Subcritical Coordinated Control Method Based on Multi-Model Predictive Control

Abstract

The invention discloses a subcritical coordinative control method based on multiple model predictive control. The method comprises the following steps: determining a plurality of preset local prediction models according to a non-linear work condition range, as well as dynamic responses of main steam pressure and power of a subcriticla machine set; designing a controller for each local prediction model, selecting a control variable, and obtaining an output increment of each local prediction model through an interpolation formula; and during each control period, obtaining an actual control output increment according to an output increment of each controller in a weighing manner; and correcting a feed-forward channel. By adopting the method disclosed by the invention, control output during a transition period can be smoothed, as a result, load and pressure response characteristics of the machine set can be improved effectively; and the subcritical coordinative control method based on multiple model predictive control can adapt to large-scale change in work conditions.

Description

technical field [0001] The invention relates to industrial process control technology, in particular to a subcritical coordinated control method based on multi-model predictive control. Background technique [0002] The thermal power unit needs to control the boiler and steam turbine from one steady state to another steady state when the load changes. The existing mature MPC technology mostly uses linear models, and the application of nonlinear process modeling and control in actual process control is very difficult. Therefore, it is a feasible method to use multi-linear models to solve the nonlinear process, but the difficulty lies in how to Establish dynamics between linear models and nonlinear process dynamics. Moreover, after the local dynamic model is established, it is also very important to design a multi-model predictive control strategy suitable for load changes, because the model is local, but the dynamic characteristics of the process are global. When using the m...

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Problems solved by technology

The existing mature MPC technology mostly uses linear models, and the application of nonlinear process modeling and control in actual process control is very difficult. Therefore, it is a feasible method to use multi-linear models to solve the nonlinear process, but the difficulty lies in how to Establishing the dynamics between linear models and nonlinear process dynamics

Moreover, after the local dynamic model is established, it is also very important to design a multi-model predictive control strategy suitable for load changes, because the model is local, but the dynamic characteristics of the process are global. When using the model for large-scale prediction , there will be a large degree of distortion

[0003] For the traditional coordinated control of boilers in thermal power plants, conventional PID+feedforward control is mostly used. The advantage is that the control structure is simple and has certain robustness, but the actual application control effect is not ideal, and it cannot adapt to large changes in load. In case of large pressure deviation

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