CN103793746A - Method for identifying parameters of coal-fired power plant boiler superheater model - Google Patents

Abstract

The invention discloses a method for identifying parameters of a coal-fired power plant boiler superheater model. The method for identifying the parameters of the coal-fired power plant boiler superheater model comprises the steps that a non-linear lumped parameter model of a boiler superheater is built, and known parameters and parameters needing identifying are determined; dead pixel processing and data smoothing processing are carried out on the known parameters to obtain a known parameter real-time database; n training sample databases in different load sections are built, and normalization processing is carried out on the parameters; a corresponding RBF neural network model is built with respect to each training database, and the n RBF neural network models are connected in parallel to form a hybrid network; an actual measurement value of a superheater system at the current time is extracted, and on-line parameter identification is carried out on the RBF neural network models; the RBF neural network models are updated at a regular time interval t. The method for identifying the parameters of the coal-fired power plant boiler superheater model overcomes the defect that fixed parameters are adopted for a conventional superheater model, realizes real-time identification of model parameters, and guarantees the identification accuracy of the model parameters.

Description

A kind of coal-fired power station boiler superheater model parameter identification method

Technical field

The present invention relates to technical field of information processing, relate in particular to a kind of coal-fired power station boiler superheater model parameter identification method.

Background technology

It is the common method that obtains its dynamic perfromance that boiler is set up to mathematical model, and the accuracy that model is set up and the global optimization control of boiler are closely bound up.Along with the increase of boiler machine pool-size, required detection and control the increasing of parameter, makes boiler structure also become gradually complicated, and that gives the foundation of model and parameter has definitely brought new challenge.

Superheater is as the important component part of boiler, and its output variable main steam temperature and main steam pressure are the important monitoring parameters of system, and therefore it being set up to accurate mathematical model is the prerequisite that above-mentioned key parameters is optimized to control.And the levels of precision of model and Model Parameter to choose precision directly related, therefore it is significant to improve parameter identification precision in model.The mathematical model great majority of existing superheater are nonlinear lumped parameter model, parameter in model is that the testing experiment by real system being carried out to different operating modes obtains mostly, parameters obtained can only reflect near the system operation situation limited testing site, be difficult to adaptive system on a large scale parameter when variable load operation change and actual motion in the impact of various disturbances.Therefore, how according to the ruuning situation of real system, the parameter of model to be carried out to accurate identification and can regularly upgrade having important practical value.

Through existing literature search is found, in " Boiler Single phase heating surface chain type novel method for modeling " literary composition on " Proceedings of the CSEE ", a kind of lumped parameter chain type novel method for modeling is proposed, for the exemplary distribution parameter object of the Single-Phase Heated Tubes such as boiler economizer, superheater, reheater, on the basis of multistage lumped parameter model, propose lumped parameter chain type novel method for modeling, and set up the lumped parameter mathematical model of the chain structure of Single-Phase Heated Tubes.The method modeling is simple, and amount of calculation is little, explicit physical meaning.But the parameter that this technology obtains, for based on calculation of design parameters gained, in actual set operational process, occurs certain deviation unavoidably, degree of accuracy is not enough to some extent, lacks certain generalization ability.

Find through retrieval again, " Dynamic Simulation Model of boiler superheater system " in " thermal power engineering " is difficult to reproduce the Complex Dynamic of boiler superheater system based on mechanism model, utilize mechanism model to take as the leading factor, dynamic neural network is revised online, has improved significantly the precision of realistic model.Its simulation result shows, the dynamic simulation that this modeling method is complex large system provides a desirable way of modeling.But the model complexity of mentioning in this technology is inadequate, accurate not to the description of object.

Therefore, those skilled in the art is devoted to develop a kind of parameter identification method for boiler superheater model, can realize on-line optimization and distinguish the real-time change situation of steam generator system.

Summary of the invention

Because the above-mentioned defect of prior art, technical matters to be solved by this invention is to provide a kind of coal fired power plant superheater model parameter identification method based on data-driven, by actual operating data relevant with model parameter in unit, set up the neural network identification model between required identified parameters, thereby can carry out according to the real-time change situation of steam generator system the on-line optimization identification of model parameter, having data easily obtains, to advantages such as the normal influence on system operation of unit are little, and avoid adopting in model the shortcoming of preset parameter, make model can follow the tracks of preferably the ruuning situation of real system, for the control optimization of system is laid a solid foundation.

For achieving the above object, the invention provides a kind of coal-fired power station boiler superheater model parameter identification method, comprise the following steps:

A) set up the non-linear lumped parameter model of boiler superheater, determine known parameters and need the parameter of identification;

B) known parameters described in step a) is carried out to bad point successively and process and data smoothing processing, known parameters after treatment is set up to known parameters real-time data base;

C) real-time data base of known parameters described in step b) is carried out to n section and divide processing, set up the training sample database of n different load section, the training sample database of each load section, according to being divided into uniformly some training sample subsets between loading zone, and is normalized the parameter in each training sample subset;

Wherein, n >=1.

D) set up a corresponding radial basis function neural network model for the each described training sample database in step c), and make n radial basis function (Radial Basis Function, RBF) neural network model compose in parallel hybrid network;

E) actual measured value of extraction current time superheater system, carries out on-line parameter identification to radial basis function neural network model, obtains the numerical value that current time needs the parameter of identification;

F) every time interval t, radial basis function neural network model is upgraded to processing, by new hybrid network, the parameter that needs identification is carried out to on-line parameter identification.

Further, in described step b), described known parameters is carried out to bad point processing and refer to: judge bad point by polynomial expression slip approximating method, and adopt and push away difference formula before 7 second orders bad point is rejected.

Further, in described step b), it is to adopt 7 weighted filtering methods to realize that described known parameters is carried out to data smoothing processing.

Further, in described step c), described known parameters real-time data base carries out n section division processing and refers to: determine that segmentation number is n, guarantee every segments database number equate or approximately equalised situation under, known parameters real-time data base is divided into n section according to payload, obtains the training sample database of n different load section.

Further, in described step c), described normalized refers to: the normalized value that obtains the parameter in training sample subset by calculating.Being calculated as follows of described normalized value:

$X^{*}=\frac{X-X_{\min }}{X_{\max }-X_{\min }}$

Wherein: be the parameter in training sample subset, X _maxthe maximum parameter in the training sample subset of X place, X _minthe minimum parameter in the training sample subset of X place, X ^*it is the normalized value of X.

Further, in described step d), describedly set up a corresponding radial basis function neural network model and comprise the following steps:

The first step, determine the output quantity of radial basis function neural network model;

Second step, from the non-linear lumped parameter model of described boiler superheater, search and the related known parameters of described output quantity, choose the input quantity of radial basis function neural network model;

The 3rd step, employing radial basis function neural network training method are trained described input quantity and described output quantity, obtain radial basis function neural network model.

Wherein, the output quantity in the first step is the parameter that needs identification.

Wherein, in the 3rd step, from known parameters, remove dependent parameter and the parameter that is difficult to obtain in known parameters, can obtain the input quantity of radial basis function neural network model.

Further, in described step e), described on-line parameter identification refers to: the actual measured value of the boiler superheater to current time is carried out bad point processing successively, data smoothing is processed and normalized, and according in the radial basis function neural network model of the corresponding load section of actual measured value input of processing as the large young pathbreaker of preload, thereby obtain the numerical value that current time needs the parameter of identification.

Further, in described step f), described renewal processing, comprises the following steps:

The first step, the actual measurement data of the superheater system in the past t time is carried out to bad point processing, data smoothing successively process and normalized, the actual measurement data after treatment of obtaining in the t time;

Second step, according to the load in each moment in the past t time, obtain over the training sample database under each moment actual measurement data after treatment in the t time, and further obtain over the affiliated training sample data subset of each moment actual measurement data after treatment in the t time;

The 3rd step, by the actual measurement data after treatment that belongs to same training sample data subset by order from front to back, replace successively original sample data, the training sample database after being upgraded;

The 4th step, for upgrade after training sample database set up a corresponding new radial basis function neural network model.

In a preferred embodiment of the present invention, a kind of coal-fired power station boiler superheater model parameter identification method, comprises the following steps: set up the non-linear lumped parameter model of boiler superheater, determine known parameters and the parameter that needs identification; Known parameters is carried out to bad point and process and data smoothing processing, obtain known parameters real-time data base; Set up the training sample database of n different load section, and parameter is normalized; Set up a corresponding RBF neural network model for each tranining database, and make n RBF neural network model compose in parallel hybrid network; Extract the actual measured value of current time superheater system, RBF neural network model is carried out to on-line parameter identification; Every time interval t, the carrying out of RBF neural network model upgraded and processed.

As can be seen here, compared with prior art, the parameter identification method of superheater model that the present invention proposes, based on data-driven, has data and easily obtains, without the advantage such as unit being tested to.The present invention, by load dividing is set up by the composite nerve network of multiple RBF neural network model combinations and carried out parameter identification, has reduced the deficiency of single Neural generalization ability, has improved the identification precision of superheater model parameter under different load; In each load section, according to the multiple training sample data of step-length Further Division subset, guarantee the harmony that data sample is selected, further improved the generalization ability of model.In addition, the present invention has set up the online timing update mechanism of neural network identification model, to reflect the situation of change of superheater system its model parameter in long-time running, for model output provides good basis to the long-term on-line tracing of real system output.The present invention has avoided adopting in conventional superheater model the shortcoming of preset parameter, has realized the real-time identification of model parameter, has guaranteed the identification precision of model parameter.

Embodiment

In one embodiment of the invention, boiler superheater meets condition below:

A, outside hot-fluid distribute along pipe range and even circumferential;

B, tube wall metal are only considered radially to conduct heat, and do not consider axial heat conduction;

C, intraductal working medium are incompressible, and parameter uniformity on flow section;

D, ignore attemperator dynamic perfromance;

E, representation parameter take superheater outlet parameter as lumped parameter model, resistance concentrates on entrance.

For the boiler superheater of the present embodiment, a kind of coal-fired power station boiler superheater model parameter identification method, comprises the following steps:

The first step, sets up the non-linear lumped parameter model of boiler superheater, determines known parameters and needs the parameter of identification.

The non-linear lumped parameter model of the present embodiment, specifically:

$D_1+D_{\mathrm{jw}}-D_2=V_g\frac{d{\mathrm{\ρ}}_2}{\mathrm{dt}}$

$\begin{array}{cc}{D}_1{H}_1+D_{\mathrm{jw}}{H}_{\mathrm{jw}}-D_2{H}_2+Q_2=V_g\frac{d\left({\mathrm{\ρ}}_2{H}_2\right)}{\mathrm{dt}}& Q_s-Q_2=C_m{M}_g\frac{d{T}_g}{\mathrm{dt}}\end{array}$

$Q_2={\mathrm{KD}}_2^{\mathrm{\α}}(T_g-\frac{T_1+T_2}{2})$

$D_1={{\mathrm{kP}}_1}^{\mathrm{\β}}\sqrt{P_1-P_2}$

Wherein: D ₁it is superheater inlet steam flow; D ₂it is superheater outlet steam flow; D _jwit is desuperheating water flow; H ₁it is superheater inlet steam enthalpy; H ₂it is superheater outlet steam enthalpy; H _jwit is desuperheating water enthalpy; V _git is superheater volume; ρ ₂it is superheater outlet vapour density; Q ₂that superheater metal pipe-wall is passed to steam working medium heat; Q _sthat flue gas is passed to superheater metal pipe-wall heat; C _mit is superheater density metal; M _git is superheater metal quality; T _git is superheater tube wall temperature; K is the coefficient of heat transfer; α is index; T ₁it is superheater inlet steam temperature; T ₂it is superheater outlet steam temperature; K is coefficient; β is index; P ₁superheater inlet steam pressure; P ₂it is superheater outlet vapor pressure.

In the present embodiment, need the parameter of identification to comprise: Coefficient K, coefficient k and index β; All the other known parameters are obtained from design data, or are recorded by on-the-spot surveying instrument.

In the present embodiment: specified steam flow D ₂909.6t/h, specified desuperheating water flow D _jw15t/h, superheater volume V _g120m ³, superheater density metal C _m7900kg/m ³, index C _mbe 0.8, it is 460J/ (kg. ℃) that metal specific heat holds, nominal steam pressure C _m14.5Mpa.

Second step, carries out bad point to known parameters and processes and data smoothing processing, obtains known parameters after treatment, thereby obtains known parameters real-time data base.

Described bad point processing is to judge bad point by polynomial expression slip approximating method, and adopts and push away difference formula before 7 second orders bad point is rejected.

Before 7 described second orders, push away difference formula, specifically: when below k point meets when formula, by k some rejecting:

$|y_k-{\hat{y}}_k|>2.2\sqrt{\underset{k=i-6}{\overset{i}{\mathrm{\Σ}}}{(y_k-{\hat{y}}_k)}^2/6},$

Wherein: $\left\{\begin{array}{c}{\hat{y}}_1=({32y}_1+{15y}_2+{3y}_3-{4y}_4-{6y}_5-{3y}_6+{5y}_7)/42\\ {\hat{y}}_2=({5y}_1+{4y}_2+{3y}_3+{2y}_4+y_5-y_7)/14\\ {\hat{y}}_3=(y_1+{3y}_2+{4y}_3+{4y}_4+{3y}_5+y_6-{2y}_7)/14\\ {\hat{y}}_4=(-{2y}_1+{3y}_2+{6y}_3+{7y}_4+{6y}_5+{3y}_6-{2y}_7)/21\\ {\hat{y}}_5=(-{2y}_1+y_2+{3y}_3+{4y}_4+{4y}_5+{3y}_6-{2y}_7)/14\\ {\hat{y}}_6=(-y_1+y_2+{2y}_4+{3y}_5+{4y}_6+{5y}_7)/14\\ {\hat{y}}_i=({5y}_{i-1}-{3y}_{i-5}-{6y}_{i-4}-{4y}_{i-3}+{3y}_{i-2}+{15y}_{i-1}+{32y}_i)/42\end{array}\right.,$

I=7,8..., y _ifor measured data, for interpolated data,

Described data smoothing processing is to adopt 7 weighted filtering methods to realize, specifically:

${\hat{y}}_m=({0.025y}_{m-3}+{0.05y}_{m-2}+{0.075y}_{m-1}+0.7y_m+{0.075y}_{m+1}+0.05y_{m+2}+0.025y_{m+3},$

Wherein:

for filtered numerical value, y _mfor the actual measured value of moment m.

For the needs of subsequent calculations, in the present embodiment by model formation:

$D_1+D_{\mathrm{jw}}-D_2=V_g\frac{d{\mathrm{\ρ}}_2}{\mathrm{dt}}$

$D_1={{\mathrm{kP}}_1}^{\mathrm{\β}}\sqrt{P_1-P_2}$

: $D_1+D_{\mathrm{jw}}-D_2=V_g(\frac{{\∂\mathrm{\ρ}}_2}{{\∂p}_2}\frac{{\mathrm{d\ρ}}_2}{\mathrm{dt}}+\frac{{\∂\mathrm{\ρ}}_2}{{\∂T}_2}\frac{{\mathrm{dT}}_2}{\mathrm{dt}})$

Be transformed to: $D_1=D_2-D_{\mathrm{jw}}+V_g(\frac{{\∂\mathrm{\ρ}}_2}{\∂p_2}\frac{{\mathrm{d\ρ}}_2}{\mathrm{dt}}+\frac{{\∂\mathrm{\ρ}}_2}{{\∂T}_2}\frac{{\mathrm{dT}}_2}{\mathrm{dt}})$

Can, in the hope of superheater inlet steam flow, for the ease of identification, be taken the logarithm in superheater inlet steam flow formula both sides by above formula and service data:

$\log \left(D_1\right)=\log \left(k\right)+\mathrm{\β}\log \left(P_1\right)+\log \sqrt{P_1-P_2}$

Arrange: $\log \left(D_1\right)-\log \left(\sqrt{P_1-P_2}\right)=\log \left(k\right)+\mathrm{\β}\log \left(P_1\right)$

Order

x=log (P ₁), a=log (k), β=b has formula:

y=ax+b。

By above-mentioned discussion, can be in the hope of (x, y) data pair by actual operating data, recycling least square method can pick out a and b, and then tries to achieve k and β, and parameter K in like manner can obtain.

The 3rd step, known parameters real-time data base is carried out to n section and divide processing, set up the training sample database of n different load section, and the training sample database of each load section is divided into some training sample subsets uniformly according to S between loading zone, and the parameter in each training sample subset is normalized.

Described n section is divided and is processed, and refers to: determine that segmentation number is n, guaranteeing that every segments database number tries one's best equal in the situation that, known parameters real-time data base is divided into n section according to payload, thereby obtains the training sample database of n different load section.

N=4 in the present embodiment, is divided into more than 85% the training sample database of load section, the training sample database of 70%～85% load section, the training sample database of 55%～70% load section, the training sample database of load 55% below section.

Described normalized, is:

$X^{*}=\frac{X-X_{\min }}{X_{\max }-X_{\min }}$

Wherein: be the parameter in training sample subset, X _maxthe maximum parameter in the training sample subset of X place, X _minthe minimum parameter in the training sample subset of X place, X ^*it is the normalized value of X.

The present embodiment is further that step-length is divided into some training sample subsets uniformly by the training sample database of each load section according to 0.5% total load.

The 4th step, sets up a corresponding RBF neural network model for each tranining database, and makes n RBF neural network model compose in parallel hybrid network.

Described set up a corresponding RBF neural network model, comprise the following steps:

1) determine the output quantity of RBF neural network model, need the parameter of identification;

2) known parameters of searching from the non-linear lumped parameter model of boiler superheater and needing the relating to parameters of identification to be, removes dependent and the amount that is difficult to obtain in known parameters, thereby obtains the input quantity of RBF neural network model;

3) adopt existing RBF neural metwork training method to train input quantity and output quantity, obtain RBF neural network model.

In the present embodiment, the input quantity of RBF neural network model is: P ₁, T _g, T ₁, D ₁and D ₂, output quantity is: Coefficient K, coefficient k and index β.

The 5th step, the actual measured value of extraction current time superheater system, carries out on-line parameter identification to RBF neural network model, obtains the numerical value of the parameter that now needs identification.

Described on-line parameter identification, be: the actual measured value of current time superheater system is carried out to bad point processing, data smoothing processing and normalized successively, and in the RBF neural network model of the corresponding load section of actual measured value input of processing according to the large young pathbreaker when preload, thereby obtain the numerical value of the parameter that now needs identification.

The 6th step, every 6 hours, RBF neural network model is upgraded to processing, the RBF neural network model after being upgraded, and make the RBF neural network model after renewal compose in parallel new hybrid network, by new hybrid network, the parameter that needs identification is carried out to on-line parameter identification.

Described renewal processing, comprises the following steps:

1) actual measurement data of the superheater system in past 6 hours is carried out successively to bad point processing, data smoothing processing and normalized, the actual measurement data after treatment of obtaining in the t time;

2) according to the load in each moment in past 6 hours, obtain over the training sample database under each moment actual measurement data after treatment in 6 hours, and further obtain over the affiliated training sample data subset of each moment actual measurement data after treatment in 6 hours;

3) by the actual measurement data after treatment that belongs to same training sample data subset by order from front to back, replace successively original sample data, the training sample database after being upgraded;

4) set up a corresponding new RBF neural network model for the training sample database after upgrading.

The present embodiment, in order to pick out exactly the variation of different load operating mode drag parameter, carries out segmentation according to the load of system, and operating mode larger difference is set up respectively to multiple corresponding neural network identification models.Set up identification model by segmentation, the deficiency of generalization ability can reduce on the one hand the operation of single Neural simulation whole system Wide Range time, thus improve the identification precision of model parameter under different load; The scale and the sample size that have reduced again on the other hand each neural network, improved network training speed, for the real-time of on-line identification provides corresponding assurance.Setting up before each neural network model, need to carry out suitable selection and pre-service to the service data of actual acquisition, further to improve generalization ability and the identification precision of neural network.In addition, for each neural network identification model has been set up the mechanism of online updating training sample, and timing is carried out the training of neural network with legacy data in measured data replacement training sample database, to be reflected in better the situation of change of time statistical significance drag parameter, thereby model and real system can be matched, for control and the optimization of system provide basis.

More than describe preferred embodiment of the present invention in detail.Should be appreciated that those of ordinary skill in the art just can design according to the present invention make many modifications and variations without creative work.Therefore, all technician in the art, all should be in by the determined protection domain of claims under this invention's idea on the basis of existing technology by the available technical scheme of logical analysis, reasoning, or a limited experiment.

Claims (8)

1. a coal-fired power station boiler superheater model parameter identification method, is characterized in that, comprises the following steps:

A) set up the non-linear lumped parameter model of boiler superheater, determine known parameters and need the parameter of identification;

B) known parameters described in step a) is carried out to bad point successively and process and data smoothing processing, known parameters after treatment is set up to known parameters real-time data base;

C) real-time data base of known parameters described in step b) is carried out to n section and divide processing, set up the training sample database of n different load section, the training sample database of each load section, according to being divided into uniformly some training sample subsets between loading zone, and is normalized the parameter in each training sample subset;

D) set up a corresponding radial basis function neural network model for the each described training sample database in step c), and make n radial basis function neural network model compose in parallel hybrid network;

E) actual measured value of extraction current time superheater system, carries out on-line parameter identification to radial basis function neural network model, obtains the numerical value that current time needs the parameter of identification;

F) every time interval t, radial basis function neural network model is upgraded to processing, by new hybrid network, the parameter that needs identification is carried out to on-line parameter identification.

2. coal-fired power station boiler superheater model parameter identification method as claimed in claim 1, it is characterized in that, in described step b), described known parameters is carried out to bad point processing to be referred to: judge bad point by polynomial expression slip approximating method, and adopt and push away difference formula before 7 second orders bad point is rejected.

3. coal-fired power station boiler superheater model parameter identification method as claimed in claim 1, is characterized in that, in described step b), it is to adopt 7 weighted filtering methods to realize that described known parameters is carried out to data smoothing processing.

4. coal-fired power station boiler superheater model parameter identification method as claimed in claim 1, it is characterized in that, in described step c), described known parameters real-time data base carries out n section division processing and refers to: determine that segmentation number is n, guarantee every segments database number equate or approximately equalised situation under, known parameters real-time data base is divided into n section according to payload, obtains the training sample database of n different load section.

5. coal-fired power station boiler superheater model parameter identification method as claimed in claim 1, is characterized in that, in described step c), described normalized refers to: the normalized value that obtains the parameter in training sample subset by calculating.

6. coal-fired power station boiler superheater model parameter identification method as claimed in claim 1, is characterized in that, in described step d), describedly sets up a corresponding radial basis function neural network model and comprises the following steps:

The first step, determine the output quantity of radial basis function neural network model;

Second step, from the non-linear lumped parameter model of described boiler superheater, search and the related known parameters of described output quantity, choose the input quantity of radial basis function neural network model;

The 3rd step, employing radial basis function neural network training method are trained described input quantity and described output quantity, obtain radial basis function neural network model.

7. coal-fired power station boiler superheater model parameter identification method as claimed in claim 1, it is characterized in that, in described step e), described on-line parameter identification refers to: the actual measured value of the boiler superheater to current time is carried out bad point processing successively, data smoothing is processed and normalized, and according in the radial basis function neural network model of the corresponding load section of actual measured value input of processing as the large young pathbreaker of preload, thereby obtain the numerical value that current time needs the parameter of identification.

8. coal-fired power station boiler superheater model parameter identification method as claimed in claim 1, is characterized in that, the described renewal processing in described step f), comprises the following steps:

The first step, the actual measurement data of the superheater system in the past t time is carried out to bad point processing, data smoothing successively process and normalized, the actual measurement data after treatment of obtaining in the t time;

Second step, according to the load in each moment in the past t time, obtain over the training sample database under each moment actual measurement data after treatment in the t time and the training sample data subset under each moment actual measurement data after treatment in the t time in the past;

The 3rd step, by the actual measurement data after treatment that belongs to same training sample data subset by order from front to back, replace successively original sample data, the training sample database after being upgraded;

The 4th step, for upgrade after training sample database set up a corresponding new radial basis function neural network model.