A Method for Hot-Spot Temperature Prediction and Thermal Capacity Estimation for Traction Transformers in High-Speed Railway Based on Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @Article{Zhou:2019:TTE,
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author = "Lijun Zhou and Jian Wang and Lujia Wang and
Shuai Yuan and Lin Huang and Dongyang Wang and Lei Guo",
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title = "A Method for Hot-Spot Temperature Prediction and
Thermal Capacity Estimation for Traction Transformers
in High-Speed Railway Based on Genetic Programming",
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journal = "IEEE Transactions on Transportation Electrification",
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year = "2019",
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volume = "5",
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number = "4",
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pages = "1319--1328",
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month = dec,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/TTE.2019.2948039",
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ISSN = "2332-7782",
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abstract = "With the rapid development of China's high-speed
railway (HSR), large-scale traction transformers have
been put into use. In order to realize the batch
prediction of hot-spot temperature (HST) of traction
transformer group and perform estimation of thermal
capacity well, this article was devoted to HST
prediction modeling for traction transformers based on
genetic programming (GP). First, the HST, load factor,
and ambient temperature data used in this article were
measured from the traction transformer A and were
further divided into training set and prediction set.
Training set was used to driven modeling by GP. An
explicit expression prediction model, which could
directly predict the dynamic HST, was established.
Then, it was confirmed that the model has high accuracy
according to the prediction set. Furthermore, the
transformers B and C that are belong to the same
railway line like A were tracked and predicted in real
time. It is verified that the model has high
generalization performance. Simultaneously, the
practical application of the model was discussed and
analyzed. The research result shows it is expected that
the proposed model could realize the batch accurate
prediction of HST for traction transformer group. It
can provide a better and more effective reference for
thermal capacity estimation, train scheduling plan, and
maintenance replacement plan of traction
transformers.",
-
notes = "Also known as \cite{8873634}",
- }
Genetic Programming entries for
Lijun Zhou
Jian Wang
Lujia Wang
Shuai Yuan
Lin Huang
Dongyang Wang
Lei Guo
Citations