一种新的数据协调模型及显著误差检测

针对传统数据协调模型会将某些测量值中存在的显著误差分散到各个协调值中去的缺陷，提出了通过添加一个基于测量值比例关系上下限的约束条件，并利用罚函数的概念将物料平衡或能量平衡的约束条件以软约束的形式表示，建立了一种新的数据协调模型．改进后的数据协调模型只会对含有显著误差的测量值给予较大的协调量，而使得显著误差对其他测量值协调结果的影响较小．因此，该数据协调模型避免了传统模型的缺陷，具有较高的鲁棒性．基于该数据协调模型的协调结果，可直接利用测量残差检测法进行显著误差检测，具有较高的错误检出率，且“虚警”的错误率较低．

Novel data reconciliation model and gross error detection

The traditional data reconciliation model tends to spread the gross errors over all the measurements. This paper proposes a new data reconciliation model to avoid the problem. In this work,some new constraints derived from the ratio of measurements were introduced and the constraints based on material balance are put into soft constraints form by using the method of penalty function. The data reconciliation procedure using the improved model tends to make measurements having gross errors have more effect than the others. Thereby,the new data reconciliation model is more robust than the old one. Based on the results of the new model,the measurement test method can be used to detect gross errors directly.The result of simulations shows that the gross error detection based on the new data reconciliation model is very sensitive to presence of gross errors and has great probability of correctly finding one or several gross errors.