CN107191328A - Blower fan Multi model Predictive Controllers, system, memory and controller - Google Patents
Blower fan Multi model Predictive Controllers, system, memory and controller Download PDFInfo
- Publication number
- CN107191328A CN107191328A CN201710501186.2A CN201710501186A CN107191328A CN 107191328 A CN107191328 A CN 107191328A CN 201710501186 A CN201710501186 A CN 201710501186A CN 107191328 A CN107191328 A CN 107191328A
- Authority
- CN
- China
- Prior art keywords
- mrow
- mover
- wind speed
- blower fan
- msub
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 claims description 29
- 230000006870 function Effects 0.000 claims description 17
- 238000005457 optimization Methods 0.000 claims description 13
- 239000011159 matrix material Substances 0.000 claims description 9
- 230000005540 biological transmission Effects 0.000 claims description 3
- 230000005055 memory storage Effects 0.000 claims description 3
- 238000005530 etching Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 14
- 230000008859 change Effects 0.000 description 8
- 238000013461 design Methods 0.000 description 7
- 230000036541 health Effects 0.000 description 7
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 3
- 230000005611 electricity Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 238000011217 control strategy Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006698 induction Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000452 restraining effect Effects 0.000 description 1
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/10—Purpose of the control system
- F05B2270/103—Purpose of the control system to affect the output of the engine
- F05B2270/1033—Power (if explicitly mentioned)
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/328—Blade pitch angle
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/40—Type of control system
- F05B2270/404—Type of control system active, predictive, or anticipative
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/70—Type of control algorithm
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Structures Of Non-Positive Displacement Pumps (AREA)
Abstract
The present invention provides a kind of blower fan Multi model Predictive Controllers, system, memory and controller, and the blower fan Multi model Predictive Controllers include：Dynamic differential between inearized model of the acquisition blower fan in each wind speed point and the inearized model according to each wind speed point of gap metric acquisition；Wind speed interval is divided according to the dynamic differential and the inearized model collection formed by the inearized model of correspondence wind speed of the wind speed interval is set up；The optimum control input that each wind speed interval is used for the predictive controller of air-blower control is obtained according to the inearized model collection of the wind speed interval；Corresponding predictive controller is called to obtain real-time optimistic control input according to the switching law between each inearized model.The present invention can realize the full working scope control of blower fan system, the linear model collection of complete, low redundancy is established in particular according to gap metric, so as to improve the multiple model predictive control effect of blower fan system, it is adaptable to the full working scope control of free-standing, distributed or grid-connected Wind turbines.
Description
Technical field
Control technology field is run the present invention relates to plant equipment, is more particularly to related to the excellent of Wind turbines full working scope operation
Change control method technical field, specially a kind of blower fan Multi model Predictive Controllers, system, memory and controller.
Copyright notice
This patent document disclosure includes material protected by copyright.The copyright is all for copyright holder.Copyright
Owner does not oppose that anyone replicates the patent document in the presence of the proce's-verbal of Patent＆Trademark Office and archives or should
Patent is disclosed.
Background technology
Energy problem is one of subject matter that world today's development faces, with the increasingly depleted and environment of traditional energy
Problem is rooted in the hearts of the people, and novel energy such as wind energy, solar energy, tide energy be of increased attention, wherein, wind energy has
The features such as pollution-free, reserves are abundant, is a kind of important regenerative resource.Wind-power electricity generation is the principal mode of wind energy utilization,
Global wind-powered electricity generation adding new capacity in 2016 exceedes 54.6GW, and the whole world adds up capacity and reaches 486.7GW, wherein, China with
23.4GW adding new capacity ranks the whole world first.Greatly develop wind power technology and have become the numerous country's solution energy in the whole world
The important means of problem and environmental problem.Control system is the important component of wind generator system, good control technology
Power generating quality can not only be improved, while can also relax unit load, extends unit service life, and then improve wind-powered electricity generation in new energy
In competitiveness.Wind turbines are substantially the nonlinear systems of a multiple-input and multiple-output belt restraining, and traditional PID control is difficult to
Optimal control effect is obtained, in the urgent need to more advanced control method improves the control effect of Wind turbines.
Retrieve and find through the open source literature to prior art, Soliman M, Malik O P, Westwick D
T.Multiple model predictive control for wind turbines with doubly fed
induction generators.IEEE Transactions on Sustainable Energy,2011,2(3):215-
225. (have the Wind turbines multiple model predictive control of double fed induction generators, International Periodicals：IEEE periodicals, sustainable energy,
2011,2(3):215-225), although author controls the full working scope that multiple model predictive control algorithm is used for Wind turbines, is formed
The wind turbine model forecast Control Algorithm of constraint is protected, but author lacks the research to linear model collection method for building up, in reality
In the operation of border, the completeness of Models Sets and low redundancy will produce material impact to the performance of multiple model predictive control algorithm.
The content of the invention
In order to solve above-mentioned and other potential technical problems, it is an object of the invention to provide a kind of blower fan multimode
Type forecast Control Algorithm, system, memory and controller, for effective control of the Wind turbines in the range of full working scope.
The embodiment provides a kind of blower fan Multi model Predictive Controllers, the blower fan multiple model predictive control
Method includes：Blower fan is obtained in the inearized model of each wind speed point and according to the inearized model of each wind speed point of gap metric acquisition
Between dynamic differential；Wind speed interval is divided according to the dynamic differential and the line by correspondence wind speed of the wind speed interval is set up
Property model formation inearized model collection；Obtaining each wind speed interval according to the inearized model collection of the wind speed interval is used for
The optimum control input of the predictive controller of air-blower control；Corresponding prediction is called according to the switching law between each inearized model
Controller obtains real-time optimistic control input.
In one embodiment of the invention, the inearized model is：Wherein,For subsequent time state vector,For state vector, For input vector, For output vector,β is propeller pitch angle, ωtFor blade rotating speed, TtwTurned round for power train
Torque, ωgFor motor speed, TgFor motor torque, β*WithThe respectively setting value of propeller pitch angle and motor torque, P is blower fan
System output power；Ai, Bi, CiFor coefficient matrix, (xi,
ui,vi) in (xi,ui) it is wind speed viCorresponding system balancing point；fwFor system state equation, i.e.,X is system
State, u inputs for system, and v is wind speed, g suffered by blower fanwFor system output equation, i.e. y=gw(x), y exports for system.
In one embodiment of the invention, obtaining the dynamic differential according to following formula is：θ (i, j)=gap (Γi,Γj),i
≠j；Wherein, θ (i, j) is blower fan in wind speed point vi,vjLocate inearized model Γi,ΓjBetween gap metric, gap (Γi,
Γj) represent to inearized model Γi,ΓjGap metric is asked for, θ (i, j) span is [0,1].
In one embodiment of the invention, each wind speed interval is obtained according to the inearized model collection of the wind speed interval and used
Specifically included in the optimum control input of the predictive controller of air-blower control：The optimization object function of optimal control is built, is established
Constraints, the object function is solved under constraints, the control at the current time for blower fan Partial controll is obtained
The optimal control sequence of input, first element for choosing the optimal control sequence is inputted as optimum control.
In one embodiment of the invention, using the Robust Predictive Control Algorithm for Solving based on LMI in institute
State the optimization object function under constraints；The optimization object function is：
The constraints meets input as follows and become
Amount, state variable： Wherein, J (k) is k time optimization object functions,For the k+j moment
System is inputted,For k+j moment system modes,System input is maintained for k+j moment r,
System mode is tieed up for k+j moment r,System output is maintained for k+j moment r, Respectively
The input of correspondence dimension, state, the output variable upper bound, j, k, r is respectively prediction time variable, and current time variable, dimension becomes
Amount, Q and R are positive definite weight matrix, nu, nxAnd nyRespectively input total dimension, the total dimension of state and the total dimension of output.
In one embodiment of the invention, the control input at the current time is：F(k)
=Yt(k)×Qt(k)-1；Wherein, u (k) inputs for current time system, and F (k) is current time feedback factor,To be current
Moment system variable, uiFor the input of current linear model correspondence equalization point, Yt(k), Qt(k) it is based on LMI
Robust Predictive Control algorithm striked by current time feedback parameter.
The embodiment provides a kind of blower fan multiple model predictive control system, the blower fan multiple model predictive control
System includes：Inearized model module, for obtaining inearized model of the blower fan in each wind speed point；Dynamic differential module, is used for
The dynamic differential between the inearized model of each wind speed point is obtained according to gap metric；Inearized model collection module, for basis
It is linear that the dynamic differential divides wind speed interval and sets up that the inearized model by correspondence wind speed of the wind speed interval formed
Change Models Sets；Predictive controller module, is used for obtaining each wind speed interval according to the inearized model collection of the wind speed interval
In the optimum control input of the predictive controller of air-blower control；Multi-model prediction module, for according between each inearized model
Switching law calls corresponding predictive controller to obtain real-time optimistic control input.
In one embodiment of the invention, the inearized model is：Wherein,For subsequent time state vector,For state vector, For input vector, For output vector,β is propeller pitch angle, ωtFor blade rotating speed, TtwTurned round for power train
Torque, ωgFor motor speed, TgFor motor torque, β*WithThe respectively setting value of propeller pitch angle and motor torque, P is blower fan
System output power；Ai, Bi, CiFor coefficient matrix, (xi,
ui,vi) in (xi,ui) it is wind speed viCorresponding system balancing point；fwFor system state equation, i.e.,X is system
State, u inputs for system, and v is wind speed, g suffered by blower fanwFor system output equation, i.e. y=gw(x)；According to being obtained following formula
Dynamic differential is：θ (i, j)=gap (Γi,Γj),i≠j；Wherein, θ (i, j) is blower fan in wind speed point vi,vjPlace's linearisation mould
Type Γi,ΓjBetween gap metric, gap (Γi,Γj) represent to inearized model Γi,ΓjAsk for gap metric, θ (i, j)
Span is [0,1].
The embodiment provides a kind of controller, including processor and memory, the memory storage has journey
Sequence is instructed, and the processor operation described program instructs to realize the step in method as described above.
The embodiment provides a kind of memory, machine readable program instruction is stored thereon with, the machine
Readable program instructions perform method as described above when running.
Following have as described above, blower fan Multi model Predictive Controllers, system, memory and the controller of the present invention have
Beneficial effect：
The present invention can realize the full working scope control of blower fan system, be established in particular according to gap metric complete, low superfluous
Remaining linear model collection, so as to improve the multiple model predictive control effect of blower fan system, this method is applied to stand alone type, distribution
The full working scope control of formula or grid-connected Wind turbines.
Brief description of the drawings
Technical scheme in order to illustrate the embodiments of the present invention more clearly, makes required in being described below to embodiment
Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for
For those of ordinary skill in the art, on the premise of not paying creative work, other can also be obtained according to these accompanying drawings
Accompanying drawing.
Fig. 1 is shown as the FB(flow block) of the blower fan Multi model Predictive Controllers of the present invention.
Fig. 2 is shown as the blower fan system structured flowchart applied in the blower fan Multi model Predictive Controllers of the present invention.
Fig. 3 is shown as machine system desired power curve figure in the blower fan health evaluating method of the present invention.
Fig. 4 is shown as the theory diagram of the blower fan health evaluation system of the present invention.
Fig. 5 is shown as the control strategy figure in the blower fan health evaluating method of the present invention.
Fig. 6 and Fig. 7 are shown as in the blower fan system different operating region in the blower fan health evaluating method of the present invention linear
The clearence degree spirogram of model.
Fig. 8 and Fig. 9 be shown as the present invention blower fan health evaluating method in part load region wind speed and correspondence wind energy
The control effect figure of usage factor.
Figure 10 and Figure 11 are shown as the blower fan health evaluating method of the present invention in complete load area (middle high wind speed) interior wind
The control effect figure of speed and correspondence power output.
Figure 12 and Figure 13 are shown as the blower fan health evaluating method of the present invention in complete load area (high wind speed) interior wind speed
And the control effect figure of correspondence power output.
Component label instructions
100 blower fan multiple model predictive control systems
110 inearized model modules
120 dynamic differential modules
130 inearized model collection modules
140 predictive controller modules
150 multi-model prediction modules
S110~S140 steps
Embodiment
Illustrate embodiments of the present invention below by way of specific instantiation, those skilled in the art can be by this specification
Disclosed content understands other advantages and effect of the present invention easily.The present invention can also pass through specific realities different in addition
The mode of applying is embodied or practiced, the various details in this specification can also based on different viewpoints with application, without departing from
Various modifications or alterations are carried out under the spirit of the present invention.It should be noted that, in the case where not conflicting, following examples and implementation
Feature in example can be mutually combined.
Fig. 1 is referred to Figure 13.It should be clear that structure, ratio, size depicted in this specification institute accompanying drawings etc., is only used
To coordinate the content disclosed in specification, so that those skilled in the art is understood with reading, being not limited to the present invention can
The qualifications of implementation, therefore do not have technical essential meaning, the tune of the modification of any structure, the change of proportionate relationship or size
It is whole, in the case where not influenceing effect of the invention that can be generated and the purpose that can reach, all should still it fall in disclosed skill
Art content is obtained in the range of covering.
The purpose of the present embodiment is to provide a kind of blower fan Multi model Predictive Controllers, system, memory and controller,
For effective control of the Wind turbines in the range of full working scope.Blower fan multiple model predictive control of the invention described in detail below
Method, system, the principle and embodiment of memory and controller, make those skilled in the art not need creative work
Understand blower fan Multi model Predictive Controllers, system, memory and the controller of the present invention.
The present invention is achieved by the following technical solutions, and it is non-linear that the present invention takes into full account that blower fan system has,
The features such as big operating mode, strong constraint, the linearization technique of blower fan system model is given, the system based on gap metric quantitative analysis
In the dynamic differential of different wind speed point Linear models, the division methods of different operating region wind speed interval are given, are established
The linear model collection of approximate blower fan system full working scope, defines the optimal control object function and constraint bar of each linear submodel
Part, the control law of each submodel is drawn using the Robust Predictive Control Algorithm for Solving based on LMI, finally by
Switching law gives the global control input of system between definition submodel.Specifically include and weigh blower fan system using gap metric
Dynamic differential, design wind speed interval division algorithm in each wind speed point Linear model, set up the linear mould of approximate blower fan system
Type collection, the linear model predictive controller of design, definition submodel switching law, design four steps of global controller, wherein profit
Blower fan system is weighed in the dynamic differential of each wind speed point Linear model with gap metric, and design wind speed interval division algorithm is built
The linear model collection for founding approximate blower fan system is the innovation of the present invention.
Specifically, it is described as shown in figure 1, The embodiment provides a kind of blower fan Multi model Predictive Controllers
Blower fan Multi model Predictive Controllers comprise the following steps：
S110, obtains blower fan in the inearized model of each wind speed point and according to the linearisation of each wind speed point of gap metric acquisition
Dynamic differential between model.
S120, according to the dynamic differential divide wind speed interval and set up the wind speed interval by correspondence wind speed it is linear
Change the inearized model collection of model formation.
S130, obtaining each wind speed interval according to the inearized model collection of the wind speed interval is used for the prediction of air-blower control
The optimum control input of controller.
S140, calls corresponding predictive controller to obtain real-time optimistic control according to the switching law between each inearized model
Input.
The step S110 in the blower fan Multi model Predictive Controllers to step S140 is described in detail below.
S110, obtains blower fan in the inearized model of each wind speed point and according to the linearisation of each wind speed point of gap metric acquisition
Dynamic differential between model.
Dynamic differential of the blower fan system in each wind speed point Linear model is weighed using gap metric.
Calculate first and obtain blower fan system in each wind speed viThe inearized model Γ at placei。
Referring to Fig. 2, being shown as blower fan system architecture diagram in the blower fan Multi model Predictive Controllers of the present invention.
It is modeled according to each subsystem of blower fan, the mathematical modeling for obtaining blower fan system is as follows：
Wherein,The physical significance of each parameter is shown in Table 1.
The parameter physical significance table of table 1
| Variable | Physical significance | Variable | Physical significance |
| v | Wind speed | Jg | Motor inertia coeffeicent |
| β | Propeller pitch angle | Ttw | Driving torque |
| τ | Blade time constant | ωt | Wind speed round |
| ρ | Atmospheric density | ωg | Engine speed |
| R | Wind wheel radius | Tg | Motor torque |
| τg | Time constant of electric motors | ks | Equivalent power transmission shaft stiffness coefficient |
| i | Gear shift ratio | Bs | Equivalent power transmission shaft damped coefficient |
| Jt | Wind wheel inertia coeffeicent | η | Generating efficiency |
Definition system is output asWherein P=η Tgωg, it is blower fan power output.With reference to above-mentioned analysis,
The mathematical modeling of blower fan system can be written as form：
Blower fan system is calculated in each wind speed viThe inearized model Γ at placei：
Wherein,For subsequent time state vector,For state vector, For
Input vector, For output vector,β is propeller pitch angle, ωtFor blade rotating speed, TtwFor
Power train torsional moment, ωgFor motor speed, TgFor motor torque, β*WithThe respectively setting value of propeller pitch angle and motor torque, P
For blower fan system power output；P=η Tgωg Ai, Bi, CiFor coefficient matrix,
(xi,ui,vi) in (xi,ui) it is wind speed viCorresponding system balancing point；fwFor system state equation, i.e.,X is
System mode, u inputs for system, and v is wind speed, g suffered by blower fanwFor system output equation, i.e. y=gw(x), y exports for system.
In this embodiment, according to above-mentioned obtained blower fan system inearized model, calculate each using gap metric
The dynamic differential of blower fan system inearized model at wind speed：
θ (i, j)=gap (Γi,Γj),i≠j；Wherein, θ (i, j) is blower fan in wind speed point vi,vjLocate inearized model
Γi,ΓjBetween gap metric, gap (Γi,Γj) represent to inearized model Γi,ΓjGap metric is asked for, θ (i, j) takes
It is [0,1] to be worth scope.Wherein gap (Γi,Γj) computing represent calculate two linear models between gap metric, can be by calling
" gap " instruction in Matlab Robust Predictive Controls tool box is asked for, and θ (i, j) span is [0,1].
According to obtained clearence degree value, the dynamic differential between each inearized model is analyzed, as θ (i, j)≤τ, τ is clearence degree
Threshold value (empirical value is 0.4≤τ≤0.6) is measured, represents that two linear system dynamic differentials are small, both can be by same feedback controller control
System, so as to be represented by a linear model.
Referring to Fig. 3, being shown as blower fan system desired power curve figure.Usually, blower fan system is divided into by wind speed size
Two working regions.Region I is referred to as part load region, and its wind speed is between incision wind speed and rated wind speed, region master
To adjust wind speed round by controlled motor torque to obtain the power coefficient of maximum；Region II is referred to as complete loading zone
Domain, its wind speed is between rated wind speed and cut-out wind speed, and the region is mainly by controlling propeller pitch angle to adjust power output.
For different working regions (part load region and complete load area), wind speed is chosen at certain intervals
Point, calculates the gap metric between each inearized model：WithWherein：N1, N2Respectively sub-load
The wind speed point number that region and complete load area are chosen.Method is as follows：
1) some wind speed point v are chosen in specified wind speed regioni, i=1,2 ..., N calculate blower fan system in above-mentioned wind
The inearized model Γ of speed pointi, i=1,2 ..., N.
2) gap metric θ (i, the j)=gap (Γ obtained between linear model are calculatedi,Γj), i, j=1,2 ..., N.
S120, according to the dynamic differential divide wind speed interval and set up the wind speed interval by correspondence wind speed it is linear
Change the inearized model collection of model formation.
Based on the clearence degree value obtained, the dynamic differential of each region inner blower system linearization model is analyzed, one is chosen
Fixed gap metric threshold tau, divides each wind speed interval, in the wind speed interval V of divisioni, i=1 in 2 ..., m, calculates blower fan system
The inearized model Γ of systemi, i=1, wherein 2 ..., m, m are the wind speed interval number obtained by dividing, and set up approximate blower fan system
Linear model collection.
When between blower fan system inearized model dynamic differential with the wind the different increase of speed difference and when increasing, can draw by the following method
Divide wind speed interval and build linear model collection：
1) gap metric threshold tau=0.47, iteration total step number K=50,100..., initialization wind speed interval number m are set
=1.
2) in wind speed region to be divided, random initializtion center wind speed point vi(i=1,2 ..., m), set wind speed area
Between and correspondence gap metric set Vi={ vi},gapi={ }, i=1,2 ..., m, and current iteration step number j=0 is set.
3) blower fan system is compared in each wind speed point vk(k=1,2 ..., N) inearized model and each center wind speed point Linear
Change the gap metric between model.The minimum center wind speed point of gap metric is chosen, each wind speed point is included into correspondence wind speed interval collection
Close, and update respective clearance metric set.
4) average value that wind speed point in center is wind speed in each wind speed interval is updated, j=j+1 is made.
If 5) j ＜ K, return 3).
If 6) the element maximum of the corresponding gap metric set of each wind speed interval is met：max(gapi)≤τ, jumps past
7)；Otherwise m=m+1 is set, is returned 2).
7) for each wind speed interval Vi, at center, wind speed point is linearized to blower fan system, obtains the linear mould of correspondence
Type collection Γi, i=1,2 ..., m.
When between blower fan system inearized model dynamic differential not with the wind the different increase of speed difference and when increasing, can be by the following method
Divide wind speed interval and build linear model collection：
1) gap metric threshold tau=0.47 is set, i=1, k is initializedi=1, wherein i are the sequence number of wind speed interval.
2) initialization wind speed interval and correspondence gap metric setCenter wind speed
3) judgeWhether threshold requirement is met, if meeting, update wind speed intervalCenter wind speed is more
It is newlyJump toward 4), if it is not satisfied, updating correspondence gap metric set gapi={ θ2(vi,p)},p
∈Vi, jump toward 5).
4) k is seti=ki+ 1, return 3).
If 5) ki=N, makes m=i, jumps toward 6), otherwise sets i=i+1, ki=ki-1, return 2).
6) for each wind speed interval Vi, at center, wind speed point is linearized to blower fan system, obtains the linear mould of correspondence
Type collection Γi, i=1,2 ..., m.
S130, obtaining each wind speed interval according to the inearized model collection of the wind speed interval is used for the prediction of air-blower control
The optimum control input of controller.
In the present embodiment, obtaining each wind speed interval according to the inearized model collection of the wind speed interval is used for blower fan control
The optimum control input of the predictive controller of system is specifically included：The optimization object function of optimal control is built, constraints is established,
The object function is solved under constraints, obtain current time for blower fan Partial controll control input it is optimal
Control sequence, first element for choosing the optimal control sequence is inputted as optimum control.
In an embodiment, using the Robust Predictive Control Algorithm for Solving based on LMI in the constraint bar
The optimization object function under part；The optimization object function is：
The constraints meets input variable as follows, state variable
Wherein, J (k) is k time optimization object functions,Etching system is inputted during for k+j,For k+j
Moment system mode,System input is maintained for k+j moment r,System mode is tieed up for k+j moment r,System output is maintained for k+j moment r,Respectively correspond to the input of dimension, it is state, defeated
Go out the variable upper bound, j, k, r is respectively prediction time variable, current time variable, dimension variable, and Q and R are positive definite weight matrix,
nu, nxAnd nyRespectively input total dimension, the total dimension of state and the total dimension of output.
It is described excellent under the constraints using the Robust Predictive Control Algorithm for Solving based on LMI
Change object function, form is as follows：
The above-mentioned optimization problem based on LMI is solved in each sampling instant, the control at current time is obtained
Input and be：
F (k)=Yt(k)×Qt(k)-1；
Wherein, u (k) inputs for current time system, and F (k) is current time feedback factor,For current time system
Variable, uiFor the input of current linear model correspondence equalization point, Yt(k), Qt(k) it is pre- for the robust based on LMI
Survey the current time feedback parameter striked by control algolithm.
S140, calls corresponding predictive controller to obtain real-time optimistic control according to the switching law between each inearized model
Input.
Specifically, using wind speed as index, the switching law between each linear model is designed.First determine whether belonging to current wind speed
Wind speed interval, calls corresponding submodel and linear predictive controller to calculate real-time control input, and next sampling instant is re-started
Judge and switch, realize the multiple model predictive control of blower fan system full working scope operation.
Specifically, global control input is calculated as follows：
U (k)=RMPCi
Wherein RMPC is the above-mentioned Robust Predictive Control algorithm based on LMI, and i selection need to cause currently
Wind speed meets v (k) ∈ Vi。
The implementation steps of the present invention can be summarized as follows：
A, offline design
1) blower fan system is directed to, its inearized model in each wind speed point is asked for, is weighed using gap metric and analyzes each
The dynamic differential of linear model.
2) suitable gap metric threshold tau is chosen, wind speed interval is divided, sets up the linear model collection of approximate blower fan system.
B, Photographing On-line
1) for each sampling instant, according to the affiliated wind speed interval of current wind speed, selection correspondence linear model.
2) based on selected linear model, suitable weight matrix Q, R are selected, it is pre- using the robust based on LMI
Survey control algolithm and calculate current system input.
To realize above-mentioned blower fan Multi model Predictive Controllers, the present embodiment also corresponds to pre- there is provided a kind of blower fan multi-model
Control system 100 is surveyed, as shown in figure 4, the blower fan multiple model predictive control system 100 includes：Inearized model module 110,
Dynamic differential module 120, inearized model collection module 130, predictive controller module 140 and multi-model prediction module 150.By
In the technology similar therefore general to principle between blower fan Multi model Predictive Controllers of blower fan multiple model predictive control system 100
Details, which is not repeated, to be repeated.
In the present embodiment, the inearized model module 110 is used to obtain inearized model of the blower fan in each wind speed point.
In the present embodiment, the dynamic differential module 120 is used for the linearisation that each wind speed point is obtained according to gap metric
Dynamic differential between model.
In the present embodiment, the inearized model collection module 130 is used to divide wind speed interval according to the dynamic differential
And set up the inearized model collection formed by the inearized model of correspondence wind speed of the wind speed interval.
In the present embodiment, the predictive controller module 140 is used for the inearized model collection according to the wind speed interval
Obtaining each wind speed interval is used for the optimum control input of predictive controller of air-blower control.
In the present embodiment, the multi-model prediction module 150, for being adjusted according to the switching law between each inearized model
Real-time optimistic control is obtained with corresponding predictive controller to input.
Embodiments of the invention also provide a kind of controller, including processor and memory, and the memory storage has journey
Sequence is instructed, and the processor operation described program instructs to realize the method in above-mentioned steps.The present embodiment to the above method
Through being described in detail, it will not be repeated here.
Embodiments of the invention also provide a kind of memory, are stored thereon with machine readable program instruction, the machine
The method in above-mentioned steps is performed when readable program instructions are run.The above method has been carried out to describe in detail for the present embodiment,
It will not be repeated here.
Further illustrate that the present invention's realizes effect with reference to instantiation.
Fig. 5 is the control strategy figure that the present invention is implemented.In the present embodiment, the method proposed in the present invention is applied to one
Class rated power is 5MW speed-changing oar-changing Wind turbines, and it is respectively 3m/s that it, which cuts wind speed, rated wind speed, excision wind speed,
11.4m/s and 25m/s, carries out emulation experiment using Matlab, successfully realizes Wind turbines in different wind speed ranges
Full working scope is controlled.Three kinds of situations of running point, respectively part load region, wind speed range are (5m/s, 10.2m/s), this
When power coefficient C is made by controlled motor torquepFor maximum (0.48)；Fully loaded region (middle high wind speed region), wind speed
Scope is (12.8m/s, 17.8m/s), is now rated value by controlling propeller pitch angle to adjust power output P；Fully loaded region (high wind
Fast region), wind speed range is (15.8m/s, 22.6m/s), is now rated value by controlling propeller pitch angle to adjust power output P.
The full working scope for having carried out blower fan system using the present invention is controlled, and all achieves preferable control effect.Fig. 6 and Fig. 7
It is the clearence degree spirogram of blower fan system different operating region intrinsically linear model.According to the wind speed interval division methods, blower fan system
The effective wind speed region division result such as table 2 of system.
The wind speed interval of table 2 divides table
Fig. 8 and Fig. 9 are shown as the control effect of blower fan system wind speed and correspondence power coefficient in part load region
Fruit is schemed.Figure 10 and Figure 11 shows control of the blower fan system in complete load area (middle high wind speed) interior wind speed and correspondence power output
Design sketch.Figure 12 and Figure 13 shows control of the blower fan system in complete load area (high wind speed) interior wind speed and correspondence power output
Design sketch.It can be drawn from Fig. 8 to Figure 13, method of the invention can preferably complete the control of blower fan system, enable the system to
Control targe is reached in different wind speed ranges and different operating region.
In summary, the present invention can realize the full working scope control of blower fan system, be established in particular according to gap metric
The linear model collection of complete, low redundancy, so as to improve the multiple model predictive control effect of blower fan system.This method is applied to only
The full working scope control of vertical, distributed or grid-connected Wind turbines.So, the present invention effectively overcomes of the prior art a variety of
Shortcoming and have high industrial utilization.
The above-described embodiments merely illustrate the principles and effects of the present invention, not for the limitation present invention.It is any ripe
Know the personage of this technology all can carry out modifications and changes under the spirit and scope without prejudice to the present invention to above-described embodiment.Cause
This, art includes usual skill complete without departing from disclosed spirit and institute under technological thought such as
Into all equivalent modifications or change, should by the present invention claim be covered.
Claims (10)
1. a kind of blower fan Multi model Predictive Controllers, it is characterised in that the blower fan Multi model Predictive Controllers include：
Acquisition blower fan is between the inearized model of each wind speed point and the inearized model according to each wind speed point of gap metric acquisition
Dynamic differential；
Wind speed interval is divided according to the dynamic differential and the inearized model shape by correspondence wind speed of the wind speed interval is set up
Into inearized model collection；
Each wind speed interval is obtained for the predictive controller of air-blower control according to the inearized model collection of the wind speed interval
Optimum control is inputted；
Corresponding predictive controller is called to obtain real-time optimistic control input according to the switching law between each inearized model.
2. blower fan Multi model Predictive Controllers according to claim 1, it is characterised in that the inearized model is：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
<mo>=</mo>
<msub>
<mi>A</mi>
<mi>i</mi>
</msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>+</mo>
<msub>
<mi>B</mi>
<mi>i</mi>
</msub>
<mover>
<mi>u</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mover>
<mi>y</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>=</mo>
<msub>
<mi>C</mi>
<mi>i</mi>
</msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>;</mo>
</mrow>
Wherein,For subsequent time state vector,For current time state vector, For input vector, For output vector,β is propeller pitch angle, ωtTurn for blade
Speed, TtwFor power train torsional moment, ωgFor motor speed, TgFor motor torque, β*WithRespectively propeller pitch angle and motor torque
Setting value, P is blower fan system power output；Ai, Bi, CiFor coefficient matrix,
(xi,ui,vi) in (xi,ui) it is wind speed viCorresponding system balancing point；fwFor system state equation, i.e.,X is
System mode, u inputs for system, and v is wind speed, g suffered by blower fanwFor system output equation, i.e. y=gw(x), y exports for system.
3. blower fan Multi model Predictive Controllers according to claim 1 or 2, it is characterised in that institute is obtained according to following formula
Stating dynamic differential is：
θ (i, j)=gap (Γi,Γj),i≠j；
Wherein, θ (i, j) is blower fan in wind speed point vi,vjLocate inearized model Γi,ΓjBetween gap metric, gap (Γi,
Γj) represent to inearized model Γi,ΓjGap metric is asked for, θ (i, j) span is [0,1].
4. blower fan Multi model Predictive Controllers according to claim 1, it is characterised in that according to the wind speed interval
The optimum control input that inearized model collection obtains the predictive controller that each wind speed interval is used for air-blower control is specifically included：
The optimization object function of optimal control is built, constraints is established, the object function is solved under constraints, obtained
It must be used for the optimal control sequence of the control input at the current time of blower fan Partial controll, choose the of the optimal control sequence
One element is inputted as optimum control.
5. blower fan Multi model Predictive Controllers according to claim 4, it is characterised in that utilize based on linear matrix not
The optimization object function of the Robust Predictive Control Algorithm for Solving of equation under the constraints；The optimization object function
For：
<mrow>
<munder>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
<mrow>
<mover>
<mi>u</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>j</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>&infin;</mi>
</mrow>
</munder>
<mi>J</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mi>J</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mi>&infin;</mi>
</munderover>
<msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>j</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mover>
<mi>u</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>j</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mi>Q</mi>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mi>R</mi>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>j</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mover>
<mi>u</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>j</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>;</mo>
</mrow>
The constraints meets input variable as follows, state variable：
<mrow>
<mo>|</mo>
<msub>
<mover>
<mi>u</mi>
<mo>&OverBar;</mo>
</mover>
<mi>r</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>j</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mo>&le;</mo>
<msub>
<mover>
<mi>u</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>r</mi>
<mo>,</mo>
<mi>max</mi>
</mrow>
</msub>
<mo>,</mo>
<mi>j</mi>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
<mo>,</mo>
<mi>r</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>n</mi>
<mi>u</mi>
</msub>
<mo>;</mo>
</mrow>
1
<mrow>
<mo>|</mo>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>r</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>j</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mo>&le;</mo>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>r</mi>
<mo>,</mo>
<mi>max</mi>
</mrow>
</msub>
<mo>,</mo>
<mi>j</mi>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
<mo>,</mo>
<mi>r</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>n</mi>
<mi>x</mi>
</msub>
<mo>;</mo>
</mrow>
<mrow>
<mo>|</mo>
<msub>
<mover>
<mi>y</mi>
<mo>&OverBar;</mo>
</mover>
<mi>r</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>j</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mo>&le;</mo>
<msub>
<mover>
<mi>y</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>r</mi>
<mo>,</mo>
<mi>max</mi>
</mrow>
</msub>
<mo>,</mo>
<mi>j</mi>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
<mo>,</mo>
<mi>r</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>n</mi>
<mi>y</mi>
</msub>
<mo>;</mo>
</mrow>
Wherein, J (k) is k time optimization object functions,Etching system is inputted during for k+j,For the k+j moment
System mode,System input is maintained for k+j moment r,System mode is tieed up for k+j moment r,System output is maintained for k+j moment r,Respectively correspond to the input of dimension, state,
The output variable upper bound, j, k, r is respectively prediction time variable, current time variable, dimension variable, and Q and R are positive definite power square
Battle array, nu, nxAnd nyRespectively input total dimension, the total dimension of state and the total dimension of output.
6. blower fan Multi model Predictive Controllers according to claim 4, it is characterised in that the control at the current time
Input and be：
<mrow>
<mi>u</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>F</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&times;</mo>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mo>;</mo>
</mrow>
F (k)=Yt(k)×Qt(k)-1；
Wherein, u (k) inputs for current time system, and F (k) is current time feedback factor,For current time system variable,
uiFor the input of current linear model correspondence equalization point, Yt(k), Qt(k) it is the Robust Predictive Control based on LMI
Current time feedback parameter striked by algorithm.
7. a kind of blower fan multiple model predictive control system, it is characterised in that the blower fan multiple model predictive control system includes：
Inearized model module, for obtaining inearized model of the blower fan in each wind speed point；
Dynamic differential module, the dynamic differential between inearized model for obtaining each wind speed point according to gap metric；
Inearized model collection module, for according to the dynamic differential divide wind speed interval and set up the wind speed interval by right
The inearized model collection for answering the inearized model of wind speed to be formed；
Predictive controller module, is used for blower fan for obtaining each wind speed interval according to the inearized model collection of the wind speed interval
The optimum control input of the predictive controller of control；
Multi-model prediction module, it is real for calling corresponding predictive controller to obtain according to the switching law between each inearized model
When optimum control input.
8. blower fan multiple model predictive control system according to claim 7, it is characterised in that the inearized model is：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
<mo>=</mo>
<msub>
<mi>A</mi>
<mi>i</mi>
</msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>+</mo>
<msub>
<mi>B</mi>
<mi>i</mi>
</msub>
<mover>
<mi>u</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mover>
<mi>y</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>=</mo>
<msub>
<mi>C</mi>
<mi>i</mi>
</msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>;</mo>
</mrow>
Wherein,For subsequent time state vector,For state vector, To be defeated
Incoming vector, For output vector,β is propeller pitch angle, ωtFor blade rotating speed, TtwFor transmission
It is torsional moment, ωgFor motor speed, TgFor motor torque, β*WithThe respectively setting value of propeller pitch angle and motor torque, P is
Blower fan system power output；Ai, Bi, CiFor coefficient matrix,
(xi,ui,vi) in (xi,ui) it is wind speed viCorresponding system balancing point；fwFor system state equation, i.e.,X is
System mode, u inputs for system, and v is wind speed, g suffered by blower fanwFor system output equation, i.e. y=gw(x), y exports for system.
Obtaining the dynamic differential according to following formula is：
θ (i, j)=gap (Γi,Γj),i≠j；
Wherein, θ (i, j) is blower fan in wind speed point vi,vjLocate inearized model Γi,ΓjBetween gap metric, gap (Γi,
Γj) represent to inearized model Γi,ΓjGap metric is asked for, θ (i, j) span is [0,1].
9. a kind of controller, including processor and memory, the memory storage have programmed instruction, it is characterised in that described
Processor operation described program instructs to realize method according to any one of claim 1 to 6.
10. a kind of memory, is stored thereon with machine readable program instruction, it is characterised in that the machine readable program refers to
Method according to any one of claim 1 to 6 is performed during order operation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710501186.2A CN107191328A (en) | 2017-06-27 | 2017-06-27 | Blower fan Multi model Predictive Controllers, system, memory and controller |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710501186.2A CN107191328A (en) | 2017-06-27 | 2017-06-27 | Blower fan Multi model Predictive Controllers, system, memory and controller |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107191328A true CN107191328A (en) | 2017-09-22 |
Family
ID=59880939
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710501186.2A Pending CN107191328A (en) | 2017-06-27 | 2017-06-27 | Blower fan Multi model Predictive Controllers, system, memory and controller |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107191328A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105425593A (en) * | 2016-01-22 | 2016-03-23 | 东华理工大学 | Multi-model smooth and stable switching control method for increase and decrease of state variables |
| CN108869175A (en) * | 2018-06-15 | 2018-11-23 | 中南大学 | A kind of Yaw control method of the Model Predictive Control MPC based on multi-step prediction |
| CN109063277A (en) * | 2018-07-12 | 2018-12-21 | 佛山科学技术学院 | A kind of dynamic pattern recognition method and device based on gap metric |
| CN110345007A (en) * | 2019-06-03 | 2019-10-18 | 天津瑞源电气有限公司 | A kind of paddle change system of wind turbines method for handover control |
| CN110966142A (en) * | 2018-09-28 | 2020-04-07 | 北京金风科创风电设备有限公司 | Control method and device for wind generating set |
| CN112580901A (en) * | 2021-02-23 | 2021-03-30 | 国能日新科技股份有限公司 | Day-ahead trading strategy method and system based on market supply and demand and regional weather prediction |
| CN112682254A (en) * | 2020-12-21 | 2021-04-20 | 北京华能新锐控制技术有限公司 | Fan active power tracking method based on dynamic multi-model predictive controller |
| CN113297728A (en) * | 2021-04-30 | 2021-08-24 | 东方电气风电有限公司 | Single-fan virtual wind speed calculation method and system based on wind field wind speed correlation |
| CN113675882A (en) * | 2021-08-20 | 2021-11-19 | 国网陕西省电力有限公司 | Wind power grid-connected system operation space division method and system based on gap measurement |
| CN114969990A (en) * | 2022-08-02 | 2022-08-30 | 中国电子科技集团公司第十研究所 | Multi-model fused avionic product health assessment method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102410138A (en) * | 2011-08-24 | 2012-04-11 | 国电联合动力技术有限公司 | Method for acquiring optimal control input of wind generating set |
| CA2836018A1 (en) * | 2012-12-18 | 2014-06-18 | General Electric Company | Control system and method for mitigating loads during yaw error on a wind turbine |
| CN106877341A (en) * | 2011-09-06 | 2017-06-20 | 通用电气公司 | Control the controller and method of power system |
-
2017
- 2017-06-27 CN CN201710501186.2A patent/CN107191328A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102410138A (en) * | 2011-08-24 | 2012-04-11 | 国电联合动力技术有限公司 | Method for acquiring optimal control input of wind generating set |
| CN106877341A (en) * | 2011-09-06 | 2017-06-20 | 通用电气公司 | Control the controller and method of power system |
| CA2836018A1 (en) * | 2012-12-18 | 2014-06-18 | General Electric Company | Control system and method for mitigating loads during yaw error on a wind turbine |
Non-Patent Citations (2)
| Title |
|---|
| 叶丽莎: "风力发电***的多模型预测控制", 《中国优秀硕士学位论文全文数据库》 * |
| 陶响元: "基于间隙度量的非线性***多模型预测控制", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105425593A (en) * | 2016-01-22 | 2016-03-23 | 东华理工大学 | Multi-model smooth and stable switching control method for increase and decrease of state variables |
| CN105425593B (en) * | 2016-01-22 | 2018-05-08 | 东华理工大学 | The multi-model smooth steady method for handover control of state variable increase and decrease |
| CN108869175A (en) * | 2018-06-15 | 2018-11-23 | 中南大学 | A kind of Yaw control method of the Model Predictive Control MPC based on multi-step prediction |
| CN108869175B (en) * | 2018-06-15 | 2019-07-09 | 中南大学 | A kind of Yaw control method of the Model Predictive Control MPC based on multi-step prediction |
| CN109063277A (en) * | 2018-07-12 | 2018-12-21 | 佛山科学技术学院 | A kind of dynamic pattern recognition method and device based on gap metric |
| CN109063277B (en) * | 2018-07-12 | 2022-12-20 | 佛山科学技术学院 | Dynamic mode identification method and device based on gap measurement |
| CN110966142A (en) * | 2018-09-28 | 2020-04-07 | 北京金风科创风电设备有限公司 | Control method and device for wind generating set |
| CN110345007A (en) * | 2019-06-03 | 2019-10-18 | 天津瑞源电气有限公司 | A kind of paddle change system of wind turbines method for handover control |
| CN112682254A (en) * | 2020-12-21 | 2021-04-20 | 北京华能新锐控制技术有限公司 | Fan active power tracking method based on dynamic multi-model predictive controller |
| CN112682254B (en) * | 2020-12-21 | 2022-09-16 | 北京华能新锐控制技术有限公司 | Draught fan active power tracking method based on dynamic multi-model predictive controller |
| CN112580901A (en) * | 2021-02-23 | 2021-03-30 | 国能日新科技股份有限公司 | Day-ahead trading strategy method and system based on market supply and demand and regional weather prediction |
| CN113297728A (en) * | 2021-04-30 | 2021-08-24 | 东方电气风电有限公司 | Single-fan virtual wind speed calculation method and system based on wind field wind speed correlation |
| CN113675882A (en) * | 2021-08-20 | 2021-11-19 | 国网陕西省电力有限公司 | Wind power grid-connected system operation space division method and system based on gap measurement |
| CN113675882B (en) * | 2021-08-20 | 2024-03-19 | 国网陕西省电力有限公司 | Wind power grid-connected system operation space division method and system based on gap measurement |
| CN114969990A (en) * | 2022-08-02 | 2022-08-30 | 中国电子科技集团公司第十研究所 | Multi-model fused avionic product health assessment method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107191328A (en) | Blower fan Multi model Predictive Controllers, system, memory and controller | |
| Abdelbaky et al. | Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines | |
| Tang et al. | Reactive power control of grid-connected wind farm based on adaptive dynamic programming | |
| Soufi et al. | Feedback linearization control based particle swarm optimization for maximum power point tracking of wind turbine equipped by PMSG connected to the grid | |
| CN105179164B (en) | Wind-energy changing system sliding-mode control and device based on T-S fuzzy models | |
| Munteanu et al. | Energy-reliability optimization of wind energy conversion systems by sliding mode control | |
| Biegel et al. | Distributed low-complexity controller for wind power plant in derated operation | |
| CN105673325A (en) | Individual pitch control method of wind driven generator set based on RBF neural network PID | |
| Ebadollahi et al. | Wind turbine torque oscillation reduction using soft switching multiple model predictive control based on the gap metric and Kalman filter estimator | |
| CN106774276A (en) | Wind power plant automatic electricity generation control system test platform | |
| CN101915203A (en) | Damping injection control method for improving power angle oscillation of water turbine generator set | |
| CN105626378A (en) | Large wind turbine unit individual pitch control method based on RBF neural network | |
| Benbouhenni et al. | Enhancement of the power quality of DFIG-based dual-rotor wind turbine systems using fractional order fuzzy controller | |
| CN102841965B (en) | The modeling method of receiving end grid security domain optimal load flow model | |
| CN103094920A (en) | Equivalence method of direct-drive-type wind turbine generator wind power plant | |
| Chandra et al. | Time-scale modeling of wind-integrated power systems | |
| CN110206686A (en) | A kind of adaptive maximum power tracking and controlling method for wind power generating set | |
| Aissaoui et al. | Power control of wind turbine based on fuzzy controllers | |
| CN102661243B (en) | Forecast correction pitch variation control method for doubly-fed induction wind power generator set | |
| Chandra et al. | Exploring the impact of wind penetration on power system equilibrium using a numerical continuation approach | |
| CN103956767B (en) | A kind of wind farm grid-connected method for analyzing stability considering wake effect | |
| Yao et al. | RBF neural network based self-tuning PID pitch control strategy for wind power generation system | |
| CN106294959A (en) | The modeling and simulating method that model reference self-adapting control combines with double-fed blower fan reduced-order model | |
| Ounnas et al. | Optimal reference model based fuzzy tracking control for wind energy conversion system | |
| Amine et al. | Adaptive fuzzy logic control of wind turbine emulator |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170922 |
|
| RJ01 | Rejection of invention patent application after publication |