Fault Identification Method for Power Measurement Terminal Communication under Ant Colony Algorithm

The proposed communication fault identification method for power metering terminals, utilizing the ant colony algorithm (ACA), presents a robust approach. By simulating the foraging behavior of ant colonies in nature, search for the optimal solution in the search space. In the process of identifying communication faults, ACA gradually approaches the real communication fault mode by continuously adjusting the search strategy. At the same time, combining the powerful learning ability of BP neural network. Experimental results validate the method’s effectiveness, achieving an impressive accuracy rate of $\mathbf{8 6 . 3 \%}$ in identifying communication faults. Leveraging ACA, the model is adeptly trained to pinpoint faults with precision. Furthermore, convergence and stability analyses underscore ACA’s superiority over genetic algorithms and particle swarm algorithms, particularly evident in its median standard deviation of errors (0.0089) across 10 runs. This underscores ACA’s exceptional performance and reliability in fault identification tasks, affirming its practical value and potential for widespread application.