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Dynamic modeling of a SOFC/MGT hybrid power system based on modified OIF Elman neural network
Author(s) -
Wu XiaoJuan,
Huang Qi,
Zhu XinJian
Publication year - 2012
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.1786
Subject(s) - artificial neural network , power (physics) , hybrid power , system dynamics , engineering , automotive engineering , control engineering , computer science , artificial intelligence , physics , quantum mechanics
SUMMARY Solid oxide fuel cell (SOFC) integrated into micro gas turbine (MGT) cycle is a promising power‐generation technology. This article proposes a modified output–input feedback (OIF) Elman neural network model to describe the nonlinear temperature and power dynamic properties of the SOFC/MGT hybrid system. A physics‐based mathematical model of a 220 kW SOFC/MGT hybrid power system is used to generate the data required for the training and prediction of the modified OIF Elman neural network identification model. Compared with the conventional Elman neural network, the simulation results show that the modified OIF Elman identification model can follow the temperature and power response of the SOFC/MGT hybrid system with higher prediction accuracy and faster convergent speed. Copyright © 2010 John Wiley & Sons, Ltd.