Hyperspectral-Based Corona Aging Evaluation for Composite Insulators
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摘要: 复合绝缘子由于其良好的憎水性和憎水迁移性在输电线路上得到了广泛应用，而电晕放电会造成复合绝缘子老化加剧而丧失性能. 为此提出了一种基于高光谱技术的复合绝缘子电晕老化状态评估方法. 首先，对全新硅橡胶复合绝缘片进行电晕老化，分析样本的傅里叶红外光谱变化，以傅里叶红外光谱图像作为老化状态分类的依据，将样品分为6个类别；然后，利用高光谱成像仪获取硅橡胶片表面不同波段的反射强度，采用主成分分析（principal component analysis，PCA）对原始谱线进行特征提取；最后，建立基于支持向量机的电晕老化状态评估（support vector machines-insulator corona aging status evaluation，SVM-CASE）模型，对60组预测数据进行分类验证，并对比分析了不同核函数对于模型评估准确率的影响. 高光谱检测及评估结果表明：不同老化时间下试样的高光谱图像有明显的区别，随着老化时间的增加，硅橡胶绝缘材料的光谱曲线在600～900 nm呈现反射率下降趋势；采用PCA算法进行特征提取后，利用polynomial核函数建立的评估模型的分类准确率达93.333%.
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关键词:
- 高光谱技术 /
- 电晕老化 /
- 傅里叶红外光谱（FTIR） /
- 支持向量机 /
- 主成分分析
Abstract: Composite insulators have been widely used in transmission lines due to good water repellency and hydrophobic migration. Corona discharges aggravate the aging of composite insulators. To this end, a method was proposed to evaluate the corona aging of composite insulators based on hyperspectral technology. Firstly, the new composite insulating silicone rubber sheet was subjected to corona aging, the Fourier infrared spectrum of the samples was analyzed, by which the samples were classified into six categories. Then, the reflection intensity at different bands on the surface of the silicone rubber sheet was obtained by using a hyperspectral imaging camera. Further, the characteristics of the original spectra lines was extracted by the principal component analysis (PCA). Finally, the support vector machines-insulator corona aging evaluation (SVM-CAE) model was established, and 60 sets of data were examined. The effects of different kernel functions on the accuracy of model evaluation were compared. The results of hyperspectral detection and evaluation show that, as the aging time increases, the high spectrum of samples under different aging times has obvious difference. With the increase of aging time, the spectral curves of silicone rubber insulation show a decreasing trend in 600 nm−900 nm bands. After the feature extraction by PCA algorithm, the model classification accuracy with polynomial kernel function was 93.333%. -
图 3 不同电晕老化时间样品的FTIR测试位置及谱线
Figure 3. FTIR test location and spectral lines of samples under different corona aging times
图 7 不同老化时间下样本752.3 nm处反射率
Figure 7. Reflectance in 752.3 nm band at different aging times
表 1 前6主成分贡献率之和
Table 1. Sum of top 6 principal component contributions
主成分 PC1 PC2 PC3 PC4 PC5 PC6 贡献率/% 89.84 93.99 94.73 94.90 94.99 95.03 
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表 2 不同核函数下测试集数据的分类准确率
Table 2. Classification accuracy of test set data under different kernel functions
核函数类型 准确率/% SVM网络参数 linear （55/60） 91.667 -c 8 -g 32 -t 0 polynomial （56/60） 93.333 -c 8 -g 32 -t 1 radial basis of function （52/60） 86.667 -c 8 -g 32 -t 2 注：括号内数据表示正确组数/预测总组数.
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