The control of ultra-supercritical(USC) power unit is a difficult issue for its characteristic of the nonlinearity, large dead time and coupling of the unit. In this paper, model predictive control(MPC) based on multi...The control of ultra-supercritical(USC) power unit is a difficult issue for its characteristic of the nonlinearity, large dead time and coupling of the unit. In this paper, model predictive control(MPC) based on multi-model and double layered optimization is introduced for coordinated control of USC unit. The linear programming(LP) combined with quadratic programming(QP) is used in steady optimization for computation of the ideal value of dynamic optimization. Three inputs(i.e. valve opening, coal flow and feedwater flow) are employed to control three outputs(i.e. load, main steam temperature and main steam pressure). The step response models for the dynamic matrix control(DMC) are constructed using the three inputs and the three outputs. Piecewise models are built at selected operation points. Double-layered multi-model predictive controller is implemented in simulation with satisfactory performance.展开更多
In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression ...In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control （SVMN-MPC） algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results.展开更多
Because model switching system is a typical form of Takagi-Sugeno（T-S） model which is an universal approximator of continuous nonlinear systems, we describe the model switching system as mixed logical dynamical （ML...Because model switching system is a typical form of Takagi-Sugeno（T-S） model which is an universal approximator of continuous nonlinear systems, we describe the model switching system as mixed logical dynamical （MLD） system and use it in model predictive control （MPC） in this paper. Considering that each local model is only valid in each local region,we add local constraints to local models. The stability of proposed multi-model predictive control （MMPC） algorithm is analyzed, and the performance of MMPC is also demonstrated on an inulti-multi-output（MIMO） simulated pH neutralization process.展开更多
This paper introduces a multi-model approach to design a robust supplementary damping controller. The designed fixed-order supplementary damping controller adjusts the voltage reference set point of SVC. There are two...This paper introduces a multi-model approach to design a robust supplementary damping controller. The designed fixed-order supplementary damping controller adjusts the voltage reference set point of SVC. There are two main objectives of the controller design, damping low frequencies oscillations and enhancing power system stability. This method relies on shaping the closed-loop sensitivity functions in the Nyquist plot under the constraints of these functions. These constraints can be linearized by choosing a desired open-loop transfer function. The robust controller is designed to minimize the error between the open-loop of the original plant model and the desired transfer functions. These outcomes can be achieved by using convex optimization methods. Convexity of the problem formulation ensures global optimality. One of the advantages of the proposed approach is that the approach accounts for multi-model uncertainty. In contrast to the methods available in the literature, the proposed approach deals with full-order model (i.e., model reduction is not required) with lower controller order. The issue of time delay of feedback signals has been addressed in this paper for different values of time delay by applying a multi-model optimization technique. The proposed approach is compared to other existing techniques to design a robust controller which is based on H2 under pole placement. Both techniques are applied to the 68-bus system to evaluate and validate the robust controller performance under different load scenarios and different wind generations.展开更多
基金Supported by the National Natural Science Foundation of China(60974119)
文摘The control of ultra-supercritical(USC) power unit is a difficult issue for its characteristic of the nonlinearity, large dead time and coupling of the unit. In this paper, model predictive control(MPC) based on multi-model and double layered optimization is introduced for coordinated control of USC unit. The linear programming(LP) combined with quadratic programming(QP) is used in steady optimization for computation of the ideal value of dynamic optimization. Three inputs(i.e. valve opening, coal flow and feedwater flow) are employed to control three outputs(i.e. load, main steam temperature and main steam pressure). The step response models for the dynamic matrix control(DMC) are constructed using the three inputs and the three outputs. Piecewise models are built at selected operation points. Double-layered multi-model predictive controller is implemented in simulation with satisfactory performance.
基金the 973 Program of China (No.2002CB312200)the National Science Foundation of China (No.60574019)
文摘In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control （SVMN-MPC） algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results.
文摘Because model switching system is a typical form of Takagi-Sugeno（T-S） model which is an universal approximator of continuous nonlinear systems, we describe the model switching system as mixed logical dynamical （MLD） system and use it in model predictive control （MPC） in this paper. Considering that each local model is only valid in each local region,we add local constraints to local models. The stability of proposed multi-model predictive control （MMPC） algorithm is analyzed, and the performance of MMPC is also demonstrated on an inulti-multi-output（MIMO） simulated pH neutralization process.
文摘This paper introduces a multi-model approach to design a robust supplementary damping controller. The designed fixed-order supplementary damping controller adjusts the voltage reference set point of SVC. There are two main objectives of the controller design, damping low frequencies oscillations and enhancing power system stability. This method relies on shaping the closed-loop sensitivity functions in the Nyquist plot under the constraints of these functions. These constraints can be linearized by choosing a desired open-loop transfer function. The robust controller is designed to minimize the error between the open-loop of the original plant model and the desired transfer functions. These outcomes can be achieved by using convex optimization methods. Convexity of the problem formulation ensures global optimality. One of the advantages of the proposed approach is that the approach accounts for multi-model uncertainty. In contrast to the methods available in the literature, the proposed approach deals with full-order model (i.e., model reduction is not required) with lower controller order. The issue of time delay of feedback signals has been addressed in this paper for different values of time delay by applying a multi-model optimization technique. The proposed approach is compared to other existing techniques to design a robust controller which is based on H2 under pole placement. Both techniques are applied to the 68-bus system to evaluate and validate the robust controller performance under different load scenarios and different wind generations.
