Artificial Intelligence Assistant Decision-Making Method for Main Distribution Power Grid Integration Based on Deep Deterministic Network

摘要

This paper studies the technology of generating DDPG (deep deterministic policy gradient) by using the deep dual network and experience pool network structure, and puts forward the sampling strategy gradient algorithm to randomly select actions according to the learned strategies (action distribution) in the continuous action space, based on the dispatching control system of the power dispatching control center of a super city power grid, According to the actual characteristics and operation needs of urban power grid, The developed refined artificial intelligence on-line security analysis and emergency response plan intelligent generation function realize the emergency response auxiliary decision-making intelligent generation function. According to the hidden danger of overload and overload found in the online safety analysis, the relevant load lines of the equipment are searched automatically. Through the topology automatic analysis, the load transfer mode is searched to eliminate or reduce the overload or overload of the equipment. For a variety of load transfer modes, the evaluation index of the scheme is established, and the optimal load transfer mode is intelligently selected. Based on the D5000 system of Metropolitan power grid, a multi-objective and multi resource coordinated security risk decision-making assistant system is implemented, which provides integrated security early warning and decision support for the main network and distribution network of city power grid. The intelligent level of power grid dispatching management and dispatching operation is improved. The state reality network can analyze the joint state observations from the action reality network, and the state estimation network uses the actor action as the input. In the continuous action space task, DDPG is better than dqn and its convergence speed is faster.