The feature extraction and pattern recognition techniques are of great importance to assess the insulation condition of gas-insulated switchgear. In this work, the ultra-high-frequency partial discharge (PD) signals generated from four types of typical insulation defects are analysed using S-transform, and the greyscale image in time-frequency representation is divided into five regions according to the cutoff frequencies of TE _{m 1} modes. Then, the three low-order moments of every subregion are extracted and the feature selection is performed based on the J criterion. To confirm the effectiveness of selected moment features after considering the electromagnetic modes, the support vector machine, k-nearest neighbour and particle swarm-optimised extreme learning machine (ELM) are utilised to classify the type of PD, and they achieve the recognition accuracies of 92, 88.5 and 95%, respectively. In addition, the results show that the ELM offers good generalisation performance at the fastest learning and testing speeds, thus more suitable for a real-time PD detection.

特征提取和模式识别技术对于评估气体绝缘开关设备的绝缘状况具有重要意义。在这项工作中，使用S变换分析了四种典型绝缘缺陷产生的超高频局部放电（PD）信号，并根据截止频率将时频表示中的灰度图像分为五个区域TE _{m 1}模式。然后，提取每个子区域的三个低阶矩，并基于J准则执行特征选择。为了在考虑电磁模式后确认所选矩特征的有效性，支持向量机k 最近邻和粒子群优化极限学习机（ELM）用于对PD的类型进行分类，它们分别达到92％，88.5％和95％的识别准确率。此外，结果表明，ELM以最快的学习和测试速度提供了良好的泛化性能，因此更适合于实时PD检测。