# Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet
## 1 Introduction
In [19], the vibration signals were converted into different images by combining the simultaneous wavelet transform and the simultaneous generalised S-transform, which effectively expanded the samples and extracted the effective features of the signals. A two-stream densely connected residual contraction network (TSDen2 NetRS) was used to achieve high-precision transformer fault diagnosis under different operating conditions. Ref. [20] proposed a new approach based on the improved empirical wavelet transform (IEWT) and sandy bee algorithm (SSA) optimisation learning machine (KELM), which not only efficiently diagnoses fault classes present in the training set, but also identifies unknown fault classes. Ref. [21] combined two parts, namely, a data measurement subsystem and data reception subsystem, to form a monitoring system based on IoT technology, and proposed a new method of transformer fault diagnosis based on an IoT monitoring system and integrated machine learning; the proposed method compensated for the deficiency of individual deep learning methods to extract feature information. However, the above study ignored the fact that during the actual operation of the transformer, the existence of the closed magnetic circuit of the core, the electrical circuit of the windings and the gate ring, and thus, the formation of the closed circuit, resulting in additional electromagnetic force on some structural parts; this produces nonlinear characteristics of the closed magnetic circuit, the electrical circuit and the gate ring, and thus introduces additional harmonic and nonharmonic components, resulting in the accuracy of the vibration-signal-based fault diagnosis. In [22; 23; 24], by studying the force characteristics and vibration characteristics of the transformer core and windings in a harmonic environment, it was found that the vibration energy distribution was shifted from the low-frequency band to the high-frequency band after the harmonics were injected, but the existing studies did not make use of this feature in the transformer fault diagnosis research. The above work shows that most of the studies on transformer fault diagnosis based on vibration signals, although significant, ignored the interference caused by harmonic and non-harmonic components of the vibration signals on the signals, thus resulting in a low fault recognition rate and degradation of the performance of the classification model. On this basis, a transformer fault diagnosis method based on SCA-VMD and improvedGoogLeNet was proposed to collect and analyse different fault signals of transformers and successfully applied in transformer fault diagnosis experiments. In comparison with existing research, the following main contributions were made: (1) This paper introduces the positive cosine algorithm to adaptively determine the VMD algorithm in the signal processing process, which cannot determine the decomposition parameters K and \(\alpha\) of the parameter problem, and defines an adaptive function of the minimum average envelope entropy for the adaptive function to verify the superiority of the performance of this paper's algorithm (SCA-VMD). The results show that the required number of iterations of this algorithm was significantly fewer than the comparative algorithm, and the operation was more efficient, which greatly improved the modal decomposition quality. The periodic map spectrum of the vibration signals decomposed based on SCA-VMD are summed up according to the modal components, the modal components are used as the horizontal axis, the frequency is used as the vertical axis, and the brightness of each point represents the energy magnitude of the modal component at the corresponding frequency to draw the feature map.