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Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

Author

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  • Yixing Du

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

  • Zhijian Hu

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)

Abstract

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6953-:d:578731
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
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