Bagging of Gaussian Process for Large Generator Eddy Current Prediction

被引:0
作者
Zhao, Jingying [1 ]
Han, Min [1 ]
Guo, Hai [2 ]
Tang, Haoran [2 ]
Dong, Na [2 ]
Zhao, Enming [3 ]
机构
[1] Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian, Peoples R China
[2] Dalian Minzu Univ, Coll Comp Sci & Engn, Dalian, Peoples R China
[3] Dali Univ, Coll Engn, Dali, Peoples R China
来源
2020 12TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI) | 2020年
关键词
large generator; eddy current loss; gaussian process regression; ensemble learning; bagging;
D O I
10.1109/icaci49185.2020.9177515
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes a large generator eddy current prediction method based on Bagging of Gaussian Process(GPR). Bagging ensemble learning model based on Gaussian Process Regression is proposed to predict eddy current loss. The slot wedge conductivity, slot wedge of the relative permeability, rotor outer diameter and stator outer diameter are as the input of the prediction model, and the eddy current loss is as the output. The experiments on the datasets calculated by Finite Element Model (FEM) show that the proposed approach has good predictive performance for Large generator rotor performance prediction and can be applied to practical projects.
引用
收藏
页码:184 / 188
页数:5
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