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首页> 外文期刊>Electric Power Components and Systems >Equivalent Salt Deposit Density Prediction of Silicone Rubber Insulators Under Simulated Pollution Conditions
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Equivalent Salt Deposit Density Prediction of Silicone Rubber Insulators Under Simulated Pollution Conditions

机译：模拟污染条件下硅橡胶绝缘子的等效盐沉积密度预测

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Insulator flashover happens when soluble and/or non-soluble contaminants cover the insulator surface which results in a reduction of the surface resistance. Significant research has been conducted to utilize leakage current (LC) to predict the contamination level on the outdoor ceramic insulators surface. This can help as a mean to warn overhead lines operators about the advent of insulator flashover. However, there have been few attempts to predict the contamination levels on the surface of non-ceramic insulators. This work aims to develop a non-intrusive technique to monitor and evaluate the surface condition of silicone rubber (SIR) insulators by predicting the equivalent salt deposit density (ESDD). Three different classifiers (K-Nearest Neighbor Classifier (KNN), Polynomial, and Neuro-fuzzy) have been utilized to predict the ESDD level of SIR samples after a salt fog test. Moreover, stepwise regression and principle component analysis (PCA) have been used as feature selection tools to optimize the classification process. The overall prediction accuracy improved from 68% to 95% when the number of classes reduced from four to two respectively.
机译：当可溶性和/或非可溶性污染物覆盖绝缘子表面时，绝缘子会发生飞弧，这会导致表面电阻降低。为了利用泄漏电流（LC）预测室外陶瓷绝缘子表面的污染水平，已经进行了重要的研究。这可以帮助警告架空线操作员有关绝缘子闪络的到来。但是，几乎没有尝试预测非陶瓷绝缘子表面的污染程度。这项工作旨在开发一种非侵入性技术，通过预测等效盐沉积密度（ESDD）来监视和评估硅橡胶（SIR）绝缘子的表面状况。盐雾测试后，已使用三种不同的分类器（K最近邻分类器（KNN），多项式和神经模糊）来预测SIR样品的ESDD水平。此外，逐步回归和主成分分析（PCA）已用作特征选择工具来优化分类过程。当类别数从四个减少到两个时，总体预测准确性从68％提高到95％。

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