Method for determining fatigue strength of offshore wind turbine, and related apparatus

Abstract

A method for determining fatigue strength of an offshore wind turbine, and a related apparatus. The method comprises: determining raw wave characterization on the basis of collected wave data of an offshore wind farm at a plurality of wind speeds (S101); performing aggregation processing on the raw wave characterization at the plurality of wind speeds to obtain equivalent wave characterization (S102); and determining the fatigue strength of a wind turbine in the offshore wind farm on the basis of the equivalent wave characterization (S103). The raw wave characterization may be raw wave energy spectra or raw wave scatter diagrams. An equivalent energy spectrum obtained by aggregating the wave energy spectra retains more detailed sea state information in the wave energy spectra, and can reflect a more multi-dimensional and more detailed representative sea state. Finally, the fatigue strength of the offshore wind turbine is determined on the basis of the equivalent energy spectrum. Therefore, by means of the equivalent energy spectrum having more dimensions and stronger continuity, the number of sea states used for calculating the fatigue strength is reduced, the calculation efficiency is improved, and the accuracy of the fatigue strength is also improved. The method further provides an alternative sea state information expression mode for subsequent calculation of the fatigue strength by means of the wave scatter diagrams, ensuring that the aggregation processing can still be continued on the basis of discrete data points in the wave scatter diagrams even in the case of data missing, thereby obtaining a more accurate representative sea state.

Description

Methods and related devices for determining the fatigue strength of offshore wind turbines

[0001] This disclosure claims priority to Chinese Patent Application No. 202412000257.3, filed with the Chinese Patent Office on December 31, 2024, entitled "A Wave Data Processing Method and Related Apparatus", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to the field of wind power generation technology, and in particular to a method and related apparatus for determining the fatigue strength of offshore wind turbines. Background Technology

[0003] Offshore wind power generation refers to the installation of offshore wind turbines in the marine environment, which convert the captured sea wind energy into electrical energy. However, offshore wind turbines are subject to fatigue and damage due to the impact of sea waves. The fatigue and damage of offshore wind turbines are usually analyzed by quantifying fatigue strength.

[0004] In related technologies, representative sea conditions are typically determined from a variety of sea conditions collected to improve the efficiency of fatigue strength calculation.

[0005] However, the representative sea state data obtained by the above method has low accuracy, resulting in inaccurate calculation of the fatigue strength of offshore wind turbines. Summary of the Invention

[0006] To address the aforementioned issues, this disclosure provides a method and related apparatus for determining the fatigue strength of offshore wind turbines, which improves the accuracy of data for representative sea conditions and enhances the accuracy of fatigue strength calculations.

[0007] Based on this, the following technical solution is disclosed:

[0008] In a first aspect, embodiments of this disclosure provide a method for determining the fatigue strength of an offshore wind turbine, the method comprising:

[0009] The original wave characterization was determined based on wave data collected from offshore wind farms at multiple wind speeds.

[0010] The original wave representations under the multiple wind speeds are subjected to condensation processing to obtain equivalent wave representations; and

[0011] The fatigue strength of the wind turbine in the offshore wind farm is determined based on the equivalent wave characterization.

[0012] Secondly, embodiments of this disclosure provide a device for determining the fatigue strength of an offshore wind turbine, the device comprising: an acquisition unit, a cohesion unit, and a determination unit;

[0013] The acquisition unit is used to determine the original wave characterization based on the collected wave data of the offshore wind farm at multiple wind speeds.

[0014] The condensation unit is used to condense the original wave representations under the multiple wind speeds to obtain equivalent wave representations; and

[0015] The determining unit is used to determine the fatigue strength of the wind turbine in the offshore wind farm based on the equivalent wave characterization.

[0016] Thirdly, embodiments of this disclosure provide a wind turbine generator set, the wind turbine generator set including a controller, the controller being used to perform the method described in the first aspect above.

[0017] Fourthly, embodiments of this disclosure provide a computer device, the computer device including a processor and a memory:

[0018] The memory is used to store computer programs and to transfer the computer programs to the processor;

[0019] The processor is configured to execute the method described in the first aspect above according to the computer program.

[0020] Fifthly, embodiments of this disclosure provide a computer-readable storage medium for storing a computer program for performing the methods described in the first aspect above.

[0021] In a sixth aspect, embodiments of this disclosure provide a computer program product including a computer program that, when run on a computer device, causes the computer device to perform the method described in the first aspect above.

[0022] As can be seen from the above technical solutions, this disclosure has at least the following beneficial effects:

[0023] The original wave characterization is determined based on wave data collected from an offshore wind farm at multiple wind speeds. The original wave characterization at these multiple wind speeds is then condensed to obtain an equivalent wave characterization. Finally, the fatigue strength of the wind turbine generators in the offshore wind farm is determined based on the equivalent wave characterization. The original wave characterization in this disclosure can be either the original wave energy spectrum or the original wave scatter plot. The equivalent energy spectrum obtained by condensing the wave energy spectrum retains more detailed sea state information, reflecting more dimensions and more detailed representative sea states. Finally, the fatigue strength of the offshore wind turbine generators is determined based on the equivalent energy spectrum. Therefore, the more dimensional and continuous equivalent energy spectrum not only reduces the number of sea states used to calculate fatigue strength and improves computational efficiency, but also improves the accuracy of fatigue strength calculation. This disclosure also provides an alternative way to express sea state information for subsequent fatigue strength calculations using wave scatter plots, ensuring that even in the event of missing data, condensation processing can continue based on discrete data points in the wave scatter plot to obtain a more accurate representative sea state. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 is a flowchart illustrating a method for determining the fatigue strength of an offshore wind turbine according to an embodiment of this disclosure;

[0026] Figure 2 is a schematic diagram of a wave energy spectrum provided in an embodiment of this disclosure;

[0027] Figure 3 is a wave scattering diagram provided in an embodiment of this disclosure;

[0028] Figure 4 is a schematic diagram of a functional relationship and equivalent wave energy provided in an embodiment of this disclosure;

[0029] Figure 5 is a schematic diagram of a process for evaluating fatigue strength according to an embodiment of this disclosure;

[0030] Figure 6 is a schematic diagram of a device for determining the fatigue strength of an offshore wind turbine provided in an embodiment of this disclosure;

[0031] Figure 7 is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure. Detailed Implementation

[0032] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0033] Wave data such as wave height, wave period, and wave occurrence frequency can be used to reflect sea conditions. Wave height is the vertical distance between adjacent wave crests and troughs. Wave period is the time required for two adjacent wave crests (or troughs) to pass through a fixed observation point. Wave occurrence frequency is the number of times the same type of wave is observed by the acquisition equipment within a certain observation period. Waves of the same type can be waves with the same wave height and wave period.

[0034] Wave cohesion: The convergence of multiple wave characteristics into a smaller number of representative sea states.

[0035] In related technologies, the fatigue strength of offshore wind turbines is generally determined by time-domain simulation calculation. However, this method collects too many sea states, which leads to long calculation time. In order to improve the computational efficiency of time-domain simulation calculation, research has found that by condensing the collected sea states into a smaller number of representative sea states, the number of sea states used to calculate fatigue strength can be greatly reduced, thus reducing the calculation time.

[0036] For example, by statistically analyzing the frequency of waves with different wave heights and periods, a wave scatter plot is obtained. This scatter plot can then be processed to generate several representative sea states. However, wave scatter plots are typically two-dimensional data charts composed of discrete data points. Expressing sea states by statistically analyzing the frequency of various wave types simplifies the complex data characteristics of sea states, resulting in low data precision. Consequently, when calculating fatigue intensity, the subtle features and dynamic changes of waves cannot be fully captured, leading to low accuracy.

[0037] Based on this, embodiments of this disclosure provide a method and related apparatus for determining the fatigue strength of offshore wind turbines. The method involves determining an original wave characterization based on wave data collected from an offshore wind farm at multiple wind speeds; performing agglomeration processing on the original wave characterization at the multiple wind speeds to obtain an equivalent wave characterization; and determining the fatigue strength of the wind turbine in the offshore wind farm based on the equivalent wave characterization. The original wave characterization in this disclosure can be an original wave energy spectrum or an original wave scatter plot. The equivalent energy spectrum obtained by agglomerating the wave energy spectrum retains more detailed sea state information, reflecting more dimensions and more detailed representative sea states. Finally, the fatigue strength of the offshore wind turbine is determined based on the equivalent energy spectrum. Thus, the more dimensional and continuous equivalent energy spectrum not only reduces the number of sea states used to calculate fatigue strength and improves computational efficiency, but also improves the accuracy of fatigue strength calculation. This disclosure also provides an alternative way to express sea state information for subsequent fatigue strength calculations using wave scatter plots, ensuring that even in the event of missing data, agglomeration processing can continue based on discrete data points in the wave scatter plot to obtain a more accurate representative sea state.

[0038] The method for determining the fatigue strength of offshore wind turbines provided in this disclosure can be applied to computer equipment with wave data processing capabilities, such as terminal devices and servers. Specifically, terminal devices can be desktop computers, laptops, mobile phones, and tablets; servers can be independent physical servers, server clusters composed of multiple physical servers, or distributed systems. Terminal devices and servers can be directly or indirectly connected via wired or wireless communication, and this disclosure does not impose any limitations. Furthermore, the method for determining the fatigue strength of offshore wind turbines provided in this disclosure can be applied to the controller of wind turbines or the central controller of offshore wind farms. The following embodiments use a server as an example to illustrate the implementation of this method for determining the fatigue strength of offshore wind turbines.

[0039] A method for determining the fatigue strength of an offshore wind turbine, wherein the method includes S1-S3:

[0040] S1: Determine the original wave characterization based on the wave data collected from the offshore wind farm at multiple wind speeds;

[0041] Offshore wind farms refer to large-scale power facilities that centrally install multiple large wind turbine generators in coastal waters (including nearshore and deep-sea areas), transmitting electricity to the onshore power grid via submarine cables. Offshore wind turbine generators are wind turbine generators used for offshore wind power generation, typically including a turbine head, tower, support structure, electrical system, and control system. Wind speed refers to the speed at which air flows relative to the calm sea surface.

[0042] By deploying monitoring equipment in areas related to offshore wind turbines, wave data such as wave height, wave period, and direction are collected at different wind speeds to construct wave representations. Since the constructed wave representations are derived from direct data acquisition, they can be called raw wave representations.

[0043] Optionally, the detection equipment can be installed inside the wind farm or in an area less than a distance threshold from the wind farm. For example, the radius of the relevant area can be five times the radius of the wind turbine's support structure, or it can be 100m.

[0044] Wave characterization is used to characterize the sea state of offshore wind farms. This wave characterization can be a wave energy spectrum or a wave scatter plot. Understandably, the wave characterization determined for each wind speed can correspond to multiple sea states.

[0045] S2: The original wave representations under multiple wind speeds are condensed to obtain equivalent wave representations.

[0046] By performing agglomeration processing on the original wave characteristics under the above multiple wind speeds, such as the original wave energy spectrum or the original wave scatter plot, multiple sea states can be agglomerated into a smaller number of representative sea states.

[0047] S3: Determine the fatigue strength of wind turbine units in offshore wind farms based on equivalent wave characterization.

[0048] By directly using the equivalent wave characterization obtained after the above condensation treatment, the fatigue strength of wind turbine units in offshore wind farms is determined, which reduces the number of sea states used to calculate fatigue strength and improves calculation efficiency.

[0049] Optionally, in the embodiments of this disclosure, fatigue strength can be represented by fatigue load.

[0050] Next, we will introduce the methods for determining the fatigue strength of offshore wind turbines based on two different wave representations: wave energy spectrum and wave scattering diagram.

[0051] In the first implementation, the wave is characterized as a wave energy spectrum. In this case, the original wave energy spectrum is determined in S1. As shown in Figure 1, the method for determining the fatigue strength of this offshore wind turbine includes S101-S103:

[0052] S101: Determine the original wave energy spectrum based on the wave data collected from offshore wind farms at multiple wind speeds.

[0053] The collected wave data can be characterized using wave energy spectrum to obtain the raw wave energy spectrum. The wave energy spectrum is used to describe the energy distribution of waves as a function of frequency (the reciprocal of the wave period) and direction.

[0054] Referring to Figure 2, which is a schematic diagram of a wave energy spectrum provided in an embodiment of this disclosure, the wave energy spectrum reflects the energy distribution of waves as frequency and direction change. The wave energy spectrum not only describes the energy variation of waves with frequency but also considers the directionality of waves, reflecting the energy changes of waves from different directions, providing a more comprehensive data dimension for sea state analysis. Compared to discrete data points, the wave energy spectrum represents energy distribution through continuous data variation relationships, avoiding the loss of detail caused by data sampling intervals and providing more accurate sea state information with less loss.

[0055] S102: The wave energy spectrum under multiple wind speeds is condensed to obtain the equivalent energy spectrum.

[0056] The equivalent energy spectrum is a representative wave energy spectrum determined from multiple original wave energy spectra. Representativeness is used to characterize the data's ability to reflect specific sea conditions.

[0057] Convergence processing is a data analysis method used to merge a set of correlated data into a smaller, more representative dataset. Convergence processing can process m wave energy spectra into n equivalent energy spectra, where m and n are positive integers, with m greater than n. This allows n equivalent energy spectra to represent n representative sea states, replacing the m wave energy spectra, thus reducing the computational workload for determining fatigue strength and improving computational efficiency.

[0058] Next, we will provide a detailed introduction to the condensation treatment:

[0059] In one possible implementation, wind speed can be divided into compartments, and condensation processing can be performed on different wind speed compartments, as detailed in A1-A3:

[0060] A1: Divide multiple wind speeds into compartments according to preset wind speed intervals to obtain multiple wind speed compartments.

[0061] A wind speed compartment is a wind speed segment divided from multiple wind speeds. Multiple wind speed compartments can be defined based on the wind speed compartment settings. These settings can include the wind speed range to be divided and the wind speed intervals set according to actual needs. For example, multiple wind speeds may include those ranging from 3 meters per second (m / s) to 25 m / s (it should be noted that wind speeds are not limited to integer values). By dividing these multiple wind speeds into compartments at 1 m / s intervals, multiple wind speed compartments can be obtained. For instance, 3.5 m / s to 4.5 m / s can be one such compartment, and 4.5 m / s to 5.5 m / s another.

[0062] A2: For each of the multiple wind speed compartments, the wave energy spectra corresponding to the multiple wind speeds contained in that wind speed compartment are averaged to obtain the sub-compartment wave energy spectra of each wind speed compartment.

[0063] For each of the multiple wind speed chambers, each of the multiple wind speeds contained within it has a corresponding original wave energy spectrum. The energy of the multiple original wave energy spectra corresponding to the multiple wind speeds in each wind speed chamber can be linearly superimposed, and the sum of the superimposed energy can be averaged to obtain the sub-chamber wave energy spectrum for each wind speed chamber. For example, the sub-chamber wave energy spectrum for the 4.5 m / s to 5.5 m / s wind speed chamber can be obtained by linearly superimposing and averaging the original wave energy spectra.

[0064] A3: Determine the equivalent energy spectrum of each wind speed compartment based on the sub-compartment wave energy spectrum of each wind speed compartment.

[0065] Specifically, the sub-wave energy spectrum of each wind speed compartment can be directly determined as the equivalent energy spectrum of each wind speed compartment. Alternatively, the sub-wave energy spectrum of each wind speed compartment can be further processed according to requirements to obtain the equivalent energy spectrum that meets the requirements. For example, the sub-wave energy spectrum of each wind speed compartment can be probability-weighted to determine the equivalent energy spectrum under the required direction.

[0066] Previous research and practice have failed to fully consider the differences in wave energy spectra under different wind speed conditions. In this disclosure, by dividing multiple wind speeds into compartments and determining the equivalent energy spectrum of each wind speed compartment, the differences in wave energy spectra under different wind speed conditions can be identified. This allows us to focus on the impact of different wind speed conditions on the fatigue strength of offshore wind turbines and improve the comprehensiveness of fatigue assessment.

[0067] Furthermore, for the same wind speed, wave energy spectra from multiple directions can be considered to further improve the accuracy of the assessment. This disclosure provides a specific implementation method for determining the equivalent energy spectrum in the desired direction through probability weighting, as detailed in A31-A33:

[0068] A31: Divide the wave into multiple directions to obtain multiple directional positions.

[0069] A directional container is a set of directions obtained by dividing multiple directions. Each directional container can include at least one of the multiple directions. For example, if the range of multiple directions is 0 degrees to 360 degrees, dividing 0 degrees to 360 degrees into containers at 30-degree intervals results in 12 directional containers. Here, 0 degrees is due north in geographical orientation, and 360 degrees is clockwise.

[0070] In one possible implementation, after obtaining user requirements, the directional range (also known as the compartment interval) of a single directional compartment can be determined based on these requirements. Then, multiple directional compartments are created by dividing the range of a single directional compartment into multiple directional compartments. For example, if the range of multiple directions is 0 degrees to 360 degrees, and the user requirement is to analyze the overall sea conditions across all directions (0 degrees to 360 degrees), the directional range of a single directional compartment is determined to be 30 degrees, resulting in 12 directional compartments divided into 0 degrees to 360 degrees. If the user requirement is to perform a detailed analysis of the sea conditions within a specific directional range, such as a detailed analysis of the sea conditions from 0 degrees to 60 degrees, the directional range of a single directional compartment is determined to be 5 degrees, resulting in 72 directional compartments divided into 0 degrees to 360 degrees. It can be seen that the compartment interval is smaller in this case compared to a user requirement covering all directions.

[0071] Therefore, the directional range of a single directional chamber is determined based on user needs. For a broad research area, a larger directional chamber can effectively reduce the number of equivalent energy spectra and improve computational efficiency. When more detailed analysis is required, a smaller directional chamber can capture more local sea state data features, resulting in more accurate fatigue strength calculations.

[0072] A32: For each wind speed compartment in multiple wind speed compartments, based on the sub-compartment wave energy spectrum of that wind speed compartment, the sub-wave energy spectrum corresponding to each directional compartment is obtained, as well as the wave occurrence probability of each directional compartment.

[0073] Sub-wave energy spectrum is a wave energy spectrum obtained by separating the sub-wave energy spectrum of multiple directional chambers. It is used to reflect the energy distribution of waves as a function of frequency and at least one direction corresponding to each directional chamber. For each wind speed chamber, the sub-wave energy spectrum corresponding to each directional chamber can be separated according to the directional range corresponding to each of the multiple directional chambers to obtain the sub-wave energy spectrum corresponding to each directional chamber.

[0074] For each directional container, the probability of wave occurrence is determined based on the number of wave occurrences in at least one direction within that directional container and the total number of wave occurrences in all directions. Specifically, the probability of wave occurrence for each directional container can be obtained by statistically analyzing the proportion of wave occurrences in at least one direction relative to the total number of wave occurrences in all directions.

[0075] A33: For each wind speed chamber, the equivalent energy spectrum of the wind speed chamber is determined based on the wave occurrence probability of each direction chamber corresponding to the wind speed chamber and the sub-wave energy spectrum of each direction chamber corresponding to the wind speed chamber.

[0076] For each wind speed chamber, the weight of each directional chamber is determined by the probability of wave occurrence in each directional chamber. Then, the energy spectrum of the sub-wave corresponding to each directional chamber is weighted based on the determined weight to obtain the equivalent energy spectrum of the wind speed chamber.

[0077] Taking the solution of the equivalent energy spectrum of a single-direction input as an example, if the 0-360 degree range is divided into 12 directional compartments at 30-degree intervals, and the wave occurrence probability of each directional compartment is P0, P1, P2, P3, P4, P5, P6, P7, P8, P9, P1, P1, P1, P2 ... 30 ..., P 330 The equivalent energy spectrum of each wind speed chamber is determined by the following formula: S ζ =S ζ0 P0+S ζ30 P 30 +…+S ζ330 P 330

[0078] Among them, S ζ The equivalent energy spectrum of each wind speed chamber, S ζ0 Let S be the sub-wave energy spectrum of the directional warehouse corresponding to 0-30 degrees, P0 be the probability of wave occurrence of the directional warehouse corresponding to 0-30 degrees, and S be the energy spectrum of the directional warehouse corresponding to 0-30 degrees. ζ30 The energy spectrum of the sub-waves in the 30-60 degree directional chamber, P 30 The probability of wave occurrence in the 30-60 degree range corresponds to the direction of the position, and so on.

[0079] If you want to solve for the equivalent energy spectrum of multiple input directions, the solution method is the same as described above. You only need to perform more detailed compartment processing according to multiple solution directions and perform the above probability weighting operation to obtain the equivalent energy spectrum of multiple directions under each wind speed compartment.

[0080] Therefore, for each wind speed chamber, the sub-wave energy spectrum of multiple directional chambers is weighted by the wave occurrence probability of its corresponding multiple directional chambers. This results in the sub-wave energy spectrum of the directional chamber with a higher wave occurrence probability contributing more to the equivalent energy spectrum. As a result, the equivalent energy spectrum can reflect the more realistic sea conditions, that is, more accurately capture the energy distribution characteristics of waves from different directions, and improve the representativeness of the equivalent energy spectrum.

[0081] S103: Determine the fatigue strength of offshore wind turbines based on the equivalent energy spectrum.

[0082] Fatigue strength is a quantitative indicator used to characterize the cumulative damage of offshore wind turbines caused by the initiation and propagation of microcracks within the material under repeated or varying wave stress.

[0083] Specifically, by using sea state information reflected in the equivalent energy spectrum, a fully coupled analysis can be performed to model the wave effects on offshore wind turbines, resulting in a simulation model. Based on this model, time-series data is output, including dynamic response parameters of the offshore wind turbine under various sea conditions. These parameters include the motion states of various structures within the turbine (displacement, velocity, acceleration, etc.), material stress, and the time-varying power generation of the turbine. Fatigue strength can then be determined by extracting data from the time-series data output by the simulation model.

[0084] One approach is the rainflow counting method, which identifies and statistically analyzes complete cycles (i.e., a complete loading-unloading process) in the time series data. Each cycle is quantified into a pair of maximum and minimum values, and its amplitude and mean stress are determined accordingly. Another approach is linear cumulative damage theory, which converts the stress amplitude of each cycle into a corresponding damage value based on the SN curve (stress-life curve). Then, according to Miner's rule, the damage values ​​caused by all cycles are summed to obtain the total cumulative damage.

[0085] As can be seen from the above technical solution, obtaining the original wave energy spectrum of offshore wind turbines at multiple wind speeds describes the energy distribution of waves as a function of frequency and direction. This original wave energy spectrum not only incorporates information about the directional dimension but also reflects the continuous relationship between wave energy and frequency and direction, providing more detailed sea state information. Then, the original wave energy spectrum at multiple wind speeds is condensed to obtain an equivalent energy spectrum. The equivalent energy spectrum obtained by condensing the original wave energy spectrum retains more detailed sea state information from the original wave energy spectrum, reflecting more dimensions and more detailed representative sea states. Finally, the fatigue strength of the offshore wind turbine is determined based on the equivalent energy spectrum. Therefore, the more dimensional and continuous equivalent energy spectrum not only reduces the number of sea states used to calculate fatigue strength, improving computational efficiency, but also improves the accuracy of fatigue strength calculation.

[0086] In the second implementation, the wave is represented as a wave scatter plot, and the original wave scatter plot is determined in S1. In this case, as shown in Figure 1, the method for determining the fatigue strength of the offshore wind turbine includes S201-S203:

[0087] S201: Determine the original wave scattering map based on the wave data collected from the offshore wind farm at multiple wind speeds.

[0088] The wave scatter plot includes multiple wave groups, with different wave groups indicating the frequency of different wave types. Waves of the same type have the same wave height and wave period. Referring to Figure 3, which is a wave scatter plot provided in an embodiment of this disclosure, it shows a wave scatter plot in all directions (0 to 360 degrees). The data in this wave scatter plot reflects the statistical distribution of wave occurrences in a specific direction under a specific wind speed. Specifically, different wave heights and periods correspond to various wave types. Frequency statistics are performed on these various wave types to obtain the wave scatter plot, which is the original wave scatter plot.

[0089] S202: The original wave scatter plots under multiple wind speeds are condensed to obtain equivalent wave scatter plots.

[0090] In one specific implementation, S202 may include B1-B3.

[0091] B1: Divide multiple wind speeds into compartments according to preset wind speed intervals to obtain multiple wind speed compartments.

[0092] For information on wind speed compartmentation, please refer to step A1; it will not be repeated here.

[0093] B2: For each of the multiple wind speed compartments, the original wave scatter plots corresponding to the multiple wind speeds contained in that wind speed compartment are averaged to obtain the sub-compartment wave scatter plots of each wind speed compartment.

[0094] Statistical analysis can yield the original wave scatter plots for each wind speed compartment within a directional range, for example, 0 to 360 degrees. For each wind speed compartment, the average of its original wave scatter plots can be taken to obtain its sub-compartment wave scatter plot. In a specific example, for the wave data of the 4.5 m / s to 5.5 m / s wind speed compartment, wave data at a wind speed of 5 m / s can be taken, and so on, to form sub-compartment wave scatter plots for each wind speed compartment at entry wind speeds of 3 m / s to exit wind speeds of 25 m / s.

[0095] B3: Determine the equivalent wave scatter diagram for each wind speed compartment based on the sub-compartment wave scatter diagram of each wind speed compartment.

[0096] Similar to A3, the sub-wave scatter plots of each wind speed compartment can be directly determined as the equivalent wave scatter plots of each wind speed compartment. Alternatively, the sub-wave scatter plots of each wind speed compartment can be further processed according to requirements to obtain the equivalent wave scatter plots that meet the requirements. For example, the sub-wave scatter plots of each wind speed compartment can be probability-weighted to determine the equivalent wave scatter plots of each wind speed compartment.

[0097] Furthermore, for the same wind speed, wave scatter plots from multiple directions can be considered to further improve the accuracy of the assessment. This disclosure provides a specific implementation method for determining the equivalent wave scatter plot, as detailed in B31-B37:

[0098] B31: Divide the wave into multiple directions to obtain multiple directional positions.

[0099] Each directional compartment includes at least one of multiple directions. Please refer to step A31 above for details.

[0100] B32: For each wind speed compartment, the sub-wave scatter plots corresponding to multiple directional compartments are obtained.

[0101] B33 divides the sub-wave scatter plot corresponding to each directional chamber to obtain multiple wave blocks.

[0102] By dividing each sub-wave scatter plot, multiple wave blocks are obtained. Each wave block includes at least one of the multiple wave groups in the original wave scatter plot. Each wave group includes the wave height, wave period, and wave occurrence number corresponding to the wave.

[0103] Different wave blocks are used to represent different representative sea states. In a specific example, the type of representative sea state can be represented by equivalent wave energy, which is the result of condensing multiple wave energies. Different equivalent wave energies correspond to different representative sea states. If the difference between the equivalent wave energies corresponding to two wave blocks is greater than a preset threshold, then these two wave blocks can be considered to represent different types of representative sea states. The preset threshold reflects the minimum difference between different representative sea states.

[0104] A detailed explanation of how the sub-wave scatter plot is divided into multiple wave blocks will be elaborated in subsequent steps B331-B335, and will not be repeated here.

[0105] B34: For each wave block, determine the equivalent wave height corresponding to the wave block based on the force coefficient, the wave height of each wave group in the wave block, the wave period, and the number of times the wave occurs.

[0106] The equivalent wave height here is a representative sea state, obtained by condensing multiple wave heights. Step B34 provides a method for calculating the equivalent wave height.

[0107] The stress coefficient is used to characterize the relationship between the equivalent wave height and the stress type of the offshore wind turbine. Based on the stress coefficient, the wave height, wave period, and wave occurrence frequency of the wave group, the equivalent wave height corresponding to each wave group can be determined by a preset model or preset formula.

[0108] In one possible implementation, the types of forces include inertial force and drag force. Inertial force is the force generated by the inertia of seawater as it accelerates or decelerates with the waves. Dragging force is a resistance force exerted on the surface of the offshore wind turbine by seawater flowing with the waves. Correspondingly, the force coefficients include an inertial force coefficient and a drag force coefficient. The inertial force coefficient characterizes the relationship between the equivalent wave height and the inertial force acting on the offshore wind turbine, while the drag force coefficient characterizes the relationship between the equivalent wave height and the drag force acting on the offshore wind turbine.

[0109] This disclosure provides a specific implementation of B34, see B341-B343:

[0110] B341: Obtain the diameter and diameter threshold of the support structure of offshore wind turbines.

[0111] The diameter of the support structure is a characteristic dimension used to characterize the scale of the interaction between the support structure and seawater. Specifically, it can be the diameter of the support structure at sea level or at the plane in contact with seawater.

[0112] B342: If the diameter of the supporting structure is less than or equal to the diameter threshold, for each wave block, the equivalent wave height corresponding to the wave block is determined based on the inertial force coefficient, the wave height of each wave group in the wave block, the wave period, and the number of wave occurrences.

[0113] If the diameter of the supporting structure is less than or equal to the diameter threshold, it means that the surface area of ​​the supporting structure in contact with seawater is small, and the drag force exerted by the seawater on the surface of the supporting structure is small. That is, the force exerted by the waves is mainly inertial force, and the equivalent wave height is determined according to the inertial force coefficient.

[0114] B343: If the diameter of the supporting structure is greater than the diameter threshold, for each wave block, the equivalent wave height corresponding to the wave block is determined based on the drag force coefficient, the wave height of each wave group in the wave block, the wave period, and the number of times the wave occurs.

[0115] If the diameter of the supporting structure is greater than the diameter threshold, it means that the surface area of ​​the supporting structure in contact with seawater is large, and the drag force exerted by the seawater on the surface of the supporting structure is greater. That is, the drag force is the dominant force of the wave. Therefore, the equivalent wave height is determined according to the drag force coefficient.

[0116] B342-B343 can be specifically represented by the following formula:

[0117] Among them, H s,,eq For the equivalent wave height, H s,,i Let T be the wave height in the i-th row of the wave scattering diagram. p,j Let p(H) be the wave period of the j-th column in the wave scatter plot. s,i ,Tp,,j ) represents the number of times the wave appears in the i-th row and j-th column of the wave scattering diagram, λ is the force coefficient, when the force coefficient is the inertial force coefficient, λ = 1, when the force coefficient is the drag force coefficient, λ = 2, m is the fatigue coefficient, the fatigue coefficient is used to characterize the material tolerance of offshore wind turbines, i is a positive integer, j is a positive integer.

[0118] Therefore, the main stress type is determined based on the diameter of the support structure of the offshore wind turbine, and then the equivalent wave height is determined by selecting different stress coefficients. This allows the equivalent wave height to be adjusted according to different stress types, making it more representative and thus making the calculated fatigue strength more accurate.

[0119] B35: Obtain the functional relationship between wave energy and wave height and wave period, and for each wave block, determine the equivalent wave period corresponding to the wave block based on the numerical relationship between the functional relationship and the equivalent wave energy corresponding to the wave block.

[0120] Equivalent wave periods can be used to characterize representative sea states and are obtained by condensing multiple wave periods. Functional relationships characterize the relationship between wave energy and wave height and period; these relationships can be used to determine the energy of waves with different heights and periods. As one implementation method, functional relationships can be wave spectra such as JONSWAP and ITTC. Then, based on the numerical relationship between the functional relationships and the equivalent wave energies corresponding to each wave block, the equivalent wave period for each wave block is determined.

[0121] For example, taking any one of multiple wave blocks as an example, refer to Figure 4. Figure 4 is a schematic diagram of a functional relationship and equivalent wave energy provided by an embodiment of this disclosure. As shown in the figure, the functional relationship is a curve with two intersection points with the horizontal line where the equivalent wave energy is located. These two intersection points correspond to two intersection periods. Since the natural frequency of a high-power offshore wind turbine is relatively small, waves with smaller frequencies (larger wave periods) are more likely to approach the natural frequency of the offshore wind turbine and cause resonance. In other words, waves with longer wave periods can cause a more significant impact. Therefore, the larger intersection period among the two intersection periods is selected as the equivalent wave period corresponding to the target wave block.

[0122] For each wave block, its corresponding equivalent wave period can be obtained.

[0123] Therefore, based on the material frequency characteristics of offshore wind turbines, a larger intersection period is selected as the equivalent wave period, making the equivalent wave period closer to the correspondence between the actual wave period and the material stress, thus improving the accuracy of the equivalent wave period.

[0124] S203: Determine the fatigue strength of offshore wind turbines based on the equivalent wave scattering diagram.

[0125] The equivalent wave scatter plot includes the equivalent wave period and the equivalent wave height.

[0126] Specifically, determining the fatigue strength of offshore wind turbines based on equivalent wave scattering diagrams can include:

[0127] B36: Determine the fatigue strength of offshore wind turbines based on the equivalent wave scattering diagrams corresponding to each wave block in each direction of the multiple wind speed cells.

[0128] The sea state information reflected by the equivalent wave state can be used to perform a fully coupled analysis and model the wave action on the offshore wind turbine to obtain a simulation model. The subsequent process is described in S103 and will not be repeated here.

[0129] Therefore, by dividing the wave scatter map into multiple sections according to multiple directions, and further processing the multiple discrete wave groups in the original wave scatter map, equivalent wave scatter maps corresponding to each wave group can be obtained, and fatigue strength can be calculated based on these equivalent wave scatter maps. In this way, the equivalent wave scatter maps obtained from each original wave scatter map can be used to describe the representative sea conditions of different directional sections, thus enabling the representation of representative sea conditions to contain more multi-dimensional information.

[0130] Typically, in the process of condensing multiple wave energies to obtain equivalent wave energy, only the quasi-static case is considered, without taking into account the dynamic excitation of the waves. Based on this, the embodiments of this disclosure provide a specific implementation method for determining equivalent wave energy, see C1-C5:

[0131] C1: Obtain the first-order vibration frequency and fatigue coefficient of the offshore wind turbine.

[0132] Among them, the first-order vibration frequency is the lowest frequency at which the structure of an offshore wind turbine can vibrate freely without external excitation, and the fatigue coefficient is used to characterize the material resistance of the offshore wind turbine. The smaller the fatigue coefficient, the higher the resistance of the supporting structure.

[0133] C2: For each wave block in each wave block, determine the wave energy corresponding to each wave group included in that wave block.

[0134] For each wave group, the wave period and wave height within the wave group can be processed using the functional relationship between wave energy and wave height and wave period to obtain the wave energy corresponding to each wave group within that wave group. For example, the corresponding wave energy can be calculated based on a preset wave spectrum model (such as the JONSWAP wave spectrum, Pierson-Moskowitz wave spectrum, or ITTC recommended spectrum) according to the wave height and wave period of the corresponding wave group.

[0135] C3: For each wave group in each wave group, the wave energy corresponding to each wave group is weighted according to the number of times the waves appear in each wave group included in the wave group, so as to obtain the weighted wave energy corresponding to each wave group included in the wave group.

[0136] Weighted wave energy is the wave energy weighted by the number of times a wave occurs. The more times a wave occurs, the greater the weighted wave energy.

[0137] C4: For each wave block in each wave block, determine the equivalent wave energy corresponding to the wave block based on the weighted wave energy, first-order vibration frequency and fatigue coefficient of each wave group included in the wave block.

[0138] The more times a wave appears in a wave group, the greater the proportion of the wave energy in the wave group to the equivalent wave energy of its corresponding wave block.

[0139] C2-C4 can be expressed by the following formula:

[0140] Among them, S ζζ (ω0) eq For equivalent wave energy, S ζζ (H s,i |T p,j |ω0) represents the wave energy of the wave group in the i-th row and j-th column of the wave scatter plot obtained by processing the first-order vibration frequency, m is the fatigue coefficient, and p(H s,i ,T p,j ) represents the number of times the wave appears in the i-th row and j-th column of the wave scatter plot, where i is a positive integer and j is a positive integer.

[0141] As one implementation method, the fatigue coefficient m can be the inverse slope of the material SN curve of the offshore wind turbine. For offshore wind turbines, m is generally taken as 4.

[0142] Therefore, by introducing a first-order vibration frequency in the process of obtaining equivalent wave energy, and considering that the resonance phenomenon caused by the wave frequency being close to the first-order vibration frequency will produce stronger fatigue strength, this dynamic excitation can more accurately capture the impact of waves on the fatigue strength of offshore wind turbines, making the equivalent wave energy more accurate.

[0143] The following provides a detailed explanation of the block division of the sub-wave scatter plot:

[0144] When dividing sub-wave scatter plots into blocks, manual division or division centered on the first-order oscillation frequency is commonly used. Both of these methods lack scientific basis, resulting in insufficient representativeness of the resulting wave blocks. Therefore, this disclosure provides a specific implementation of B33, see B331-B335:

[0145] B331: For each sub-wave scatter plot, divide the sub-wave scatter plot to obtain multiple initial wave blocks.

[0146] The initial wave block is the wave block that has not yet been updated. As one implementation, the initial wave block can be divided randomly.

[0147] B332: For each sub-wave scatter plot, determine the wave energy corresponding to each wave group of that sub-wave scatter plot.

[0148] The wave period and wave height in a wave group can be processed by the functional relationship between wave energy and wave height and wave period to obtain the wave energy corresponding to each wave group.

[0149] B333: For each sub-wave scatter plot, based on the difference between the wave energy corresponding to each wave group in the sub-wave scatter plot and the equivalent wave energy corresponding to each initial wave block in the sub-wave scatter plot, determine the initial wave block with the smallest wave energy difference as the target initial wave block; if the target initial wave block is different from the initial wave block corresponding to the wave group, then move the wave group to the target initial wave block to obtain the updated wave block corresponding to the wave group; if the target initial wave block is the same as the initial wave block corresponding to the wave group, then do not perform a move operation on the wave group.

[0150] In a specific example, if N initial wave blocks are obtained after the first division, and the i-th wave block includes K wave groups, then for the j-th wave group among the K wave groups: determine the difference between the equivalent wave energy of the j-th wave group and each of the N initial wave blocks, and take the initial wave block with the smallest energy difference from the j-th wave group among the N initial wave blocks as the target initial wave block; if the target initial wave block is different from the initial wave block of the j-th wave group, for example, it is the p-th (p≠i) initial wave block, then move the j-th wave group to the target initial wave block to obtain the updated wave block corresponding to the j-th wave group; if the target initial wave block is the same as the j-th initial wave block, that is, it is still the i-th initial wave block, then the j-th wave group still belongs to the i-th initial wave block, where 1≤i≤N, 1≤j≤K, 1≤p≤N, and N and K are any integers greater than or equal to 1.

[0151] For each wave group, a corresponding updated wave block is obtained. If N initial wave blocks are obtained through step B331, N updated wave blocks can be obtained.

[0152] B334: For each initial wave block, determine the energy difference between the equivalent wave energy of the initial wave block and the equivalent wave energy of each updated wave block. If each energy difference is less than the difference threshold, then the updated wave block is determined as the multiple wave blocks; otherwise, repeat steps B32-B33 until the difference between the equivalent wave energy corresponding to the multiple updated wave blocks obtained in this update and the equivalent wave energy corresponding to the multiple updated wave blocks obtained in the previous update is less than the difference threshold, and then the multiple updated wave blocks obtained in this update are determined as multiple wave blocks.

[0153] The difference threshold is used to characterize the upper limit of the expected difference between the equivalent wave energies of the two updated wave blocks obtained from the two consecutive divisions. If the (r-1)th update yields multiple (r-1)... th Update the equivalent wave energy corresponding to each wave block and the multiple r values ​​obtained from the r-th update. th If the difference between the equivalent wave energies corresponding to the updated wave blocks is less than the difference threshold, it means that further updating the wave blocks will not produce significant changes. Therefore, the multiple r-th partitions obtained from the r-th partition are... th The updated wave block is determined to be multiple wave blocks, completing the process of dividing the target wave scatter plot. r is a positive integer greater than or equal to 1. When r = 0, it corresponds to the initial wave block obtained through step B331.

[0154] By dividing the wave groups according to the minimum energy difference, each wave group is assigned to the most similar wave block during multiple updates, which enhances the consistency within each wave block. Through multiple update operations, the division of the sub-wave scatter plot is gradually optimized, making each wave block more representative.

[0155] Optionally, after acquiring wave data, the data completeness can be assessed. Data completeness is an indicator of the completeness of wave energy spectrum data, used to characterize the proportion of missing data in the acquired wave data. The higher the data completeness, the lower the missing proportion. For example, data completeness can be a percentage value, ranging from 0% to 100%. When the data completeness is 100%, it means that there is no missing data in the wave data; while when the data completeness is below 100%, it means that there is missing data in the wave data.

[0156] The completeness of wave data is determined based on wave data from offshore wind turbines at multiple wind speeds. For example, the completeness of wave data can be determined using the following formula:

[0157] The number of valid data points can be obtained by identifying and excluding outlier and missing data points in the wave data. The total number of data points is the total number of all data points in the wave data. This identification can be achieved through boundary value judgment, statistical analysis of data point distribution, or time continuity verification. For example, energy values ​​exceeding a preset physical reasonable range can be identified as outlier data points, and missing or empty data points within a continuous time period can be identified as missing data points. Alternatively, box plots or standard deviation methods can be used to identify and remove data points that significantly deviate from the statistical distribution.

[0158] Based on this, step S1: determining the original wave representation based on the collected wave data of the offshore wind farm at multiple wind speeds may include: determining the data completeness of the wave data, which is used to characterize the degree of data missing in the wave data; in response to the data completeness being greater than or equal to a completeness threshold, the original wave representation is determined to be the original wave energy spectrum; in response to the data completeness being less than the completeness threshold, the original wave representation is determined to be the original wave scatter plot. The completeness threshold is an evaluation index used to reflect the level of data completeness.

[0159] In other words, when the data integrity of the collected wave data is greater than or equal to the integrity threshold, the original wave energy spectrum can be used as the original wave characterization, and the aforementioned steps S201-S103 can be executed.

[0160] When the data is complete, the wave energy spectrum can more fully and meticulously express the directional information and continuous energy distribution of waves. Thus, when the data is complete, using the condensation processing of the wave energy spectrum to determine the fatigue strength can make full use of the multidimensionality and continuity of the wave energy spectrum, making the sea state information of the equivalent energy spectrum richer, thereby improving the accuracy of fatigue strength.

[0161] When the data integrity of the collected wave data is less than the integrity threshold, the original wave energy spectrum can be used as the original wave characterization, and the aforementioned steps S201-S203 can be executed. The wave scatter plot provides an alternative way of expressing sea state information for subsequent fatigue strength calculations, ensuring that even in the case of missing data, the discrete data points in the wave scatter plot can still be aggregated to obtain a more accurate representative sea state.

[0162] The following is a specific embodiment of this solution:

[0163] Referring to Figure 5, which is a schematic flowchart of a fatigue strength assessment provided by an embodiment of this disclosure, environmental data of the relevant area of ​​the offshore wind turbine is first collected. Wind parameters such as wind direction and wind speed are collected by wind-measuring radar, and wave data such as wave height, wave period, wave occurrence frequency, wave energy, and direction are collected by wave-measuring radar. Then, it is checked whether the monitoring period of the measured wind parameters and wave data meets the data analysis requirements. Wind parameters are monitored in 10-minute intervals as one sample, and wave data are monitored in 1-minute intervals as one sample. If the requirements are not met, data collection continues. If the requirements are met, the collected data is input into the data storage and processing analysis system for data processing. If there are missing values ​​in the data, the missing values ​​are filtered out. If the proportion of missing values ​​in the sample is less than or equal to 1%, the data above and below the sampling time is interpolated and replaced. If the proportion of missing values ​​in the sample is greater than 1%, the sample is deleted. If there are no missing values ​​in the data, it is checked whether there are discrete values ​​in the data that exceed 3 times / m times the standard deviation. These discrete values ​​are considered abnormal data. If there are discrete values ​​in the data, the discrete values ​​are filtered out using the box plot method, and the data above and below the sampling time is interpolated and replaced. If the data has no discrete values, determine whether the wave data acquisition period is greater than 3 months. If so, perform compartmentalization and aggregation operations on the wave energy spectrum to obtain an equivalent wave energy spectrum, and determine the fatigue strength of the offshore wind turbine based on the equivalent wave energy spectrum. Otherwise, perform compartmentalization, block division, and aggregation operations on the wave scatter plot to obtain an equivalent wave scatter plot, and determine the fatigue strength of the offshore wind turbine based on the equivalent wave scatter plot.

[0164] Referring to Figure 6, Figure 6 shows a device for determining the fatigue strength of an offshore wind turbine provided in an embodiment of this disclosure. The device 600 includes: an acquisition unit 601, a cohesion unit 602, and a determination unit 603.

[0165] The acquisition unit 601 is used to determine the original wave characterization based on the collected wave data of the offshore wind farm at multiple wind speeds.

[0166] The condensation unit 602 is used to condense the original wave characterization under multiple wind speeds to obtain an equivalent wave characterization.

[0167] The determining unit 603 is used to determine the fatigue strength of wind turbine units in offshore wind farms based on equivalent wave characterization.

[0168] As can be seen from the above technical solution, the device for determining the fatigue strength of offshore wind turbines includes an acquisition unit, a condensation unit, and a determination unit. The acquisition unit determines the original wave characterization based on wave data collected from the offshore wind farm at multiple wind speeds. The condensation unit performs condensation processing on the original wave characterization at multiple wind speeds to obtain an equivalent wave characterization. The determination unit then determines the fatigue strength of the wind turbines in the offshore wind farm based on the equivalent wave characterization. The original wave characterization in this disclosure can be the original wave energy spectrum or the original wave scatter plot. The equivalent energy spectrum obtained by condensing the wave energy spectrum retains more detailed sea state information, reflecting more dimensions and more detailed representative sea states. Finally, the fatigue strength of the offshore wind turbine is determined based on the equivalent energy spectrum. Therefore, the more dimensional and continuous equivalent energy spectrum not only reduces the number of sea states used to calculate fatigue strength and improves computational efficiency but also improves the accuracy of fatigue strength calculation. This disclosure also provides an alternative way of expressing sea state information for subsequent fatigue strength calculations using wave scatter plots, ensuring that even in the event of missing data, condensation processing can continue based on discrete data points in the wave scatter plot to obtain a more accurate representative sea state.

[0169] As an exemplary embodiment, the above-mentioned device for determining the fatigue strength of offshore wind turbines is integrated into the central controller of the wind farm or the controller of the wind turbine.

[0170] As one possible implementation, the original wave characterization includes an original wave energy spectrum, which describes the energy distribution of the wave as a function of frequency and direction. The condensed unit is specifically used for:

[0171] The original wave energy spectra under the multiple wind speeds are condensed to obtain the equivalent energy spectra.

[0172] The determining unit is specifically used to: determine the fatigue strength of the wind turbine in the offshore wind farm based on the equivalent energy spectrum.

[0173] As one possible implementation, the multiple wind speeds are divided into multiple wind speed compartments, and the original wave energy spectrum includes the sub-compartmental wave energy spectrum of each wind speed compartment; the determining unit is specifically used for:

[0174] The wave is divided into multiple directions to obtain multiple directional positions, each directional position including at least one of the multiple directions; and

[0175] For each of the plurality of wind speed chambers,

[0176] Based on the wave energy spectrum of the wind speed chamber, the sub-wave energy spectra corresponding to the multiple directional chambers are obtained, along with the wave occurrence probability of each directional chamber. The wave occurrence probability of each directional chamber is determined based on the number of wave occurrences in at least one direction within the directional chamber and the total number of wave occurrences in all directions.

[0177] The equivalent energy spectrum of the wind speed chamber is determined based on the wave occurrence probability of each directional chamber and the energy spectrum of the sub-waves corresponding to the multiple directional chambers.

[0178] As one possible implementation, the original wave characterization includes an original wave scatter plot, which describes the number of times waves with different wave heights and different wave periods occur at each of the multiple wind speeds. The agglomeration unit is specifically used to: perform agglomeration processing on the original wave scatter plots at the multiple wind speeds to obtain an equivalent wave scatter plot; and the determination unit is specifically used to: determine the fatigue strength of the wind turbine units in the offshore wind farm based on the equivalent wave scatter plot.

[0179] In one specific embodiment, the plurality of wind speeds are divided into a plurality of wind speed compartments, and the original wave scattering map includes the compartmentalized wave scattering map of each wind speed compartment; the condensation unit is specifically used for:

[0180] The wave is divided into multiple directions to obtain multiple directional positions, each directional position including at least one of the multiple directions; and

[0181] For each of the plurality of wind speed chambers,

[0182] Based on the wave scattering diagram of the wind speed compartment, the sub-wave scattering diagrams corresponding to the multiple directional compartments are obtained respectively;

[0183] The sub-wave scatter plot corresponding to each directional chamber is divided to obtain multiple wave blocks. Different wave blocks are used to represent different types of representative sea states. The difference between the equivalent wave energy corresponding to any two wave blocks is greater than a preset threshold. Each wave block includes at least one wave group. Each wave group includes the wave height, wave period and wave occurrence number corresponding to the same type of wave.

[0184] The stress coefficient is obtained, and for each wave group, the equivalent wave height corresponding to that wave group is determined based on the stress coefficient, the wave height, wave period, and wave occurrence frequency of each wave group within that wave group. The stress coefficient is used to characterize the relationship between the equivalent wave height and the stress type of the offshore wind turbine.

[0185] Obtain the functional relationship between wave energy and wave height and wave period, and for each wave block, determine the equivalent wave period corresponding to the wave block based on the numerical relationship between the functional relationship and the equivalent wave energy corresponding to the wave block;

[0186] The determining unit is specifically used to: determine the fatigue strength of the offshore wind turbine based on the equivalent wave scattering diagrams corresponding to each wave block of each of the multiple directional chambers in the multiple wind speed chambers, wherein the equivalent wave scattering diagrams include the equivalent wave period and the equivalent wave height.

[0187] As one possible implementation, the equivalent wave energy corresponding to each wave block is obtained in the following way:

[0188] The first-order frequency and fatigue coefficient of the offshore wind turbine are obtained. The first-order frequency is the lowest frequency at which the offshore wind turbine can vibrate freely without external excitation. The fatigue coefficient is used to characterize the material tolerance of the offshore wind turbine.

[0189] For each wave block in the aforementioned wave blocks.

[0190] Determine the wave energy corresponding to each wave group included in the wave block;

[0191] The wave energy of each wave group within the wave block is weighted according to the frequency of wave occurrence, resulting in the weighted wave energy for each wave group within the wave block; and

[0192] The equivalent wave energy corresponding to the wave block is determined based on the weighted wave energy, the first-order vibration frequency, and the fatigue coefficient corresponding to each wave group included in the wave block.

[0193] As one possible implementation, the agglomeration unit divides the corresponding sub-wave scatter plot in each directional chamber to obtain multiple wave blocks, including:

[0194] The sub-wave scatter plot is divided to obtain multiple initial wave blocks;

[0195] For each wave group in the sub-wave scatter plot, the following wave group block update operation is performed:

[0196] Determine the wave energy corresponding to each wave group in the sub-wave scatter plot; and

[0197] Based on the difference between the wave energy corresponding to each wave group in the sub-wave scatter plot and the equivalent wave energy corresponding to each initial wave block in the sub-wave scatter plot, the initial wave block with the smallest wave energy difference is determined as the target initial wave block; if the target initial wave block is different from the initial wave block corresponding to the current wave group, the wave group is moved to the target initial wave block to obtain the updated wave block corresponding to the current wave group; if the target initial wave block is the same as the initial wave block corresponding to the current wave group, no movement operation is performed on the wave group; and

[0198] For each initial wave block, the energy difference between the equivalent wave energy of the initial wave block and the equivalent wave energy of each updated wave block is determined. If each energy difference is less than the difference threshold, the updated wave blocks corresponding to the initial wave blocks are determined as the multiple wave blocks. Otherwise, the multiple updated wave blocks are determined as new initial wave blocks, and the wave block update operation is repeated until the difference between the equivalent wave energy of the multiple updated wave blocks obtained by the current wave block update operation and the equivalent wave energy of the multiple updated wave blocks obtained by the previous wave block update operation is less than the difference threshold. Then, the multiple updated wave blocks obtained by the current wave block update operation are determined as the multiple wave blocks.

[0199] As one possible implementation, the condensation unit is specifically used for:

[0200] Obtain the diameter and diameter threshold of the support structure of the offshore wind turbine;

[0201] If the diameter of the supporting structure is less than or equal to the diameter threshold, for each wave block, the equivalent wave height corresponding to that wave block is determined based on the inertial force coefficient, the wave height of each wave group within that wave block, the wave period, and the number of wave occurrences; and

[0202] If the diameter of the support structure is greater than the diameter threshold, for each wave block, the equivalent wave height corresponding to the wave block is determined based on the drag force coefficient, the wave height, wave period, and wave occurrence frequency of each wave group in the wave block.

[0203] As one possible implementation, the condensation unit is specifically used for:

[0204] For each wave block,

[0205] Based on the two intersection points between the curve corresponding to the functional relationship and the straight line obtained based on the numerical value of the equivalent wave energy corresponding to the wave block, determine the two intersection point periods corresponding to the two intersection points respectively; and

[0206] The larger of the two intersection periods is determined as the equivalent wave period corresponding to the wave block.

[0207] According to exemplary embodiments of the present disclosure, a wind turbine generator set may also be provided, the wind turbine generator set including a controller for performing the environmental turbulence correction method for the wind turbine generator set as described in the exemplary embodiments above.

[0208] Referring to Figure 7, this disclosure also provides a computer device, which includes a memory 701 and a processor 702:

[0209] The memory is used to store computer programs and to transfer the computer programs to the processor;

[0210] The processor is used to execute the method of the above method embodiment according to the computer program.

[0211] This disclosure also provides a computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program, the computer program being used to execute the method of the above-described method embodiments.

[0212] This disclosure also provides a computer program product including a computer program, which, when run on a computer device, causes the computer device to perform the methods described in the above method embodiments.

[0213] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems or apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

[0214] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0215] It should be understood that in this disclosure, "at least one item" means one or more, and "more than one" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0216] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0217] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0218] The above description of the disclosed embodiments enables those skilled in the art to make or use this disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining the fatigue strength of an offshore wind turbine, wherein, The method includes: The original wave characterization was determined based on wave data collected from offshore wind farms at multiple wind speeds. The original wave representations under the multiple wind speeds are subjected to condensation processing to obtain equivalent wave representations; and The fatigue strength of the wind turbine in the offshore wind farm is determined based on the equivalent wave characterization.

2. The method according to claim 1, wherein, The original wave characterization includes the original wave energy spectrum, which is used to describe the energy distribution of the wave as it varies with frequency and direction; The process of condensing the original wave representations under the multiple wind speeds to obtain equivalent wave representations includes: condensing the original wave energy spectra under the multiple wind speeds to obtain the equivalent energy spectra; and Determining the fatigue strength of the wind turbine in the offshore wind farm based on the equivalent wave characterization includes: determining the fatigue strength of the wind turbine in the offshore wind farm based on the equivalent energy spectrum.

3. The method according to claim 2, wherein, The multiple wind speeds are divided into multiple wind speed compartments, and the original wave energy spectrum includes the sub-compartmental wave energy spectrum of each wind speed compartment; the process of condensing the original wave energy spectrum under the multiple wind speeds to obtain an equivalent energy spectrum includes: The wave is divided into multiple directions to obtain multiple directional positions, each directional position including at least one of the multiple directions; and For each of the plurality of wind speed chambers, Based on the wave energy spectrum of the wind speed chamber, the sub-wave energy spectra corresponding to the multiple directional chambers are obtained, along with the wave occurrence probability of each directional chamber. The wave occurrence probability of each directional chamber is determined based on the number of wave occurrences in at least one direction within the directional chamber and the total number of wave occurrences in all directions. The equivalent energy spectrum of the wind speed chamber is determined based on the wave occurrence probability of each directional chamber and the energy spectrum of the sub-waves corresponding to the multiple directional chambers.

4. The method according to claim 1, wherein, The original wave characterization includes an original wave scatter plot, which is used to describe the number of times waves with different wave heights and different wave periods occur at each of the multiple wind speeds. The process of converging the original wave representations under the multiple wind speeds to obtain equivalent wave representations includes: converging the original wave scatter maps under the multiple wind speeds to obtain equivalent wave scatter maps; and The step of determining the fatigue strength of the wind turbine in the offshore wind farm based on the equivalent wave characterization includes: determining the fatigue strength of the wind turbine in the offshore wind farm based on the equivalent wave scattering diagram.

5. The method according to claim 4, wherein, The multiple wind speeds are divided into multiple wind speed compartments, and the original wave scatter map includes the compartmentalized wave scatter map of each wind speed compartment; the process of agglomerating the original wave scatter maps under the multiple wind speeds to obtain an equivalent wave scatter map includes: The wave is divided into multiple directions to obtain multiple directional positions, each directional position including at least one of the multiple directions; and For each of the plurality of wind speed chambers, Based on the wave scattering diagram of the wind speed compartment, the sub-wave scattering diagrams corresponding to the multiple directional compartments are obtained respectively; The sub-wave scatter plot corresponding to each directional chamber is divided to obtain multiple wave blocks. Different wave blocks are used to represent different types of representative sea states. The difference between the equivalent wave energy corresponding to any two wave blocks is greater than a preset threshold. Each wave block includes at least one wave group. Each wave group includes the wave height, wave period and wave occurrence number corresponding to the same type of wave. The stress coefficient is obtained, and for each wave group, the equivalent wave height corresponding to that wave group is determined based on the stress coefficient, the wave height, wave period, and wave occurrence frequency of each wave group within that wave group. The stress coefficient is used to characterize the relationship between the equivalent wave height and the stress type of the offshore wind turbine. Obtain the functional relationship between wave energy and wave height and wave period, and for each wave block, determine the equivalent wave period corresponding to that wave block based on the numerical relationship between the functional relationship and the equivalent wave energy corresponding to that wave block; and wherein, The step of determining the fatigue strength of wind turbines in the offshore wind farm based on the equivalent wave scattering diagram includes: The fatigue strength of the offshore wind turbine is determined based on the equivalent wave scatter diagrams corresponding to each wave block of each of the multiple directional chambers in the multiple wind speed chambers. The equivalent wave scatter diagrams include the equivalent wave period and the equivalent wave height.

6. The method according to claim 5, wherein, The equivalent wave energy corresponding to each wave block is obtained in the following way: The first-order frequency and the fatigue coefficient of the offshore wind turbine are obtained. The first-order frequency is the lowest frequency at which the offshore wind turbine can vibrate freely without external excitation. The fatigue coefficient is used to characterize the material resistance of the offshore wind turbine. as well as For each wave block in the aforementioned wave blocks. Determine the wave energy corresponding to each wave group included in the wave block; The wave energy corresponding to each wave group in the wave block is weighted according to the number of times each wave group appears, so as to obtain the weighted wave energy corresponding to each wave group in the wave block. as well as The equivalent wave energy corresponding to the wave block is determined based on the weighted wave energy, the first-order vibration frequency, and the fatigue coefficient corresponding to each wave group included in the wave block.

7. The method according to claim 5, wherein, The sub-wave scatter plot corresponding to each directional chamber is divided to obtain multiple wave blocks, including: The sub-wave scatter plot is divided to obtain multiple initial wave blocks; For each wave group in the sub-wave scatter plot, the following wave group block update operation is performed: Determine the wave energy corresponding to each wave group in the sub-wave scatter plot; and Based on the difference between the wave energy corresponding to each wave group in the sub-wave scatter plot and the equivalent wave energy corresponding to each initial wave block in the sub-wave scatter plot, the initial wave block with the smallest wave energy difference is determined as the target initial wave block; if the target initial wave block is different from the initial wave block corresponding to the current wave group, the wave group is moved to the target initial wave block to obtain the updated wave block corresponding to the current wave group; if the target initial wave block is the same as the initial wave block corresponding to the current wave group, no movement operation is performed on the wave group; and For each initial wave block, the energy difference between the equivalent wave energy of the initial wave block and the equivalent wave energy of each updated wave block is determined. If each energy difference is less than the difference threshold, the updated wave blocks corresponding to the initial wave blocks are determined as the multiple wave blocks. Otherwise, the multiple updated wave blocks are determined as new initial wave blocks, and the wave block update operation is repeated until the difference between the equivalent wave energy of the multiple updated wave blocks obtained by the current wave block update operation and the equivalent wave energy of the multiple updated wave blocks obtained by the previous wave block update operation is less than the difference threshold. Then, the multiple updated wave blocks obtained by the current wave block update operation are determined as the multiple wave blocks.

8. The method according to claim 5, wherein, If the force coefficient includes an inertial force coefficient and a drag force coefficient, then determining the equivalent wave height corresponding to each wave block based on the force coefficient, the wave height of each wave group within that wave block, the wave period, and the number of wave occurrences includes: Obtain the diameter and diameter threshold of the support structure of the offshore wind turbine; If the diameter of the supporting structure is less than or equal to the diameter threshold, for each wave block, the equivalent wave height corresponding to that wave block is determined based on the inertial force coefficient, the wave height of each wave group within that wave block, the wave period, and the number of wave occurrences; and If the diameter of the support structure is greater than the diameter threshold, for each wave block, the equivalent wave height corresponding to the wave block is determined based on the drag force coefficient, the wave height, wave period, and wave occurrence frequency of each wave group in the wave block.

9. The method according to claim 5, wherein, For each wave block, determining the equivalent wave period corresponding to that wave block based on the numerical relationship between the functional relationship and the equivalent wave energy of that wave block includes: For each wave block, Based on the two intersection points between the curve corresponding to the functional relationship and the straight line obtained based on the numerical value of the equivalent wave energy corresponding to the wave block, determine the two intersection point periods corresponding to the two intersection points respectively; and The larger of the two intersection periods is determined as the equivalent wave period corresponding to the wave block.

10. The method according to claim 1, wherein, The determination of the original wave characterization based on wave data collected from offshore wind farms at multiple wind speeds includes: Determine the data completeness of the wave data, wherein the data completeness is used to characterize the degree of data missing in the wave data; If the data integrity is greater than or equal to the integrity threshold, then the original wave characterization is determined to be the original wave energy spectrum; If the data integrity is less than the integrity threshold, the original wave characterization is determined to be an original wave scatter plot.

11. A device for determining the fatigue strength of an offshore wind turbine, wherein, The device includes: an acquisition unit, a coagulation unit, and a determination unit; The acquisition unit is used to determine the original wave characterization based on the collected wave data of the offshore wind farm at multiple wind speeds. The condensation unit is used to condense the original wave representations under the multiple wind speeds to obtain equivalent wave representations; and The determining unit is used to determine the fatigue strength of the wind turbine in the offshore wind farm based on the equivalent wave characterization.

12. The apparatus as claimed in claim 11, characterized in that, The device is integrated into the central controller of the offshore wind farm or the controller of the wind turbine generator set.

13. A wind turbine generator set, characterized in that, The wind turbine generator set includes a controller for performing the method as described in any one of claims 1-10.

14. A computer device, wherein, The computer device includes a processor and memory: The memory is used to store computer programs and to transfer the computer programs to the processor; The processor is configured to perform the method according to any one of claims 1-10 according to the computer program.

15. A computer-readable storage medium, wherein, The computer-readable storage medium is used to store a computer program for performing the method according to any one of claims 1-10.

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