Dynamic parameter setting method for superheater mechanism model by combining with field data

Abstract

The invention discloses a dynamic parameter setting method for a superheater mechanism model by combining with field data. The method comprises the following steps that: according to a basic physicallaw, from the internal working process of a system, establishing a system mechanism simulation model; taking a superheater outlet parameter as a feature parameter, and carrying out modeling on the superheater in five sections in order to improve model accuracy; in addition, adding the dynamic parameter [Alpha] of the model; and according to practical operation features, carrying out optimal regulation on the simulation model. By use of the method, the superheater mechanism model is combined with a transfer function distinguishing model based on field data, a particle swarm intelligent algorithm is used to automatically set dynamic parameters in the mechanism model according to an error function on the basis of the time constant of the transfer function model distinguished by the field data, simplification and deficiency for the dynamic features of the practical process when the mechanism model is established can be effectively improved, and dynamic feature simulation accuracy and the development efficiency of a simulation system are improved.

Description

technical field

[0001] The invention relates to a parameter setting method, in particular to a dynamic parameter setting method of a superheater mechanism model combined with on-site data, and belongs to the technical field of superheater dynamic characteristic simulation modeling. Background technique

[0002] With the continuous expansion of the scale of thermal power units and the continuous improvement of the level of science and technology, the unit unit is developing towards the direction of large capacity, high parameters and high automation. It provides better guidance for state, scheme optimization, parameter setting, etc. The key to realizing these requirements is to establish a highly reliable simulation model.

[0003] Since most thermal objects have characteristics such as complexity, nonlinearity, and time-varying parameters, it is difficult to establish a simulation model applicable to a full range, and in the process of developing the simulation machine, the...

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