基于二次维数约简的油中溶解气体浓度预测

doi:10.19595/j.cnki.1000-6753.tces.160867

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Abstract Aiming at the testing problem of dissolved gases content in transformer oil,a new prediction model based on twice dimensionality reduction was proposed.Firstly,mutual information variable selection method was used to select relevant input variables of the prediction model;Secondly,the relevant variables were reconstructed in the phase space where feature extraction was carried out by using kernel principal component analysis (KPCA) for the purpose of data dimension reduction,denoising and eliminating relativity of variables,meanwhile,the parameters of KPCA were determined by Renyi information entropy.At last,kernel extreme learning machine (KELM) was employed to forecast dissolved gases content in transformer oil,and kernel principal components were used as the inputs of KELM.Compared with gray model and the prediction model which only adopt variable selection method or feature extraction method,experimental results show that the proposed prediction model has a better prediction and generalization.

Key words： Dissolved gases in oil kernel extreme learning machine kernel principal component analysis Renyi entropy variable selection feature extraction

Received: 05 June 2016 Published: 10 November 2017

PACS:	TM411
	TP183

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	Tang Yongbo
	Xiong Yinguo

Cite this article:

Tang Yongbo,Xiong Yinguo. Prediction Model for Dissolved Gases Content in Transformer Oil Based on Twice Dimensionality Reduction[J]. Transactions of China Electrotechnical Society, 2017, 32(21): 194-202.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.160867 OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I21/194

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