Ant colony optimization algorithm and FCM cluster analysis based on the Power quality assessment methodology

In the power market environment, the quantitative description of the comprehensive assessment of power quality is an important basis for power quality assessment. At present, there are more and more monitoring data of power quality, in order to evaluate the power quality more quickly and easily, this paper proposes a power quality assessment method based on ant colony optimization algorithm and FCM clustering analysis, which combines ant colony algorithm and FCM algorithm, and makes use of the advantages of the ant colony algorithm's robustness, global optimality searching, and evolutionary fuzzy clustering, to get the initialized clustering centers of FCM algorithm, and effectively solves the problem of the fuzzy clustering algorithm's tendency to fall into local optimal solution and dependence on initial clustering centers, and finally through the optimization method. The fuzzy clustering algorithm is easy to fall into the local optimal solution, the problem of dependence on the initial clustering center, and finally through the optimization method to get the optimal number of clusters, the output of the optimal clustering center under the optimal number of clusters, and the clustering results will be divided, and the results of the evaluation of the quality of electrical energy will be obtained. By comparing with the traditional FCM clustering method, the results show that the optimized algorithm has a greater improvement in accuracy as well as clustering effectiveness.