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WO2019127974A1 - Procédé et appareil d'optimisation de charges d'éolienne - Google Patents

Procédé et appareil d'optimisation de charges d'éolienne Download PDF

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Publication number
WO2019127974A1
WO2019127974A1 PCT/CN2018/082347 CN2018082347W WO2019127974A1 WO 2019127974 A1 WO2019127974 A1 WO 2019127974A1 CN 2018082347 W CN2018082347 W CN 2018082347W WO 2019127974 A1 WO2019127974 A1 WO 2019127974A1
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WO
WIPO (PCT)
Prior art keywords
load
value
control parameter
predetermined
wind turbine
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Application number
PCT/CN2018/082347
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English (en)
Chinese (zh)
Inventor
马羽龙
彭柳
赵树椿
Original Assignee
新疆金风科技股份有限公司
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Publication of WO2019127974A1 publication Critical patent/WO2019127974A1/fr

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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 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/386—
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • 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
    • Y02E10/76—Power conversion electric or electronic aspects

Definitions

  • the present application relates generally to the field of wind power generation technology and, more particularly, to a method and apparatus for optimizing the load of a wind turbine.
  • the design of a wind turbine is usually started from the load calculation. To do this, it is necessary to establish a mathematical model of the wind turbine and determine the control strategy. The adjustment of the parameters related to the control strategy will obviously affect the results of the load calculation.
  • a method for optimizing a load of a wind power generator comprising the steps of: acquiring at least one control parameter of a wind power generator, wherein the plurality of wind power generator sets The load of the position is affected by the at least one control parameter; acquiring a load value of the plurality of positions of the wind turbine set corresponding to the at least one control parameter; loading the plurality of positions using a predetermined evaluation model The value is quantized into a single evaluation value; determining whether the evaluation value satisfies a predetermined condition; when the evaluation value does not satisfy the predetermined condition, the predetermined optimization algorithm is used to optimize the at least one control parameter based on the evaluation value to update the at least one control parameter, And performing the step of acquiring at least one control parameter of the wind power generator again, wherein the optimization problem of optimizing the control parameter is such that the evaluation value corresponding to the updated control parameter satisfies a predetermined condition; when the evaluation value satisfies the predetermined condition
  • an apparatus for optimizing a load of a wind power generator comprising: a control parameter acquisition module that acquires at least one control parameter of a wind power generator, wherein the wind power generation The load of the plurality of positions of the unit is affected by the at least one control parameter; the load acquisition module acquires a load value of the plurality of positions of the wind power generator corresponding to the at least one control parameter; a load quantization module, The load value of the plurality of locations is quantized into a single evaluation value using a predetermined evaluation model; the evaluation value judging module judges whether the evaluation value satisfies a predetermined condition; and the control parameter update module uses a predetermined optimization algorithm when the evaluation value does not satisfy the predetermined condition Updating the at least one control parameter based on the evaluation value to update the at least one control parameter, and the control parameter obtaining module acquires the at least one control parameter again, wherein the optimization problem of optimizing the control parameter is such that after updating The evaluation value corresponding
  • a computer readable storage medium storing a computer program that, when executed by a processor, implements the method for optimizing a load of a wind turbine.
  • a control system for a wind power generator set comprising: a processor; a memory storing a computer program, when the computer program is executed by the processor, implementing the above A method for optimizing the load of a wind turbine.
  • the method and apparatus for optimizing the load of a wind power generator can quantify multiple loads of the wind power generator and optimize the parameters of the wind turbine generator controller model based on the quantized values using an optimization algorithm. To help get optimized load results.
  • FIG. 1 shows a flow chart of a method for optimizing a load of a wind power plant according to an embodiment of the present application
  • FIG. 2 is a block diagram showing the structure of an apparatus for optimizing the load of a wind power generator according to an embodiment of the present application.
  • FIG. 1 shows a flow chart of a method for optimizing the load of a wind turbine according to an embodiment of the present application.
  • step S10 at least one control parameter of the wind turbine may be acquired, and loads of the plurality of locations of the wind turbine are affected by the at least one control parameter.
  • control parameters affecting the loads of the plurality of locations of the wind turbine and related to the control strategy of the wind turbine may be obtained as at least one control parameter.
  • a control parameter of a predetermined controller model of the wind turbine may be obtained as at least one control parameter. It should be noted that when the control parameter of the predetermined controller model is acquired as the at least one control parameter, the acquired at least one control parameter may be a control parameter of the same controller model, or may be a control parameter of different multiple controller models. This application does not limit this.
  • the predetermined controller model may be a pitch controller model, a torque controller model, or a filter model.
  • the optimization may take into account multiple loads among the fatigue load and the ultimate load at various locations of the wind turbine.
  • the load at a plurality of positions may include a fatigue load of a bending moment Mx in a first direction of the bottom of the tower, a fatigue load of a bending moment My in the second direction of the bottom of the tower, and a fatigue load of a bending moment My in the second direction of the blade root.
  • the loads at multiple locations may be two or more of the above loads, as well as other types of loads.
  • the predetermined controller model may be a pitch controller model for the above loads.
  • the control parameters of the pitch controller model can be KPPS and KIPS.
  • acquiring the control parameter may refer to acquiring the current value of the control parameter.
  • any value within the range of values of the control parameter can be obtained as the current value.
  • the range of values of the control parameters can be set in advance according to experience.
  • the method illustrated in FIG. 1 may also include the step of initializing at least one control parameter.
  • a control parameter of a predetermined controller model may be initialized, and an initialization value of the control parameter may be acquired as a current value.
  • step S20 load values of a plurality of locations of the wind turbine set corresponding to at least one control parameter may be acquired.
  • the load at a plurality of positions in one embodiment of the present application may be the fatigue load of the bottom Mx, the fatigue load of the bottom My, the fatigue load of the blade root My, the ultimate load of the bottom My, and the limit of the blade root My.
  • the overall model of the wind turbine may be simulated based on the current value of the acquired control parameters to obtain the plurality of load values corresponding to the control parameters.
  • the whole machine model of the wind power generator is simulated based on the acquired at least one control parameter, and the value of the predetermined operating parameter of the wind power generator set is also obtained. This will be described in more detail below.
  • step S30 the plurality of load values are quantized into a single evaluation value using a predetermined evaluation model.
  • a plurality of load values may be quantized to an evaluation value associated with the minimum load using a predetermined evaluation model to perform subsequent optimization based thereon.
  • the predetermined evaluation model may be various functions capable of implementing the evaluation function of an embodiment, and the quantized evaluation value is a value of the function.
  • the predetermined evaluation model may be a fitness function.
  • the predetermined evaluation model may be the product of the sum of the plurality of loads (eg, the accumulated value) and the constraint value.
  • the predetermined evaluation model may be a product of a weighted sum (eg, an accumulated weighting value) of the plurality of loads respectively weighted and a constraint value.
  • respective weights may be applied to a plurality of loads in the evaluation model according to different degrees of attention to the plurality of loads, and different weights may be applied to the plurality of loads in consideration of different magnitudes between the plurality of loads, but the application is not limited thereto. this.
  • the constraint values as described above may reflect constraints for predetermined operational parameters that need to be met for normal operation of the wind turbine.
  • the constraint value can be determined based on constraints required for the predetermined operational parameters to be met by the normal operation of the wind turbine.
  • the predetermined operational parameter may include at least one of a generator rotational speed and a cabin vibration acceleration, but the application is not limited thereto.
  • the constraints described above may include the generator speed being within a predetermined range of generator speeds and the cabin vibration acceleration being within a predetermined range of cabin vibration acceleration.
  • the value of the predetermined operational parameter can be obtained by simulation based on the current value of the acquired at least one control parameter in step S20. Therefore, the constraint value can be determined by judging whether the value of the predetermined operational parameter obtained by the simulation satisfies the above constraint condition.
  • the determining the constraint value according to the constraint condition for the predetermined operational parameter that is required to be met by the normal operation of the wind power generator may include: setting the constraint value to a predetermined value when the predetermined operational parameter satisfies the constraint condition; When the predetermined operational parameter does not satisfy the constraint, the constraint value is set to a value greater than the predetermined value.
  • the predetermined value can be set in advance.
  • the constraint value may be determined to be greater than the predetermined value according to the degree to which the abnormality of the predetermined operating parameter can be accepted (or the magnitude of the abnormality). degree.
  • the constraint value when the generator rotational speed obtained by simulation according to the current value of the control parameter exceeds a predetermined range of the generator rotational speed, the constraint value may be set to a value greater than a predetermined value, thereby increasing the sum of the load values as the quantized evaluation values.
  • the product of the constraint value is merely an example, and the case where the predetermined control parameter of the present application does not satisfy the constraint and the setting of the constraint value are not limited thereto.
  • step S40 it is judged whether or not the evaluation value satisfies the predetermined condition.
  • step S50 the control parameter is updated based on the evaluation value to update the control parameter using the predetermined optimization algorithm, and returns to step S10 to acquire the control parameter again.
  • the optimization problem of optimizing the control parameters is such that the evaluation value corresponding to the updated control parameter (that is, the evaluation value obtained by the steps S20 and S30 for the updated control parameter) satisfies the predetermined condition.
  • the predetermined condition may mean that the evaluation value reaches a minimum value.
  • the predetermined condition may mean that the evaluation value corresponding to the acquired at least one control parameter reaches a minimum value.
  • the evaluation value may be determined to be reached. Minimum value.
  • control parameters can be optimized based on the quantized evaluation values to update the control parameters using various existing optimization algorithms.
  • the optimization algorithm used may be a genetic algorithm or a simplex method, but the application is not limited thereto.
  • the evaluation value corresponding to the acquired at least one control parameter may be The minimum values of all the previous evaluation values are compared, and based on the comparison result, according to the correspondence relationship between the evaluation values and the control parameters, the optimization direction and the amplitude at which the control parameters are to be adjusted next are determined, thereby updating the control parameters. Then, returning to step S10, the at least one control parameter is acquired again.
  • the number of predetermined thresholds may be set for the number of the at least one control parameter.
  • the difference between the first control parameters (eg, KPPS) acquired twice before and after may be referred to as a first difference
  • the difference between the second control parameters (eg, KIPS) acquired twice before and after may be referred to as a second difference.
  • a first predetermined threshold and a second predetermined threshold may be set for the first difference and the second difference, respectively.
  • the same predetermined threshold may be set for the first difference and the second difference. It should be understood that the number of control parameters and predetermined thresholds is not limited thereto.
  • step S60 when the evaluation value satisfies the predetermined condition, the acquired at least one control parameter is taken as the final control parameter.
  • control parameters can be used in the design, simulation and actual operation of subsequent wind turbines to conveniently and efficiently optimize the corresponding loads.
  • Table 1 shows an example of comparing the loads of the above-described plurality of positions before and after the method as described in FIG. 1 and the values of the control parameters.
  • the optimization algorithm is used to update the control parameters of the pitch controller model, so that the fatigue load of the bottom Mx, the fatigue load of the bottom of the tower My, the fatigue load of the blade root My, The ultimate load of the bottom of My Tower has been reduced to some extent. Although the ultimate load of the blade root My is slightly higher than that before the optimization, the load of the wind turbine is generally optimized from the system point of view.
  • FIG. 2 is a block diagram showing the structure of an apparatus for optimizing the load of a wind power generator according to an embodiment of the present application.
  • the apparatus for optimizing the load of a wind power generator includes: a control parameter acquisition module 10, a load acquisition module 20, a load quantization module 30, an evaluation value judgment module 40, and a control parameter update module. 50 and control parameter providing module 60.
  • the control parameter obtaining module 10 acquires at least one control parameter of the wind power generator set, and the loads of the plurality of positions of the wind power generator set are affected by the at least one control parameter.
  • control parameters related to the control strategy of the wind turbine ie, for setting the control strategy
  • a control parameter of a predetermined controller model of the wind turbine may be obtained as at least one control parameter.
  • the control parameter of the predetermined controller model is a control parameter of the wind turbine associated with a plurality of loads of the wind turbine, the plurality of loads being affected by the control parameter.
  • control parameter obtaining module 10 acquires the control parameter of the predetermined controller model as the at least one control parameter
  • the acquired at least one control parameter may be the control parameter of the same controller model, or may be different controllers.
  • the control parameters of the model are not limited in this application.
  • the predetermined controller model may be a pitch controller model, a torque controller model, or a filter model.
  • control parameter acquisition module 10 may obtain the current value of the control parameter.
  • the control parameter acquisition module 10 may obtain any value within the range of values of the control parameter as the current value.
  • the apparatus may further include an initialization module (not shown) for initializing the at least one control parameter.
  • the initialization module can initialize control parameters of a predetermined controller model.
  • the control parameter acquisition module may obtain an initialization value of the control parameter as the current value.
  • the load acquisition module 20 may acquire load values for a plurality of locations of the wind turbine set corresponding to the at least one control parameter.
  • the load acquisition module 20 may simulate a complete machine model of the wind turbine based on the current value of the acquired at least one control parameter, thereby obtaining load values for the plurality of locations corresponding to the at least one control parameter.
  • the load acquisition module 20 may also simulate the overall model of the wind turbine based on the acquired at least one control parameter to obtain a value of a predetermined operational parameter of the wind turbine.
  • the load quantification module 30 quantizes the plurality of load values into a single evaluation value using a predetermined evaluation model.
  • the load quantification module 30 may quantize the plurality of load values into one of the evaluation values associated with the minimum load using a predetermined evaluation model to perform subsequent optimizations based thereon.
  • the predetermined evaluation model may be various functions capable of implementing the evaluation function in some embodiments of the present application, and the quantized evaluation value is a value of the function.
  • the predetermined evaluation model may be a fitness function.
  • the predetermined evaluation model may be the product of the sum of the plurality of loads (eg, the accumulated value) and the constraint value.
  • the predetermined evaluation model may be a product of a weighted sum (eg, an accumulated weighting value) of the plurality of loads respectively weighted to the constraint value.
  • the load quantification module 30 may apply respective weights to a plurality of loads in the evaluation model according to different degrees of attention to the plurality of loads, and may also apply respective weights to the plurality of loads in consideration of different magnitudes between the plurality of loads, but This application is not limited to this.
  • the constraint values as described above may reflect constraints for predetermined operational parameters that need to be met for normal operation of the wind turbine.
  • the load quantification module 30 may determine the constraint value based on constraints for the predetermined operational parameters that need to be met for normal operation of the wind turbine.
  • the predetermined operational parameter may include at least one of a generator rotational speed and a cabin vibration acceleration, but the application is not limited thereto.
  • the constraints described above may include the generator speed being within a predetermined range of generator speeds and the cabin vibration acceleration being within a predetermined range of cabin vibration acceleration.
  • the value of the predetermined operational parameter of the wind turbine can be simulated by the load acquisition module 20 based on the acquired at least one control parameter. Therefore, the constraint value can be determined by the load quantization module 30 by determining whether the value of the simulated predetermined operational parameter satisfies the above constraint.
  • the step of determining the constraint value by the load quantification module 30 may include: when the predetermined operational parameter satisfies the constraint condition, the load quantization module 30 sets the constraint value to a predetermined value; when the predetermined operational parameter does not satisfy the constraint condition The load quantization module 30 sets the constraint value to a value greater than a predetermined value.
  • the predetermined value may be preset by the load quantization module 30.
  • the load quantization module 30 may determine the constraint value according to the degree to which the abnormality of the predetermined operational parameter can be accepted (or the magnitude of the abnormality). A degree greater than a predetermined value.
  • the load quantification module 30 may set the constraint value to a value greater than a predetermined value, thereby increasing the The product of the weighted value of the quantized evaluation value and the product of the constraint value.
  • this is merely an example, and the application is not limited thereto.
  • the evaluation value determination module 40 determines whether the evaluation value satisfies a predetermined condition.
  • the control parameter update module 50 updates the control parameter based on the evaluation value to update the control parameter using a predetermined optimization algorithm, and the control parameter acquisition module 10 acquires the control parameter again.
  • the optimization problem of optimizing the control parameters is such that the evaluation value corresponding to the updated control parameter satisfies a predetermined condition.
  • the predetermined condition may mean that the evaluation value reaches a minimum value.
  • the predetermined condition may mean that the evaluation value quantized by the load quantization module 30 corresponding to the at least one control parameter acquired by the control parameter acquisition module 10 reaches a minimum value.
  • the evaluation value determining module 40 may determine that the evaluation value reaches a minimum value.
  • the control parameter update module 50 may optimize the control parameters based on the quantized evaluation values using existing various optimization algorithms to update the control parameters.
  • the optimization algorithm used by the control parameter update module 50 may be a genetic algorithm or a simplex method, but the application is not limited thereto.
  • the control parameter update module 50 may select at least one acquired by the control parameter acquisition module 10 in the predetermined optimization algorithm.
  • the evaluation value quantized by the load quantization module 30 corresponding to the control parameter is compared with the minimum value of all the previous evaluation values, and based on the comparison result, according to the correspondence relationship between the evaluation value and the control parameter, it is determined that the control parameter is next.
  • the direction and magnitude of the adjustment are optimized to update the control parameters.
  • control parameter acquisition module 10 acquires the at least one control parameter
  • the load acquisition module 20 acquires the load values of the plurality of locations corresponding to the at least one control parameter
  • the load quantization module 30 uses the predetermined evaluation model to the plurality of locations.
  • the load value of the position is quantized into a single evaluation value
  • the evaluation value judging module 40 judges whether the evaluation value satisfies a predetermined condition.
  • the control parameter update module 50 uses the predetermined optimization algorithm to calculate the at least one control parameter based on the evaluation value. optimize.
  • the evaluation value judging module 40 may set the number of predetermined thresholds for the number of the at least one control parameter.
  • the difference between the first control parameters acquired twice before and after may be referred to as the first difference
  • the difference between the second control parameters acquired twice before and after may be referred to as the second difference.
  • a first predetermined threshold and a second predetermined threshold may be set for the first difference and the second difference, respectively.
  • the same predetermined threshold may be set for the first difference and the second difference. It should be understood that the number of control parameters and predetermined thresholds is not limited thereto.
  • the control parameter providing module 60 when the evaluation value judging module 40 judges that the evaluation value satisfies the predetermined condition, the control parameter providing module 60 provides at least one control parameter acquired by the control parameter obtaining module 10 as the final control parameter.
  • the control parameter providing module 60 can provide the control parameters for design, simulation, and actual operation of the subsequent wind turbine to conveniently and efficiently optimize the corresponding load.
  • Embodiments of the present application also provide a control system.
  • the control system includes a processor and a memory.
  • the memory is used to store computer programs.
  • the computer program is executed by a processor such that the processor executes a computer program for the method of optimizing the load of the wind turbine as described above.
  • Embodiments of the present application also provide a computer readable storage medium storing a computer program.
  • the computer readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform the method described above for optimizing the load of the wind turbine.
  • the computer readable recording medium is any data storage device that can store data read by a computer system. Examples of the computer readable recording medium include read only memory, random access memory, read-only optical disk, magnetic tape, floppy disk, optical data storage device, and carrier wave (such as data transmission via the Internet via a wired or wireless transmission path).
  • the above method and apparatus for optimizing the load of a wind power generator using the embodiment of the present application can automatically update the control parameters based on the controller model of the wind turbine using an optimization algorithm, instead of based on the overall model of the wind turbine
  • the linearized data analyzes the time domain and frequency domain and manually adjusts the control parameters, thereby greatly saving labor and resource costs in the design process of the wind turbine.
  • the above method and apparatus for optimizing the load of the wind power generator according to the embodiment of the present application can flexibly select a plurality of optimization algorithms, and the method and the device can be extended not only to the load but also to the power generation amount, the power curve, and the like. Optimization of various indicators and update of related control parameters.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (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)
  • Wind Motors (AREA)

Abstract

L'invention concerne un procédé d'optimisation des charges d'une éolienne. Ledit procédé comprend les étapes consistant à : obtenir au moins un paramètre de commande d'une éolienne; obtenir des valeurs de charge à de multiples positions de l'éolienne; quantifier les valeurs de charge aux multiples positions en valeurs d'évaluation individuelles; déterminer si les valeurs d'évaluation satisfont une condition prédéterminée; si les valeurs d'évaluation ne satisfont pas la condition prédéterminée, optimiser l'au moins un paramètre de commande à l'aide d'un algorithme d'optimisation prédéterminé; et si les valeurs d'évaluation satisfont la condition prédéterminée, utiliser l'au moins un paramètre de commande comme paramètre de commande final. En utilisant le procédé et l'appareil, de multiples charges d'une éolienne peuvent être quantifiées, et sur la base des valeurs quantifiées, les paramètres de dispositif de commande de l'éolienne sont optimisés à l'aide d'un algorithme d'optimisation, pour obtenir un résultat de charge optimisé.
PCT/CN2018/082347 2017-12-25 2018-04-09 Procédé et appareil d'optimisation de charges d'éolienne WO2019127974A1 (fr)

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CN201711421605.8 2017-12-25
CN201711421605.8A CN109962493A (zh) 2017-12-25 2017-12-25 用于优化风力发电机组的载荷的方法和装置

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CN111339709B (zh) * 2020-03-27 2021-08-06 上海电气风电集团股份有限公司 海上固定式风机基础的强度校核方法、系统和电子设备
CN111997831B (zh) * 2020-09-01 2021-11-19 新疆金风科技股份有限公司 风电机组的载荷控制方法和装置

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CN105408625A (zh) * 2013-07-30 2016-03-16 维斯塔斯风力系统集团公司 基于叶片上测量的负荷和加速度的风力涡轮机的操作方法及装置
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