Multi-model disturbance estimation predictive-control method for superheated steam temperature of thermal power generating unit

Abstract

The invention discloses a multi-model disturbance estimation predictive-control method for superheated steam temperature of a thermal power generating unit. According to the method, a superheated steam temperature system serves a controlled object, cooling spray water flow serves as system control input, superheated steam temperature serves as system output, a plurality of disturbance models are established to respectively expand the system state on the basis of a controlled object state space model, and a disturbance model set is formed. Sub-models in the disturbance model set are subjected to weighted averaging, thus the influence of immeasurable disturbance upon the system output is fitted. According to the concept of predictive control, optimization solution is performed once for each step so that optimal system controlled input is obtained. Compared with common predictive control algorithms, the method has the advantages that immeasurable disturbance can be inhibited more effectively, and steam temperature is kept around a set value stably; when no immeasurable disturbance occurs, the method is as effective as the common predictive control algorithms, and tracking regulation performance is fine.

Description

Technical field [0001] The invention relates to an optimized control method for a superheated steam temperature system, in particular to a multi-model disturbance estimation predictive control method for the superheated steam temperature of a thermal power unit against unmeasured disturbances, and belongs to the thermal automatic control technology. Background technique [0002] The superheated steam temperature is directly related to the safety and economy of the operation of the thermal power unit, and is one of the process parameters that need to be monitored during the operation of the unit. [0003] Due to the long pipeline of the superheater and the large inertia and delay of the controlled object of the steam temperature, the traditional control method is often difficult to achieve satisfactory control results. In recent years, predictive control algorithms have achieved certain results in the application of power station steam temperature control systems. However, when mod...

AI Technical Summary

Problems solved by technology

However, when the model-based predictive control algorithm deals with unmeasurable disturbances on the flue gas side caused by changes in coal quality, the control effect is not ideal due to the lack of modeling of unmeasurable disturbances.

At present, most multi-model predictive controllers are mainly to solve the nonlinearity of large-scale operating conditions of the unit, and do not consider active anti-interference through disturbance modeling

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