# Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques
## 1 Introduction  
The most costly and significant components of power systems are power transformers. For network operations to be stable and safe, they are essential. In fact, a significant breakdown of an electricity system caused by power transformer failure can result in blackouts, expensive repairs, and enormous financial losses [1]. Thus, to operate and sustain power system networks, early transformer problem detection is essential. Dissolvedgas analysis (DGA), a chromatographic measurement of dissolved gas in oil, is one of the most commonly used methods for the early identification of defects in transformer inactive portions [2; 3]. The fact that this method is non-intrusive and suitable for real-time monitoring accounts for its widespread use. The basic idea of the method is to routinely collect samples of transformer insulation oil to identify the gases that have been dissolved in it because of the insulation system's deterioration [4]. Since its discovery in the 1940s, gas chromatography has allowed for the identification of several dissolved gases [5]. The temperature and/or energy generated by the defect promotes the formation of gas. Different kinds of decomposition processes might happen depending on the type of defect. Transformer oil deteriorates due to electrical or thermal problems, producing flammable gases including ethylene (C\({}_{2}\)H\({}_{4}\)), acetylene (C\({}_{2}\)H\({}_{2}\)), methane (CH\({}_{4}\)), ethane (C\({}_{2}\)H\({}_{6}\)), and hydrogen (H\({}_{2}\)). Carbon monoxide (CO) and carbon dioxide (CO\({}_{2}\)) are released during the breakdown of cellulose insulation, and these gases are indicative of a thermal defect. There is also the production of other gases, such as nitrogen (N\({}_{2}\)) and oxygen (O\({}_{2}\)) [6]. The results of the identification and quantification of the gases must be analyzed to evaluate the transformer's condition. Transformer fault detection involves the use of the dissolved gas analysis (DGA) technique, which measures the changes in the insulating oil in the event of a failure. Gas concentrations in the insulating oil are monitored, and defect detection utilizing several interpretation techniques in the DGA approach is performed [7]. Conventional techniques are those included in standard practice that have been in use for a long time. In these methods, the rules used for fault detection are developed based on historical fault datasets and expert experience. These techniques use rules, graphical representations, and various gas rates and gas percentages to diagnose faults [7]. This method of fault identification might lead to incorrect diagnoses in certain situations and impossible diagnoses in others. Intelligent classification techniques are beginning to be applied to overcome these problems. These techniques are artificial intelligence-based and computer-assisted. In this work, gas concentrations acquired from the DGA approach for power system fault diagnostics have been used to diagnose faults. By combining intelligent methods--i.e., DGA interpretation--with conventional approaches, the goal was to improve fault diagnostic performance. For this reason, the gas ratios and gas percentages utilized in traditional approaches, namely Duval's triangle method, the Doernenburg ratio method, and the Rogers ratio method, were used as the input dataset for classification algorithms. The classification algorithms have been developed using machine learning techniques such as random forest (RF), neural network (NN), K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and Naive Bayes (NB) [8; 9; 10]. The employment of machine learning has been used by numerous researchers to ensure better DGA accuracy. SVM has been used to identify transformer faults by DGA, and studies have highlighted the notably better performance of SVM over fuzzy logic and traditional methods [11; 12]. In [13], machine learning was used to explain fault types in transformers by DGA.