Modeling and supervisory control design for a combined cycle power plant

The traditional control strategy based on PID controllers may be unsatisfactory when dealing with processes with large time delay and constraints. This paper presents a supervisory model based constrained predictive controller (MPC) for a combined cycle power plant (CCPP). First, a non-linear dynamic model of CCPP using the laws of physics was proposed. Then, the supervisory control using the linear constrained MPC method was designed to tune the performance of the PID controllers by including output constraints and manipulating the set points. This scheme showed excellent tracking and disturbance rejection results and improved performance compared with a stand-alone PID controller’s scheme

Distributed nonlinear state-dependent model predictive control and estimation for power generation plants

Centralized model predictive control (MPC) is often considered impractical, inflexible and unsuitable for controlling large-scale systems due to several factors such as large computational effort and difficulty to meet all operational objectives. Therefore, industrial large-scale systems are usually controlled by a distributed control framework. In this thesis, novel sequential nonlinear Distributed Model Predictive Control (DMPC) algorithms for large-scale systems that can handle constraints are proposed. The proposed algorithms are based on nonlinear MPC strategy, which uses a state-dependent nonlinear model to reduce the complexity of solving optimization problem. In this distributed framework, the overall system is divided into several interconnected subsystems and each subsystem is controlled by local MPC. These local MPCs solve convex optimization problem and exchange information via one directional communication channel at each sampling time to achieve the global performance. The proposed algorithms are applied to an industrial power plant model to improve power generation efficiency. A non-linear dynamic model of Combined Cycle Power Plant (CCPP) using the laws of physics was first developed and simulated using decentralized PID controllers. Then, a supervisory controller using linear constrained MPC was designed to tune the performance of the PID controllers. Next, a supervisory centralized nonlinear model predictive control (NMPC) algorithm based on state-dependent models was developed to control the nonlinear plant over a wide operating range. Finally, two sequential DMPC algorithms based on state-dependent models were developed. The lack of states measurement were handled by designing nonlinear distributed state estimation algorithms using state-dependent differential Riccati equation (SDDRE) Kalman filter. Numerical simulation results show that the performance of the proposed DMPC algorithms is close to the centralized NMPC but computationally more efficient compared to the centralized one.Centralized model predictive control (MPC) is often considered impractical, inflexible and unsuitable for controlling large-scale systems due to several factors such as large computational effort and difficulty to meet all operational objectives. Therefore, industrial large-scale systems are usually controlled by a distributed control framework. In this thesis, novel sequential nonlinear Distributed Model Predictive Control (DMPC) algorithms for large-scale systems that can handle constraints are proposed. The proposed algorithms are based on nonlinear MPC strategy, which uses a state-dependent nonlinear model to reduce the complexity of solving optimization problem. In this distributed framework, the overall system is divided into several interconnected subsystems and each subsystem is controlled by local MPC. These local MPCs solve convex optimization problem and exchange information via one directional communication channel at each sampling time to achieve the global performance. The proposed algorithms are applied to an industrial power plant model to improve power generation efficiency. A non-linear dynamic model of Combined Cycle Power Plant (CCPP) using the laws of physics was first developed and simulated using decentralized PID controllers. Then, a supervisory controller using linear constrained MPC was designed to tune the performance of the PID controllers. Next, a supervisory centralized nonlinear model predictive control (NMPC) algorithm based on state-dependent models was developed to control the nonlinear plant over a wide operating range. Finally, two sequential DMPC algorithms based on state-dependent models were developed. The lack of states measurement were handled by designing nonlinear distributed state estimation algorithms using state-dependent differential Riccati equation (SDDRE) Kalman filter. Numerical simulation results show that the performance of the proposed DMPC algorithms is close to the centralized NMPC but computationally more efficient compared to the centralized one

Sequential distributed model predictive control for state-dependent nonlinear systems

In this paper, sequential nonlinear Distributed Model Predictive Control (DMPC) algorithms for large-scale systems that can handle constraints are proposed. The proposed algorithms are based on nonlinear MPC strategy, which uses a statedependent nonlinear model to avoid the complexity of the nonlinear programming (NLP) problem. In this distributed framework, local MPCs solve convex optimization problem and exchange information via one directional communication channel at each sampling time to achieve the global control objectives of the system. Numerical simulation results show that the performance of the proposed DMPC algorithms is close to the centralized NMPC but computationally more efficient compared to the centralized one