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An Improved SVM-Based Cognitive Diagnosis Algorithm for Operation States of Distribution Grid

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Abstract

Intelligent diagnosis of operation states of distribution grid is a prerequisite to the self-healing ability of a smart grid. In this paper, an improved support vector machine (SVM)-based cognitive diagnosis algorithm is proposed to cognize the current operation state of distribution grid by classifying the disturbance into different operation states. Based on the current measurement in distribution grid, wavelet-packet time entropy is developed to extract features of the operation states. Considering the rejection recognition of multi-class classification, an improved SVM multi-class classifier based on a kernel metric is constructed. To investigate the performance of the proposed cognitive diagnosis algorithm, simulations of real distribution grid cases are carried out in PSCAD–EMTDC. Compared with wavelet-packet energy and Fuzzy C-means, the simulation results demonstrate that the proposed cognitive diagnosis algorithm can achieve higher accuracy and more robust performance on different grids and fault conditions.

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Acknowledgments

The work is funded by the National Science Foundation of China (51277135, 50707021) and Hubei Power Supply Corporation.

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Authors and Affiliations

  1. School of Electrical Engineering, Wuhan University, Luo-jia-shan, Wuchang District, Wuhan City, 430072, Hubei Province, People’s Republic of China

    Jun Yang & Lingyun Gong

  2. Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI, 02881, USA

    Yufei Tang, Jun Yan & Haibo He

  3. Wuhan Power Supply Corporation, Wuhan City, 430072, Hubei Province, People’s Republic of China

    Leiqi Zhang & Gang Li

Authors
  1. Jun Yang
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  2. Lingyun Gong
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  4. Jun Yan
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Corresponding author

Correspondence to Lingyun Gong.

Appendix

Appendix

See Fig. 10 and Table 2.

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Yang, J., Gong, L., Tang, Y. et al. An Improved SVM-Based Cognitive Diagnosis Algorithm for Operation States of Distribution Grid. Cogn Comput 7, 582–593 (2015). https://doi.org/10.1007/s12559-015-9323-2

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  • DOI: https://doi.org/10.1007/s12559-015-9323-2

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