摘要
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.
作者
张怀天
刘科研
盛万兴
孟晓丽
ZHANG Huaitian;LIU Keyan;SHENG Wanxing;MENG Xiaoli(China Electric Power Research Institute,Beijing 100192,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2021年第12期4502-4509,共8页
High Voltage Engineering
基金
国家电网公司科技项目(配电网海量数据质量提升与数据修复技术研究与开发)(PD71-17-003)。