复合绝缘子电晕放电紫外图像参数的变化特性及放电量估计

Variation Characteristic of Composite Insulator Corona Discharge Ultraviolet Image Parameter and Estimation of Discharge Magnitude

日盲紫外成像是一种非接触式放电检测方法,该文提出了一种综合考虑观测距离和仪器增益两个因素的复合绝缘子视在放电量的估计方法。根据放电紫外图像特征,采用数字图像处理算法分割出了紫外图像中的放电光斑区域,在此基础上定义了"光斑面积"参数。试验研究了光斑面积与放电量、仪器增益和观测距离之间的关系。研究表明:光斑面积随放电强度的增强而增加,但两者具有一定的非线性特性,在50%～80%的增益范围内,光斑面积与增益之间近似满足指数函数关系,当增益分别为50%、60%、70%和80%时,光斑面积与距离之间近似满足幂函数关系。在此基础上,根据实验样本数据,建立了自适应模糊逻辑推理系统,从而实现了视在放电量的估计,测试结果表明该模型具有较高的预测精度。

Ultraviolet （UV） imaging is a non-contact discharge detection method, a method for estimating the apparent discharge magnitude on composite insulator is proposed in this paper, which considers the two factors of observe distance and imager’s gain. According to the discharge UV image feature, the discharge facular regions in UV images were extracted with digital image processing algorithm, and then the facular area parameter was defined. In laboratory, the relationship of facular area to apparent discharge magnitude, imager’s gain and observe distance were studied respectively. Research shows that facular area increases with the enhancement of discharge intensity, but it has non-linear characteristic, in the gain range of 50%-80%, facular area to gain has approximate exponential function relation, and when the gain is 50%,60%,70% and 80% respectively, the facular area to the observe distance has approximate power function relation. Based on the above research, with the sample data, an adaptive neuro-fuzzy inference system （ANFIS） model was established and realized the apparent discharge magnitude estimation, test shows that it has high prediction accuracy.