Neural network modeling of the differential expansion of a 200 MW steam turbine

A new neural network approach is applied to the modeling of the differential expansion of a 200 MW steam turbine. This approach uses BP network with internal lags and is useful for modeling the process in which outputs change slowly with inputs.