Dynamic neural network controller model of PEM fuel cell system

This paper presents the artificial intelligence techniques to control a proton exchange membrane fuel cell system process, using particularly a methodology of dynamic neural network. In this work a dynamic neural network control model is obtained by introducing a delay line in the input of the neural network. A static production system including a PEMFC is subjected to variations of active and reactive power. Therefore the goal is to make the system follow these imposed variations. The simulation requires the modelling of the principal element (PEMFC) in dynamic mode. The simulation results demonstrate that the model-based dynamic neural network control scheme is appropriate for controlling, the stability of the identification and the tracking error were analyzed, and some reasons for the usefulness of this methodology are given.

[1]  S. Iniyan,et al.  A review of energy models , 2006 .

[2]  S. Srinivasan,et al.  Quantum jumps in the PEMFC science and technology from the 1960s to the year 2000 Part I. Fundamental scientific aspects , 2001 .

[3]  Daniel Hissel,et al.  Characterisation and modelling of a 5 kW PEMFC for transportation applications , 2006 .

[4]  T. Vezirolu,et al.  Long-term environmental and socio-economic impact of a hydrogen energy program in Brazil , 2001 .

[5]  Won-Yong Lee,et al.  Empirical modeling of polymer electrolyte membrane fuel cell performance using artificial neural networks , 2004 .

[6]  Mohammad S. Alam,et al.  Evolutionary programming-based methodology for economical output power from PEM fuel cell for micro-grid application , 2005 .

[7]  Mohammad S. Alam,et al.  A dynamic model for a stand-alone PEM fuel cell power plant for residential applications , 2004 .

[8]  F. R. Foulkes,et al.  Fuel Cell Handbook , 1989 .

[9]  Brant A. Peppley,et al.  On board hydrogen purification for steam reformation/ PEM fuel cell vehicle power plants , 1996 .

[10]  Paola Costamagna,et al.  Quantum jumps in the PEMFC science and technology from the 1960s to the year 2000 ☆: Part II. Engineering, technology development and application aspects , 2001 .

[11]  Mohammad S. Alam,et al.  Impact of hydrogen production on optimal economic operation of a grid-parallel PEM fuel cell power plant , 2006 .

[12]  José L. Figueiredo,et al.  Enhanced electrocatalytic activity of carbon-supported MnOx/Ru catalysts for methanol oxidation in fuel cells , 2006 .

[13]  M. Hatti,et al.  Neural Network Controller for P E M Fuel Cells , 2007, 2007 IEEE International Symposium on Industrial Electronics.

[14]  Bimal K. Bose,et al.  Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective , 2007, IEEE Transactions on Industrial Electronics.