Machine learning based multi-method interpretation to enhance dissolved gas analysis for power transformer fault diagnosis

Accurate interpretation of dissolved gas analysis (DGA) measurements for power transformers is essential to ensure overall power system reliability. Various DGA interpretation techniques have been proposed in the literature, including the Doernenburg Ratio Method (DRM), Roger Ratio Method (RRM), IEC Ratio Method (IRM), Duval Triangle Method (DTM), and Duval Pentagon Method (DPM). While these techniques are well documented and widely used by industry, they may lead to different conclusions for the same oil sample. Additionally, the ratio-based methods may result in an out-of-code condition if any of the used gases fall outside the specified limits. Incorrect interpretation of DGA measurements can lead to mismanagement and may lead to catastrophic consequences for operating power transformers. This paper presents a new interpretation technique for DGA aimed at improving its accuracy and consistency. The proposed multi-method approach employs s scoring index and random forest machine learning principles to integrate existing interpretation methods into one comprehensive technique. The robustness of the proposed method is assessed using DGA data collected from several transformers under various health conditions. Results indicate that the proposed multi-method, based on the scoring index and random forest; offers greater accuracy and consistency than individual conventional interpretation methods alone. Furthermore, the multi-method based on random forest demonstrated higher accuracy than employing the scoring index only.