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A Hierarchical Self-Adaptive Method for Post-Disturbance Transient Stability Assessment of Power Systems Using an Integrated CNN-Based Ensemble Classifier

Author

Listed:
  • Ruoyu Zhang

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

  • Junyong Wu

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

  • Yan Xu

    (School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore)

  • Baoqin Li

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

  • Meiyang Shao

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

Abstract

Data-driven approaches using synchronous phasor measurements are playing an important role in transient stability assessment (TSA). For post-disturbance TSA, there is not a definite conclusion about how long the response time should be. Furthermore, previous studies seldom considered the confidence level of prediction results and specific stability degree. Since transient stability can develop very fast and cause tremendous economic losses, there is an urgent need for faster response speed, credible accurate prediction results, and specific stability degree. This paper proposed a hierarchical self-adaptive method using an integrated convolutional neural network (CNN)-based ensemble classifier to solve these problems. Firstly, a set of classifiers are sequentially organized at different response times to construct different layers of the proposed method. Secondly, the confidence integrated decision-making rules are defined. Those predicted as credible stable/unstable cases are sent into the stable/unstable regression model which is built at the corresponding decision time. The simulation results show that the proposed method can not only balance the accuracy and rapidity of the transient stability prediction, but also predict the stability degree with very low prediction errors, allowing more time and an instructive guide for emergency controls.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3217-:d:259593
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    References listed on IDEAS

    as
    1. 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.
    2. 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. Qiufang Zhang & Zheng Shi & Ying Wang & Jinghan He & Yin Xu & Meng Li, 2020. "Security Assessment and Coordinated Emergency Control Strategy for Power Systems with Multi-Infeed HVDCs," Energies, MDPI, vol. 13(12), pages 1-21, June.
    2. 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.

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