为了深入研究变压器振动信号包含的大量故障信息，提出了一种基于S变换奇异值分解(ST-SVD)与鲸鱼优化支持向量机(WOA-SVM)模型的变压器绕组松动故障诊断方法。首先，基于变压器故障模拟试验平台采集变压器绕组处于不同状态下的振动信号。其次，对变压器振动信号进行S变换获取其时频矩阵。再次，计算出时频矩阵对应的幅值矩阵进行SVD，并定义特征向量。最后，采用鲸鱼优化算法优化SVM模型参数，并输入特征向量完成故障诊断。试验结果表明，所提方法故障识别准确率高于传统方法模型，适用于变压器绕组松动故障诊断。

In order to further study the large amount of fault information contained in transformer vibration signals, a fault diagnosis method for transformer winding looseness based on S-transform singular value decomposition (ST-SVD) and support vector machine optimized by whale optimization algorithm (WOA-SVM) model was proposed. Firstly, based on the transformer fault simulation experiment platform, the vibration signals of transformer windings in different states were collected. Secondly, the time-frequency matrix of the transformer vibration signal was obtained by S-transformation. Thirdly, calculating the amplitude matrix corresponding to the time-frequency matrix for SVD, and defining the feature vector. Finally, the parameters of SVM model were optimized by whale optimization algorithm, and the fault diagnosis was completed by inputting feature vectors. The experimental results show that the accuracy of fault identification of the proposed method is higher than that of the traditional method model, and it is suitable for the diagnosis of transformer winding looseness fault.

国家自然科学基金项目(51577050)；国网江苏省电力有限公司科技项目(J2021053)