Research on auxiliary decision-making of power grid fault recovery based on generative adversarial imitation learning

With the development of artificial intelligence technology, deep learning methods with big data analysis capabilities provide great potential for the safe operation of power grids. In this paper, based on the combined load data of wind, light and electricity in a northwest region of China, the generator network formulates the unit output plan after the fault occurs. Based on the information of actual power load and actual new energy output before and after the fault, aiming at minimizing the system power generation cost, and considering the constraints of safe operation of the system, an expert system generation fault recovery strategy for this grid fault is formed. It is found that the fault recovery strategy in the early stage of training is quite different from the fault recovery strategy generated by expert system, and the error value is large. With the complete training of the generative adversarial network, the fault recovery auxiliary decision-making scheme approaching the fault recovery expert system can be given in various load and new energy output scenarios, and the error between the two is maintained within 5 %. The research on power grid fault recovery strategy based on generative adversarial imitation learning network model shows the autonomous and safe fault recovery ability of force control system.