Abstract
The control of an ultra-supercritical (USC) boiler–turbine power plant is critical in maintaining the safety of the sustainable power grid. However, it is challenging due to the internal nonlinearity, hard manipulation constraints, and widespread uncertainties. To this end, a fuzzy extended state observer (FESO)-based stable fuzzy predictive control (SFPC) approach is developed in this paper. First, the control difficulties of the USC boiler–turbine unit are analyzed. Then, based on a Takagi–Sugeno (T–S) fuzzy model, a new FESO is developed for nonlinear systems to achieve a more precise observation performance. The gain of FESO is determined by solving a series of linear matrix inequalities, while guaranteeing the stability of FESO. Then, by combining the proposed FESO with the SFPC, an integrated FESO–SFPC algorithm is devised. The disturbance rejection ability of the FESO–SFPC algorithm is analyzed theoretically. Simulation results on a 1000 MW USC boiler–turbine power plant model further validate the effectiveness of the proposed method.
Highlights
Ultra-supercritical (USC) power plants have been gaining increasing attention in modern coal-fired power industry because of their high efficiency with low emission
model predictive control (MPC) has been used in the dynamic stabilization of DC microgrids [10], control of electric vehicles [11], load frequency control [12], and control of DC–DC converters [13], achieving good simulation and experimental results
We aim to propose a control strategy that is able to regulate an input-constrained nonlinear USC boiler–turbine unit to overcome the defects of the existing controls
Summary
Ultra-supercritical (USC) power plants have been gaining increasing attention in modern coal-fired power industry because of their high efficiency with low emission. To achieve a sustainable future for renewable energy, a conventional power plant is required to be able to change its power output rapidly to balance the grid load in the presence of intermittent renewable generation [2]. It is extremely challenging to control a USC boiler–turbine unit because of the nonlinearity, the coupling among multi-variables, and the hard constraints on the manipulated variables. To overcome these issues, various control strategies for a boiler–turbine system have been studied, such as robust control [2], optimal control [3], intelligent control [4], sliding model control [5], active disturbance rejection control [6], model predictive control (MPC) [7], etc. MPC has been used in the dynamic stabilization of DC microgrids [10], control of electric vehicles [11], load frequency control [12], and control of DC–DC converters [13], achieving good simulation and experimental results
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