Interpreting dissolved gases in transformer oil: A new method based on the analysis of labelled fault data

Abstract

Abstract In this contribution, a new dissolved gas analysis (DGA) method combining key gases and ratio approaches for power transformer fault diagnostic is presented. It is based on studying subsets and uses the five main hydrocarbon gases including hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2). The proposed method uses 475 samples from the dataset divided into subsets formed from the maximum and minimum(s) concentrations of the whole dataset. It has been tested on 117 DGA sample data and validated on the International Electrotechnical Commission (IEC) TC10 database. The performance of the proposed diagnostic method was evaluated and compared with the following diagnostic methods: IEC ratios method, Duval's triangle (DT), three ratios technique (TRT), Gouda's triangle (GT), and self‐organizing map (SOM) clusters. The results found were analysed by computer simulations using MATLAB software. The proposed method has a diagnosis accuracy of 97.42% for fault types, as compared to 93.16% of TRT, 96.58% of GT method, 97.25% of SOM clusters method and 98.29% of DT method. However, in terms of fault severity, the proposed method has a diagnostic accuracy of 90.59% as compared to 78.90% of SOM clusters method, 83.76% of TRT, 88.03% of DT method, and 89.74% of GT method.

Keywords

• Organic insulation
• Transformers and reactors
• Data handling techniques
• Power engineering computing
• Neural nets