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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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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