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Estimation of discharge magnitude of composite insulator surface corona discharge based on ultraviolet imaging method

机译:基于紫外线成像方法的复合绝缘子表面电晕放电量估算

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摘要

Ultraviolet (UV) imaging is a non-contact discharge detection method, in this study, a method, which considers the two factors of observation distance and imager's gain, of estimating the apparent discharge magnitude on composite insulator was proposed. According to the discharge UV image feature, the discharge emitted radiation regions in UV images were extracted with digital image processing algorithm, and then the emitted radiation area parameter was defined. In laboratory, the relationship of emitted radiation area to apparent discharge magnitude, imager's gain and observation distance were studied respectively. Research shows that emitted radiation area increases with the enhancement of discharge intensity, but it has non-linear characteristic; in the gain range of 50 to 80%, emitted radiation area to gain has approximate exponential function relation, when the gain is 50, 60, 70 and 80% respectively, the emitted radiation area to the observation distance has approximate power function relation. Based on the above research, an adaptive neuro-fuzzy inference system (ANFIS) model was established and realized the estimation of apparent discharge magnitude, test shows that it has high prediction accuracy.
机译:紫外线(UV)成像是一种非接触式放电检测方法,在此研究中,提出了一种考虑观察距离和成像器增益两个因素的方法,用于估计复合绝缘子的视在放电量。根据放电紫外图像特征,利用数字图像处理算法提取紫外图像中的放电发射辐射区域,然后定义发射辐射面积参数。在实验室中,分别研究了发射辐射面积与视在放电量,成像器增益和观察距离的关系。研究表明,随着辐射强度的增大,辐射面积增大,但具有非线性特征。在增益范围为50%到80%时,要获得的发射辐射区域具有近似指数函数关系,当增益分别为50%,60%,70%和80%时,发射辐射区域与观察距离之间具有近似幂函数关系。在上述研究的基础上,建立了自适应神经模糊推理系统(ANFIS)模型,并实现了视在放电量的估算,测试表明其具有较高的预测精度。

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