Analysis Method of Transformer Winding Looseness Fault Based on Chaos Theory and K-means Algorithm Optimized by Mayfly Optimization Algorithm

In order to analyze the state of transformer windings more accurately and effectively, a method for analyzing the characteristics of transformer winding looseness fault based on the chaos theory and K-means algorithm optimized by mayfly optimization algorithm is proposed. Firstly, the phase space of transformer vibration signal is reconstructed using C-C method, and the chaotic characteristics of transformer vibration signal are analyzed. The correlation dimension and Kolmogorov entropy are obtained and taken as the chaotic features. Then, the mayfly optimization algorithm is introduced into K-means clustering analysis to optimize the cluster center selection of high-dimensional phase space trajectory, and the sum of cluster center distance of phase trajectory and vector offset are obtained and taken as geometric features. The experimental results show that the maximum Lyapunov exponents of transformer vibration signals are all greater than 0, which is suitable for the analysis of chaotic characteristics. The chaos features calculated by transformer vibration signals can characterize the states of transformer windings. At the same time, the cluster centers obtained by the mayfly optimized K-means algorithm can be used as feature points to extract the geometric features of the whole phase space trajectory, which distinguish whether the winding looseness fault occurs. The combination of the two features can realize the accurate monitoring of transformer winding states, thus providing a theoretical basis for the online maintenance of transformer windings. © 2024 Science Press. All rights reserved.