基于PCA与SSA-LightGBM的油浸式变压器故障诊断方法

Oil-immersed transformer fault diagnosis method based on PCA and SSA-LightGBM

针对现阶段油浸式变压器故障复杂性导致诊断精度不高的问题,提出一种基于主元分析(PCA)与麻雀搜索算法-轻量级梯度提升机(SSA-LightGBM)的油浸式变压器故障诊断方法。采集油中溶解气体数据,结合无编码比值方法构建17维故障特征矩阵,并对特征矩阵进行标准化处理得到联合特征。利用主元分析法进行特征融合,消除变量之间的信息冗余,构造融合特征。构建基于SSA-LightGBM变压器诊断模型,并采用十折交叉验证法验证该模型的分类能力。试验结果表明:提出的模型平均故障诊断精度为93.6%,与GA-LightGBM和GWO-LightGBM故障诊断模型相比,诊断精度分别提高了8.1和5.7百分点,验证了该方法能够有效提高油浸式变压器的故障诊断性能。

In view of low diagnosis accuracy on oil-immersed transformer faults caused by the fault complexity at present,an oil-immersed transformer fault diagnosis method based on PCA and SSA-LightGBM is proposed.The data of dissolved gas in oil are collected,and a 17-dimensional fault feature matrix is constructed with codeless ratio method. The joint feature is obtained in the normalized matrix.Feature fusion is performed by using principal component analysis which can eliminate the redundant information between variables,and construct fusion features. The SSA-LightGBM transformer diagnosis model is constructed,and the ten-fold cross-validation method is used to verify the classification ability of the model.The experimental results show that the average fault diagnosis accuracy of the proposed model is 93.6%,which is 8.1and 5.7 percentage points higher than that of GA-LightGBM and GWO-LightGBM fault diagnosis models,respectively, verifying that this method can effectively improve the fault diagnosis performance for transformers.