A Comprehensive and Practical Method for Transformer Fault Analysis With Historical Data Trend Using Fuzzy Logic

Dissolved gas analysis (DGA) of transformer oil is an important tool to identify incipient faults in 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 article, a comprehensive three-stage fuzzy logic approach is proposed to emulate best practices in the industry for transformer health analysis using the current as well as the historical data from previous samples. In the first stage, a fuzzy logic is developed for an oil sampling precheck for better laboratory 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 the additional advantages of saving time and cost.