摘要
针对当前电力系统动态状态估计主要采用的扩展卡尔曼滤波(EKF)法存在鲁棒性差、建模具有不确定性等缺点,提出一种强跟踪滤波动态状态估计算法。该算法在扩展卡尔曼滤波器中引入时变次优渐消因子,在线调整状态预报误差协方差矩阵和相应的增益矩阵,使状态估计残差方差最小。同时,引入广域测量系统(WAMS)/数据采集与监视控制(SCADA)系统的混合量测数据,增加了系统的冗余量测,进一步提高了动态状态估计的性能。仿真结果表明,所提方法在正常情况以及负荷突变、存在坏数据、网络拓扑错误各种情况下具有较好的预测和滤波效果。
As EKF（Extended Kalman Filter） method has the defects of bad robustness and uncertain models,the strong tracking filter is proposed for power system dynamic state estimation,which introduces the suboptimal time-varying fading factor to EKF,online rectifies the state forecast error covariance matrix and the corresponding gain matrix to minimize the state estimation residual variance.On the other hand,hybrid measurements of WAMS（Wide Area Measurement System） and SCADA（Supervisory Control And Data Acquisition） system are applied in power system dynamic state estimation to improve its performance.Simulative results show that,the proposed method has better forecasting and filtering performance under various conditions： normal operation,sudden load change,bad measurements,topology error,etc.
出处
《电力自动化设备》
EI
CSCD
北大核心
2012年第9期101-105,116,共6页
Electric Power Automation Equipment
基金
中央高校基本科研业务费专项资金资助项目(11QG59)
河北省自然科学基金资助项目(E2010001693)~~
关键词
电力系统
广域测量系统
状态估计
强跟踪滤波
预测
electric power systems
wide area measurement system
state estimation
strong tracking filter
forecasting
