CN107061158B - A kind of prediction of low wind speed leeward power generator and tracking and controlling method - Google Patents
A kind of prediction of low wind speed leeward power generator and tracking and controlling method Download PDFInfo
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- CN107061158B CN107061158B CN201710501708.9A CN201710501708A CN107061158B CN 107061158 B CN107061158 B CN 107061158B CN 201710501708 A CN201710501708 A CN 201710501708A CN 107061158 B CN107061158 B CN 107061158B
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- 210000005036 nerve Anatomy 0.000 claims description 3
- 238000010248 power generation Methods 0.000 claims 1
- 230000001276 controlling effect Effects 0.000 description 12
- 230000005611 electricity Effects 0.000 description 6
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- 230000009514 concussion Effects 0.000 description 2
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Classifications
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- 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
- F05B2260/00—Function
- F05B2260/82—Forecasts
- F05B2260/821—Parameter estimation or prediction
-
- 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
- F05B2270/32—Wind speeds
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- 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
- F05B2270/327—Rotor or generator speeds
-
- 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
- F05B2270/709—Type of control algorithm with neural networks
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- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
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- 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
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- General Engineering & Computer Science (AREA)
- Control Of Eletrric Generators (AREA)
- Wind Motors (AREA)
Abstract
The invention discloses a kind of prediction of low wind speed leeward power generator and tracking and controlling methods, comprising: 1) predicts the optimal angular speed of wind turbines rotor；2) design controller carries out tracing control to the angular speed of wind turbines rotor.The prediction of the low wind speed leeward power generator of the present invention and tracking and controlling method, the wind speed of wind field can be entered Accurate Prediction following a period of time, the perfect forecast angular velocity data close with future time instance wind turbines rotor true angular velocity is obtained, perfect forecast angular speed is tracked by accurately controlling wind turbines rotor angular speed, the problem of can be avoided control not in time, the wind energy utilization for improving wind-driven generator, makes wind-driven generator at low wind speeds with maximum power output；And designed RBF neural adaptive controller only depends on system angle velocity error, for internal system partial function and unknown parameters the case where can also control wind-driven generator revolving speed.
Description
Technical field
The present invention relates to wind-power electricity generation run control technology field, in particular to a kind of low wind speed leeward power generator it is pre-
Survey and tracking and controlling method.
Background technique
1) the non-renewable energy resources storage such as world's coal, charcoal, petroleum is in short supply at present, and new energy such as wind energy, solar energy etc. belongs to
Renewable resource, inexhaustible, nexhaustible and widely distributed, cleanliness without any pollution obtained the weight of countries in the world in recent years
Depending on research.China has a vast territory, possesses wind energy resources abundant, therefore wind-power electricity generation is greatly developed in China.
2) currently, wind-power electricity generation there are some defects on wind driven generator output power tracing control.Such as when wind into
After entering wind field, fan blade of wind driven generator rotation is pushed to generate electricity, the wind speed that is measured using wind-driven generator internal sensor is turned
There are certain deficiencies for the method that the data such as speed are regulated and controled, since wind-driven generator inertia is larger, in the presence of controlling upper meeting
Prolong, leads to the problem of control not in time；For another example current wind-driven generator mainly uses PID control method, and this control method is deposited
The deficiencies of time is long tracking, and is easy to appear concussion, and stability is weak.Control is not in time or controller low efficiency will have a direct impact on
Wind-driven generator causes the waste of wind-resources to the utilization rate of wind energy resources, especially specified lower than wind-driven generator in wind speed
Wind speed, in the case that generated output is relatively low.
3) publication No. is that the patent of invention of CNIO556992OA discloses " a kind of control method and control of wind-driven generator
System ", the invention obtain current wind speed and propeller pitch angle angle, calculate propeller pitch angle desired angle using current wind speed, control pitch
Angle angleonly tracking desired angle, this mode use real-time wind speed, there are problems that control not in time.
4) patent of invention that publication No. is CNIO49638llA discloses a kind of " multistage aerogenerator of adaptive wind speed
And its control method ", which acquires the wind velocity signal of wind field in real time, but wind may have already passed through wind field when control, there is control
The problem of making not in time.
5) publication No. is that the patent of invention of CNIO5257475A discloses a kind of " controlling party of Stall Type wind power generating set
Method ", this method form that model foundation is directly proportional to the cube of revolving speed at output power, and then calculate control amount --- electricity
Magnetic torque.This method is inaccurate for establishing for model, can only carry out qualitative analysis, not be suitable in engineering practice.
6) patent of invention that publication No. is CNIO4612898A discloses that " a kind of wind electricity change paddle is away from Multivariable Fuzzy nerve net
Network PID control method ", the invention mainly use the control method of Fuzzy Neural PID, and this method is for single wind speed feelings
Condition, stability is good, high reliablity, but requires for being difficult to reach control when the wind speed of complex situations, be easy to produce concussion or
The problem of controlling not in time.
7) publication No. is that the patent of invention of CNIO4408223A discloses a kind of " tracking optimal control side of Wind turbines
Method ", the invention are controlled using electromagnetic torque of the Robust Adaptive Control mode to wind-driven generator, the benefit of this method
It is that can control wind-driven generator to a certain extent preferably to track ideal curve, but having some deficiency is exactly not to be added
The equipment such as anemometer tower do not describe the acquisition modes of ideal curve.
Summary of the invention
In view of this, the object of the present invention is to provide a kind of predictions of low wind speed leeward power generator and tracing control side
Method Accurate Prediction following a period of time can enter the wind speed of wind field to realize, and then improve the control efficiency of wind-driven generator, mention
Wind energy utilization of the high wind-driven generator in low wind speeds.
The prediction of the low wind speed leeward power generator of the present invention and tracking and controlling method, comprising:
1) the optimal angular speed of wind turbines rotor is predicted, comprising:
I, anemometer tower is placed at 1-2 kilometers of wind field periphery, measurement will enter the wind speed of wind field；
II, according to surveyed air speed data, pass through formula ω*=λoptWind turbines rotor perfect forecast is calculated in v/R
Angular velocity omega*, wherein λoptFor optimum tip-speed ratio, v is the wind speed that anemometer tower measures, and R is wind turbines rotor radius；
2) design controller carries out tracing control to the angular speed of wind turbines rotor, comprising:
A, blower fan system model is designed are as follows:
Wherein, J=Jr+n2Je, JrFor the rotary inertia of rotor at wind wheel, JeFor generator amature rotary inertia, B is transmission
System total damping coefficient, TrFor the pneumatic torque that wind wheel obtains, n is the transmission ratio for connecting the gearbox of blower and generator, TeFor
Generator electromagnetic torque；Tr, B etc. belong to system unknowns；
B, wind wheel angular velocity omega, wind wheel perfect forecast angular velocity omega are obtained*, define error e=ω-ω*, to both sides derivation
It obtainsArrangement obtains:
Blower fan system does not determine partUse RBF neuralEstimate
Meter, whereinIt is node Gaussian radial basis function, W is the power of each node
Value, ε is the threshold value of neural network；
C, controller is designed using the RBF neural method based on limited liapunov's method:
Wherein, γ is a BLF parameter greater than zero of designed, designed, K0It is the control ginseng greater than zero of designed, designed
Number, nerve network controller:
Adaptive updates rate:
WhereinFor the estimated value of W,For the estimated value of ε, γ=0.02, K are chosen0=3；
D, the error signal e at this moment is inputted into wind driven generator controller, obtains the control signal T of subsequent timee；
E, control signal input wind-driven generator is obtained into the wind wheel angular velocity omega of subsequent time.
Further, the wind wheel angular velocity omega in the step B is measured by angular-rate sensor and is obtained.
Further, the node Gaussian radial basis function number is 50.
Further, the node Gaussian radial basis function central point is chosen according to constant gradient mode, big in sample rate
Place increases data point.
Beneficial effects of the present invention:
1, the prediction of the low wind speed leeward power generator of the present invention and tracking and controlling method, can Accurate Prediction it is one section following when
Between enter wind field wind speed, obtained the perfect forecast angular speed close with future time instance wind turbines rotor true angular velocity
Data track perfect forecast angular speed by accurately controlling wind turbines rotor angular speed, can be avoided and control not in time
Problem improves the wind energy utilization of wind-driven generator, makes wind-driven generator at low wind speeds with maximum power output.
2, the prediction of the low wind speed leeward power generator of the present invention and tracking and controlling method, design based on limited Li Yapu
The RBF neural adaptive controller of promise husband's method, which only depends on system angle velocity error, for internal system
The case where partial function and unknown parameters, can also control wind-driven generator revolving speed.
Detailed description of the invention
Fig. 1 is the overall structure block diagram of wind powered generator system；
Fig. 2 is the flow chart of the prediction and tracking and controlling method of wind-driven generator；
Fig. 3 is simulation wind speed curve figure；
Fig. 4 is the tracking effect figure of practical wind wheel angular speed；
Fig. 5 is error curve diagram.
Specific embodiment
The invention will be further described with reference to the accompanying drawings and examples.
The prediction of the low wind speed leeward power generator of the present embodiment and tracking and controlling method, comprising:
1) the optimal angular speed of wind turbines rotor is predicted, comprising:
A, anemometer tower is placed at 1-2 kilometers of wind field periphery, measurement will enter the wind speed of wind field；
B, according to surveyed air speed data, pass through formula ω*=λoptWind turbines rotor perfect forecast is calculated in v/R
Angular velocity omega*, wherein λoptFor optimum tip-speed ratio, v is the wind speed that anemometer tower measures, and R is wind turbines rotor radius；
2) design controller carries out tracing control to the angular speed of wind turbines rotor, comprising:
A, blower fan system model is designed are as follows:
Wherein, J=Jr+n2Je, JrFor the rotary inertia of rotor at wind wheel, JeFor generator amature rotary inertia, B is transmission
System total damping coefficient, TrFor the pneumatic torque that wind wheel obtains, n is the transmission ratio for connecting the gearbox of blower and generator, TeFor
Generator electromagnetic torque；Tr, B etc. belong to system unknowns；
B, wind wheel angular velocity omega, wind wheel perfect forecast angular velocity omega are obtained by sensor measurement*, define error e=ω-
ω*, both sides derivation is obtainedArrangement obtains:
Blower fan system does not determine partUse RBF neuralEstimate
Meter, whereinIt is node Gaussian radial basis function, described in the present embodiment
Node Gaussian radial basis function number is 50, and node Gaussian radial basis function central point is chosen according to constant gradient mode, in sample
The big place of density increases data point, and W is the weight of each node, and ε is the threshold value of neural network；
C, controller is designed using the RBF neural method based on limited liapunov's method:
Wherein, γ is a BLF parameter greater than zero of designer's designed, designed, K0It is being greater than for designer's designed, designed
Zero control parameter, nerve network controller:
Adaptive updates rate:
WhereinFor the estimated value of W,For the estimated value of ε, γ=0.02, K are chosen0=3；
D, the error signal e at this moment is inputted into wind driven generator controller, obtains the control signal T of subsequent timee；
E, control signal input wind-driven generator is obtained into the wind wheel angular velocity omega of subsequent time.
The prediction of the low wind speed leeward power generator of the present embodiment and the implementing procedure of tracking and controlling method are as shown in Fig. 2,
Specific steps are as follows:
Step Step 1, control starts；
Step Step 2, anemometer tower measurement will enter the wind speed v of wind field；
Step Step 3 utilizes formula ω*=λoptV/R calculates wind wheel perfect forecast angular velocity omega*；
Step Step 4 utilizes sensor measurement current wind generator wind wheel angular velocity omega；
Step Step 5, current wind wheel angular velocity omega and wind wheel perfect forecast angular velocity omega*It makes the difference, obtains angular speed error
e；
This moment error signal is inputted wind driven generator controller by step Step 6, obtains subsequent time control signal
Te；
Control signal input wind-driven generator is obtained the wind wheel angular velocity omega of subsequent time by step Step 7；
Step Step 8, first time loop control terminate.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although referring to compared with
Good embodiment describes the invention in detail, those skilled in the art should understand that, it can be to skill of the invention
Art scheme is modified or replaced equivalently, if but without departing from the objective and range of technical solution of the present invention, just it should cover at this
In the scope of the claims of invention.
Claims (4)
1. prediction and the tracking and controlling method of a kind of low wind speed leeward power generator, it is characterised in that: include:
1) the optimal angular speed of wind turbines rotor is predicted, comprising:
I, anemometer tower is placed at 1-2 kilometers of wind field periphery, measurement will enter the wind speed of wind field；
II, according to surveyed air speed data, pass through formula ω*=λoptWind turbines rotor perfect forecast angle speed is calculated in v/R
Spend ω*, wherein λoptFor optimum tip-speed ratio, v is the wind speed that anemometer tower measures, and R is wind turbines rotor radius；
2) design controller carries out tracing control to the angular speed of wind turbines rotor, comprising:
A, blower fan system model is designed are as follows:
Wherein, J=Jr+n2Je, JrFor the rotary inertia of rotor at wind wheel, JeFor generator amature rotary inertia, B is transmission system
Total damping coefficient, TrFor the pneumatic torque that wind wheel obtains, n is the transmission ratio for connecting the gearbox of blower and generator, TeFor power generation
Electromechanical magnetic torque；Tr, B belong to system unknowns；
B, wind wheel angular velocity omega, wind wheel perfect forecast angular velocity omega are obtained*, define error e=ω-ω*, both sides derivation is obtainedArrangement obtains:
Blower fan system does not determine partUse RBF neuralEstimation,
Wherein,It is node Gaussian radial basis function, W is the weight of each node,
ε is the threshold value of neural network；
C, controller is designed using the RBF neural method based on limited liapunov's method:
Wherein, γ is a BLF parameter greater than zero of designed, designed, K0It is the control parameter for being greater than zero of designed, designed, nerve
Network controller:
Adaptive updates rate:
WhereinFor the estimated value of W,For the estimated value of ε, γ=0.02, K are chosen0=3；
D, the error signal e at this moment is inputted into wind driven generator controller, obtains the control signal T of subsequent timee；
E, control signal input wind-driven generator is obtained into the wind wheel angular velocity omega of subsequent time.
2. prediction and the tracking and controlling method of low wind speed leeward power generator according to claim 1, it is characterised in that: institute
The wind wheel angular velocity omega stated in step B measures acquisition by angular-rate sensor.
3. prediction and the tracking and controlling method of low wind speed leeward power generator according to claim 1, it is characterised in that: institute
Stating node Gaussian radial basis function number is 50.
4. prediction and the tracking and controlling method of low wind speed leeward power generator according to claim 3, it is characterised in that: institute
It states node Gaussian radial basis function central point to choose according to constant gradient mode, increases data point in the big place of sample rate.
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| CN110094298B (en) | 2018-01-31 | 2020-05-26 | 北京金风科创风电设备有限公司 | Adaptive adjustment method and device for switching-out strategy |
| CN108717266B (en) * | 2018-05-30 | 2021-03-12 | 迪比（重庆）智能科技研究院有限公司 | Neural self-adaptive tracking control method for wind field fan power based on disturbance observer |
| CN110032238B (en) * | 2019-04-28 | 2020-06-12 | 闽江学院 | Maximum power tracking method for wind turbine power generation yaw control system |
| CN111091236B (en) * | 2019-11-27 | 2023-06-20 | 长春吉电能源科技有限公司 | Multi-classification deep learning short-term wind power prediction method classified according to pitch angles |
| CN111963372B (en) * | 2020-09-01 | 2021-12-17 | 北京石油化工学院 | Tracking control method for optimal rotating speed of wind driven generator |
| CN112343770B (en) * | 2020-11-17 | 2021-09-24 | 北京石油化工学院 | Observer-based wind driven generator optimal rotation speed finite time tracking control method |
| CN114660946B (en) * | 2022-05-09 | 2023-06-16 | 电子科技大学 | Fuzzy self-adaptive dynamic surface control method of time-lag forming process system |
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| EP2148225B1 (en) * | 2008-07-22 | 2016-11-02 | Siemens Aktiengesellschaft | Method and arrangement for the forecast of wind-resources |
| US8076789B2 (en) * | 2010-12-21 | 2011-12-13 | General Electric Company | System and method for controlling wind turbine power output |
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Inventor after: Song Yongduan Inventor after: Liu Xiulan Inventor after: Li Yue Inventor after: Lai Junfeng Inventor before: Song Yongduan Inventor before: Li Yue Inventor before: Lai Junfeng |
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Effective date of registration: 20210615 Address after: Room 731 and 732, 7th floor, 557 Renmin Road, Dongge sub district office, Pingdu City, Qingdao City, Shandong Province 266701 Patentee after: QINGDAO GELAIRUI INTELLIGENT CONTROL TECHNOLOGY Co.,Ltd. Address before: 400030 No. 1 Huiquan Road, Shapingba District, Chongqing, 13, attachment 4. Patentee before: STAR INSTITUTE OF INTELLIGENT SYSTEMS |