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Power Quality Identification Based on S-transform and RBF Neural Network

  • Ganyun Lv
  • Xiaodong Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

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.

Keywords

Sine Wave Power Quality Correct Ratio Good Generalization Ability Power Quality Disturbance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ganyun Lv
    • 1
  • Xiaodong Wang
    • 1
  1. 1.Department of Information Science and EngineeringZhejiang Normal UniversityJinhuaChina

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