Application Of Improved Predictive Functional Control In Thermal Process

In recent years, with the rapid growth of the national economy, people’s living standards continue to improve, the national security and stability requirements for the power supply is increasing, the economical and environmental protection requirements become increasingly strong. In the case of energy structure adjustment is more inclined to clean electricity energy, power efficiency and low energy consumption have become the focus of future development. Improve efficiency is the inevitable way to achieve a higher efficiency. As an important control object, thermal efficiency is not only closely related to the safety of electric power production, but also one of the direct factors of power plant efficiency.At present, the actual use of power plant is still a large part of the PID control, for large inertia and strong hysteresis main steam temperature object, the control effect is general, it is difficult to achieve a good quality. With the promotion of the efficiency of the power plant, the requirement of improving the control system becomes more and more urgent. In this paper, we try to improve the thermal control system to do the following:1. According to the traditional PID control in the main steam temperature control is difficult to obtain satisfactory control effect in present situation, combining the situation that the dynamic time-domain optimization plays an important role in the control system on dynamic performance and steady-state performance, the scheme is given in this paper which is based on dynamic time-domain optimization multi-model predictive function control. The scheme is more rapid and robust in the tracking of the reference trajectory and the set value. The corresponding predictive controller is designed based on the main steam temperature model of several typical working conditions established in advance. By using the matching error strategy, the optimal controller is selected, and the weight of the model is calculated online. Based on the error performance index and the fuzzy weighted switching strategy, the control scheme of the multi-model weighted predictive function is presented. The simulation results show that this method is superior to the traditional PID control and the predictive functional control based on the fixed model.2. In view of the situation that the predictive function control is easy to be unbalanced in the large scale object fluctuation, fuzzy control is added to the predictive functional control in dynamic compensation, to compensate for the deficiency of the predictive function in the control of the object model mismatch From the simulation analysis, the control effect is good, and has certain robustness.