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Feature Reduction for Power System Transient Stability Assessment Based on Neighborhood Rough Set and Discernibility Matrix

Author

Listed:
  • Bingyang Li

    (Department of Electrical Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Jianmei Xiao

    (Department of Electrical Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Xihuai Wang

    (Department of Electrical Engineering, Shanghai Maritime University, Shanghai 201306, China)

Abstract

In machine learning-based transient stability assessment (TSA) problems, the characteristics of the selected features have a significant impact on the performance of classifiers. Due to the high dimensionality of TSA problems, redundancies usually exist in the original feature space, which will deteriorate the performance of classification. To effectively eliminate redundancies and obtain the optimal feature set, a new feature reduction method based on neighborhood rough set and discernibility matrix is proposed in this paper. First, 32 features are selected to structure the initial feature set based on system principle. An evaluation index based on neighborhood rough set theory is used to characterize the separability of classification problems in the specified feature space. By constructing the discernibility matrix of input features, a feature selection strategy is designed to find the optimal feature set. Finally, comparative experiments based on the proposed feature reduction method and several common feature reduction techniques used in TSA are applied to the New England 39 bus system and Australian simplified 14 generators system. The experimental results illustrate the effectiveness of the proposed feature reduction method.

Suggested Citation

  • Bingyang Li & Jianmei Xiao & Xihuai Wang, 2018. "Feature Reduction for Power System Transient Stability Assessment Based on Neighborhood Rough Set and Discernibility Matrix," Energies, MDPI, vol. 11(1), pages 1-19, January.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:185-:d:126685
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    Citations

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    Cited by:

    1. Jieyu Li & Ping-an Zhong & Feilin Zhu & Juan Chen & Minzhi Yang & Jisi Fu & Weifeng Liu, 2020. "Reduction of the Criteria System for Identifying Effective Reservoirs in the Joint Operation of a Flood Control System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 71-85, January.
    2. Zhen Chen & Xiaoyan Han & Chengwei Fan & Tianwen Zheng & Shengwei Mei, 2019. "A Two-Stage Feature Selection Method for Power System Transient Stability Status Prediction," Energies, MDPI, vol. 12(4), pages 1-15, February.

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