基于AF/UKF组合滤波的配电物联网数据平差方法

Data Adjustment Method for Distribution Internet of Things Based on AF/UKF Combined Filter

为解决因历史数据或滤波历史值误差过大造成预测值不准、进而导致滤波效果下降的问题,提出了一种基于AF/UKF组合滤波的配电物联网数据平差方法,向无迹卡尔曼滤波(unscented Kalman filter,UKF)的预测环节引入自适应滤波(adaptive filter,AF),并加入基于统计规律的判断,使得滤波系统对暂态或故障态时间具有一定的判断能力。以某实际电网85节点系统为例,通过稳态SCADA数据采集系统模拟、暂态μPMU系统模拟,进行仿真验证。仿真结果表明,该方法相比于传统无迹卡尔曼滤波,其数据平差结果具有更高的估计精度,经AF/UKF组合滤波处理后,估计值的绝对误差平均值和最大值均较小,且在系统处于剧烈变化时具有更好的跟踪性能。

In order to solve the problem of filtering effectiveness degradation caused by inaccurate predictive values with too large errors of historical data and filtered historical values,this paper proposes a data adjustment method for distribution internet of things(D-IoT)based on AF/UKF being combined filter.In the method,adaptive filter(AF)is introduced into the prediction part of unscented Kalman filter(UKF)and a judgment based on statistical laws is added,thus,the filtering system has a certain ability to judge the transient or fault time.Simulation verification is performed by taking an actual 85-node system of actual power grid as an example through steady-state SCADA data acquisition system simulation and transientμPMU system simulation.Experimental results show that the proposed method has higher estimation accuracy than traditional unscented Kalman filter.After treatment of AF/UKF being combined filter,the average and maximum absolute error of the estimated values are smaller,and the system has better tracking performance when the system is in drastic changes.