|
二维码（扫一下试试看！） |
| Study on the Factors Affecting AC Breakdown Strength of Insulating Paper based on Multivariate Statistical Analysis |
| Received:July 06, 2020 |
| DOI：10.11981/j.issn.1000-6842.2021.02.59 |
| Key Words:oil-paper insulation;AC breakdown strength;grey relational analysis;principal component analysis;best subset selection |
| Fund Project:国家重点研发计划（2017YFB0308200）。 |
| Author Name | Affiliation | Postcode | | WU Dongle* | China National Pulp and Paper Research Institute Co.， Ltd.， Beijing， 100102
National Paper Quality Supervision and Inspection Center， Beijing， 100102
National Engineering Lab for Pulp and Paper， Beijing， 100102 | 100102 | | LIU Qunhua | China National Pulp and Paper Research Institute Co.， Ltd.， Beijing， 100102
National Engineering Lab for Pulp and Paper， Beijing， 100102 | 100102 | | SUN Shengran | China National Pulp and Paper Research Institute Co.， Ltd.， Beijing， 100102
National Engineering Lab for Pulp and Paper， Beijing， 100102 | 100102 | | XU Kaili | China National Pulp and Paper Research Institute Co.， Ltd.， Beijing， 100102
National Engineering Lab for Pulp and Paper， Beijing， 100102 | 100102 | | LIU Wen | China National Pulp and Paper Research Institute Co.， Ltd.， Beijing， 100102
National Engineering Lab for Pulp and Paper， Beijing， 100102 | 100102 |
|
| Hits: 2060 |
| Download times: 1746 |
| Abstract: |
| In order to improve the manufacturing technology and quality level of insulating paper for ultra-high voltage （UHV） transformers and study the correlation of "material-structure-performance"， a quantified analysis model of the factors affecting the AC breakdown strength of insulating paper based on gray relational analysis （GRA） was proposed， and the principal component analysis （PCA） and the best subset selection method were used for multi-parameter optimization to construct a multiple linear regression model for the AC breakdown strength. The results showed that the gray correlation order affecting AC breakdown strength of insulating paper was： fiber length>fines content>fiber width>density>air permeability>porosity>thickness. Principal component analysis showed that 95.76% of the messages in the original parameters could be explained by three extracted principal components， and the best subset selection method optimized the parameters into three. The multiple linear regression model had a high degree of fitting and the relative deviation of the prediction results between the simulated sample and the verified sample was basically within 10%， indicating that the regression model was of good prediction capability. |
| View Full Text View/Add Comment Download reader HTML |
