Abstract
A large number of tasks for analyzing the state of power transformers are solved on the basis of mathematical models, the validity of which is undeniable. The disadvantage of standard methods for diagnosing current-carrying parts of transformers is the requirement to remove voltage. The applied diagnostic methods without stress relief require improvement in terms of increasing accuracy, speed and ensuring predictive response. The paper presents methods for identifying the parameters of mathematical models of power transformers using artificial neural networks.
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