Optimizing Online Sequential Extreme Learning Machine Parameters and Application to Transformer Fault Diagnosis

Abstract

Abstract. In order to solve the problem that the (OS-ELM) is used in the fault diagnosis of the transformer, the genetic algorithm (Algorithm Genetic) is applied to the on-line extreme learning machine, and a new method of transformer fault diagnosis is proposed. In this method, the number of hidden layer neurons of the Block L, the data set size N, and the hidden layer activation function are selected by the Algorithm Genetic optimization algorithm. Through simulation test, the fault diagnosis of transformer is 99.56%, and the test time is 0.0024 s. Compared with the optimization, the diagnostic accuracy and the test time of the transformer fault are improved obviously.