基于GLR-NT的显著误差检测与数据协调

Gross Error Detection and Data Reconciliation Based on A GLR-NT Combined Method

提出了一种广义似然比法(Generalized Likelihood Ratio,GLR)与节点检测法(NodalTest,NT)组合的显著误差检测和稳态数据协调方法。该方法充分发挥了GLR法和NT法的优点,采用逐次侦破、补偿校正的策略,避免了传统显著误差侦破方法中系数矩阵降秩问题,并且融入了测量变量的上、下限约束,最终实现显著误差的侦破、识别、处理和测量数据的协调。仿真结果显示:该方法对多显著误差特别是误差幅度较小或出现节点大显著误差相互抵消的情况具有较好的性能,优于单独的GLR法和NT-MT法。一个实例验证了该算法的有效性。

By combining generalized likelihood ratio and nodal test,this paper proposes a new method for gross error detection and data reconciliation,GLR-NT combining method.This method makes full use of the advantages of both generalized likelihood ratio and nodal test,and adopts a strategy of detecting and compensating in successive iteration.Thus,the decreasing problem of coefficient matrix rank in traditional method may be effectively avoided.Moreover,by integrating the constraint of bounds on measurement variables,the proposed method can achieve the identification and processing of gross errors,and the reconciliation of measurement data.The simulation results show that the proposed method is superior to both sole GLR method and NT-MT method,and can attain better performance for the system with more than one error,especially when the magnitude of gross error is smaller or several biased stream are counteracted at the same node.Finally,an actual example is provided to illustrate the effectiveness of the proposed method.