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| Reinforcement Learning-based Resilience Analysis of Wind Power Grid Under Integration Strategy |
| LI Weisha, WANG Shuliang, SONG Bo
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| School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221116, China |
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Abstract To explore the impact of wind power station grid-connection sites on the resilience of power networks, this paper introduces a new analytical framework for assessing the resilience of wind power network. By integrating the network's structural and functional models and applying relevant resilience assessment metrics, we propose a Q-Learning-based grid-connection strategy to identify the optimal grid-connection locations for wind power station. We validate this strategy using the IEEE 118 power grid model, which incorporates wind power grid-connection. Our research shows that the Q-Learning-based grid-connection strategy surpasses traditional heuristic methods and genetic algorithms in reducing operational costs and the risk of overload, highlighting the crucial role of strategic grid-connection in strengthening the network's resilience.
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Received: 27 March 2024
Published: 03 June 2025
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