Bayesian Optimization-Based Gradient Boosting Method of Fault Detection in Oil-Immersed Transformer and Reactors

Abstract

This article aims at developing a gradient boosting (GB) model with parameters optimized by the Bayesian optimization (BO) method for detecting various types of fault in an oil-immersed transformer and reactor. The developed model depicts higher accuracy in comparison with other machine learning (ML) techniques, such as XGBoost, AdaBoost, random forest, and support vector machine parameters being optimized by BO. A comparison of the skill score depicts the superiority of the developed Bayesian optimization-based gradient boosting (BO-GB) model with respect to Bayesian optimized other ML techniques. Further comparison of the model with conventional Duval pentagon verifies the accuracy of detecting the faults, which were missed by the former. Also, the software has been developed based on the proposed BO-GB method for the on-site detection of the fault. In addition, actual values of dissolved gases with respect to fault confirmed after the internal inspection of the transformer and reactor are tabulated and compared. The data so presented will help in utilizing the authentic dissolved gas data in addition to the already available IEC TC10 databases for future works. The study undertaken in this article will help utilities in diagnosing the incipient faults more accurately and precisely, thereby preventing hazardous operating conditions along with minimizing the downtime cost.