Dynamic modeling of a SOFC/MGT hybrid power system based on modified OIF Elman neural network |
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| Authors: | Xiao‐Juan Wu Qi Huang Xin‐Jian Zhu |
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| Affiliation: | 1. School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China;2. Institute of Fuel Cell, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China |
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| Abstract: | 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. |
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| Keywords: | solid oxide fuel cell (SOFC) micro gas turbine (MGT) output– input feedback(OIF) Elman neural network particle swarm optimization (PSO) |
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