• solid oxide fuel cell (SOFC);
  • micro gas turbine (MGT);
  • optimization;
  • iterative method;
  • particle swarm optimization (PSO)


For a solid oxide fuel cell (SOFC) and micro gas turbine (MGT) hybrid system, optimal control of load changes requires optimal dynamic scheduling of set points for the system's controllers. Thus, this paper proposes an improved iterative particle swarm optimization (PSO) algorithm to optimize the operating parameters under various loads. This method combines the iteration method and the PSO algorithm together, which can execute the discrete PSO iteratively until the control profile would converge to an optimal one. In MATLAB environment, the simulation results show that the SOFC/MGT hybrid model with the optimized parameters can effectively track the output power with high efficiency. Hence, the improved iterative PSO algorithm can be helpful for system analysis, optimization design, and real-time control of the SOFC/MGT hybrid system. Copyright © 2011 John Wiley & Sons, Ltd.