Multi-objective economic model predictive control for gas turbine system based on quantum simultaneous whale optimization algorithm

The gas turbine system is a major contributor of the thermal power generation process and an important complementary to the future high penetration of intermittent renewable generations. The rapidity, efficiency, and economy operation of the gas turbine system calls for advanced control algorithms and associated parameter optimization methods, which are intractable due to the strong coupling impact of complex turbine systems. In this paper, a multi-objective economic model predictive control (MOEMPC) method based on quantum simultaneous whale optimization algorithm is proposed for gas turbine system control. Firstly, the objective function of the MOEMPC strategy is formulated simultaneously considering economic indexes, terminal cost function, and stability constraints. The economic indexes reflect the change of energy consumption and throttle loss in real-time, while the terminal cost function and stability constraint guarantee the tracking accuracy under variable operation point and external disturbance. Secondly, a novel quantum simultaneous whale optimization algorithm is adopted to handle the optimization problem of MOEMPC. The introductions of the quantum coding and simultaneous search in original whale optimization algorithm have improved the convergence rate and accuracy immediately. Finally, the proposed method is applied into the controller design of gas turbine system in combined cycle unit. The simulation results have indicated the superiority of the presented strategy in terms of desired economic performance, high precision, rapidity, and robustness.