摘要
当前广域测量系统(Wide Area Measurement System,WAMS)和数据监控及采集系统(Supervisory Control and Data Acquisition,SCADA)组成了状态估计数据采集的混合量测系统,使得基于混合量测数据兼容方案的电力系统实时在线估计成为可能。传统无迹卡尔曼滤波(Unscented Kalman Filter,UKF)动态状态估计由于预测步精度不足和后验校正步纠偏能力有限,已不能满足电力系统对状态估计精度的要求。为此,文章从误差补偿思想出发,引入基于布雷顿秩1的拟牛顿算法作为UKF估计后验校正步的误差补偿,提出了基于误差补偿的UKF新算法应用于动态状态估计。通过IEEE 30节点系统上的仿真分析,验证了该算法在控制估计误差上的有效性和具有良好的抗差性能。
Current hybrid measurement system is combined of wide area measurement system（ WARMS） and supervisory control and data acquisition（ SCADA）,which makes it possible to power system real-time online estimate on the basis of analysis of hybrid measurement data compatible solution. Traditional unscented Kalman filter UKF dynamic state estimation can not satisfy the requirement of power system on the state estimation accuracy,because of the shortages of prediction accuracy and posterior correction ability. For this,this paper embarking from error compensation thought,introduces quasi-Newton algorithm based on rank-1 Bretton as the error compensation of UKF estimated posterior correction,and a new UKF dynamic state estimation algorithm based on error compensation is proposed. The simulation results of Institute of IEEE 30 node systems show the validity and good robustness.
出处
《电测与仪表》
北大核心
2015年第19期123-128,共6页
Electrical Measurement & Instrumentation
