State Evaluation Method of Distribution Terminal Based on Deep Reinforcement Learning

Abstract

The reliable operation of the distribution terminal is a key link to realize distribution automation. It is particularly important to efficiently and accurately evaluate the operation state of the distribution terminal. In order to realize accurate state perception of distribution terminals, a state evaluation method based on deep reinforcement learning is proposed to support the reliable operation of the distribution network. First, the fault causes of terminal equipment and the collected datasets are introduced. On this basis, the multilayer network structure is used to analyze the terminal state. Q-reinforcement learning network is used to optimize the convolution neural network, solve the overfitting problem of the deep network model, and continuously extract the data features. At the same time, in order to increase the objectivity and reliability of the evaluation method, the membership function optimization is also introduced into the model to further ensure the accuracy of the state analysis method. Simulation results show that the recognition accuracy of the proposed method is 94.23%, which shows excellent evaluation performance.