摘要：This paper proposes a reinforcement learning-based approach that optimises bus and line control methods to solve the problem of short circuit currents in power systems. Expansion of power grids leads to concentrated power output and more lines for large-scale transmission, thereby increasing short circuit currents. The short circuit currents must be managed systematically by controlling the buses and lines such as separating, merging, and moving a bus, line, or transformer. However, there are countless possible control schemes in an actual grid. Moreover, to ensure compliance with power system reliability standards, no bus should exceed breaker capacity nor should lines or transformers be overloaded. For this reason, examining and selecting a plan requires extensive time and effort. To solve these problems, this paper introduces reinforcement learning to optimise control methods. By providing appropriate rewards for each control action, a policy is set, and the optimal control method is obtained through a maximising value method. In addition, a technique is presented that systematically defines the bus and line separation measures, limits the range of measures to those with actual power grid applicability, and reduces the optimisation time while increasing the convergence probability and enabling use in actual power grid operation. In the future, this technique will contribute significantly to establishing power grid operation plans based on short circuit currents.
