Fault Diagnosis of Data-Driven Photovoltaic Power Generation System Based on Deep Reinforcement Learning

Abstract

Aiming at the problem of fault diagnosis of the photovoltaic power generation system, this paper proposes a photovoltaic power generation system fault diagnosis method based on deep reinforcement learning. This method takes data-driven as the starting point. Firstly, the compressed sensing algorithm is used to fill the missing photovoltaic data and then state, action, strategy, and return functions from the environment. Based on the interaction rules and other factors, the fault diagnosis model of the photovoltaic power generation system is established, and the deep neural network is used to approximate the decision network to find the optimal strategy, so as to realize the fault diagnosis of the photovoltaic power generation system. Finally, the effectiveness and accuracy of the method are verified by simulation. The simulation results show that this method can accurately diagnose the fault types of the photovoltaic power generation system, which is of great significance to enhance the security of the photovoltaic power generation system and improve the intelligent operation and maintenance level of the photovoltaic power generation system.