关键词: 小波包变换, S变换, 二叉树, 支持向量机, 电能质量

Abstract: While transient power quality disturbances are made classifications, single feature can not be fully representative of transient disturbance, it may cause errors. In order to overcome the weaknesses, this paper extracts scheme of power quality multi-classification based on combined features and binary tree support vector machine. Using wavelet packet transform to decompose the disturbance signal is extracted under specific band signal energy, and the feature information is extracted by modulus matrix of perturbation signal using S transform. Then the multi-band signal energy and the corresponding S conversion feature information grouped together to get a combination of features. Finally, the binary tree support vector machine classifier with clustering ideas is trained and tested. The simulation result shows that the proposed method can effectively classify disturbance signals with higher recognition accuracy and faster recognition speed, the average recognition rate increases more than 6%, total test time shortened 0.06 s, the training time is reduced to 1.8 s.

Key words: wavelet package transform, S transform, binary tree, support vector machine, power quality