Transformer DGA fault diagnosis based on the random forest feature optimization and MAEPSO-ELM algorithm
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    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.

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丁学辉,许海林,罗颖婷,杨鑫,鄂盛龙.基于随机森林特征优选与MAEPSO-ELM算法的变压器DGA故障诊断[J].电力科学与技术学报英文版,2022,37(2):181-187. DING Xuehui, XU Hailin, LUO Yingting, YANG Xin, E Shenglong. Transformer DGA fault diagnosis based on the random forest feature optimization and MAEPSO-ELM algorithm[J]. Journal of Electric Power Science and Technology,2022,37(2):181-187.

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  • Received:
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  • Online: May 26,2022
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