基于无迹卡尔曼滤波的电力系统抗差动态估计

孙怡; 何光宇; 翟少鹏

孙怡,何光宇,翟少鹏.基于无迹卡尔曼滤波的电力系统抗差动态估计[J].电测与仪表,2020,57(4):1-6.
Sun Yi,He Guangyu,Zhai Shaopeng.Robust Dynamic State Estimation for Power System Based on Unscented Kalman Filter[J].Electrical Measurement & Instrumentation,2020,57(4):1-6.

基于无迹卡尔曼滤波的电力系统抗差动态估计

Robust Dynamic State Estimation for Power System Based on Unscented Kalman Filter

DOI：10.19753/j.issn1001-1390.2020.04.001

中文关键词: 无迹卡尔曼滤波抗差估计测点正常率不良数据

英文关键词:Unscented Kalman Filter, robust estimation, normal rate, bad data

基金项目:

作者中文名	作者英文名	单位
孙怡	Sun Yi	上海交通大学
何光宇	He Guangyu	上海交通大学
翟少鹏	Zhai Shaopeng	上海交通大学

摘要点击次数: 2153

中文摘要:

为解决电力系统动态状态估计准确性易受量测不良数据影响的问题，本文提出基于无迹卡尔曼滤波(Unscented Kalman Filter, UKF)的电力系统抗差动态估计方法。在预测步中引入时变噪声估计器处理未知系统噪声；利用新息向量判断量测是否存在异常，并使用基于测点正常率最大的静态估计方法辨识不良数据；然后构建更新因子矩阵降低不良数据在动态估计更新过程中的影响。将算法运用于IEEE14节点标准系统中，仿真结果表明该方法估计结果准确且抗差效果良好。。

英文摘要:

The bad data may decrease the accuracy of power system dynamic state estimation, and even lead to the divergency of the results. This paper proposes a robust dynamic estimation algorithm based on Unscented Kalman Filter (UKF). Noise statistical estimator is introduced to deal with time-varying noise in the prediction step. Innovation vectors are used to judge whether the measurement is abnormal or not, and static estimation method based on maximum normal rate is used to identify bad data. The algorithm has been applied to IEEE 14-bus system. The simulation results show that the estimation results are accurate and robust.

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