Power quality identification based on s-transform and RBF neural network

  • Authors:

  • Ganyun Lv;Xiaodong Wang

  • 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

  • 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 power quality (PQ) disturbances identification method based on S-transform time-frequency analysis and RBF network. 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 events because of its superior performance under noise. Using the time-frequency characteristics, a set of features is extracted for identification of power quality disturbances. Finally, a RBF network is developed for classification of the power quality disturbances. The proposed method is simple and reached 97.5% identification correct ratio under high signal to noise ratio for those most important disturbances in power system.