Enhancing the Diagnostic Accuracy of DGA Techniques Based on IEC-TC10 and Related Databases

Abstract

Investigating and enhancing the accuracy of widespread dissolved gas analysis (DGA) techniques based on IEC-TC10 and related databases was implemented. The current work aimed to help the experts to diagnose the transformers’ faults accurately. The major drawback of the dissolved gas techniques is no decision diagnosis for the cases that lie out of the specified codes of the traditional DGA methods. In this article, fuzzy logic (FL) and artificial neural network (ANN) were applied to the standard DGA techniques such as the Dornenburg ratio method, Rogers ratio method, and IEC Standard Code 60599. This paper provided a new concept using artificial intelligence for enhancing the diagnostic accuracy of the conventional DGA method such as Dornenburg ratio, Rogers’ ratio, and IEC standard which suffer from a poor diagnostic accuracy and fail to interpret the cause of the faults in most cases. In this article the FL and ANN accuracy results were compared with that of other diagnostic techniques in the literature. The results revealed that the artificial intelligence methods improve the diagnostic accuracy of the conventional DGA techniques from 41.95, 76.76, and 51.44% to 58.97 (ANN), 89.02 (FL), and 62.67% (FL) for Rogers, Dornenburg, and IEC standard code, respectively.

Keywords

• Dissolved gas analysis
• fault diagnosis
• power transformer oil
• heptagon graph DGA
• and Duval triangle