基于改进卡尔曼滤波的配电网动态数据平差方法

受设备、天气等多方面因素影响,电网量测数据不可避免的存在误差。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,本文提出了一种鲁棒性无迹卡尔曼滤波算法(RUKF),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UKF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。多个实验表明本文提出的RUKF算法对数据平差计算可以提供有效的理论支撑。

Research on Power System Data Adjustment Method Based on Robust Unscented Kalman Filter

Affected by many factors such as equipment and weather, there is an inevitable error in the measurement data of the power grid. Before the actual application, the appropriate estimation method should be used for data adjustment. In order to reduce the influence of bad data on estimation accuracy, this paper proposes a robust unscented Kalman filter algorithm (RUKF), which introduces a bad data detection method based on operation mode before performing unscented Kalman filtering. The measured change trend adjusts the threshold to avoid missed detection and false detection of bad data. Taking IEEE 33-bus and a real 107-node system as examples, simulations and experiments are implemented. The experimental results show that, in the presence of bad data, the data adjustment results obtained by RUKF compared with the traditional UKF have higher estimation accuracy and improve the reliability of data estimation. It shows that the RUKF algorithm proposed in this paper can provide effective theoretical support for data adjustment calculation.