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A Parallel RBFNN Classifier Based on S-Transform for Recognition of Power Quality Disturbances

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Advances in Neural Networks – ISNN 2007 (ISNN 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4492))

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Abstract

This paper proposes a novel parallel RBFNN (Radial Basis Function Neural Network) classifier based on S-transform for recognition and classification of PQ (Power Quality) disturbances. S-transform is used to extract feature vectors, while the constructed parallel RBFNN classifier is used to recognize and classify PQ disturbances according to the extracted feature vectors. The parallel RBFNN classifier consists of eight sub-networks, each of which is only able to recognize one type of disturbance. In order to improve the convergence performance of RBFNN and optimize the number of hidden layer nodes, a dynamic clustering algorithm which clusters all training samples to determine the number of hidden layer nodes is proposed. Simulation and test results demonstrate that the method proposed to recognize and classify PQ disturbances is correct and feasible, and that the RBFNN classifier based on the dynamic clustering algorithm has a faster convergence speed and a higher correct identification rate.

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Authors and Affiliations

  1. Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, dianqi@hit.edu.cn, China

    Weiming Tong & Xuelei Song

Authors
  1. Weiming Tong
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  2. Xuelei Song
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Editor information

Derong Liu Shumin Fei Zengguang Hou Huaguang Zhang Changyin Sun

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© 2007 Springer Berlin Heidelberg

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Tong, W., Song, X. (2007). A Parallel RBFNN Classifier Based on S-Transform for Recognition of Power Quality Disturbances. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_89

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  • DOI: https://doi.org/10.1007/978-3-540-72393-6_89

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72392-9

  • Online ISBN: 978-3-540-72393-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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