## 2 Literature Review  
Considering related research studies, research [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16] mentioned fault-detection methods using artificial neural networks (ANNs) [7; 8; 9; 10; 11; 12; 13; 14; 15; 16], finite elements [17; 18; 19; 20; 21; 22], fuzzy logic [23; 24; 25; 26; 27; 28], hybrid systems [29; 30; 31; 32; 33; 34; 35; 36], transient-based protection [37; 38; 39; 40; 41; 42; 43; 44; 45], etc. [46]. For transformer defect diagnostics, a novel multi-input and multi-output polynomial neural network (PNN) is proposed [10]. The five types of characteristic gas that match the four fault categories of sample data are used to train and produce a single-output PNN I classification model. Then, there is a model for detecting transformer faults based on multiple outputs, PNN II, which is designed to target high-energy discharge, low-energy discharge, and thermal heating fault types. The results of simulations and tests demonstrate that accuracy can reach 100%. The frequency response traces of the tested winding were analyzed using convolutional neural networks (CNN) [11]. The proposed method has been shown to be capable of accurately locating faults. To detect DSV location in transformer winding, convolutional neural networks were used to extract key information from frequency response traces. The location of all applied DSV faults in the transformer winding was pinpointed with 100% accuracy. In paper [13], the discrete time-series convolution neural network (DTCNN) is constructed. HDCNN's performance has been verified. The conditions of a suitable source dataset for TRU fault diagnosis, as well as the transfer layers from the pre-trained HDCNN, are discussed on this foundation. The comparison of transfer learning to other algorithms under various noise conditions shows that it is an effective technique to build a diagnosis network for similar equipment and can often lead to higher performance. Finite element analysis of internal winding faults in a distribution transformer is presented in the study [17]. The transformer's terminal behaviors are investigated using an indirect coupling of the finite element approach and circuit modeling. The circuit model was exported and used in a circuit analysis program to investigate the transformer'sterminal characteristics. For comparison, previous experimental data were employed. We can observe that the FEA transformer model can accurately predict the terminal values of a distribution transformer's internal winding defect. Soft computing models such as fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS) have been used to implement dissolved gas analysis (DGA) [23]. For diagnostic capability, the models were tested using a reported fault database. The ANFIS model is a dependable system with a high degree of precision. Because the ANFIS model is so simple to create, it can overcome the constraints of traditional DGA-based transformer fault diagnosis approaches. The hyperbolic S-transform-based approach in [29] was used to distinguish between internal faults, external faults, incipient faults, and inrush currents. This method was operated in two stages. External faults were first distinguished from other disturbances while an internal disturbance was being detected. The system was then subjected to the absolute deviation of the S-transform matrix. Analyzing diverse industry standards of dis-solved gas analysis [36] yields new defect features. To improve diagnosis accuracy, two effective data pre-processing methods are used. As fault classifiers, a least-square support vector machine, a support vector machine, and a support vector data description are constructed in experiments, and the best parameters of the three classifiers are found via particle-swarm optimization. A detailed comparison of the performance of three-support vector machine-based classifiers is made for the first time in the field of dissolved gas analysis.