Search

Your search keyword '"Gong, Ruohan"' showing total 11 results
Start Over Author "Gong, Ruohan" Search Limiters Available in Library Collection
11 results on '"Gong, Ruohan"'

4. A Method for Hot Spot Temperature Prediction of a 10 kV Oil-Immersed Transformer

Author

Cihan Duan, Gong Ruohan, Yiming Xie, Yu Quan, Yongqing Deng, Jiangjun Ruan, and Daochun Huang

Subjects
General Computer Science, 020209 energy, 02 engineering and technology, 01 natural sciences, law.invention, law, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, support vector regression, multi-physical field analysis, Transformer, Mathematics, 010302 applied physics, Maximum temperature, business.industry, oil-immersed transformer, General Engineering, Structural engineering, Support vector machine, Mean absolute percentage error, Hot spot temperature, lcsh:Electrical engineering. Electronics. Nuclear engineering, Orthogonal array, business, lcsh:TK1-9971
Abstract

This paper proposed a prediction method to predict a 10-kV oil-immersed transformer hot spot temperature (HST). A set of feature temperature points on the transformer iron shell is proposed based on fluid-thermal field calculation. These feature points, as well as transformer load rate, are taken as the input parameters of a machine learning model established by support vector regression (SVR), thus to describe their relationships with the HST. This model is trained by nine samples selected by L9(34) orthogonal array and applied to predict the HST of 20 test samples. The training samples are all obtained by simulation, and the test samples have consisted of simulation and transformer temperature rise test results. With effective parameter optimization of the SVR model, the predicted results agree well with the experimental and simulation data, the mean absolute percentage error (MAPE) is 1.55%, and the maximum temperature difference is less than 3 °C. The results validated the validity and the generalization performance of the prediction model.

Published
2019

Catalog

Books, media, physical & digital resources
See catalog results