Model Predictive Control Strategy for a Combined-Cycle Power-Plant Boiler

Abstract

Combined-cycle power plants recycle steam or gas to generate additional power and reduce emissions. In this research work, the boiler of a combined-cycle power plant is controlled using three control strategies, which are designed and compared, for the variables drum water level ($L$) and superheated steam pressure ($p_{s}$). A conventional PI controller is designed using the Lambda-tuning technique to obtain the optimal controller's gains. In addition, a fuzzy logic-based controller that considers the error and the error's rate-of-change is applied. Finally, a model predictive control (MPC) is applied, which objective function is to minimize the steady state error and the variation of the control actions, thus the fuel consumption is reduced. The controllers' performance is compared by analyzing maximum overshoot, settling time, steady-state error, and mainly fossil fuel consumption, which influences the operating cost. The results show a proper performance of the three control techniques. However, MPC control achieves a higher reduction of fuel consumption.