Open AccessUse of Artificial Neural Networks for the Simulation of Combined Cycle Transients
Author(s) -
Umberto Desideri,
Francesco Fantozzi,
Gianni Bidini,
Philippe Mathieu
Publication year - 1997
Publication title -
volume 2: coal, biomass and alternative fuels; combustion and fuels; oil and gas applications; cycle innovations
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1115/97-gt-442
Subject(s) - artificial neural network , transient (computer programming) , steam turbine , computer science , block (permutation group theory) , power station , combined cycle , duty cycle , turbine , control engineering , electric power system , engineering , automotive engineering , power (physics) , artificial intelligence , voltage , electrical engineering , mechanical engineering , geometry , mathematics , operating system , physics , quantum mechanics
Neural networks can be used reliably to obtain the response of complex energetic systems such as combined cycles (CC), during slow transient and consequently as part of an on-line monitoring system. A CC power plant is simulated by dividing it into three blocks representative of the three main elements of the CC: namely the gas turbine, the heat recovery steam generator, and the steam turbine
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