A simulation method and system for offshore wind turbines under comprehensive airflow factors

Abstract

The present application proposes a simulation method and system for offshore wind turbines under comprehensive airflow factors, the method comprising: obtaining the wind vector at each moment corresponding to the offshore wind turbine based on a time-domain wind model; inputting the wind vector, wind load time series, and correction coefficient of wind flow shape change into an aerodynamic model for solution to obtain a time series of wind pressure distribution; inputting the time series of wind pressure distribution and the time series of offshore wind turbine torque into a structural dynamics model for solution to obtain the deformation of the wind blades in the offshore wind turbine and the correction coefficient of wind flow shape change corresponding to the deformation; performing iterative calculation based on the deformation of the wind blades and the correction coefficient of wind flow shape change corresponding to the deformation to determine the control signal of the offshore wind turbine, and then simulating the offshore wind turbine based on the control signal. The technical solution proposed in the present application can quickly and accurately perform integrated simulation and solution of wind turbines.

Description

Technical Field

[0001] The present application relates to the technical field of wind turbine simulation, and in particular to a simulation method and system for offshore wind turbines under comprehensive airflow factors. Background Art

[0002] Due to abundant wind energy resources, the wind power industry has experienced rapid growth over the past decade, with installed capacity increasing annually. Compared to onshore wind power, offshore wind power, with its abundant resources, high efficiency, and environmental friendliness, is expected to become the mainstay of the future wind power industry. Offshore wind power is gradually developing towards scale, deep-sea expansion, and affordable prices.

[0003] Despite the vast development prospects, offshore wind power development faces significant technical challenges. Wind turbine blades are key components of wind turbines, and their performance directly impacts the efficiency and stability of the entire turbine. They are also the most complex components subject to stress. They are long in span and short in chord, offering excellent flexibility. They are elongated elastic bodies prone to deformation, and involve a strong coupling of multiple disciplines, including aerodynamics, structural dynamics, and mechanical dynamics. Modeling and simulating the electromechanical characteristics of the wind turbine under the influence of complex, time-varying wind conditions requires significant computational resources. Furthermore, solving such complex systems of equations involving multiple nonlinear boundaries takes a long time, making them incapable of meeting the demands of real-time simulation.

[0004] Currently, the calculation of the complex fluid-structure interaction characteristics of offshore wind turbine aerodynamic transmission under mixed wind conditions primarily involves two steps: wind resource generation and blade stress distribution. Wind resource generation involves developing various mathematical models based on wind tower observation data. This includes mathematical and statistical evaluation methods that convert meteorological station and tower observation data into wind energy, wind power, and other wind resource assessment parameters. Furthermore, numerical simulation methods utilize computer simulation techniques combined with tower observation data and mesoscale data to analyze near-surface wind energy resources. Applying these two wind resource models, blade element momentum theory or computational fluid dynamics is used to determine the wind pressure distribution on the wind turbine blades. This result is then used in fluid-structure interaction to determine the stress, strain, and shape variable distributions of the blades. The locations of maximum stress, strain, and deformation are analyzed, providing data reference for fatigue life and fracture analysis, and providing a basis for blade improvement and selection, as well as for the development of wind turbine control logic. However, existing methods fail to achieve both modeling accuracy and rapid solution speed. Summary of the Invention

[0005] The present application provides a simulation method and system for offshore wind turbines under comprehensive airflow factors, so as to at least solve the technical problem that existing methods cannot simultaneously take into account both modeling accuracy and solution speed.

[0006] The first embodiment of the present application provides a simulation method for an offshore wind turbine under comprehensive airflow factors, the method comprising:

[0007] Obtain the wind vector at each moment corresponding to the offshore wind turbine based on the time domain wind model;

[0008] Inputting the wind vector, wind load time series and wind flow shape change correction coefficient into an aerodynamic model for solution to obtain a time series of wind pressure distribution;

[0009] Inputting the time series of wind pressure distribution and the time series of offshore wind turbine torque into a structural dynamics model for solution, thereby obtaining a deformation of a wind blade in the offshore wind turbine and a correction coefficient of a wind flow shape change corresponding to the deformation;

[0010] An iterative calculation is performed based on the deformation amount of the wind blade and a correction coefficient of the wind flow shape change corresponding to the deformation amount to determine a control signal of the offshore wind turbine, and then the offshore wind turbine is simulated based on the control signal.

[0011] Preferably, the step of inputting the wind vector, wind load time series, and correction coefficient of wind flow shape change into an aerodynamic model for solution to obtain a time series of wind pressure distribution includes:

[0012] Based on the correction coefficient of the wind vector and the wind flow shape change, and using an aerodynamic BEM algorithm or an aerodynamic CFD algorithm to calculate, a torque corresponding to the offshore wind turbine is obtained;

[0013] The torque is matched to the corresponding wind speed moment by using the wind load time series to obtain the time series of wind pressure distribution corresponding to the offshore wind turbine.

[0014] Preferably, performing iterative calculation based on the deformation amount of the wind blade and the correction coefficient of the wind flow shape change corresponding to the deformation amount to determine the control signal of the offshore wind turbine includes:

[0015] Step F1: Processing the k-th calculated deformation of the wind blade using the response time sequence to obtain the angular acceleration;

[0016] Step F2: determining a time series of the offshore wind turbine torque based on the angular acceleration;

[0017] Step F3: Determine whether the time series of the offshore wind turbine torque satisfies If so, proceed to step F4; otherwise, use the correction coefficient of the wind flow shape change corresponding to the deformation calculated at the kth iteration to correct the aerodynamic model and generate a time series of wind pressure distribution. Then, input the generated time series of wind pressure distribution and the time series of offshore wind turbine torque into the structural dynamics model for solution to obtain the deformation of the offshore wind turbine blade calculated at the k+1th iteration and the correction coefficient of the wind flow shape change corresponding to the deformation. Set k=k+1 and return to step F1.

[0018] Where, is the random effect value at the kth iteration i, is the random effect value at the k-1th iteration i, k is the number of iterations, and δ is a given positive decimal.

[0019] Step F4: Compare the time series of the offshore wind turbine torque with the electromagnetic torque output by the induction motor of the offshore wind turbine, and determine a control signal for the offshore wind turbine based on the comparison result.

[0020] Preferably, the control signal of the offshore wind turbine includes:

[0021] The wind turbine's start and stop signals, direction adjustment signals, speed regulation signals, pitch control signals and yaw signals.

[0022] Preferably, the time-domain wind model, the aerodynamic model and the structural dynamics model are solved based on the boundary conditions of fluid-solid coupling;

[0023] The time-domain wind model, the aerodynamic model and the structural dynamics model are established using an integrated solution architecture.

[0024] A second embodiment of the present application provides a simulation system for an offshore wind turbine under comprehensive airflow factors, the system comprising:

[0025] An acquisition module, used to obtain the wind vector corresponding to the offshore wind turbine at each moment based on the time domain wind model;

[0026] A first solving module is used to input the wind vector, wind load time series and wind flow shape change correction coefficient into an aerodynamic model for solving to obtain a time series of wind pressure distribution;

[0027] A second solving module is configured to input the time series of the wind pressure distribution and the time series of the offshore wind turbine torque into a structural dynamics model for solving, thereby obtaining a deformation of the wind blades in the offshore wind turbine and a correction coefficient of the wind flow shape change corresponding to the deformation;

[0028] The determination module is used to perform iterative calculation based on the deformation of the wind blade and the correction coefficient of the wind flow shape change corresponding to the deformation to determine the control signal of the offshore wind turbine, and then simulate the offshore wind turbine based on the control signal.

[0029] Preferably, the first solution module includes:

[0030] a calculation unit, configured to calculate based on the wind vector and the correction coefficient of the wind flow shape change and using an aerodynamic BEM algorithm or an aerodynamic CFD algorithm to obtain a torque corresponding to the offshore wind turbine;

[0031] The first determining unit is configured to use a wind load time series to match the torque with the corresponding wind speed moment, so as to obtain a time series of wind pressure distribution corresponding to the offshore wind turbine.

[0032] Preferably, the determining module is specifically configured to:

[0033] Step E1: Processing the k-th calculated deformation of the wind blade using the response time sequence to obtain the angular acceleration;

[0034] Step E2: determining a time series of the offshore wind turbine torque based on the angular acceleration;

[0035] Step E3: Determine whether the time series of the offshore wind turbine torque satisfies If so, proceed to step E4; otherwise, use the correction coefficient of the wind flow shape change corresponding to the deformation calculated at the kth iteration to correct the aerodynamic model and generate a time series of wind pressure distribution. Then, input the generated time series of wind pressure distribution and the time series of offshore wind turbine torque into the structural dynamics model for solution to obtain the deformation of the offshore wind turbine blade calculated at the k+1th iteration and the correction coefficient of the wind flow shape change corresponding to the deformation. Set k=k+1 and return to step E1.

[0036] Where, is the random effect value at the kth iteration i, is the random effect value at the k-1th iteration i, k is the number of iterations, and δ is a given positive decimal.

[0037] Step E4: Compare the time series of the offshore wind turbine torque with the electromagnetic torque output by the induction motor of the offshore wind turbine, and determine a control signal for the offshore wind turbine based on the comparison result.

[0038] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method described in the first aspect is implemented.

[0039] A fourth embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first embodiment.

[0040] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0041] The present application proposes a simulation method and system for offshore wind turbines under comprehensive airflow factors, the method comprising: obtaining the wind vector at each moment corresponding to the offshore wind turbine based on a time-domain wind model; inputting the wind vector, wind load time series, and correction coefficient of wind flow shape change into an aerodynamic model for solution to obtain a time series of wind pressure distribution; inputting the time series of wind pressure distribution and the time series of offshore wind turbine torque into a structural dynamics model for solution to obtain the deformation of the wind blades in the offshore wind turbine and the correction coefficient of wind flow shape change corresponding to the deformation; performing iterative calculation based on the deformation of the wind blades and the correction coefficient of wind flow shape change corresponding to the deformation to determine the control signal of the offshore wind turbine, and then simulating the offshore wind turbine based on the control signal. The technical solution proposed in the present application can quickly and accurately perform integrated simulation and solution of wind turbines.

[0042] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0044] Figure 1 This is a flow chart of a simulation method for an offshore wind turbine under comprehensive airflow factors according to one embodiment of the present application;

[0045] Figure 2 This is a structural diagram of a simulation system for an offshore wind turbine under comprehensive airflow factors according to one embodiment of the present application;

[0046] Figure 3 This is a structural diagram of a first solution module provided according to one embodiment of the present application. DETAILED DESCRIPTION

[0047] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0048] The present application proposes a simulation method and system for offshore wind turbines under comprehensive airflow factors, the method comprising: obtaining the wind vector at each moment corresponding to the offshore wind turbine based on a time-domain wind model; inputting the wind vector, wind load time series, and correction coefficient of wind flow shape change into an aerodynamic model for solution to obtain a time series of wind pressure distribution; inputting the time series of wind pressure distribution and the time series of offshore wind turbine torque into a structural dynamics model for solution to obtain the deformation of the wind blades in the offshore wind turbine and the correction coefficient of wind flow shape change corresponding to the deformation; performing iterative calculation based on the deformation of the wind blades and the correction coefficient of wind flow shape change corresponding to the deformation to determine the control signal of the offshore wind turbine, and then simulating the offshore wind turbine based on the control signal. The technical solution proposed in the present application can quickly and accurately perform integrated simulation and solution of wind turbines.

[0049] The following describes a simulation method and system for an offshore wind turbine under comprehensive airflow factors according to an embodiment of the present application with reference to the accompanying drawings.

[0050] Example 1

[0051] Figure 1 This is a flow chart of a simulation method for an offshore wind turbine under comprehensive airflow factors according to one embodiment of the present application. Figure 1 As shown, the method includes:

[0052] Step 1: Obtain the wind vector at each moment corresponding to the offshore wind turbine based on the time domain wind model;

[0053] Step 2: Input the wind vector, wind load time series, and correction coefficient of wind flow shape change into an aerodynamic model for solution to obtain a time series of wind pressure distribution;

[0054] In the embodiment of the present disclosure, step 2 specifically includes:

[0055] Based on the correction coefficient of the wind vector and the wind flow shape change, and using an aerodynamic BEM algorithm or an aerodynamic CFD algorithm to calculate, a torque corresponding to the offshore wind turbine is obtained;

[0056] The torque is matched to the corresponding wind speed moment by using the wind load time series to obtain the time series of wind pressure distribution corresponding to the offshore wind turbine.

[0057] Step 3: Input the time series of wind pressure distribution and the time series of offshore wind turbine torque into a structural dynamics model for solution to obtain the deformation of the offshore wind turbine blades and the correction coefficient of the wind flow shape change corresponding to the deformation;

[0058] Step 4: performing iterative calculation based on the deformation of the wind blade and the correction coefficient of the wind flow shape change corresponding to the deformation to determine the control signal of the offshore wind turbine, and then simulating the offshore wind turbine based on the control signal.

[0059] In the embodiment of the present disclosure, step 4 specifically includes:

[0060] Step F1: Processing the k-th calculated deformation of the wind blade using the response time sequence to obtain the angular acceleration;

[0061] Step F2: determining a time series of the offshore wind turbine torque based on the angular acceleration;

[0062] Step F3: Determine whether the time series of the offshore wind turbine torque satisfies If so, proceed to step F4; otherwise, use the correction coefficient of the wind flow shape change corresponding to the deformation calculated at the kth iteration to correct the aerodynamic model and generate a time series of wind pressure distribution. Then, input the generated time series of wind pressure distribution and the time series of offshore wind turbine torque into the structural dynamics model for solution to obtain the deformation of the offshore wind turbine blade calculated at the k+1th iteration and the correction coefficient of the wind flow shape change corresponding to the deformation. Set k=k+1 and return to step F1.

[0063] Where, is the random effect value at the kth iteration i, is the random effect value at the k-1th iteration i, k is the number of iterations, and δ is a given positive decimal.

[0064] Specifically, the iterative calculation includes:

[0065] The first step is to calculate the random effects The initial value of . Let ψ, σ 2 and The value of is ψ(t), (σ (t) ) 2 and β (t) , where k = 0, calculate

[0066]

[0067] Where, ψ is σ 2 and The value of , σ is the between-group variance, is the predicted value of the regression coefficient, is the test result of the regression equation with regression coefficient i, Z i is the regression equation when the regression coefficient is i, I niis the covariance parameter, y i is the i-dimensional observation phasor, v i is the fixed effect of the regression equation, u i is the initial value of the random effect for iteration, is the random effect value at the t-th iteration i;

[0068] The second step is to calculate

[0069]

[0070] Calculate using the following formula

[0071]

[0072] Let k = k + 1 and iterate until the desired accuracy is reached. The iterative termination condition is:

[0073]

[0074] Step F4: Compare the time series of the offshore wind turbine torque with the electromagnetic torque output by the induction motor of the offshore wind turbine, and determine a control signal for the offshore wind turbine based on the comparison result.

[0075] It should be noted that the control signals of the offshore wind turbine include:

[0076] The wind turbine's start and stop signals, direction adjustment signals, speed regulation signals, pitch control signals and yaw signals.

[0077] In the embodiment of the present disclosure, the time-domain wind model, the aerodynamic model and the structural dynamics model are solved based on the boundary conditions of fluid-structure coupling;

[0078] The time-domain wind model, the aerodynamic model and the structural dynamics model are established using an integrated solution architecture.

[0079] In the embodiment of the present disclosure, Figure 2 The following shows the components and data flow of the integrated solution architecture:

[0080] ① Time domain wind model: Through the analysis of the dynamic characteristics of the atmospheric boundary layer, discretization of the dynamic equations and numerical simulation of turbulence, multi-scale and multi-regional single-node vector modeling of wind speed and wind direction is carried out according to the nonlinear complexity of the sea surface, including numerical simulation of steady wind, single-point wind, turbulent wind and transient wind.

[0081] Among them, the Charnock parameterized model is used to simulate the physical process of the marine atmospheric boundary layer, and the actual state wind speed equation of the offshore wind contour line at height Z1 based on the neutral equivalent wind speed is:

[0082]

[0083] Where abs(F) is the actual wind speed, F2 is the wind speed at height G2, F1 is the wind speed at height G1, and F * is the atmospheric boundary layer wind speed, χ is the slope of the zero pressure gradient boundary layer function in the logarithmic wind contour line, is the turbulent viscosity coefficient, g is the surface roughness of the wind, and a is the power exponent in the power-law wind profile.

[0084] ② Aerodynamic model: Combining the relative wind speed vector at each finite element of the wind turbine blade with the wind field wake wind direction vector, the wind pressure distribution at the root and tip of the wind turbine blade is calculated through the key nonlinear equations and boundary conditions in the CFD or BEM algorithm, while considering the impact of significantly increased turbulence on blade aerodynamics in the wake condition.

[0085] ③ The wind load time series serves as the interface between the time-domain wind model and structural dynamics. Its interface algorithm is based on the time-domain conservation equations of motion for mass, momentum, and energy, solving the large eddy simulation equations based on time variables. The average force in the wind flow field is decomposed into normal stress and shear stress through the differential equations for compressible variable-viscosity fluid motion, and the computational effort is reduced by filtering out high-wavenumber pulsating components. At the same time, for the turbulent factors in the wind resource, the Reynolds average method is used to calculate the nonlinear equations for the wind flow field wave momentum. The unknown quantities generated during the closure of the nonlinear equations are associated with the turbulence model to form a k-equation model or a Spalart-Allmaras single-equation model.

[0086] Among them, the Releaux average method equation is:

[0087]

[0088]

[0089] Where, is the average velocity component at time i, is the average value of the product of the velocity component at time i and the velocity component at time j, is the Reynolds stress coefficient, x j is the first independent variable at time j, x i is the first independent variable at time i, is the average value of the product of the fluctuation component at time i and the fluctuation component at time j;

[0090] The turbulent stress equation using the k-ε two-equation model is:

[0091]

[0092] γi =c μ ρk 2 / ε

[0093] Where, (τ ij ) t is the time constant, γ i is the turbulent viscosity, ε is the turbulent kinetic energy dissipation term, ρ is the fluid density, c μ is the empirical coefficient, k is the turbulent kinetic energy coefficient, δ ij is the change between time i and time j, u i is the turbulent velocity at time i, u j is the turbulent velocity at time j, x j is the first independent variable at time j.

[0094] General governing equations:

[0095]

[0096] Where, represents a scalar, τ is the time constant, S is the average strain rate, div is the division operator, grand is the percentage, and ρ is the fluid density;

[0097] ④ Structural dynamics model: Modal and deformation analysis of the physical characteristics of the wind turbine body under multiple loads, strong deformation, and complex motion conditions based on time series analysis, taking into account the dynamics of the multi-degree-of-freedom transmission system of the doubly fed or semi-direct drive wind turbine.

[0098] ⑤ The response time series serves as the interface between structural dynamics and mechanical dynamics. Its interface algorithm is based on modal analysis in the time domain to solve the equilibrium equations of load, internal force, displacement, etc. based on time variables. Through the analysis of fluid-solid coupling boundary conditions, the stress, strain and shape variable distribution of the wind turbine blades are obtained, and the location of the maximum stress, strain and deformation and the magnitude of the force are analyzed.

[0099] The interaction between fluids and solids causes changes in their respective shapes, masses, kinetic energy, and energy. The fluid-solid coupling component uses the Naverstoke equations for fluids and the principle of solid virtual work to solve for these changes.

[0100] Among them, the mass equation of the Naverstock equations is:

[0101]

[0102] Momentum equation:

[0103]

[0104] Energy equation:

[0105]

[0106] μ is the dynamic viscosity, λ is the i is the mean free path, I is the time-averaged velocity, p is the pressure, R i is the thermodynamic constant, e is the total energy of the object;

[0107] ⑥ Ultimate and fatigue loads: Considering the dual constraints of the machine and the grid, that is, the main control logic of the wind turbine under the power generation and load limitations, and considering the relationship between the average wind speed plus the turbulent wind speed and the design strength when the transmission chain state changes frequently due to large turbulence.

[0108] ⑦ Feedback the time-domain structural deformation variables obtained from the fluid-structure coupling analysis in the structural dynamics model to the aerodynamic model to update the time series of wind pressure distribution;

[0109] ⑧ Feedback the mechanical force output from the response time series to the wind pressure of the wind load time series and send them together to the structural dynamics model to update the numerical results.

[0110] It should be noted that the integrated solution architecture provided in the method of this embodiment, that is, the multi-level collaborative rapid solution architecture utilizes the nonlinear multivariate regression statistical principle in the multi-level collaborative architecture in statistics, combined with the rapid numerical analysis of the nonlinear mixed effect model, and forms the various boundary conditions involved in the key component nodes in the overall system of multi-physical quantity coupling into overall solution constraints, distinguishes between fixed effects and random effects between levels, and uses time as a unified scale to perform overall joint rapid solution of wind turbines from wind resources to loads, thereby achieving integrated simulation and rapid solution of the multi-physical quantity model of the wind turbine body.

[0111] In summary, the simulation method for offshore wind turbines under comprehensive airflow factors proposed in this embodiment has the following effects:

[0112] 1. Use multi-level models to jointly model complex coupled systems, eliminating the traditional data formatting process between physical systems;

[0113] 2. Using mathematical analysis methods to fit the fluid-solid characteristics of the fan's pneumatic transmission components, replacing traditional finite element simulation methods, improves the timeliness of the model and helps achieve real-time simulation of the fan's dynamics;

[0114] 3. Adopting an integrated solution architecture to model and test the influence factors of variables on the whole and variables on variables, it can analyze the impact of complex wind resources on specific intermediate variables in the pneumatic transmission model;

[0115] 4. The architecture uses time series as the basic variable. The corresponding time quantity contained in each data generation is in line with the essence of dynamics theory, providing a standardized interface for real-time data input and real-time simulation.

[0116] Example 2

[0117] Figure 2 This is a structural diagram of a simulation system for an offshore wind turbine under comprehensive airflow factors according to an embodiment of the present application. Figure 2 As shown, the system includes:

[0118] An acquisition module 100 is configured to acquire the wind vector corresponding to the offshore wind turbine at each moment based on a time domain wind model;

[0119] A first solution module 200 is configured to input the wind vector, wind load time series, and correction coefficient of wind flow shape change into an aerodynamic model for solution to obtain a time series of wind pressure distribution;

[0120] The second solution module 300 is configured to input the time series of wind pressure distribution and the time series of offshore wind turbine torque into a structural dynamics model for solution to obtain a deformation of a wind turbine blade and a correction coefficient of wind flow shape change corresponding to the deformation;

[0121] The determination module 400 is configured to perform iterative calculations based on the deformation of the blade and a correction coefficient of the wind flow shape change corresponding to the deformation to determine a control signal for the offshore wind turbine, and then simulate the offshore wind turbine based on the control signal.

[0122] In the embodiment of the present disclosure, Figure 3 As shown, the first solution module 200 includes:

[0123] A calculation unit 201 is configured to calculate a torque corresponding to the offshore wind turbine based on the wind vector and the correction coefficient of the wind flow shape change and using an aerodynamic BEM algorithm or an aerodynamic CFD algorithm;

[0124] The first determining unit 202 is configured to use a wind load time series to match the torque with the corresponding wind speed moment, and obtain a time series of wind pressure distribution corresponding to the offshore wind turbine.

[0125] In the embodiment of the present disclosure, the determining module 400 is specifically configured to:

[0126] Step E1: Processing the k-th calculated deformation of the wind blade using the response time sequence to obtain the angular acceleration;

[0127] Step E2: determining a time series of the offshore wind turbine torque based on the angular acceleration;

[0128] Step E3: Determine whether the time series of the offshore wind turbine torque satisfies If so, proceed to step E4; otherwise, use the correction coefficient of the wind flow shape change corresponding to the deformation calculated at the kth iteration to correct the aerodynamic model and generate a time series of wind pressure distribution. Then, input the generated time series of wind pressure distribution and the time series of offshore wind turbine torque into the structural dynamics model for solution to obtain the deformation of the offshore wind turbine blade calculated at the k+1th iteration and the correction coefficient of the wind flow shape change corresponding to the deformation. Set k=k+1 and return to step E1.

[0129] Where, is the random effect value at the kth iteration i, is the random effect value at the k-1th iteration i, k is the number of iterations, and δ is a given positive decimal.

[0130] Step E4: Compare the time series of the offshore wind turbine torque with the electromagnetic torque output by the induction motor of the offshore wind turbine, and determine a control signal for the offshore wind turbine based on the comparison result.

[0131] It should be noted that the control signals of the offshore wind turbine include:

[0132] The wind turbine's start and stop signals, direction adjustment signals, speed regulation signals, pitch control signals and yaw signals.

[0133] Furthermore, the time-domain wind model, the aerodynamic model and the structural dynamics model are solved based on the boundary conditions of fluid-structure coupling;

[0134] The time-domain wind model, the aerodynamic model and the structural dynamics model are established using an integrated solution architecture.

[0135] In summary, the simulation system for offshore wind turbines under comprehensive airflow factors proposed in this embodiment can quickly and accurately perform integrated simulation solutions for wind turbines.

[0136] Example 3

[0137] To implement the above embodiments, the present disclosure further proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first embodiment is implemented.

[0138] Example 4

[0139] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the first embodiment is implemented.

[0140] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0141] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0142] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A simulation method for offshore wind turbines under comprehensive airflow factors, characterized in that: The method comprises: Obtain the wind vector at each moment corresponding to the offshore wind turbine based on the time domain wind model; Inputting the wind vector, wind load time series and wind flow shape change correction coefficient into an aerodynamic model for solution to obtain a time series of wind pressure distribution; Inputting the time series of wind pressure distribution and the time series of offshore wind turbine torque into a structural dynamics model for solution, thereby obtaining a deformation of a wind blade in the offshore wind turbine and a correction coefficient of a wind flow shape change corresponding to the deformation; performing iterative calculations based on the deformation of the blade and a correction coefficient of the wind flow shape change corresponding to the deformation to determine a control signal for the offshore wind turbine, and then simulating the offshore wind turbine based on the control signal; The iterative calculation based on the deformation of the wind blade and the correction coefficient of the wind flow shape change corresponding to the deformation to determine the control signal of the offshore wind turbine includes: Step F1: Processing the k-th calculated deformation of the wind blade using the response time sequence to obtain the angular acceleration; Step F2: determining a time series of the offshore wind turbine torque based on the angular acceleration; Step F3: Determine whether the time series of the offshore wind turbine torque satisfies If so, proceed to step F4; otherwise, use the correction coefficient of the wind flow shape change corresponding to the deformation calculated at the kth iteration to correct the aerodynamic model and generate a time series of wind pressure distribution. Then, input the generated time series of wind pressure distribution and the time series of offshore wind turbine torque into the structural dynamics model for solution to obtain the deformation of the offshore wind turbine blade calculated at the k+1th iteration and the correction coefficient of the wind flow shape change corresponding to the deformation. Set k=k+1 and return to step F1; Where, is the random effect value at the kth iteration i, is the random effect value at the k-1th iteration i, k is the number of iterations, and δ is a given positive decimal; Step F4: Compare the time series of the offshore wind turbine torque with the electromagnetic torque output by the induction motor of the offshore wind turbine, and determine a control signal for the offshore wind turbine based on the comparison result.

2. The method according to claim 1, wherein The wind vector, wind load time series and correction coefficient of wind flow shape change are input into the aerodynamic model for solution to obtain the time series of wind pressure distribution, including: Based on the correction coefficient of the wind vector and the wind flow shape change, and using an aerodynamic BEM algorithm or an aerodynamic CFD algorithm to calculate, a torque corresponding to the offshore wind turbine is obtained; The torque is matched to the corresponding wind speed moment by using the wind load time series to obtain the time series of wind pressure distribution corresponding to the offshore wind turbine.

3. The method according to claim 1, wherein The control signal of the offshore wind turbine includes: The wind turbine's start and stop signals, direction adjustment signals, speed regulation signals, pitch control signals and yaw signals.

4. The method according to claim 1, wherein The time-domain wind model, the aerodynamic model and the structural dynamics model are solved based on the boundary conditions of fluid-solid coupling; The time-domain wind model, the aerodynamic model and the structural dynamics model are established using an integrated solution architecture.

5. A simulation system for offshore wind turbines under comprehensive airflow factors, characterized in that: The system comprises: An acquisition module, used to obtain the wind vector corresponding to the offshore wind turbine at each moment based on the time domain wind model; A first solving module is used to input the wind vector, wind load time series and wind flow shape change correction coefficient into an aerodynamic model for solving to obtain a time series of wind pressure distribution; A second solving module is configured to input the time series of the wind pressure distribution and the time series of the offshore wind turbine torque into a structural dynamics model for solving, thereby obtaining a deformation of the wind blades in the offshore wind turbine and a correction coefficient of the wind flow shape change corresponding to the deformation; a determination module, configured to perform iterative calculation based on the deformation amount of the wind blade and a correction coefficient of the wind flow shape change corresponding to the deformation amount to determine a control signal of the offshore wind turbine, and then simulate the offshore wind turbine based on the control signal; The determining module is specifically configured to: Step E1: Processing the k-th calculated deformation of the wind blade using the response time sequence to obtain the angular acceleration; Step E2: determining a time series of the offshore wind turbine torque based on the angular acceleration; Step E3: Determine whether the time series of the offshore wind turbine torque satisfies If so, proceed to step E4; otherwise, use the correction coefficient of the wind flow shape change corresponding to the deformation calculated at the kth iteration to correct the aerodynamic model and generate a time series of wind pressure distribution. Then, input the generated time series of wind pressure distribution and the time series of offshore wind turbine torque into the structural dynamics model for solution to obtain the deformation of the offshore wind turbine blade calculated at the k+1th iteration and the correction coefficient of the wind flow shape change corresponding to the deformation. Set k=k+1 and return to step E1; Where, is the random effect value at the kth iteration i, is the random effect value at the k-1th iteration i, k is the number of iterations, and δ is a given positive decimal; Step E4: Compare the time series of the offshore wind turbine torque with the electromagnetic torque output by the induction motor of the offshore wind turbine, and determine a control signal for the offshore wind turbine based on the comparison result.

6. The system according to claim 5, wherein: The first solving module includes: a calculation unit, configured to calculate based on the wind vector and the correction coefficient of the wind flow shape change and using an aerodynamic BEM algorithm or an aerodynamic CFD algorithm to obtain a torque corresponding to the offshore wind turbine; The first determining unit is configured to use a wind load time series to match the torque with the corresponding wind speed moment, so as to obtain a time series of wind pressure distribution corresponding to the offshore wind turbine.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 4 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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