## 1 Introduction
### Dissolved Gas Analysis  
In most power system configurations under normal conditions, power transformers operate continuously providing the required power at the designed voltage level. Over time, the insulation of these transformers tends to gradually degrade due to various factors such as temperature, humidity, aging, loading stress, and oil quality. These elements make the transformers susceptible to insulation breakdowns that lead to transformer faults which interrupt the power system. To prevent interruptions, various power transformer monitoring techniques are employed to assess the condition of transformers, such as the dissolved gas analysis (DGA) technique. DGA is a routine test that monitors the presence and concentration of dissolved gases in the transformer oil as a result of electrical and thermal faults [5]. The dissolved gases present in the transformer oil that indicate incipient faults are Methane (CH\({}_{4}\)), Ethane (C\({}_{2}\)H\({}_{6}\)), Ethylene (C\({}_{2}\)H\({}_{4}\)), Acetylene (C\({}_{2}\)H\({}_{2}\)), Hydrogen (H\({}_{2}\)), Carbon Monoxide (CO), and Carbon Dioxide (CO\({}_{2}\)). Generally, the procedure followed to conduct the analysis is as follows.  
* Extract a small oil portion from the transformer using a specialized syringe.
* Extract the gases in the oil collected from the transformer in the laboratory.
* Use chromatography to analyze and identify the individual concentrations of the extracted gases.  
* Then, use the identified gas concentration to diagnose the incipient faults that may occur in the transformer using the DGA-based interpretation techniques.  
Various DGA-based interpretation techniques are widely used for interpreting DGA data to identify incipient transformer faults governed by different international standards and to classify transformer faults such as thermal faults, electrical faults, or partial discharge faults [13; 14; 15; 16]. These methods include the key gas method, Dornenburg ratio method, Rogers ratio method, Nomograph Method, IEC Ration Method, Duval triangle method, and the CIGRE method.  
With the exponential deployment of artificial intelligence in various industries, the utilization of machine learning algorithms in electrical equipment has seen substantial growth over the past decade, especially in condition monitoring applications.  
For power transformers, machine learning algorithms have been employed for analyzing key diagnosis data associated with DGA which are used for fault detection and prediction with the intent of improving the accuracy and reliability of conventional methods based on the transformer's liquid insulation properties and oil concentration obtained from laboratory results [5; 17; 18; 19].  
The proposed systematic literature review investigates the current scholarly works on the application of machine learning algorithms for DGA-based fault classification in power transformers. This review assesses different machine learning algorithms and models that are currently employed for transformer fault diagnosis. Furthermore, this SLR searches for gaps in the methods used in comparison with the conventional DGA interpretation techniques to pave the way for further research and developments in the research area. To better ascertain the state of the art concerning the application of machine learning (ML) algorithms in classifying and interpreting incipient transformer faults based on dissolved gas analysis, a systematic literature review (SLR) was conducted. The primary goal of this SLR was to evaluate the extent to which researchers have investigated the application of ML using transformer oil characteristics obtained from DGA results to identify possible methodological or theoretical gaps in the existing literature. For a guarded search of the literature, a set of restrictive boundaries and criteria were applied, this included research period range, keywords, research domain, language, and publication type as shown in Sections 2.1 and 2.3.