Considered here is concept of steam turbine
stress control, which is based on Nonlinear AutoRegressive
neural networks with eXogenous inputs. Using NARX
neural networks,whichwere trained based on experimentally
validated FE model allows to control stresses in protected
thickwalled steam turbine element with FE model
quality. Additionally NARX neural network, which were
trained base on FE model, includes: nonlinearity of steam
expansion in turbine steam path during transients, nonlinearity
of heat exchange inside the turbine during transients
and nonlinearity of material properties during transients.
In this article NARX neural networks stress controls
is shown as an example of HP rotor of 18K390 turbine.
HP part thermodynamic model as well as heat exchange
model in vicinity of HP rotor,whichwere used in FE model
of the HP rotor and the HP rotor FE model itself were validated
based on experimental data for real turbine transient
events. In such a way it is ensured that NARX neural
network behave as real HP rotor during steam turbine transient
events