Research on Fault Detection and Simulation of Power Distribution Network Based on Fuzzy Clustering Algorithm

The power distribution network directly targets a large number of electricity users, and its operating environment is very complex. Various complex faults often occur during the operation process. How to reduce the number of faults during the operation of the distribution network and improve the reliability of the power supply system has become a difficult problem that the power system needs to focus on solving. In this regard, this article uses fuzzy clustering algorithm to detect faults in power distribution networks, and simulation experiments show that the number of ACA(Ant Colony Algorithm) misclassified samples is relatively large, which is far from the actual situation. However, the number of misclassified samples in this algorithm is 7. Compared to the number of misclassified samples in ACA, the value of our algorithm is at the optimal value. In the experiment of misdiagnosis rate, it can also be found that the misdiagnosis rate of our algorithm is also relatively low, only 4.9%. The objective function value is 0.52, which is also better than ACA 0.8. Through experimental results, it can be seen that the algorithm in this paper can accurately distinguish circuit faults. The line selection technology for single-phase grounding faults in distribution networks has become a focus of attention for power workers. It is necessary to carefully analyze the problem, accurately identify the faulty line, and ensure the safe, normal, and effective operation of the entire distribution network.