针对变压器绕组松动故障诊断中特征量难以选取，依赖人工经验的问题，提出了一种基于自动编码器降噪，格拉姆角场（GAF）和深度残差网络（ResNet）进行识别的变压器绕组松动诊断方法。该方法直接从GAF图像中自动学习有效的故障特征，不需要手动提取特征量。首先，将振动信号经过自动编码器进行降噪，获得信噪比更高的振动信号。然后，采用GAF方法将振动信号转化为二维图像，生成图像数据集，在此基础上训练ResNet，构建适用于变压器绕组松动故障分类识别的网络模型。最后，搭建变压器绕组松动故障试验平台，采集绕组在不同松动和试验电流下的振动信号并进行分析。试验结果表明，所提诊断方法对变压器绕组松动识别准确率达95%以上，能够有效识别松动相和松动程度，适用于变压器绕组松动故障的识别和诊断。

Aiming at the problem that the feature quantity is difficult to select in the fault diagnosis of transformer winding looseness and relying on manual experience, a diagnosis method of transformer winding looseness based on automatic encoder noise reduction, gramian angle field (GAF) and depth residual network (ResNet) recognition is proposed. The method automatically learns effective fault features from GAF images without manually extracting the feature quantity. Firstly, the vibration signal is denoised through an automatic encoder to obtain a vibration signal with a higher signal-to-noise ratio. Then, the GAF method is used to convert the vibration signal into a two-dimensional image and generate an image dataset. Based on this, ResNet is trained to construct a network model suitable for classification and recognition of transformer winding looseness faults. Finally, a transformer winding looseness fault test platform is built to collect vibration signals of the winding under different looseness and experimental currents for analysis. The experimental results show that the proposed diagnosis method has an accuracy of over 95% in identifying transformer winding looseness, and can effectively identify the looseness phase and degree. It is suitable for identifying and diagnosing transformer winding looseness faults.

国家自然科学基金(51577050)；国家电网江苏省电力有限公司重点科技项目(J2022047)