Wind turbine wake velocity distribution determination method, apparatus, device, and medium

Abstract

The application relates to the technical field of wind farm micro-siting, and particularly provides a wind turbine wake velocity distribution determination method, device, equipment and medium, and aims to solve the problem of errors in wake calculation based on linear expansion distribution of wind turbine wake along the flow direction distance. To this end, the method provided by the application comprises the following steps: obtaining wind turbine information and wind condition information, calculating a wind turbine wake expansion rate and a wind turbine wake maximum speed loss distribution according to the wind turbine information and the wind condition information, determining a wind turbine wake width distribution according to the wind turbine information, the wind turbine wake expansion rate and the wind turbine wake maximum speed loss distribution, and determining a wind turbine wake velocity distribution by adopting a Gaussian wake model according to the wind turbine information and the wind turbine wake width distribution. The application achieves the purpose of more accurately calculating the wind turbine wake velocity distribution in a wind farm, helps to optimize the wind farm site selection, and further improves the overall power generation efficiency of the wind farm.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind farm micro-siting, and particularly relates to a wind turbine wake velocity distribution determination method, device, equipment and medium. BACKGROUND

[0002] At present, in the wake flow evaluation of wind turbines, it is generally believed that the wake of a wind turbine expands linearly with the distance along the flow direction. However, in a farther area, the trend of the wake width growth gradually decays, showing a nonlinear expansion characteristic. The traditional wake calculation does not consider this nonlinear change, resulting in errors in the wake calculation, and then affecting the calculation and prediction of the power generation.

[0003] Correspondingly, there is a need in the art for a new wind turbine wake velocity distribution determination scheme to solve the above problems. SUMMARY

[0004] In order to overcome the above defects, the present application is proposed to solve or at least partially solve the technical problem that there are errors in the wake calculation based on the linear expansion of the wind turbine wake with the distance along the flow direction.

[0005] In a first aspect, a wind turbine wake velocity distribution determination method is provided, the method comprising: obtaining wind turbine information and wind condition information; calculating a wake expansion rate and a wake maximum speed loss distribution of the wind turbine according to the wind turbine information and the wind condition information; determining a wake width distribution of the wind turbine according to the wind turbine information, the wake expansion rate and the wake maximum speed loss distribution; and determining a wake velocity distribution of the wind turbine by using a Gaussian wake model according to the wind turbine information and the wake width distribution.

[0006] In one technical solution of the above wind turbine wake velocity distribution determination method, the calculation of the wake expansion rate and the wake maximum speed loss distribution of the wind turbine according to the wind turbine information and the wind condition information comprises: calculating the wake expansion rate of the wind turbine according to the wind condition information; determining a near-wake region length according to the wind turbine information and the wind condition information; and determining the wake maximum speed loss distribution according to the near-wake region length, the wind turbine information and the wind condition information.

[0007] In one technical solution of the above wind turbine wake velocity distribution determination method, the determination of the wake velocity distribution of the wind turbine by using the Gaussian wake model according to the wind turbine information and the wake width distribution comprises: determining a wake speed loss distribution of the wind turbine by using the Gaussian wake model according to the wind turbine information and the wake width distribution; and determining the wake velocity distribution of the wind turbine according to the wake speed loss distribution.

[0008] In one technical solution of the above-mentioned method for determining the wake velocity distribution of a wind turbine, the wind condition information includes the inflow turbulence intensity, and the step of calculating the wake expansion rate of the wind turbine based on the wind condition information includes: calculating the wake expansion rate of the wind turbine based on the inflow turbulence intensity using the following formula:

[0009] k w =0.38I u +0.004

[0010] Where, k w I represents the wake expansion rate. u This indicates the turbulence intensity in the inflow direction.

[0011] In one technical solution of the above-mentioned method for determining the wake velocity distribution of a wind turbine, the wind turbine information includes the thrust coefficient, the wind condition information includes the inflow wind speed, and the step of determining the maximum wake velocity deficit distribution based on the near-wake region length, the wind turbine information, and the wind condition information includes: determining the maximum wake velocity deficit distribution using the following formula based on the near-wake region length, the thrust coefficient, and the inflow wind speed:

[0012]

[0013] in, This represents the maximum velocity deficit distribution in the wake, ΔU. max U represents the maximum velocity loss in the wake. ∞ x represents the inflow velocity. NW C represents the length of the near-wake region. T The thrust coefficient is represented by x, which represents the distance along the axial direction of the wind turbine in the preset coordinate system.

[0014] In one technical solution of the above-mentioned method for determining the wake velocity distribution of a wind turbine, the wind turbine information further includes the rotor diameter. The step of determining the wake width distribution of the wind turbine based on the wind turbine information, the wake expansion rate, and the maximum wake velocity deficit distribution includes: determining the wake width distribution using the following formula based on the rotor diameter, the thrust coefficient, and the maximum wake velocity deficit distribution:

[0015]

[0016]

[0017]

[0018] in, represents the wake width distribution, x represents the distance along the axial direction of the wind turbine in a preset coordinate system, k w represents the wake expansion rate, D represents the rotor diameter, C T represents the thrust coefficient, represents the wake maximum speed deficit distribution.

[0019] In one of the technical solutions of the method for determining the wake velocity distribution of the wind turbine, the wind turbine information further comprises a hub height of the wind turbine and a spanwise center position of the wind turbine, and the wake velocity distribution of the wind turbine is determined by using the Gaussian wake model according to the wind turbine information and the wake width distribution, comprising: the wake velocity distribution of the wind turbine is determined by using the following formula according to the wake width distribution, the hub height of the wind turbine and the spanwise center position of the wind turbine:

[0020]

[0021] wherein, represents the wake velocity deficit distribution, C T represents the thrust coefficient, represents the wake width distribution, D represents the rotor diameter, x, y and z respectively represent the distance along the axial direction, the radial direction and the vertical direction of the wind turbine in a preset coordinate system, z h represents the hub height of the wind turbine, y h represents the spanwise center position of the wind turbine.

[0022] In a second aspect, a device for determining the wake velocity distribution of a wind turbine is provided, comprising: an information acquisition module for acquiring wind turbine information and wind condition information; a data calculation module for calculating the wake expansion rate and the wake maximum speed deficit distribution of the wind turbine according to the wind turbine information and the wind condition information; a wake width determination module for determining the wake width distribution of the wind turbine according to the wind turbine information, the wake expansion rate and the wake maximum speed deficit distribution; and a wake velocity determination module for determining the wake velocity distribution of the wind turbine by using the Gaussian wake model according to the wind turbine information and the wake width distribution.

[0023] In a third aspect, an intelligent device is provided, comprising at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores a computer program, and the computer program is executed by the at least one processor to implement the method in any one of the technical solutions of the method for determining the wake velocity distribution of a wind turbine.

[0024] In a fourth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored in the computer-readable storage medium, the program codes being adapted to be loaded and run by a processor to execute the method according to any one of the technical solutions of the method for determining a wake velocity distribution of a wind turbine.

[0025] The one or more technical solutions of the present application have at least one or more of the following beneficial effects:

[0026] In the implementation of the technical solutions of the present application, by obtaining wind turbine information and wind condition information, the wake expansion rate and the maximum wake speed loss distribution of the wind turbine are calculated according to the above information, and then the wake width distribution of the wind turbine is determined according to the obtained maximum wake speed loss distribution, and then the wake velocity distribution of the wind turbine is determined according to the wake width distribution using the Gaussian wake model. The purpose of more accurately calculating the wake velocity distribution in the wind farm is achieved, which helps to optimize the selection of the location of the wind farm, and thus improves the overall power generation efficiency of the wind farm. BRIEF DESCRIPTION OF DRAWINGS

[0027] The disclosure of the present application will become more apparent with reference to the drawings. It is easily understood by those skilled in the art that these drawings are only for illustrative purposes, and are not intended to limit the scope of protection of the present application. Among them:

[0028] Figure 1 is a main step flow diagram of a method for determining a wake velocity distribution of a wind turbine according to an embodiment of the present application;

[0029] Figure 2 is a schematic diagram of a wake speed loss distribution in the related art;

[0030] Figure 3 is a main step flow diagram of a wind turbine wake calculation method considering an ultra-long wake area according to an embodiment of the present application;

[0031] Figure 4 is a thrust coefficient curve diagram according to an embodiment of the present application;

[0032] Figure 5 is a wake velocity distribution diagram of a wind turbine according to an embodiment of the present application;

[0033] Figure 6 is a main structure block diagram of a device for determining a wake velocity distribution of a wind turbine according to an embodiment of the present application;

[0034] Figure 7 is a main structure diagram of an intelligent device according to an embodiment of the present application.

[0035] Reference signs:

[0036] 11: memory; 12: processor. DETAILED DESCRIPTION

[0037] Some embodiments of the present application will now be described with reference to the drawings. It will be understood by those skilled in the art that these embodiments are merely for the purpose of illustrating the technical principles of the present application, and are not intended to limit the scope of protection of the present application.

[0038] In the description of the present application, the terms "first", "second", and the like are used to distinguish similar objects, and are not necessarily used to describe a particular order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in other than the order illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a list of steps or units is not necessarily limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to such processes, methods, products or devices. The terms "mount", "connect", "connect" should be interpreted broadly, for example, can be fixed connection, can also be detachable connection, or integral connection; can be mechanical connection, can also be electrical connection; can be directly connected, can also be indirectly connected through intermediate medium, can also be the communication between two elements, can be wireless connection, can also be wired connection.

[0039] In addition, "module" and "processor" can include hardware, software or a combination of both. A module can include hardware circuit, various suitable sensors, communication port, memory, and can also include software part such as program code, and can be a combination of software and hardware. The processor can be a central processor, microprocessor, image processor, digital signal processor or any other suitable processor. The processor has data and / or signal processing function. The processor can be implemented in software, hardware or a combination of both. The computer readable storage medium includes any suitable medium that can store program code, such as magnetic disk, hard disk, optical disk, flash memory, read-only memory, random access memory and the like.

[0040] Furthermore, if the term "and / or" appears in this application, it includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or a solution that simultaneously satisfies A and B. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B," and can include only A, only B, or A and B. The singular forms of the terms "a" and "this" can also include plural forms.

[0041] The wake of a wind turbine refers to the region where airflow is affected after passing over a wind turbine. Specifically, when air flows past the turbine blades, the turbine absorbs some wind energy and converts it into electrical energy. During this process, the air velocity decreases and the turbulence intensity increases. The region containing this air is the wake region of the wind turbine. The wake effect refers to the wake region formed downstream of the wind turbine as it absorbs energy from the wind. The wake effect causes uneven wind speed distribution within the wind farm, affecting the operation of the wind turbines and further impacting the wind farm's operating conditions and output. Currently, in the quantitative assessment of wind turbine wakes, it is generally believed that the wake expands linearly with distance. This pattern exists within a distance of 4D to 10D behind the wind turbine, but in more distant regions, the wake width increase gradually decreases, exhibiting non-linear expansion characteristics. Traditional wake calculations do not consider this non-linear change, leading to errors in wake calculations within the wind farm and affecting the calculation and prediction of power generation.

[0042] To address the aforementioned problems, this application provides a method for determining the wake velocity distribution of a wind turbine. (See appendix.) Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a method for determining the wake velocity distribution of a wind turbine according to an embodiment of this application. Figure 1 As shown, the method mainly includes the following steps S2 to S8:

[0043] Step S2: Obtain wind turbine information and wind condition information.

[0044] In this embodiment, the wind turbine information refers to the wind turbine's own parameter information, such as the wind turbine's rotor diameter, hub height, thrust coefficient curve, etc.; the wind condition information refers to the environmental wind information, such as the inflow wind speed, (inflow) turbulence intensity, etc.

[0045] In one implementation, wind turbine information and wind condition information can be obtained through equipment data collection, experimental measurements, etc. For example, the inflow wind speed and inflow turbulence intensity can be directly obtained using a wind turbine laser wind radar, and the thrust coefficient and other information can be determined based on the inflow wind speed and the wind turbine thrust coefficient curve.

[0046] Step S4: Calculate the wake expansion rate and maximum wake velocity loss distribution of the wind turbine based on the wind turbine information and wind condition information.

[0047] In this embodiment, the wake expansion rate refers to the rate of volume change of the wake during its expansion process. The wake is the airflow region formed at the tail of the wind turbine during operation, and its expansion rate reflects the extent to which the wake volume increases with the axial distance of the wind turbine. The wake velocity deficit can be calculated based on the difference between the inflow wind speed and the wake velocity. Studies have shown that the velocity in the actual wake region of a wind turbine is not uniformly distributed; the velocity deficit in the far-field wake region exhibits self-similarity and approximately follows a Gaussian distribution. Figure 2 As shown in the figure. Point M represents the maximum velocity deficit in the wake, and the dashed line represents the axial direction of the wind turbine. In this embodiment, the distribution of the maximum velocity deficit in the wake is the variation law of the maximum velocity deficit in the wake along the axial direction of the wind turbine.

[0048] This embodiment calculates the wake expansion rate and maximum wake velocity deficit distribution of the wind turbine by using wind turbine information and wind condition information, and then calculates the wake width distribution of the wind turbine, ultimately achieving the purpose of determining the wake velocity distribution of the wind turbine.

[0049] Step S6: Determine the wake width distribution of the wind turbine based on the wind turbine information, wake expansion rate, and wake maximum velocity loss distribution.

[0050] In this embodiment, the wake width distribution of the wind turbine is derived from the wake maximum velocity loss distribution, wind turbine information, and wake expansion rate calculated in step S4.

[0051] Step S8: Determine the wake velocity distribution of the wind turbine using a Gaussian wake model based on the wind turbine information and wake width distribution.

[0052] In this embodiment, the Gaussian wake model is used to determine the wake velocity distribution of the wind turbine based on the wind turbine information and the wake width distribution derived in step S6. The Gaussian wake model can refer to the algorithm model in the prior art, which will not be described in detail here.

[0053] Based on the methods described in steps S2 to S8 above, wind turbine information and wind condition information are obtained. The wake expansion rate and maximum wake velocity deficit distribution of the wind turbine are calculated based on this information. Then, the wake width distribution of the wind turbine is determined based on the obtained maximum wake velocity deficit distribution. Subsequently, the wake velocity distribution of the wind turbine is determined using a Gaussian wake model based on this wake width distribution. This achieves the goal of more accurately calculating the wake velocity distribution in the wind farm, which helps to optimize the selection of wind farm locations and thus improve the overall power generation efficiency of the wind farm.

[0054] The following sections will further explain steps S4 to S8.

[0055] In one embodiment of this application, step S4 may further include steps S42 to S46:

[0056] Step S42: Calculate the wake expansion rate of the wind turbine based on the wind condition information.

[0057] In this embodiment, the wind condition information includes the inflow turbulence intensity, and the wake expansion rate can be calculated based on the inflow turbulence intensity using the following empirical formula:

[0058] k w =0.38I u +0.004

[0059] Where, k w I represents the wake expansion rate. u This indicates the turbulence intensity in the inflow direction.

[0060] Step S44: Determine the length of the near-wake zone based on the wind turbine information and wind condition information.

[0061] In this embodiment, the length of the near-wake region is determined based on the theory of turbulent shear layers. Specifically, the turbulent shear layer refers to the region in a fluid where turbulence occurs when the fluid encounters a boundary surface or when the fluid velocity changes drastically. In other words, after the incoming wind passes through the wind turbine, its energy and velocity decrease, while the intensity of the ambient turbulence increases. The wake region has a different wind speed than the outside wind, and there is an energy exchange between the two. A more pronounced layer exists where the energy exchange is weaker, while the layer is less pronounced where the energy exchange is stronger. The region with a more pronounced layer is the near-wake region.

[0062] In one implementation, the near-wake region length is calculated using the following formula:

[0063]

[0064] Where, x NW Indicates the length of the near-wake region, D represents the rotor diameter, and C represents the length of the near-wake region. TI represents the thrust coefficient. u Indicates the inflow turbulence intensity, x0 = 1D, Sc t =0.5, σ e =0.18, S′=0.043. It should be noted that the above parameters are the optimal values ​​determined based on a large number of simulation fittings.

[0065] In one implementation, the thrust coefficient can be determined based on the thrust coefficient curve and the inflow velocity.

[0066] Step S46: Determine the maximum velocity loss distribution of the wake based on the length of the near-wake region, wind turbine information, and wind condition information.

[0067] In this embodiment, the wind turbine information includes the thrust coefficient, and the wind condition information includes the inflow wind speed. The maximum velocity deficit distribution in the wake is determined using the following formula based on the near-wake region length, thrust coefficient, and inflow wind speed:

[0068]

[0069] in, Denotes the maximum velocity deficit distribution in the wake, ΔU max U represents the maximum velocity loss in the wake. ∞ Indicates the inflow velocity, x NW C represents the length of the near-wake region. T denoted by thrust coefficient, and x represents the distance along the axial direction of the wind turbine in the preset coordinate system.

[0070] In one embodiment of this application, step S6 may further include step S62:

[0071] Step S62: Determine the wake width distribution using the following formula based on the rotor diameter, thrust coefficient, wake expansion rate, and maximum wake velocity deficit distribution:

[0072]

[0073]

[0074]

[0075] in, The wake width distribution is represented by x, which represents the distance along the axial direction of the wind turbine in the preset coordinate system, and k. w Indicates the wake expansion rate, D represents the rotor diameter, and C represents the wind turbine diameter. T Indicates the thrust coefficient. This represents the maximum velocity deficit distribution in the wake.

[0076] In one embodiment of this application, step S8 may further include steps S82 and S84:

[0077] Step S82: Based on the wind turbine information and the wake width distribution, the Gaussian wake model is used to determine the wake velocity loss distribution of the wind turbine.

[0078] In this embodiment, the wind turbine information also includes the hub height and spanwise center position of the wind turbine. Based on the wind turbine information and the wake width distribution, a Gaussian wake model is used to determine the wake velocity deficit distribution of the wind turbine, including:

[0079] Based on the wake width distribution, the hub height of the wind turbine, and the spanwise center position of the wind turbine, the wake velocity deficit distribution of the wind turbine is determined using the following formula:

[0080]

[0081] in, C represents the wake velocity deficit distribution. T Indicates the thrust coefficient. This represents the wake width distribution, where D represents the rotor diameter, and x, y, and z represent the distances along the axial, radial, and vertical directions of the wind turbine in the preset coordinate system, respectively. h Indicates the hub height of the wind turbine, y h This indicates the wind turbine unit extending towards the center.

[0082] In one implementation, the preset coordinate system can be selected from positions such as the hub center of the wind turbine or the center point of the wind turbine on the ground, depending on specific needs.

[0083] Step S84: Determine the wake velocity distribution of the wind turbine based on the wake velocity loss distribution.

[0084] In this embodiment, after obtaining the wake velocity deficit distribution, the wake velocity distribution of the wind turbine can be determined based on the inflow wind speed.

[0085] In one application scenario according to an embodiment of this application, a method for calculating the wake of a wind turbine considering the ultra-long wake region is provided, as detailed in the appendix. Figure 3 The method mainly includes the following steps S31 to S33:

[0086] Step S31: Obtain the wind turbine diameter, thrust curve, inflow wind speed, and turbulence intensity.

[0087] In this embodiment, the diameter of the wind turbine is the diameter of the wind rotor. The inflow wind speed and turbulence intensity can be directly obtained using the wind turbine lidar. The thrust coefficient can be determined based on the inflow wind speed and the wind turbine thrust coefficient curve.

[0088] As an example, Table 1 is a table of wind turbine parameter information:

[0089] Table 1. Wind Turbine Parameter Information Table

[0090] Parameter Value (unit: meter) Height of fan hub 70 Diameter of fan wheel 80

[0091] As shown in Table 1, the unit model is Vestas-V80, and the wind turbine hub height Z... h The wind turbine has a rotor diameter D of 70 meters, an inflow velocity of 7 m / s, a turbulence intensity of 0.12, and a thrust coefficient curve as shown in the figure. Figure 4 As shown, Figure 4 The horizontal axis represents wind speed, and the vertical axis represents the thrust coefficient C. T .according to Figure 4 It can be determined that when the (inflow) wind speed is 7 m / s, the corresponding thrust coefficient C is... T =0.8.

[0092] Step S32: Determine the wake expansion rate and the length of the near-wake region of the wind turbine based on the wind turbine diameter, thrust curve, inflow wind speed, and turbulence intensity.

[0093] In this embodiment, the wake expansion rate within 8D is first calculated, and then k is calculated. w =0.0496. Then, the length of the near-wake region of the wind turbine is determined based on the inflow direction and turbulence intensity, that is, by substituting the above known information into the following formula:

[0094]

[0095] Where x0 = 1D, Sc t =0.5, σ e =0.18, S′=0.043, we can get x NW =214.9m, then the maximum velocity deficit distribution in the wake is calculated based on the length of the near-wake region:

[0096]

[0097] Next, based on the above maximum velocity deficit distribution in the wake, the wake width distribution of the entire wake region is calculated:

[0098]

[0099] in,

[0100]

[0101] Step S33: Use the Gaussian wake model to represent the wake evolution distribution of the wind turbine and calculate the wake velocity distribution of the wind turbine.

[0102] In this embodiment, based on the wake width distribution calculated in step S32, a Gaussian wake model is used to represent the wake evolution distribution of the wind turbine. The above-mentioned known information is substituted into the following formula for calculating the wake velocity deficit distribution of the wind turbine:

[0103]

[0104] By following the steps above, the wake velocity distribution diagram of the wind turbine can be obtained, as shown in the attached diagram. Figure 5 As shown in the diagram, x / d and y / d represent the axial and radial velocity distributions of the wind turbine, respectively. This velocity distribution diagram illustrates the wind speed reduction at different locations downstream of the wind turbine, playing a crucial role in the micro-site selection and layout optimization of wind farms. Specifically, by adjusting the position of the wind turbine, the impact of wake on downstream wind turbines can be reduced, thereby improving the overall power generation efficiency of the wind farm.

[0105] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent to the technical solutions described in this application and therefore will also fall within the protection scope of this application.

[0106] Another aspect of this application provides a device for determining the wake velocity distribution of a wind turbine generator, such as... Figure 6 As shown, the device includes: an information acquisition module 2, used to acquire wind turbine information and wind condition information; a data calculation module 4, used to calculate the wake expansion rate and maximum wake velocity deficit distribution of the wind turbine based on the wind turbine information and wind condition information; a wake width determination module 6, used to determine the wake width distribution of the wind turbine based on the wind turbine information, wake expansion rate, and maximum wake velocity deficit distribution; and a wake velocity determination module 8, used to determine the wake velocity distribution of the wind turbine using a Gaussian wake model based on the wind turbine information and wake width distribution.

[0107] It is understandable that the above-mentioned device is used to perform Figure 1 The method embodiments shown are similar in technical principle, the technical problems solved and the technical effects produced. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device and related descriptions can be referred to the content described in the method embodiments, and will not be repeated here.

[0108] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0109] Another aspect of this application provides a computer-readable storage medium.

[0110] In one embodiment of a computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program for performing the wake velocity distribution determination method of the wind turbine generator described in the above-described method embodiments. This program can be loaded and run by a processor to implement the wake velocity distribution determination method of the wind turbine generator described above. For ease of explanation, only the parts related to the embodiments of this application are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this application. The computer-readable storage medium can be a storage device comprising various electronic devices. Optionally, in the embodiments of this application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0111] Another aspect of this application provides a smart device.

[0112] In one embodiment of a smart device according to this application, the smart device may include at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program, which, when executed by the at least one processor, implements the methods described in any of the above embodiments. See Appendix Figure 7 , Figure 7 The image exemplarily illustrates a communication connection between memory 11 and processor 12 via a bus.

[0113] In some embodiments of this application, the smart device may further include at least one sensor for sensing information. The sensor is communicatively connected to any type of processor mentioned in this application. Optionally, the smart device described in this application may be, but is not limited to, a mobile phone, tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, in-vehicle device, ultra-mobile personal computer (UMPC), etc., and this application embodiment does not limit this.

[0114] The technical solution of this application has been described above with reference to one embodiment shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A method for determining the wake velocity distribution of a wind turbine generator, characterized in that, The method includes: Obtain information about wind turbines and wind conditions; Calculate the wake expansion rate and maximum wake velocity loss distribution of the wind turbine based on the wind turbine information and wind condition information. The wake width distribution of the wind turbine is determined based on the wind turbine information, the wake expansion rate, and the maximum wake velocity deficit distribution. The wake velocity distribution of the wind turbine is determined using a Gaussian wake model based on the wind turbine information and the wake width distribution. The step of determining the wake velocity distribution of the wind turbine using a Gaussian wake model based on the wind turbine information and the wake width distribution includes: Based on the wind turbine information and the wake width distribution, the wake velocity loss distribution of the wind turbine is determined using a Gaussian wake model. The wake velocity distribution of the wind turbine is determined based on the wake velocity deficit distribution. The wind turbine information also includes the rotor diameter and thrust coefficient. Determining the wake width distribution of the wind turbine based on the wind turbine information, the wake expansion rate, and the maximum wake velocity deficit distribution includes: The wake width distribution is determined using the following formula based on the rotor diameter, the thrust coefficient, the wake expansion rate, and the maximum wake velocity deficit distribution: in, The wake width distribution is represented by x, which represents the distance along the axial direction of the wind turbine in the preset coordinate system. The term "wake expansion rate" is represented by D, and "wind turbine diameter" is represented by D. This represents the thrust coefficient. This represents the maximum velocity deficit distribution in the wake; The wind turbine information also includes the hub height and spanwise center position of the wind turbine. The step of determining the wake velocity deficit distribution of the wind turbine using a Gaussian wake model based on the wind turbine information and the wake width distribution includes: Based on the wake width distribution, the hub height of the wind turbine, and the spanwise center position of the wind turbine, the wake velocity deficit distribution of the wind turbine is determined using the following formula: in, The vector represents the wake velocity deficit distribution, where x, y, and z represent the distances along the axial, radial, and vertical directions of the wind turbine in a preset coordinate system, respectively. This indicates the hub height of the wind turbine. This indicates the position of the wind turbine unit extending towards the center.

2. The method according to claim 1, characterized in that, The calculation of the wake expansion rate and maximum wake velocity deficit distribution of the wind turbine based on the wind turbine information and wind condition information includes: Calculate the wake expansion rate of the wind turbine based on the wind condition information; The length of the near-wake region is determined based on the wind turbine information and wind condition information. The maximum velocity loss distribution of the wake is determined based on the near-wake region length, the wind turbine information, and the wind condition information.

3. The method according to claim 2, characterized in that, The wind condition information includes the inflow direction and turbulence intensity. The calculation of the wake expansion rate of the wind turbine based on the wind condition information includes: The wake expansion rate of the wind turbine is calculated using the following formula based on the inflow turbulence intensity: in, This represents the wake expansion rate. This indicates the turbulence intensity in the inflow direction.

4. The method according to claim 2, characterized in that, The wind condition information includes the inflow wind speed. Determining the maximum velocity deficit distribution in the wake based on the near-wake region length, the wind turbine information, and the wind condition information includes: The maximum velocity deficit distribution in the wake is determined using the following formula based on the near-wake region length, the thrust coefficient, and the inflow wind speed: in, This represents the maximum velocity deficit distribution in the wake. This indicates the maximum velocity loss in the wake. This indicates the inflow velocity. This indicates the length of the near-wake region. The thrust coefficient is represented by x, which represents the distance along the axial direction of the wind turbine in the preset coordinate system.

5. A device for determining the wake velocity distribution of a wind turbine generator according to any one of claims 1 to 4, characterized in that, The device includes: The information acquisition module is used to acquire information about wind turbine units and wind conditions. The data calculation module is used to calculate the wake expansion rate and the maximum wake velocity loss distribution of the wind turbine based on the wind turbine information and wind condition information. The wake width determination module is used to determine the wake width distribution of the wind turbine based on the wind turbine information, the wake expansion rate, and the maximum wake velocity loss distribution. The wake velocity determination module is used to determine the wake velocity distribution of the wind turbine based on the wind turbine information and the wake width distribution using a Gaussian wake model.

6. A smart device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores a computer program that, when executed by the at least one processor, implements the method of any one of claims 1 to 4.

7. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the method of any one of claims 1 to 4.

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