Simulation of Distribution Terminal Automation Joint Debugging Model Based on Machine Learning Algorithm

In the increasingly advanced environment of technology, the power distribution network is gradually developing towards automation, and the functions of its distribution automation terminal equipment are also becoming more and more complete. Distribution automation, as an important part of China’s smart grid construction, plays an important role in improving the production, operation and management level of the distribution network. From the perspective of power construction, the application of distribution automation equipment in the growth of power systems is an inevitable trend in the information society. The research on distribution automation terminal equipment is of crucial significance for the growth of power distribution network automation. At present, the joint debugging between distribution terminals and the main station relies on manual completion. However, with the increasing quantity of distribution terminals, the efficiency of joint debugging is becoming lower and the standardization level is not high. Based on this, this article constructs a distribution terminal automation joint debugging model based on machine learning algorithms to improve work efficiency and facilitate the promotion and application of distribution automation. The simulation results show that the model can achieve important functions such as monitoring the operation status of the distribution network, automatic fault isolation, and fast power restoration, improving the power supply reliability of the distribution network.