Dissolved Gas Analysis of Generator Step Up Transformer in Grati Power Plant Using Random Forest Based Method

Transformers are one of the important electrical equipment in the power system. To prevent some electrical contact on the component in transformers, an insulator or dielectric material is needed likely insulating oil. DGA test is important for diagnosis and deciding the maintenance of transformers. Duval Triangle and Duval Pentagon methods are DGA identification methods with the highest of accuracy compared to other methods. The data used in this article is from the DGA measurement test of transformers GT 3.1 Steam and Gas Power Plant Grati. The DGA data was analyzed by Random Forest based-model of Duval Triangle and Pentagon method, in accordance to IEEE C57.104-2019 and IEC 60599-2015 guidelines. Random Forest based-model has the best performance in implemented Duval method than others. The result of DGA identification using Random Forest based-model showed PD and S for Duval Triangle, and S for Duval Pentagon and from the results of identification using the Duval Triangle and Pentagon it does not always show the same results on the same test sample, so it is necessary to identify the history of DGA testing to get accurate results. This article presents the use of the combined Duval Triangle and Pentagon for diagnosis transformers

Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique

Funding Information: The authors appreciate Taif University Researchers Supporting Project TURSP 2020/34, Taif University, Taif, Saudi Arabia for supporting this work. Further, this study is partly funded by DIPA Polinema. Publisher Copyright: © 2023Power transformers are considered one of the power system's most critical and expensive assets. In this regard, it is vital to assess the fault within the power transformer considering numerous operational aspects. In the literature, dissolved gas analysis (DGA) is the routine in-service test for power transformers and one of the most important tests to ensure sufficient system reliability. Specifically, this test can detect dissolved gases in transformer oil which are then interpreted to detect the fault type of the transformer. Previous studies reported that the graphical Duval pentagon is one of the most accurate and consistent DGA interpretation techniques. However, it still has limitations on the complexity of the implementation in large amounts of data. To cover these issues, this study mitigates the limitation and complexity of implementing the graphical Duval Pentagon Method (DPM) in large amounts of data. To reach this goal, we develop a precise machine-learning-based fault identification model by employing the Random Forest algorithm with Synthetic minority over-sampling technique (SMOTE) preprocessing. The proposed Random Forest models with SMOTE perform satisfactorily in diagnosing faults for the evaluation dataset, with a total accuracy of 96.2% for DPM1 and 96.5% for DPM2. The proposed models were also compared to other machine learning algorithms, performing better both in classification accuracy and consistency due to uncertainty.Peer reviewe