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Article

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

1
State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2
Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China
*
Author to whom correspondence should be addressed.
Academic Editors: Abdelali El Aroudi, Pedro Rodriguez and Tomislav Dragicevic
Energies 2021, 14(12), 3430; https://doi.org/10.3390/en14123430
Received: 18 May 2021 / Revised: 3 June 2021 / Accepted: 5 June 2021 / Published: 10 June 2021
(This article belongs to the Special Issue Artificial Intelligence in Power Systems Operation and Control)
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. View Full-Text
Keywords: ADASYN; augmented dataset; differential evolution; model interpretation; preventive control; transient stability ADASYN; augmented dataset; differential evolution; model interpretation; preventive control; transient stability
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MDPI and ACS Style

Ren, J.; Li, B.; Zhao, M.; Shi, H.; You, H.; Chen, J. Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset. Energies 2021, 14, 3430. https://doi.org/10.3390/en14123430

AMA Style

Ren J, Li B, Zhao M, Shi H, You H, Chen J. Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset. Energies. 2021; 14(12):3430. https://doi.org/10.3390/en14123430

Chicago/Turabian Style

Ren, Junyu, Benyu Li, Ming Zhao, Hengchu Shi, Hao You, and Jinfu Chen. 2021. "Optimization for Data-Driven Preventive Control Using Model Interpretation and Augmented Dataset" Energies 14, no. 12: 3430. https://doi.org/10.3390/en14123430

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