Nonlinear multivariable hierarchical model predictive control for boiler-turbine system

Abstract

Economic optimal control has been a major concern in modern power plant. The HMPC (hierarchical model predictive control) incorporates both the plant-wide economic process optimization and regulatory process control into a hierarchical control structure, in which the model predictive control technology has been an effective tool for solving the higher-layer economic optimization problems. Since the power plants are typically nonlinear multivariable large-scale processes, applications of the HMPC can be computationally extensive and resulting in nonlinear and non-convex optimization problems. Since the power plant dynamics changes with load, fuzzy model representing the local input–output relations of the nonlinear power plant system is incorporated to facilitate the convex QP (quadratic program) routine, and thus realize the HMPC. Detailed analysis on power plant steam-boiler generation system has been made to demonstrate the effectiveness of the proposed nonlinear HMPC.