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,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。在预测过程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IEEE 14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。
The bad data may decrease the accuracy of dynamic estimation in power system,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 the maximum normal rate is used to identify bad data.And then,the update factor matrix is constructed to reduce the influence of bad data in the dynamic estimation update process.The algorithm has been applied to IEEE 14-bus system.The simulation results show that the estimation results of the proposed method are accurate and robust.
作者
孙怡
何光宇
翟少鹏
Sun Yi;He Guangyu;Zhai Shaopeng(Department of Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《电测与仪表》
北大核心
2020年第4期1-6,共6页
Electrical Measurement & Instrumentation
基金
国家重点研发计划(2017YFB0902800)。
