CN104748807B - A kind of power station main steam flow on-line calculation method based on flux modification - Google Patents
A kind of power station main steam flow on-line calculation method based on flux modification Download PDFInfo
- Publication number
- CN104748807B CN104748807B CN201410764596.2A CN201410764596A CN104748807B CN 104748807 B CN104748807 B CN 104748807B CN 201410764596 A CN201410764596 A CN 201410764596A CN 104748807 B CN104748807 B CN 104748807B
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- flow
- mtd
- main steam
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a kind of power station main steam flow on-line calculation method based on flux modification, with the online computation model of mass flow equilibrium equation amendment main steam flow, the model completes flow capacity checking on the basis of the condensing water flow that nozzle is measured, under different load operating mode, main steam flow value is obtained using iterative method calculation and check, main steam flow measurement amendment Fu Liugeer formula after being checked with this, final set up obtains the online computation model of main steam flow.This method can quickly reflect the variation tendency of main steam flow, while can ensure that the degree of accuracy of flow is high.Consider that the situation of sensor drift distortion occurs in Field adjustment stage pressure measuring point, according to incidence coefficient, judge first stage pressure value whether distortion, and then propose first stage pressure hard measurement alternative scheme, it is ensured that the stability of the online computing module of main steam flow.The online calculative strategy of main steam flow proposed by the present invention can electric power supply plant refer to and carry out economic analysis.
Description
Technical field
The present invention relates to a kind of power station main steam flow on-line calculation method, comprehensive pivot analysis and SVMs are had concurrently
Soft-sensing model belong to machine learning modeling field.
Background technology
In process industrial, exist some can not direct measurement or measurement have very large time delay variable, it is necessary to by it is soft survey
Amount technology sets up model and it is estimated.In the monitoring of fired power generating unit on-line performance and Optimal Management System, heat consumption rate, coal consumption
Need to use main steam flow in the calculating of the economic indicators such as rate, the accuracy that main steam flow is calculated will directly influence unit
Performance calculating and the reliability of running optimizatin.At present calculate unit main steam flow method mainly have the direct method of measurement,
Connect mensuration and artificial intelligence model method.
The direct method of measurement refers to by installing orifice plate constant pitch stream device come direct measurement, but this method can cause very big section
Stream loss.The general principle of the indirect method of measurement is to calculate main steam flow based on pressure after governing stage, and this method is by through-flow
The influence of part fouling, can reduce the accuracy that steam flow calculates result.Main steam flow mould based on neural network
Type will be that machine learning method is applied in hard measurement, but it has the shortcomings that adaptability, robustness are poor.
Main steam flow measurement model is, it is necessary to consider the sensitivity and accuracy of model.Therefore, the present invention combines stream
Amount equilibrium equation and Fu Liugeer formula obtain main steam flow computation model.Although because Fu Liugeer formula can be accurate
Reflection be in variable working condition when main steam flow variation tendency;The main steam flow calculating side checked based on condensing water flow
The main steam flow value obtained under method, its quiescent conditions is calibrated really.According to the characteristics of both main steam flow computation models, this
The main steam flow that invention is obtained after being checked with condensing water flow corrects Fu Liugeer formula so that in a wide range of variable working condition
Calculate obtained main steam flow accuracy and response promptness is all improved.Further, consider from robustness and angle, work as tune
When assistant warden pressure-measuring-point breaks down, judge that failure occurs and uses hard measurement substitute revision of option in time, it is ensured that model is normal.
The content of the invention
Goal of the invention:In order to overcome the deficiencies in the prior art, the present invention provides a kind of electricity based on flux modification
Station owner's steam flow on-line calculation method.
Technical scheme:In order to solve the above technical problems, a kind of power station main steam flow based on flux modification of the present invention
On-line calculation method, the step includes as follows:
(1) field data is exported to data-interface via the network switch;
(2) batch capture condensing water flow Dns, feedwater flow Dgs, middle pressure gate bar steam loss Dkf, high pressure door rod steam loss
Daf, reheating spray flow Dzr, overheat spray flow Dgr, unit load Pload, pressure P after level10, temperature T after level10, and it is at different levels
Height adds outlet heat regenerative system parameter, and filter removes wherein unstable data, sets up sample database;
(3) by the sample database in step (2), module is checked by the laggard inbound traffics of load classification, flow G is obtained10;
(3.1) the condensing water flow D under each load section is obtained via Data Input Interfacens, feedwater flow Dgs, middle pressure gate
Bar steam loss Dkf, high pressure door rod steam loss Daf, reheating spray flow Dzr, overheat spray flow DgrAnd the high turnover added at different levels
Mouth parameter, calculates extraction flow;
The step (3.1) concretely comprises the following steps:Calculate extraction flow
Dkf=k1*Dfw+k2
Daf=k3*Dfw+k4
hwi,hwi+1,hi, hsiRepresent that this section draw gas respectively goes out saliva enthalpy, Inlet water enthalpy, and draw gas enthalpy, hydrophobic enthalpy, enthalpy by
Pressure and temperature is obtained by water vapour calculation formula;
DkfFor middle pressure gate bar air leakage;DafFor high pressure door rod air leakage;Coefficient k1、k2、k3、k4By obtained by thermal test;
DzrpsFor reheating spray flow;DgrpsFor overheat spray flow, extracted out if crossing hot water spray from No. 1 high plus outlet, DgrpsTake 0.
(3.2) according to conservation of mass formula Dfw_js=Dns+d1+d2+d3+d4+Dkf+Daf-Dzr-Dgr, with extraction flow
d1,...,d4Calculation formula simultaneous, calculates feedwater flow initial value and is set to Dgs;
(3.3)|Dfw_js-Dgs| during > difference in flow limit values, Dgs=Dfw_js, and enter step (3.2), iterate to calculate, until |
Dfw_js-Dgs| < difference in flow limit values, output now feedwater flow, and then calculate main steam flow G10, t/h.
(4) under each load section, the main steam flow expression formula that pressure and temperature is represented after governing stage:
In formula,P10For with G10Pressure value after corresponding governing stage, unit is Mpa;T10For the tune under declared working condition
Temperature value after assistant warden, unit is K, sets up unit load PloadWith coefficient kM,Functional relation, kM=f
(Pload);
(5) real time data unit load P is pressedload, first stage pressure P0With temperature T after level0, according to formulaObtain and calculate main steam flow G;
(6) judge to calculate the correlation between main steam flow and load:In formula, X,
Y represents two data samples of main steam flow and load, ρ respectivelyXYFor the coefficient correlation between X, Y;DX, DY are respectively variable X
With Y variance;E (X), E (Y), E (XY) are X, Y, XY average respectively,
If correlation coefficient ρXY>When 0.8, it is determined as that calculating main steam flow is credible, the main steam in being calculated as performance
Traffic source;Otherwise, it is determined that being first stage pressure sensor fault, into step (7) first stage pressure hard measurement correction module;
(7) the first stage pressure P of first stage pressure sensor under normal circumstances is obtained0And extraction pressure P at different levels1,...,
P8, after such sample pivot analysis, as the input of SVMs, the output valve set up after model is that first stage pressure is soft
Measured value, replaces first stage pressure sensor values, and then enter step (5) with this result;
(7.1) influence steam turbine first stage pressure P is obtained via Data Input Interface0Each parameter:Heat regenerative system is at different levels to take out
Steam pressure Pj(j=1,2 ... 8), as the input parameter of pivot analysis, it is designated as Xold(n × m), n represents measurement sampling
Number of times, m represents to measure attribute number;
(7.2) according to following formula, input data is standardized, X (n, m) is obtained
In formula:I=1,2 ..., n, j=1,2..., m, average (xold(:, j)) and represent j-th of variable down-sampling point
Average, std (xold(:, j)) and represent the standard deviation of j-th of variable down-sampling point;
(7.3) X (n, m) covariance matrix COV (X), and its eigenvalue λ are calculatediWith characteristic vector pi
COV(X)pi=λipi
SVD decomposition is carried out to COV (X), and pivot analysis is carried out to X, k pivot is chosen, k is constant, determines score square
The process of battle array T so far pivot analysis terminates;
(7.4) by k obtained pivot and first stage pressure sampled value P0Input respectively as SVMs and defeated
Go out, wherein n/2 groups data first standardize inputoutput data as test data as training data, in addition n/2 groups;
Optimum linearity decision function y (x)=sgn [w ψ (x)+b] is constructed in high-dimensional feature space, following formula target is taken
Function:
In formula, constraintsW is weight factor, and C is penalty parameter, and b is deviation
Value, C ∈ R+It is punishment parameter, ξi=[ξ1,…,ξn]T,It is that a Nonlinear Mapping can be xiHeight is mapped to from the input space
The feature space of (or even infinite dimension) is tieed up to realize that the nonlinear regression in the input space is converted into high-dimensional feature space
Linear regression, constrained optimization can be converted into unconstrained optimization by constructing Lagrange functions, will be asked according to KKT conditions
Majorization of solutions problem can finally be converted into solution linear equation:
Wherein, output data y=[y1,…,yn]T;Unit array Iv=[1 ..., 1]T;Lagrange multipliers a=
[a1,…,an]T;Ω={ Ωij| i, j=1 ... n };K () is kernel function, herein may be used
From Radial basis kernel function K (xi,xj)=exp [- | | xi-xj||2/(2σ2)];
(7.5) calculated value of SVMs part of detecting is hard measurement income value, into step (5), uses this result
Replace first stage pressure sensor values.
So far, the main part that main steam flow is calculated has been completed.Consider unit through-flow characteristic at runtime compared with
Long or change during midway through big light maintenance, step 1-4 can periodically or irregularly repeat core according to on-site actual situations
Calculate so that actually through-flow performance is consistent main steam computing module with unit.
Beneficial effect:The present invention in terms of existing technologies, with advantages below:
(1) the online computation model of main steam flow of mass flow equilibrium equation amendment Fu Liugeer formula, uses condensate
Flow carrys out iteration and checks feedwater flow, obtains the higher main steam flow of accuracy to correct Fu Liugeer formula so as to calculate.
Finally correcting obtained Fu Liugeer formula, not only accuracy is high, and dynamic response is in time, more can truly reproduce main steaming
Steam flow value.
(2) PCA and SVMs is combined to model Steam Turhine Adjustment stage pressure, can preferably calibrating (base measuring) pressure sensor
The deviation occurred during error.
(3) with the change of the through-flow characteristic of unit, the parameter in the computation model that upgrades in time, it is ensured that model calculation value and reality
Border main steam flow is consistent.
(4) provided for power plant's monitoring information system Premium Features module (condition monitoring and fault diagnosis etc.) and refer to mould
Type.
Brief description of the drawings
Fig. 1 is the flow chart of the present invention.
Fig. 2 is power of the assembling unit curve map in the present embodiment.
Fig. 3 is the change curve of two kinds of main steam flow calculated values in the present embodiment.
Fig. 4 is the coefficient correlation graph of a relation between different main steam flow calculated values and load in the present embodiment.
Embodiment
The present invention is further described below in conjunction with the accompanying drawings.
By taking the overcritical resuperheat condensing-type units of certain power station 600MW as an example, on April 01st, 2014 in collection SIS systems
10 points to 2014 April 10 day 10 points of data, acquisition interval 1 minute.Framework of the present invention mainly has input data pretreatment, stream
Amount checks the nucleus modules such as module, main steam flow computing module, first stage pressure hard measurement, and detailed process is as shown in Figure 1:
1) field data is exported to data-interface via the network switch;
2) with 1 minute collection condensing water flow (D of time intervalns), feedwater flow (Dgs), middle pressure gate bar steam loss (Dkf),
High pressure door rod steam loss (Daf), reheating spray flow (Dzr), overheat spray flow (Dgr), unit load (Pload), pressure after level
(P10), temperature (T after level10), and the high import and export parameter added at different levels, filter is removed after wherein unstable data, remaining
9372 groups of samples.
3) by the sample data obtained by step 2, module is checked by the laggard inbound traffics of load section classification, flow G is obtained10。
4) load (P is set upload) and coefficient kM Functional relation, kM=f (Pload), it is real-time meter
Main steam flow is calculated to prepare.
5) access real-time unit load (Pload), first stage pressure (P0) and level after temperature (P0), according to formulaObtain and calculate main steam flow G.
6) judge to calculate the correlation between main steam flow and load:If phase relation
Number ρXY>When 0.8, it is determined as that calculating main steam flow is credible, the main steam flow source in being calculated as performance;Otherwise, sentence
It is set to first stage pressure sensor fault, into step first stage pressure hard measurement correction module.
7) first stage pressure (P of first stage pressure sensor under normal circumstances is obtained0) and extraction pressure at different levels
(P1,...,P8), after such sample pivot analysis, as the input of SVMs, the output valve set up after model is regulation
Stage pressure hard measurement value.First stage pressure sensor values is replaced with this result, and then enters step 5.
8) main part that main steam flow is calculated has been completed.Consider unit through-flow characteristic it is longer at runtime or
Changed when person is midway through big light maintenance, step 1-4 can be according to on-site actual situations, regular or irregular repeat calculation stream
Amount so that actually through-flow performance is consistent main steam computing module with unit.
Calculated examples:Assuming that the standard value 50t/h under feedwater flow value off-design operating mode, with the condensation under design conditions
Water-carrying capacity iterates to calculate the feedwater flow value after being checked, and it is compared with exact value, its result of calculation such as table 1 below
It is shown.
The feedwater flow of table 1 checks the error analysis of model
As can be seen from Table 1, missing relatively between the feedwater flow and exact value that are obtained by condensing water flow calculation and check
Difference is no more than 1%, shows, can be according to the less condensate of measurement error when feedwater flow measuring point error is larger in scene
Flow iterative calculation obtains relatively more accurate feedwater flow value.
After the governing stage that main steam flow, the unit obtained by condensing water flow calculation and check is collected pressure value and
Exhaust temperature of HP value, obtains coefficient kMIt is worth for 2.45196, so as to obtain the unit main steam flow timely monitor model.
Unit is gathered in the operational parameter value in certain varying duty stage to verify the dynamic characteristic of model, unit is in the varying duty
The change curve of power is as shown in Fig. 2 using main steam flow computational methods proposed by the present invention, calculating is obtained at this under state
In the main steam flow value in varying duty stage, its change curve below figure 3 (D1 is the curve of top in figure) shown in D1.In the change
Under load condition, D2 in the obtained main steam flow curve of cyclical fluctuations such as Fig. 3 is added with overheat desuperheat water spray with unit feedwater flow
Shown in (D2 is the curve of lower section in figure).
Using the algorithm of matrix times window, two kinds of coefficient correlations calculated between main steam flow and load are calculated respectively
Value.It is 50 to take time window span, i.e., every 50 data since first sample data obtain 431 as a matrix
Sample matrix.Phase relation between main steam flow and the sample matrix of load that the computational methods proposed by this invention are obtained
Numerical value represents that feedwater flow measuring point calculates the phase relation between obtained main steam flow and the sample matrix of load with variable C1
Numerical value is represented with variable C2, is illustrated in figure 4 C1 and C2 correlation curve.
As seen from Figure 4, C1 values perseverance is more than C2, in sudden load change, and C2 amplitudes of variation for C1 compared with becoming apparent from, this table
It is bright during load fluctuation, using feedwater flow measured value calculate main steam flow computation model retardance it is bigger.This
Show to use feedwater flow measuring point to calculate main steam flow value compared to direct using present invention main steam flow computation model used
Dynamic characteristic is more preferable.
Described above is only the preferred embodiment of the present invention, it should be pointed out that:For the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (2)
1. a kind of power station main steam flow on-line calculation method based on flux modification, it is characterised in that:Step includes as follows:
(1) field data is exported to data-interface via the network switch;
(2) batch capture condensing water flow Dns, feedwater flow Dgs, middle pressure gate bar steam loss Dkf, high pressure door rod steam loss Daf, then
Thermal jet water-carrying capacity Dzr, overheat spray flow Dgr, unit load Pload, pressure P after level10, temperature T after level10, and it is at different levels it is high plus
Heat regenerative system parameter is imported and exported, filter removes wherein unstable data, sets up sample database;
(3) by the sample database in step (2), module is checked by the laggard inbound traffics of load classification, main steam flow G is obtained10;
(4) under each load section, the main steam flow expression formula that pressure and temperature is represented after governing stage:In formula,P10For with G10Pressure value after corresponding governing stage, unit is Mpa;T10After the governing stage under declared working condition
Temperature value, unit is K, sets up unit load PloadWith coefficient kM,Functional relation, kM=f (Pload);
(5) real time data unit load P is pressedload, first stage pressure P0With temperature T after level0, according to formula
Obtain and calculate main steam flow G;
(6) judge to calculate the correlation between main steam flow and load:In formula, X, Y difference
Represent two data samples of main steam flow and load, ρXYFor the coefficient correlation between X, Y;DX, DY are respectively variable X and Y
Variance;E (X), E (Y), E (XY) are X, Y, XY average respectively,
If correlation coefficient ρXY>When 0.8, it is determined as that calculating main steam flow is credible, the main steam flow in being calculated as performance
Source;Otherwise, it is determined that being first stage pressure sensor fault, into step (7) first stage pressure hard measurement correction module;
(7) the first stage pressure P of first stage pressure sensor under normal circumstances is obtained0And extraction pressure P at different levels1,...,P8,
After such sample pivot analysis, as the input of SVMs, the output valve set up after model is the soft survey of first stage pressure
Value, first stage pressure sensor values is replaced with this result;
The step (7) specifically includes following content:
(7.1) influence steam turbine first stage pressure P is obtained via Data Input Interface0Each parameter:Heat regenerative system extraction pressures at different levels
Pj(j=1,2 ... 8), as the input parameter of pivot analysis, it is designated as Xold(n × m), n represents measurement sampling number, n
Represent measurement attribute number;
(7.2) according to following formula, input data is standardized, X (n, m) is obtained
<mrow>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>o</mi>
<mi>l</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>a</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
<mi>a</mi>
<mi>g</mi>
<mi>e</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>o</mi>
<mi>l</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>(</mo>
<mrow>
<mo>:</mo>
<mo>,</mo>
<mi>j</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mi>s</mi>
<mi>t</mi>
<mi>d</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>o</mi>
<mi>l</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>(</mo>
<mrow>
<mo>:</mo>
<mo>,</mo>
<mi>j</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
</mrow>
In formula:I=1,2 ..., n, j=1,2..., m, average (xold(:, j)) and represent the equal of j-th variable down-sampling point
Value, std (xold(:, j)) and represent the standard deviation of j-th of variable down-sampling point;
(7.3) X (n, m) covariance matrix COV (X), and its eigenvalue λ are calculatediWith characteristic vector pi
<mrow>
<mi>C</mi>
<mi>O</mi>
<mi>V</mi>
<mrow>
<mo>(</mo>
<mi>X</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mrow>
<msup>
<mi>X</mi>
<mi>T</mi>
</msup>
<mi>X</mi>
</mrow>
<mrow>
<mi>m</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</mfrac>
</mrow>
COV(X)pi=λipi
SVD decomposition is carried out to COV (X), and pivot analysis is carried out to X, k pivot is chosen, k is constant, determines score matrix T extremely
The process of this pivot analysis terminates;
(7.4) by k obtained pivot and first stage pressure sampled value P0Respectively as the input and output of SVMs, its
Middle n/2 groups data first standardize inputoutput data as test data as training data, in addition n/2 groups;
Optimum linearity decision function y (x)=sgn [w ψ (x)+b] is constructed in high-dimensional feature space, following formula object function is taken:
<mrow>
<mi>min</mi>
<munder>
<mi>J</mi>
<mrow>
<mi>w</mi>
<mo>,</mo>
<mi>b</mi>
<mo>,</mo>
<mi>&xi;</mi>
</mrow>
</munder>
<mrow>
<mo>(</mo>
<mi>w</mi>
<mo>,</mo>
<mi>&xi;</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mn>2</mn>
</mfrac>
<msup>
<mi>w</mi>
<mi>T</mi>
</msup>
<mi>w</mi>
<mo>+</mo>
<mi>C</mi>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msub>
<mi>&xi;</mi>
<mi>i</mi>
</msub>
</mrow>
In formula, constraintsW is weight factor, and C is penalty parameter, and b is deviation, C
∈R+It is punishment parameter, ξi=[ξ1,…,ξn]T,It is that a Nonlinear Mapping can be xiHigher-dimension is mapped to from the input space
Feature space is converted into the linear regression in high-dimensional feature space so as to the nonlinear regression realized in the input space, passes through structure
Unconstrained optimization can be converted into constrained optimization by making Lagrange functions, according to KKT conditions that the optimization problem of solution is final
Solution linear equation can be converted into:
<mrow>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<msubsup>
<mi>I</mi>
<mi>v</mi>
<mi>T</mi>
</msubsup>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>I</mi>
<mi>v</mi>
</msub>
</mtd>
<mtd>
<mrow>
<mi>&Omega;</mi>
<mo>+</mo>
<msup>
<mi>c</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mi>I</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mi>b</mi>
</mtd>
</mtr>
<mtr>
<mtd>
<mi>a</mi>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mi>y</mi>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein, y=[y1,…,yn]T;Iv=[1 ..., 1]T;A=[a1,…,an]T;Ω={ Ωij| i, j=1 ... n };K () is kernel function, and Radial basis kernel function K (x are can select hereini,xj)=exp [- |
|xi-xj||2/(2σ2)];
(7.5) calculated value of SVMs part of detecting is hard measurement income value, into step (5), is replaced with this result
First stage pressure sensor values.
2. the power station main steam flow on-line calculation method according to claim 1 based on flux modification, it is characterised in that:
The step (3) concretely comprises the following steps:
(3.1) the condensing water flow D under each load section is obtained via Data Input Interfacens, feedwater flow Dgs, middle pressure gate bar leakage vapour
Measure Dkf, high pressure door rod steam loss Daf, reheating spray flow Dzr, overheat spray flow DgrAnd the high import and export parameters added at different levels,
Calculate extraction flow d1,...,d4;
The extraction flow d1,...,d4Calculation formula it is as follows:
<mrow>
<msub>
<mi>d</mi>
<mn>1</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>D</mi>
<mrow>
<mi>f</mi>
<mi>w</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>w</mi>
<mn>1</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>w</mi>
<mn>2</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mo>(</mo>
<msub>
<mi>h</mi>
<mn>1</mn>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>s</mi>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mfrac>
</mrow>
<mrow>
<msub>
<mi>d</mi>
<mn>2</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>D</mi>
<mrow>
<mi>f</mi>
<mi>w</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>w</mi>
<mn>2</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>w</mi>
<mn>3</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>d</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>s</mi>
<mn>1</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>s</mi>
<mn>2</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mo>(</mo>
<msub>
<mi>h</mi>
<mn>2</mn>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>s</mi>
<mn>2</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mfrac>
</mrow>
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>d</mi>
<mn>3</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>D</mi>
<mrow>
<mi>f</mi>
<mi>w</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>w</mi>
<mn>3</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>w</mi>
<mn>4</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>d</mi>
<mn>1</mn>
</msub>
<mo>+</mo>
<msub>
<mi>d</mi>
<mn>2</mn>
</msub>
<mo>)</mo>
</mrow>
<mrow>
<mo>(</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>s</mi>
<mn>2</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>s</mi>
<mn>3</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>h</mi>
<mn>3</mn>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>s</mi>
<mn>3</mn>
</mrow>
</msub>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<msub>
<mi>D</mi>
<mrow>
<mi>a</mi>
<mi>f</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>g</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>s</mi>
<mn>3</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>D</mi>
<mrow>
<mi>k</mi>
<mi>f</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>z</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>s</mi>
<mn>3</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>h</mi>
<mn>3</mn>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>s</mi>
<mn>3</mn>
</mrow>
</msub>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>d</mi>
<mn>4</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>D</mi>
<mrow>
<mi>f</mi>
<mi>w</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>D</mi>
<mrow>
<mi>z</mi>
<mi>r</mi>
<mi>p</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>D</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>p</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>)</mo>
<mo>(</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>w</mi>
<mn>4</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>w</mi>
<mn>5</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mrow>
<msub>
<mi>h</mi>
<mn>4</mn>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>w</mi>
<mn>5</mn>
</mrow>
</msub>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>-</mo>
<mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>d</mi>
<mn>1</mn>
</msub>
<mo>+</mo>
<msub>
<mi>d</mi>
<mn>2</mn>
</msub>
<mo>+</mo>
<msub>
<mi>d</mi>
<mn>3</mn>
</msub>
<mo>)</mo>
<mo>(</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>s</mi>
<mn>3</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>w</mi>
<mn>5</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mrow>
<msub>
<mi>h</mi>
<mn>4</mn>
</msub>
<mo>-</mo>
<msub>
<mi>h</mi>
<mrow>
<mi>w</mi>
<mn>5</mn>
</mrow>
</msub>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
2
Dkf=k1*Dfw+k2
Daf=k3*Dfw+k4
hwi,hwi+1,hi, hsiRepresent that this section draw gas respectively goes out saliva enthalpy, Inlet water enthalpy, and draw gas enthalpy, hydrophobic enthalpy, and enthalpy is by pressure
Obtained with temperature by water vapour calculation formula;
DkfFor middle pressure gate bar air leakage;DafFor high pressure door rod air leakage;Coefficient k1、k2、k3、k4By obtained by thermal test;DzrpsFor
Reheating spray flow;DgrpsFor overheat spray flow, extracted out if crossing hot water spray from No. 1 high plus outlet, DgrpsTake 0;
(3.2) according to conservation of mass formula Dfw_js=Dns+d1+d2+d3+d4+Dkf+Daf-Dzr-Dgr, with extraction flow d1,...,d4
Calculation formula simultaneous, calculates feedwater flow initial value and is set to Dgs;
(3.3)|Dfw_js-Dgs| during > difference in flow limit values, Dgs=Dfw_js, and enter step (3.2), iterate to calculate, until |
Dfw_js-Dgs| < difference in flow limit values, output now feedwater flow, and then calculate main steam flow G10, t/h.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410764596.2A CN104748807B (en) | 2014-12-12 | 2014-12-12 | A kind of power station main steam flow on-line calculation method based on flux modification |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410764596.2A CN104748807B (en) | 2014-12-12 | 2014-12-12 | A kind of power station main steam flow on-line calculation method based on flux modification |
Publications (2)
Publication Number | Publication Date |
---|---|
CN104748807A CN104748807A (en) | 2015-07-01 |
CN104748807B true CN104748807B (en) | 2017-11-03 |
Family
ID=53588836
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201410764596.2A Active CN104748807B (en) | 2014-12-12 | 2014-12-12 | A kind of power station main steam flow on-line calculation method based on flux modification |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN104748807B (en) |
Families Citing this family (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105841781B (en) * | 2016-03-16 | 2018-09-28 | 中国大唐集团科学技术研究院有限公司华东分公司 | A method of calibration steam turbine heating steam flow |
CN106124119B (en) * | 2016-08-01 | 2019-02-12 | 中国神华能源股份有限公司 | Steam turbine extraction pressure flexible measurement method |
CN106289416B (en) * | 2016-08-16 | 2019-03-22 | 中国航空工业集团公司沈阳发动机设计研究所 | A kind of critical Venturi nozzle method of calculating flux |
CN106682376B (en) * | 2017-04-01 | 2020-03-10 | 国网河南省电力公司电力科学研究院 | Whole-process steam turbine modeling and identification method for actual characteristics of parameters changing along with working conditions |
CN109813400B (en) * | 2019-03-23 | 2024-02-06 | 重庆市计量质量检测研究院 | Boiler main steam flowmeter online checking and testing system based on internet of things technology |
CN110702175B (en) * | 2019-09-11 | 2021-11-19 | 湖南大唐先一科技有限公司 | Online soft measurement device and method for main steam flow of steam turbine of thermal power plant |
CN111006731B (en) * | 2019-12-10 | 2021-07-13 | 海默潘多拉数据科技(深圳)有限公司 | Intelligent oil well flow measuring method based on multiphase flowmeter |
CN111351531B (en) * | 2020-03-16 | 2021-11-12 | 中国大唐集团科学技术研究院有限公司华东电力试验研究院 | Main steam flow online measurement method |
CN111982245B (en) * | 2020-07-13 | 2023-08-08 | 中广核核电运营有限公司 | Main steam flow calibration method, device, computer equipment and storage medium |
CN112800694B (en) * | 2021-01-15 | 2022-08-30 | 贵州黔西中水发电有限公司 | Soft measurement method for main steam flow of 600MW condensing steam turbine |
CN114922707A (en) * | 2022-02-09 | 2022-08-19 | 华能曲阜热电有限公司 | Industrial steam supply flow automatic adjusting device |
CN116402411B (en) * | 2023-06-09 | 2024-05-14 | 济南作为科技有限公司 | Consumption difference analysis method, device, equipment and storage medium |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102840889A (en) * | 2012-09-25 | 2012-12-26 | 华北电力大学(保定) | Soft measuring method for main steam flow of unit system utility boiler |
CN103048020A (en) * | 2013-01-22 | 2013-04-17 | 山东电力集团公司电力科学研究院 | Main steam flow online calculation method of power station based on performance testing data |
CN104048842A (en) * | 2014-05-29 | 2014-09-17 | 华中科技大学 | On-line monitoring method for heat rate of steam turbine on basis of soft measurement technology |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2011021758A (en) * | 2009-07-13 | 2011-02-03 | Jfe Engineering Corp | Method of correcting fuel charging amount for boiler |
-
2014
- 2014-12-12 CN CN201410764596.2A patent/CN104748807B/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102840889A (en) * | 2012-09-25 | 2012-12-26 | 华北电力大学(保定) | Soft measuring method for main steam flow of unit system utility boiler |
CN103048020A (en) * | 2013-01-22 | 2013-04-17 | 山东电力集团公司电力科学研究院 | Main steam flow online calculation method of power station based on performance testing data |
CN104048842A (en) * | 2014-05-29 | 2014-09-17 | 华中科技大学 | On-line monitoring method for heat rate of steam turbine on basis of soft measurement technology |
Non-Patent Citations (4)
Title |
---|
基于简化热平衡方程的再热蒸汽流量实时软测量;闫姝 等;《中国电机工程学报》;20110215;第31卷(第5期);第114-119页 * |
弗留格尔公式的修正应用;许立敏;《浙江电力》;19941231;第1-4页 * |
汽轮机主蒸汽流量在线计算方法及应用;汪军 等;《热力发电》;20101231;第39卷(第10期);第42-45页 * |
电厂主蒸汽流量测量与计算方法分析比较;赵晶睛, 林中达;《燃气轮机技术》;20071231;第20卷(第4期);第39-46页 * |
Also Published As
Publication number | Publication date |
---|---|
CN104748807A (en) | 2015-07-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN104748807B (en) | A kind of power station main steam flow on-line calculation method based on flux modification | |
CN104048842B (en) | A kind of thermal loss of steam turbine rate on-line monitoring method based on soft-measuring technique | |
CN102494714B (en) | Synchronous reckoning method of utility boiler efficiency and coal heat value as well as ash content and moisture content | |
CN103530504B (en) | The computing system and method for feasible traffic coverage under cogeneration units electricity determining by heat | |
CN106249728B (en) | A kind of thermal power generation unit on-line performance monitoring method based on characteristics of components | |
CN102778538B (en) | Soft measuring method based on improved SVM (Support Vector Machine) for measuring boiler unburned carbon content in fly ash | |
CN109471420B (en) | CVA-SFA-based method for monitoring control performance of air preheater of large coal-fired power generator set of intelligent power plant | |
Teichgraeber et al. | Optimal design and operations of a flexible oxyfuel natural gas plant | |
CN110532674A (en) | A kind of coal-fired power station boiler fire box temperature measurement method | |
CN103293013B (en) | A kind of cooling tower thermal performance appraisal procedure and system | |
CN106018730B (en) | Ature of coal device for measuring moisture and method based on coal pulverizer inlet First air amendment | |
CN106936627A (en) | A kind of thermal power generating equipment performance monitoring method based on big data analysis mining | |
CN105242660A (en) | Multi-modal cigarette primary processing process online monitoring and fault diagnosis method based on relative change analysis | |
CN104536290A (en) | Soft measuring method and system based on kernel principal component analysis and radial basis function neural network | |
Strušnik | Integration of machine learning to increase steam turbine condenser vacuum and efficiency through gasket resealing and higher heat extraction into the atmosphere | |
CN110173710A (en) | A kind of energy-saving climatic dynamics dynamic regulation device and method for eliminating plume | |
Blanco et al. | New investigation on diagnosing steam production systems from multivariate time series applied to thermal power plants | |
CN101813920A (en) | Virtual redundancy method for temperature sensor of power station turboset | |
CN109144028A (en) | A kind of rectifying column efficiency deterioration detecting | |
CN102494895B (en) | Analyzing method for energy saving and optimization of steam turbine set of power station | |
CN102117365A (en) | On-line modeling and optimizing method suitable for recovering coking coarse benzene | |
CN104360984A (en) | Calculating method for measuring thermal performance of filler of lower cooling tower based on two-point boundary value | |
CN115510904A (en) | Boiler heating surface ash deposition monitoring method based on time sequence prediction | |
CN109655275B (en) | Gas turbine cycle heat economy diagnosis method based on sensitivity coefficient method | |
CN103699790B (en) | A kind of real-time detection method of coal fired power plant furnace outlet flue gas mean temperature |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |