Varying set-point with adaptive predictive supervisory controller for gas turbine of GILAN combined cycle

Electric power companies pay attention to the load following capability and economical operation of a thermal power plant. However, conventional control strategy in industrial control systems can't always provide the optimum solution for such subjects. To cope with these problems, the optimization at supervisory level of a combined cycle power plant control is investigated. The objective of this paper is to present new supervisory predictive controller based on multiple models. Multiple models are considered in order to characterize the nonlinear behavior of a gas turbine. Adaptive predictive supervisory controller optimizes an objective function in order to determine the set-point based on economic and operational criteria. The proposed adaptive predictive supervisory controller is applied to gas turbine of GILAN combined cycle power plant model and it is compared with the supervisory controller based on linear model and regulatory controller with constant set-point. The simulation results show the improvement in system operation with adaptive predictive supervisory control system. The efficiency of the whole system in terms of fuel consumption is increased.