基于振动分析法的变压器故障分类和识别

为了实现变压器故障的直观分类和故障识别,在分析变压器振动机理的基础上,提出一种基于主成分分析和KNN分类识别的变压器故障测量方法。该方法采用EMMD(集合经验模式分解)方法提取变压器不同运行状态下振动信号的特征矢量,将该特征矢量通过主成分分析投影到直观的二维图像中。利用KNN分类识别实现故障分类和自动故障识别。试验结果表明,该方法可以实现对变压器正常状态、绕组变形、铁芯故障3种状态直观分类,并对测试样本进行快速的自动模式识别。

The Classification and Recognition of Transformer Fault Based on Vibration Analysis

In order to achieve the fault identification and classification intuitively of transformer fault , this paper pro-poses a method of transformer fault detection method based on PCA ( principal component analysis ) and KNN ( K-Nearest Neighbor ) classification and recognition .In this paper , vibration signals from different transformer states are decomposed by EMMD ( ensemble empirical mode decomposition ) to abstract feature vectors which are projected onto a visual two-dimensional image .KNN classification is applied to verify fault classification and achieve automatic fault identification .Experimental results show that this method can achieve classification of a normal state of transformer , winding deformation and the core fault respectively , which can realize automatically pattern recognition of test sample .