CN107061158A - 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 PDF

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CN107061158A
CN107061158A CN201710501708.9A CN201710501708A CN107061158A CN 107061158 A CN107061158 A CN 107061158A CN 201710501708 A CN201710501708 A CN 201710501708A CN 107061158 A CN107061158 A CN 107061158A
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wind
speed
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msup
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CN107061158B (en
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宋永端
李岳
赖俊峰
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Qingdao Gelairui Intelligent Control Technology Co ltd
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Star Institute of Intelligent Systems
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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
    • 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
    • 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
    • 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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  • 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)
  • 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 method, including：1) the optimal angular speed of wind turbines rotor is predicted；2) design controller is tracked 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, by accurately controlling wind turbines rotor angular speed to track perfect forecast angular speed, the problem of control can be avoided not in time, the wind energy utilization of wind-driven generator is improved, 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, wind-driven generator rotating speed can also be controlled when internal system partial function and unknown parameters.

Description

A kind of prediction of low wind speed leeward power generator and tracking and controlling method
Technical field
The present invention relates to wind-power electricity generation run control technology field, more particularly to a kind of low wind speed leeward power generator it is pre- Survey and tracking and controlling method.
Background technology
1) the non-renewable energy resources storage such as world's coal, charcoal, oil is in short supply at present, and new energy such as wind energy, solar energy etc. belong to Renewable resource, inexhaustible, nexhaustible and widely distributed, cleanliness without any pollution obtained the weight of countries in the world in the last few years Depending on research.China is vast in territory, possesses abundant wind energy resources, therefore wind-power electricity generation is greatly developed in China.
2) at present, there are some defects in wind-power electricity generation on wind driven generator output power tracing control.Such as when wind enters Enter after wind field, promote fan blade of wind driven generator rotation to be generated electricity, the wind speed that is measured using wind-driven generator internal sensor, turn There is certain deficiency in the method that the data such as speed are regulated and controled, because wind-driven generator inertia is larger, in control in the presence of meeting Prolong, the problem of producing control not in time；For another example current wind-driven generator mainly uses PID control method, and this control method is deposited It is long in the tracking time, easily shake, stability is weak to wait not enough.Control is not in time or controller efficiency is low to directly affect Wind-driven generator causes the waste of wind-resources to the utilization rate of wind energy resources, especially specified less than wind-driven generator in wind speed Wind speed, in the case that generated output is relatively low.
3) publication No. discloses " a kind of control method of wind-driven generator and control for CNIO556992OA patent of invention System ", the invention obtains current wind speed and propeller pitch angle angle, and propeller pitch angle desired angle is calculated using current wind speed, controls pitch Angle angleonly tracking desired angle, this mode uses real-time wind speed, the problem of there is control not in time.
4) publication No. discloses a kind of " multistage aerogenerator of adaptive wind speed for CNIO49638llA patent of invention And its control method ", the invention gathers the wind velocity signal of wind field in real time, but wind may pass through wind field during control, there is control The problem of making not in time.
5) publication No. discloses a kind of " controlling party of Stall Type wind power generating set for CNIO5257475A patent of invention Model is created as the form that power output is directly proportional to the cube of rotating speed by method ", this method, and then calculates controlled quentity controlled variable --- electricity Magnetic torque.This method is not accurate enough for the foundation of model, can only carry out qualitative analysis, not be suitable in engineering practice.
6) publication No. discloses that " a kind of wind electricity change paddle is away from Multivariable Fuzzy nerve net for CNIO4612898A patent of invention Network PID control method ", the invention mainly uses the control method of Fuzzy Neural PID, and this method is for single wind speed feelings Condition, stability is good, reliability is high, but for complex situations wind speed when be difficult to reach that control is required, easily produce concussion or The problem of controlling not in time.
7) publication No. discloses a kind of " the tracking optimal control side of Wind turbines for CNIO4408223A patent of invention Method ", the invention is controlled using Robust Adaptive Control mode to the electromagnetic torque of wind-driven generator, the benefit of this method It is that wind-driven generator can be controlled preferably to track ideal curve to a certain extent, but has some deficiency to be exactly not add The equipment such as anemometer tower, do not describe the acquisition modes of ideal curve.
The content of the invention
In view of this, it is an object of the invention to provide a kind of prediction 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, carry 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, including：
1) the optimal angular speed of wind turbines rotor is predicted, including：
Ith, anemometer tower is positioned over wind field periphery 1-2 kms, measurement will enter the wind speed of wind field；
IIth, according to surveyed air speed data, formula ω is passed through*=λoptV/R is calculated and is obtained wind turbines rotor perfect forecast Angular velocity omega*, wherein λoptFor optimum tip-speed ratio, v is the wind speed that anemometer tower is measured, and R is wind turbines rotor radius；
2) design controller is tracked control to the angular speed of wind turbines rotor, including：
A, design blower fan system model are：
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, TrThe pneumatic torque obtained for wind wheel, n is the gearratio of the gearbox of connecting fan and generator, TeFor Generator electromagnetic torque；Tr, B etc. belong to system unknowns；
B, acquisition wind wheel angular velocity omega, wind wheel perfect forecast angular velocity omega*, define error e=ω-ω*, to both sides derivation ObtainArrangement is obtained：
Blower fan system does not determine partUse RBF neuralEstimate Meter, wherein,It is node Gaussian radial basis function, W is the power of each node Value, ε is the threshold value of neutral net；
C, use based on limited liapunov's method RBF neural method design controller：
Wherein, γ is a BLF parameter more than zero of designed, designed, K0It is the control ginseng for being more than zero of designed, designed Number, nerve network controller：
Adaptive updates rate：
WhereinFor W estimate,For ε estimate, γ=0.02, K is chosen0=3；
D, the error signal e input wind driven generator controller by this moment, obtain the control signal T of subsequent timee；
E, control signal is inputted into wind-driven generator obtain 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 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 increase data point.
Beneficial effects of the present invention：
1st, the prediction of low wind speed leeward power generator of the invention and tracking and controlling method, can Accurate Prediction it is following one section 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, by accurately controlling wind turbines rotor angular speed to track perfect forecast angular speed, can avoid controlling 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.
2nd, the prediction of low wind speed leeward power generator of the invention and tracking and controlling method, its design based on limited Li Yapu The RBF neural adaptive controller of promise husband's method, the controller only depends on system angle velocity error, for internal system The situation of partial function and unknown parameters can also be controlled to wind-driven generator rotating speed.
Brief description of the drawings
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 actual wind wheel angular speed；
Fig. 5 is error curve diagram.
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, including：
1) the optimal angular speed of wind turbines rotor is predicted, including：
A, anemometer tower is positioned over to wind field periphery 1-2 kms, measurement will enter the wind speed of wind field；
B, according to surveyed air speed data, pass through formula ω*=λoptV/R is calculated and is obtained wind turbines rotor perfect forecast Angular velocity omega*, wherein λoptFor optimum tip-speed ratio, v is the wind speed that anemometer tower is measured, and R is wind turbines rotor radius；
2) design controller is tracked control to the angular speed of wind turbines rotor, including：
A, design blower fan system model are：
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, TrThe pneumatic torque obtained for wind wheel, n is the gearratio of the gearbox of connecting fan and generator, TeFor Generator electromagnetic torque；Tr, B etc. belong to system unknowns；
B, pass through sensor measurement obtain wind wheel angular velocity omega, wind wheel perfect forecast angular velocity omega*, definition error e=ω- ω*, both sides derivation is obtainedArrangement is obtained：
Blower fan system does not determine partUse RBF neuralEstimate Meter, wherein,It 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 increase data point of density, W is the weights of each node, and ε is the threshold value of neutral net；
C, use based on limited liapunov's method RBF neural method design controller：
Wherein, γ is a BLF parameter more than zero of designer's designed, designed, K0It is being more than for designer's designed, designed Zero control parameter, nerve network controller：
Adaptive updates rate：
WhereinFor W estimate,For ε estimate, γ=0.02, K is chosen0=3；
D, the error signal e input wind driven generator controller by this moment, obtain the control signal T of subsequent timee；
E, control signal is inputted into wind-driven generator obtain 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 as shown in Figure 2, Concretely comprise the following steps：
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, current wind generator wind wheel angular velocity omega is measured using sensor；
Step Step 5, current wind wheel angular velocity omega and wind wheel perfect forecast angular velocity omega*Make the difference, obtain angular speed error e；
Step Step 6, inputs wind driven generator controller by this moment error signal, obtains subsequent time control signal Te；
Step Step 7, the wind wheel angular velocity omega that wind-driven generator obtains subsequent time is inputted by control signal；
Step Step 8, first time loop control terminates.
Finally illustrate, the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted, although with reference to compared with The present invention is described in detail good embodiment, it will be understood by those within the art that, can be to skill of the invention Art scheme is modified or equivalent substitution, if but without departing from the objective and scope of technical solution of the present invention, just it should cover at this Among the right 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：Including：
1) the optimal angular speed of wind turbines rotor is predicted, including：
Ith, anemometer tower is positioned over wind field periphery 1-2 kms, measurement will enter the wind speed of wind field；
IIth, according to surveyed air speed data, formula ω is passed through*=λoptV/R, which is calculated, obtains wind turbines rotor perfect forecast angle speed Spend ω*, wherein λoptFor optimum tip-speed ratio, v is the wind speed that anemometer tower is measured, and R is wind turbines rotor radius；
2) design controller is tracked control to the angular speed of wind turbines rotor, including：
A, design blower fan system model are：
<mrow> <mi>J</mi> <mover> <mi>&amp;omega;</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>+</mo> <mi>B</mi> <mi>&amp;omega;</mi> <mo>=</mo> <msub> <mi>T</mi> <mi>r</mi> </msub> <mo>-</mo> <msub> <mi>nT</mi> <mi>e</mi> </msub> </mrow>
Wherein, J=Jr+n2Je, JrIt is used to inertia, J for the rotation of rotor at wind wheeleFor generator amature rotary inertia, B is power train System total damping coefficient, TrThe pneumatic torque obtained for wind wheel, n is the gearratio ratio of the gearbox of connecting fan and generator, TeFor Generator electromagnetic torque；Tr, B etc. belong to system unknowns；
B, acquisition wind wheel angular velocity omega, wind wheel perfect forecast angular velocity omega*, define error e=ω-ω*, both sides derivation is obtainedArrangement is obtained：
<mrow> <mover> <mi>e</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>=</mo> <mfrac> <mn>1</mn> <mi>J</mi> </mfrac> <mrow> <mo>(</mo> <msub> <mi>T</mi> <mi>r</mi> </msub> <mo>-</mo> <mi>B</mi> <mi>&amp;omega;</mi> <mo>)</mo> </mrow> <mo>-</mo> <msup> <mover> <mi>&amp;omega;</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>*</mo> </msup> <mo>-</mo> <mfrac> <mi>n</mi> <mi>J</mi> </mfrac> <msub> <mi>T</mi> <mi>e</mi> </msub> </mrow>
Blower fan system does not determine partUse RBF neuralEstimation, Wherein,It is node gaussian radial basis function basic function, W is the power of each node Value, ε is the threshold value of neutral net；
C, use based on limited liapunov's method RBF neural method design controller：
<mrow> <mi>u</mi> <mo>=</mo> <msub> <mi>T</mi> <mi>e</mi> </msub> <mo>=</mo> <mfrac> <mi>J</mi> <mi>n</mi> </mfrac> <mfrac> <mrow> <msup> <mi>&amp;gamma;</mi> <mn>2</mn> </msup> <mo>-</mo> <msup> <mi>e</mi> <mn>2</mn> </msup> </mrow> <mrow> <msup> <mi>&amp;gamma;</mi> <mn>2</mn> </msup> <mo>-</mo> <msup> <mi>e</mi> <mn>2</mn> </msup> <mo>+</mo> <mn>1</mn> </mrow> </mfrac> <mrow> <mo>(</mo> <msub> <mi>k</mi> <mn>0</mn> </msub> <mi>e</mi> <mo>+</mo> <msub> <mi>u</mi> <mi>c</mi> </msub> <mo>)</mo> </mrow> </mrow>
Wherein, γ is a BLF parameter more than zero of designed, designed, K0It is the control parameter for being more than zero of designed, designed, nerve Network controller：
Adaptive updates rate：
WhereinFor W estimate,For ε estimate, γ=0.02, K is chosen0=3；
D, the error signal e input wind driven generator controller by this moment, obtain the control signal T of subsequent timee；
E, control signal is inputted into wind-driven generator obtain 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 It is 50 to state node Gaussian radial basis function number.
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 State node Gaussian radial basis function central point to choose according to constant gradient mode, in the big place increase data point of sample rate.
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CN108717266A (en) * 2018-05-30 2018-10-30 迪比（重庆）智能科技研究院有限公司 Neural adaptive tracking control method of the wind field power of fan based on disturbance observer
CN110032238A (en) * 2019-04-28 2019-07-19 闽江学院 A kind of wind turbine power generation yaw control system maximum power tracing method
WO2019148770A1 (en) * 2018-01-31 2019-08-08 北京金风科创风电设备有限公司 Cut-out strategy adaptive adjustment method and apparatus
CN111091236A (en) * 2019-11-27 2020-05-01 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
CN111963372A (en) * 2020-09-01 2020-11-20 北京石油化工学院 Tracking control method for optimal rotating speed of wind driven generator
CN112343770A (en) * 2020-11-17 2021-02-09 北京石油化工学院 Observer-based wind driven generator optimal rotation speed finite time tracking control method
CN114660946A (en) * 2022-05-09 2022-06-24 电子科技大学 Fuzzy self-adaptive dynamic surface control method of time-lag forming process system

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WO2019148770A1 (en) * 2018-01-31 2019-08-08 北京金风科创风电设备有限公司 Cut-out strategy adaptive adjustment method and apparatus
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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
CN110032238A (en) * 2019-04-28 2019-07-19 闽江学院 A kind of wind turbine power generation yaw control system maximum power tracing method
CN111091236A (en) * 2019-11-27 2020-05-01 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
CN111963372A (en) * 2020-09-01 2020-11-20 北京石油化工学院 Tracking control method for optimal rotating speed of wind driven generator
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
CN112343770A (en) * 2020-11-17 2021-02-09 北京石油化工学院 Observer-based wind driven generator optimal rotation speed finite time tracking control method
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
CN114660946A (en) * 2022-05-09 2022-06-24 电子科技大学 Fuzzy self-adaptive dynamic surface control method of time-lag forming process system
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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