# A Deep Parallel Diagnostic Method for Transformer Dissolved Gas Analysis
## 1 Introduction  
As the pivotal equipment of the power system, a power transformer's health directly affects the safety and stability of the entire power grid. Therefore, it is of great significance to study the fault diagnostic method of a transformer [1]. With the continuous development of computer storage and sensor technology, the online monitoring DGA data of power transformers will show an explosive growth trend [2; 3], which has put forward higher requirements on the learning ability, feature extraction ability, and adaptability of transformer diagnostic methods. The dissolved gas analysis (DGA) is an online monitoring technology to analyze the composition and content of dissolved gas in transformer oil [4]. By studying the correlation between the dissolved gas and the fault states of the transformer, the health status of the transformer can be effectively diagnosed and the latent faults can be eliminated in time. A lot of studies have been conducted on traditional diagnostic methods based on DGA data. Traditional DGA interpretation methods such as IEC Ratio, Rogers Ratio, and Doernenburg Ratio are simple and easy to implement and have been widely used in engineering practice. Yet, there are still problems, such as the absolute diagnostic boundaries and missing codes [5; 6]. In addition, although traditional intelligent methods have achieved certain effects, they still have certain limitations on popularization and application due totheir shortcomings in learning ability, processing efficiency, and feature extraction ability. For example, the learning ability of the fuzzy methods are not satisfying [7; 8]. Neural networks (NN) tend to fall into local optimal solutions [9; 10]. The K-nearest neighbor (KNN) method is inefficient in high-dimensional space [11]. The support vector machine (SVM) is essentially a two-classifier, which makes it more troublesome to deal with multi-classification problems [12; 13]. There are also some relevant diagnosis studies on combining traditional ratio methods and intelligent methods, which are combinations of previous researches, and have achieved certain effects [14; 15; 16]. However, the features extracted by the ratio methods are still limited. And the anti-interference ability of the combined models need further testing. In recent years, due to the strong ability of deep learning to extract complex non-linear features, some papers have tried to introduce it into the field of transformer fault diagnosis. And the fault diagnostic accuracy of deep learning methods has been significantly improved compared to traditional machine learning diagnostic models'. The authors in [17] proposed a DBN-based fault diagnostic method that used the uncoded ratio of DGA data as the model input. Compared with the traditional methods', the accuracy was significantly improved. The authors in [18] introduced a method for identifying and locating winding faults based on CNN and transformer impulse tests. And the results showed that the method was effective. The authors in [19] studied the internal fault diagnosis of transformers based on CNN to enhance the differential protection performance, and verified the effectiveness of the method through different reliability indicators. However, these papers only considered a single deep learning method and did not compare the pros and cons of different deep learning methods. Still, they did not find a way to integrate different deep learning methods to fully mine the transformer fault characteristics from different perspectives and further improve the accuracy of fault diagnosis. Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN) are two of the most important frameworks in deep learning field [20; 21; 22]. CNN has been applied in mature fields such as visual recognition, image processing, and fault diagnosis [23; 24; 25]. As an improved model of RNN, long short-term memory (LSTM) was introduced to make up for the defects of long-term memory loss, gradient dissipation or explosion in the feedback process of the RNN model [26; 27].