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A Method for Hot Spot Temperature Prediction of a 10 kV Oil-Immersed Transformer
- Source :
- IEEE Access, Vol 7, Pp 107380-107388 (2019)
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
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.
- 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
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....e3e14395395fa88650897b3239219917