Wind turbine gearbox load spectrum construction optimization method and device

Abstract

The application relates to a wind turbine gearbox load spectrum construction optimization method and device, which comprises the following steps: constructing a pure torsional dynamics model of a wind turbine gearbox, reading a mixed optimization wind spectrum and generating an input torque in combination with an OpenFAST wind field simulation model, calculating the total torsional deformation displacement of the sun gear and the planetary gear, the gear ring and the planetary gear in each planetary gear train of the wind turbine gearbox to the meshing line projection, calculating the dynamic meshing force of each meshing pair of the wind turbine gearbox, converting the dynamic meshing force into time sequence dynamic meshing force, and inputting the time sequence dynamic meshing force into a contact stress formula and a bending stress formula respectively to obtain the contact stress time sequence data and the bending stress time sequence data of each meshing pair, and statistically processing the contact stress time sequence data and the bending stress time sequence data to obtain the gearbox fatigue load spectrum. The application solves the problems of life assessment deviation, difficulty in balancing lightweight and reliability caused by the traditional load spectrum.

Description

Technical Field

[0001] This application relates to the field of load spectrum construction, and in particular to a method for optimizing the construction of load spectrum for wind turbine gearboxes, as well as a corresponding apparatus. Background Technology

[0002] With the rapid development of offshore wind power towards deep-sea and ultra-large-scale applications, floating wind turbines (FOWTs) have become core equipment for developing deep-sea wind energy resources. As the core transmission component of wind turbines, the gearbox needs to operate for extended periods in the extremely harsh marine environment, enduring coupled loads from wind, waves, and currents. Its reliability and power performance directly determine the service life and operating efficiency of the turbine. However, the deep-sea environment has unique characteristics significantly different from nearshore and onshore environments: atmospheric stability varies dramatically in time and space, and turbulence exhibits characteristics of "significant low-frequency fluctuations, prominent large-scale coherent structures, and strong wave-induced turbulence coupling." Traditional load spectrum compilation methods based on flat terrain and neutral atmospheric conditions are no longer sufficient to meet engineering requirements.

[0003] Currently, the industry widely uses the Kaimal and Mann spectra recommended by the IEC 61400-1 standard for wind field simulation and load spectrum compilation. However, these spectral models have significant limitations: the Kaimal spectrum overemphasizes high-frequency turbulent components, neglecting the influence of low-frequency, large-amplitude turbulence in the deep sea; while the Mann spectrum considers spatial correlation, it does not fully couple ocean atmospheric stability and wave-induced turbulence effects; neither has been adapted to the large-scale coherent structures of complex terrain in the deep sea, resulting in significant deviations between the load spectra compiled based on them and actual operating conditions. This leads to problems such as inaccurate gearbox fatigue life assessment and a prominent contradiction between lightweight design and reliability. Therefore, developing wind spectrum models and load spectrum compilation methods adapted to deep-sea turbulent conditions has become a key technical bottleneck for improving the design accuracy and service reliability of deep-sea wind turbine gearboxes.

[0004] Existing methods for compiling wind turbine load spectra largely rely on traditional turbulence spectrum models, failing to adequately consider the low-frequency characteristics of deep-sea turbulence and the impact of atmospheric stability. This results in significant deviations between the load spectra and actual deep-sea operating conditions. For example, patent application CN120409360A discloses a simulation analysis method for floating wind turbines under wind and wave environments that considers atmospheric stability. This method couples the WRF-SWAN-OpenFAST platform, introduces Richardson's number for atmospheric stability classification, and establishes a linked simulation mechanism between the wind field and wave field, improving the physical consistency of the environmental simulation. However, this method does not optimize the wind spectrum model for the low-frequency, large-scale structure of deep-sea turbulence, still relying on traditional Kaimal or von Karman spectra. Furthermore, it does not directly connect environmental simulation with the compilation of gearbox dynamic meshing force and fatigue load spectra, thus failing to directly support gearbox load optimization design.

[0005] On the other hand, patent application CN121031102A proposes a numerical simulation method for scale conversion of inflow turbulence. This method achieves efficient simulation of multi-scale turbulence through scale decomposition and filtering techniques, providing a methodology for the accurate characterization of complex turbulence. However, this method focuses on the scale matching problem in turbulence simulation, failing to consider the coupling effect of atmospheric stability and wind and wave turbulence in the deep sea, and it does not extend to the load spectrum compilation of gearbox contact stress and bending stress, thus failing to meet the needs of fatigue life assessment for deep-sea wind turbine gearboxes.

[0006] In summary, existing methods for constructing wind turbine gearbox load spectra do not directly integrate environmental simulation with the compilation of dynamic meshing forces and fatigue load spectra, thus failing to directly support gearbox load optimization design. Furthermore, they do not consider the coupling effects of atmospheric stability and wind / wave turbulence in deep-sea environments, thus failing to meet the needs of fatigue life assessment for deep-sea wind turbine gearboxes. Summary of the Invention

[0007] The purpose of this application is to solve the above-mentioned problems by providing a method for constructing and optimizing the load spectrum of a wind turbine gearbox, as well as a corresponding device.

[0008] To achieve the various objectives of this application, the following technical solution is adopted:

[0009] A method for constructing and optimizing the load spectrum of a wind turbine gearbox, proposed to meet one of the purposes of this application, includes:

[0010] The first spatial distance-wind speed coherence model corresponding to the Kemal wind spectrum of the wind turbine gearbox is called. Based on the measured significant wave height, the first coherence length of the downwind turbulence under the deep-sea turbulence condition is corrected. The second coherence length of the vertical turbulence under the deep-sea turbulence condition is corrected according to the Monin-Obuhoff length. The attenuation coefficient under various atmospheric conditions is determined to construct the second spatial distance-wind speed coherence model under the deep-sea turbulence condition.

[0011] An anisotropic correction function under the deep-sea turbulent conditions is constructed. Based on the anisotropic correction function, a spatial spectrum tensor of the Mann spectrum is constructed to replace the first spatial distance-wind speed coherence model of the Kemal wind spectrum. Based on the time domain power spectrum of the Kemal wind spectrum and the anisotropic correction function, a hybrid optimized wind spectrum based on the time domain energy distribution is constructed.

[0012] A pure torsional dynamics model of the wind turbine gearbox is constructed. The OpenFAST wind field simulation model is used to read the hybrid optimized wind spectrum and generate input torque. The input torque is input into the pure torsional dynamics model to calculate the projection of the total torsional deformation displacement between the sun gear and planet gears, and between the ring gear and planet gears in each stage of the planetary gear train of the wind turbine gearbox onto the line of meshing.

[0013] Based on the projected amount, the dynamic meshing force of each meshing pair in the wind turbine gearbox is calculated by combining the time-varying meshing stiffness and meshing damping of each meshing pair. The dynamic meshing force is converted into time-series dynamic meshing force and input into the contact stress formula and bending stress formula respectively to obtain the contact stress time-series data and bending stress time-series data of each meshing pair.

[0014] The contact stress time series data and bending stress time series data are statistically analyzed to obtain the gearbox fatigue load spectrum with stress amplitude and average stress as variables corresponding to the number of cycles, thus completing the construction of the wind turbine gearbox load spectrum.

[0015] Optionally, the steps of correcting the first coherence length of downwind turbulence under deep-sea turbulence conditions based on measured significant wave heights, and correcting the second coherence length of vertical turbulence under the same conditions based on the Moning-Obukhov length, include:

[0016] Obtain the first coherence length of the downwind turbulence and the second coherence length of the vertical turbulence in the first spatial distance-wind speed coherence model;

[0017] Based on the measured significant wave height under the deep-sea turbulence condition, a wave height influence coefficient is introduced to correct the first coherence length of the downwind turbulence, thus obtaining the third coherence length of the downwind turbulence under the deep-sea turbulence condition.

[0018] Based on the Monin-Obukhov length, an atmospheric stability influence coefficient is introduced to correct the second coherence length of the vertical turbulence, thus obtaining the fourth coherence length of the vertical turbulence under the deep-sea turbulence condition.

[0019] Optionally, the steps for determining the attenuation coefficient under various atmospheric conditions include:

[0020] The Monin-Obukhov length under deep-sea turbulent conditions is obtained, and various atmospheric conditions under deep-sea turbulent conditions are determined based on the Monin-Obukhov length. The atmospheric conditions include atmospheric unstable conditions, atmospheric neutral conditions, and atmospheric stable conditions.

[0021] Expressions for the attenuation coefficient related to the Moning-Obukhov length are constructed to determine the attenuation coefficient under various atmospheric conditions.

[0022] Optionally, the steps for constructing the second spatial distance-wind speed coherence model include:

[0023] The third coherence length of downwind turbulence under the deep-sea turbulence condition, the fourth coherence length of vertical turbulence under the deep-sea turbulence condition, and the attenuation coefficient under various atmospheric conditions are obtained.

[0024] The third coherence length and the fourth coherence length are used as the corrected coherence lengths of the first spatial distance-wind speed coherence model. Based on the corrected coherence lengths and the attenuation coefficients under the various atmospheric conditions, a second spatial distance-wind speed coherence model under the deep-sea turbulent conditions is constructed.

[0025] Optionally, the step of constructing the anisotropy correction function under the deep-sea turbulent condition includes:

[0026] The wavenumber vector, the turbulence anisotropy parameters of the deep-sea turbulence condition, and the Monin-Obukhov length are obtained under the deep-sea turbulence condition. The wavenumber vector includes a downwind wavenumber component, a spanwise wavenumber component, and a vertical wavenumber component.

[0027] Based on the downwind wavenumber component, spanwise wavenumber component, and vertical wavenumber component, the wavenumber modulus is calculated and determined; based on the wavenumber vector, the wavenumber modulus, the turbulence anisotropy parameter, and the Monin-Obukhov length, the anisotropy correction function for the deep-sea turbulence condition is constructed.

[0028] Optionally, the steps for constructing the time-domain power spectrum of the Kemal wind spectrum include:

[0029] The average wind speed at the hub height of the wind turbine gearbox, the measured height, the friction speed, and the actual frequency are obtained. The dimensionless frequency is calculated based on the measured height, the average wind speed at the hub height, and the actual frequency.

[0030] The dimensionless frequency, the actual frequency, and the friction velocity are used to construct the time-domain power spectrum of the Kemal wind spectrum.

[0031] Optionally, the step of constructing a hybrid optimized wind spectrum based on the time-domain energy distribution according to the time-domain power spectrum of the Kemal wind spectrum and the anisotropy correction function includes:

[0032] Obtain the time-domain power spectrum of the Kemal wind spectrum, and an anisotropic correction function adapted to deep-sea turbulent conditions;

[0033] The square root value of the anisotropy correction function is calculated and determined. The square root value is then fused with the time domain power spectrum of the Kemmar wind spectrum to construct a hybrid optimized wind spectrum based on the time domain energy distribution. When the anisotropy parameter is zero, the hybrid optimized wind spectrum degenerates into a traditional isotropic Kemmar wind spectrum.

[0034] Optionally, the step of constructing the spatial spectral tensor of the Mann spectrum based on the anisotropy correction function includes:

[0035] The empirical constant of the Mann spectrum, the turbulent kinetic energy dissipation rate, and the anisotropy correction function under the deep-sea turbulent conditions were obtained.

[0036] The empirical constant of the Mann spectrum, the turbulent kinetic energy dissipation rate, the wavenumber modulus, and the anisotropy correction function are used to construct a spatial spectral tensor of the Mann spectrum adapted to the deep-sea turbulent conditions.

[0037] Optionally, the anisotropic correction function characterizes the non-uniformity of turbulence intensity, energy distribution, and vortex scale in the three spatial directions of downwind, span, and vertical. At the same time, it couples the modulation effect of atmospheric stability on the spatial structure of turbulence to optimize the directional characteristics of the Mann spectrum tensor.

[0038] A wind turbine gearbox load spectrum construction and optimization apparatus provided for another purpose of this application includes:

[0039] The coherence model optimization module is set to call the first spatial distance-wind speed coherence model corresponding to the Kemal wind spectrum of the wind turbine gearbox, correct the first coherence length of the downwind turbulence under the deep-sea turbulence condition based on the measured significant wave height, correct the second coherence length of the vertical turbulence under the deep-sea turbulence condition according to the Monin-Obukhov length, and determine the attenuation coefficient under various atmospheric conditions to construct the second spatial distance-wind speed coherence model under the deep-sea turbulence condition.

[0040] The hybrid optimized wind spectrum construction module is configured to construct an anisotropic correction function under the deep-sea turbulent conditions, construct the spatial spectrum tensor of the Mann spectrum based on the anisotropic correction function to replace the first spatial distance-wind speed coherence model of the Kemal wind spectrum, and construct a hybrid optimized wind spectrum based on the time domain energy distribution based on the time domain power spectrum of the Kemal wind spectrum and the anisotropic correction function.

[0041] The projection calculation module is configured to construct a pure torsional dynamics model of the wind turbine gearbox, read the hybrid optimized wind spectrum in conjunction with the OpenFAST wind field simulation model and generate an input torque, input the input torque into the pure torsional dynamics model, and calculate the projection of the total torsional deformation displacement between the sun gear and planet gears, and between the ring gear and planet gears in each stage of the planetary gear train of the wind turbine gearbox to the line of meshing.

[0042] The stress calculation module is configured to calculate the dynamic meshing force of each meshing pair of the wind turbine gearbox based on the projected amount and the time-varying meshing stiffness and meshing damping of each meshing pair. The dynamic meshing force is converted into time-series dynamic meshing force and input into the contact stress formula and bending stress formula respectively to obtain the contact stress time-series data and bending stress time-series data of each meshing pair.

[0043] The load spectrum construction module is configured to statistically analyze the contact stress time series data and bending stress time series data to obtain the gearbox fatigue load spectrum with stress amplitude and average stress as variables corresponding to the number of cycles, thus completing the construction of the wind turbine gearbox load spectrum.

[0044] Compared to existing technologies, this application addresses the problems of existing wind turbine gearbox load spectrum construction methods, which do not directly link environmental simulation with gearbox dynamic meshing force and fatigue load spectrum compilation, thus failing to directly support gearbox load optimization design, and do not consider the coupling effects of deep-sea atmospheric stability and wind wave turbulence, thus failing to meet the needs of fatigue life assessment for deep-sea wind turbine gearboxes. This application includes, but is not limited to, the following beneficial effects:

[0045] Firstly, this application addresses the original spatial distance-wind speed coherence model of the KEMAL wind spectrum. Combining this with deep-sea turbulence characteristics, it corrects the downwind coherence length using measured significant wave heights and the vertical coherence length based on the Monin-Obukhov length. Differential attenuation coefficients are determined for three atmospheric conditions: unstable, neutral, and stable. This constructs a second coherence model adapted to deep-sea wind-wave coupling and the spatiotemporal variations of atmospheric stability. Next, an anisotropic correction function is constructed to fuse the Mann spectrum spatial spectral tensor, replacing the original spatial distance-wind speed coherence model of the KEMAL wind spectrum. This results in a hybrid optimized wind spectrum based on the time-domain power spectrum of the KEMAL wind spectrum. This hybrid optimized wind spectrum retains the advantages of the time-domain energy distribution of the KEMAL wind spectrum while overcoming the shortcomings of traditional spectral models that neglect low-frequency large-scale turbulence and fail to consider the spatial anisotropy of turbulence. It accurately characterizes the characteristics of deep-sea turbulence: "significant low-frequency fluctuations, prominent large-scale coherent structures, and strong wave-induced turbulence coupling," laying a wind field foundation that closely matches actual conditions for subsequent load calculations.

[0046] Secondly, this application overcomes the drawback of the existing technology where "environmental simulation and gearbox load calculation are disconnected." It constructs a pure torsional dynamic model of the wind turbine gearbox and combines it with the OpenFAST wind field simulation model. The hybrid optimized wind spectrum is directly converted into the gearbox input torque. The torsional deformation displacement and meshing line projection of each planetary gear train meshing pair are accurately calculated through the dynamic model. Combined with the time-varying meshing stiffness and meshing damping of the meshing pairs, the dynamic meshing force is obtained, and finally converted into time-series data of contact stress and bending stress. This full-chain parameter transfer mechanism realizes the direct mapping from deep-sea turbulence parameters to gearbox component-level loads, effectively eliminating the errors caused by indirect conversions in multiple stages in traditional methods. This significantly improves the calculation accuracy of the gearbox's dynamic meshing force, contact stress, and bending stress, making the load data more closely reflect the actual service stress state of the gearbox.

[0047] Thirdly, based on accurate contact stress time-series data and bending stress time-series data, this application statistically obtains a gearbox fatigue load spectrum with stress amplitude and average stress as variables corresponding to the number of cycles. This load spectrum fully reflects the actual fatigue load characteristics of each meshing pair of the gearbox under the conditions of multi-field coupling of wind, waves, and currents in deep-sea environments and dynamic changes in atmospheric stability. Compared with traditional load spectra compiled based on neutral atmosphere and flat terrain spectrum models, the load spectrum of this application can provide core data support that conforms to engineering practice for accurate fatigue life assessment, lightweight structural design, and reliability optimization of key components of deep-sea wind turbine gearboxes. It effectively solves the industry pain points of life assessment deviation caused by traditional load spectra and the difficulty in balancing lightweighting and reliability.

[0048] Furthermore, this application is not only applicable to wind turbine gearboxes, but its core concept can also be extended to the compilation of load spectra for other core transmission components such as the main shaft and bearings of deep-sea floating wind turbines. It provides a reference for the precise load design of core components of deep-sea wind power equipment, and has important technical support significance for promoting the development of offshore wind power towards deep-sea and ultra-large scale. Attached Figure Description

[0049] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0050] Figure 1 An exemplary network architecture used in the wind turbine gearbox load spectrum construction and optimization method of this application;

[0051] Figure 2 This is a schematic diagram showing the variation of turbulence intensity in the yz plane of the wind turbine gearbox in an embodiment of this application;

[0052] Figure 3 This is a schematic diagram illustrating the change in wind speed at the hub height of the wind turbine gearbox in an embodiment of this application.

[0053] Figure 4 This is a schematic diagram of the pure torsional dynamics model of the wind turbine gearbox in the embodiments of this application;

[0054] Figure 5 This is a schematic diagram of the dynamic meshing forces of each stage of the wind turbine gearbox under the mixed optimized wind spectrum condition in the embodiments of this application;

[0055] Figure 6 This is a schematic diagram of the contact stress fatigue load spectrum and bending stress fatigue load spectrum of the low-speed stage sun gear of a wind turbine gearbox based on the hybrid optimized wind spectrum in the embodiments of this application.

[0056] Figure 7This is a schematic diagram of the contact stress fatigue load spectrum and bending stress fatigue load spectrum of the low-speed stage planetary gear of the wind turbine gearbox based on the hybrid optimized wind spectrum in the embodiments of this application.

[0057] Figure 8 This is a schematic diagram of the contact stress fatigue load spectrum and bending stress fatigue load spectrum of the intermediate stage sun gear of the wind turbine gearbox based on the hybrid optimized wind spectrum in the embodiments of this application;

[0058] Figure 9 This is a schematic diagram of the contact stress fatigue load spectrum and bending stress fatigue load spectrum of the intermediate stage planetary gear of the wind turbine gearbox based on the hybrid optimized wind spectrum in the embodiments of this application;

[0059] Figure 10 This is a schematic diagram of the contact stress fatigue load spectrum and bending stress fatigue load spectrum of the high-speed stage large gear of the wind turbine gearbox based on the hybrid optimized wind spectrum in the embodiments of this application.

[0060] Figure 11 This is a schematic diagram of the contact stress fatigue load spectrum and bending stress fatigue load spectrum of the high-speed stage pinion of the wind turbine gearbox based on the hybrid optimized wind spectrum in the embodiments of this application;

[0061] Figure 12 This is a comparison of short-term fatigue damage of the Kaimal wind spectrum after correction for deep-sea turbulence conditions, the original Kaimal wind spectrum, and the hybrid optimized wind spectrum in the embodiments of this application.

[0062] Figure 13 This is a schematic diagram of the wind turbine gearbox load spectrum construction and optimization device in the embodiments of this application. Detailed Implementation

[0063] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0064] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0065] Those skilled in the art will understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0066] Those skilled in the art will understand that although the various methods in this application are described based on the same concept and thus present commonality among them, they can be performed independently unless otherwise specified. Similarly, the various embodiments disclosed in this application are all based on the same inventive concept; therefore, concepts expressed in the same way, as well as concepts that are appropriately changed for convenience but are expressed differently, should be understood equivalently.

[0067] Unless otherwise expressly stated, the various embodiments disclosed in this application can be combined in a cross-cutting manner to flexibly construct new embodiments, as long as such combination does not depart from the inventive spirit of this application and can meet the needs of the prior art or solve a certain deficiency in the prior art. Those skilled in the art should be aware of such modifications.

[0068] Please see Figure 1 In one embodiment of the wind turbine gearbox load spectrum construction and optimization method of this application, the method includes:

[0069] Step S10: Call the first spatial distance-wind speed coherence model corresponding to the Kemal wind spectrum of the wind turbine gearbox, correct the first coherence length of the downwind turbulence under the deep-sea turbulence condition based on the measured significant wave height, correct the second coherence length of the vertical turbulence under the deep-sea turbulence condition according to the Monin-Obukhov length, and determine the attenuation coefficient under various atmospheric conditions to construct the second spatial distance-wind speed coherence model under the deep-sea turbulence condition.

[0070] The wind turbine gearbox load spectrum construction and optimization system in the terminal equipment can call the first spatial distance-wind speed coherence model (original spatial distance-wind speed coherence model) corresponding to the Kaimal wind spectrum of the wind turbine gearbox. Based on the measured significant wave height, it corrects the first coherence length of the downwind turbulence under the deep-sea turbulence condition. Based on the Monin-Obukhov length, it corrects the second coherence length of the vertical turbulence under the same deep-sea turbulence condition. It also determines the attenuation coefficients under various atmospheric conditions to construct the second spatial distance-wind speed coherence model under the same deep-sea turbulence condition. The wind turbine gearbox can be a three-stage planetary gearbox transmission system of a 7.25MW wind turbine, etc.

[0071] In some embodiments, the steps of correcting the first coherence length of downwind turbulence under deep-sea turbulence conditions based on measured significant wave heights, and correcting the second coherence length of vertical turbulence under the same deep-sea turbulence conditions based on the Moning-Obukhov length, include:

[0072] Step S101: Obtain the first coherence length of the downwind turbulence and the second coherence length of the vertical turbulence in the first spatial distance-wind speed coherence model (original spatial distance-wind speed coherence model);

[0073] Step S102: Based on the measured significant wave height under the deep-sea turbulence condition, a wave height influence coefficient is introduced to correct the first coherence length of the downwind turbulence, thereby obtaining the third coherence length of the downwind turbulence under the deep-sea turbulence condition.

[0074] Step S103: Based on the Monin-Obukhov length, an atmospheric stability influence coefficient is introduced to correct the second coherence length of the vertical turbulence, thereby obtaining the fourth coherence length of the vertical turbulence under the deep-sea turbulence condition.

[0075] Specifically, this application takes the three-stage planetary gearbox transmission system of a 7.25MW wind turbine as an example. Based on the deep-sea turbulent environment, atmospheric stability conditions are considered to increase the low-frequency large-scale turbulent structure. The first spatial distance-wind speed coherence model corresponding to the Kémar wind spectrum of the wind turbine gearbox is optimized. The expression of the first spatial distance-wind speed coherence model is as follows:

[0076] ;

[0077] in, Representing two points in the original space , Wind speed coherence between different spatial points characterizes the degree of correlation between wind speeds at different spatial points. It represents the original fixed attenuation coefficient (dimensionless), which characterizes the rate attenuation of coherence with distance / frequency; Indicates the reference frequency; Indicates the average wind speed at the wheel hub height; Indicates the distance between two points in space; It represents the original coherence length and characterizes the spatial scale of the turbulent coherence structure.

[0078] Furthermore, the formula for calculating the original coherence length is expressed as follows:

[0079] ;

[0080] in, It represents the original coherence length, that is, the first coherence length of downwind turbulence and the second coherence length of vertical turbulence, which characterizes the spatial scale of the turbulent coherence structure; This indicates the measured height, which is usually taken as the hub height of the wind turbine gearbox.

[0081] Furthermore, based on the measured significant wave height under the deep-sea turbulent conditions, a wave height influence coefficient is introduced to correct the first coherence length of the downwind turbulence, thus obtaining the third coherence length of the downwind turbulence under the deep-sea turbulent conditions. The formula for calculating the third coherence length of the downwind turbulence under the deep-sea turbulent conditions is as follows:

[0082] ;

[0083] in, It represents the third coherence length of downwind turbulence under deep-sea turbulence conditions; This represents the wave height influence coefficient, which is adapted to the modulation of downwind coherence by wave-induced turbulence. It represents the measured significant wave height, characterizing the actual scale of ocean waves; This indicates the reference wave height, and its base value can be taken as 5m. This indicates the measured height, which is usually taken as the hub height of the wind turbine gearbox.

[0084] Furthermore, based on the Monin-Obukhov length, an atmospheric stability influence coefficient is introduced to correct the second coherence length of the vertical turbulence, resulting in the fourth coherence length of the vertical turbulence under the deep-sea turbulence condition. The formula for calculating the fourth coherence length of the vertical turbulence under the deep-sea turbulence condition is as follows:

[0085] ;

[0086] in, It represents the fourth coherence length of vertical turbulence under deep-sea turbulent conditions; This represents the atmospheric stability influence coefficient, which can take any value between 0.2 and 0.4. L represents the Monin-Obukhov length. When L < 0, it represents an unstable atmospheric condition; when L → ∞, it represents a neutral atmospheric condition; and when L > 0, it represents a stable atmospheric condition. Represents the absolute value of the Mönning-Obukhov length; This represents the reference height, and its value can be 100. This indicates the measured height, which is usually taken as the hub height of the wind turbine gearbox.

[0087] In a further embodiment, the step of determining the attenuation coefficient under various atmospheric conditions includes:

[0088] Step S1001: Obtain the Monin-Obukhov length under deep-sea turbulent conditions, and determine various atmospheric conditions under deep-sea turbulent conditions based on the Monin-Obukhov length, wherein the atmospheric conditions include atmospheric unstable conditions, atmospheric neutral conditions and atmospheric stable conditions.

[0089] Step S1002: Construct attenuation coefficient expressions related to the Moning-Obukhov length to determine the attenuation coefficient under various atmospheric conditions.

[0090] Specifically, based on the Moning-Obukhov length, various atmospheric conditions under the deep-sea turbulent conditions are determined. When L < 0, the atmospheric condition is characterized as unstable; when L → ∞, the atmospheric condition is characterized as neutral; and when L > 0, the atmospheric condition is characterized as stable. This indicates the length of the Monin-Obukhov line;

[0091] When L < 0, it represents an unstable atmospheric condition, and the expression for the attenuation coefficient under an unstable atmospheric condition is as follows:

[0092] ;

[0093] As L→∞, the atmospheric condition is characterized as a neutral atmospheric condition, and the expression for the attenuation coefficient under the neutral atmospheric condition is as follows:

[0094] ;

[0095] When L>0, the atmospheric condition is characterized as a stable atmospheric condition, and the expression for the attenuation coefficient under the stable atmospheric condition is as follows:

[0096] ;

[0097] in, It represents the corrected attenuation coefficient under atmospheric unstable, atmospheric neutral, and atmospheric stable conditions, which changes dynamically with atmospheric stability and wave conditions. It represents the influence coefficient of wave parameters on the attenuation coefficient, characterizing the modulation of coherence attenuation by waves; It represents the measured significant wave height, characterizing the actual scale of ocean waves; This indicates the reference wave height, and its base value can be 5m.

[0098] In a further embodiment, the step of constructing the second spatial distance-wind speed coherence model includes:

[0099] Step S10001: Obtain the third coherence length of the downwind turbulence under the deep-sea turbulence condition, the fourth coherence length of the vertical turbulence under the deep-sea turbulence condition, and the attenuation coefficient under various atmospheric conditions.

[0100] Step S10002: Use the third coherence length and the fourth coherence length as the corrected coherence lengths of the first spatial distance-wind speed coherence model, and construct a second spatial distance-wind speed coherence model under the deep-sea turbulence condition based on the corrected coherence lengths and the attenuation coefficients under the various atmospheric conditions.

[0101] Specifically, based on the corrected coherence length and the attenuation coefficients under various atmospheric conditions, a second spatial distance-wind speed coherence model is constructed under the deep-sea turbulence condition. The expression for the second spatial distance-wind speed coherence model is as follows:

[0102] ;

[0103] in, This represents the corrected spatial points under deep-sea turbulent conditions. , Wind speed coherence between the two sides; This represents the corrected attenuation coefficient under unstable atmospheric conditions, neutral atmospheric conditions, and stable atmospheric conditions. Indicates the reference frequency; Indicates the average wind speed at the wheel hub height; Indicates the distance between two points in space; This represents the corrected coherence length. The coherence length of downwind turbulence under deep-sea turbulence conditions is... The coherence length of vertical turbulence under deep-sea turbulent conditions is .

[0104] Step S20: Construct the anisotropic correction function under the deep-sea turbulent conditions; construct the spatial spectrum tensor of the Mann spectrum based on the anisotropic correction function to replace the first spatial distance-wind speed coherence model of the Kemal wind spectrum; and construct a hybrid optimized wind spectrum based on the time domain energy distribution based on the time domain power spectrum of the Kemal wind spectrum and the anisotropic correction function.

[0105] The first spatial distance-wind speed coherence model corresponding to the Kemal wind spectrum of the wind turbine gearbox is invoked. Based on the measured significant wave height, the first coherence length of the downwind turbulence under the deep-sea turbulence condition is corrected. The second coherence length of the vertical turbulence under the same deep-sea turbulence condition is corrected according to the Moning-Obuhoff length. Attenuation coefficients under various atmospheric conditions are determined to construct the second spatial distance-wind speed coherence model under the deep-sea turbulence condition. Then, an anisotropy correction function under the same deep-sea turbulence condition is constructed, and based on the anisotropy correction function, a model is constructed... The spatial spectrum tensor of the Mann spectrum is used to replace the first spatial distance-wind speed coherence model of the Kemal wind spectrum. Based on the time-domain power spectrum of the Kemal wind spectrum and the anisotropy correction function, a hybrid optimized wind spectrum based on the time-domain energy distribution is constructed. The anisotropy correction function characterizes the non-uniformity of turbulence intensity, energy distribution and vortex scale in the three spatial directions of downwind, span and vertical in the deep sea, and simultaneously couples the modulation effect of atmospheric stability on the spatial structure of turbulence to optimize the directional characteristics of the Mann spectrum spatial spectrum tensor.

[0106] In some embodiments, the step of constructing the anisotropic correction function under the deep-sea turbulent conditions includes:

[0107] Step S21: Obtain the wavenumber vector under deep-sea turbulent conditions, the turbulence anisotropy parameters of the deep-sea turbulent conditions, and the Monin-Obukhov length, wherein the wavenumber vector includes a downwind wavenumber component, a spanwise wavenumber component, and a vertical wavenumber component.

[0108] Step S22: Calculate and determine the wavenumber modulus based on the downwind wavenumber component, spanwise wavenumber component, and vertical wavenumber component; construct the anisotropy correction function for the deep-sea turbulent condition based on the wavenumber vector, the wavenumber modulus, the turbulence anisotropy parameter, and the Monin-Obukhov length.

[0109] Specifically, based on the downwind wavenumber component, spanwise wavenumber component, and vertical wavenumber component, the wavenumber modulus is calculated and determined, wherein the calculation formula for the wavenumber modulus is expressed as:

[0110] ,

[0111] in, Represents wavenumber vector Downwind wavenumber component in the downwind direction (x); Represents wavenumber vector Spanning wavenumber components in the spanwise direction (y); Represents wavenumber vector Vertical wavenumber components in the vertical (z) direction; The wavenumber modulus directly reflects the spatial scale of the turbulent vortex (the larger the wavenumber, the smaller the vortex scale).

[0112] Furthermore, the anisotropy correction function under the deep-sea turbulence condition (adapting to the differences in deep-sea turbulence direction) is expressed as follows:

[0113] ;

[0114] in, Represents the wavenumber vector; Represents wavenumber vector Downwind wavenumber component in the downwind direction (x); Represents wavenumber vector Spanning wavenumber components in the spanwise direction (y); Represents wavenumber vector Vertical wavenumber components in the vertical (z) direction; Represents the anisotropic correction function under deep-sea turbulent conditions; It represents the anisotropic parameter of turbulence (dimensionless), and assigns differentiated weights to wavenumbers in different directions (downwind, spanwise, or vertical); The wave number modulus directly reflects the spatial scale of the turbulent vortex (the larger the wave number, the smaller the vortex scale). The Monin-Obukhov length represents atmospheric conditions. It represents the natural exponential function, characterizing the modulation of turbulent spatial structure by atmospheric stability.

[0115] Based on the above embodiments, the step of constructing the spatial spectral tensor of the Mann spectrum according to the anisotropic correction function includes:

[0116] Step S201: Obtain the Mann spectrum empirical constant, turbulent kinetic energy dissipation rate, and anisotropy correction function under the deep-sea turbulent conditions;

[0117] Step S202: Construct a spatial spectral tensor of the Mann spectrum adapted to the deep-sea turbulent conditions using the Mann spectrum empirical constant, turbulent kinetic energy dissipation rate, wavenumber modulus, and the anisotropy correction function.

[0118] Specifically, the spatial spectral tensor adapted to the deep-sea turbulent conditions is expressed as follows:

[0119] ;

[0120] in, The spatial spectral tensor representing the Mann spectrum characterizes the energy distribution of turbulence in the spatial frequency domain; Empirical constants representing the Mann spectrum; It represents the turbulent kinetic energy dissipation rate, which characterizes the rate at which turbulent energy is dissipated; The wave number modulus directly reflects the spatial scale of the turbulent vortex (the larger the wave number, the smaller the vortex scale). Represents the anisotropic correction function under deep-sea turbulent conditions; It represents the anisotropic parameter of turbulence (dimensionless), and assigns differentiated weights to wavenumbers in different directions (downwind, spanwise, or vertical); The ______ represents the Monin-Obukhov length, which characterizes atmospheric conditions.

[0121] In a further embodiment, the step of constructing the time-domain power spectrum of the Kemal wind spectrum includes:

[0122] Step S2001: Obtain the average wind speed at the hub height of the wind turbine gearbox, the measured height, the friction speed, and the actual frequency. Calculate the dimensionless frequency based on the measured height, the average wind speed at the hub height, and the actual frequency.

[0123] Step S2002: Use the dimensionless frequency, the actual frequency, and the friction velocity to construct the time-domain power spectrum of the Kemal wind spectrum.

[0124] Specifically, the formula for calculating the original time-domain power spectrum of the Kemal wind spectrum is as follows:

[0125] ;

[0126] in, The time-domain power spectrum of the Kaimal wind spectrum represents the energy distribution of turbulence in the time-frequency domain. Represents dimensionless frequency (dimensionless). Indicates the measured height; It represents the friction velocity and characterizes the shear intensity of near-surface turbulence; Indicates the actual frequency; This indicates the average wind speed at the hub height.

[0127] Furthermore, the formula for calculating dimensionless frequency is expressed as:

[0128] ;

[0129] in, Represents dimensionless frequency (dimensionless). Indicates the measured height; Indicates the actual frequency; This indicates the average wind speed at the hub height.

[0130] Based on the above embodiments, the steps for constructing a hybrid optimized wind spectrum based on the time-domain energy distribution, according to the time-domain power spectrum of the Kemal wind spectrum and the anisotropy correction function, include:

[0131] Step S20001: Obtain the time domain power spectrum of the Kemal wind spectrum and the anisotropic correction function adapted to deep-sea turbulent conditions;

[0132] Step S20002: Calculate and determine the square root value of the anisotropy correction function, and fuse the square root value with the time domain power spectrum of the Kemmar wind spectrum to construct a hybrid optimized wind spectrum based on the time domain energy distribution. The hybrid optimized wind spectrum degenerates into a traditional isotropic Kemmar wind spectrum when the anisotropy parameter is zero.

[0133] Specifically, the square root value of the anisotropy correction function is calculated and determined. This square root value is then fused with the time-domain power spectrum of the Kemal wind spectrum to construct a hybrid optimized wind spectrum based on the time-domain energy distribution. This simplifies the original hybrid spectrum expression. The hybrid spectrum is based on the time-domain energy distribution of the Kemal spectrum, and... Directly superimposed spatial anisotropic features. When hour, This degenerates into an isotropic model, ensuring compatibility with traditional Kemal spectra; when At that time, the weight of downwind turbulence is the highest, which closely matches the measured characteristics; wherein, the expression for the hybrid optimized wind spectrum based on the time-domain energy distribution is:

[0134] ;

[0135] in, Represents the anisotropic correction function under deep-sea turbulent conditions; The time-domain power spectrum of the Kaimal wind spectrum represents the energy distribution of turbulence in the time-frequency domain. This represents a hybrid optimized wind spectrum based on the energy distribution in the time domain.

[0136] Step S30: Construct a pure torsional dynamics model of the wind turbine gearbox, read the hybrid optimized wind spectrum and generate input torque in conjunction with the OpenFAST wind field simulation model, input the input torque into the pure torsional dynamics model, and calculate the projection of the total torsional deformation displacement between the sun gear and planet gears, and between the ring gear and planet gears in each stage of the planetary gear train of the wind turbine gearbox onto the meshing line.

[0137] An anisotropic correction function for the deep-sea turbulent conditions is constructed. Based on the anisotropic correction function, a spatial spectral tensor of the Mann spectrum is constructed to replace the first spatial distance-wind speed coherence model of the Kemal wind spectrum. Based on the time-domain power spectrum of the Kemal wind spectrum and the anisotropic correction function, a hybrid optimized wind spectrum based on the time-domain energy distribution is constructed. Then, a pure torsional dynamics model of the wind turbine gearbox is constructed. The hybrid optimized wind spectrum is read in conjunction with the OpenFAST wind field simulation model to generate an input torque. The input torque is input into the pure torsional dynamics model to calculate the projection of the total torsional deformation displacement between the sun gear and planet gears, and between the ring gear and planet gears in each stage of the planetary gear train of the wind turbine gearbox onto the meshing line. The pure torsional dynamics model can be a ten-degree-of-freedom pure torsional dynamics model, etc.

[0138] In some embodiments, a ten-degree-of-freedom pure torsional dynamic model of the wind turbine gearbox is constructed, wherein the dynamic equations of the ten-degree-of-freedom pure torsional dynamic model include:

[0139] In this designation, subscript 1 indicates the low-speed stage, with corresponding components including the sun gear, planet gears, ring gear, and planet carrier; subscript 2 indicates the medium-speed stage, with corresponding components including the sun gear, planet gears, ring gear, and planet carrier; and subscript h indicates the high-speed stage, with corresponding component being the high-speed shaft. Indicates the sun wheel; subscript Indicates a planetary gear; subscript Indicates a gear ring; subscript Indicates planetary support; subscript Indicates high-speed stage gear; subscript This indicates the planetary gear number (n=1,2,3, representing the nth planetary gear in a three-level planetary gear system).

[0140] It is the input torque, the torque transmitted from the wind turbine to the low-speed stage sun gear; It is the output torque, representing the torque output from the high-speed shaft of the high-speed stage to the generator; Indicates angular velocity; Indicates angular acceleration; It represents the moment of inertia, characterizing the ability of a component to resist torsional deformation; It represents the time-varying meshing stiffness, a stiffness coefficient that characterizes the change in gear meshing position; Represents the load distribution function; Indicates relative displacement; Represents relative velocity, relative displacement of the meshing point. The first derivative with respect to time; Meshing damping represents the energy dissipation caused by elastic deformation and friction during gear meshing. Indicates the radius of the base circle.

[0141] This represents the product of the moment of inertia and angular acceleration of the low-speed sun gear. This represents the total meshing reaction torque of the three planetary gears in the low-speed stage to the sun gear; This represents the time-varying stiffness of the nth sun gear-planet gear meshing pair in the low-speed stage; This represents the load distribution coefficient of the sun gear-planet gear meshing pair; Indicates the base circle radius of the low-speed stage sun gear; This indicates the damping of the sun gear-planet gear meshing pair; This indicates the relative velocity along the meshing line of the sun gear-planet gear meshing pair; This represents the product of the moment of inertia and angular acceleration of the low-speed gear ring. This represents the product of the moment of inertia and angular acceleration of the nth planetary gear in the low-speed stage.

[0142] This represents the product of the combined moment of inertia and angular acceleration of the low-speed planetary carrier and the medium-speed sun gear. The low-speed planetary carrier and the medium-speed sun gear are rigidly connected and are the same moving part.

[0143] This indicates that it has been incorporated into the low-speed stage planetary support program; This represents the stiffness and damping of the nth sun gear-planet gear meshing pair in the intermediate speed stage; This represents the product of the moment of inertia and angular acceleration of the intermediate-speed gear ring. This represents the product of the moment of inertia and angular acceleration of the nth planetary gear in the intermediate speed stage.

[0144] This represents the product of the combined moment of inertia and angular acceleration between the intermediate-speed planetary carrier and the high-speed pinion. The intermediate-speed planetary carrier and the high-speed input gear are rigidly connected.

[0145] This represents the torque balance term for high-speed gear meshing. Indicates the time-varying stiffness of the high-speed gear pair; Indicates the relative displacement of the meshing line of the high-speed gear; Indicates the base circle radius of the high-speed stage pinion; This represents the product of the moment of inertia and angular acceleration of the large gear in the high-speed stage.

[0146] This indicates torque balance at the output end of the high-speed shaft; This indicates the base circle radius of the high-speed stage large gear.

[0147] Furthermore, before analyzing fatigue damage in different gear stages, it is necessary to statistically analyze the dynamic meshing force of the gears in sequence. The projection of the sun gear and planet gears in each stage of the planetary gear train of the wind turbine gearbox is calculated using the following formula:

[0148] ;

[0149] in, This represents the total relative projection of the torsional deformation displacement of each component of the nth sun gear-planet gear meshing pair in the low-speed stage onto the line of engagement. This represents the projection of the torsional angular displacement of the low-speed stage sun gear in the direction of the meshing line; the negative sign indicates that it is opposite to the direction of the meshing line. This represents the projection of the torsional angular displacement of the nth planetary gear in the low-speed stage onto the meshing line. This represents the projection of the low-speed stage planetary carrier's torsional angular displacement along the meshing line (the planetary carrier's revolution motion needs to be multiplied by...). Projected onto the line of engagement.

[0150] The projection of the torsional deformation displacement between the ring gear and planet gears in each stage of the planetary gear train of the wind turbine gearbox onto the line of meshing is calculated using the following formula:

[0151] ;

[0152] in, This represents the total relative projection of the torsional deformation displacement of each component of the nth gear ring-planet gear meshing pair in the low-speed stage onto the line of engagement. This represents the projection of the torsional angular displacement of the nth planetary gear in the low-speed stage onto the meshing line. This represents the projection of the torsional angular displacement of the low-speed planetary carrier onto the meshing line.

[0153] Step S40: Based on the projection amount, and combined with the time-varying meshing stiffness and meshing damping of each meshing pair, calculate the dynamic meshing force of each meshing pair in the wind turbine gearbox, convert the dynamic meshing force into a time-series dynamic meshing force, and input it into the contact stress formula and bending stress formula respectively to obtain the contact stress time-series data and bending stress time-series data of each meshing pair.

[0154] A pure torsional dynamics model of the wind turbine gearbox is constructed. The OpenFAST wind field simulation model is used to read the hybrid optimized wind spectrum and generate input torque. The input torque is then input into the pure torsional dynamics model. The total torsional deformation displacement between the sun gear and planet gears, and between the ring gear and planet gears in each stage of the planetary gear train of the wind turbine gearbox is calculated and projected onto the line of action. Based on the projection, the dynamic meshing force of each meshing pair is calculated by combining the time-varying meshing stiffness and meshing damping of each meshing pair. The dynamic meshing force is converted into time-series dynamic meshing force and input into the contact stress formula and bending stress formula, respectively, to obtain the contact stress time-series data and bending stress time-series data of each meshing pair.

[0155] In some embodiments, based on the projected amount, and in conjunction with the time-varying meshing stiffness and meshing damping of each meshing pair, the dynamic meshing force of each meshing pair in the wind turbine gearbox is calculated, wherein the calculation formula for the dynamic meshing force of the sun gear-planet gear meshing pair is expressed as:

[0156] ;

[0157] in, This represents the dynamic meshing force of the nth sun gear-planet gear meshing pair in the low-speed stage;

[0158] It represents the time-varying meshing stiffness of the sun gear-planet gear meshing pair, which changes periodically with the gear meshing position;

[0159] This represents the projection of the torsional deformation displacement of the sun gear-planet gear meshing pair onto the line of engagement. This indicates the meshing damping of the sun gear-planet gear meshing pair; express The derivative with respect to time is the relative velocity of the meshing pair in the direction of the line of engagement.

[0160] Furthermore, the formula for calculating the dynamic meshing force of the gear ring-planet gear meshing pair is expressed as follows:

[0161] ;

[0162] in, This represents the dynamic meshing force of the nth gear ring-planet gear meshing pair in the low-speed stage; Indicates the time-varying meshing stiffness of the gear ring-planet gear meshing pair; This represents the projection of the torsional deformation displacement of the gear ring-planet gear meshing pair onto the line of action; This indicates the meshing damping of the ring gear-planet gear meshing pair; express This represents the derivative with respect to time, i.e., the relative velocity of the gear-planet gear meshing pair in the direction of the meshing line.

[0163] In some embodiments, dynamic meshing force is first used. Substitute into the contact stress formula Bending stress formula The contact stress time series data and bending stress time series data of each meshing pair are obtained; then the contact stress time series data and bending stress time series data are segmented, and the number of iterations is calculated using the formula. The number of cycles corresponding to each stress block was statistically obtained, which provides a basis for the subsequent construction of fatigue load spectrum (rainflow counting method).

[0164] Among them, the formula for the number of iterations The calculation formula is expressed as:

[0165] ;

[0166] in, Indicates the first The number of stress cycles corresponding to each stress block; Indicates the stress block number; Indicates the first The total number of time periods contained in each stress block; Indicates the first The time period sequence number within each stress block; Indicates the number of planetary gears; Indicates the first The duration of each time period; Indicates the first The average angular velocity of the gear over a time period; This represents the conversion factor between radians and circumferences.

[0167] Furthermore, the contact stress formula is expressed as:

[0168] ;

[0169] in, It represents contact stress and characterizes the compressive stress between the meshing surfaces of gears; It represents the elastic modulus, which is determined by the elastic modulus and Poisson's ratio of the gear material; This represents the area coefficient (contact coefficient), which is related to the gear tooth profile and meshing position. It represents the overlap coefficient, characterizing the stress distribution effect during multi-tooth meshing;

[0170] Indicates the time-sequential dynamic meshing force. For gear pairs, For transmission stage, The planetary wheel number is m=1, which represents a low-speed planetary system, and m=2, which represents a medium-speed planet. Indicates tooth width, the axial width of the gear teeth; Indicates the pitch circle diameter of the driving gear; The transmission ratio is represented by the ratio of the number of teeth on the driven gear to the number of teeth on the driving gear. Indicates external meshing Internal meshing .

[0171] The formula for bending stress is expressed as:

[0172] ;

[0173] in, It represents bending stress, characterizing the bending fatigue stress at the root of the gear teeth; Indicates the time-sequential dynamic meshing force. For gear pairs, For transmission stage, The planetary wheel number is m=1, which represents a low-speed planetary system, and m=2, which represents a medium-speed planet. This indicates the usage factor, which takes into account the correction for operating conditions, impacts, and load fluctuations. This represents the dynamic load coefficient, which takes into account dynamic load corrections for gear manufacturing errors and operational vibrations. This represents the tooth load distribution coefficient, which takes into account the correction for uneven load distribution along the tooth width; This represents the inter-tooth load distribution coefficient, which corrects for uneven load distribution when multiple pairs of teeth are meshing. Indicates tooth width, the axial width of the gear teeth; The module represents the basic dimensions of the gear teeth. It represents the tooth form factor, which is related to the number of teeth and tooth profile of the gear, and reflects the degree of stress concentration at the tooth root; This represents the stress correction factor, taking into account the stress concentration effect of the tooth root transition fillet.

[0174] Step S50: Statistically analyze the contact stress time series data and bending stress time series data to obtain the gearbox fatigue load spectrum with stress amplitude and average stress as variables corresponding to the number of cycles, thus completing the construction of the wind turbine gearbox load spectrum.

[0175] Based on the projected values, the dynamic meshing force of each meshing pair in the wind turbine gearbox is calculated by combining the time-varying meshing stiffness and meshing damping of each meshing pair. The dynamic meshing force is then converted into a time-series dynamic meshing force and input into the contact stress formula and bending stress formula, respectively. After obtaining the contact stress time-series data and bending stress time-series data of each meshing pair, the contact stress time-series data and bending stress time-series data are statistically analyzed to obtain the gearbox fatigue load spectrum with stress amplitude and average stress as variables corresponding to the number of cycles, thus completing the construction of the wind turbine gearbox load spectrum.

[0176] Specifically, the contact stress time series data and bending stress time series data (stress curves that change continuously over time) obtained in step S40 can be statistically analyzed using the rainflow counting method to extract two core fatigue characteristic quantities: stress amplitude and average stress. The number of stress cycles corresponding to each combination of characteristic quantities is then counted, ultimately forming a gearbox fatigue load spectrum with stress amplitude and average stress as two variables, corresponding one-to-one with the number of cycles. This load spectrum is the direct basis for subsequently using the Palmgren-Miner rule to calculate gear fatigue damage and assess the service life of the gearbox.

[0177] The core function of the rainflow counting method is to accurately extract effective fatigue stress cycles from irregular random stress time series data, and to count the number of cycles corresponding to different stress amplitudes and average stresses. This method is perfectly suited to the dynamic characteristics of gear stress under deep-sea turbulent conditions, which are irregular, asymmetric, and variable in amplitude.

[0178] The rainflow counting method rotates the continuous stress-time curve by 90° and uses the falling "rainflow" method to identify, count, and merge irregular stress fluctuations according to the fatigue damage equivalence principle. Finally, it obtains the stress amplitude and average stress of each independent stress cycle and counts the number of cycles with the same characteristic quantity combination. This solves the problem that random variable amplitude stress cycles cannot be directly used for fatigue analysis.

[0179] In some embodiments, please refer to Figures 2 to 12 ,in, Figure 2 This is a schematic diagram showing the variation of turbulence intensity in the yz plane of the wind turbine gearbox in an embodiment of this application; Figure 3 This is a schematic diagram illustrating the change in wind speed at the hub height of the wind turbine gearbox in an embodiment of this application. Figure 4 This is a schematic diagram of the pure torsional dynamics model of the wind turbine gearbox in the embodiments of this application; Figure 5 This is a schematic diagram of the dynamic meshing forces of each stage of the wind turbine gearbox under the mixed optimized wind spectrum condition in the embodiments of this application; Figure 6 This is a schematic diagram of the contact stress fatigue load spectrum and bending stress fatigue load spectrum of the low-speed stage sun gear of a wind turbine gearbox based on the hybrid optimized wind spectrum in the embodiments of this application. Figure 7 This is a schematic diagram of the contact stress fatigue load spectrum and bending stress fatigue load spectrum of the low-speed stage planetary gear of the wind turbine gearbox based on the hybrid optimized wind spectrum in the embodiments of this application. Figure 8 This is a schematic diagram of the contact stress fatigue load spectrum and bending stress fatigue load spectrum of the intermediate stage sun gear of the wind turbine gearbox based on the hybrid optimized wind spectrum in the embodiments of this application; Figure 9 This is a schematic diagram of the contact stress fatigue load spectrum and bending stress fatigue load spectrum of the intermediate stage planetary gear of the wind turbine gearbox based on the hybrid optimized wind spectrum in the embodiments of this application; Figure 10 This is a schematic diagram of the contact stress fatigue load spectrum and bending stress fatigue load spectrum of the high-speed stage large gear of the wind turbine gearbox based on the hybrid optimized wind spectrum in the embodiments of this application. Figure 11 This is a schematic diagram of the contact stress fatigue load spectrum and bending stress fatigue load spectrum of the high-speed stage pinion of the wind turbine gearbox based on the hybrid optimized wind spectrum in the embodiments of this application; Figure 12 This is a comparison of short-term fatigue damage of the Kaimal wind spectrum after correction for deep-sea turbulent conditions, the original Kaimal wind spectrum, and the hybrid optimized wind spectrum in the embodiments of this application.

[0180] As can be seen from the above embodiments, compared with the prior art, this application addresses the problems of existing wind turbine gearbox load spectrum construction methods that do not directly connect environmental simulation with gearbox dynamic meshing force and fatigue load spectrum compilation, thus failing to directly support gearbox load optimization design, and do not consider the coupling effect of deep-sea atmospheric stability and wind wave turbulence, thus failing to meet the needs of deep-sea wind turbine gearbox fatigue life assessment. This application includes, but is not limited to, the following beneficial effects:

[0181] Firstly, this application addresses the original spatial distance-wind speed coherence model of the KEMAL wind spectrum. Combining this with deep-sea turbulence characteristics, it corrects the downwind coherence length using measured significant wave heights and the vertical coherence length based on the Monin-Obukhov length. Differential attenuation coefficients are determined for three atmospheric conditions: unstable, neutral, and stable. This constructs a second coherence model adapted to deep-sea wind-wave coupling and the spatiotemporal variations of atmospheric stability. Next, an anisotropic correction function is constructed to fuse the Mann spectrum spatial spectral tensor, replacing the original spatial distance-wind speed coherence model of the KEMAL wind spectrum. This results in a hybrid optimized wind spectrum based on the time-domain power spectrum of the KEMAL wind spectrum. This hybrid optimized wind spectrum retains the advantages of the time-domain energy distribution of the KEMAL wind spectrum while overcoming the shortcomings of traditional spectral models that neglect low-frequency large-scale turbulence and fail to consider the spatial anisotropy of turbulence. It accurately characterizes the characteristics of deep-sea turbulence: "significant low-frequency fluctuations, prominent large-scale coherent structures, and strong wave-induced turbulence coupling," laying a wind field foundation that closely matches actual conditions for subsequent load calculations.

[0182] Secondly, this application overcomes the drawback of the existing technology where "environmental simulation and gearbox load calculation are disconnected." It constructs a pure torsional dynamic model of the wind turbine gearbox and combines it with the OpenFAST wind field simulation model. The hybrid optimized wind spectrum is directly converted into the gearbox input torque. The torsional deformation displacement and meshing line projection of each planetary gear train meshing pair are accurately calculated through the dynamic model. Combined with the time-varying meshing stiffness and meshing damping of the meshing pairs, the dynamic meshing force is obtained, and finally converted into time-series data of contact stress and bending stress. This full-chain parameter transfer mechanism realizes the direct mapping from deep-sea turbulence parameters to gearbox component-level loads, effectively eliminating the errors caused by indirect conversions in multiple stages in traditional methods. This significantly improves the calculation accuracy of the gearbox's dynamic meshing force, contact stress, and bending stress, making the load data more closely reflect the actual service stress state of the gearbox.

[0183] Thirdly, based on accurate contact stress time-series data and bending stress time-series data, this application statistically obtains a gearbox fatigue load spectrum with stress amplitude and average stress as variables corresponding to the number of cycles. This load spectrum fully reflects the actual fatigue load characteristics of each meshing pair of the gearbox under the conditions of multi-field coupling of wind, waves, and currents in deep-sea environments and dynamic changes in atmospheric stability. Compared with traditional load spectra compiled based on neutral atmosphere and flat terrain spectrum models, the load spectrum of this application can provide core data support that conforms to engineering practice for accurate fatigue life assessment, lightweight structural design, and reliability optimization of key components of deep-sea wind turbine gearboxes. It effectively solves the industry pain points of life assessment deviation caused by traditional load spectra and the difficulty in balancing lightweighting and reliability.

[0184] Furthermore, this application is not only applicable to wind turbine gearboxes, but its core concept can also be extended to the compilation of load spectra for other core transmission components such as the main shaft and bearings of deep-sea floating wind turbines. It provides a reference for the precise load design of core components of deep-sea wind power equipment, and has important technical support significance for promoting the development of offshore wind power towards deep-sea and ultra-large scale.

[0185] Please see Figure 13A wind turbine gearbox load spectrum construction and optimization device provided for one of the purposes of this application includes a coherence model optimization module 1100, a hybrid optimization wind spectrum construction module 1200, a projection calculation module 1300, a stress calculation module 1400, and a load spectrum construction module 1500. The coherence model optimization module 1100 is configured to call the first spatial distance-wind speed coherence model corresponding to the Kemal wind spectrum of the wind turbine gearbox, correct the first coherence length of the downwind turbulence under the deep-sea turbulence condition based on the measured significant wave height, correct the second coherence length of the vertical turbulence under the deep-sea turbulence condition based on the Moning-Obukhov length, and determine the attenuation coefficient under various atmospheric conditions to construct the second spatial distance-wind speed coherence model under the deep-sea turbulence condition; the hybrid optimized wind spectrum construction module 1200 is configured to construct the anisotropy correction function under the deep-sea turbulence condition, construct the spatial spectrum tensor of the Mann spectrum based on the anisotropy correction function to replace the first spatial distance-wind speed coherence model of the Kemal wind spectrum, and construct the hybrid optimized wind spectrum based on the time domain energy distribution based on the time domain power spectrum of the Kemal wind spectrum and the anisotropy correction function; the projection calculation module 1300 is configured to construct the pure projection of the wind turbine gearbox. A torsional dynamics model, in conjunction with an OpenFAST wind field simulation model, reads the hybrid optimized wind spectrum and generates an input torque. This input torque is then input into the pure torsional dynamics model to calculate the projection of the total torsional deformation displacement between the sun gear and planet gears, and between the ring gear and planet gears in each stage of the wind turbine gearbox onto the line of engagement. A stress calculation module 1400 is configured to calculate the dynamic meshing force of each meshing pair in the wind turbine gearbox based on the projection, combined with the time-varying meshing stiffness and meshing damping of each meshing pair. The dynamic meshing force is converted into a time-series dynamic meshing force and input into the contact stress formula and bending stress formula, respectively, to obtain the contact stress time-series data and bending stress time-series data of each meshing pair. A load spectrum construction module 1500 is configured to statistically analyze the contact stress time-series data and bending stress time-series data to obtain the gearbox fatigue load spectrum with stress amplitude and average stress as variables corresponding to the number of cycles, thus completing the construction of the wind turbine gearbox load spectrum.

[0186] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for constructing and optimizing the load spectrum of a wind turbine gearbox, characterized in that, include: The first spatial distance-wind speed coherence model corresponding to the Kemal wind spectrum of the wind turbine gearbox is called. Based on the measured significant wave height, the first coherence length of the downwind turbulence under the deep-sea turbulence condition is corrected. The second coherence length of the vertical turbulence under the deep-sea turbulence condition is corrected according to the Monin-Obuhoff length. The attenuation coefficient under various atmospheric conditions is determined to construct the second spatial distance-wind speed coherence model under the deep-sea turbulence condition. An anisotropic correction function under the deep-sea turbulent conditions is constructed. Based on the anisotropic correction function, a spatial spectrum tensor of the Mann spectrum is constructed to replace the first spatial distance-wind speed coherence model of the Kemal wind spectrum. Based on the time domain power spectrum of the Kemal wind spectrum and the anisotropic correction function, a hybrid optimized wind spectrum based on the time domain energy distribution is constructed. A pure torsional dynamics model of the wind turbine gearbox is constructed. The OpenFAST wind field simulation model is used to read the hybrid optimized wind spectrum and generate input torque. The input torque is input into the pure torsional dynamics model to calculate the projection of the total torsional deformation displacement between the sun gear and planet gears, and between the ring gear and planet gears in each stage of the planetary gear train of the wind turbine gearbox onto the line of meshing. Based on the projected amount, the dynamic meshing force of each meshing pair in the wind turbine gearbox is calculated by combining the time-varying meshing stiffness and meshing damping of each meshing pair. The dynamic meshing force is converted into time-series dynamic meshing force and input into the contact stress formula and bending stress formula respectively to obtain the contact stress time-series data and bending stress time-series data of each meshing pair. The contact stress time series data and bending stress time series data are statistically analyzed to obtain the gearbox fatigue load spectrum with stress amplitude and average stress as variables corresponding to the number of cycles, thus completing the construction of the wind turbine gearbox load spectrum.

2. The method for constructing and optimizing the load spectrum of a wind turbine gearbox according to claim 1, characterized in that, The steps of correcting the first coherence length of downwind turbulence under deep-sea turbulence conditions based on measured significant wave heights, and correcting the second coherence length of vertical turbulence under the same conditions based on the Moning-Obukhov length, include: Obtain the first coherence length of the downwind turbulence and the second coherence length of the vertical turbulence in the first spatial distance-wind speed coherence model; Based on the measured significant wave height under the deep-sea turbulence condition, a wave height influence coefficient is introduced to correct the first coherence length of the downwind turbulence, thus obtaining the third coherence length of the downwind turbulence under the deep-sea turbulence condition. Based on the Monin-Obukhov length, an atmospheric stability influence coefficient is introduced to correct the second coherence length of the vertical turbulence, thus obtaining the fourth coherence length of the vertical turbulence under the deep-sea turbulence condition.

3. The method for constructing and optimizing the load spectrum of a wind turbine gearbox according to claim 1, characterized in that, The steps for determining the attenuation coefficient under various atmospheric conditions include: The Monin-Obukhov length under deep-sea turbulent conditions is obtained, and various atmospheric conditions under deep-sea turbulent conditions are determined based on the Monin-Obukhov length. The atmospheric conditions include atmospheric unstable conditions, atmospheric neutral conditions, and atmospheric stable conditions. Expressions for the attenuation coefficient related to the Moning-Obukhov length are constructed to determine the attenuation coefficient under various atmospheric conditions.

4. The method for constructing and optimizing the load spectrum of a wind turbine gearbox according to claims 2 to 3, characterized in that, The steps for constructing a second spatial distance-wind speed coherence model include: The third coherence length of downwind turbulence under the deep-sea turbulence condition, the fourth coherence length of vertical turbulence under the deep-sea turbulence condition, and the attenuation coefficient under various atmospheric conditions are obtained. The third coherence length and the fourth coherence length are used as the corrected coherence lengths of the first spatial distance-wind speed coherence model. Based on the corrected coherence lengths and the attenuation coefficients under the various atmospheric conditions, a second spatial distance-wind speed coherence model under the deep-sea turbulent conditions is constructed.

5. The method for constructing and optimizing the load spectrum of a wind turbine gearbox according to claim 1, characterized in that, The steps for constructing the anisotropic correction function under the deep-sea turbulent condition include: The wavenumber vector, the turbulence anisotropy parameters of the deep-sea turbulence condition, and the Monin-Obukhov length are obtained under the deep-sea turbulence condition. The wavenumber vector includes a downwind wavenumber component, a spanwise wavenumber component, and a vertical wavenumber component. Based on the downwind wavenumber component, spanwise wavenumber component, and vertical wavenumber component, the wavenumber modulus is calculated and determined; based on the wavenumber vector, the wavenumber modulus, the turbulence anisotropy parameter, and the Monin-Obukhov length, the anisotropy correction function for the deep-sea turbulence condition is constructed.

6. The method for constructing and optimizing the load spectrum of a wind turbine gearbox according to claim 1, characterized in that, The steps for constructing the time-domain power spectrum of the Kemal wind spectrum include: The average wind speed at the hub height of the wind turbine gearbox, the measured height, the friction speed, and the actual frequency are obtained. The dimensionless frequency is calculated based on the measured height, the average wind speed at the hub height, and the actual frequency. The dimensionless frequency, the actual frequency, and the friction velocity are used to construct the time-domain power spectrum of the Kemal wind spectrum.

7. The method for constructing and optimizing the load spectrum of a wind turbine gearbox according to claims 5 to 6, characterized in that, The steps for constructing a hybrid optimized wind spectrum based on the time-domain energy distribution, according to the time-domain power spectrum of the Kemal wind spectrum and the anisotropy correction function, include: Obtain the time-domain power spectrum of the Kemal wind spectrum, and an anisotropic correction function adapted to deep-sea turbulent conditions; The square root value of the anisotropy correction function is calculated and determined. The square root value is then fused with the time domain power spectrum of the Kemmar wind spectrum to construct a hybrid optimized wind spectrum based on the time domain energy distribution. When the anisotropy parameter is zero, the hybrid optimized wind spectrum degenerates into a traditional isotropic Kemmar wind spectrum.

8. The method for constructing and optimizing the load spectrum of a wind turbine gearbox according to claim 5, characterized in that, The steps for constructing the spatial spectral tensor of the Mann spectrum based on the anisotropic correction function include: The empirical constant of the Mann spectrum, the turbulent kinetic energy dissipation rate, and the anisotropy correction function under the deep-sea turbulent conditions were obtained. The empirical constant of the Mann spectrum, the turbulent kinetic energy dissipation rate, the wavenumber modulus, and the anisotropy correction function are used to construct a spatial spectral tensor of the Mann spectrum adapted to the deep-sea turbulent conditions.

9. The method for constructing and optimizing the load spectrum of a wind turbine gearbox according to any one of claims 1 to 8, characterized in that, The anisotropic correction function characterizes the non-uniformity of turbulence intensity, energy distribution, and vortex scale in the three spatial directions of downwind, span, and vertical. It also couples the modulation effect of atmospheric stability on the spatial structure of turbulence, thereby optimizing the directional characteristics of the Mann spectrum tensor.

10. A device for constructing and optimizing the load spectrum of a wind turbine gearbox, characterized in that, include: The coherence model optimization module is set to call the first spatial distance-wind speed coherence model corresponding to the Kemal wind spectrum of the wind turbine gearbox, correct the first coherence length of the downwind turbulence under the deep-sea turbulence condition based on the measured significant wave height, correct the second coherence length of the vertical turbulence under the deep-sea turbulence condition according to the Monin-Obukhov length, and determine the attenuation coefficient under various atmospheric conditions to construct the second spatial distance-wind speed coherence model under the deep-sea turbulence condition. The hybrid optimized wind spectrum construction module is configured to construct an anisotropic correction function under the deep-sea turbulent conditions, construct the spatial spectrum tensor of the Mann spectrum based on the anisotropic correction function to replace the first spatial distance-wind speed coherence model of the Kemal wind spectrum, and construct a hybrid optimized wind spectrum based on the time domain energy distribution based on the time domain power spectrum of the Kemal wind spectrum and the anisotropic correction function. The projection calculation module is configured to construct a pure torsional dynamics model of the wind turbine gearbox, read the hybrid optimized wind spectrum in conjunction with the OpenFAST wind field simulation model and generate an input torque, input the input torque into the pure torsional dynamics model, and calculate the projection of the total torsional deformation displacement between the sun gear and planet gears, and between the ring gear and planet gears in each stage of the planetary gear train of the wind turbine gearbox to the line of meshing. The stress calculation module is configured to calculate the dynamic meshing force of each meshing pair of the wind turbine gearbox based on the projected amount and the time-varying meshing stiffness and meshing damping of each meshing pair. The dynamic meshing force is converted into time-series dynamic meshing force and input into the contact stress formula and bending stress formula respectively to obtain the contact stress time-series data and bending stress time-series data of each meshing pair. The load spectrum construction module is configured to statistically analyze the contact stress time series data and bending stress time series data to obtain the gearbox fatigue load spectrum with stress amplitude and average stress as variables corresponding to the number of cycles, thus completing the construction of the wind turbine gearbox load spectrum.

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