Abstract
Accuracy of operational data of a power plant is essential for power plant performance monitoring and fault diagnosis. However, due to inevitable occurrence of systematic and measurement errors in the course of obtaining operational data, these errors can only be reduced to a certain level but never be eliminated. In this work, we propose a data reconciliation based approach to reduce the errors of operational data thus enhance the accuracy of the data. The reconciled data can then be used in performance monitoring and fault diagnosis systems to improve their performances. The proposed method is based on more efficient use of redundant data and a first-principle mathematical model of a power plant. Then an optimization process is performed where the weighted least square form of aggregated differences between measured data and their estimated values are minimized. To illustrate the capability of the proposed method, we provide a case study of data reconciliation for feedwater heater heat balance analysis in a 660 MW coal- fired power plant in China. Results show that uncertainty of four key parameters, namely feed water mass flow rate, condensate mass flow rate, deaerator pressure and outlet temperature, can be reduced by 24 %, 30 %, 5 % and 65 %, whilst the uncertainty of other parameters are also reduced to various extent. Moreover, the results also indicate that the proposed approach is effective over a wide range of measured data quality, where quality of some data could be much worse than others and the estimated measurement uncertainties of operational data may not be accurate.
