CN106200416B - The regulator control system and method that atmospheric temperature influences combined cycle unit power - Google Patents
The regulator control system and method that atmospheric temperature influences combined cycle unit power Download PDFInfo
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Abstract
The regulator control system and method that a kind of atmospheric temperature of electric power energy-saving technical field influences combined cycle unit power, sample data acquisition is carried out by sensing measurement module first, it obtains under different atmospheric temperatures, when standard generated output, measuring point initial data and generated output data on combined cycle generating unit are stored in database module using the corresponding measuring point initial data of same atmospheric temperature and generated output data as a sample group；Then simulation algorithm model chooses several sample group building raw sample data collection from database module, and determines that raw sample data concentrates the maximum value and minimum value of measuring point initial data and generated output data；Atmospheric temperature is calculated finally by BP neural network and changes the fair curve influenced on combined cycle generating unit generated output.The present invention can carry out the amendment of combined cycle generating unit generated output without carrying out test of many times debugging, provide solid foundation for set optimization, energy-saving and emission-reduction assessment.
Description
Technical field
The present invention relates to a kind of technologies in electric power energy-saving field, and specifically a kind of atmospheric temperature is to combined cycle unit
The regulator control system and method that power influences.
Background technique
In recent years, coal price is high and the requirement of energy-saving and emission-reduction, promotes power plant to be reformed to reduce energy consumption, improves
Economy, therefore very high requirement is proposed for the evaluation criterion after power plant's performance evaluation.The comparable performance of unit needs
Performance parameter, the i.e. performance possessed by unit under given boundary condition provided under a certain design conditions.It carries out at the scene
When performance test, for various reasons, all boundary conditions can not be made to be able to satisfy design condition, therefore, it is necessary to performance
Boundary condition when test is modified, that is, is adapted to design condition, and reasonable evaluation could be provided to unit.Usually after amendment
Obtained fair curve is provided by manufactory, but unit, after running after a period of time, especially machine group parts are through celebrating a festival
It can be transformed, changed relative to original design, fair curve originally is no longer applicable in.
At present for pure burning low combustion value furnace gas gas-steam combined cycle set, still lack atmospheric temperature variation pair
The fair curve that generated output influences.The amendment that generated output is completed according to existing technical method, needs to carry out very much
Field test, and other each parameters adjusted according to the timing node of atmospheric temperature and be tested in phase same level, power grid and
Power plant can not achieve, and empirical risk is big, and the time is long, costly.
Summary of the invention
The present invention In view of the above shortcomings of the prior art, proposes a kind of atmospheric temperature to combined cycle unit power
The regulator control system and method for influence can carry out pure burning low combustion value furnace gas-Steam Combined without carrying out test of many times debugging
The amendment of circulating generation unit performance, and realize the feedback regulation to unit, it is provided conscientiously for set optimization, energy-saving and emission-reduction assessment
Reliable foundation.
The present invention is achieved by the following technical solutions：
The present invention relates to a kind of regulation systems for influencing modification method on combined cycle unit power based on above-mentioned atmospheric temperature
System, including：Sensing measurement module, database module and simulation algorithm model, wherein：Sensing measurement module and simulation calculation mould
Block is connected and exports acquired original data, and simulation algorithm model is connected with database module and exports measuring point initial data and power generation
Power data, database module are connected with simulation algorithm model and export raw sample data collection, and simulation algorithm model is according to hair
Electrical power correction model and the operation of fair curve feedback regulation unit, to increase or decrease generated output.
The present invention relates to the modification methods that the atmospheric temperature of above system influences combined cycle unit power, pass through first
Sensing measurement module carries out raw data acquisition, and is handled by the nondimensionalization that simulation algorithm model carries out data, obtains not
With under atmospheric temperature, a certain standard generated output when, measuring point initial data on blast furnace gas-steam combined cycle power generating unit
And generated output data, it is deposited using the corresponding measuring point initial data of same atmospheric temperature and generated output data as a sample group
It is stored in database module；Then simulation algorithm model chooses several sample group building raw sample datas from database module
Collection, and determine that raw sample data concentrates the maximum value and minimum value of measuring point initial data and generated output data；It will finally survey
Intermediate objective data are calculated by BP neural network as input layer data after point initial data normalization, by intermediate objective
The target output data obtained after data and generated output data normalization carries out error calculation, obtains generated output amendment mould
Type；One group of measuring point initial data close to nominal parameter is chosen as radix, only changes atmospheric temperature, simulation algorithm model is being sent out
Atmospheric temperature variation is calculated on the basis of electrical power correction model to repair on what combined cycle generating unit generated output influenced
Positive curve.
The standard generated output is 80MW, 90MW, 100MW, 110MW or 120MW.
The raw sample data collection be include the available minimum generated output of combined cycle generating unit operation and
Multiple sample groups including maximum power generation.
The BP neural network calculating includes the following steps：
S1, the normalization of raw sample data collection：Wherein：I table
Show measuring point serial number, i=1,2 ..., n；P indicates that raw sample data concentrates the serial number of sample group, p=1,2 ..., N；N is original
The sample group quantity that beginning sample data is concentrated；x′ipIndicate that raw sample data concentrates the measuring point of i-th of measuring point of pth sample group former
Beginning data, xipFor x 'ipValue after normalization, referred to as measuring point input data；x′0pIndicate the power generation function that raw sample data is concentrated
Rate data, x0pFor x '0pValue after normalization, referred to as target output data；x′imin、x′imaxRespectively indicate raw sample data collection
In i-th of measuring point minimum value and maximum value；x′0min、x′0maxIndicate raw sample data centralized power generation power minimum value and
Maximum value；
S2Pair, determine the number of intermediate hidden node and initialize the connection weight matrix of pth sample group, wherein p=1, i.e.,
The input layer data weight matrix V of 1st sample groupijWith intermediate hidden layer data weight matrix WjIt is initialized：For in connection weight matrix
Each element assigns initial value and obtains original connection weight matrix for the random number between 0~1, wherein：J indicates intermediate hidden node sequence
Number, j=1,2 ..., m；VijIt is n × m rank weight matrix, WjIt is 1 × m rank weight matrix, m is intermediate the number of hidden nodes, and m is according to input
How much determinations of layer data, input layer data amount check is more, and m value is bigger, and when input layer is 3~20 data, m value can be
It is chosen between 10~30；
S3, for the original connection weight matrix of pth sample group training, establish generated output correction model：
S31, determine intermediate hidden node data ypj：Wherein：For in
Between hidden node data calculation formula index, e be natural logrithm the truth of a matter；
S32, calculate intermediate objective data dp：Wherein：For intermediate objective
The index of data calculation formula；
S33, calculate target output data x0pCorresponding intermediate objective data dpError Ep：
S34, according to the original connection weight matrix of pth sample group, the connection weight matrix V of adjusting training p+1 sample groupij' and
Wj′：V′ij=Vij+ΔVijp, W 'j=Wj+ΔWjp, Δ Vijp=η δyjxi(p-1)+αΔVij(p-1), Δ Wjp=η δ0y(p-1)j+αΔ
Wj(p-1), wherein：ΔWjp、ΔVijpFor calculate pth sample group arrive+1 sample group of pth when adjustment connection weight matrix increment；Δ
Wj(p-1)、ΔVij(p-1)Adjust the increment of connection weight matrix when for -1 sample group of pth to pth sample group, Δ W when p=1j0And Δ
Vij0As value of the original connection weight matrix of the 1st sample group after α rests and reorganizes calculating；y(p-1)j、xi(p-1)For -1 sample group of pth
Intermediate hidden node data and i-th of measuring point input data；α, η be in order to speed up the convergence rate and be arranged two coefficients,
The arbitrary number between 0~1 is generally taken, starts to take 0.5 when operation, can carry out increasing by 5% or reduce according to convergence state later
5% adjustment；Intermediate variable δ0And δyjFor correction factor：
S35, p=p+1 is enabled, step S is repeated34After the connection weight matrix training for completing whole sample groups, calculates total output and miss
Poor EN：If ENIt is unsatisfactory for required precision, repeats step S2Again assignment calculates, until ENPrecision
Reach requirement；The V of required precision will be metij、Wj、m、x′0min、x′0max、x′iminWith x 'imaxChange as atmospheric temperature to coal
The generated output correction model that gas-steam combined cycle power generating unit generation power influences is stored in simulation algorithm model；
S4, generated output amendment：Every 2 DEG C of selections, one temperature within the scope of 2 DEG C~35 DEG C atmospheric temperatures, and keep it
Remaining measuring point initial data is in the state close to nominal parameter；The measuring point close to nominal parameter under the conditions of above-mentioned temperature is original
Data x '1p、x′2p、…、x′np, utilize the x ' in generated output correction modeliminWith x 'imaxRepeat S1, x is obtained after normalization1p、
x2p、…、xnp, in conjunction with the V in generated output correction modelij、Wjλ is calculated with mpj、λpWith intermediate objective data dp, last anti-normalizing
Change obtains generated output data X ' at revised a certain temperature0=dp(x′0max-x′0min)+x′0min, and obtain 2 DEG C~35 DEG C
The fair curve that atmospheric temperature within the scope of atmospheric temperature influences generated output.
Technical effect
Compared with prior art, the method that the present invention uses BP neural network is based on field measurement data and artificial intelligence
Combination, dependence to actual tests is reduced under conditions of guaranteeing precision；Only need to change atmospheric temperature just can be sent out
The output valve of electrical power, the pure operation for burning low-heat value gas gas-steam combined cycle set of feedback regulation on this basis, with
Generated output is increased or decreased, while providing solid foundation for set optimization, energy-saving and emission-reduction assessment.It is big due to using
Live actual information is measured, therefore resulting correction result meets the requirement of engineer application generally within 5%.
Detailed description of the invention
Fig. 1 is that present invention amendment atmospheric temperature changes the schematic diagram influenced on generated output；
Fig. 2 is system construction drawing in the present invention；
Fig. 3 is that BP neural network models schematic diagram in the present invention.
Specific embodiment
It elaborates below to the embodiment of the present invention, the present embodiment carries out under the premise of the technical scheme of the present invention
Implement, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to following implementation
Example.
Embodiment 1
As depicted in figs. 1 and 2, combined cycle unit power is influenced to correct based on above-mentioned atmospheric temperature for the present embodiment
The regulator control system of method, including：Sensing measurement module, database module and simulation algorithm model, wherein：Sensing measurement module
It is connected with simulation algorithm model and exports acquired original data, simulation algorithm model is connected with database module and exports measuring point original
Beginning data and generated output data, database module are connected with simulation algorithm model and export raw sample data collection, emulation meter
It calculates module to be run according to generated output correction model and fair curve feedback regulation unit, to increase or decrease generated output.
As shown in Figure 1, the present embodiment is related to the modification method of above system, include the following steps：
1) raw data acquisition is carried out by sensing measurement module, and carries out the dimensionless of data by simulation algorithm model
Change processing, obtains under different atmospheric temperatures, when a certain standard generated output, on blast furnace gas-steam combined cycle power generating unit
The measuring point initial data and generated output data of 15 measuring points, the corresponding measuring point initial data of same atmospheric temperature and power generation function
Rate data are stored in database module as a sample group；
2) simulation algorithm model chooses 250 sample group building raw sample data collection from database module, and determines
The maximum value and minimum value of raw sample data concentration measuring point initial data and generated output data；
3) intermediate objective will be calculated by BP neural network as input layer data after the normalization of measuring point initial data
The target output data obtained after intermediate objective data and generated output data normalization is carried out error calculation, obtained by data
Generated output correction model；
4) one group of measuring point initial data close to nominal parameter is chosen as radix, only changes atmospheric temperature, simulation calculation
Atmospheric temperature variation is calculated to combined cycle generating unit generated output in module on the basis of generated output correction model
The fair curve of influence.
It is as shown in table 1 below 15 measuring points in the present embodiment：
1 measuring point list of table
It is as shown in table 2 below raw sample data collection
2 raw sample data collection of table
As shown in figure 3, the BP neural network calculating includes the following steps：
S1, the normalization of raw sample data collection：Each measuring point original input data x ' is determined firstipMaximum value and minimum
Value, as shown in table 3 below, is then normalized, obtains table 4；
Each measuring point initial data x ' in 3250 sample groups of tableipAnd generated output data x '0pMaximum value and minimum value
Wherein corresponding generated output data maximums x '0maxWith minimum value x '0minRespectively 116.6 and 72.7；
4 250 sample group measuring point input data x of tableipWith target output data x0p
S2, determine the number m=12 of intermediate hidden node, and initialize the connection weight matrix of pth sample group, wherein p=
1, i.e., to the input layer data weight matrix V of the 1st sample groupijWith intermediate hidden layer data weight matrix WjIt is initialized：For connection weight
Each element assigns initial value and obtains original connection weight matrix for the random number between 0~1 in matrix, wherein：J indicates intermediate hidden layer
Node ID, j=1,2 ..., m；VijIt is 15 × 12 rank weight matrixs, WjIt is 1 × 12 rank weight matrix；
S3, for the original connection weight matrix of pth sample group training, establish generated output correction model：
S31, determine intermediate hidden node data ypj：Wherein：For in
Between hidden node data calculation formula index, e be natural logrithm the truth of a matter；
S32, calculate intermediate objective data dp：Wherein：For intermediate objective
The index of data calculation formula；
S33, calculate target output data x0pCorresponding intermediate objective data dpError Ep：
S34, according to the original connection weight matrix of pth sample group, the connection weight matrix V ' of adjusting training p+1 sample groupijWith
W′j：V′ij=Vij+ΔVijp, W 'j=Wj+ΔWjp, Δ Vijp=η δyjxi(p-1)+αΔVij(p-1), Δ Wjp=η δ0y(p-1)j+αΔ
Wj(p-1), wherein：ΔWjp、ΔVijpFor calculate pth sample group arrive+1 sample group of pth when adjustment connection weight matrix increment；Δ
Wj(p-1)、ΔVij(p-1)Adjust the increment of connection weight matrix when for -1 sample group of pth to pth sample group, Δ W when p=1j0And Δ
Vij0As value of the original connection weight matrix of the 1st sample group after α rests and reorganizes calculating；y(p-1)j、xi(p-1)For -1 sample group of pth
Intermediate hidden node data and i-th of measuring point input data；α, η be in order to speed up the convergence rate and be arranged two coefficients；
The arbitrary number between 0~1 is generally taken, starts to take 0.5 when operation, can carry out increasing by 5% or reduce according to convergence state later
5% adjustment；Intermediate variable δ0And δyjFor correction factor：
S35, p=p+1 is enabled, step S is repeated34After the connection weight matrix training for completing whole sample groups, calculates total output and miss
Poor EN：If ENIt is unsatisfactory for required precision, repeats step S2Again assignment calculates, until ENPrecision
Reach requirement；The V of required precision will be metij、Wj、m、x′0min、x′0max、x′iminWith x 'imaxChange as atmospheric temperature to coal
The generated output correction model that gas-steam combined cycle power generating unit generation power influences is stored in simulation algorithm model；
S4, generated output amendment：Every 2 DEG C of selections, one temperature within the scope of 2 DEG C~35 DEG C atmospheric temperatures, and keep it
Remaining measuring point initial data is in the state close to nominal parameter；Acquire the measuring point original close to nominal parameter under the conditions of above-mentioned temperature
Beginning data x '1p、x′2p、…、x′np, utilize the x ' in generated output correction modeliminWith x 'imaxRepeat S1, obtained after normalization
x1p、x2p、…、xnp, in conjunction with the V in generated output correction modelij、Wjλ is calculated with mpj、λpWith intermediate objective data dp, last anti-
Normalization obtains generated output data X ' at revised a certain temperature0=dp(x′0max-x′0min)+x′0min, and obtain 2 DEG C~
The fair curve that atmospheric temperature within the scope of 35 DEG C of atmospheric temperatures influences generated output.
Claims (7)
1. the regulator control system that a kind of atmospheric temperature influences modification method on combined cycle unit power, which is characterized in that including：It passes
Sensed quantity module, database module and simulation algorithm model, wherein：Sensing measurement module is connected and defeated with simulation algorithm model
Acquired original data out, simulation algorithm model are connected with database module and export measuring point initial data and generated output data,
Database module is connected with simulation algorithm model and exports raw sample data collection, and simulation algorithm model is corrected according to generated output
Model and the operation of fair curve feedback regulation unit, to increase or decrease generated output；The sensing measurement module carries out former
The acquisition of beginning data, and handled by the nondimensionalization that simulation algorithm model carries out data, it obtains under different atmospheric temperatures, a certain mark
When quasi- generated output, measuring point initial data and generated output data on blast furnace gas-steam combined cycle power generating unit, same
The corresponding measuring point initial data of one atmospheric temperature and generated output data are stored in database module as a sample group；So
Post-simulation computing module chooses several sample group building raw sample data collection from database module, and determines original sample number
According to the maximum value and minimum value for concentrating measuring point initial data and generated output data；It will finally make after the normalization of measuring point initial data
Intermediate objective data are calculated by BP neural network for input layer data, intermediate objective data and generated output data are returned
The target output data obtained after one change carries out error calculation, obtains generated output correction model；It chooses close to nominal parameter
One group of measuring point initial data only changes atmospheric temperature, simulation algorithm model is on the basis of generated output correction model as radix
On atmospheric temperature be calculated change the fair curve that influences on combined cycle generating unit generated output.
2. a kind of atmospheric temperature influences modification method to combined cycle unit power, which is characterized in that according to claim 1
System is realized.
3. according to the method described in claim 2, it is characterized in that, the raw sample data collection be include combined cycle generation
Unit runs multiple sample groups including available minimum generated output and maximum power generation.
4. according to the method described in claim 2, it is characterized in that, the described BP neural network calculating includes the following steps：
S1, the normalization of raw sample data collection：Wherein：I indicates measuring point
Serial number, i=1,2 ..., n；P indicates that raw sample data concentrates the serial number of sample group, p=1,2 ..., N；N is original sample
Sample group quantity in data set；x′ipIndicate that raw sample data concentrates the measuring point initial data of i-th of measuring point of pth sample group,
xipFor x 'ipValue after normalization, referred to as measuring point input data；x′0pIndicate the generated output data that raw sample data is concentrated,
x0pFor x '0pValue after normalization, referred to as target output data；x′imin、x′imaxRaw sample data is respectively indicated to concentrate i-th
The minimum value and maximum value of measuring point；x′0min、x′0maxIndicate the minimum value and maximum value of raw sample data centralized power generation power；
S2, determine the number of intermediate hidden node and initialize the connection weight matrix of pth sample group, wherein p=1, i.e., to the 1st sample
The input layer data weight matrix V of this groupijWith intermediate hidden layer data weight matrix WjIt is initialized：For member each in connection weight matrix
Element assigns initial value and obtains original connection weight matrix for the random number between 0~1, wherein：J indicates intermediate hidden node serial number, j=
1,2 ..., m；VijIt is n × m rank weight matrix, WjIt is 1 × m rank weight matrix, m is intermediate the number of hidden nodes；
S3, for the original connection weight matrix of pth sample group training, establish generated output correction model：
S31Determine intermediate hidden node data ypj：Wherein：For intermediate hidden layer
The index of node data calculation formula, e are the truth of a matter of natural logrithm；
S32, calculate intermediate objective data dp：Wherein：For intermediate objective data
The index of calculation formula；
S33, calculate target output data x0pCorresponding intermediate objective data dpError Ep：
S34, according to the original connection weight matrix of pth sample group, the connection weight matrix V of adjusting training p+1 sample groupij' and Wj′：
Vij'=Vij+ΔVijp, Wj'=Wj+ΔWjp, Δ Vijp=η δyjxi(p-1)+αΔVij(p-1), Δ Wjp=η δ0y(p-1)j+αΔWj(p-1),
Wherein：ΔWjp、ΔVijpFor calculate pth sample group arrive+1 sample group of pth when adjustment connection weight matrix increment；ΔWj(p-1)、Δ
Vij(p-1)Adjust the increment of connection weight matrix when for -1 sample group of pth to pth sample group, Δ W when p=1j0With Δ Vij0As the 1st
Value of the original connection weight matrix of sample group after α rests and reorganizes calculating；y(p-1)j、xi(p-1)For the intermediate hidden layer of -1 sample group of pth
Node data and i-th of measuring point input data；Two coefficients and value that α, η are in order to speed up the convergence rate and are arranged are 0~1
Between, start to take 0.5 when operation, can carry out increasing the adjustment of 5% or reduction by 5% according to convergence state later；Intermediate variable
δ0And δyjFor correction factor：
S35, p=p+1 is enabled, step S is repeated34After the connection weight matrix training for completing whole sample groups, total output error E is calculatedN：If ENIt is unsatisfactory for required precision, repeats step S2Again assignment calculates, until ENPrecision reach and want
It asks；The V of required precision will be metij、Wj、m、x′0min、x′0max、x′iminWith x 'imaxChange as atmospheric temperature to coal gas-steam
The generated output correction model that combined cycle generating unit generated output influences is stored in simulation algorithm model；
S4, generated output amendment：Every 2 DEG C of selections, one temperature within the scope of 2 DEG C~35 DEG C atmospheric temperatures, and keep remaining survey
Point initial data is in the state close to nominal parameter；By the measuring point initial data close to nominal parameter under the conditions of above-mentioned temperature
x′1p、x′2p、…、x′np, utilize the x ' in generated output correction modeliminWith x 'imaxRepeat S1, x is obtained after normalization1p、
x2p、…、xnp, in conjunction with the V in generated output correction modelij、Wjλ is calculated with mpj、λpWith intermediate objective data dp, last anti-normalizing
Change obtains generated output data X ' at revised a certain temperature0=dp(x′0max-x′0min)+x′0min, and obtain 2 DEG C~35 DEG C
The fair curve that atmospheric temperature within the scope of atmospheric temperature influences generated output.
5. method according to claim 2 or 4, characterized in that the measuring point totally 15, measure following items respectively：
Blast furnace gas flow, blast furnace gas calorific value, blast furnace gas feed air temperature, gas heater outlet temperature, blast furnace gas hydrogen
Fraction, blast furnace gas carbon monoxide volume fraction, blast furnace gas carbon dioxide volume fraction, blast furnace gas nitrogen integral
Number, high-pressure steam pressure, high steam temperature, steam turbine exhaust pressure, Inlet Temperature of Circulating Water, waste heat boiler exhaust gas temperature,
Atmospheric pressure and atmospheric temperature.
6. according to the method described in claim 2, it is characterized in that, the standard generated output be 80MW, 90MW, 100MW,
110MW or 120MW.
7. according to the method described in claim 4, it is characterized in that, the m according to input layer data how much determinations, input layer
Data amount check is more, and m value is bigger, and when input layer is 3~20 data, m value is chosen between 10~30.
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| US6681155B1 (en) * | 1998-08-31 | 2004-01-20 | Mitsubishi Chemical Corporation | Optimizing control method and optimizing control system for power plant |
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| US6681155B1 (en) * | 1998-08-31 | 2004-01-20 | Mitsubishi Chemical Corporation | Optimizing control method and optimizing control system for power plant |
| CN101929684A (en) * | 2010-07-30 | 2010-12-29 | 上海交通大学 | Coal consumption calculating method of composite firing low heat value gas unit |
| CN102799161A (en) * | 2012-08-13 | 2012-11-28 | 浙江大学 | Performance index correcting and comparing method and regulation control system of combined cycle generating unit |
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| 联合循环机组建模及环境参数对其性能的影响;张杨林子 等;《电力科学与工程》;20160630;第32卷(第6期);68-72 * |
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