Enhanced transformer fault forecasting based on dissolved gases concentration and their rate of change

Abstract

A method for using forecasting power transformer faults may include receiving dissolved gas data of power transformers; receiving features of the power transformers; generating, based on the features and the dissolved gas data, clusters of transformers, wherein each respective cluster of the clusters comprises transformers exhibiting similarities to each other; selecting, for each respective cluster and respective gas of the first dissolved gas data, from among machine learning models trained to minimize a difference between a forecast gas concentration and an actual gas concentration, a machine learning model; generating, using a selected machine learning model, a forecasted concentration of the respective gas of the respective cluster; estimating, using forecasted gas concentration, a ROC of the respective gas of the respective cluster; predicting, based on a comparison of the ROC to an ROC alarm threshold, a future fault of a transformer; and generating an alert indicating the transformer maintenance is required.