Abstract
Transformer is an important device in the power system. Dissolve gas analysis is an oil testing method for distribution transformer fault diagnosis. In this paper, support vector machine with binary decision tree has been presented for the fault prediction using the DGA gases and verified with graphical representation of different outputs. SVM is an artificial intelligence technique based on statistical learning theory. The SVM classification method is compared with IEC three ratio method, key gas method, and ANN method, and then, the test results show that SVM has better accuracy in fault detection of transformer than other conventional methods.
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Sahoo, S., Chowdary, K.V.V.S.R., Das, S. (2021). DGA and AI Technique for Fault Diagnosis in Distribution Transformer. In: Sherpa, K.S., Bhoi, A.K., Kalam, A., Mishra, M.K. (eds) Advances in Smart Grid and Renewable Energy. ETAEERE ETAEERE 2020 2020. Lecture Notes in Electrical Engineering, vol 691. Springer, Singapore. https://doi.org/10.1007/978-981-15-7511-2_4
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DOI: https://doi.org/10.1007/978-981-15-7511-2_4
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