Dissertation > Industrial Technology > Electrotechnical > Power generation, power plants > Power plant > Thermal power plants, thermal power stations > Boilers and combustion systems

Model Predictive Control of Air System of Thermal Boiler Based-on Subspace Identification Method

Author WangChuan
Tutor XueMeiSheng
School University of Science and Technology of China
Course Control Theory and Control Engineering
Keywords Thermal Power Plant Air System Subspace Identification Method Principal Component Analysis Model Optimal Order Model Predictive Control Data-driven
CLC TM621.2
Type Master's thesis
Year 2010
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With the increasing competition from the power market, the shortage of global resources, and the severe requirement on environment protection, it is quite urgent for the thermal power units to improve the operation level of the boiler combustion system, and to meet the need of energy saving and emission reduction. This dissertation mainly focuses on the advanced control strategy design of air system of thermal plant, based-on the theoretical research and industrial process data analysis. On the basis of Subspace Identification Method (SIM), a novel method of Model Predictive Controller design is proposed for the air system of thermal power station. First of all, the basic framework and characteristics of SIM is analyzed systematically, including the fundamental theory and algorithms. It can be concluded that the classic SIM can be summarized into three key steps. Step 1: The projection of the row space of specific data Hankel matrices is calculated. Step 2: with the tool of singular value decomposition, the extended observability matrix and a Kalman filter estimation of the state sequences can be obtained from the projection. Step 3: the system matrices are extracted from the observability matrices and/or the estimated state sequences. Then, the performance of SIM is improved by using Principal Component Analysis (PCA). Meanwhile, the subject on how to determine the optimal order of state-space model is also discussed.Model Predictive Control (MPC) technology has gained considerable application in industrial process for its good control performance. In this dissertation, the system architecture and algorithm theory of MPC is introduced, followed by MPC design method based-on state space model obtained by SIM. Then, by comparison between SIM and Model Algorithm Control (MAC), a data-driven MPC design method is proposed by using input and output Hankel matrices to obtain impulse response sequences of the system directly.Air system is one of the most important components of the combustion system in thermal power plant. Its working efficiency and control performance has a direct impact on the thermal boiler’s economy and safety. The air system controls and optimizes the combustion process by adjusting the amount of fuel, air input and air output. The air control systems can be divided into four parts: Prime Air Control System, Second Air Control System, Oxygen Control System and Boiler Pressure Control System. A further discussion is made upon the air control systems’capability and their present control strategies. Compared with traditional MPC, the MPC based-on SIM gives a better control performance. What is more, focusing on the problem of determination of the SIM model order, a relevant solution has been proposed with the consideration of the feature of the plant.

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