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Surface discharge detection method of contaminated insulators based on ultraviolet images' parameters

机译:基于紫外图像参数的绝缘子表面放电检测方法

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Insulator flashover is a great threat of power system's safe and stable operation and surface discharge detection of contaminated insulators is an effective approach of flashover warning. Surface discharge detection method of contaminated insulators based on ultraviolet images' parameters is proposed in this paper. Experiment system of contaminated insulators surface discharge is designed to acquire discharge magnitude and ultraviolet(UV) images synchronously. After frame image process of UV videos, we define average facula area in 100 successive UV image frames as UV images' feature parameter. As shooting parameters vary in practical application, effects of imager gain and shooting distance on facula area under the same discharge magnitude are also studied. Then a model of support vector regression is built to predict discharge magnitude using the UV image parameter. And genetic algorithm is adopted to optimize kernel parameters and punishment parameters. The regression result indicates that discharge magnitude calculated by UV image only have an error of less than 9% compared with measured discharge magnitude. It meets the accuracy requirement of engineering application and thus provide a new surface discharge detection method of contaminated insulators based on ultraviolet images' parameters.
机译:绝缘子闪络是电力系统安全稳定运行的巨大威胁,绝缘子表面放电的检测是一种有效的闪络预警方法。提出了一种基于紫外线图像参数的绝缘子表面放电检测方法。设计了污染绝缘子表面放电实验系统,以同步获取放电量和紫外线图像。在对UV视频进行帧图像处理后,我们将100个连续的UV图像帧中的平均光斑面积定义为UV图像的特征参数。随着实际应用中拍摄参数的变化,还研究了在相同放电量下成像器增益和拍摄距离对光斑面积的影响。然后,建立支持向量回归模型,以使用UV图像参数预测放电量。并采用遗传算法对核参数和惩罚参数进行优化。回归结果表明,与实测的放电量相比,UV图像计算的放电量仅具有小于9%的误差。满足工程应用的精度要求,从而提供了一种基于紫外线图像参数的绝缘子表面放电检测的新方法。

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