| Following the national policy of energy saving and emission reduction,ultra-supercritical(USC)boiler-turbine units,which operate at higher temperature and pressure level,have been greatly promoted and gradually become the main devices in the power industry.Due to the severe nonlinearity,coupling among variables,large time-delay,tight operating constraints,vulnerability to unmeasurable disturbance such as the variation of fuel,the control performance is limited under the conventional control methods.Given these reasons,advanced control strategy is investigated based on the USC boiler-turbine unit to improve the control performance of the power plants.The main contributions of this thesis are as follows:(1)The differential-algebraic equations(DAEs)based predictive controller and the multi-model based predictive controller are developed for the USC boiler-turbine unit.In the DAEs based predictive control,Lagrange interpolation polynomials and Radau collocation methods are used to discretize the process variables,and the objective function is discretized by Gaussian quadrature.Collocation equations and continuity equations are formulated to convert the optimization problem into a nonlinear programming problem.To eliminate the steady-state deviation,integral action is introduced by adding the accumulated tracking error in the output equations.In the multi-model based predictive control,multi-model for the boiler-turbine unit is established on the basis of nonlinearity analysis.To achieve the offset-free tracking of the set-points,multi-model based predictive control in incremental form is used.By comparison of the control performance and the computing time for these two controllers,multi-model based predictive control is chosen as the basis for the advanced algorithm design in the following chapters.(2)To optimize the economic objectives of the boiler-turbine unit in transient process,such as fuel consumption,throttle loss and so on,two improved Utopia tracking based multi-objective predictive control are proposed,in which the hierarchical structure is utilized.In the upper layer,quasi infinite horizon model predictive control and fuzzy control are designed for the steady-state compromise solution,respectively.The stability constraint for the multi-objective predictive control in the lower layer is established via the suboptimality condition of the value function in the compromise solution tracking problem.In addition,the multi-objective operation mode of the boiler-turbine unit is proposed,in which the economic objectives are optimized while the output power is regulated.Simulation results on a boiler-turbine unit demonstrate the effectiveness of the proposed approach.(3)To overcome the influence of internal nonlinearity and unknown disturbances simultaneously,an extended state observer(ESO)based stable model predictive control is proposed for the USC boiler-turbine unit.The stable model predictive controller is devised on the multi-model using output cost function for the purpose of wide range load tracking.Input constraints are taken into consideration when the control action is solved.The improved ESO which can estimate plant behavior variations and unknown disturbances regardless of the direct feedthrough characteristic of the unit,is synthesized with the predictive controller to enhance its disturbance rejection property.Closed-loop stability of the ovrall control system is guaranteed.The proposed method is validated through simulations on a boiler-turbine unit.(4)Based on the multi-objective predictive control and anti-disturbance control of the boiler-turbine unit,a feasible disturbance rejection structure for the multi-objective predictive control is proposed.The lumped disturbance is estimated by the extended state observer,and then used for the compromise solution correction calculation.The influence of disturbance can be attenuated from the output channel in steady state.The simulations results on a boiler-turbine unit verify the merits of the proposed algorithm. |
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