Abstract: In data reconciliation, it is common practice to detect gross errors and delete those measurements with gross error(MGEs) before using the algorithm of reconciliation. Each measurements deletion affects the structure of measurement network as well as reconciliation precision. Since some MGEs deletion will perhaps result in the unreasonable solution of the least squares algorithm of data reconciliation, MGEs can not be deleted totally at one time. They must be treated differently in order to delete as many MGEs as possible to ensure reconciliation precision. To solve this problem, a new method of redundancy analysis is proposed in this paper. Supposing one MGE is deleted and treated as unmeasured variable, the corresponding formulation of the data reconciliation problem is adjusted and its reconciliation precision can be calculated. Thereupon, the first order redundancy degree of the deleted MGE is defined. After all of the MGEs are checked, the MGE with the largest first order redundancy degree will be deleted and treated as unmeasured. In the same way, the higher order redundancy degree of the remained MGEs can be calculated, and they can be removed one by one until the deletion of each one left will lead to no redundancy of the network. In this way, the information in the data can be used sufficiently and the reconciliation precision is ensured. Simulation result demonstrates the efficiency of the proposed method.

摘要： 提出一种数据协调中的冗余性分析新方法 .根据尽量删除带有显著误差的变量 ,同时确保协调精度的原则 ,对所有检测出来的带有显著误差的变量 ,通过计算不同阶次的冗余度 ,按照冗余度的大小逐个选择可以删除的变量 .这样既充分利用了数据信息 ,又确保了协调精度 .仿真结果证实了该方法的有效性 .