Predictive Model for Incipient Faults in Oil-Filled Transformers

Abstract

The power transformer is an invaluable piece of device in the power system. To prevent catastrophic failures and the ensuing power outages, the status of a transformer linked to a system must be examined for any possible faults. Despite using DGA as a global tool for detecting faults, it is limited by the inability to accurately solve the problem associated with results variability due to the intrinsic nature of the IEC TC 10 database. This study proposed a data-driven fault/defect diagnostic model using four ensemble models with three base classifiers respectively. The base classifiers are comprised of SVM, C4.5 decision tree, and naive Bayes while the ensemble methods are comprised of stacking, voting, boosting and bagging respectively. The DGA dataset used comprises seven features and 168 instances split into training (i.e. 56%) and test (i.e. 44%) datasets respectively. The results indicate that C4.5 obtained a 98.33% accuracy while stacking obtained a 99.89% accuracy as the best-performing base and ensemble models respectively. The high classification performance accuracy achieved by our proposed models indicates its capacity for real-world applications. It can be applied to advance automation in mobile-based technology.