Abstract：This paper presents a new approach based on S-transform and support vector machine (SVM) for identification of power quality complex disturbances. Firstly, original power quality signals are processed by S-transform and features were extracted from the result of S-transform at different frequency areas. Then, 2 types of most distinguished feature are selected by statistic feature selection. The selected features are used as the input vector of SVM and to train a SVM based classifier for power quality disturbances recognition. Furthermore, the SVM classifier is used to classify the short-time power quality disturbances. The proposed method reduces the computing costs of feature calculation, meanwhile saves the time of training and classification. 8 types of power quality disturbances including 2 types of complex disturbances are accurate identified. The simulation results show the validity of this method.
黄南天, 徐殿国, 刘晓胜. 基于S变换与SVM的电能质量复合扰动识别[J]. 电工技术学报, 2011, 26(10): 23-30.
Huang Nantian, Xu Dianguo, Liu Xiaosheng. Identification of Power Quality Complex Disturbances Based on S-Transform and SVM. Transactions of China Electrotechnical Society, 2011, 26(10): 23-30.
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