CN106682369A - Heating pipe network hydraulic simulation model identification correction method and system, method of operation - Google Patents
Heating pipe network hydraulic simulation model identification correction method and system, method of operation Download PDFInfo
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Abstract
The invention relates to a heating pipe network hydraulic simulation model identification correction method and a system. The heating pipe network hydraulic simulation model identification correction method comprises the steps of S1, selecting multiple groups of actually measured data of a heating system under steady work conditions, S2, calculating theoretical values of the drag coefficient of all pipe section, and S3, establishing a hydraulic simulation model of heating pipe network, using particle swarm optimization method accompanied by multiple groups of actually measured data, identifying and calculating various resistance coefficient correction amount vectors, and through the various resistance coefficient correction amount vectors in the heating pipe network hydraulic simulation model, making the comparison between the flow conditions and the actual measured data under multiple working conditions, identifying the optimal resistance coefficient correction amount. The corrected model can better describe the actual physical system, and increase the calculation precision of the hydraulic calculation of heating pipe network. The application of a parallel computing architecture for the implementation for the identification of the particle swarm algorithm drag coefficient correction with high identification efficiency.
Description
Technical field
The invention belongs to heating field, and in particular to a kind of heating network waterpower phantom Identification Correction Method and be
System, operational approach.
Background technology
Foundation meet actual heating network operation characteristic waterpower phantom be realize heat supply network wisdomization manage it is important
Basis.With the expanding day and increasingly sophisticated, the waterpower of the strict accurate heating network of foundation of network structure of heat supplying scale
Phantom becomes further difficult.The generally parameter such as attachment structure, node absolute altitude of pipe network is relatively easy to obtain, and the resistance of ducting
Characteristic is affected by various physical parameters, and is changed over, by exact mechanism calculate obtain theoretical drag characteristic often with
There is deviation in actual characteristic, cause the hydraulic pipeline phantom set up can not simulation actual operating mode very well.For this purpose, needing
Actual motion historical data is made full use of, using the method amendment pipeline section drag characteristic of identification.Current most of heating networks
There are confession, pressure of return water measuring point and flow measuring point at thermal substation, and there is no measuring point at other pipeline sections and node, thus be difficult to
The resistance coefficient of whole pipe network pipeline section is obtained by the method for directly calculating.
The content of the invention
It is an object of the invention to provide a kind of heating network waterpower phantom Identification Correction Method and system, to solve to supply
Hot pipe network waterpower phantom is unable to the technical problem of the actual pipe network operation of accurate description.
In order to solve above-mentioned technical problem, the invention provides a kind of heating network waterpower phantom identification and modification side
Method, comprises the steps：
Step S1, chooses heating system multigroup measured data under steady state operating conditions；
Step S2, calculates the resistance coefficient theoretical value of each pipeline section；And
Step S3, sets up heating network waterpower phantom, and using particle cluster algorithm multigroup measured data identification meter is combined
Each resistance coefficient correction is calculated, and obtains corrected resistance coefficient.
Further, heating system multigroup measured data under steady state operating conditions is chosen in step S1, i.e.,
Multiple steady state operating conditions of identification heating network system, choose the measured data of multigroup steady state operating condition, its
Method includes：
Step S11, floor data pretreatment；
Step S12, seeks the steady state condition time interval of each thermal substation；
Step S13, seeks the steady state condition time interval of whole heating network system；And
Step S14, chooses multigroup measured data.
Further, the method for the resistance coefficient theoretical value of each pipeline section is calculated in step S2 to be included：
The resistance coefficient theoretical value of each pipeline section is calculated by following mechanism formula；I.e.
δiFor the resistance coefficient (Pa/ (t/h) of pipeline section i2)；K is the equivalent absolute roughness of tube wall；diFor internal diameter of the pipeline, list
Position is m；liFor length of pipe section, unit is m；ldiFor pipeline section local resistance equivalent length, unit is m；ρ is the average of hot water in pipe
Density, unit is kg/m3；D is pipeline section quantity.
Further, the method for heating network waterpower phantom is set up in step S3 to be included：
Heating network is converted to the Directed Graph Model being made up of node and section, i.e. pipe network figure；Wherein
Node represents the point that there is flow turnover, is represented with set V, V={ V1,V2,…,Vn, in formula, n is in pipe network
Node number；
Section represents the connection pipeline section between node, is represented with set E, E={ E1,E2,…,Em, D is for sector number in formula
Pipeline section quantity；
Be converted to directed graph, then it represents that for G=<V,E>；
The incidence matrix A and fundamental circuit matrix B of heating network are obtained according to network graph theory, wherein A is n × D rank matrixes,
B is s × D rank matrixes, and s is fundamental circuit number s=D-n+1；
Heating network waterpower phantom is built, i.e.,
AGT=0；
BΔHT=0；
In formula, G is the row vector for recording each pipeline section inner volume flow in pipe network figure, i.e.,
G=[G1,G2,…,GD]；
Δ H is the column vector for recording each pipeline section drag overall loss in pipe network figure, i.e.,
Δ H=ζ * | G | * G+Z-Hb；
In formula, ζ is the resistance coefficient matrix of each pipeline section in pipe network；
ζ=diag { δ1+Δδ1,δ2+Δδ2,…,δD+ΔδD}；
Wherein δ1、δ2、……、δDFor resistance coefficient theoretical value, Δ δ1、Δδ2、……、ΔδDFor resistance coefficient correction,
Need the parameter for recognizing；D represents the dimension of resistance coefficient correction to be identified；
Z is the column vector Z=[Z of the node potential energy of pipeline section two difference1,Z2,…,ZD]T；
HbFor pump head column vector H in heat supply networkb=[Hb1,Hb2,…,HbD]T；
It is following form by its abstract representation when heating network waterpower phantom is applied to identification：
Y=Func (X, δ)
X represents multigroup measured data of input in formula；Y represents output vector；δ represent the resistance coefficient of pipeline section to be identified to
Amount, δ=[δ1+Δδ1,δ2+Δδ2,…,δD+ΔδD]。
Further, calculate each resistance coefficient with reference to the identification of multigroup measured data using particle cluster algorithm in step S3 to repair
Positive quantity, i.e.,
Different resistance coefficient corrections are produced in every generation using particle cluster algorithm, and is emulated by heating network waterpower
Different resistance coefficient correction vector flow regimes of pipe network under multiple operating modes are contrasted with corresponding measured data in model,
Pick out the resistance coefficient correction of optimum.
Further, the side that multigroup measured data recognizes each resistance coefficient correction vector of calculating is combined using particle cluster algorithm
Method comprises the steps：
Step S31, particle cluster algorithm parameter setting, i.e.,
Microgranule number N, microgranule dimension D, and decision variable upper bound U=[Δ δ1u,Δδ2u,…,ΔδDu] and lower bound L=
[Δδ1l,Δδ2l,…,ΔδDl]；
Step S32, initialization, i.e.,
Resistance coefficient theoretical value is read, N number of microgranule, i.e. N groups resistance coefficient correction vector is generated；
Step S33, sets up the object function for the identification of resistance coefficient correction, to calculate the object function of each microgranule
Value；
The object function is as follows：
When resistance coefficient correction amount δ1,Δδ2,…,ΔδDAfter as decision variable set-point, by heating network waterpower
Phantom is calculated corresponding output data；
To each microgranuleK ∈ { 1,2 ..., N }, by itself and resistance coefficient theoretical value vector
[δ1,δ2,…,δD] be added, obtain the resistance coefficient vector that the microgranule is represented
By δkInput heating network waterpower phantom, and the 1st, 2 ... ... are utilized successively, NCGroup measured data carries out potamometer to it
Calculate, obtain the calculation of pressure value of each thermal substation under corresponding operating mode；
In formula,
PCij：By the calculation of pressure value of the calculated thermal substation i of phantom under steady state condition j；
POij：The corresponding pressure measuring values of thermal substation i under steady state condition j；
NP：Pressure-measuring-point quantity；
Step S34, microgranule flight produces microgranule of future generation；
Step S35, reaches the operation algebraically of setting, and algorithm terminates, otherwise returns to step S33 and continue executing with.
Another aspect, present invention also offers a kind of heating network waterpower phantom identification and modification system, including：
Particle cluster algorithm unit, produces the corresponding microgranule of resistance coefficient correction per a generation, and microgranule is write into data
Storehouse, and read the target function value of each microgranule；
Object function computing unit, sets up the object function for the identification of resistance coefficient correction, drives multiple heating tubes
Net waterpower phantom, that is, producing N number of phantom carries out parallel simulation calculating, and obtains microgranule from data base, by imitative
True mode calculates the target function value of microgranule and write back data storehouse.
Database Unit, stores colony and the target function value of resistance coefficient correction vector, and the meter of object function
Calculation state, and also the result of optimization calculating is also stored in data base by the measured data of steady state condition and per a generation.
Further, the object function is as follows：
When resistance coefficient correction amount δ1,Δδ2,…,ΔδDAfter as decision variable set-point, by heating network waterpower
Phantom is calculated corresponding output data；
To each microgranuleK ∈ { 1,2 ..., N }, by itself and resistance coefficient theoretical value vector
[δ1,δ2,…,δD] be added, obtain the resistance coefficient vector that the microgranule is represented
By δkInput heating network waterpower phantom, and the 1st, 2 ... ... are utilized successively, NCGroup measured data carries out potamometer to it
Calculate, obtain the calculation of pressure value of each thermal substation under corresponding operating mode；
In formula,
PCij：By the calculation of pressure value of the calculated thermal substation i of phantom under steady state condition j；
POij：The corresponding pressure measuring values of thermal substation i under steady state condition j；
NP：Pressure-measuring-point quantity.
The third aspect, present invention also offers a kind of operation side of heating network waterpower phantom identification and modification system
Method, comprises the steps：
Step Sa, sets up the topology controlment of pipe network to be identified, to generate the unique ID of each part of pipe network；
Step Sb, obtains operating mode measured data and calculates the preparation of resistance coefficient theoretical value；
Step Sc, arranges the algorithm parameter in particle cluster algorithm unit, starts the unit；
Step Sd, starts object function computing unit.
Further, preparation includes described in step Sb：By the ID of pipeline section to be identified and resistance coefficient theoretical value
Accordingly arrange in configuration file, read for particle cluster algorithm unit；And
By in multigroup measured data write into Databasce of screening, inquire about for object function computing unit, each group of operating mode number
According to including operating mode numbering, thermal source or the corresponding flow of thermal substation ID, thermal source or thermal substation and supplying pressure of return water.
The invention has the beneficial effects as follows, the heating network waterpower phantom Identification Correction Method and system of the present invention can show
The precision for improving heating network waterpower phantom simulation calculation is write, it adopts particle cluster algorithm theoretical in the resistance coefficient of pipeline section
Resistance coefficient correction is recognized on the basis of value, multigroup operating mode service data is make use of during calculating, it is not enough in observation station and cannot
Relatively accurate result can be obtained in the case of by Analytical Solution；Particle cluster algorithm resistance coefficient is carried out using parallel architecture
The identification of correction, computational efficiency is high.The identification of heating network resistance coefficient can not only be applied to set up High Precision Simulation model,
Also a kind of diagnostic method is provided for pipe network obstruction.
Description of the drawings
With reference to the accompanying drawings and examples the present invention is further described.
Fig. 1 is a kind of flow chart of heating network waterpower phantom Identification Correction Method of the invention；
Fig. 2 is the structure chart of heating network waterpower phantom identification and modification system of the present invention；
Fig. 3 is the heating network system of the embodiment of the present invention.
Specific embodiment
In conjunction with the accompanying drawings, the present invention is further detailed explanation.These accompanying drawings are simplified schematic diagram, only with
The basic structure of the illustration explanation present invention, therefore it only shows the composition relevant with the present invention.
Embodiment 1
As shown in figure 1, a kind of heating network waterpower phantom Identification Correction Method of the present invention, comprises the steps：
Step S1, chooses heating system multigroup measured data under steady state operating conditions；
Step S2, calculates the resistance coefficient theoretical value of each pipeline section；And
Step S3, sets up heating network waterpower phantom, and using particle cluster algorithm multigroup measured data identification meter is combined
Each resistance coefficient correction is calculated, and obtains corrected resistance coefficient.
Further, step S1 is chosen heating system multigroup measured data under steady state operating conditions and is transported according to heat supply network history
Row floor data chooses multigroup measured data, and heat supply network history run floor data is that the measured data of a heating period is included but not
Be limited to each thermal substation primary side import and export pressure and flow, it is also possible to gather confession, pressure of return water and the flow of thermal source etc. it is related ginseng
Number.
The present embodiment 1 will be carried out expansion and said by taking thermal substation as an example to heating network waterpower phantom Identification Correction Method
It is bright.
Specifically, step S1 is launched to comprise the steps：
Step S11, floor data pretreatment makes the time of each thermal substation --- flow curve figure (broken line graph),
Obvious irrational data are will be considered to by analysis to be rejected, number such as excessive or too small caused by sensor or transmission fault
According to then with the average filling of data before and after disallowable data.
Step S12, asks the steady state condition time interval of each thermal substation, i.e., the condition with stability of flow as stable conditions, stream
The number of times of amount fluctuation can be approximately the number of times of working conditions change, if the time of each thermal substation --- it is adjacent in flow curve figure to turn
The relative change rate of point flow such as but not limited to represents flowed fluctuation more than 3%.Flowed fluctuation number of times is drawn according to curve chart
Most thermal substation sk, operating mode base station is defined as, and mark off the steady state condition time interval of each thermal substationWherein siRepresent thermal substation i, niIt is steady that the flow histories data of expression thermal substation i can be divided
State operating mode time interval number,... represent each time interval.
Step S13, seeks the steady state condition time interval of whole heating network system, i.e., with thermal substation that flowed fluctuation is most
skOn the basis of, ask the steady state condition time interval of other each thermal substations withCommon factorThe steady state condition time interval of as whole heat network system, wherein nrFor occur simultaneously number, i.e.,
Steady state condition number, nr≤nk。
Step S14, chooses multigroup measured data, i.e., from each steady state condition time interval One group of complete floor data of middle selection, many datas of same steady state condition time interval are taken and are asked
Average mode, finally gives NCGroup steady state condition data, as multigroup measured data.
Further, the resistance coefficient theoretical value of each pipeline section of the step S2 calculating calculates each pipe by following mechanism formula
The resistance coefficient theoretical value of section；I.e.
δiFor the resistance coefficient (Pa/ (t/h) of pipeline section i2)；K for tube wall equivalent absolute roughness, general K=0.0005m；
diFor internal diameter of the pipeline, unit is m；liFor length of pipe section, unit is m；ldiFor pipeline section local resistance equivalent length, unit is m；ρ
For the average density of hot water in pipe, unit is kg/m3；D is pipeline section quantity.
Further, the method for heating network waterpower phantom is set up in step S3 to be included：
Heating network is converted to the Directed Graph Model being made up of node and section, i.e. pipe network figure；Wherein
Node represents the point that there is flow turnover, is represented with set V, V={ V1,V2,…,Vn, in formula, n is in pipe network
Node number；
Section represents the connection pipeline section between node, is represented with set E, E={ E1,E2,…,Em, D is for sector number in formula
Pipeline section quantity；
Be converted to directed graph, then it represents that for G=<V,E>；
The incidence matrix A and fundamental circuit matrix B of heating network are obtained according to network graph theory, wherein A is n × D rank matrixes,
B is s × D rank matrixes, and s is fundamental circuit number s=D-n+1；
Heating network waterpower phantom is built, i.e.,
AGT=0；
BΔHT=0；
In formula, G is the row vector for recording each pipeline section inner volume flow in pipe network figure, i.e.,
G=[G1,G2,…,GD]；
Δ H is the column vector for recording each pipeline section drag overall loss in pipe network figure, i.e.,
Δ H=ζ * | G | * G+Z-Hb；
In formula, ζ is the resistance coefficient matrix of each pipeline section in pipe network；
ζ=diag { δ1+Δδ1,δ2+Δδ2,…,δD+ΔδD}；
Wherein δ1、δ2、……、δDFor resistance coefficient theoretical value, Δ δ1、Δδ2、……、ΔδDFor resistance coefficient correction,
Need the parameter for recognizing；D represents the dimension of resistance coefficient correction to be identified；
Z is the column vector Z=[Z of the node potential energy of pipeline section two difference1,Z2,…,ZD]T；
HbFor pump head column vector H in heat supply networkb=[Hb1,Hb2,…,HbD]T；
It is following form by its abstract representation when heating network waterpower phantom is applied to identification：
Y=Func (X, δ)
X represents multigroup measured data of input in formula, from choose steady state condition measured data in obtain, including but do not limit
In the pressure reduction (difference of inlet and outlet pressure) at total pressure of return water, each thermal substation primary side flow and least favorable station；The backwater pressure of thermal source
Power；Y represents output vector, including the inlet and outlet pressure for reaching each thermal substation primary side；δ represents the resistance coefficient of pipeline section to be identified
Vector,
δ=[δ1+Δδ1,δ2+Δδ2,…,δD+ΔδD]。
Further, calculate each resistance coefficient with reference to the identification of multigroup measured data using particle cluster algorithm in step S3 to repair
Positive quantity, i.e., produce different resistance coefficient correction vectors using particle cluster algorithm in every generation, and by heating network waterpower
Different resistance coefficient correction vector flow regimes of pipe network under multiple operating modes are carried out with corresponding measured data in phantom
Contrast, picks out the resistance coefficient correction of optimum.
Specifically, the method bag that multigroup measured data recognizes each resistance coefficient correction of calculating is combined using particle cluster algorithm
Include following steps：
Step S31, particle cluster algorithm parameter setting, i.e.,
Microgranule number N, microgranule dimension D, run algebraically Ngen, and decision variable upper bound U=[Δ δ1u,Δδ2u,…,Δ
δDu] and lower bound L=[Δ δ1l,Δδ2l,…,ΔδDl]；Wherein decision variable bound is traditionally arranged to be resistance coefficient theoretical value
+ 30%, -30%, adjustable above-mentioned parameter is run multiple times this algorithm to obtain more preferable identification result.
Step S32, initialization, i.e.,
Resistance coefficient theoretical value is read, N number of microgranule, i.e. N groups resistance coefficient is generated at random between decision variable bound
Correction vector
Step S33, sets up the object function for the identification of resistance coefficient correction, to calculate the object function of each microgranule
Value；
The object function is as follows：
When resistance coefficient correction amount δ1,Δδ2,…,ΔδDAfter as decision variable set-point, by heating network waterpower
Phantom is calculated corresponding output data；
To each microgranuleK ∈ { 1,2 ..., N }, by itself and resistance coefficient theoretical value vector [δ1,
δ2,…,δD] be added, obtain the resistance coefficient vector that the microgranule is represented
By δkInput heating network waterpower phantom, and the 1st, 2 ... ... are utilized successively, NCGroup measured data carries out potamometer to it
Calculate, obtain the calculation of pressure value of each thermal substation under corresponding operating mode；
In formula,
PCij：By the calculation of pressure value of the calculated thermal substation i of phantom under steady state condition j；
POij：The corresponding pressure measuring values of thermal substation i under steady state condition j；
NP：Pressure-measuring-point quantity；
Step S34, microgranule flight produces microgranule of future generation；
Step S35, reaches the operation algebraically N of settinggen, algorithm terminate, otherwise return to step S33 and continue executing with.
Wherein, algebraically N is rungenCan be arranged as required to respective value, such as but not limited to 100,150,200,300
Deng.
Embodiment 2
On the basis of embodiment 1, the present embodiment 2 provides a kind of heating network waterpower phantom identification and modification system,
Including：
Particle cluster algorithm unit, produces the corresponding microgranule of resistance coefficient correction vector per a generation, and microgranule is write
Data base, and read the target function value of each microgranule；
Object function computing unit, sets up the object function for the identification of resistance coefficient correction, drives multiple heating tubes
Net waterpower phantom, that is, producing N number of phantom carries out parallel simulation calculating, and obtains microgranule from data base, by imitative
True mode calculates the target function value of microgranule and write back data storehouse.
Database Unit, stores colony and the target function value of resistance coefficient correction vector, and the meter of object function
Calculation state, and also the result of optimization calculating is also stored in data base by the measured data of steady state condition and per a generation.
The object function is as follows：
When resistance coefficient correction amount δ1,Δδ2,…,ΔδDAfter as decision variable set-point, by heating network waterpower
Phantom is calculated corresponding output data；
To each microgranuleK ∈ { 1,2 ..., N }, by itself and resistance coefficient theoretical value vector
[δ1,δ2,…,δD] be added, obtain the resistance coefficient vector that the microgranule is represented
By δkInput heating network waterpower phantom, and the 1st, 2 ... ... are utilized successively, NCGroup measured data carries out potamometer to it
Calculate, obtain the calculation of pressure value of each thermal substation under corresponding operating mode；
In formula,
PCij：By the calculation of pressure value of the calculated thermal substation i of phantom under steady state condition j；
POij：The corresponding pressure measuring values of thermal substation i under steady state condition j；
NP：Pressure-measuring-point quantity.
Embodiment 3
On the basis of embodiment 1 and 2, the present embodiment 3 provides a kind of for the heating network waterpower phantom of embodiment 2
The operational approach of identification and modification system, comprises the steps：
Step Sa, sets up the topology controlment of pipe network to be identified, to generate the unique ID of each part of pipe network；
Step Sb, obtains operating mode measured data and calculates the preparation of resistance coefficient theoretical value；
Step Sc, arranges the algorithm parameter in particle cluster algorithm unit, starts the unit；
Step Sd, starts object function computing unit.
Preparation includes described in step Sb：The ID of pipeline section to be identified and resistance coefficient theoretical value is accordingly whole
In managing configuration file, read for particle cluster algorithm unit；And
By in multigroup measured data write into Databasce of screening, inquire about for object function computing unit, each group of operating mode number
According to including operating mode numbering, thermal source or the corresponding flow of thermal substation ID, thermal source or thermal substation and supplying pressure of return water.
Embodiment 4
The present embodiment 4 is the resistance coefficient amendment for illustrating the present invention using each pipeline section in particle cluster algorithm computation model
The method of amount.
As Fig. 3 illustrates a heating network system, the system includes 1 thermal source, 4 thermal substations, 1 circulating pump, supply,
Backwater pipeline section totally 19, is represented respectively with S1-S19.
The measured data of 5 group steady state operating condition of the table 1 to choose.
Table 2 is the resistance coefficient theoretical value of pipeline section in pipe network.
Specifically, following step is included using the method for the resistance coefficient correction of each pipeline section in particle cluster algorithm computation model
Suddenly：
Step S31, particle cluster algorithm parameter setting：Operation algebraically Ngen=200, microgranule number N=50, microgranule dimension are
Pipeline section number D=19, decision variable upper bound U=[2.913,2.913,0.5958,0.5958,1.3965,1.3965,2.7255,
2.7255,2.6298,2.6298,0.1461,0.1461,3.0537,3.0537,2.2866,2.2866,0.4563,0.4563,
2.9103],
Decision variable lower bound L=- [2.913,2.913,0.5958,0.5958,1.3965,1.3965,2.7255,
2.7255,2.6298,2.6298,0.1461,0.1461,3.0537,3.0537,2.2866,2.2866,0.4563,0.4563,
2.9103]。
Step S32, initialization.Resistance coefficient theoretical value is read, it is random between decision variable upper bound U and lower bound L to generate N
The resistance coefficient correction vector of=50 microgranules, i.e., 50 groups for water return pipeline.
Step S33, calculates the target function value of microgranule.
The object function of the particle cluster algorithm resistance coefficient correction identification set up is as follows：
The object function itself is not the display expression formula of decision variable, as given decision variable Δ δ1,Δδ2,…,Δ
δDValue after, pressure data is calculated by heating network waterpower phantom.It is 50 groups of resistances by N=50 microgranule of generation
Hydraulic pipeline phantom is input into respectively after force coefficient correction vector and resistance coefficient theoretical value addition of vectors, it is then sharp successively
With the 1st, the the 2nd ... ..., NC=5 groups of floor datas carry out water force to it, obtain the piezometer of thermal substation under different operating modes
Calculation value.
Constraint：Can automatically meet quality, energy by calling the water force module of heating network waterpower phantom
Conservation constraints.
In formula,
Δδ1,Δδ2,…,ΔδD：Resistance coefficient correction to be identified, the present embodiment D=19；
PCij：By the calculation of pressure value of the calculated thermal substation i of model under operating mode j；
POij：The corresponding pressure measuring values of thermal substation i under operating mode j；
NP：Pressure-measuring-point quantity, the confession of 4 thermal substations of the present embodiment, pressure of return water measuring point quantity are NP=4 × 2=8；
Step S34, microgranule flight produces microgranule of future generation；
Microgranule coordinate is without departing from the scope that U and L is limited in microgranule flight course.
Step S35, reaches the operation algebraically N of settinggen=200, algorithm terminates, and otherwise returns to step S33 and continues executing with.
By identification, the microgranule i.e. resistance coefficient correction for optimizing is obtained as shown in table 3, the target function value of the microgranule is
0.14691。
Table 3 is that the embodiment of the present invention recognizes the resistance coefficient correction for calculating.
Calculate the target letter of the microgranule that resistance coefficient theoretical value is represented, i.e. resistance coefficient correction vector for [0,0 ..., 0]
Numerical value is 0.15732, it is seen that the resistance coefficient after identification is better than resistance coefficient theoretical value, illustrates that the system number can more describe reality
The running status of border heat supply network.
With the above-mentioned desirable embodiment according to the present invention as enlightenment, by above-mentioned description, relevant staff is complete
Entirely various change and modification can be carried out in the range of without departing from this invention technological thought.The technology of this invention
Property scope is not limited to the content in description, it is necessary to its technical scope is determined according to right.
Claims (10)
1. a kind of heating network waterpower phantom Identification Correction Method, it is characterised in that comprise the steps：
Step S1, chooses heating system multigroup measured data under steady state operating conditions；
Step S2, calculates the resistance coefficient theoretical value of each pipeline section；And
Step S3, sets up heating network waterpower phantom, combines the identification of multigroup measured data using particle cluster algorithm and calculates each
Resistance coefficient correction, and obtain corrected resistance coefficient.
2. heating network waterpower phantom Identification Correction Method according to claim 1, it is characterised in that
Heating system multigroup measured data under steady state operating conditions is chosen in step S1, i.e.,
Multiple steady state operating conditions of identification heating network system, choose the measured data of multigroup steady state operating condition, its method
Including：
Step S11, floor data pretreatment；
Step S12, seeks the steady state condition time interval of each thermal substation；
Step S13, seeks the steady state condition time interval of whole heating network system；And
Step S14, chooses multigroup measured data.
3. heating network waterpower phantom Identification Correction Method according to claim 2, it is characterised in that
The method of the resistance coefficient theoretical value of each pipeline section is calculated in step S2 to be included：
The resistance coefficient theoretical value of each pipeline section is calculated by following mechanism formula；I.e.
δiFor the resistance coefficient (Pa/ (t/h) 2) of pipeline section i；K is the equivalent absolute roughness of tube wall；diFor internal diameter of the pipeline, unit is
m；liFor length of pipe section, unit is m；ldiFor pipeline section local resistance equivalent length, unit is m；ρ is the average close of hot water in pipe
Degree, unit is kg/m3；D is pipeline section quantity.
4. heating network waterpower phantom Identification Correction Method according to claim 3, it is characterised in that
The method of heating network waterpower phantom is set up in step S3 to be included：
Heating network is converted to the Directed Graph Model being made up of node and section, i.e. pipe network figure；Wherein
Node represents the point that there is flow turnover, is represented with set V, V={ V1,V2,…,Vn, in formula, n is the node in pipe network
Number；
Section represents the connection pipeline section between node, is represented with set E, E={ E1,E2,…,Em, D is pipeline section for sector number in formula
Quantity；
Be converted to directed graph, then it represents that for G=<V,E>；
The incidence matrix A and fundamental circuit matrix B of heating network are obtained according to network graph theory, wherein A is n × D rank matrixes, and B is s
× D rank matrixes, s is fundamental circuit number s=D-n+1；
Heating network waterpower phantom is built, i.e.,
AGT=0；
BΔHT=0；
In formula, G is the row vector for recording each pipeline section inner volume flow in pipe network figure, i.e.,
G=[G1,G2,…,GD]；
Δ H is the column vector for recording each pipeline section drag overall loss in pipe network figure, i.e.,
Δ H=ζ * | G | * G+Z-Hb；
In formula, ζ is the resistance coefficient matrix of each pipeline section in pipe network；
ζ=diag { δ1+Δδ1,δ2+Δδ2,…,δD+ΔδD}；
Wherein δ1、δ2、……、δDFor resistance coefficient theoretical value, Δ δ1、Δδ2、……、ΔδDFor resistance coefficient correction, that is, need
The parameter to be recognized；D represents the dimension of resistance coefficient correction to be identified；
Z is the column vector Z=[Z of the node potential energy of pipeline section two difference1,Z2,…,ZD]T；
HbFor pump head column vector H in heat supply networkb=[Hb1,Hb2,…,HbD]T；
It is following form by its abstract representation when heating network waterpower phantom is applied to identification：
Y=Func (X, δ)
X represents multigroup measured data of input in formula；Y represents output vector；δ represents the resistance coefficient vector of pipeline section to be identified, δ
=[δ1+Δδ1,δ2+Δδ2,…,δD+ΔδD]。
5. heating network waterpower phantom Identification Correction Method according to claim 4, it is characterised in that
The identification of multigroup measured data is combined in step S3 using particle cluster algorithm and calculate each resistance coefficient correction, i.e.,
Different resistance coefficient corrections are produced in every generation using particle cluster algorithm, and by heating network waterpower phantom
Middle different resistance coefficient correction vector flow regimes of pipe network under multiple operating modes are contrasted with corresponding measured data, are recognized
Go out the resistance coefficient correction of optimum.
6. heating network waterpower phantom Identification Correction Method according to claim 5, it is characterised in that
Multigroup measured data is combined using particle cluster algorithm and recognizes the method for calculating each resistance coefficient correction vector including as follows
Step：
Step S31, particle cluster algorithm parameter setting, i.e.,
Microgranule number N, microgranule dimension D, and decision variable upper bound U=[Δ δ1u,Δδ2u,…,ΔδDu] and lower bound L=[Δs
δ1l,Δδ2l,…,ΔδDl]；
Step S32, initialization, i.e.,
Resistance coefficient theoretical value is read, N number of microgranule, i.e. N groups resistance coefficient correction vector is generated；
Step S33, sets up the object function for the identification of resistance coefficient correction, to calculate the target function value of each microgranule；
The object function is as follows：
When resistance coefficient correction amount δ1,Δδ2,…,ΔδDAfter as decision variable set-point, emulated by heating network waterpower
Model is calculated corresponding output data；
To each microgranuleK ∈ { 1,2 ..., N }, by itself and resistance coefficient theoretical value vector [δ1,
δ2,…,δD] be added, obtain the resistance coefficient vector that the microgranule is represented
By δkInput heating network waterpower phantom, and the 1st, 2 ... ... are utilized successively, NCGroup measured data carries out potamometer to it
Calculate, obtain the calculation of pressure value of each thermal substation under corresponding operating mode；
In formula,
PCij：By the calculation of pressure value of the calculated thermal substation i of phantom under steady state condition j；
POij：The corresponding pressure measuring values of thermal substation i under steady state condition j；
NP：Pressure-measuring-point quantity；
Step S34, microgranule flight produces microgranule of future generation；
Step S35, reaches the operation algebraically of setting, and algorithm terminates, otherwise returns to step S33 and continue executing with.
7. a kind of heating network waterpower phantom identification and modification system, it is characterised in that include：
Particle cluster algorithm unit, produces the corresponding microgranule of resistance coefficient correction per a generation, and by microgranule write into Databasce, and
And the target function value of each microgranule of reading；
Object function computing unit, sets up the object function for the identification of resistance coefficient correction, drives multiple heating network water
Power phantom, that is, producing N number of phantom carries out parallel simulation calculating, and obtains microgranule from data base, by emulating mould
Type calculates the target function value of microgranule and write back data storehouse.
Database Unit, stores colony and the target function value of resistance coefficient correction vector, and the calculating shape of object function
State, and also the result of optimization calculating is also stored in data base by the measured data of steady state condition and per a generation.
8. heating network waterpower phantom identification and modification system according to claim 7, it is characterised in that
The object function is as follows：
When resistance coefficient correction amount δ1,Δδ2,…,ΔδDAfter as decision variable set-point, emulated by heating network waterpower
Model is calculated corresponding output data；
To each microgranuleK ∈ { 1,2 ..., N }, by itself and resistance coefficient theoretical value vector [δ1,
δ2,…,δD] be added, obtain the resistance coefficient vector that the microgranule is represented
By δkInput heating network waterpower phantom, and the 1st, 2 ... ... are utilized successively, NCGroup measured data carries out potamometer to it
Calculate, obtain the calculation of pressure value of each thermal substation under corresponding operating mode；
In formula,
PCij：By the calculation of pressure value of the calculated thermal substation i of phantom under steady state condition j；
POij：The corresponding pressure measuring values of thermal substation i under steady state condition j；
NP：Pressure-measuring-point quantity.
9. a kind of operational approach of heating network waterpower phantom identification and modification system according to claim 7, including
Following steps：
Step Sa, sets up the topology controlment of pipe network to be identified, to generate the unique ID of each part of pipe network；
Step Sb, obtains operating mode measured data and calculates the preparation of resistance coefficient theoretical value；
Step Sc, arranges the algorithm parameter in particle cluster algorithm unit, starts the unit；
Step Sd, starts object function computing unit.
10. operational approach according to claim 9, it is characterised in that
Preparation includes described in step Sb：The ID of pipeline section to be identified and resistance coefficient theoretical value are accordingly arranged
In configuration file, read for particle cluster algorithm unit；And
By in multigroup measured data write into Databasce of screening, inquire about for object function computing unit, each group of floor data bag
Include operating mode numbering, thermal source or the corresponding flow of thermal substation ID, thermal source or thermal substation and supply pressure of return water.
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