Transformer evaluation strategy based on improved machine learning

Abstract

A transformer state assessment method based on an improved extreme learning machine is proposed in this paper, which introduces an adaptive evolution algorithm into the extreme learning machine. This method is used in the evaluation model to evaluate the state of the transformer. The algorithm is trained and tested through sample sets. Furthermore, the test results are analyzed to prove the feasibility of the proposed control strategy.