A Machine Learning Framework for Improved Fault Diagnosis in Service Transformers Using Dissolved Gas Analysis
TL;DR
This article explores machine learning techniques (MLTs) as a modern alternative to enhance the interpretation of DGA data for early-stage fault detection in service transformers, and demonstrates that random forest and gradient boosting outperform others, achieving up to 98% accuracy.
Abstract
The service transformer stands as a pervasive and essential component within the energy infrastructure. Service transformers are critical elements in power distribution, and their longevity hinges on effective monitoring. Traditional fault detection methods, especially dissolved gas analysis (DGA), though widely used, often suffer from delays due to manual sampling and lab-based analysis. This article explores machine learning techniques (MLTs) as a modern alternative to enhance the interpretation of DGA data for early-stage fault detection in service transformers. The method relies on the use of multiple DGA datasheets to investigate the fault typing capabilities and suitability of different MLTs. It evaluates multiple algorithms—logistic regression, support vector machines (SVM), random forest, and gradient boosting—using actual DGA datasets. To validate the best class algorithms, this article also looks at performance accuracy and then evaluates the top-performing algorithm. The results demonstrate that random forest and gradient boosting outperform others, achieving up to 98% accuracy, and are especially useful for condition monitoring professionals dealing with insulating oil analysis, as compared to the accuracy achieved of 81.4% with SVM and 76% with artificial neural network (ANN) in the case of previously published work.