Floating fan mooring fatigue calculation method and device and storage medium

Abstract

The invention discloses a floating type wind turbine mooring fatigue calculation method and device and a storage medium, and relates to the technical field of offshore wind power generation. The calculation method comprises the following steps: selecting a characteristic sea condition parameter combination from sea condition long-term distribution to obtain a characteristic sea condition subset; constructing a floating type fan full-coupling dynamic model; taking the sample points in the characteristic sea condition subset as the input of the floating type wind turbine fully-coupled dynamic model, and combining data processing and fatigue criteria to obtain mooring fatigue damage corresponding to each sample point; constructing a fatigue damage prediction model according to the sample points in the feature sea condition subset and the corresponding mooring fatigue damage; and using the fatigue damage prediction model to predict floating fan mooring fatigue damage. According to the method, on the premise that the precision and the engineering applicability are guaranteed, the calculation efficiency of floating type wind turbine mooring fatigue evaluation is remarkably improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of offshore wind power generation, and particularly relates to a floating wind turbine mooring fatigue calculation method, device and storage medium. BACKGROUND

[0002] With the global offshore wind power accelerating development towards the deep sea, the floating wind power technology has become the core direction of future clean energy layout due to its huge development potential. Unlike traditional fixed wind turbines, the floating wind turbine is anchored to the seabed through a mooring system (anchor chain, steel cable or synthetic fiber cable), so that it can maintain a stable posture under the combined action of wind, wave and current. However, this dynamic characteristic also causes the mooring system to bear complex alternating loads for a long time, including low-frequency wind turbine aerodynamic load, wave excitation force, high-frequency wind turbine operation vibration, extreme sea state impact and vortex-induced vibration, etc. Fatigue damage thus becomes one of the important causes of mooring system failure.

[0003] The current mooring fatigue evaluation generally refers to the experience of offshore oil and gas platforms, and adopts full-coupling time-domain simulation combined with rainflow counting method and T-N curve for life prediction, but this method has the problem of low calculation efficiency. The mooring tension of the floating wind turbine needs to be accurately solved through aerodynamic-hydrodynamic-aerodynamic-servo control full-coupling simulation, and a single working condition simulation needs to take tens of minutes to one hour, and the fatigue analysis needs to cover the long-term distribution of sea conditions (usually thousands of working conditions), so the calculation cost is difficult to support engineering application. Although the frequency domain simplification method can improve the efficiency, it is difficult to deal with the nonlinear response caused by wind turbine active control, irregular waves and other factors.

[0004] In summary, it is of great significance to develop a mooring fatigue calculation method that takes into account efficiency, accuracy and engineering applicability to reduce the operation and maintenance risk of deep-sea wind power and improve economic efficiency. SUMMARY

[0005] In view of the above defects in the prior art, the purpose of the present application is to provide a floating wind turbine mooring fatigue calculation method, device and storage medium, which significantly improves the calculation efficiency of floating wind turbine mooring fatigue evaluation under the premise of ensuring accuracy and engineering applicability.

[0006] The present application solves the above technical problems by the following technical solutions: a floating wind turbine mooring fatigue calculation method, comprising:

[0007] selecting a characteristic sea condition parameter combination from the long-term distribution of sea conditions to obtain a characteristic sea condition subset;

[0008] constructing a full-coupling dynamic model of the floating wind turbine;

[0009] Taking the sample points in the characteristic sea state subset as inputs of the full-coupling dynamic model of the floating wind turbine, and combining data processing and fatigue criteria, a mooring fatigue damage corresponding to each sample point is obtained;

[0010] According to the sample points in the characteristic sea state subset and the corresponding mooring fatigue damage, a fatigue damage prediction model is constructed;

[0011] The fatigue damage prediction model is used to predict the mooring fatigue damage of the floating wind turbine.

[0012] In the prediction model construction stage, the present application selects a small number (relative to the number of sea state long-term distribution) of representative characteristic sea states from the massive sea state long-term distribution to perform full-coupling dynamic simulation to obtain mooring fatigue damage labels, greatly shortening the simulation time, improving the construction efficiency of the sample data set of the prediction model, and further improving the construction efficiency of the prediction model; the mooring fatigue damage labels are obtained through full-coupling dynamic simulation technology, greatly improving the label accuracy of each sample point, and further improving the prediction performance of the prediction model, ensuring the prediction accuracy in the application stage.

[0013] In the actual application stage, since the fatigue damage prediction model has automatically learned and embedded complex, nonlinear mapping relationships through sample points-mooring fatigue damage labels, the complex physical process is embedded into an efficient prediction model, so the calculation in the application stage is extremely fast, but its accuracy essentially comes from high-precision full-coupling dynamic simulation, ensuring calculation efficiency while ensuring calculation accuracy, thereby overcoming the defects of insufficient efficiency or accuracy of traditional methods.

[0014] The present application perfectly combines the accuracy of full-coupling dynamic simulation with the speed of the prediction model, through one-time, manageable investment (constructing the fatigue damage prediction model) in the early stage, in exchange for nearly infinite, fast and accurate fatigue damage calculation capability in the later stage, thereby truly realizing the ultimate goal of balancing efficiency, accuracy and engineering applicability.

[0015] Further, the clustering algorithm is used to select the characteristic sea state parameter combination from the sea state long-term distribution, specifically including:

[0016] Selecting a sea state parameter combination from the sea state long-term distribution to obtain a sea state total sample set;

[0017] Calculating the normalized distance between each sample point in the sea state total sample set;

[0018] Selecting, from the sea state total sample set, a sample point with the maximum sum of normalized distances from other sample points as the first sample point of the characteristic sea state subset;

[0019] calculating distances of the remaining sample points in the total sea state sample set to the feature sea state subset, and adding the sample point with the largest distance to the feature sea state subset;

[0020] repeating the above steps until the feature sea state subset contains M sample points; wherein M << N, N represents the number of sample points in the total sea state sample set.

[0021] In the embodiment, a clustering algorithm is used to filter a small number of feature sea states from the massive original sea state long-term distribution, and the feature sea states can represent the statistical characteristics of the original sea state long-term distribution. Only the small number of feature sea states need to be subjected to full-coupling dynamic simulation, so that the corresponding mooring fatigue damage can be obtained, the simulation time is greatly shortened, and the construction efficiency of the sample data set of the fatigue damage prediction model is improved.

[0022] Further, the construction process of the full-coupling dynamic model of the floating wind turbine includes:

[0023] The floating wind turbine system is discretized into a multi-body system composed of rigid bodies and flexible bodies, to serve as a load application object and provide motion feedback for a control module;

[0024] An aerodynamic load calculation module and a hydrodynamic load calculation module are constructed, and a wind turbine control module based on proportional integral control is integrated;

[0025] Data exchange and iteration calculation among the modules are coordinated by a central time step coordinator;

[0026] The aerodynamic load output by the aerodynamic load calculation module, the hydrodynamic load output by the hydrodynamic load calculation module, and the control instruction output by the wind turbine control module are jointly input as system excitation to the multi-body system, so that full-coupling dynamic simulation of aerodynamic-hydrodynamic-structure-control multi-physical fields is realized.

[0027] In the embodiment, data exchange and iteration are performed at each time step by the central time step coordinator, and the effects of aerodynamics and hydrodynamics on the structure, the reaction of the structure to the load, and the real-time adjustment of the generator torque and the blade pitch angle by the wind turbine control module according to the wind turbine motion information and the generator speed are dynamically considered, so that the aerodynamic and hydrodynamic effects on the structure are dynamically considered, the reaction of the structure to the load is dynamically considered, and the generator torque and the blade pitch angle are dynamically adjusted by the wind turbine control module according to the wind turbine motion information and the generator speed, the aerodynamic load and the dynamic response of the whole system are changed, and the closed-loop dynamics of the wind turbine-floater-mooring is truly reflected.

[0028] The full-coupling dynamic model constructed in the embodiment seamlessly integrates aerodynamics, hydrodynamics, structure and control in one framework, avoids data interface errors and time synchronization problems caused by using different software for collaborative simulation, and the high-precision output of the full-coupling dynamic model is a prerequisite for accurate fatigue damage assessment.

[0029] Further, the flexible body includes a blade, a tower, and a mooring line;

[0030] Both the blade and the tower are discretized into a plurality of beam elements or rod elements, and are represented by a modal superposition method for their elastic deformation;

[0031] The mooring line is discretized into a plurality of beam elements or rod elements to simulate its dynamic response by using a finite element method.

[0032] Further, the aerodynamic load calculation module is based on the momentum blade element theory and introduces a non-steady aerodynamic correction model for calculation to improve the calculation accuracy of the non-steady aerodynamic load;

[0033] The hydrodynamic load calculation module calculates by using a method combining the potential flow theory and the Morison equation; wherein, based on the potential flow theory, the radiation force and the diffraction force acting on the foundation floating body are calculated by using a frequency domain-time domain conversion method; based on the Morison equation, the flow load acting on the slender member is calculated, and a quadratic drag term is introduced in the equation to accurately represent the viscous effect of each member of the foundation floating body.

[0034] Further, the calculation process of the mooring fatigue damage corresponding to each sample point is as follows:

[0035] The sample points in the characteristic sea state subset are input into the full-coupling dynamic model of the floating wind turbine to obtain a mooring tension time history;

[0036] The rainflow counting method is used to process the data of the mooring tension time history, and the T-N curve and the Miner fatigue criterion are combined to calculate the corresponding mooring fatigue damage.

[0037] Further, the fatigue damage prediction model is a proxy model or a deep learning model.

[0038] Further, when the fatigue damage prediction model is a proxy model, the specific construction process of the fatigue damage prediction model includes:

[0039] Regression analysis is performed on the sample points in the characteristic sea state subset and their corresponding mooring fatigue damage to establish a mapping relationship between the characteristic sea state and the mooring fatigue damage, and a fatigue damage prediction model is obtained.

[0040] In this embodiment, the mapping relationship between the characteristic sea state and the mooring fatigue damage is established by means of the proxy model, so that the calculation of the mooring fatigue damage is converted into a simple algebraic operation process, avoiding frequent numerical simulation and significantly improving the calculation efficiency.

[0041] Based on the same concept, the present invention also provides an electronic device, including a memory, a processor, and a computer program or instructions stored in the memory, wherein the processor executes the computer program or instructions to implement the floating wind turbine mooring fatigue calculation method as described above.

[0042] Based on the same concept, the present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implements the floating wind turbine mooring fatigue calculation method as described above.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] This invention takes characteristic sea states selected from long-term sea state distributions as input and mooring fatigue damage obtained through fully coupled dynamic simulation as output. It establishes a mapping relationship between the two with the help of a prediction model (surrogate model or deep learning model), which significantly improves the computational efficiency of floating wind turbine mooring fatigue assessment while ensuring accuracy and engineering applicability. Attached Figure Description

[0045] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of the floating wind turbine mooring fatigue calculation method in an embodiment of the present invention;

[0047] Figure 2 This is a top view of the three-column semi-submersible wind turbine mooring system in an embodiment of the present invention;

[0048] Figure 3 This is a side view of the three-column semi-submersible wind turbine mooring system in an embodiment of the present invention;

[0049] Figure 4 This is a schematic diagram of the convergence of mooring fatigue calculation for a floating wind turbine in an embodiment of the present invention;

[0050] Figure 5 This is a comparison chart of the prediction results of the proxy model and the numerical simulation results in an embodiment of the present invention. Detailed Implementation

[0051] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0052] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0053] Embodiment 1

[0054] As Figure 1 shown, the mooring fatigue calculation method for a floating wind turbine provided in the embodiment of the present invention includes the following steps:

[0055] Step 1: Select a combination of characteristic sea condition parameters from the long-term distribution of sea conditions to obtain a subset of characteristic sea conditions.

[0056] The long-term distribution of sea conditions refers to the probability statistical law of the occurrence of various marine environmental parameters (such as wind speed, wind direction, wave height, wave period, wave direction, flow velocity, flow direction, etc.) during the service period of a floating wind turbine in a specific sea area, and describes the occurrence frequency of different sea condition combinations in the form of a joint probability matrix.

[0057] The long-term distribution of sea conditions can be obtained through oceanographic hydrological measured data or numerical hindcast data based on buoys. The long-term distribution of sea conditions mainly includes, but is not limited to: the marginal probability distribution of wind speed the joint probability distribution of significant wave height - spectral peak period under different wind speeds the joint probability distribution of wind direction - wave direction under different wind speeds the joint probability distribution of flow velocity - flow direction under different wind speeds the joint probability distribution of flow velocity - flow direction . The joint probability distribution of the seven marine environmental parameters of wind speed wind direction significant wave height spectral peak period wave direction flow velocity flow direction is calculated by the following formula:

[0058] (1)

[0059] In a specific embodiment of the present invention, a clustering algorithm is used to select characteristic sea state parameter combinations from the long-term sea state distribution, specifically including:

[0060] Step 1.1: Select combinations of sea state parameters from the long-term sea state distribution to obtain the total sea state sample set.

[0061] The marine environmental parameter combinations with a joint probability of zero (i.e., sea state parameter combinations) are removed, and the remaining marine environmental parameter combinations with a joint probability of non-zero constitute the total sea state sample set.

[0062] Step 1.2: Calculate the normalized distance between each sample point in the total sea state sample set.

[0063] For any sample point i in the total sea state sample set, it can be represented as ,in, Let $\frac{1}{2}$ represent the wind speed, wind direction, significant wave height, spectral peak period, wave direction, current velocity, and current direction at the $i$-th sample point, respectively. Since the magnitudes of the various marine environmental parameters differ, normalization is required. Wind speed, significant wave height, spectral peak period, and current velocity are scalars, and are normalized using their maximum and minimum values ​​in the overall sea state sample set.

[0064] , (2)

[0065] , (3)

[0066] in, and These represent the normalized wind speed, meaningful wave height, spectral peak period, and current velocity, respectively. and These represent the maximum wind speed, maximum meaningful wave height, maximum spectral peak period, and maximum current velocity in the total sea state sample set, respectively. and These represent the minimum wind speed, minimum meaningful wave height, minimum spectral peak period, and minimum current velocity in the total sea state sample set, respectively.

[0067] Wind direction, wave direction, and current direction are rounded variables. Normalization is performed using the following formula:

[0068] (4)

[0069] in, , and These represent the normalized wind direction, wave direction, and current direction, respectively.

[0070] Normalized distance between any two sample points i and j in the total sea state sample set for:

[0071] (5)

[0072] The sample categories are divided based on the normalized distance between sample points in the total sea state sample set. That is, the criterion for determining whether two sample points belong to different categories is whether the distance between them is large enough.

[0073] Step 1.3: Select the sample point with the largest sum of normalized distances to other sample points from the total sea state sample set as the first sample point of the feature sea state subset. .

[0074] Step 1.4: Calculate the distances from the remaining N-1 sample points in the total sea state sample set to the feature sea state subset, and add the sample point with the largest distance to the feature sea state subset.

[0075] In this embodiment, any remaining sample point i in the total sea state sample set is connected to the feature sea state subset. distance Defined as:

[0076] (6)

[0077] in, This represents the difference between sample point i in the total sea state sample set and the last sample point in the feature sea state subset. The distance between them; This represents the sample point i in the total sea state sample set and the sample points in the characteristic sea state subset. The minimum distance between them.

[0078] Step 1.5: Repeat step 1.4 until the feature sea state subset contains M sample points; where M is much smaller than N, and N represents the number of sample points in the total sea state sample set.

[0079] The value of M is set according to the actual situation and is generally determined through convergence analysis. That is, the value of M is gradually increased until the mooring fatigue damage predicted by the fatigue damage prediction model does not change significantly with the increase of M. At this point, convergence is considered achieved, and the value of M at this point is the final value of M.

[0080] Step 2: Construct a fully coupled dynamic model of the floating wind turbine.

[0081] In a specific embodiment of the present invention, the process of constructing the fully coupled dynamic model of the floating wind turbine includes:

[0082] Step 2.1: Discretize the floating wind turbine system into a multibody system composed of rigid and flexible bodies, so as to serve as the object of load application and provide motion feedback for the control module.

[0083] Specifically, the floating wind turbine system is discretized into a multibody system consisting of blades, hub, drive shaft, nacelle, tower, floating foundation, and mooring system; among them, the hub, drive shaft, nacelle, and floating foundation are modeled as rigid bodies, while the blades, tower, and mooring cable are modeled as flexible bodies.

[0084] In this embodiment, both the blades and the tower are discretized into multiple beam elements or rod elements, and the modal superposition method is used to represent the elastic deformation of the blades and the tower. The mooring cable is discretized into multiple beam elements or rod elements using the finite element method to simulate its dynamic response. The low-speed shaft in the drive shaft is simulated using a rotational inertia-stiffness-damper mechanical model to simulate its dynamic characteristics.

[0085] Step 2.2: Construct an aerodynamic load calculation module and a hydrodynamic load calculation module, and integrate a wind turbine control module based on proportional-integral control.

[0086] Specifically, a three-dimensional turbulent wind field is generated using an exponential-law wind speed profile model, a fluctuating wind power spectrum, and a turbulent integral scale. The aerodynamic loads on the wind turbine blades are calculated using momentum blade element theory. An unsteady aerodynamic correction model is introduced to enhance the predictive capability of aerodynamic forces under unsteady flow and improve the calculation accuracy of unsteady aerodynamic loads. In this embodiment, the unsteady aerodynamic correction model is at least one of the following: tip stall, hub stall, Glauert correction model, skew wake correction model, dynamic inflow model, and dynamic stall model.

[0087] The hydrodynamic load calculation module employs a combination of potential flow theory and the Morison equation to calculate the hydrodynamic loads (i.e., hydrodynamic loads) on the floating foundation. Potential flow theory calculates the radiation and diffraction forces acting on the floating foundation using a frequency-to-time domain transformation method (involving convolution and Fourier transform). Based on the Morison equation, it calculates the flow loads acting on slender components and introduces a quadratic drag term into the Morison equation to accurately characterize the viscous effects of each component of the floating foundation. Together, these two methods constitute the complete hydrodynamic load calculation.

[0088] Specifically, the frequency domain hydrodynamic coefficients (such as added mass and radiation damping) of the floating foundation are solved based on potential flow theory, combined with the wave spectrum, and converted into time domain radiation and diffraction forces through convolution and Fourier transform.

[0089] The integrated wind turbine control module, based on proportional-integral control, calculates the adjustment values ​​of generator torque and blade pitch angle according to wind turbine motion information (such as platform tilt angle) and generator speed (such as deviation signal between actual speed and target speed). It adjusts generator torque and blade pitch angle in real time, controls the power output of wind turbine, changes aerodynamic load and the dynamic response of the entire system, and truly reflects the closed-loop dynamics of wind turbine-floating body-mooring.

[0090] Step 2.3: Coordinate data exchange and iterative calculations between modules through the central time step coordinator;

[0091] Under the scheduling of the central time step coordinator, the aerodynamic load calculation module, the hydrodynamic load calculation module, and the wind turbine control module perform independent calculations based on the current system state. The aerodynamic loads output by the aerodynamic load calculation module, the hydrodynamic loads output by the hydrodynamic load calculation module, and the control commands output by the wind turbine control module are integrated into system excitations through a clear data interface and input to the multibody system. The multibody system solves for new states and feeds them back to the aerodynamic load calculation module, the hydrodynamic load calculation module, and the wind turbine control module, and this process is repeated until convergence. Ultimately, through this iteration, a precise, fully coupled dynamic simulation of the aerodynamic-hydraulic-structural-control multiphysics field is achieved.

[0092] Step 3: Using the sample points in the feature sea state subset as input to the fully coupled dynamic model of the floating wind turbine, and combining data processing and fatigue criteria, the mooring fatigue damage corresponding to each sample point is obtained.

[0093] Sample points from a subset of characteristic sea states are input into a fully coupled dynamic model of a floating wind turbine, and the mooring tension time history is obtained through simulation calculations. The rainflow counting method is used to process the mooring tension time history data, and the corresponding mooring fatigue damage is calculated by combining the TN curve (tension-life curve) and the Miner fatigue criterion. The TN curve of the mooring cable is represented as follows:

[0094] (7)

[0095] in, The number of cycles allowed is indicated; T represents the ratio of tension amplitude to the minimum breaking force of the mooring cable; m represents the slope of the TN curve; and K represents the TN curve constant.

[0096] According to Miner's fatigue criterion, the mooring fatigue damage at the i-th sample point in the characteristic sea state subset is:

[0097] (8)

[0098] in, This represents the mooring fatigue damage at the i-th sample point in the feature sea state subset, i.e., the true value of mooring fatigue damage. This indicates the number of times the k-th tension amplitude occurs, obtained by rainflow counting statistics on the mooring tension time history. This represents the permissible number of cycles at the k-th tension amplitude calculated using the TN curve; This indicates the quantity of tension amplitude.

[0099] Step 4: Construct a fatigue damage prediction model based on the sample points in the feature sea state subset and their corresponding mooring fatigue damage.

[0100] In a specific embodiment of the present invention, the fatigue damage prediction model can be either a deep learning model or a surrogate model. When it is a deep learning model, the specific construction process of the fatigue damage prediction model is as follows:

[0101] A training dataset is constructed based on sample points in the feature sea state subset and their corresponding mooring fatigue damage.

[0102] Using sample points from a subset of characteristic sea states as input and the corresponding mooring fatigue damage (true value) as output (i.e., label), the deep learning model is trained using the training dataset to obtain a fatigue damage prediction model.

[0103] In this embodiment, the deep learning model can be a convolutional neural network, a recurrent neural network and its variants, a Transformer model, etc.

[0104] When using a surrogate model, the specific construction process of the fatigue damage prediction model is as follows:

[0105] Regression analysis was performed on sample points in the characteristic sea state subset and their corresponding mooring fatigue damage to establish the mapping relationship between characteristic sea states and mooring fatigue damage, thus obtaining a fatigue damage prediction model.

[0106] In this embodiment, the surrogate model uses the Kriging function. The Kriging function consists of a regression function and related basis functions. The Kriging function is used for a given combination of sea state parameters. Predicted value of mooring fatigue damage at the location It can be written as:

[0107] (9)

[0108] (10)

[0109] in, This represents a 1×M cross-correlation vector, describing a combination of sea state parameters. Spatial correlation with each sample point in the feature sea state subset S; Let S represent the autocorrelation coefficient matrix of the feature sea state subset S, with a size of M×M; This represents a column vector consisting of the true values ​​of mooring fatigue damage at each sample point in the feature sea state subset S, with a size of M×1; This represents a column vector with all elements being 1 and a size of M×1. The superscript T represents the intermediate vector; the superscript T represents the matrix transpose.

[0110] For any two combinations of sea state parameters and The cross-correlation coefficient (or autocorrelation coefficient) is defined as follows: (Sample points in the total sea state sample set and sample points in the characteristic sea state subset, or two sample points in the characteristic sea state subset).

[0111] (11)

[0112] in, This represents the cross-correlation coefficient or autocorrelation coefficient. and The cross-correlation coefficients are the sample points in the total sea state sample set and the sample points in the feature sea state subset S, respectively. and The cross-correlation coefficient is the number of two sample points corresponding to the feature sea state subset S. n represents the parameter dimension of the sample point; in this embodiment, n=7. This represents the importance weight of the i-th parameter; the larger the value, the greater the impact of the parameter on the output. and They represent and The i-th parameter in; This represents the smoothness parameter of the i-th parameter, and its value is usually between 1 and 2. , We can use the identity relation Estimate the coefficients by using the maximum likelihood method. , Solve the problem.

[0113] Step 5: Use a fatigue damage prediction model to predict fatigue damage of floating wind turbine mooring.

[0114] The sample points from the total sample set of sea states to be predicted are input into the fatigue damage prediction model. The output of the fatigue damage prediction model is the predicted mooring fatigue damage value for the corresponding sample point. Based on the predicted mooring fatigue damage value of each sample point, and combined with the joint probability distribution, the predicted mooring fatigue damage value (i.e., the cumulative predicted mooring fatigue damage value) during the service life of the floating wind turbine is calculated. The specific formula is as follows:

[0115] (12)

[0116] Where D represents the predicted cumulative fatigue damage of the mooring; N represents the number of sample points in the total sample set of the sea state to be predicted; This represents the predicted mooring fatigue damage value of the i-th sample point in the total sea state sample set to be predicted, which is obtained from the fatigue damage prediction model. This represents the probability of occurrence of the i-th sample point in the total sample set of sea states to be predicted; It represents the number of days in a year, which is 365. represents the number of years of service; h represents the simulation duration for a single sample point.

[0117] For example, the service life of a floating wind turbine is 25 years, the duration of a single sea state simulation is 3 hours, and the cumulative fatigue damage of mooring is: .

[0118] Taking a three-post semi-submersible wind turbine mooring system as an example, such as Figure 2 and Figure 3 As shown, the mooring system includes seven mooring cables: L1 mooring cable 31, L2 mooring cable 32, L3 mooring cable 33, L4 mooring cable 34, L5 mooring cable 35, L6 mooring cable 36, and L7 mooring cable 37. The three-column semi-submersible wind turbine foundation float mainly consists of one wind turbine-side column 38 and two non-wind turbine-side columns 39 and 310. Mooring cables L1 to L3 are moored on the wind turbine-side column 38, mooring cables L4 and L5 are moored on the non-wind turbine-side column 39, and mooring cables L6 and L7 are moored on the non-wind turbine-side column 310. The wind turbine tower 311 is installed on the wind turbine-side column 38. This embodiment mainly focuses on the fatigue condition of the mooring cables at the cable guide hole A1, the three bottom transition points A2 to A4, the shackle connection point A5, and the mud entry point A6.

[0119] The number of sample points M in the characteristic sea state subset is a crucial factor affecting the prediction accuracy of fatigue damage prediction models. To determine the value of M, convergence analysis is required. For example... Figure 4 As shown, the fatigue damage prediction model (taking the surrogate model as an example) predicts the cumulative fatigue damage at the guide holes of the seven mooring cables of the above-mentioned three-post semi-submersible wind turbine mooring system under five conditions: M=400, 600, 800, and 1000. When the predicted value does not change significantly with the increase of M, the surrogate model is considered to have reached convergence, and its corresponding predicted value is the final predicted value of cumulative mooring fatigue damage; otherwise, it is necessary to further increase the number of sample points in the characteristic sea state subset. Figure 4 It can be seen that as the number of sample points M in the feature sea state subset increases, the surrogate model tends to stabilize. When M=1000, the surrogate model can be considered to have reached the convergence state, and the final value of M is 1000.

[0120] To further verify the prediction accuracy of the surrogate model, 170 sample points were randomly selected from the total sample set of sea states to be predicted. The predicted value of mooring fatigue damage at the L1 mooring cable guide hole by the surrogate model was compared with the mooring fatigue damage result obtained directly through fully coupled dynamic numerical simulation (i.e., the numerical simulation result). Figure 5 As shown. Figure 5 In the graph, the horizontal axis represents the sample point number, and the vertical axis represents the predicted mooring fatigue damage value for each sample point. The black solid line represents the numerical simulation result, and other colors represent the prediction results of the surrogate model using different M values. Figure 5 It can be seen that the predicted values ​​of the surrogate model are generally in good agreement with the numerical simulation results, and the degree of agreement increases with the increase of the value of M. When M is 1000, the predicted values ​​of the surrogate model are in the best agreement with the numerical simulation results.

[0121] Table 1 shows the predicted cumulative fatigue damage (including safety factor) over 25 years for the seven mooring cables of the three-post semi-submersible wind turbine mooring system at six verification points, calculated using the method of this invention. The predicted cumulative fatigue damage is calculated first using formula (12) and then multiplied by the safety factor. In this embodiment, the safety factor is set to 5 according to design specifications. Table 1 shows that the predicted cumulative fatigue damage, including the safety factor, is less than 1, meeting the fatigue strength requirements. Under the influence of wind and waves, the mooring cables at the bottom transition point are repeatedly raised and lowered, resulting in a large stress variation amplitude and thus the largest predicted cumulative fatigue damage.

[0122] Table 1. Predicted cumulative fatigue damage values ​​of different mooring cables including safety factors

[0123] Example 2

[0124] This invention also provides an electronic device, which includes a memory, a processor, and a computer program or instructions stored in the memory. The processor executes the computer program or instructions to implement the floating wind turbine mooring fatigue calculation method in this invention.

[0125] Although not shown, the electronic device includes a processor that can perform various appropriate operations and processes based on programs and / or data stored in read-only memory (ROM) or loaded from a storage portion into random access memory (RAM). The processor can be a multi-core processor or may contain multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more specialized coprocessors, such as a central processing unit, graphics processing unit (GPU), neural network processor (NPU), digital signal processor (DSP), etc. Various programs and data required for device operation are also stored in RAM. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0126] The processor and memory described above are used together to execute programs / instructions stored in the memory. When the program / instructions are executed by the computer, they can implement the methods, steps, or functions described in the above embodiments.

[0127] Although not shown, embodiments of the present invention also provide a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implements the floating wind turbine mooring fatigue calculation method of the present invention.

[0128] Readable storage media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0129] The above description only discloses specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or modifications that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calculating the mooring fatigue of a floating wind turbine, characterized in that, The calculation method includes: A subset of characteristic sea state parameters is obtained by selecting characteristic sea state parameter combinations from the long-term sea state distribution; Construct a fully coupled dynamic model of a floating wind turbine; Using the sample points in the subset of the characteristic sea states as input to the fully coupled dynamic model of the floating wind turbine, and combining data processing and fatigue criteria, the mooring fatigue damage corresponding to each sample point is obtained. A fatigue damage prediction model is constructed based on the sample points in the feature sea state subset and their corresponding mooring fatigue damage. The fatigue damage prediction model described above is used to predict fatigue damage of floating wind turbine mooring.

2. The method for calculating the mooring fatigue of a floating wind turbine according to claim 1, characterized in that, Clustering algorithms are used to select characteristic sea state parameter combinations from the long-term sea state distribution, specifically including: By selecting combinations of sea state parameters from the long-term sea state distribution, a total sea state sample set is obtained; Calculate the normalized distance between each sample point in the total sea state sample set; The sample point with the largest sum of normalized distances to other sample points in the total sea state sample set is selected as the first sample point of the feature sea state subset; Calculate the distance from the remaining sample points in the total sea state sample set to the feature sea state subset, and add the sample point with the largest distance to the feature sea state subset; Repeat the above steps until the feature sea state subset contains M sample points; where M << N, and N represents the number of sample points in the total sea state sample set.

3. The method for calculating the mooring fatigue of a floating wind turbine according to claim 1, characterized in that, The process of constructing the fully coupled dynamic model of the floating wind turbine includes: The floating wind turbine system is discretized into a multibody system composed of rigid and flexible bodies, which serves as the object of load application and provides motion feedback for the control module; An aerodynamic load calculation module and a hydrodynamic load calculation module were constructed, and a wind turbine control module based on proportional-integral control was integrated. The central time step coordinator coordinates the data exchange and iterative calculations between modules. The aerodynamic loads output by the aerodynamic load calculation module, the hydrodynamic loads output by the hydrodynamic load calculation module, and the control commands output by the wind turbine control module are all used as system excitation inputs to the multibody system, thereby realizing the fully coupled dynamic simulation of aerodynamic-hydrodynamic-structural-control multiphysics fields.

4. The method for calculating the mooring fatigue of a floating wind turbine according to claim 3, characterized in that, The flexible body includes blades, towers, and mooring cables; Both the blades and the tower are discretized into multiple beam elements or rod elements, and their elastic deformation is represented by the modal superposition method. The mooring cable is discretized into multiple beam or rod elements using the finite element method to simulate its dynamic response.

5. The method for calculating the mooring fatigue of a floating wind turbine according to claim 3, characterized in that, The aerodynamic load calculation module is based on momentum blade element theory and introduces an unsteady aerodynamic correction model for calculation to improve the accuracy of unsteady aerodynamic load calculation. The hydrodynamic load calculation module uses a combination of potential flow theory and Morison equations for calculation. Based on potential flow theory, the radiation force and diffraction force acting on the foundation float are calculated using a frequency domain-time domain conversion method. Based on Morison equations, the flow load acting on slender components is calculated, and a quadratic drag term is introduced into the equations to accurately characterize the viscous effect of each component of the foundation float.

6. The method for calculating the mooring fatigue of a floating wind turbine according to claim 1, characterized in that, The calculation process for mooring fatigue damage corresponding to each sample point is as follows: The sample points in the feature sea state subset are input into the fully coupled dynamic model of the floating wind turbine to obtain the mooring tension time history; The mooring tension time history was processed using the rainflow counting method, and the corresponding mooring fatigue damage was calculated by combining the TN curve and Miner fatigue criterion.

7. The method for calculating the mooring fatigue of a floating wind turbine according to any one of claims 1 to 6, characterized in that, The fatigue damage prediction model is a surrogate model or a deep learning model.

8. The method for calculating the mooring fatigue of a floating wind turbine according to claim 7, characterized in that, When the fatigue damage prediction model is a surrogate model, the specific construction process of the fatigue damage prediction model includes: Regression analysis is performed on the sample points in the subset of characteristic sea states and their corresponding mooring fatigue damage to establish the mapping relationship between characteristic sea states and mooring fatigue damage, thereby obtaining a fatigue damage prediction model.

9. An electronic device comprising a memory, a processor, and a computer program or instructions stored in the memory, characterized in that, The processor executes the computer program or instructions to implement the floating wind turbine mooring fatigue calculation method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by the processor, they implement the method for calculating the mooring fatigue of a floating wind turbine as described in any one of claims 1 to 8.

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