A Comprehensive Review of Transformer Fault Diagnosis Studies Based on Dissolved Gas Analysis: Classical Methods, Historical Development of the Devices Used, Artificial Intelligence Based Methods, Accuracy of Classifications of Predictions

Transformers are critical and expensive components of power systems. Therefore, it is important that these systems operate at optimum performance levels and sustainable economic conditions. In the normal operating environments, mineral oil passes on a slow and natural deterioration, while under conditions of thermal or electrical stress, the deterioration ratio increases. Due to breakdown, the hydrocarbon gases H2, CH4, C2H6, C2H4, C2H2, CO, and CO2) are composed in the transformer mineral oil. There are several conventional methods for identifying and classifying incipient faults in power transformers based on dissolved gas analysis (DGA). However, these methods have the disadvantage of not being able to distinguish situations where multiple electrical or thermal faults occur simultaneously. Due to the serious disadvantage of traditional DGA methods in terms of accuracy and consistency estimation compared to algorithm calculations made with artificial intelligence techniques, researchers have started to work intensively on artificial intelligence techniques in recent years. This comprehensive review aims to combine and present in a single source basic information about classical methods of power transformer fault diagnosis for DGA, historical development of the devices used, artificial intelligence-based methods, accuracy classifications of predictions. This investigation also revealed the contribution of the parameter optimisation process to eliminate the imbalance of the dataset in the accuracy of prediction when applying artificial intelligence techniques in DGA. In this study, the prediction performance of each research method performed with artificial intelligence techniques in fault diagnosis among the compared methods was analysed. This review emphasises the importance of eliminating dataset imbalance by performing parameter optimisation in artificial intelligence technique with an in-depth research-orientated perspective. As a result, this study not only encourages new ideas, but also provides a comprehensive source of literature for future accessibility of the subject.