Simulation of Fault Recovery Optimization Decision Model for Power Distribution Network Based on Ant Colony Optimization Algorithm

In this article, the simulation of optimal decision model for fault recovery of power distribution network (PDN) based on ant colony optimization (ACO) algorithm is mainly studied. Firstly, aiming at the shortcomings of traditional ACO algorithm, this article improves it, introduces elite strategy and dynamically adjusts pheromone volatilization rate to improve the search ability and robustness of the algorithm. Then, an optimized decision-making model of PDN fault recovery is established by using the improved ACO algorithm. The objective function is to minimize the fault recovery time and the power outage loss, and the topological structure and operation constraints of the PDN are considered. Finally, the proposed algorithm and model are verified and analyzed by simulation experiments. Experiments show that the accuracy of the algorithm can reach $94 \%$ and the error is only 1.35. In addition, compared with GA and AFSA, ACO algorithm improves the solution accuracy by more than $10 \%$ and shortens the solution time by more than $30 \%$. The comparative experimental results verify the effectiveness and superiority of the proposed algorithm and model in PDN fault recovery. The research results have important theoretical and practical significance for improving the efficiency and effect of PDN fault recovery, and provide a new solution and method for other similar problems.