# A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis
## 2 Materials and Methods
### Deep Parallel Diagnosis  
#### 2.2.1 CNN-Based Transformer DGA Diagnostic Method  
As shown in Table 2, transforming measured data such as waveforms into pictures through certain data visualization techniques and inputting them to CNN for diagnosis is a commonly used method in the current researches of diagnostic methods [42]. DGA fault data contains rich information on transformer fault states. The reason for the relatively lower accuracy of traditional methods is that it is difficult to find accurate mathematical models to characterize the complex nonlinear relationship between DGA data and transformer fault states. In order to fully mine the transformer state information  
Figure 2: Illustrate the local reachability density, when \(D_{i,j}^{\min}\) is bigger and \(D_{i,j}^{maj}\) is smaller.  
contained in the DGA data, the normalized numerical DGA data are converted into the more intuitive images, which are convenient for CNN to process.  
The continuous improvement of equipment manufacturing technologies and fault monitoring methods may lead to insufficient DGA fault samples. Transfer learning can map the knowledge in the B domain to the A domain. When using transfer learning, the CNN parameters after pre-trained on the public dataset are set to initial values and part of the network is frozen for training. Transfer learning is used to enable the network to obtain the fine characteristics of the equipment to be diagnosed, making up for the lack of power equipment's fault samples. With the transfer learning method, a small amount of data can still achieve a high training accuracy [43].  
At present, there are dozens of CNN image recognition models suitable for large image databases. Among them, MobileNet-V2 [44] (its basic module is shown in Figure 3) is a lightweight neural network for limited computing resource environment proposed by the Google team in 2018. MobileNet-V2 is based on the basic concepts of MobileNet-V1 [45]. MobileNet-V1 innovatively proposed to use depth-wise separable convolution instead of traditional convolution operations. Although this can reduce the number of parameters and operations, it will also cause a loss of features and decrease accuracy. MobileNet-V2 has made certain relevant improvements against the shortcomings of MobileNet-V1 and proposed two innovative design ideas: inverted residuals and linear bottlenecks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Data** & **Examples** & **Data Visualization Method** \\ \hline Monitoring image & Trim away the extra parts and highlight the detected or pivotal parts. \\ \hline Waveform graph & Perform a decomposition, spectrum analysis, and other process to convert the curve into a characteristic spectrum. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data visualization methods.  
Inverted residuals: the depth-wise convolution of MobileNet-V2 was preceded by a \(1\times 1\) \"expansion\" layer. Its purpose is to expand the number of channels in the data before the data enters deep convolutions, enrich the number of features, and improve accuracy.  
Linear bottlenecks: MobileNet-V2 proposed to replace the ReLU activation function with a linear activation function after layers with fewer channels. Due to the introduction of the \"expansion layer\", a large number of features output by the convolution layer need to be \"compressed\" to reduce the amount of calculation. As the number of channels decreases, if the activation function is still ReLU, the features will be destroyed. This is because ReLU's outputs for negative inputs are all zeros; the original features are already \"compressed\" and if they pass through ReLU, a lot of them will be lost.  
Due to the large cost of accidents in large power transformers, the efficiency requirements for the fault diagnosis algorithm are numerous. MobileNet-V2 can obtain a higher accuracy without too much calculating time and computing resource. Therefore, the MobileNet-V2 model was introduced in this diagnostic scenario as the pre-trained model for transfer learning.