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.
Highlights
Power transformers are the most expensive and vital equipment in the electric power system due to their power transformation functions
This paper provided a new concept using artificial intelligence for enhancing the diagnostic accuracy of the conventional dissolved gas analysis (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
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
Summary
Power transformers are the most expensive and vital equipment in the electric power system due to their power transformation functions. This paper systematically discusses the accuracy of the different conventional, Fuzzy Logic, Artificial Neural network-based DGA ratio techniques, and DGA techniques suggested by [23,24,25] to detect and identify transformer malfunctions in each fault type. These techniques are implemented for fault diagnosing and decision-making for oil-immersed transformers and summarize existing problems. Sayed A Ward et al [26] used novel combined techniques based on DGA and partial discharge sensors to improve the final decision accuracy
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