Abstract
This paper presents a Neural Network (NN) approach for steam turbines modelling. NN models can predict generated power as well as different steam features that cannot be directly monitored through sensors, such as pressures and temperatures at drums outlet and steam quality. The investigated models have been trained and validated on a dataset created through the internal sizing design tool and tested by exploiting field data coming from a real-world power plant, in which a High Pressure and a Low Pressure turbines are installed. The proposed approach is applied to identify the variation of the characteristics from data measurable on the operating field, by means of suitable monitoring and control algorithms that are implemented directly on the PLC.
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References
Medina-Flores, J.M., Picn-Nez, M.: Modelling the power production of single and multiple extraction steam turbines. Chem. Eng. Sci. 65(9), 2811–2820 (2010)
Chaibakhsh, A., Ghaffari, A.: Steam turbine model. Simul. Model. Pract. Theory 16(9), 1145–1162 (2008)
Varbanov, P.S., Doyle, S., Smith, R.: Modelling and optimization of utility systems. Chem. Eng. Res. Des. 82(5), 561–578 (2004)
Ray, A.: Dynamic modelling of power plant turbines for controller design. Appl. Math. Model. 4(2), 109–112 (1980)
Lu, S., Hogg, B.W.: Dynamic nonlinear modelling of power plant by physical principles and neural networks. Int. J. Electr. Power Energy Syst. 22(1), 67–78 (2000)
Larry, D.S., Hall, C.: Fluid Mechanics and Thermodynamics of Turbomachinery. Butterworth-Heinemann (2013)
Mavromatis, S.P., Kokossis, A.C.: Conceptual optimisation of utility networks for operational variationsI. Targets and level optimisation. Chem. Eng. Sci. 53(8), 1585–1608 (1998)
Aguilar, O., Perry, S.J., Kim, J.K., Smith, R.: Design and optimization of flexible utility systems subject to variable conditions: Part 1: modelling framework. Chem. Eng. Res. Des. 85(8), 1136–1148 (2007)
Ganjehkaviri, A., Jaafar, M.M., Hosseini, S.E.: Optimization and the effect of steam turbine outlet quality on the output power of a combined cycle power plant. Energy Convers. Manag. 89, 231–243 (2015)
Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6), 989–993 (1994)
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Dettori, S., Colla, V., Salerno, G., Signorini, A. (2018). A Neural Network-Based Approach for Steam Turbine Monitoring. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_20
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DOI: https://doi.org/10.1007/978-3-319-56904-8_20
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