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Method for identifying superheater model parameters based on data drive

A model parameter and data-driven technology, applied in the field of information processing, can solve problems such as inaccurate object description and insufficient model complexity, and achieve the effects of improving generalization ability, identification accuracy and ensuring balance

Inactive Publication Date: 2010-09-08
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the model mentioned in this technology is not complex enough, and the description of the object is not precise enough

Method used

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  • Method for identifying superheater model parameters based on data drive
  • Method for identifying superheater model parameters based on data drive
  • Method for identifying superheater model parameters based on data drive

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Experimental program
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Embodiment

[0035] The boiler high-temperature superheater in this embodiment satisfies the following conditions:

[0036] A. The external heat flow is evenly distributed along the length and circumference of the tube;

[0037] B. The tube wall metal only considers radial heat transfer, not axial heat transfer;

[0038] C. The working medium in the tube is incompressible, and the parameters on the flow section are uniform;

[0039] D. Neglect the dynamic characteristics of the desuperheater;

[0040] E. Taking the superheater outlet parameter as the representative parameter of the lumped parameter model, the resistance is concentrated at the inlet.

[0041] This embodiment includes the following steps:

[0042] The first step is to establish a nonlinear lumped parameter model of the boiler superheater, and determine the known operating parameters of the boiler and the parameters to be identified.

[0043] The nonlinear lumped parameter model of the present embodiment is specifically: ...

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Abstract

A method for identifying superheater model parameters based on data drive belongs to the technical field of information processing and comprises the following steps: establishing a non-linear lumped parameter model of the boiler superheater and determining the known boiler operation state parameters and the parameters needing to be identified; carrying out bad pixel processing and data smoothing on the known boiler operation state parameters to obtaine a real-time database of the known boiler operation state parameters; establishing training sample databases in n-numbered different load sections and normalizing the parameters; establishing a corresponding RBF neural network model aiming at each training database and connecting n-numbered RBF neural network models in parallel to form a hybrid network; extracting the actual measured value of the superheater system at the present moment and carrying out online parameter identification on the RBF neural network models; and updating the RBF neural network models every t. The method avoids the defect of adopting the fixed parameters in the conventional superheater models, realizes real-time identification of the parameters of the models and ensures the identification precision of the parameters of the models.

Description

technical field [0001] The invention relates to a method in the technical field of information processing, in particular to a data-driven superheater model parameter identification method. Background technique [0002] Establishing a mathematical model for a boiler is a common method to obtain its dynamic characteristics, and the accuracy of the model is closely related to the overall optimal control of the boiler. With the increase of the capacity of the boiler unit, the structure of the boiler has gradually become more complicated, and the increase of the required detection and control parameters has brought new challenges to the establishment of the model and the determination of its parameters. [0003] The superheater is an important part of the boiler. The output parameters of the superheater, the main steam temperature and the main steam pressure, are important monitoring parameters of the system. Therefore, the establishment of an accurate mathematical model for it i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/02F22G5/20
Inventor 王景成陈旭史元浩吕鹏宏王斌袁景淇
Owner SHANGHAI JIAO TONG UNIV
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