Gross error detection in steam turbine measurements based on data reconciliation of inequality constraints

Abstract

Maintaining the online calculation accuracy of isentropic efficiency of a steam turbine stage is challenging due to widely existing gross errors in steam turbine measurements. They invalidate modelling results and hinder model-based monitoring and optimization. Data reconciliation is a mathematical method for gross error detection and has been applied in various industrial systems. In power plant systems, previous studies focus on gross error detection of flow rate measurements in regenerative systems based on equality constraints, which is insufficient for gross error detection of steam turbine systems. We propose a data reconciliation model adding inequality constraints to solve the problem. Statistical test is used to detect gross errors in steam turbine systems. Then an in-service 660 MW ultra-supercritical double reheat power plant is selected as a case study. Gross errors of flow rate measurements are detected and eliminated firstly. Then nonlinear inequality constraints, entropy increase of each stage, are added for further detection. Results show that the proposed model effectively detects gross errors in the steam turbine system and further improve the thermal calculation accuracy by 3.1–5.7%. It provides quantitative guidance for the calibration and maintenance of measurement instruments and facilitates performance monitoring and operation optimization in in-service power plants.