Method, device and equipment for determining wake flow velocity distribution of wind turbine generator and medium

Abstract

The invention relates to the technical field of wind power plant micro-siting, particularly provides a wake flow velocity distribution determination method and device of a wind turbine generator, equipment and a medium, and aims at solving the problem that errors exist in wake flow calculation based on the fact that wake flow of the wind turbine generator is in linear expansion distribution along with the flow direction distance. In order to achieve the purpose, the method comprises the steps that wind turbine generator information and wind condition information are obtained, and the wake flow expansion rate and wake flow maximum speed loss distribution of a wind turbine generator are calculated according to the wind turbine generator information and the wind condition information, and according to the wind turbine generator information, the wake flow expansion rate and the wake flow maximum speed loss distribution, wake flow width distribution of the wind turbine generator is determined, and according to the wind turbine generator information and the wake flow width distribution, a Gaussian wake flow model is adopted to determine wake flow speed distribution of the wind turbine generator. The method achieves the purpose of more accurately calculating the wake flow velocity distribution in the wind power plant, facilitates the optimization of the position selection of the wind power plant, and improves the overall power generation efficiency of the wind power plant.

Description

Technical Field

[0001] The present application relates to the technical field of micro-site selection for wind farms, and in particular to a method, device, equipment and medium for determining the wake velocity distribution of a wind turbine. Background Art

[0002] At present, in the quantitative evaluation of wind turbine wake, it is generally believed that the wake of a wind turbine expands linearly with the flow distance. However, in the farther area, the growth trend of the wake width will gradually decay, showing nonlinear expansion characteristics. Traditional wake calculations do not take this nonlinear change into account, resulting in errors in wake calculations, which in turn affects the calculation and prediction of power generation.

[0003] Accordingly, the art needs a new solution for determining the wake velocity distribution of a wind turbine to solve the above problems. Summary of the invention

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

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

[0006] In a technical solution of the method for determining the wake velocity distribution of the above-mentioned wind turbine, the calculation of the wake expansion rate and the wake maximum velocity loss distribution of the wind turbine according to the wind turbine information and the wind condition information includes: calculating the wake expansion rate of the wind turbine according to the wind condition information; determining the length of the near-wake area according to the wind turbine information and the wind condition information; and determining the wake maximum velocity loss distribution according to the near-wake area length, the wind turbine information and the wind condition information.

[0007] In a technical solution of the method for determining the wake velocity distribution of the above-mentioned wind turbine, the wake velocity distribution of the wind turbine is determined by using a Gaussian wake model based on the wind turbine information and the wake width distribution, including: determining the wake velocity loss distribution of the wind turbine by using a Gaussian wake model based on the wind turbine information and the wake width distribution; and determining the wake velocity distribution of the wind turbine based on the wake velocity loss distribution.

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

[0009] k w =0.38I u +0.004

[0010] Among them, k w represents the wake expansion rate, I u represents the inflow turbulence intensity.

[0011] In a technical solution of the method for determining the wake velocity distribution of the above-mentioned wind turbine generator set, the wind turbine generator set information includes a thrust coefficient, the wind condition information includes an inflow wind speed, and the method for determining the wake maximum velocity loss distribution according to the near-wake area length, the wind turbine generator set information, and the wind condition information includes: using the following formula to determine the wake maximum velocity loss distribution according to the near-wake area length, the thrust coefficient, and the inflow wind speed:

[0012]

[0013] in, represents the maximum velocity loss distribution of the wake, ΔU max represents the maximum wake velocity loss, U ∞ represents the inflow wind speed, x NW represents the length of the near wake region, C T represents the thrust coefficient, and x represents the distance along the axial direction of the wind turbine in a preset coordinate system.

[0014] In a technical solution of the method for determining the wake velocity distribution of the above-mentioned wind turbine, the wind turbine information also includes a rotor diameter, and determining the wake width distribution of the wind turbine according to the wind turbine information, the wake expansion rate, and the wake maximum velocity loss distribution includes: using the following formula to determine the wake width distribution according to the rotor diameter, the thrust coefficient, the wake expansion rate, and the wake maximum velocity loss distribution:

[0015]

[0016]

[0017]

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

[0019] In a technical solution of the method for determining the wake velocity distribution of the above-mentioned wind turbine, the wind turbine information also includes the hub height of the wind turbine and the span-wise center position of the wind turbine. The method of determining the wake velocity loss distribution of the wind turbine using a Gaussian wake model according to the wind turbine information and the wake width distribution includes: determining the wake velocity loss distribution of the wind turbine using the following formula according to the wake width distribution, the hub height of the wind turbine and the span-wise center position of the wind turbine:

[0020]

[0021] in, represents the wake velocity loss distribution, C T represents the thrust coefficient, represents the wake width distribution, D represents the wind rotor diameter, x, y, z represent the distances in the axial, radial and vertical directions of the wind turbine in the preset coordinate system, respectively, h represents the hub height of the wind turbine, y h Indicates the span-wise 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, the device comprising: an information acquisition module, used to acquire wind turbine information and wind condition information; a data calculation module, used to calculate the wake expansion rate and the maximum wake velocity loss distribution of the wind turbine according to the wind turbine information and wind condition information; a wake width determination module, used to determine the wake width distribution of the wind turbine according to the wind turbine information, the wake expansion rate and the maximum wake velocity loss distribution; a wake velocity determination module, used to determine the wake velocity distribution of the wind turbine using a 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 communicatively connected to the at least one processor; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any one of the technical solutions of the method for determining the wake velocity distribution of the above-mentioned wind turbine is implemented.

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

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

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

[0027] The disclosure of the present application will become easier to understand with reference to the accompanying drawings. It is easy for those skilled in the art to understand 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 schematic flow chart of main steps of a method for determining the wake velocity distribution of a wind turbine according to an embodiment of the present application;

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

[0030] Figure 3 This is a schematic flow chart of the main steps of a method for calculating the wake of a wind turbine generator set considering an ultra-long wake area according to an embodiment of the present application;

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

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

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

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

[0035] Reference numerals:

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

[0037] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0038] In the description of the present application, the terms "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, it can be indirectly connected through an intermediate medium, it can also be the internal communication of two elements, it can be a wireless connection, or it can be a wired connection.

[0039] In addition, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, and memories, and may also include software parts, such as program codes, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware, or a combination of the two. Computer-readable storage media include any suitable media that can store program codes, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc.

[0040] In addition, if the meaning of "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 satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "one" and "this" can also include plural forms.

[0041] The wake of a wind turbine refers to the area where the air flow is affected after passing through the wind turbine. Specifically, when air flows through the blades of a wind turbine, the wind turbine absorbs part of the wind energy and converts it into electrical energy. During this process, the air speed will decrease and the turbulence intensity will increase. The area where this part of the gas is located is the wake area of ​​the wind turbine. The wake effect of a wind turbine refers to the formation of a wake area with a decreasing wind speed downstream of the wind turbine while the wind turbine obtains energy from the wind. The wake effect will cause uneven wind speed distribution in the wind farm, affect the operating conditions of the wind turbines in the wind farm, and further affect the operating conditions and output of the wind farm. At present, in the quantitative evaluation of the wake of wind turbines, it is generally believed that the wake of wind turbines expands linearly with the flow distance. This law exists within a distance of 4D to 10D behind the wind turbine, while in the farther area, the growth trend of the wake width will gradually decay, showing a nonlinear expansion characteristic. Traditional wake calculations do not take this nonlinear change into account, resulting in errors in the calculation of wakes in wind farms, affecting the calculation and prediction of power generation.

[0042] In order to solve the above problems, the present application provides a method for determining the wake velocity distribution of a wind turbine. Figure 1 , Figure 1 FIG. 1 is a flow chart showing the main steps of a method for determining the wake velocity distribution of a wind turbine according to an embodiment of the present application. Figure 1 As shown, the method mainly includes the following steps S2 to S8:

[0043] Step S2, obtaining wind turbine information and wind condition information.

[0044] In this embodiment, the wind turbine information represents the parameter information of the wind turbine itself, such as: the rotor diameter, hub height, thrust coefficient curve and other information of the wind turbine; the wind condition information represents the ambient wind information, such as: inflow wind speed, (inflow) turbulence intensity and other information.

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

[0046] Step S4, calculating the wake expansion rate and the wake maximum velocity loss distribution of the wind turbine according to the wind turbine information and the wind condition information.

[0047] In this embodiment, the wake expansion rate refers to the volume change rate of the wake during the expansion process, wherein the wake is the airflow area formed at the tail of the wind turbine when it is running, and its expansion rate reflects the extent to which the wake volume expands with the increase of the axial distance of the wind turbine. The wake velocity loss can be calculated based on the difference between the inflow wind speed and the wake velocity. Studies have shown that the velocity in the real wake area of ​​the wind turbine is not uniformly distributed, and the velocity loss in the far-field wake area is self-similar and approximately Gaussian distributed, such as Figure 2 Wherein, point M represents the maximum velocity loss of the wake, and the dotted line represents the axial direction of the wind turbine. In this embodiment, the distribution of the maximum velocity loss of the wake is the variation rule of the maximum velocity loss of the wake along the axial distance of the wind turbine.

[0048] This embodiment calculates the wake expansion rate and the wake maximum velocity loss distribution of the wind turbine set through wind turbine set information and wind condition information, and then calculates the wake width distribution of the wind turbine set, thereby finally achieving the purpose of determining the wake velocity distribution of the wind turbine set.

[0049] Step S6, determining the wake width distribution of the wind turbine according to the wind turbine information, the wake expansion rate and the wake maximum velocity loss distribution.

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

[0051] Step S8, determining the wake velocity distribution of the wind turbine using a Gaussian wake model according to the wind turbine information and the wake width distribution.

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

[0053] Based on the method described in steps S2 to S8 above, wind turbine information and wind condition information are obtained, and the wake expansion rate and the maximum wake velocity loss distribution of the wind turbine are calculated based on the above information, and then the wake width distribution of the wind turbine is determined based on the obtained wake maximum velocity loss distribution, and then the wake velocity distribution of the wind turbine is determined using the Gaussian wake model based on the wake width distribution. The purpose of more accurately calculating the wake velocity distribution in the wind farm is achieved, which helps to optimize the location selection of the wind farm, thereby improving the overall power generation efficiency of the wind farm.

[0054] The above steps S4 to S8 are further explained below.

[0055] In one implementation of the embodiment of the present application, the above step S4 may further include the following steps S42 to S46:

[0056] Step S42, calculating the wake expansion rate of the wind turbine according to the wind condition information.

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

[0058] k w =0.38I u +0.004

[0059] Among them, k w represents the wake expansion rate, I u It represents the inflow turbulence intensity.

[0060] Step S44, determining the length of the near wake area according to the wind turbine information and wind condition information.

[0061] In this embodiment, the length of the near-wake zone is determined based on the turbulent shear layer theory. Specifically, the turbulent shear layer refers to an area in the fluid where turbulence occurs when the fluid encounters a boundary surface or the fluid velocity changes dramatically. Specifically, after the incoming wind passes through the wind turbine, the energy and speed will decrease, the turbulence intensity of the ambient flow will increase, the wind speed in the wake area is different from that in the outside world, and there is a mutual exchange of energy between the two. There will be a more obvious layer in places where the energy exchange is relatively weak, and the layer is not obvious in places where the energy exchange is relatively strong. The area with a more obvious layer is the near-wake zone.

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

[0063]

[0064] Among them, x NW represents the length of the near wake area, D represents the diameter of the wind wheel, C Trepresents the thrust coefficient, I u represents the inflow turbulence intensity, x 0 =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 fits.

[0065] In one embodiment, the thrust coefficient may be determined based on a thrust coefficient curve and an inflow wind speed.

[0066] Step S46, determining the wake maximum velocity loss distribution according to the near wake area length, 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 following formula is used to determine the wake maximum velocity loss distribution according to the length of the near wake area, the thrust coefficient, and the inflow wind speed:

[0068]

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

[0070] In one implementation of the embodiment of the present application, the above step S6 may further include the following step S62:

[0071] Step S62, using the following formula to determine the wake width distribution according to the wind rotor diameter, thrust coefficient, wake expansion rate and wake maximum velocity loss distribution:

[0072]

[0073]

[0074]

[0075] in, represents the wake width distribution, x represents the distance along the axis of the wind turbine in the 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 velocity loss distribution.

[0076] In one implementation of the embodiment of the present application, the above step S8 may further include the following steps S82 and S84:

[0077] Step S82, determining the wake velocity loss distribution of the wind turbine using a Gaussian wake model according to the wind turbine information and the wake width distribution.

[0078] In this embodiment, the wind turbine information also includes the hub height of the wind turbine and the span-wise center position of the wind turbine. The wake velocity loss distribution of the wind turbine is determined using a Gaussian wake model according to the wind turbine information and the wake width distribution, including:

[0079] The following formula is used to determine the wind turbine wake velocity loss distribution based on the wake width distribution, the wind turbine hub height, and the wind turbine span center position:

[0080]

[0081] in, represents the wake velocity loss distribution, C T represents the thrust coefficient, represents the wake width distribution, D represents the rotor diameter, 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 Indicates the span center position of the wind turbine.

[0082] In one implementation, the preset coordinate system may be selected according to specific requirements, such as the hub center of the wind turbine set or the center point of the wind turbine set on the ground.

[0083] Step S84, determining the wake velocity distribution of the wind turbine according to the wake velocity deficit distribution.

[0084] In this embodiment, after the wake velocity loss distribution is obtained, the wake velocity distribution of the wind turbine generator set can be determined according to the inflow wind speed.

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

[0086] Step S31, obtaining the diameter of the wind turbine, 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 by using the wind turbine laser radar. The thrust coefficient can be determined according to the inflow wind speed and the thrust coefficient curve of the wind turbine.

[0088] As an example, Table 1 is a wind turbine parameter information table: Table 1. Wind turbine parameter information table parameter Value (unit: meter) Fan hub height 70 Fan rotor diameter 80

[0089] As shown in Table 1, the unit model is Vestas-V80 unit, and the fan hub height Z h The wind turbine rotor diameter D is 70 meters, the inflow wind speed is 7m / s, the turbulence intensity is 0.12, and the thrust coefficient curve is as follows: Figure 4 As shown, Figure 4 The horizontal axis represents wind speed, and the vertical axis represents thrust coefficient C T .according to Figure 4 It can be determined that when the (inflow) wind speed is 7m / s, the corresponding thrust coefficient C T =0.8.

[0090] Step S32, determining the wake expansion rate and the length of the near-wake region of the wind turbine according to the diameter of the wind turbine, the thrust curve, the inflow wind speed and the turbulence intensity.

[0091] 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 area of ​​the wind turbine is determined according to the turbulence intensity of the inflow direction, that is, the above known information is substituted into the following formula:

[0092]

[0093] Among them, x 0 =1D,Sc t =0.5,σ e = 0.18, S′ = 0.043, we can get x NW =214.9m, and then calculate the maximum velocity loss distribution of the wake according to the length of the near wake area:

[0094]

[0095] Then, the wake width distribution of the entire wake area is obtained based on the above wake maximum velocity loss distribution:

[0096]

[0097] in,

[0098]

[0099] Step S33: using a Gaussian wake model to represent the evolution distribution of the wind turbine wake, and calculating the wind turbine wake velocity distribution.

[0100] In this embodiment, according to the wake width distribution calculated in step S32, a Gaussian wake model is used to represent the evolution distribution of the wind turbine wake, and the above known information is substituted into the following wind turbine wake velocity loss distribution calculation formula:

[0101]

[0102] Through the above steps, the wake velocity distribution diagram of the wind turbine can be obtained, as shown in the attached figure. Figure 5 As shown in the figure. x / d and y / d represent the velocity distribution of the wind turbine in the axial and radial directions respectively. The velocity distribution diagram shows the wind speed reduction at different positions downstream of the wind turbine, which plays a very important role in the micro-site selection of wind farms and the optimization of wind turbine layout. Specifically, by adjusting the position of the wind turbine, the influence of the wake on the downstream wind turbine can be reduced, thereby improving the power generation efficiency of the entire wind farm.

[0103] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effect of the present application, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted schemes are equivalent to the technical schemes described in this application, and therefore will also fall within the scope of protection of this application.

[0104] Another aspect of the present application also provides a device for determining the wake velocity distribution of a wind turbine generator set, such as Figure 6 As shown, the device includes: an information acquisition module 2, which is used to acquire wind turbine information and wind condition information; a data calculation module 4, which is used to calculate the wake expansion rate and the maximum wake speed loss distribution of the wind turbine according to the wind turbine information and the wind condition information; a wake width determination module 6, which is used to determine the wake width distribution of the wind turbine according to the wind turbine information, the wake expansion rate and the maximum wake speed loss distribution; a wake speed determination module 8, which is used to determine the wake speed distribution of the wind turbine using a Gaussian wake model according to the wind turbine information and the wake width distribution.

[0105] It is understandable that the above device is used to perform Figure 1 The method embodiments shown in the drawings have similar technical principles, technical problems solved and technical effects produced. Technical personnel in this technical field can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the device can refer to the contents described in the method embodiments, which will not be repeated here.

[0106] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0107] Another aspect of the present application also provides a computer-readable storage medium.

[0108] In an embodiment of a computer-readable storage medium according to the present application, the computer-readable storage medium may be configured to store a program for executing the method for determining the wake velocity distribution of a wind turbine set in the above-mentioned method embodiment, and the program may be loaded and run by a processor to implement the method for determining the wake velocity distribution of the above-mentioned wind turbine set. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium may be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.

[0109] Another aspect of the present application also provides a smart device.

[0110] In an embodiment of an intelligent device according to the present application, the intelligent device may include at least one processor; and a memory connected to the at least one processor in communication; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any of the above embodiments is implemented. Figure 7 , Figure 7 FIG. 4 exemplarily shows that the memory 11 and the processor 12 are communicatively connected via a bus.

[0111] In some embodiments of the present application, the smart device may further include at least one sensor, and the sensor is used to sense information. The sensor is communicatively connected to any type of processor mentioned in the present application. Optionally, the smart device described in the present application may be, but is not limited to, a mobile phone, a tablet computer, a desktop, a laptop, a handheld computer, a notebook computer, a vehicle-mounted device, an ultra-mobile personal computer (UMPC), etc., and the embodiments of the present application are not limited to this.

[0112] So far, the technical solution of the present application has been described in conjunction with an embodiment shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present 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 fall within the protection scope of the present application.

Claims

1. A method for determining the wake velocity distribution of a wind turbine, characterized in that: The method comprises: Obtain wind turbine information and wind condition information; Calculate the wake expansion rate and the wake maximum speed loss distribution of the wind turbine according to the wind turbine information and wind condition information; Determine the wake width distribution of the wind turbine according to the wind turbine information, the wake expansion rate and the wake maximum velocity loss distribution; The wake velocity distribution of the wind turbine is determined by adopting a Gaussian wake model according to the wind turbine information and the wake width distribution.

2. The method according to claim 1, characterized in that The step of calculating the wake expansion rate and the wake maximum velocity loss distribution of the wind turbine according to the wind turbine information and the wind condition information includes: Calculating the wake expansion rate of the wind turbine generator set according to the wind condition information; Determine the length of the near wake area according to the wind turbine information and wind condition information; The wake maximum velocity loss distribution is determined according to the near wake area length, the wind turbine group information and the wind condition information.

3. The method according to claim 2, characterized in that Determining the wake velocity distribution of the wind turbine using a Gaussian wake model according to the wind turbine information and the wake width distribution includes: Determine the wake velocity loss distribution of the wind turbine using a Gaussian wake model according to the wind turbine information and the wake width distribution; The wake velocity distribution of the wind turbine generator set is determined according to the wake velocity deficit distribution.

4. The method according to claim 2, characterized in that: The wind condition information includes the inflow direction turbulence intensity, and the step of calculating the wake expansion rate of the wind turbine generator set according to the wind condition information includes: The wake expansion rate of the wind turbine generator set is calculated according to the inflow turbulence intensity using the following formula: k w =0.38I u +0.004 Among them, k w represents the wake expansion rate, I u represents the inflow turbulence intensity.

5. The method according to claim 3, characterized in that: The wind turbine generator set information includes a thrust coefficient, the wind condition information includes an inflow wind speed, and determining the wake maximum velocity loss distribution according to the near wake area length, the wind turbine generator set information, and the wind condition information includes: The following formula is used to determine the wake maximum velocity loss distribution according to the near wake area length, the thrust coefficient and the inflow wind speed: in, represents the maximum velocity loss distribution of the wake, ΔU max represents the maximum wake velocity loss, U ∞ represents the inflow wind speed, x NW represents the length of the near wake region, C T represents the thrust coefficient, and x represents the distance along the axial direction of the wind turbine in a preset coordinate system.

6. The method according to claim 5, characterized in that The wind turbine information also includes a rotor diameter. The step of determining the wake width distribution of the wind turbine according to the wind turbine information, the wake expansion rate, and the wake maximum velocity loss distribution includes: The wake width distribution is determined according to the wind rotor diameter, the thrust coefficient, the wake expansion rate, and the wake maximum velocity loss distribution using the following formula: in, represents the wake width distribution, x represents the distance along the axial direction of the wind turbine in the preset coordinate system, k w represents the wake expansion rate, D represents the wind wheel diameter, C T represents the thrust coefficient, represents the wake maximum velocity loss distribution.

7. The method according to claim 6, characterized in that The wind turbine information also includes the hub height of the wind turbine and the span-wise center position of the wind turbine. The determining the wake velocity loss distribution of the wind turbine using a Gaussian wake model according to the wind turbine information and the wake width distribution includes: The wake velocity loss distribution of the wind turbine is determined by 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: in, represents the wake velocity loss distribution, C T represents the thrust coefficient, represents the wake width distribution, D represents the wind rotor diameter, x, y, z represent the distances in the axial, radial and vertical directions of the wind turbine in the preset coordinate system, respectively, h represents the hub height of the wind turbine, y h Indicates the span-wise center position of the wind turbine.

8. A device for determining the wake velocity distribution of a wind turbine, characterized in that: The device comprises: An information acquisition module is used to obtain wind turbine information and wind condition information; A data calculation module, used for calculating the wake expansion rate and the wake maximum speed loss distribution of the wind turbine according to the wind turbine information and wind condition information; A wake width determination module, used to determine the wake width distribution of the wind turbine according to the wind turbine information, the wake expansion rate and the wake maximum velocity loss distribution; The wake velocity determination module is used to determine the wake velocity distribution of the wind turbine using a Gaussian wake model according to the wind turbine information and the wake width distribution.

9. A smart device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores a computer program, and when the computer program is executed by the at least one processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and executed by a processor to execute the method according to any one of claims 1 to 7.

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