# Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet
## 1 Introduction  
With the significant enhancement in the comprehensive globalisation of the world and the rapid social and economic development, the scale of the power grid is constantly expanding, and the user requirements for the safety and stability of electric power equipment are getting higher and higher [1]. The transformer, as one of the key pivotal pieces of equipment for transforming voltage, regulating power, improving voltage stability and improving power quality, can ensure uninterrupted power supply to end users, as well as the safety and stability of the power system [2; 3; 4]. Therefore, it is important to set up a perfect and accurate fault early warning mechanism to monitor and diagnose the condition of a power transformer in real time to obtain the internal mechanical status of the power transformer and to prevent accidents in a timely and effective manner [5]. At present, the methods for transformer mechanical fault diagnosis are mainly divided into those based on transformer electrical characteristics and those based on transformer vibration characteristics. Methods based on electrical characteristics mainly include the low-voltage impulse method, frequency response analysis method and short-circuit impedance method [6; 7], but they are not widely used because they generally require power outage diagnosis. The transformer vibration method mainly refers to the vibration signal analysis method, which is used to diagnose the winding status online by collecting the vibration signals on the surface of the transformer tank, which has no electrical connection with the transformer; this method is more accurate, easier to operate, safer and more convenient compared with other methods, and is a hot spot of current research [8; 9]. The research related to transformer vibration signals began in the 1980s. In the mid-to-late 1990s,refs. [10; 11] proposed the use of vibration analysis to analyse the vibration signals on the surface of transformers. The vibration signal analysis method can identify the electromagnetic force, mechanical movement, structural deformation and other physical phenomena inside the transformer, which can reveal the overall working status of the transformer and possible faults [12; 13] and is more intuitive and easier to interpret than other methods. The transformer vibration signal research mainly considers the vibration mechanism via the extraction of vibration signal eigenvalues and real-time calculation of the transformer box surface vibration signal changes [14; 15; 16], and by using the same transformer historical data or the same type of transformer data for comparison and analysis of the normal operating conditions, you can judge the working state of the transformer under test. So far, related researchers have achieved certain research results in the study of transformer vibration characteristics and fault diagnosis methods based on vibration signals. In [17], by mining the fault information contained in the vibration signals, continuous wavelet transform (CWT) was used for the feature extraction, and the vibration signals were converted into RGB images with a time-frequency relationship; then, an improved convolutional neural network (CNN) model was used to complete the image recognition task of transformer fault diagnosis. In [18], a transformer fault diagnosis method based on the combination of time-shifted multiscale bubble entropy (TSMBE) and stochastic configuration network (SCN) was proposed. The method introduces bubble entropy to overcome the shortcomings of traditional entropy models that rely too much on hyperparameters. Based on the bubble entropy, a TSMBE tool was proposed to measure the signal complexity, and then, the TSMBE of the transformer vibration signal was extracted as the fault feature. Finally, the fault features were input into the random configuration network model to achieve the accurate identification of different transformer state signals.