基于数据融合电晕放电紫外计数值的数据处理

电晕不是一个稳定的放电现象,往往受到测量条件等因素的影响,使得紫外测量得到的数据一般是波动的,仅凭某一紫外计数值或有限次测量结果的简单算术平均值是很难判断电晕放电的强弱。因此,为了提高电晕放电紫外计数值的测量准确性和可靠性,基于数据融合技术,采用算术平均值和分批估计相结合的数据融合方法,对电晕放电紫外计数值进行了处理,在一定程度上消除了电晕放电紫外测量过程中外界因素的干扰,使得结果更能有效反映电晕放电的强弱,从而为后续故障诊断提供依据。文中介绍了该方法的基本原理以及详细的计算步骤,最后以实例将该方法与简单的算术平均值方法进行了比较,验证了该方法的有效性。

Data Process of Ultraviolet Counts for Corona Discharge Detection Based on Data Fusion Technology

The corona is not a steady discharge because of the effect of detection conditions,so the data of ultraviolet(UV) counts is fluctuant.It is difficult to estimate the corona discharge with only one data or the average of several UV counts.To improve the precision and reliability of UV counts for corona discharge measurement,the authors use the methods of arithmetic average and batch estimation for processing the data of UV counts of corona discharge based on the principle of data fusion technology.As a result,the interference in the course of UV detection of corona discharge is suppressed to a certain extent,hence the detection result gets more accurate.