摘要
提出了在快速分解正交变换状态估计算法中检测与辨识坏数据的新方法。该方法成功地将假设检验辨识法（HTI）和量测补偿法的思想应用于基于快速分解状态估计算法的坏数据检测与辨识，用基于对增广的量测雅可比矩阵进行Givens行变换的方法计算和更新残差协方差矩阵，在建立可疑量测集时，考虑有功类量测误差对无功类量测残差的影响和无功类量测误差对有功类量测残差的影响。算例说明，该方法检测与辨识坏数据的能力较强。
Based on methods of hypothesis testing identification and measurement compensation. a new method is proposed to detect and identify bad data in state estimation using fast decoupled orthogonal trans formation. This method fully utilizes the characteristics of fast decoupled orthogonal transformation. Row-oriented Givens rotation of augmented measurement Jacobian matrix is used to calculate and update residual covariance matrix. When creating the set of suspect measurements. interaction of real measurements and reactive measurements is considered. Test results show that the proposed method can effectively detect and identify bad data.
出处
《电力系统自动化》
EI
CSCD
北大核心
1999年第20期1-4,26,共5页
Automation of Electric Power Systems
基金
国家重点基础研究专项经费资助!G19980203
国家电力公司科技项目!1999SPKJ010-20
关键词
状态估计
坏数据检测
快速分解
正交变换
电业
state estimation bad data fast decoupling orthogonal trans formation
