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A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier

Author

Listed:
  • Yanzhen Zhou

    (School of Electrical Engineering, Beijing Jiaotong Univerisity, Beijing 100044, China)

  • Junyong Wu

    (School of Electrical Engineering, Beijing Jiaotong Univerisity, Beijing 100044, China)

  • Zhihong Yu

    (China Electric Power Research Institute, Beijing 100192, China)

  • Luyu Ji

    (School of Electrical Engineering, Beijing Jiaotong Univerisity, Beijing 100044, China)

  • Liangliang Hao

    (School of Electrical Engineering, Beijing Jiaotong Univerisity, Beijing 100044, China)

Abstract

Machine learning techniques have been widely used in transient stability prediction of power systems. When using the post-fault dynamic responses, it is difficult to draw a definite conclusion about how long the duration of response data used should be in order to balance the accuracy and speed. Besides, previous studies have the problem of lacking consideration for the confidence level. To solve these problems, a hierarchical method for transient stability prediction based on the confidence of ensemble classifier using multiple support vector machines (SVMs) is proposed. Firstly, multiple datasets are generated by bootstrap sampling, then features are randomly picked up to compress the datasets. Secondly, the confidence indices are defined and multiple SVMs are built based on these generated datasets. By synthesizing the probabilistic outputs of multiple SVMs, the prediction results and confidence of the ensemble classifier will be obtained. Finally, different ensemble classifiers with different response times are built to construct different layers of the proposed hierarchical scheme. The simulation results show that the proposed hierarchical method can balance the accuracy and rapidity of the transient stability prediction. Moreover, the hierarchical method can reduce the misjudgments of unstable instances and cooperate with the time domain simulation to insure the security and stability of power systems.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:778-:d:79079
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    Citations

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    Cited by:

    1. Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
    2. Ruoyu Zhang & Junyong Wu & Yan Xu & Baoqin Li & Meiyang Shao, 2019. "A Hierarchical Self-Adaptive Method for Post-Disturbance Transient Stability Assessment of Power Systems Using an Integrated CNN-Based Ensemble Classifier," Energies, MDPI, vol. 12(17), pages 1-20, August.
    3. Yi Tang & Feng Li & Chenyi Zheng & Qi Wang & Yingjun Wu, 2018. "PMU Measurement-Based Intelligent Strategy for Power System Controlled Islanding," Energies, MDPI, vol. 11(1), pages 1-15, January.
    4. Yixing Du & Zhijian Hu, 2021. "Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network," Sustainability, MDPI, vol. 13(12), pages 1-21, June.
    5. 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.
    6. Dan Huang & Qiyu Chen & Shiying Ma & Yichi Zhang & Shuyong Chen, 2018. "Wide-Area Measurement—Based Model-Free Approach for Online Power System Transient Stability Assessment," Energies, MDPI, vol. 11(4), pages 1-20, April.

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