基于特征提取与 INGO-SVM 的变压器故障诊断方法

Transformer fault diagnosis method based on feature extraction and INGO-SVM

针对使用支持向量机(support vector machine,SVM)对变压器进行故障诊断时有效特征提取困难、模型参数难以选择的问题,提出一种基于特征提取与INGO-SVM的变压器故障诊断方法。首先,使用核主成分分析(kernel principal component analysis,KPCA)方法对构建的21维待选特征进行特征融合和低维敏感特征提取。其次,使用佳点集、随机反向学习和维度交叉学习等策略对北方苍鹰优化算法(northern goshawk optimization,NGO)进行改进。通过2个典型测试对改进北方苍鹰优化算法(improved northern goshawk optimization,INGO)进行性能测试,验证了INGO算法的优越性。然后,基于KPCA提取的低维敏感特征,使用INGO对SVM的参数进行组合寻优,建立基于KPCA特征提取与INGO-SVM的变压器故障诊断模型。最后,对不同变压器故障诊断模型进行实例仿真对比实验。结果表明:所提方法故障诊断精度高、稳定性好,更适用于变压器的故障诊断。

It is difficult to extract effective features and select model parameters when using a support vector machine(SVM)for transformer fault diagnosis.A transformer fault diagnosis method based on feature extraction and an improved northern goshawk optimization(INGO)algorithm optimized SVM is proposed.First,kernel principal component analysis(KPCA)is used to conduct feature fusion and low dimensional sensitive feature extraction for the 21 dimensional candidate feature.Secondly,strategies such as good point set,random opposition-based learning,and dimensional cross learning are used to improve the northern goshawk optimization(NGO)algorithm.The performance of the INGO algorithm is tested using two typical test functions,verifying its superiority.Then,based on the low dimensional sensitive feature extracted by KPCA,INGO is used to optimize the parameters of the SVM,and a transformer fault diagnosis model is established based on KPCA feature extraction and INGO-SVM.Finally,simulation and comparative experiments are conducted on different transformer fault diagnosis models.The results show that the proposed method has high accuracy and good stability in fault diagnosis,and is more suitable for transformer fault diagnosis.