Method, system and equipment for calculating velocity distribution of wind turbine tail flow field and medium

Abstract

The invention belongs to the technical field of wind power, and provides a wind turbine tail flow field speed distribution calculation method, system, device and medium, and the method comprises the steps: obtaining wind speed, wind wheel rotation speed and airfoil aerodynamic data, building a prediction function based on blade deformation data output load distribution for a wind turbine, inputting current blade deformation data to obtain load distribution, and calculating the wind turbine tail flow field speed distribution. Calculating blade exhibition item circular rector distribution in combination with related data, obtaining trailing vortex and shedding vortex intensity according to the circular rector distribution so as to obtain a wake control point position vector, and solving speed distribution of any position in a downstream wake field of the wind turbine based on the wake control point position vector. The process aims at more accurately predicting related characteristics of the wind turbine tail flow field, and the problem that an existing wake flow field prediction model cannot consider the real shape of the wake is solved. The method can consider the real shape of the wake to obtain accurate wake flow field velocity distribution.

Description

Technical Field

[0001] The present invention relates to the field of wind power technology, and in particular to a method, system, device and medium for calculating the velocity distribution of a wind turbine wake field. Background Art

[0002] When a wind turbine is running, a wake field will be generated behind it. The existence of the wind turbine wake field has a direct impact on the surrounding environment and the operation of other wind turbines in the wind farm. Predicting the distribution of wind turbine wakes and related characteristics of the wake field can help optimize the layout and design of wind turbines and improve the power generation efficiency and stability of wind farms.

[0003] The invention patent with the publication number CN114091377A discloses a method for calculating the wake wind speed of a dynamic double Gaussian wind turbine based on spatial variation, determining the wind turbine rotor diameter and thrust coefficient, and calculating the initial wake radius; collecting the incoming wind speed of the wind turbine, determining the initial wake expansion coefficient, power exponent, and the radial distance of the wake wind speed minimum point relative to the hub centerline, and calculating the effective rotor diameter; dividing the wind turbine wake area into the wake area adjacent to the rotor, the near wake area, and the far wake area, determining the corresponding wake expansion coefficient and power exponent, and calculating the wake radius distribution in combination with the initial wake radius; establishing the wake unilateral Gaussian profile distribution function on both sides of the hub centerline, and calculating the wake double Gaussian distribution profile; combining the average momentum flow equation and thrust calculation formula flowing through the rotor, calculating the maximum normalized velocity attenuation, and then calculating the velocity attenuation in the wake area, and determining the velocity distribution in the wake area. This technology improves the accuracy of calculating the wake wind speed distribution in the entire wake area.

[0004] The invention patent with publication number CN116415421A discloses a method and device for modeling the flow field in the wake area of ​​a wind turbine, the method comprising: determining the radius of the wake area of ​​the wind turbine according to the basic parameters required for flow field modeling and the linear expansion assumption of the wake; determining the location of the maximum velocity loss in the wake area based on the actual evolution law of the wake; dividing the wake area of ​​the wind turbine based on the radius of the wake area of ​​the wind turbine, the location of the maximum velocity loss in the wake area, and the wake influence of the wind turbine blades; modeling the flow field of the wake area of ​​the wind turbine after the division of the areas by area, and establishing a flow field model to be solved in the wake area of ​​the wind turbine; solving the flow field model to be solved in the wake area of ​​the wind turbine to obtain a flow field model. The method and device for modeling the flow field provided by this technology take into account the actual generation and evolution law of the wind turbine wake, accurately model the flow field in the wake area of ​​the wind turbine, and improve the accuracy of wake calculation.

[0005] The invention patent with publication number CN109255184B discloses a method and system for determining the wind speed distribution of the entire wake field of a wind turbine. The method includes: determining the wind speed distribution of the near wake of the wind turbine according to the vortex wake model; determining the wind speed distribution of the far wake of the wind turbine according to the engineering wake model; determining the wind speed at the 2D position and the 3D position according to the near wake wind speed distribution and the far wake wind speed distribution respectively; correcting the wake radius according to the wind speed at the 2D position and the 3D position and the correction formula; and then correcting the near wake wind speed distribution and the far wake wind speed distribution according to the corrected wake radius to obtain the corrected near wake wind speed distribution and the far wake wind speed distribution; determining the wind speed distribution of the entire wake field of the wind turbine according to the corrected near wake wind speed distribution and the corrected far wake wind speed distribution. The method and system for determining the wind speed distribution of the entire wake field of a wind turbine provided by this technology can fit the wind speed distribution of the entire wake field, which not only meets the engineering needs in terms of accuracy, but also greatly reduces the amount of calculation.

[0006] However, in the prior art, the prediction of the wake field is based on an empirical wake model, which only focuses on the macroscopic velocity characteristics of the wake field and cannot consider the true shape of the wake. Summary of the invention

[0007] To solve the above problems, the present disclosure provides a method, system, equipment and medium for calculating the velocity distribution of the wind turbine wake field. By improving the technology for determining the induced velocity in the prediction of the development of the wind wheel wake, the problem that the existing wake field prediction model cannot take into account the true shape of the wake is solved.

[0008] The present invention is achieved through the following technical solutions:

[0009] Calculation method of wind turbine wake velocity distribution:

[0010] Obtain the average incoming wind speed data, wind rotor speed and airfoil family aerodynamic data;

[0011] For a given model of wind turbine in a wind farm, a blade load prediction function is constructed to output blade load distribution based on blade deformation data; the blade deformation data of the wind turbine blade at the current moment is input into the blade load prediction function, and the blade load distribution at the current moment is output;

[0012] Calculate the wind turbine blade circulation distribution at the current moment according to the average incoming wind speed data, the wind rotor speed, the airfoil family aerodynamic data, and the blade load distribution data at the current moment;

[0013] According to the circulation distribution of the wind turbine blade at the current moment, the tail vortex and shedding vortex strength of the blade are obtained, and the position vector of the wake control point is obtained according to the tail vortex and shedding vortex strength;

[0014] The velocity distribution at any spatial position in the wake field downstream of the wind turbine is solved based on the position vector of the wake control point.

[0015] Further,

[0016] The method of constructing a blade load prediction function for outputting blade load distribution based on blade deformation data comprises:

[0017] Generate blade deformation data and corresponding blade load distribution data through a finite element structural model as a machine learning training data set;

[0018] The neural network is trained using the machine learning training data set to obtain the blade load prediction function; the blade load prediction function is:

[0019] L j =f(u x ,u y ,u z )

[0020] Where, L j is the lift at the jth airfoil section of the blade; f is the blade load prediction function obtained through training; u x ,u y ,u z They are the flapping, shimmying and torsional deformation of the blades respectively.

[0021] Further,

[0022] The blade deformation data includes:

[0023] The flapping of the blades, the vibration of the blades, and the torsional deformation of the blades.

[0024] Further,

[0025] The calculation formula of the blade span annular distribution is:

[0026]

[0027] Where ρ is the air density; dl is the length of the airfoil; N and T are the normal and tangential direction vectors at the airfoil section, respectively; V rel,j Relative inflow velocity at the airfoil section; L j is the lift at the airfoil section; j is the number of the airfoil section; V rel,j Synthesized based on incoming wind speed and wind wheel speed.

[0028] Further,

[0029] The calculation formula for the trailing vortex and shedding vortex strength of the blade is:

[0030]

[0031]

[0032] In the formula, and are the trailing vortex and shedding vortex intensities, respectively; the superscript i represents the time; the subscript j represents the airfoil section number.

[0033] Further,

[0034] The calculation formula of the position vector of the trail control point is:

[0035]

[0036] Where r is the position vector of the wake control point; V0 is the free flow velocity; V avg is the average induced velocity at the rotor plane; k is the induced velocity attenuation coefficient, k changes with the wake life angle ζ;

[0037] The induced velocity attenuation coefficient k varies with the life angle. The induced velocity attenuation coefficient k is 1 at the starting position of the wake, 0.5 at the ending position of the wake, and calculated by life angle interpolation at the middle position of the wake.

[0038] Further,

[0039] The Biot-Savart law is used to calculate the velocity distribution at any spatial position in the wake field downstream of the wind turbine. The expression is:

[0040]

[0041] Where r1, r2 are the position vectors of the wake control point from the vortex segment to the space point, and l is the length of the vortex segment.

[0042] A calculation system for wind turbine wake velocity distribution, characterized in that it includes:

[0043] A data acquisition module is used to obtain average incoming wind speed data, wind rotor speed and airfoil family aerodynamic data;

[0044] The blade load distribution output module is used to construct a blade load prediction function for a given model of wind turbine in a wind farm, which outputs blade load distribution based on blade deformation data; input the blade deformation data of the wind turbine blade at the current moment into the blade load prediction function, and output the blade load distribution at the current moment;

[0045] The blade spread circulation distribution calculation module is used to calculate the wind turbine blade spread circulation distribution at the current moment according to the average incoming wind speed data, the wind rotor speed, the airfoil family aerodynamic data, and the blade load distribution data at the current moment;

[0046] The wake spatial distribution construction module is used to obtain the tail vortex and shedding vortex strength of the blade according to the wind turbine blade spread circulation distribution at the current moment, and obtain the position vector of the wake control point according to the tail vortex and shedding vortex strength;

[0047] The wake field distribution construction module is used to solve the velocity distribution of any spatial position in the wake field downstream of the wind turbine based on the position vector of the wake control point.

[0048] Compared with the prior art, the present invention has the following advantages:

[0049] The present invention establishes a blade load prediction function that outputs blade load distribution based on blade deformation data, and can accurately invert blade load distribution according to blade geometric deformation monitored in real time; blade load directly affects the formation and development of wind wheel wake;

[0050] Based on the accurate blade load distribution, combined with other relevant data such as the average incoming wind speed data, the rotor speed and the aerodynamic data of the airfoil family, the circulation distribution of the wind turbine blade can be estimated more accurately; then, the tail vortex and the shedding vortex strength of the blade can be obtained according to the circulation distribution. The strength of the tail vortex and the shedding vortex determines the dynamic development and spatial distribution of the rotor wake, so that the characteristics of the rotor wake can be accurately solved; based on the accurately obtained wake control point position vector, the velocity distribution at any spatial position in the downstream wake field of the wind turbine can be further solved, realizing the real-time prediction of the geometric contour of the wind turbine wake and the velocity distribution of the wake field.

[0051] Such real-time prediction can fully and accurately describe the shape of the wake and the characteristics of the wake field, and solves the problem in the prior art that the true shape of the wake cannot be considered.

[0052] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A schematic diagram of the method of the present invention is shown. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0056] Figure 1 A schematic diagram of the method according to the present invention is shown, and the specific implementation details of the present invention include:

[0057] Step 1: Establish a "blade deformation data-blade load distribution" machine learning training data set for a given model of wind turbine in the wind farm, and train the blade load prediction function.

[0058] 1.1) In step 1, the “blade deformation data-blade load distribution” machine learning training data set is generated through a finite element structural model or a wind turbine blade multi-body dynamics model, and the specific steps are as follows:

[0059] Blade geometry modeling: Use professional CAD software to accurately draw the three-dimensional geometry of a given model of wind turbine blade, including the blade's airfoil, length, width, thickness, and internal structural details.

[0060] Finite element meshing: Import the geometric model of the blade into the finite element analysis software.

[0061] Select the appropriate mesh type (such as tetrahedral mesh or hexahedral mesh), and mesh the blade according to the structural characteristics of the blade and the analysis accuracy requirements. Encrypt the mesh at key locations (such as the blade root and areas where the airfoil changes greatly) to improve the calculation accuracy.

[0062] Material property definition: Assign corresponding material properties to each part of the blade, such as elastic modulus, Poisson's ratio, density, etc. These properties should be determined according to the material actually used in the blade.

[0063] Boundary conditions and load settings: Define the boundary conditions of the blades. Usually the root of the blade is fixed to simulate its installation on the wind turbine.

[0064] Apply a variety of different types and sizes of loads, including aerodynamic loads (calculated based on different wind speeds, wind directions and turbulence intensities), centrifugal forces (taking into account the rotation speed of the blades), gravity, etc.

[0065] Finite element solution calculation: Run finite element analysis calculation to obtain the deformation data (such as displacement, strain) and load distribution data (such as bending moment, torque, shear force) of the blade under different load conditions.

[0066] The required blade deformation data and load distribution data are extracted from the finite element analysis results. The blade deformation data and load distribution data correspond one to one.

[0067] Label the data to clarify the load conditions and deformation results corresponding to each data sample.

[0068] The labeled data is the machine learning training data set.

[0069] 1.2) In step 1, the specific steps of training the blade load prediction function are:

[0070] The blade deformation data-blade load distribution machine learning training data set is used to train the neural network and construct the blade load prediction function. The neural network can be a bp neural network.

[0071] The functional form of the blade load prediction function is:

[0072] L j =f(u x ,u y ,u z )

[0073] Where, L j is the lift at the jth airfoil section of the blade; f is the black box function obtained through training; u x ,u y ,u z They are the flapping, shimmying and torsional deformation of the blades respectively.

[0074] The input of the blade load prediction function is the blade deformation data, including the flapping, shimmy and torsional deformation of the blade, and the output of the blade load prediction function is the lift distribution of the blade.

[0075] Step 2: Based on the blade deformation data monitoring device, the geometric deformation of the blade at the current moment is obtained, and the blade load distribution at the current moment is inverted through the blade load prediction function.

[0076] Step 3: Estimate the wind turbine blade circulation distribution at the current moment based on the blade load distribution at the current moment combined with the average incoming wind speed data, rotor speed and airfoil family aerodynamic data monitored by the SCADA system.

[0077] The blade span circulation distribution is calculated by the following formula:

[0078]

[0079] Where ρ is the air density; dl is the length of the airfoil; N and T are the normal and tangential direction vectors at the airfoil section, respectively; V rel,j Relative inflow velocity at the airfoil section; L jis the lift at the airfoil section, j is the number of the airfoil section, and the blade is divided into several sections along the length direction, for example, the number of section 20 is 20.

[0080] Where V rel,j The acquisition needs to be based on the synthesis of incoming wind speed and rotation speed (conventional vector synthesis). The wind speed and wind rotor speed are provided by the SCADA system.

[0081] Step 4: Obtain the tail vortex and shedding vortex strength of each blade according to the circulation distribution of each blade at the current moment, and solve the dynamic development and spatial distribution of the wind wheel wake based on this.

[0082] 4.1) In step 4, the calculation formula for the trailing vortex and shedding vortex strength based on the blade span annular distribution is as follows:

[0083] The trailing vortex strength is the circulation of the j+1th vortex segment on the blade minus the circulation of the jth vortex segment on the blade.

[0084] The shedding vortex intensity is the circulation of the vortex section on the blade at the i+1th moment minus the circulation of the vortex section on the blade at the i-th moment.

[0085] Its mathematical expression is:

[0086]

[0087]

[0088] In the formula, and are the trailing vortex and shedding vortex intensities, respectively; the superscript i represents the time; the subscript j represents the airfoil section number.

[0089] 4.2) The dynamic development and spatial distribution of the wind wheel wake in step 4 specifically include:

[0090] Although traditional CFD numerical simulation and vortex wake models can predict the development of wind turbine wakes, their solution speed is slow, requiring huge computing resources, and are not suitable for wind farm wake control.

[0091] Therefore, in order to achieve a fast solution of the wind turbine wake distribution to meet the control requirements of the wind farm, the wake geometry contour solution is accelerated by setting the wake induced velocity attenuation coefficient k instead of the Biot-Savart law in the traditional numerical solution scheme:

[0092]

[0093] Where r is the position vector of the wake control point; V0 is the free flow velocity; V avg is the average induced velocity at the wind rotor plane; k is the induced velocity attenuation coefficient, which changes with the wake life angle ζ.

[0094] The induced velocity attenuation coefficient k changes with the life angle. The induced velocity attenuation coefficient is 1 at the start of the wake, 0.5 at the end of the wake, and calculated by life angle interpolation at the middle of the wake. This method can determine the induced velocity more quickly, avoid complex calculations, greatly reduce the amount of calculations, and thus significantly reduce the computing resources required for prediction.

[0095] The interpolation calculation is the basic algorithm and will not be introduced in detail here.

[0096] Step 5: Solve the velocity distribution of any spatial position in the wake field downstream of the wind turbine based on the position vector of the wake control point.

[0097] The velocity distribution at any spatial position in the wake field downstream of the wind turbine is estimated using the Biot-Savart law:

[0098]

[0099] Where r1, r2 are the vectors from the vortex segment to the space point, and l is the length of the vortex segment.

[0100] Based on the method of the present invention, the embodiment of the present disclosure also provides a prediction system corresponding to the above method, which includes:

[0101] A data acquisition module is used to obtain the average incoming wind speed data, the rotor speed and the aerodynamic data of the airfoil family; a blade load distribution output module is used to construct a blade load prediction function based on the blade deformation data to output the blade load distribution for a given model of wind turbine in the wind farm; the blade deformation data of the wind turbine blade at the current moment is input into the blade load prediction function, and the blade load distribution at the current moment is output; a blade spread circulation distribution calculation module is used to calculate the wind turbine blade spread circulation distribution at the current moment based on the average incoming wind speed data, the rotor speed, the aerodynamic data of the airfoil family and the blade load distribution data at the current moment; a wake spatial distribution construction module is used to obtain the blade's wake vortex and shedding vortex intensity based on the wind turbine blade spread circulation distribution at the current moment, and obtain the wake control point position vector; a wake field distribution construction module is used to solve the velocity distribution at any spatial position in the downstream wake field of the wind turbine based on the wake control point position vector.

[0102] Based on the same inventive concept as the above disclosed content, an embodiment of the present disclosure also provides a prediction device corresponding to the above method, which includes at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above method.

[0103] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between lines. An indirect connection method can be applied to the embodiments of the present disclosure as long as the purpose of the present disclosure is achieved.

[0104] Based on the same inventive concept, the present disclosure also provides a computer storage medium on which executable instructions are stored. When the instructions are executed by a processor, the processor performs the above method.

[0105] Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for calculating the velocity distribution of a wind turbine wake field, characterized in that: include: Obtain the average incoming wind speed data, wind rotor speed and airfoil family aerodynamic data; For a given model of wind turbine in a wind farm, a blade load prediction function is constructed to output blade load distribution based on blade deformation data; the blade deformation data of the wind turbine blade at the current moment is input into the blade load prediction function, and the blade load distribution at the current moment is output; Calculate the wind turbine blade circulation distribution at the current moment according to the average incoming wind speed data, the wind rotor speed, the airfoil family aerodynamic data, and the blade load distribution data at the current moment; According to the circulation distribution of the wind turbine blade at the current moment, the tail vortex and shedding vortex strength of the blade are obtained, and the position vector of the wake control point is obtained according to the tail vortex and shedding vortex strength; The velocity distribution at any spatial position in the wake field downstream of the wind turbine is solved based on the position vector of the wake control point.

2. The method for calculating the velocity distribution of the wind turbine wake field according to claim 1, characterized in that: The method of constructing a blade load prediction function for outputting blade load distribution based on blade deformation data comprises: Generate blade deformation data and corresponding blade load distribution data through a finite element structural model as a machine learning training data set; The neural network is trained using the machine learning training data set to obtain the blade load prediction function; the blade load prediction function is: L j =f(u x ,and y ,and z ) Where, L j is the lift at the jth airfoil section of the blade; f is the blade load prediction function obtained through training; u x ,u y ,u z They are the flapping, shimmying and torsional deformation of the blades respectively.

3. The method for calculating the velocity distribution of the wind turbine wake field according to claim 1, characterized in that: The blade deformation data includes: The flapping of the blades, the vibration of the blades, and the torsional deformation of the blades.

4. The method for calculating the velocity distribution of the wind turbine wake field according to claim 1, characterized in that: The calculation formula of the blade span annular distribution is: Where ρ is the air density; dl is the length of the airfoil section; N and T are the normal and tangential direction vectors at the airfoil section, respectively; V rel,j Relative inflow velocity at the airfoil section; L j is the lift at the airfoil section; j is the number of the airfoil section; V rel,j Synthesized based on incoming wind speed and wind wheel speed.

5. The method for calculating the velocity distribution of the wind turbine wake field according to claim 4, characterized in that: The calculation formula for the trailing vortex and shedding vortex strength of the blade is: In the formula, and are the trailing vortex and shedding vortex intensities, respectively; the superscript i represents the time; the subscript j represents the airfoil section number.

6. The method for calculating the velocity distribution of the wind turbine wake field according to claim 1, characterized in that: The calculation formula of the position vector of the trail control point is: Where r is the position vector of the wake control point; V0 is the free flow velocity; V avg is the average induced velocity at the rotor plane; k is the induced velocity attenuation coefficient, k changes with the wake life angle ζ; The induced velocity attenuation coefficient k varies with the life angle. The induced velocity attenuation coefficient k is 1 at the starting position of the wake, 0.5 at the ending position of the wake, and calculated by life angle interpolation at the middle position of the wake.

7. The method for calculating the velocity distribution of the wind turbine wake field according to claim 1, characterized in that: The Biot-Savart law is used to calculate the velocity distribution at any spatial position in the wake field downstream of the wind turbine. The expression is: Where r1, r2 are the position vectors of the wake control point from the vortex segment to the space point, and l is the length of the vortex segment.

8. A calculation system for the velocity distribution of the wind turbine wake field, characterized in that: include: A data acquisition module is used to obtain average incoming wind speed data, wind rotor speed and airfoil family aerodynamic data; The blade load distribution output module is used to construct a blade load prediction function for a given model of wind turbine in a wind farm, which outputs blade load distribution based on blade deformation data; input the blade deformation data of the wind turbine blade at the current moment into the blade load prediction function, and output the blade load distribution at the current moment; The blade spread circulation distribution calculation module is used to calculate the wind turbine blade spread circulation distribution at the current moment according to the average incoming wind speed data, the wind rotor speed, the airfoil family aerodynamic data, and the blade load distribution data at the current moment; The wake spatial distribution construction module is used to obtain the tail vortex and shedding vortex strength of the blade according to the wind turbine blade spread circulation distribution at the current moment, and obtain the position vector of the wake control point according to the tail vortex and shedding vortex strength; The wake field distribution construction module is used to solve the velocity distribution of any spatial position in the wake field downstream of the wind turbine based on the position vector of the wake control point.

9. A device for calculating the velocity distribution of a wind turbine wake field, comprising: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for constructing the wind turbine wake field distribution as described in any one of claims 1 to 7.

10. A computer storage medium having executable instructions stored thereon, wherein when the instructions are executed by a processor, the processor implements the method according to any one of claims 1 to 7.

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