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| Research on the Near-Infrared Spectroscopy Detection Method of Water Absorption Rate of Silicone Rubber Based on CPO-SVM |
| WU Tian1, 3*, WANG Ling-zhi1, 3, QIU Zhong-hua2, LI Ming-dian1, WU Chen1, 3, WU Bin-fan1, 3, GU Tong1, 3 |
1. College of Electricity and New Energy, China Three Gorges University, Yichang 443002, China
2. State Grid Sichuan Extra High Voltage Company, Chengdu 610041, China
3. Hubei Transmission Line Engineering Technology Research Center, Yichang 443002, China
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Abstract Silicone rubber composite insulators are commonly utilized for external insulation in overhead transmission lines due to their exceptional insulation, weather resistance, and anti-pollution flashover performance. However, traditional methods for assessing the water absorption rate of silicone rubber involve destructive sampling techniques, such as the fuchsin identification method and the weighing method. These methods do not align with the requirements for on-site detection and maintaining the structural integrity of the insulators. Currently, there is a lack of a non-destructive, in-situ method for detecting the water-absorption characteristics of silicone rubber materials used in composite insulators. Therefore, this study introduces near-infrared spectroscopy to detect the water-absorption rate of the silicone-rubber outer sheath of composite insulators. Taking newly manufactured silicone rubber test specimens of composite insulators as the research object, the original data were first subjected to abnormal sample elimination using the Local Outlier Factor (LOF) algorithm and the Mahalanobis distance algorithm (MA), and then the near-infrared spectral data were preprocessed using methods such as Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), Savitzky-Golay smoothing filter, first derivative, and second derivative. Subsequently, the Competitive Adaptive Reweighted Sampling (CARS) algorithm, the Interval Combination Optimization (ICO) algorithm, and the Successive Projections Algorithm (SPA) were used to screen for redundant wavelengths among the characteristic wavelengths and to establish SVM models, respectively. Finally, the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Crown Pigeon Optimization (CPO) algorithms were used to optimize the model parameters. The research results show that the SNV-CARS-CPO-SVM model achieves good discrimination of the water absorption rate of silicone rubber test pieces, with an accuracy of 96.64% on the test set. This indicates that CARS can select high-quality features, effectively remove redundancies and noise, and compared with the PSO-SVM and GWO-SVM optimization models, the CPO-SVM model's classification accuracy rate has increased by 2.65% and 3.68%, respectively, demonstrating significant advantages. This study presents a novel approach for identifying the water-absorption rate of silicone rubber and other high-voltage insulating materials.
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Received: 2025-05-21
Accepted: 2025-09-18
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Corresponding Authors:
WU Tian
E-mail: wutian_08@163.com
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