部放电信号进行小波包分解，利用小波包系数构建小波包系数矩阵;然后，对小波包系数矩阵进行奇异值分解，定义奇异值能量百分比作为局部放电信号的特征向量，并利用 M-ary 算法将支持向量机二分类扩展到多分类，使用粒子群算法对支持向量机参数进行优化;最后，将特征向量作为输入，使用支持向量机对 4 种放电信号进行识别，并与 BP 神经网络的识别效果进行对比． 结果表明:利用奇异值能量百分比构建的放电信号特征向量能够很好反映原始信号的特征;基于支持向量机能够有效对放电信号进行识别，平均识别率达到 95%，随着分解尺度增大，4 种放电信号的平均识别率增大，但增大的幅度减小;支持向量机和 BP 神经网络均能够很好识别 4 种放电信号，且支持向量机相比 BP 神经网络，具有更好的识别效果．

Aiming at pattern recognition of on-line partial discharge (PD) monitoring，the wavelet packet coeffi-cient matrix is constructed on the basis of wavelet packet decomposition of de-noised PD signal after the wavelet packet decomposition of de-noised partial discharge signal is done． Then，by the singular value decomposition of the wavelet packet coefficient matrix，the singular value energy percentage is defined as the feature vector of the partial discharge signal． Two classifications of the supportive vector machine are extended to multi one by M-ary al-gorithm，and the particle swarm optimization algorithm is used to optimize the parameters of supportive vector ma-chine． Finally，input is regarded as the feature vectors，supportive vector machines are used to recognize 4 kinds of discharge signals，and a comparison of recognition effect is made by means of BP neural network． The results show that the feature vector of the singular value energy percentage can reflect the characteristics of the original signal well． Based on supportive vector machines，the discharge signals can be effectively identified with a 95% average recognition rate． And with the increase of decomposition scale，the average recognition rate of 4 kinds of discharge signal increases，but the increment decreases． Supportive vector machine and BP neural network can well identify 4 kinds of discharge signals，and the former has a better recognition effect．