基于离群点算法的在线监测传感器的设计与研究

Design and research of online monitoring sensor based on outlier algorithm

为解决现有用电异常大数据计算模型由于原始数据不准确导致存在漏报、误报、数据处理复杂等问题,研制了一种用于分布安装在一次侧的线路的节点的在线监测传感器,通过对硬件的设计确保数据采集的可靠性;建立传感器采样误差数学模型,分析传感器铁芯引入气隙后测量后误差形成的机理,并对其进行仿真优化。引入采集电流信号波动指标及变异系数,选择数据样本质心,并根据质心及离群点算法筛选处离群点,大大降低了数据计算复杂程度,提高了用电异常判别的可靠性。功能性测试表明,该传感器采集的电流数据准确度高度,相对误差值小于0.2%,数据同步误差不大于5μs,为计算模型提供可靠的采集数据。

In order to solve the problems of missing alarms,false alarms,and complex data processing due to inaccurate original data in the existing big data calculation model of abnormal electricity consumption,an online monitoring sensor for distributed installation of line nodes on the primary side was developed.The reliability of data acquisition is ensured through the design of the hardware;the mathematical model of the sensor sampling error is established,the mechanism of the error formation after the sensor core is introduced into the air gap is analyzed,and the simulation optimization is carried out.The fluctuating index and coefficient of variation of the collected current signal are introduced,the centroid of the data sample is selected,and the outliers are screened according to the centroid and outlier algorithm,which greatly reduces the complexity of data calculation and improves the reliability of electricity abnormality discrimination.Functional tests show that the current data collected by the sensor is highly accurate,the relative error is less than 0.2%,and the data synchronization error is less than 5μs,providing reliable collection data for the calculation model.