Abstract
Dissolved Gas Analysis (DGA) of transformer oil is an important tool to identify incipient faults of transformer. The challenge with the existing research on DGA is that only a single sample is used for the analysis which might lead to inaccurate results. In this paper a comprehensive three stages fuzzy logic approach is proposed to emulate best practices in the industry for transformer health analysis using the current as well as historical data from previous samples. In the first stage, a fuzzy logic is developed for an oil sampling pre-check for better lab acceptance rate which helps to save time and cost. In the second stage fuzzy, the latest IEEE Std C57.104 – 2019 is used to determine the status of transformer health by considering the gas formation rate from past samples and observing the trend. Finally, the third stage fuzzy is used to identify the transformer fault type and determine the corresponding down time. The proposed approach is tested on real data from the industry and the results demonstrate accurate transformer health identification with additional advantages of saving time and cost.
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