CLC number: TP273; TM911.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2009-10-18
Cited: 1
Clicked: 7106
Jun LI, Nan GAO, Guang-yi CAO, Heng-yong TU, Ming-ruo HU, Xin-jian ZHU, Jian LI. Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model[J]. Journal of Zhejiang University Science A, 2010, 11(1): 61-70.
@article{title="Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model",
author="Jun LI, Nan GAO, Guang-yi CAO, Heng-yong TU, Ming-ruo HU, Xin-jian ZHU, Jian LI",
journal="Journal of Zhejiang University Science A",
volume="11",
number="1",
pages="61-70",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0800887"
}
%0 Journal Article
%T Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model
%A Jun LI
%A Nan GAO
%A Guang-yi CAO
%A Heng-yong TU
%A Ming-ruo HU
%A Xin-jian ZHU
%A Jian LI
%J Journal of Zhejiang University SCIENCE A
%V 11
%N 1
%P 61-70
%@ 1673-565X
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0800887
TY - JOUR
T1 - Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model
A1 - Jun LI
A1 - Nan GAO
A1 - Guang-yi CAO
A1 - Heng-yong TU
A1 - Ming-ruo HU
A1 - Xin-jian ZHU
A1 - Jian LI
J0 - Journal of Zhejiang University Science A
VL - 11
IS - 1
SP - 61
EP - 70
%@ 1673-565X
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0800887
Abstract: In this paper, an application of a nonlinear predictive controller based on a self recurrent wavelet network (SRWN) model for a direct internal reforming solid oxide fuel cell (DIR-SOFC) is presented. As operating temperature and fuel utilization are two important parameters, the SOFC is identified using an SRWN with inlet fuel flow rate, inlet air flow rate and current as inputs, and temperature and fuel utilization as outputs. To improve the operating performance of the DIR-SOFC and guarantee proper operating conditions, the nonlinear predictive control is implemented using the off-line trained and on-line modified SRWN model, to manipulate the inlet flow rates to keep the temperature and the fuel utilization at desired levels. Simulation results show satisfactory predictive accuracy of the SRWN model, and demonstrate the excellence of the SRWN-based predictive controller for the DIR-SOFC.
[1] Arriagada, J., Olausson, P., Selimovic, A., 2002. Artificial neural network simulator for SOFC performance prediction. Journal of Power Sources, 112(1):54-60.
[2] Campanari, S., 2001. Thermodynamic model and parametric analysis of a tubular SOFC module. Journal of Power Sources, 92(1-2):26-34.
[3] Chan, S.H., Ho, H.K., Tian, Y., 2003. Multi-level modeling of SOFC-gas turbine hybrid system. International Journal of Hydrogen Energy, 28(8):889-900.
[4] Chui, C.K., 1992. An Introduction to Wavelets. Academic Press, New York, p.98.
[5] Entchev, E., Yang, L., 2007. Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation. Journal of Power Sources, 170(1):122-129.
[6] Jurado, F., 2006a. A method for the identification of solid oxide fuel cells using a Hammerstein model. Journal of Power Sources, 154(1):145-152.
[7] Jurado, F., 2006b. Predictive control of solid oxide fuel cells using fuzzy Hammerstein models. Journal of Power Sources, 158(1):245-253.
[8] Karoliussen, H., Nisancioglu, K., Solheim, A., 1998. Use of effective conductivities and unit cell-based supraelements in the numerical simulation of solid oxide fuel cell stacks. Journal of Applied Electrochemistry, 28(3):283-288.
[9] Larminie, J., Dicks, A., 2000. Fuel Cell Systems Explained. Wiley, New York, p.164.
[10] Li, J., Cao, G.Y., Zhu, X.J., Tu, H.Y., 2007. Two-dimensional dynamic simulation of a direct internal reforming solid oxide fuel cell. Journal of Power Sources, 171(2):585-600.
[11] Lin, C.H., Wang, C.H., 2006. Adaptive wavelet networks for power-quality detection and discrimination in a power system. IEEE Transaction on Power Delivery, 21(3):1106-1113.
[12] Lin, C.L., Shieh, N.C., Tung, P.C., 2002. Robust wavelet neuro control for linear brushless motors. IEEE Transaction on Aerospace and Electronic System, 38(3):918-932.
[13] Oussar, Y., Rivals, I., Personnaz, L., Dreyfus, G., 1998. Training wavelet networks for nonlinear dynamic input-output modeling. Neurocomputing, 20(1-3):173-188.
[14] Schumacher, J.O., Gemmar, P., Denne, M., Zedda, M., Stueber, M., 2004. Control of miniature proton exchange membrane fuel cells based on fuzzy logic. Journal of Power Sources, 129(2):143-151.
[15] Selimovic, A., Palsson, J., 2002. Networked solid oxide fuel cell stacks combined with a gas turbine cycle. Journal of Power Sources, 106(1-2):76-82.
[16] Shen, C., Cao, G.Y., Zhu, X.J., Sun, X.J., 2002. Nonlinear modeling and adaptive fuzzy control of MCFC stack. Journal of Process Control, 12(8):831-839.
[17] Stiller, C., Thorud, B., Seljebo, S., Mathisen, O., Karoliussen, H., Bolland, O., 2005. Finite-volume modeling and hybrid-cycle performance of planar and tubular solid oxide fuel cells. Journal of Power Sources, 141(2):227-240.
[18] Yoo, S.J., Choi, Y.H., Park, J.B., 2006. Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: adaptive learning rates approach. IEEE Transaction on Circuits and Systems I, 53(6):1381-1394.
[19] Zhang, Q., Benveniste, A., 1992. Wavelet networks. IEEE Transactions on Neural Networks, 3(6):889-898.
[20] Zhu, J., 2002. Intelligent Predictive Control Technology and Application. Zhejiang University Press, Hangzhou, China, p.36 (in Chinese).
Open peer comments: Debate/Discuss/Question/Opinion
<1>