The large-scale access of distributed power sources will accelerate the formation of active distribution networks, which will have a greater impact on the safe and stable operation of distribution networks. In this context, this study constructs a state monitoring model for high proportion distributed energy access distribution network. Firstly, the objective optimization function of distribution network state is established, and then the model is solved by combining genetic algorithm and ant colony optimization algorithm. Simulation experiments are carried out with the IEEE 33-node distribution system as an example. The GA-CAO algorithm in this paper accurately portrays the state change characteristics of the distribution network, and its errors in estimating each state of the distribution network are smaller than those of the GA algorithm, with the root-mean-square error and the average absolute error being reduced by 1.07%~2.46% and 0.35%~3.02%, respectively. Experiments show that the method in this paper improves the accuracy of distribution network condition monitoring and has obvious advantages over other distribution network condition monitoring models, and the monitoring results can provide valuable information for distribution network enterprises as well as managers. In addition, the distribution network can be regulated by optimizing load management and scheduling, applying power management system and optimizing the design of distribution network architecture for the access of high proportion of distributed energy sources, which can promote the safe and stable operation of the distribution network.
