## 4 Discussion
Some of the studied algorithms have hybrid algorithms adapted with an attempt to improve their performance such as those of ANN found in this study which includes CNN, GRNN, improved BP neural network, deep believe network, and Adaptive Neuro-Fuzzy Inference System, all of which contributes an additional combined 10%, which bring the overall total of neural networks contribution to 27%. Other machine learning algorithms that had fewer contributions and are being explored in the studied literature include the k-Means Clustering algorithm, improved fuzzy c-means clustering, Bayesian Networks, hybrid gray wolf optimization algorithm, Gaussian Process Multi-classification, Lasso regression, Hidden Markov model, Logistic Regression, among others. The combined contribution of these algorithms is 26%. Figure 9 shows the distribution of machine learning algorithms used for transformer monitoring using DGA data. **Q6. **: _Which software and engineering tools are frequently used to compute and analyze DGA data?_  
From the papers studied, MATLAB is the most commonly used software for modeling and computing machine learning algorithms for transformer DGA applications with a percentage of 31%, surprisingly, Python is used by only 2% of the papers studied, and LabView just 1%. The rest of the documents, which contributed 66%, did not specify the software or engineering tool used for computation. Figure 10 shows the distribution of the software used. **Q7. **: _Which methods and industrial specifications are utilized for DGA data collection, analysis, and interpretation of results?_  
A large number of the papers studied do not specify the globally adopted industrial standards used in the analysis. This causes concern as having an accurate depiction of standards would aid in understanding which ones are the most effective and accurate. From the collected studies, 36% of the papers used IEC 60599, while 9% specified C57 IEEE 104 standards, and the remaining 55% of papers did not specify the standard used. Figure 11 shows the distribution of standards used for DGA interpretation. **Q8. **: _Which DGA gas components are analyzed?_  
The studied papers apply a holistic approach when describing the gases studies, with almost all of them mentioning all gases, i.e., hydrogen (H2), methane (CH4), ethylene (C2H4), ethane (C2H6), acetylene (C2H2), carbon monoxide (CO), and carbon dioxide (CO2). This could be due to the methods used in these papers being adapted from the traditional DGA interpretation techniques, namely, the Dornenburg ratio method, the Rogers ratio method, the Nomograph Method, the IEC Ration Method, the Duval triangle method, and the CIGRE method. **Q9. **: _What are the sources of DGA datasets used in the literature?_  
The study shows that a majority of the DGA datasets used to conduct simulations are obtained from the literature (34%) and the IEC TC10 Database (32%), and a combined percentage of 25% is obtained from transformers used in 16 different industrial power utilities, manufacturers, substations, and private companies. The rest of the 9% of sources of the datasets are not specified in the papers. Figure 12 shows the distribution of the sources. Shortcomings and Limitations Identified in the Literature Relating to DGA-Based Transformer Fault Classification
* Unspecified transformer type and capacity MVA sizes: A great majority of the papers do not specify the transformer type and their capacity; this could have provided indicative insights into which transformer types and sizes are most prone to which transformer fault based on the DGA interpreted results. * Unspecified oil type of DGA samples: Many of the papers do not provide the type of insulation oil that was used from which the DGA results were extracted. This could have provided valuable insights into which insulation oil between mineral oil, vegetable oil, esters oils, etc., is the most reliable and which ones exhibit high concentrations of gases over a period of usage.