摘要
受设备、天气等多方面因素影响,电网量测数据不可避免地存在误差。在实际应用前,应选用合适的估计方法进行数据平差。为减小不良数据对估计精度的影响,提出一种鲁棒性无迹卡尔曼滤波(robust unscented Kalman filer,RUKF)算法,在进行无迹卡尔曼滤波之前引入基于运行模式的不良数据检测方法,通过分析量测量的变化趋势调整阈值,避免出现不良数据的漏检与误检现象。以IEEE 33-bus与某实际107节点系统为例进行仿真验证。实验结果表明,在存在不良数据的情况下,RUKF与传统无迹卡尔曼滤波相比,求得的数据平差结果具有更高的估计精度,提高了数据估算的鲁棒性。多个实验表明本文的RUKF算法对数据平差计算可以提供有效的理论支撑。
Affected by many factors such as equipment and weather,there are inevitable errors in the measurement data of the power grid.Before actual application,appropriate estimation method should be used for data adjustment.In order to reduce the influence of bad data on estimation accuracy,a robust unscented Kalman filter(RUKF)algorithm was proposed.The algorithm introduced a bad data detection method based on operation mode before performing unscented Kalman filtering.The measured change trend adjusted the threshold to avoid missed detection and false detection of bad data.By taking IEEE 33-bus and a real 107-node system as examples,simulations and experiments were implemented.The experimental results show that the data adjustment results obtained by RUKF compared with the traditional UKF have higher estimation accuracy and improve the reliability of data estimation,in the presence of bad data.It shows that the proposed RUKF algorithm can provide effective theoretical support for data adjustment calculation.
作者
刘科研
盛万兴
胡丽娟
LIU Ke-yan;SHENG Wan-xing;HU Li-juan(China Electric Power Research Institute, Beijing 100192, China)
出处
《科学技术与工程》
北大核心
2020年第31期12857-12862,共6页
Science Technology and Engineering
基金
国家电网公司科技项目(PD71-17-003)。
