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Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset

Author

Listed:
  • Junyu Ren

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Benyu Li

    (Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China)

  • Ming Zhao

    (Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China)

  • Hengchu Shi

    (Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China)

  • Hao You

    (Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China)

  • Jinfu Chen

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Transient stability preventive control (TSPC) ensures that power systems have a sufficient stability margin by adjusting power flow before faults occur. The generation of TSPC measures requires accuracy and efficiency. In this paper, a novel model interpretation-based multi-fault coordinated data-driven preventive control optimization strategy is proposed. First, an augmented dataset covering the fault information is constructed, enabling the transient stability assessment (TSA) model to discriminate the system stability under different fault scenarios. Then, the adaptive synthetic sampling (ADASYN) method is implemented to deal with the imbalanced instances of power systems. Next, an instance-based machine model interpretation tool, Shapley additive explanations (SHAP), is embedded to explain the TSA model’s predictions and to find out the most effective control objects, thus narrowing the number of control objects. Finally, differential evolution is deployed to optimize the generation of TSPC measures, taking into account the security and economy of TSPC. The proposed method’s efficiency and robustness are verified on the New England 39-bus system and the IEEE 54-machine 118-bus system.

Suggested Citation

  • Junyu Ren & Benyu Li & Ming Zhao & Hengchu Shi & Hao You & Jinfu Chen, 2021. "Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset," Energies, MDPI, vol. 14(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3430-:d:572413
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    Cited by:

    1. Jinghan He & Ninghui Han & Ziqi Wang, 2021. "Optimization Method for Multiple Measures to Mitigate Line Overloads in Power Systems," Energies, MDPI, vol. 14(19), pages 1-19, September.

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