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

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.