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

A device for using forecasting power transformer faults, the device comprising memory coupled to at least one processor, the at least one processor configured to:

• receive dissolved gas data of power transformers;
• receive features of the power transformers;
• generate, 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;
• select, for each respective cluster and for each respective gas of the dissolved gas data, from among multiple machine learning models trained to minimize a difference between a forecast gas concentration and an actual gas concentration, a machine learning model associated with forecasting concentration of the respective gas of the respective cluster;
• generate, using a selected machine learning model, a forecasted gas concentration of the respective gas of the respective cluster;
• estimate, using forecasted gas concentration, a rate of change (ROC) of the respective gas of the respective cluster;
• predict, based on comparisons of the forecasted gas concentration and the ROC to respective alarm thresholds, a future fault of a transformer;
• and
• generate an alert that transformer maintenance is required prior to the predicted future fault.

A system for using forecasting power transformer faults, the system comprising:

• a dissolved gas analyzer device;
• and
• memory coupled to at least one processor, the at least one processor configured to:
• receive, from the dissolved gas analyzer device, dissolved gas data of power transformers;
• receive features of the power transformers;
• generate, 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;
• select, for each respective cluster and for each respective gas of the dissolved gas data, from among multiple machine learning models trained to minimize a difference between a forecast gas concentration and an actual gas concentration, a machine learning model associated with forecasting concentration of the respective gas of the respective cluster;
• generate, using a selected machine learning model, a forecasted gas concentration of the respective gas of the respective cluster;
• estimate, using the forecasted gas concentration, a rate of change (ROC) of the respective gas of the respective cluster;
• predict, based on comparisons of the forecasted gas concentration and the ROC to respective alarm thresholds, a future fault of a transformer;
• and
• generate an alert indicating that transformer maintenance is required prior to the predicted future fault of the transformer.