Emergency control methods for power systems based on improved deep reinforcement learning
In order to achieve fast and accurate transient stability analysis and emergency control, this paper proposes a transient stability emergency control method based on improved deep reinforcement learning. In order to fully explore the temporal and spatial variation trend of transient response, a mult...
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| Veröffentlicht in: | Journal of physics. Conference series 2024-10, Vol.2858 (1), p.12035 |
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| container_title | Journal of physics. Conference series |
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| creator | Zhang, Jie Zhu, Yihua Liang, Zhuohang Ma, Qinfeng Zhang, Qingqing Liu, Mingshun An, Su Pu, Qingxin Dai, Jiang |
| description | In order to achieve fast and accurate transient stability analysis and emergency control, this paper proposes a transient stability emergency control method based on improved deep reinforcement learning. In order to fully explore the temporal and spatial variation trend of transient response, a multi-dimensional feature containing information such as transient situation energy is constructed, and the deep reinforcement learning model is transformed based on the time-space graph neural network. On this basis, an emergency control model is constructed, and the power grid knowledge is integrated into the emergency control decision-making scheme to reduce the exploration of invalid decision-making and improve the performance of the model. The effectiveness of the proposed method is verified in the IEEE-39 system. |
| doi_str_mv | 10.1088/1742-6596/2858/1/012035 |
| format | Article |
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In order to fully explore the temporal and spatial variation trend of transient response, a multi-dimensional feature containing information such as transient situation energy is constructed, and the deep reinforcement learning model is transformed based on the time-space graph neural network. On this basis, an emergency control model is constructed, and the power grid knowledge is integrated into the emergency control decision-making scheme to reduce the exploration of invalid decision-making and improve the performance of the model. 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| subjects | Control methods Control stability Decision making Deep learning Emergency response Graph neural networks Multidimensional methods Stability analysis Transient response Transient stability |
| title | Emergency control methods for power systems based on improved deep reinforcement learning |
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