Temperature and Stress On-line Prediction in Steam Turbine Rotor using Neural Networks

Abstract

Considered here are Nonlinear Auto-Regressive neural networks with exogenous inputs (NARX) as a mathematical model of a steam turbine rotor used for the on-line prediction of turbine temperature and stress. In this paper on-line prediction is presented on the basis of one critical location in a high pressure steam turbine rotor, according to power plant common measurements, i.e., turbine speed, turbine load as well as steam temperature and pressure before turbine control valve. In order to obtain neural networks that will correspond to the temperature and stress the critical rotor location, an FE rotor model was built. Neural networks trained using the FE rotor model not only have FEM accuracy, but also include nonlinearity related to nonlinear steam turbine expansion, nonlinear heat exchange inside the turbine and nonlinear rotor material properties during transient conditions. Simultaneous neural networks are algorithms which can be implemented in turbine controllers. This allows for the application of neural networks to control steam turbine stress in industrial power plants.