S-transform based LS-SVM recognition method for identification of PQ disturbances

  • Authors:

  • Ganyun Lv;Xiushan Cai;Xaidong Wang;Haoran Zhang

  • Affiliations:

  • Department of Information Science and Engineering, Zhejiang Normal University, Jinhua, Zhejiang, China;Department of Information Science and Engineering, Zhejiang Normal University, Jinhua, Zhejiang, China;Department of Information Science and Engineering, Zhejiang Normal University, Jinhua, Zhejiang, China;Department of Information Science and Engineering, Zhejiang Normal University, Jinhua, Zhejiang, China

  • Venue:
  • ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
  • Year:
  • 2006

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Abstract

This paper presents a new method based on S-transform time-frequency analysis and multi-lay SVMs classifier for identification of power quality (PQ) disturbances. The proposed technique consists of time-frequency analysis, feature extraction and pattern classification. Though there are several time-frequency analysis methods existing in the literature, this paper uses S-transform to obtain the time-frequency characteristics of PQ disturbances because of its superior performance under noise. With the time-frequency characteristics of ST result, a set of features is extracted for identification of PQ disturbances. Then PQ disturbances training samples were contrsucted with the features, and a multi-lay LS-SVMs classifier was trained by the training sample. Finally, the trained multi-lay LS-SVMs classifier was developed for classification of the PQ disturbances. The proposed method has an excellent performance on training speed and correct ratio. The correct ratio of identification could reach 98.3% and the training time of the N-1 classifier was only about 0.2s.