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Keywords

fault diagnosis, random forest, multi-scale cooperative mutation particle swarm, extreme learning machine

Abstract

A transformer DGA fault diagnosis method is proposed based on the random forest feature optimization and multi-scale cooperative mutation particle swarm limit learning machine for the problems that different input characteristics effects the diagnosis results and the low accuracy of particle swarm algorithm optimization limit learning machine. Firstly, the candidate feature set is established on the basis of the DGA data in the fault sample. The random forest algorithm is utilized to calculate the feature importance scores and rank them in a descending order. The optimal input features are then selected by the sequence forward selection method. Next, aiming at the problem of difficult parameter selection of extreme learning machine, a multi-scale cooperative mutation particle swarm optimization algorithm is introduced for optimization. Finally, the method is compared for the diagnostic performance with the IEC three-ratio method and different combinations of extreme learning machines. An example shows that the proposed method has higher diagnostic accuracy.

DOI

10.19781/j.issn.1673-9140.2022.02.021

First Page

181

Last Page

187

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