Abstract
Power transformer is an important apparatus in power system. Insulation used in transformer is of solid insulation system and liquid dielectrics. It is important to investigate the cause of the insulation degradation with respect to aging affects. The standalone parameters of transformer oil are analyzed for various breakdown voltages. The absence of a well-defined scheme to correlate the various parameters of transformer oil reinforces the need of this transformer oil testing studies. In this work, a change in the breakdown voltage of insulating oil was observed, and its inter-relationship was determined using feed forward neural network (FFNN) trained with quasi-Newton and conjugate gradient learning methods. An expert system is created using FFNN implemented using MATLAB software to accurately determine the sensitivity and also the fault in system due to aging of transformer oil.
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Srividhya, V. et al. (2021). Determination of Breakdown Voltage for Transformer Oil Testing Using ANN. In: Sharma, H., Saraswat, M., Yadav, A., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. CIS 2020. Advances in Intelligent Systems and Computing, vol 1334. Springer, Singapore. https://doi.org/10.1007/978-981-33-6981-8_35
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DOI: https://doi.org/10.1007/978-981-33-6981-8_35
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