Research On Advanced Control Strategy Of Thermal Process In Power Plant--Multivariable And Bond Graph Control

Although DCS is widely applied into power plant, its control strategy still depends on conventional PID theory. Due to the high complexity of fossil-fueled power plant, which involves equipments of multi-field and multivariable with the characteristics of nonlinear, large delay and time varying, the control performance is somewhat stunted by conventional control strategy based on single-loop, fixed parameter model. Therefore, it is important to develop advanced control strategies for thermal process control. In this dissertation advance control strategies of multivariable thermal process has been explored, and a control method based on bond graph model has also been discussed preliminarily. Facing nonlinear, multivariable process, a fuzzy neural network control strategy is presented. It not only realizes decoupling model with nonlinear, but also overcomes the low quality of linear controller. Both a learning algorithm of fuzzy neural network named small dynamic universe and a fuzzy neural network controller (FNNC) with simplified rules are firstly proposed. Then multivariable control scheme of FNNC with nonlinear decoupling based on distributed predictive compensation or neural network compensator is given. For the multivariable process with large delay, an internal model decoupling control structure is provided. An internal model control, which implies the principle of Smith estimator and inverse control, combines decoupling technology to achieve good performance for multivariable process with large delay. According to the cancellation method, IMC for multivariable process with or without large delay are designed individually. Besides, the MAC multivariable control algorithm based on prediction approach is proposed, which includes no input constraint condition and input constraint condition. In order to improve real-time performance of the algorithm, for large delay multivariable process a predictive functional control (PFC) with low complexity of calculation is proposed. A transparency control that incorporates P criterion decoupling into predictive functional control for first order plant of large delay is developed. Furthermore, a multivariable PFC is firstly presented and control performance is tested and studied on delay-balanced system and delay-unbalanced system. For enlarging the scope to advanced control of thermal process, on the other hand, the dissertation also preliminarily research on bond model control, which is a new control field in the world. A bond graph model is firstly introduced into thermaldynamics in our country and a control algorithm based on bond graph model is developed. Bond graph model of vaporization system of natural circulation boiler is successfully built and a simulation curve of the drum level demonstrates the high precision of the model. Further, a hybrid qualitative and quantitative control method based on bond graph of no causality stroke is put forward and can achieve a control performance of both robustness and precision. Simulation results of above advanced control strategy are also carried out. Tests on ball mill system with nonlinear coupling and reheat steam temperature with large time constant, large delay using steam-steam exchanger show that multivariable control strategies developed have important theoretical and practical advantage for process control of power plant. The bond graph control opens a new window to research on control theory and thermal process control.