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Quantitative prediction of aging state of oil-paper insulation based on Raman spectroscopy
- Source :
- AIP Advances, Vol 11, Iss 3, Pp 035001-035001-8 (2021)
- Publication Year :
- 2021
- Publisher :
- AIP Publishing LLC, 2021.
-
Abstract
- Accurate monitoring of insulation aging of oil-paper insulation power equipment such as oil-immersed transformers is a key and difficult point in the field of high voltage research. In this paper, a method based on Raman spectroscopy to diagnose the aging degree of oil-paper insulation is discussed. Raman detections of the samples were carried out on a self-built Raman detection platform. The partial least squares method was used to extract and analyze the spectral features. The aging time of the sample was used to supervise the feature extraction of oil-paper insulation Raman data, and the intrinsic mathematical relationship between the Raman features of oil-paper insulation and the aging was excavated. Finally, a quantitative aging diagnostic model based on Raman spectral features of oil-paper insulation to predict its aging state was built with the assistance of the support vector regression method. The results of aging time prediction for 30 test samples show that the mean square error is 0.0123 and the square of correlation coefficient is 0.987. The proposed method provides a new idea for Raman aging diagnosis of oil-paper insulation.
- Subjects :
- 010302 applied physics
Materials science
Correlation coefficient
Mean squared error
Acoustics
Feature extraction
General Physics and Astronomy
High voltage
02 engineering and technology
021001 nanoscience & nanotechnology
01 natural sciences
lcsh:QC1-999
law.invention
Support vector machine
symbols.namesake
law
0103 physical sciences
Partial least squares regression
symbols
0210 nano-technology
Raman spectroscopy
Transformer
lcsh:Physics
Subjects
Details
- Language :
- English
- ISSN :
- 21583226
- Volume :
- 11
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- AIP Advances
- Accession number :
- edsair.doi.dedup.....7b74a95abfa340741e850ca65e3b917d