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1 2014

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Steam turbine stress control using NARX neural network

Dominiczak K., Rządkowski R., Radulski W.

Zeszyty Naukowe. Cieplne Maszyny Przepływowe - Turbomachinery / Politechnika Łódzka

2014

nr 145

37--38

Considered here is concept of steam turbine stress control, which is based on Nonlinear AutoRegressive neural networks with eXogenous inputs. Using NARX neural networks, which were trained based on experimentally validated FE model allows to control stresses in protected thick-walled 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. Moreover NARX neural network allows to predict strength parameters (stress and temperature in protected steam turbine thick-walled element) for few time steps ahead. This leads to high accuracy of stress control. 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, which were used in FE model of the HP rotor and the HP rotor FE model itself were validated bas 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.