Deep Reinforcement Learning Based Dispatching Method for Power Grid Facing False Topology Attack and Topology Optimization
With the integration of high-penetration renewable energy and the increasing frequency of false topology attack,the traditional power grid control methods and power generation output adjustment scheduling methods are no longer able to meet the needs of power system operation.Therefore,this article designs and implements the deep reinforcement learning based scheduling method for power grid facing the false topology attack and topology optimization.This method enables intelligent agents to make decisions and execute actions to adjust the power grid topology structure through the deep reinforcement learning under normal load fluctuations and random topology attacks,thereby improving the security of the power system operation.Finally,the effectiveness of the proposed method is verified through simulation based on IEEE 14-bus system data.
power system operationdeep reinforcement learningtopology optimizationfalse topology attack
万方数据
维普
