A Dynamic Parameter Tuning Method of Superheater Mechanism Model Combined with Field Data

Abstract

The invention discloses a dynamic parameter setting method of a superheater mechanism model combined with field data. The method starts from the internal working process of the system according to the basic physical laws, establishes a system mechanism simulation model, and uses the superheater outlet parameters as characteristic parameters. In order to improve Model accuracy, the superheater is divided into five sections for modeling, and the model dynamic parameter α is added to optimize and adjust the simulation model according to the actual operating characteristics. This method combines the superheater mechanism model and the transfer function identification model based on field data, using particle swarm intelligence Algorithm, in the case of closed-loop operation of thermal power units, according to the time constant of the transfer function model identified from the field data, the dynamic parameter size in the mechanism model is automatically adjusted according to the error function, which can effectively improve the dynamic characteristics of the actual process when the mechanism model is established. Simplify and improve the simulation accuracy of dynamic characteristics and the development efficiency of the simulation system.

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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Problems solved by technology

[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, there is no uniform, recognized methodology or It is a development specification, and there will be some simplification or neglect when establishing the mathematical model of the equipment. The mathematical model thus established is not accurate enough, and it is difficult to meet various use needs, which greatly reduces the credibility of the simulation results and cannot be well reflect the dynamic characteristics of the system

[0004] At present, most superheater simulation models use lumped parameter models, and the adjustment of dynamic characteristics of superheater simulation models mainly depends on the operating experience of experts. Developers need to spend a lot of time to repeatedly debug model parameters based on experience, and the work intensity is high and the efficiency is relatively high. However, the current thermal power units widely use DCS and SIS systems, which can easily obtain the historical data of unit operation. How to make full use of these field data to optimize the parameters of the simulation model, reduce the debugging time of developers, and improve the simulation accuracy of the dynamic characteristics of the model It has become one of the important research contents in the field of system simulation

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