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Method for identifying oxygen object model of boiler combustion system

An object model and boiler combustion technology, applied in the field of system identification, can solve problems such as easy to fall into local optimum and premature hybrid leapfrog algorithm, etc., and achieve fast global convergence, good ergodicity, and good identification effect

Active Publication Date: 2019-09-17
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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  • Application Information

AI Technical Summary

Problems solved by technology

Since it was proposed, it has been widely used in workshop scheduling, optimization problems, power grid planning, etc., but the hybrid leapfrog algorithm is prone to premature maturity and easy to fall into local optimum

Method used

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  • Method for identifying oxygen object model of boiler combustion system
  • Method for identifying oxygen object model of boiler combustion system
  • Method for identifying oxygen object model of boiler combustion system

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Experimental program
Comparison scheme
Effect test

Embodiment

[0045] This application proposes an identification method for an oxygen content object model of a boiler combustion system, which includes the following steps:

[0046] S1. In the population initialization of the hybrid leapfrog algorithm, chaotic initialization is used instead of random initialization, and the local variation Gaussian factor is introduced to improve the local search strategy, and an improved hybrid leapfrog algorithm is obtained, such as figure 1 shown;

[0047] S2. Collect the actual operating data of the boiler combustion system of the thermal power plant as sample data;

[0048] S3. Using the improved hybrid leapfrog algorithm to identify the oxygen level object model through the sample data, and obtain an identification result.

[0049] Step S1 specifically includes:

[0050] S11, using the Logistic equation to generate a chaotic sequence to produce the initial frog population, the Logistic equation is:

[0051]

[0052] Among them, S k Indicates a...

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Abstract

The invention relates to a method for identifying an oxygen object model of a boiler combustion system, which comprises the following steps of S1, replacing the random initialization with the chaos initialization when the population initialization of a basic shuffled frog leaping algorithm is carried out, and introducing a local variation Gaussian factor to improve a local search strategy so as to obtain an improved shuffled frog leaping algorithm; S2, collecting the actual operation data of a boiler combustion system of the thermal power plant, and selecting a data segment with obviously changed oxygen content as the sample data; and S3, identifying the oxygen content object model through the sample data by using an improved shuffled frog leaping algorithm to obtain an identification result. Compared with the prior art, the chaotic initialization and the local variation factors are introduced to improve the basic shuffled frog leaping algorithm, the local searching speed is higher, the local optimum can be effectively avoided, the global convergence speed is higher, the stability is better, and the overall identification effect is better.

Description

technical field [0001] The invention relates to the technical field of system identification, in particular to an identification method for an oxygen amount object model of a boiler combustion system. Background technique [0002] With the continuous development of large-capacity and high-parameter thermal power units, the safe operation of boiler systems has become more and more important. Among them, the boiler combustion system is the heart of the thermal power unit, and its importance is self-evident. The establishment of an accurate combustion process model is a guarantee for the optimization of the thermal control system. The research on boiler system modeling is divided into mechanism modeling and test modeling. Although the model of mechanism modeling has strong adaptability, the research objects in the industrial field are usually very complicated, and the internal mechanism is unclear. Considering the characteristics of the actual object and the accuracy of the mo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06F17/11G06F17/15G06N3/00
CPCG06F17/11G06F17/15G06N3/006G06F2119/08G06F30/20
Inventor 孙宇贞黄晓筱郭皓文彭道刚李帅唐毅伟
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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