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.

Aiming at the problems of time-consuming and low accuracy in the existing state evaluation methods of distribution equipment, a state evaluation method of distribution equipment based on health index in big data environment is proposed. Firstly, in order to optimize the time-consuming of big data analysis on large-scale and distributed clusters, a distribution equipment condition monitoring data platform in big data environment is designed, and a hive based relational online analysis method (ROLAP) is proposed. Secondly, the health index (HI) is introduced as the evaluation index to evaluate the health status of distribution equipment. According to the different influence degree of different fault factors on the equipment status, a comprehensive multifactor fault rate correction model is obtained, and the method based on success flow is used to solve the model to improve the accuracy of state evaluation. Finally, experiments show that when the data volume of distribution equipment is 60 GB, the time of the proposed method is only 30.0 s, which is far lower than 73.6 s and 82.5 s of the comparison method. The evaluation accuracy of the proposed method is 95.1%, while the evaluation accuracy of the comparison method is only 82.4% and 73.1%, respectively. Therefore, the proposed method can effectively improve the efficiency of distribution equipment condition evaluation.