基于矩阵特征值分析和SOA优化模糊聚类的变压器故障诊断

Transformer Fault Diagnosis Based on Matrix Eigenvalue Analysis and Optimized Fuzzy Clustering of Seeker Optimization Algorithm

摘要:

考虑变压器故障诊断的不确定性,构建了变压器模糊聚类模型,提出了用矩阵特征值分析方法得出样本集最佳分类数,实现了无监督的故障诊断。针对模糊C均值算法用于变压器故障诊断存在的问题,提出用人群搜索算法(SOA)得到较优的初始聚类中心。SOA算法是一种新型的启发式智能算法,克服了粒子群算法(PSO)和遗传算法(GA)等智能优化算法收敛性差、局部寻优的缺陷。仿真结果表明,该算法收敛速度更快,且具有更好的全局搜索能力,比传统的智能算法具有更高的有效性和鲁棒性,为变压器故障诊断聚类分析提供了参考。

Abstract:

A fuzzy clustering model is presented considering the uncertainty of transformer fault diagnosis，and a matrix eigenvalue analysis method is proposed to estimate the correct number of clusters which can implement the unsupervised fault diagnosis． Aiming at the problem existed in fuzzy c － means clustering algorithm which is applied to transformer fault diagnosis， seeker optimization algorithm ( SOA) is introduced to obtain the optimized initial clustering center． SOA simulates human random search behavior and overcomes the defects of particle swarm optimization ( PSO) and genetic algorithm ( GA) with local search and poor convergence． Simulation results show that SOA has a higher convergence speed and a better global searching ability． Comparing with the traditional intelligent optimization algorithms，SOA is more effective and robust，which can give a reference for transformer fault diagnosis．