An Effective Nonlinear Multivariable HMPC for USC Power Plant Incorporating NFN-Based Modeling


The ultra-supercritical (USC) unit is a complicated power generation technology with high plant efficiency, high coal utilization, and low emission. However, it is troublesome to realize a coordinate management for the USC unit to attain fast and stable dynamic response during load tracking and grid frequency disturbances, as a result of it's complex, nonlinear, and large scale. This paper presents a nonlinear hierarchical model predictive control (HMPC) to incorporate both the plant-wide economic method optimization and the regulatory process control into a hierarchical control structure, in that the model predictive control (MPC) technology is utilised to solve the multilayer optimization problem. Whereas the nonlinear HMPC optimization issues will be nonconvex, the neuro-fuzzy network (NFN) modeling on USC is incorporated to facilitate the convex quadratic program (QP) routine. Detailed analysis on load tracking and grid frequency disturbances via simulations has been addressed to demonstrate the effectiveness of the proposed nonlinear HMPC.

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