Simulation of Automatic Classification Model of Electric Power Data Based on Fuzzy Clustering Algorithm

The reliability and efficiency of power system are very important for maintaining the normal operation of modern society. In order to improve the management and monitoring of power system, this study discusses the automatic classification model of power data based on fuzzy clustering algorithm. The goal of this model is to automatically identify different load patterns, faults and anomalies in power data, so as to realize more intelligent power system management. In this study, aiming at the shortcomings of traditional fuzzy c means (FCM) algorithm, an adaptive FCM algorithm is proposed. The algorithm does not depend on any initial conditions, and integrates the clustering validity function into the algorithm, thus avoiding the subjectivity in the selection of the number of clusters, improving the practicability of FCM algorithm, and achieving the goal of no manual intervention in the whole clustering process. The research results show that the automatic classification model of electric power data based on fuzzy clustering algorithm has obvious advantages in accuracy and efficiency. The algorithm can effectively identify different load patterns and help power system managers to better understand the operation status of the system. This study provides strong support for the development of automatic classification model of power data, and provides an important tool for intelligent management and monitoring of power system. This research provides useful guidance for the improvement and optimization of power system in the future.