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A Two-Stage Feature Selection Method for Power System Transient Stability Status Prediction

Author

Listed:
  • Zhen Chen

    (State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China)

  • Xiaoyan Han

    (State Grid Sichuan Electric Power Company, Chengdu 610041, China)

  • Chengwei Fan

    (State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China)

  • Tianwen Zheng

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610213, China
    Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Shengwei Mei

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

Abstract

Transient stability status prediction (TSSP) plays an important role in situational awareness of power system stability. One of the main challenges of TSSP is the high-dimensional input feature analysis. In this paper, a novel two-stage feature selection method is proposed to handle this problem. In the first stage, the relevance between features and classes is measured by normalized mutual information (NMI), and the features are ranked based on the NMI values. Then, a predefined number of top-ranked features are selected to form the strongly relevant feature subset, and the remaining features are described as the weakly relevant feature subset, which can be utilized as the prior knowledge for the next stage. In the second stage, the binary particle swarm optimization is adopted as the search algorithm for feature selection, and a new particle encoding method that considers both population diversity and prior knowledge is presented. In addition, taking the imbalanced characteristics of TSSP into consideration, an improved fitness function for TSSP feature selection is proposed. The effectiveness of the proposed method is corroborated on the Northeast Power Coordinating Council (NPCC) 140-bus system.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:689-:d:207749
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    References listed on IDEAS

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    1. 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.
    2. Marek Śmieja & Dawid Warszycki, 2016. "Average Information Content Maximization—A New Approach for Fingerprint Hybridization and Reduction," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-14, January.
    3. Yanzhen Zhou & Junyong Wu & Zhihong Yu & Luyu Ji & Liangliang Hao, 2016. "A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier," Energies, MDPI, vol. 9(10), pages 1-20, September.
    4. Luyu Ji & Junyong Wu & Yanzhen Zhou & Liangliang Hao, 2016. "Using Trajectory Clusters to Define the Most Relevant Features for Transient Stability Prediction Based on Machine Learning Method," Energies, MDPI, vol. 9(11), pages 1-19, November.
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    Cited by:

    1. Xiaoming Mao & Junxian Chen, 2019. "A Fast Method to Compute the Dynamic Response of Induction Motor Loads Considering the Negative-Sequence Components in Stability Studies," Energies, MDPI, vol. 12(9), pages 1-19, May.
    2. Juan Carlos Bravo-Rodríguez & Francisco J. Torres & María D. Borrás, 2020. "Hybrid Machine Learning Models for Classifying Power Quality Disturbances: A Comparative Study," Energies, MDPI, vol. 13(11), pages 1-20, June.
    3. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, June.

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