Power decoupling control of a solid oxide fuel cell and micro gas turbine hybrid power system

Solid Oxide Fuel Cell (SOFC) integrated into Micro Gas Turbine (MGT) is a multivariable nonlinear and strong coupling system. To enable the SOFC and MGT hybrid power system to follow the load profile accurately, this paper proposes a self-tuning PID decoupling controller based on a modified output–input feedback (OIF) Elman neural network model to track the MGT output power and SOFC output power. During the modeling, in order to avoid getting into a local minimum, an improved particle swarm optimization (PSO) algorithm is employed to optimize the weights of the OIF Elman neural network. Using the modified OIF Elman neural network identifier, the SOFC/MGT hybrid system is identified on-line, and the parameters of the PID controller are tuned automatically. Furthermore, the corresponding decoupling control law is achieved by the conventional PID control algorithm. The validity and accuracy of the decoupling controller are tested by simulations in MATLAB environment. The simulation results verify that the proposed control strategy can achieve favorable control performance with regard to various load disturbances.