Emergency control methods for power systems based on improved deep reinforcement learning
Year: 2024
Type: conference-paper
Abstract: 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.
Authors: Jie Zhang, Yihua Zhu, Zhuohang Liang, Qinfeng Ma, Qingqing Zhang, Mingshun Liu, Su An, Qingxin Pu, Jiang Dai
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403244547