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- W4313304634 abstract "A methodology to diagnose transformer faults based on dissolved gas analysis (DGA) is proposed. Since a fault is more sensitive to the ratios of gas contents, a general ratio feature extraction framework is proposed to generate a feature subset as the input of the diagnosis model. The feature subset is evaluated by the improved case-based reasoning (CBR) and optimized by using a proposed new algorithm called the k-optimal algorithm (k-OA). The comparison results between the k-OA and the genetic algorithm (GA) show that the k-OA is more efficient in solving such a combinatorial optimization problem. The obtained optimal feature subset is used to diagnose a public transformer fault dataset, and a 92.6% diagnosis accuracy is observed as compared to that of only 85.1% diagnosis accuracy by using the original features." @default.
- W4313304634 created "2023-01-06" @default.
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- W4313304634 date "2023-04-14" @default.
- W4313304634 modified "2023-09-26" @default.
- W4313304634 title "A methodology for transformer fault diagnosis based on the feature extraction from DGA data" @default.
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- W4313304634 doi "https://doi.org/10.3233/jae-220169" @default.
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