A reverse reconstruction method and system of an approximate aeroelastic model of an in-service wind turbine and related devices

Abstract

The application provides a kind of reverse reconstruction method, system and related device of in-service wind turbine whole machine approximate aeroelastic model, belong to wind power generation technical field, method includes: first according to rated power and wind wheel diameter from aeroelastic simulation model library select matched preset basic model;Then the actual technical parameters of unit, blade and tower information are updated to the model, obtain updated model;Subsequently, the blade mass and stiffness distribution in the model are calibrated according to the blade geometric information;Finally, the aerodynamic performance is calibrated by comparing the design power curve and thrust curve, so as to obtain the whole machine approximate aeroelastic model.The application can construct high-fidelity whole machine aeroelastic model without original factory confidential data, and provide independent and reliable digital simulation tool for in-service unit load analysis, life evaluation and operation optimization.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation technology, specifically relating to a method, system and related devices for reverse reconstruction of an approximate aeroelastic model of an in-service wind turbine. Background Technology

[0002] A wind turbine aeroelastic model is a core tool for calculating turbine loads, assessing fatigue life, analyzing dynamic characteristics, verifying control strategies, and predicting power generation performance. A high-fidelity aeroelastic model can accurately simulate the aerodynamic-servo-elastic coupling response of a wind turbine under complex wind conditions, serving as the cornerstone for design certification, safety assessment, and optimized operation.

[0003] However, for operational wind turbines, their complete and certified aeroelastic models are typically kept as core technical secrets by the turbine manufacturers and are generally not disclosed to wind farm owners, operators, or third-party technical service providers. This results in a lack of effective digital simulation tools for relevant entities when conducting in-depth turbine inspections, remaining life assessments, site-specific adaptability analyses, feasibility studies for technological upgrades (such as lengthening blades and upgrading towers), and accident inversion analyses. They often have to rely on empirical formulas or simplified models, leading to insufficient accuracy in the analysis results and potentially causing technical risks or misjudgments of economic viability.

[0004] Currently, attempts to reverse engineer in-service units in the industry mostly rely on limited publicly available technical parameters and operational data (Supervisory Control And Data Acquisition, SCADA). The methods are fragmented and have significant limitations: First, key structural dynamic characteristics (such as higher-order modes of blades and towers) and detailed aerodynamic, mass, and stiffness distribution data are lacking; second, there is a lack of systematic multi-source data fusion and model calibration processes, resulting in low accuracy and poor reliability of the reconstructed models, making them difficult to apply to load and life assessment scenarios with strict accuracy requirements.

[0005] Therefore, there is an urgent need for a systematic, practical, and relatively accurate method that can reverse engineer an approximate whole-machine aeroelastic model for engineering analysis of in-service wind turbines without requiring the original manufacturer's confidential model. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system and related apparatus for reverse reconstruction of an approximate aeroelastic model of an in-service wind turbine, which solves the above-mentioned shortcomings in the construction of existing approximate aeroelastic models of in-service wind turbines.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for reverse reconstruction of an approximate aeroelastic model of an in-service wind turbine generator, comprising the following steps: Select a gaseous simulation model from the known gaseous simulation model library that is equal to or close to the rated power and rotor diameter of the in-service wind turbine to be tested, and use it as the preset basic model for the in-service wind turbine to be tested. The acquired technical parameters, blade geometry, airfoil aerodynamic parameters, and tower parameter distribution information of the wind turbine under test are updated into the preset basic model to obtain the updated basic model. Based on the blade geometry information of the in-service wind turbine under test, the blade mass and stiffness distribution in the updated basic model are compared and calibrated until the accuracy requirements are met, thus obtaining the calibrated basic model. Based on the design power curve and design thrust curve of the in-service wind turbine under test, the calibrated basic model is compared and calibrated until the accuracy requirements are met, so as to obtain the approximate aeroelastic model of the whole machine corresponding to the in-service wind turbine under test.

[0008] Preferably, the blade mass and stiffness distribution in the updated basic model are compared and calibrated based on the blade geometry information of the in-service wind turbine to be tested until the accuracy requirements are met, thus obtaining the calibrated basic model. The specific method is as follows: S1, Perform modal testing on the blades of the in-service wind turbine under test to obtain the measured flapping natural frequency and the measured shimmy natural frequency of the blades; S2, based on the blade geometry information of the wind turbine unit under test, the blade mass and stiffness distribution in the updated basic model are calibrated to obtain the calibrated preliminary basic model. S3, Calculate the flapping natural frequency and oscillation natural frequency of the blade in the preliminary basic model after calibration; S4, compare the swing natural frequency and the oscillation natural frequency with the corresponding measured swing natural frequency and measured oscillation natural frequency, respectively, where: If the error between the measured swing natural frequency and the swing natural frequency or the measured oscillation natural frequency and the oscillation natural frequency is greater than the set threshold, then return to S2; Otherwise, complete the calibration of the blades in the updated base model to obtain the calibrated base model.

[0009] Preferably, the blades of the in-service wind turbine under test are subjected to modal testing using the exciter hammering method or the environmental vibration method to obtain the measured flapping natural frequency and the measured oscillation natural frequency of the blades.

[0010] Preferably, the measured natural waving frequencies include the first-order natural waving frequency and the second-order natural waving frequency; The measured natural frequencies of the oscillation include the first-order natural frequency and the second-order natural frequency.

[0011] Preferably, the calibrated basic model is compared and calibrated according to the design power curve and design thrust curve of the wind turbine under test until the accuracy requirements are met, thus obtaining the approximate aeroelastic model of the entire wind turbine under test. The specific method is as follows: Under standard wind conditions, calculate the static power curve and thrust curve corresponding to the calibrated basic model; The design power curve and design thrust curve are compared with the static power curve and thrust curve, where: If, at the same wind speed, the error between the static power value or thrust value and the corresponding design value is greater than the preset difference value, then the parameters affecting power and thrust in the calibrated basic model are calibrated and returned to S1. Otherwise, an approximate aeroelastic model of the entire in-service wind turbine unit to be tested is obtained.

[0012] Secondly, the present invention provides a reverse reconstruction system for an approximate aeroelastic model of an in-service wind turbine generator, comprising: The model selection unit is used to select an aeroelastic simulation model from the known aeroelastic simulation model library that is equal to or close to the rated power and rotor diameter of the wind turbine unit under test, and use it as the preset basic model corresponding to the wind turbine unit under test. The model update unit is used to update the acquired technical parameters, blade geometry information, airfoil aerodynamic parameters and tower parameter distribution information of the wind turbine under test to the preset basic model, so as to obtain the updated basic model. The model calibration unit is used to compare and calibrate the blade mass and stiffness distribution in the updated basic model based on the blade geometry information of the wind turbine under test until the accuracy requirements are met, thus obtaining the calibrated basic model. Meanwhile, the calibrated basic model is compared and calibrated according to the design power curve and design thrust curve of the wind turbine under test until the accuracy requirements are met, so as to obtain the approximate aeroelastic model of the whole wind turbine under test.

[0013] Thirdly, the present invention provides an electronic device including a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the reverse reconstruction method of an approximate aeroelastic model of an in-service wind turbine.

[0014] Fourthly, the present invention provides a computing device cluster, comprising at least one computing device, each computing device including a processor and a memory; The processor of the at least one computing device is used to execute instructions stored in the memory of the at least one computing device, so that the cluster of computing devices executes the reverse reconstruction method of an approximate aeroelastic model of an in-service wind turbine.

[0015] Fifthly, the present invention provides a computer program product containing computer-executable instructions, which, when executed, implement the reverse reconstruction method for an approximate aeroelastic model of an in-service wind turbine unit.

[0016] Sixthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the reverse reconstruction method for an approximate aeroelastic model of an in-service wind turbine generator.

[0017] Compared with the prior art, the beneficial effects achieved by the present invention are: This invention provides a reverse reconstruction method for an approximate aeroelastic model of an in-service wind turbine. By selecting a pre-set base model that matches the macroscopic parameters of the target turbine, a reliable initial framework is provided for reverse reconstruction, effectively avoiding the complexity and uncertainty of modeling from scratch. Then, by importing the actual technical parameters of the turbine, blade geometry, airfoil aerodynamic parameters, and tower parameter distribution information obtained from field testing and simulation analysis, key data updates are performed on the pre-set base model, laying the data foundation for model fidelity. Next, using the actual dynamic characteristics of the blades obtained from field modal testing as a benchmark, the blade mass and stiffness distribution in the model are iteratively calibrated to ensure the accuracy of the model at the structural dynamics level. Finally, using the official design power and thrust curves of the turbine as the final benchmark, the parameters affecting aerodynamic performance and control logic in the model are calibrated overall, thereby ensuring a high degree of consistency between the model and the actual turbine in terms of overall aerodynamic response and energy capture characteristics. Through this system's reverse reconstruction process based on dual calibration and verification, a high-fidelity approximate aeroelastic model of the entire unit can be constructed without requiring original factory confidential data. This provides an independent, reliable, and practical digital simulation tool for load analysis, life assessment, technical modification verification, and operation optimization of in-service units. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an embodiment of the present invention. Detailed Implementation

[0019] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0020] It should be understood that, when used in this application specification, the term "comprising" indicates the presence of the described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0021] It should also be understood that the term “and / or” as used in this application specification means any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations.

[0022] As used in this application specification, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0023] Furthermore, in the description of this application, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0025] Example 1 This embodiment provides a method for reverse reconstruction of an approximate aeroelastic model of an in-service wind turbine, comprising the following steps: Step 1: Select an aeroelastic simulation model from the known aeroelastic simulation model library that is equal to or close to the rated power and rotor diameter of the wind turbine to be tested in service, and use it as the preset basic model corresponding to the wind turbine to be tested in service. Step 2: Update the obtained technical parameters, blade geometry information, airfoil aerodynamic parameters, and tower parameter distribution information of the wind turbine under test to the preset basic model to obtain the updated basic model; Step 3: Based on the blade geometry information of the in-service wind turbine to be tested, compare and calibrate the blade mass and stiffness distribution in the updated basic model until the accuracy requirements are met, and obtain the calibrated basic model. Step 4: Based on the design power curve and design thrust curve of the wind turbine under test, the calibrated basic model is compared and calibrated until the accuracy requirements are met, so as to obtain the approximate aeroelastic model of the whole wind turbine under test.

[0026] This method can effectively solve the industry pain point that operators and third-party assessment agencies cannot conduct in-depth analysis of load, life, performance and other aspects due to the inability to obtain the original factory model, and provides a reliable digital analysis foundation for the refined management, safety assessment, efficiency improvement and life extension of in-service wind turbine units.

[0027] Example 2 Based on Example 1, this example provides a reverse reconstruction method for an approximate aeroelastic model of an in-service wind turbine. The method involves comparing and calibrating the blade mass and stiffness distribution in the updated basic model according to the blade geometry information of the in-service wind turbine under test until the accuracy requirements are met, thus obtaining the calibrated basic model. The specific method is as follows: S1, Perform modal testing on the blades of the in-service wind turbine under test to obtain the measured flapping natural frequency and the measured shimmy natural frequency of the blades; S2, based on the blade geometry information of the wind turbine unit under test, the blade mass and stiffness distribution in the updated basic model are calibrated to obtain the calibrated preliminary basic model. S3, Calculate the flapping natural frequency and oscillation natural frequency of the blade in the preliminary basic model after calibration; S4, compare the swing natural frequency and the oscillation natural frequency with the corresponding measured swing natural frequency and measured oscillation natural frequency, respectively, where: If the error between the measured swing natural frequency and the swing natural frequency or the measured oscillation natural frequency and the oscillation natural frequency is greater than the set threshold, then return to S2; Otherwise, complete the calibration of the blades in the updated base model to obtain the calibrated base model.

[0028] Example 3 Based on Example 1, this example provides a reverse reconstruction method for an approximate aeroelastic model of an in-service wind turbine. The method involves comparing and calibrating the calibrated base model with the design power curve and design thrust curve corresponding to the in-service wind turbine under test until the accuracy requirements are met, thus obtaining the approximate aeroelastic model of the in-service wind turbine under test. The specific method is as follows: S1, under standard wind conditions, calculate the static power curve and thrust curve corresponding to the calibrated basic model; S2, compare the design power curve and design thrust curve with the static power curve and thrust curve, where: If, at the same wind speed, the error between the static power value or thrust value and the corresponding design value is greater than the preset difference value, then the parameters affecting power and thrust in the calibrated basic model are calibrated and returned to S1. Otherwise, an approximate aeroelastic model of the entire in-service wind turbine unit to be tested is obtained.

[0029] Example 4 like Figure 1 As shown in the figure, the reverse reconstruction method of the approximate aeroelastic model of an in-service wind turbine provided in this embodiment specifically includes the following steps: Step 1: Collect documents such as purchase contracts, technical agreements or specifications, type certification certificates, and component drawings of the wind turbines to be tested, and obtain the technical parameters, design power curves, and design thrust curves of the wind turbines to be tested.

[0030] In this embodiment, the acquired wind turbine technical parameters typically include, but are not limited to: turbine class, rated power, rated wind speed, speed range, rated speed, rotor diameter, hub center height, hub diameter, generator and hub weight, nacelle dimensions and weight, tower weight, operating wind speed range, design life, annual average wind speed at hub center height, reference wind speed, average inflow tilt angle, 50-year return period extreme wind speed at hub center height, air density, blade type and length, blade weight, blade cone angle, tilt angle, hub material and drawings, pitch system type, main shaft material and drawings, gearbox transmission ratio, tower material and drawings, etc.

[0031] Step 2: Perform modal testing on the blades of the in-service wind turbine to be tested, and obtain the measured flapping natural frequency and the measured shimmy natural frequency of the blades, where: The measured natural frequencies of the wave include the first-order natural frequency of the wave and the second-order natural frequency of the wave; The measured natural frequencies of the oscillation include the first-order natural frequency and the second-order natural frequency.

[0032] In this embodiment, the blade is subjected to modal testing using either the exciter hammering method or the environmental vibration-based method.

[0033] Step 3: Use a high-precision 3D laser scanner to scan the shape data of each blade of the in-service wind turbine under test when it is stationary. All blade profile data are aligned and averaged to eliminate the influence of manufacturing tolerances and installation errors, and key geometric information at different spanwise positions of the blade is obtained. The key geometric information includes airfoil profile, chord length, pre-bending distance, thickness, torsion angle, and main beam cap position.

[0034] Step 4: Use computational fluid dynamics software to perform two-dimensional turbulence simulations on the airfoils at different spanwise positions obtained in Step 3. Set up a range covering the expected angle of attack and the design Reynolds number, solve the Navier-Stokes equations, and calculate complete aerodynamic parameters for each airfoil at different angles of attack, such as surface pressure coefficient distribution, lift coefficient, drag coefficient, and pitching moment coefficient, to form a blade airfoil aerodynamic parameter library.

[0035] In this embodiment, the computational fluid dynamics software is ANSYS Fluent.

[0036] The expected angle of attack range for coverage is set to -180° to 180°.

[0037] Step 5: Using structural finite element analysis software, and combining the detailed tower drawings and material properties obtained in Step 1, establish the finite element model of the tower.

[0038] The obtained finite element model is analyzed and calculated to extract the tower parameter distribution information, which includes the equivalent diameter of the tower along the height direction, the mass per unit length, the bending stiffness EI, and the axial stiffness EA.

[0039] In this embodiment, the structural finite element analysis software is SAP2000.

[0040] Step 6: Select a general-purpose wind turbine aeroelastic simulation software and choose a preset basic model from the software model library that corresponds to the wind turbine with a rated power and rotor diameter that are equal to or close to the wind turbine being tested in service.

[0041] The technical parameters obtained in step 1, the blade geometry information obtained in step 3, the airfoil aerodynamic parameter library obtained in step 4, and the tower parameter distribution information obtained in step 5 are updated one by one into the preset basic model, replacing their original parameters, to obtain the updated basic model.

[0042] Step 7: Based on the common sense of blade design and the blade length, shape and weight information in Step 1 and Step 3, make preliminary modifications to the default or incompletely defined blade mass and stiffness distribution in the updated basic model to obtain the calibrated preliminary basic model. The flapping natural frequency and teeter natural frequency of the blades in the preliminary basic model after calibration are calculated, where: The flapping natural frequencies include the first-order flapping natural frequency and the second-order flapping natural frequency of the blade; The natural frequencies of the oscillation include the first-order natural frequency of the blade and the second-order natural frequency of the blade oscillation.

[0043] In this embodiment, the basic principles of blade design are the variation of mass distribution along the span and the relationship between stiffness and geometry.

[0044] Step 8: Compare the blade natural frequencies calculated in Step 7 with the measured natural frequencies obtained in Step 2, where: If the relative error between the blade's natural frequency and the measured natural frequency is less than 1%, then the blade calibration in the updated basic model is completed, the calibrated basic model is obtained, and the process proceeds to step 9. Otherwise, return to step 7 and adjust the mass or stiffness distribution parameters of the blades accordingly based on the direction of the error.

[0045] Step 9: Under standard wind conditions in the aeroelastic software, calculate the static power curve and thrust curve corresponding to the calibrated basic model.

[0046] In this embodiment, the quasi-wind condition is the IEC standard wind condition.

[0047] Step 10: Compare the design power curve and design thrust curve with the static power curve and thrust curve, wherein: If, at the same wind speed, the difference between the static power or thrust value and the corresponding design value is greater than or equal to the preset difference value, it indicates that the model still needs adjustment in terms of overall aerodynamic performance or control logic. At this point, keeping the tower, calibrated blades, and main geometric parameters unchanged, fine-tune other parameters in the aeroelastic model that may affect power and thrust, and return to step 9; Otherwise, an approximate aeroelastic model of the entire in-service wind turbine unit to be tested is obtained.

[0048] In this embodiment, one or more of the following are finely adjusted: generator efficiency curve, transmission system loss curve, air density correction coefficient, pitch angle control parameter and air density correction coefficient, to reduce the impact on power and thrust.

[0049] In this embodiment, the difference value is 5%.

[0050] Step 11: The approximate aeroelastic model of the entire wind turbine unit under test is reconstructed.

[0051] Step 12: The constructed approximate aeroelastic model of the in-service wind turbine can be used to carry out a series of subsequent high-value work, including but not limited to: Remaining life assessment: Input the actual wind spectrum of the site to perform fatigue load simulation and damage calculation.

[0052] Load calculation and safety verification: Calculate the ultimate load for specific complex wind conditions or post-technical upgrades.

[0053] Dynamic characteristics and stability analysis: This study investigates the modal and vibration characteristics of the unit under specific operating conditions.

[0054] Performance prediction and optimization: assess the potential for increased power generation under different control strategies.

[0055] Design consistency verification: Compare and analyze the conformity between the actual operating characteristics of the unit and the original design.

[0056] Example 5 This embodiment provides a reverse reconstruction system for an approximate aeroelastic model of an in-service wind turbine, comprising: The model selection unit is used to select an aeroelastic simulation model from the known aeroelastic simulation model library that is equal to or close to the rated power and rotor diameter of the wind turbine unit under test, and use it as the preset basic model corresponding to the wind turbine unit under test. The model update unit is used to update the acquired technical parameters, blade geometry information, airfoil aerodynamic parameters and tower parameter distribution information of the wind turbine under test to the preset basic model, so as to obtain the updated basic model. The model calibration unit is used to compare and calibrate the blade mass and stiffness distribution in the updated basic model based on the blade geometry information of the wind turbine under test until the accuracy requirements are met, thus obtaining the calibrated basic model. Meanwhile, the calibrated basic model is compared and calibrated according to the design power curve and design thrust curve of the wind turbine under test until the accuracy requirements are met, so as to obtain the approximate aeroelastic model of the whole wind turbine under test.

[0057] Example 6 This embodiment also provides a computing device. The computing device includes a bus, a processor, a memory, and a communication interface. The processor, memory, and communication interface communicate with each other via the bus. The computing device can be a server or a terminal device. It should be understood that this application does not limit the number of processors and memory in the computing device.

[0058] A bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, a bus can include a path for transmitting information between various components of a computing device (e.g., memory, processor, communication interfaces).

[0059] The processor may include any one or more of the following: central processing unit (CPU), graphics processing unit (GPU), tensor processing unit (TPU), application specific integrated circuit (ASIC), field-programmable gate array (FPGA), microprocessor (MP), or digital signal processor (DSP).

[0060] Memory can include volatile memory, such as random access memory (RAM). Processors can also include non-volatile memory. volatile memory, such as read-only memory (ROM). ROM (memory only), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0061] The memory stores executable program code, which the processor executes to implement the functions of the aforementioned units, thereby achieving, for example, the method described in Embodiment 1. That is, the memory may store instructions for the methods and functions relating to the computing device in any of the above embodiments.

[0062] The communication interface uses transceiver modules such as, but not limited to, network interface cards and transceivers to enable communication between computing devices and other devices or communication networks.

[0063] Example 7 This embodiment also provides a computing device cluster. The computing device cluster includes at least one computing device. The computing device can be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device can also be a terminal device such as a desktop computer, a laptop computer, or a smartphone.

[0064] The computing device cluster includes at least one computing device. The memory of one or more computing devices in the computing device cluster may store the same instructions for performing the methods and functions related to the computing devices in any of the above embodiments.

[0065] In some possible implementations, the memory of one or more computing devices in the computing device cluster may also store partial instructions for performing the methods and functions of the computing devices involved in any of the above embodiments. In other words, a combination of one or more computing devices can jointly execute the instructions for performing the methods and functions of the computing devices.

[0066] It should be noted that the memory in different computing devices within a computing device cluster can store different instructions, which are used to execute parts of the device's functions.

[0067] In some possible implementations, one or more computing devices in a computing device cluster can be connected via a network. This network can be a wide area network (WAN) or a local area network (LAN), etc. Two computing devices are connected via the network. Specifically, they connect to the network through communication interfaces on each computing device.

[0068] Embodiments of this disclosure also provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods and functions related to a computing device in any of the above embodiments.

[0069] Example 8 This embodiment also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, cause the processor to perform the methods and functions of the computing device involved in any of the above embodiments.

[0070] Generally, the various embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software, which can be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of this disclosure are shown and described as block diagrams, flowcharts, or represented using some other illustration, it should be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as, as non-limiting examples, in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0071] Example 9 This embodiment provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods as described above with reference to the accompanying drawings. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0072] Computer program code used to implement the methods of this disclosure may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the computer or other programmable data processing apparatus, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be performed. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0073] In the context of this disclosure, computer program code or related data may be carried on any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0074] Computer-readable media can be any tangible medium that contains or stores programs for or relating to an instruction execution system, apparatus, or device, or a data storage device such as a data center containing one or more available media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. More detailed examples of computer-readable storage media include electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0075] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for reverse reconstruction of an approximate aeroelastic model of an in-service wind turbine generator set, characterized in that, Includes the following steps: Select a gaseous simulation model from the known gaseous simulation model library that is equal to or close to the rated power and rotor diameter of the in-service wind turbine to be tested, and use it as the preset basic model for the in-service wind turbine to be tested. The acquired technical parameters, blade geometry, airfoil aerodynamic parameters, and tower parameter distribution information of the wind turbine under test are updated into the preset basic model to obtain the updated basic model. Based on the blade geometry information of the in-service wind turbine under test, the blade mass and stiffness distribution in the updated basic model are compared and calibrated until the accuracy requirements are met, thus obtaining the calibrated basic model. Based on the design power curve and design thrust curve of the in-service wind turbine under test, the calibrated basic model is compared and calibrated until the accuracy requirements are met, thus obtaining the approximate aeroelastic model of the whole unit corresponding to the in-service wind turbine under test.

2. The method for reverse reconstruction of an approximate aeroelastic model of an in-service wind turbine as described in claim 1, characterized in that, Based on the blade geometry information of the in-service wind turbine under test, the blade mass and stiffness distribution in the updated basic model are compared and calibrated until the accuracy requirements are met, thus obtaining the calibrated basic model. The specific method is as follows: S1, Perform modal testing on the blades of the in-service wind turbine under test to obtain the measured flapping natural frequency and the measured shimmy natural frequency of the blades; S2, based on the blade geometry information of the wind turbine unit under test, the blade mass and stiffness distribution in the updated basic model are calibrated to obtain the calibrated preliminary basic model. S3, Calculate the flapping natural frequency and oscillation natural frequency of the blade in the preliminary basic model after calibration; S4, compare the swing natural frequency and the oscillation natural frequency with the corresponding measured swing natural frequency and measured oscillation natural frequency, respectively, where: If the error between the measured swing natural frequency and the swing natural frequency or the measured swing natural frequency and the swing natural frequency is greater than the set threshold, then return to S2; Otherwise, complete the calibration of the blades in the updated base model to obtain the calibrated base model.

3. The method for reverse reconstruction of an approximate aeroelastic model of an in-service wind turbine as described in claim 2, characterized in that, Modal tests were conducted on the blades of the in-service wind turbine under test using either the exciter hammering method or the environmental vibration method to obtain the measured flapping natural frequency and the measured oscillation natural frequency of the blades.

4. The method for reverse reconstruction of an approximate aeroelastic model of an in-service wind turbine as described in claim 2, characterized in that, The measured natural frequencies of the wave include the first-order natural frequency of the wave and the second-order natural frequency of the wave; The measured natural frequencies of the oscillation include the first-order natural frequency and the second-order natural frequency.

5. The method for reverse reconstruction of an approximate aeroelastic model of an in-service wind turbine as described in claim 1, characterized in that, Based on the design power curve and design thrust curve of the in-service wind turbine under test, the calibrated basic model is compared and calibrated until the accuracy requirements are met, thus obtaining the approximate aeroelastic model of the entire wind turbine under test. The specific method is as follows: S1, under standard wind conditions, calculate the static power curve and thrust curve corresponding to the calibrated basic model; S2, compare the design power curve and design thrust curve with the static power curve and thrust curve, where: If, at the same wind speed, the error between the static power value or thrust value and the corresponding design value is greater than the preset difference value, then the parameters affecting power and thrust in the calibrated basic model are calibrated and returned to S1. Otherwise, an approximate aeroelastic model of the entire in-service wind turbine unit to be tested is obtained.

6. A reverse reconstruction system for an approximate aeroelastic model of an in-service wind turbine generator set, characterized in that, include: The model selection unit is used to select an aeroelastic simulation model from the known aeroelastic simulation model library that is equal to or close to the rated power and rotor diameter of the wind turbine unit under test, and use it as the preset basic model corresponding to the wind turbine unit under test. The model update unit is used to update the acquired technical parameters, blade geometry information, airfoil aerodynamic parameters and tower parameter distribution information of the wind turbine under test to the preset basic model, so as to obtain the updated basic model. The model calibration unit is used to compare and calibrate the blade mass and stiffness distribution in the updated basic model based on the blade geometry information of the wind turbine under test until the accuracy requirements are met, thus obtaining the calibrated basic model. Meanwhile, the calibrated basic model is compared and calibrated according to the design power curve and design thrust curve of the wind turbine under test until the accuracy requirements are met, so as to obtain the approximate aeroelastic model of the whole wind turbine under test.

7. An electronic device, characterized in that, The device includes a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the electronic device performs a reverse reconstruction method for an approximate aeroelastic model of an in-service wind turbine as described in any one of claims 1 to 5.

8. A computing device cluster, characterized in that, It includes at least one computing device, each computing device including a processor and memory; The processor of the at least one computing device is used to execute instructions stored in the memory of the at least one computing device, so that the cluster of computing devices executes a reverse reconstruction method for an approximate aeroelastic model of an in-service wind turbine unit according to any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product contains computer-executable instructions, which, when executed, implement a reverse reconstruction method for an approximate aeroelastic model of an in-service wind turbine unit as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement a method for reverse reconstruction of an approximate aeroelastic model of an in-service wind turbine as described in any one of claims 1 to 5.

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