基于VMD和WOA-SVM的变压器绕组松动故障诊断

为了更加准确有效地诊断变压器绕组松动故障，提出了一种基于变分模态分解(VMD)和鲸鱼优化支持向量机(WOA-SVM)的变压器绕组松动故障诊断方法。首先，对某10 kV变压器进行模拟故障试验，测量其振动信号；随后，采用VMD将非平稳的振动信号分解成多个本征模态函数(IMF)，并计算各层IMF的能量熵，构成特征向量；最后，将特征向量输入鲸鱼算法(WOA)优化的支持向量机(SVM)中训练出分类模型，实现变压器绕组松动故障诊断。结果表明，所提方法适用于变压器绕组松动故障诊断，并且相较于传统的改进SVM分类模型，所提方法的故障识别准确率更高。

Fault Diagnosis for Winding Looseness of Transformer Based on VMD and WOA-SVM

In order to diagnose transformer winding looseness fault more accurately and effectively, a fault diagnosis method for transformer winding looseness based on variational mode decomposition (VMD) and support vector machine optimized by whale optimization algorithm (WOA-SVM) is proposed. Firstly, a fault simulation experiment is carried out on a 10 kV transformer to measure its vibration signal. Then, VMD is used to decompose the non-stationary vibration signal into multiple intrinsic mode functions (IMF), and the energy entropy of each IMF is calculated to constitute feature vectors. Finally, the feature vectors are input into the WOA-SVM to train the classification model, and the fault diagnosis of transformer winding looseness is realized. The results show that the proposed method is applicable to the fault diagnosis of transformer winding looseness, and its fault identification accuracy is higher than the traditional improved SVM classification model.