中文摘要：

为对不同类型局部放电信号进行识别,笔者提出一种新的特征提取方法。首先,制作了4种典型的局部放电人工缺陷模型,并通过S变换对采集的局部放电UHF信号进行时频分析;然后,采用双向二维主成分分析（2DPCA）对S变换幅值矩阵进行压缩以提取特征;最后,引入基于粒子群算法优化参数的支持向量机对样本特征集进行模式识别。识别结果表明：4种特征维数组合中,（10,5）组合的平均识别率最高,（5,5）组合最低;粒子群优化算法的引入大幅提高了支持向量机的分类性能,平均识别率均在94.43%以上,最高可达到97.67%。由此可见,经过S变换和双向2DPCA提取的特征集在维数显著约减的同时,保留了原始数据大部分信息量,能够获得较为理想的分类识别率。

英文摘要：

A new feature extraction method is proposed to recognize different types of partial discharge （PD） signals. Firstly, four typical categories of PD artificial defect models are made and S transform （ST） is employed to obtain a time-frequency representation of the recorded UHF signals. Then, two-directional twodimensional principal component analysis （（2D）2PCA） is applied to compress the ST amplitude （STA） matrix to extract features. Finally, support vector machine （SVM） combined with particle swarm optimization （PSO） algorithm is introduced to accomplish the recognition of experimental samples. Classification results demonstrate that the average recognition rate of （10,5） combination is the highest while the one of （5,5） combination is the lowest among four kinds of feature dimension combinations. Moreover, PSO can obviously improve the classification performance of SVM. Specifically, all the average recognition rates of PSO-SVM are higher than 94. 43% and the maximum value comes to 97. 67%. Therefore,the feature sets extracted by ST and （2D）2PCA can not only achieve dramatic dimension reduction, but also retain the major information of original data. It is proved that the proposed algorithm can obtain ideal results in PD pattern recognition.