Aging Degree Evaluation of Composite Insulator Based on Hyperspectral Technology

被引：0
|
作者：
Zhang X. [1 ]
Zhang Y. [1 ]
Guo Y. [1 ]
Liu K. [1 ]
Wu G. [1 ]
机构：
[1] School of Electrical Engineering, Southwest Jiaotong University, Chengdu
来源：
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2021年 / 36卷 / 02期
关键词：
Aging degree; Composite insulator; Deep extreme learning machine; Fourier transform infrared; Hyperspectral technology;
D O I：
10.19595/j.cnki.1000-6753.tces.191654
中图分类号：
学科分类号：
摘要：
There is no convenient and fast method for the insulation aging degree detection. In this paper, a non-contact and fast non-destructive testing method for surface aging of composite insulators based on hyperspectral technology is proposed. Firstly, Fourier-infrared tests were carried out on samples with different aging degrees, and the group changes on the surface of the samples and the influence on hydrophobicity were analyzed. Secondly, the hyperspectral imager (900~1 700nm) was used to obtain the spectral information of the aged samples. Combined with the Fourier mid-infrared spectroscopy, the relationship between the group content and the spectral information of the aged samples was determined, so as to qualitatively analyze the degree of aging. Finally, an aging degree evaluation model based on the deep extreme learning machine was established, and 60 groups of data to be tested were predicted to achieve accurate grading of the insulator aging degree. The classification accuracy rate was 96.67%. Compared with BP neural network model and support vector machine model, it is shown that the model used in this paper has both rapidity and accuracy, which provides a new idea for on-line detection of the aging degree of external insulation surface. © 2021, Electrical Technology Press Co. Ltd. All right reserved.
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页码：388 / 396
页数：8
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