基于格拉姆角场图像融合与深度学习算法的大型变压器绕组故障诊断方法OA

Winding fault detection is crucial to ensure the safe and reliable operation of transformers.In order to break through the limitations of the traditional frequency response information characterization and to improve the fault diagno-sis accuracy,this paper proposes an image fusion framework based on GAF-VSM-WLSO and combined with a novel HDC-CBAM-ResNet34 classification model to realize the accurate diagnosis of transformer winding faults.First,the frequency response information is transformed into two-dimensional GASF and GADF images by Gram angle field,and then the two types of images are fused using visual saliency map with weighted least squares optimization algorithm to generate VSM-WLSO fused images that retain global structure and local details,which solves the problem of low redun-dancy in the representation of a single feature.Second,the HDC-CBAM-ResNet34 classification model fusing hybrid dilation convolution and attention mechanism is constructed,which takes ResNet34 as the benchmark,introduces hybrid dilation convolution(HDC)so as to expand the receptive field for capturing multi-scale features,and incorporates a con-volutional block attention mechanism(CBAM)module to enhance key feature information.Consequently,high-accuracy recognition of the type,degree,and location of transformer winding faults is achieved.Finally,the performance of the model proposed in this paper is verified and comparatively analyzed through ablation experiments and comparisons with current mainstream models.The experiment results show that the method proposed in this paper performs well in the classification of fault type,fault location,and fault degree,and its recognition accuracy and F1 parameter are above 96％.