数据融合用于MOA在线监测数据的处理

Research of Data Fusion in Processing On-line Monitoring Data of Metal Oxide Surge Arrester

为解决MOA在线监测测量数据产生的误差,采用算术平均值和分批估计相结合的数据融合方法,平滑MOA在线监测过程中外界因素的干扰,保留了真实信号的基本特征,使其结果更能有效反映MOA当前的绝缘状况。两种方法处理MOA在线监测数据的标准差结果显示数据融合方法能得到比简单的算术平均值更加精确的数据。该方法程序简单、可操作性强、适于现场应用。

Due to influences of electromagnetic interference, environmental factors and aberration of operating voltage, on-line monitoring data of MOA is fluctuant. If only with these results to estimate the insulation status of MOA directly, a wrong decision will be made inevitable, so how to get the accurate on-line monitoring data of MOA is always the main topic of research. At present, the method of arithmetic average is used to process the on-line monitoring data of MOA at the most times, and use the value of arithmetic average as the final results, but as we know this method only can be used on the conditions that the measuring data have normal school and the measuring have enough times, it is obvious that the method of arithmetic average will bring the big measuring error for processing the limited on-line monitoring data of MOA, so it need us to introduce the more advanced mathematical methods to analyze the on-line monitoring data of MOA. Based on the principles of data fusion technology, this paper uses the methods of arithmetic average and batch estimation to smooth the interference that consists in the course of on-line monitoring, and remain its basic characteristics of the real signals for MOA, which will make the results reflect the insulation status well and truly. And what is more, the computing program of arithmetic is simple, its maneuverability is big and it is very fit for application in the field. Finally, in order to estimate the effectiveness of data fusion technology, these two methods are used to process the on-line monitoring data of MOA from the field and compare the computing results processed by these two methods, from the root-mean-square error, it is concluded that the data fusion technology will bring us the more accurate on-line monitoring data of MOA.