Methods, equipment, and media for optimizing wind farm layout considering the influence of surrounding wind farms

Abstract

This invention relates to the field of wind power generation technology, specifically providing a method, equipment, and medium for optimizing wind farm layout considering the influence of surrounding wind farms, aiming to solve the problem of micro-site selection for wind farms. The method includes: establishing an initial population for a genetic algorithm based on the regional range and wind turbine parameter information of the target wind farm; determining the wake velocity deficit generated by the surrounding wind farms based on the wind turbine parameter information, wind resource distribution information, and wind turbine wake model of the surrounding wind farms; determining the wind speed in front of the hub of the wind turbines within the target wind farm based on the wake velocity deficit; and then determining the total power generation of the wind farm corresponding to each individual in the initial population. The scheme provided by this invention considers the inter-field wake influence range brought by surrounding wind farms under different inflow wind speeds and wind directions, taking the maximization of the total power generation of the target wind farm as the optimization objective, and using a genetic algorithm to obtain a reasonably arranged wind turbine layout scheme, thereby increasing the total power generation of the wind farm.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, specifically providing a method, equipment, and medium for optimizing wind farm layout considering the influence of surrounding wind farms. Background Technology

[0002] With the continuous development of wind power technology, wind turbines are becoming larger and more large-scale, leading to increasingly clustered wind farms. This may result in more severe mutual influences between wind farms. The wake of a wind farm can significantly affect the surrounding flow field distribution, thereby impacting the power generation efficiency of other wind farms. Therefore, during the planning and design phase of wind farm micro-site selection, the influence of surrounding wind farms must be considered to ensure a reasonable layout of wind turbines and improve overall power generation.

[0003] Accordingly, there is a need in this field for a new wind farm layout optimization scheme that takes into account the influence of surrounding wind farms to solve the above problems. Summary of the Invention

[0004] In order to overcome the above-mentioned defects, the present invention is proposed to provide a solution or at least a partial solution to the technical problem that the existing wind farm layout schemes fail to take into account the influence of surrounding wind farms, resulting in low power generation efficiency of the constructed wind farms.

[0005] In a first aspect, the present invention provides a method for optimizing wind farm layout considering the influence of surrounding wind farms, the method comprising:

[0006] Obtain the regional scope, wind turbine parameter information, and wind resource distribution information of the target wind farm and its surrounding wind farms;

[0007] An initial population for a genetic algorithm is established based on the regional range and wind turbine parameter information of the target wind farm. The initial population contains multiple individuals, each of which represents a wind farm turbine layout scheme.

[0008] Based on the wind turbine parameter information, wind resource distribution information and preset wind turbine wake model of the surrounding wind farm, the wake velocity loss generated by the surrounding wind farm is determined, and the wind speed in front of the hub of the wind turbine in the target wind farm is determined based on the wake velocity loss.

[0009] Based on the wind speed in front of the hub of the wind turbine, the total power generation of the wind farm corresponding to the individuals in the initial population is determined. With the goal of maximizing the total power generation of the wind farm, the wind farm turbine layout scheme is optimized using a genetic algorithm to obtain the wind turbine layout scheme of the target wind farm.

[0010] In some implementations, the wind turbine parameter information includes the number of wind turbines, and the initial population for establishing the genetic algorithm based on the regional range of the target wind farm and the wind turbine parameter information includes:

[0011] The coordinate constraint condition for wind turbine units is set as follows: the distance between any two wind turbine units in the wind farm is greater than n times the diameter of the wind turbine, where n is a positive integer greater than 1.

[0012] Based on the number of wind turbine units and the coordinate constraints of the wind turbine units, the coordinates of each wind turbine unit are randomly generated within the area of ​​the wind farm, and an initial population of the genetic algorithm is established based on the coordinates.

[0013] In some implementations, determining the wake velocity deficit generated by the surrounding wind farm based on wind turbine parameter information, wind resource distribution information, and a preset wind turbine wake model includes:

[0014] The inflow turbulence intensity of the wind turbine is obtained, and the wake expansion rate and near-wake region length of the wind turbine are determined based on the inflow turbulence intensity and the wind turbine parameter information. The maximum loss distribution of the wake is calculated based on the near-wake region length.

[0015] The wake region width is determined based on the wake expansion rate of the wind turbine and the maximum wake loss distribution.

[0016] Based on the wake width, the wind turbine parameter information, and the wind resource distribution information, the wake velocity deficit of the wind turbine is solved using a preset wind turbine wake model.

[0017] Furthermore, the preset wind turbine wake model adopts a Gaussian wake model, which is determined by the following formula:

[0018]

[0019] Where ΔU represents the wake velocity deficit of the wind turbine, U ∞ For the ambient airflow velocity. y is the width of the wake region, z is the radial coordinate of the wind turbine, and z is the vertical coordinate. h The hub height of the wind turbine, y h For the wind turbine unit extending towards the center, C T denoted as thrust coefficient, and D is the rotor diameter of the wind turbine.

[0020] Furthermore, obtaining the inflow turbulence intensity of the wind turbine generator includes:

[0021] For each wind turbine in the target wind farm, based on the additional directional turbulence intensity at multiple points on the rotor of the current wind turbine from the upstream wind turbine, the additional directional turbulence intensity at multiple points on the rotor of the current wind turbine is obtained by superposition according to the following formula:

[0022]

[0023] Wherein, ΔIu i (x,y,z) represents the additional turbulence intensity in the flow direction at point (x,y,z) on the rotor of the current i-th unit; ΔIu ji q represents the additional directional turbulence intensity of the j-th unit at point (x,y,z) on the rotor of the i-th unit; ij Let q be a binary variable, and let q be a variable that is downstream of the i-th unit if and only if the i-th unit is downstream of the j-th unit. ij =1, otherwise q ij =0; N is the number of wind turbines in the wind farm.

[0024] Based on the ambient incoming turbulence intensity and the additional flow-direction turbulence intensity, the inflow turbulence intensity of the current wind turbine is obtained according to the following formula:

[0025]

[0026] Where Iu represents the inflow turbulence intensity of the wind turbine, ΔIu represents the additional flow-direction turbulence intensity, and I b0 This indicates the intensity of turbulence from the surrounding environment.

[0027] In some implementations, determining the hub-front wind speed of the wind turbines within the target wind farm based on the wake velocity deficit includes:

[0028] The inflow velocity loss at multiple points on the rotor of the current wind turbine is calculated based on the superposition of the wake velocity loss of the upstream wind turbine on the current wind turbine.

[0029] Based on the inflow velocity deficit at multiple points on the rotor of the current wind turbine, the wind speed in front of the turbine hub is determined by averaging the values ​​and using the following formula:

[0030]

[0031] Among them, U i U represents the wind speed in front of the hub of the i-th wind turbine under preset wind conditions; ∞ For the ambient airflow velocity; ΔU i (x,y,z) represents the inflow velocity deficit at multiple points on the rotor of the current i-th unit.

[0032] Furthermore, the step of calculating the inflow velocity loss at multiple points on the rotor of the current wind turbine based on the superposition of the wake velocity loss of the upstream wind turbine on the current wind turbine includes:

[0033] For each wind turbine in the target wind farm, based on the wake velocity deficit of the upstream wind turbine at the current wind turbine location, the inflow velocity deficit at multiple points on the rotor of the current wind turbine is determined by superposition according to the following formula.

[0034]

[0035] Wherein, ΔU i (x,y,z) represents the inflow velocity deficit at point (x,y,z) on the rotor of the i-th unit; ΔU ij The wake velocity deficit of the j-th unit at the point (x,y,z) on the rotor of the i-th unit; q ij Let q be a binary variable, and let q be a variable that is downstream of the i-th unit if and only if the i-th unit is downstream of the j-th unit. ij =1, otherwise q ij =0; N is the number of wind turbines in the wind farm.

[0036] In some implementations, determining the total power generation of the wind farm corresponding to individuals in the initial population based on the wind speed in front of the wind turbine hub and the wind energy efficiency curve of the wind turbines in the target wind farm includes:

[0037] The output power of the wind turbine under preset wind conditions is determined based on the wind speed and wind energy efficiency curve in front of the wind turbine hub. The wind energy efficiency curve is the correspondence between wind speed and wind turbine power.

[0038] Based on the current output power of the wind turbines, the total power generation of the wind farm is determined according to the following formula:

[0039] Among them, P total W represents the total power generation of the wind farm. jk Indicates wind speed as u j Wind direction is θ k Wind conditions; P i For unit i in wind condition W jk Output power at f(W) jk (W) is the wind condition. jk Frequency of occurrence; N θ For wind direction quantity; N u N represents the number of wind speed ranges taken under a single wind direction; N represents the number of wind turbine units in the wind farm.

[0040] In a second aspect, the present invention provides an electronic device comprising at least one processor and at least one memory, the memory being adapted to store a plurality of program codes, the program codes being adapted to be loaded and run by the processor to execute the wind farm layout optimization method considering the influence of surrounding wind farms as described in any of the above-described technical solutions of the wind farm layout optimization method considering the influence of surrounding wind farms.

[0041] In a third aspect, the present invention provides a computer-readable storage medium storing a plurality of program codes adapted to be loaded and run by a processor to perform the wind farm layout optimization method considering the influence of surrounding wind farms as described in any of the above-described technical solutions of the wind farm layout optimization method considering the influence of surrounding wind farms.

[0042] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects:

[0043] In implementing the technical solution of this invention, an initial population of a genetic algorithm is established based on the regional range and wind turbine parameter information of the target wind farm. The wake velocity deficit generated by the surrounding wind farms is determined based on the wind turbine parameter information, wind resource distribution information, and a preset wind turbine wake model. The wind speed in front of the hub of the wind turbines within the target wind farm is determined based on the wake velocity deficit. The total power generation of the wind farm corresponding to each individual in the initial population is determined based on the wind speed in front of the hub and the wind resource distribution information of the target wind farm. With maximizing the total power generation of the wind farm as the optimization objective, the genetic algorithm is used to optimize the wind farm turbine layout scheme to obtain the wind turbine layout scheme of the target wind farm. The solution provided by this invention considers the inter-farm wake influence range brought by surrounding wind farms under different inflow wind speeds and wind directions. It optimizes the wind farm turbine layout scheme with maximizing the total power generation of the target wind farm as the optimization objective, thereby rationally arranging the wind turbine layout, increasing the total power generation of the wind farm, and improving the comprehensive benefits of the wind farm throughout its entire life cycle. Attached Figure Description

[0044] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. Wherein:

[0045] Figure 1 This is a flowchart illustrating the main steps of a wind farm layout optimization method that takes into account the influence of surrounding wind farms, as provided in this application.

[0046] Figure 2 This is a schematic diagram of the rose wind direction diagram, thrust curve, and wind energy efficiency curve used in the embodiments of this application;

[0047] Figure 3 yes Figure 1 A flowchart illustrating the main steps of a specific implementation of step S13;

[0048] Figure 4 This is a schematic diagram illustrating an application scenario for wind farm layout optimization provided in this application.

[0049] Figure 5 This is a schematic diagram of a specific wind farm layout effect obtained by using the wind farm layout optimization method provided in this application that takes into account the influence of surrounding wind farms. Detailed Implementation

[0050] Some embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0051] In the description of this invention, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and may also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. 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 terms "a" or "this" can also include plural forms.

[0052] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a wind farm layout optimization method considering the influence of surrounding wind farms according to an embodiment of the present invention. Figure 1 As shown, the wind farm layout optimization method considering the influence of surrounding wind farms in this embodiment of the invention mainly includes the following steps S11 to S14.

[0053] Step S11: Obtain the regional range, wind turbine parameter information, and wind resource distribution information of the target wind farm and its surrounding wind farms.

[0054] In this embodiment, the wind turbine parameter information includes at least the number of wind turbines, rotor diameter, hub height, thrust coefficient, and wind turbine power, and the wind resource distribution information includes at least wind speed, wind direction angle, wind direction and wind speed change frequency, ambient incoming wind speed, and ambient incoming turbulence intensity.

[0055] Step S12: Establish the initial population of the genetic algorithm based on the regional range of the target wind farm and the wind turbine parameter information.

[0056] In this embodiment, the initial population contains multiple individuals, each representing a wind farm turbine layout scheme. This step may specifically include: setting a wind turbine coordinate constraint that the distance between any two wind turbines in the wind farm is greater than n times the rotor diameter, where n is a positive integer greater than 1; based on the number of wind turbines and the wind turbine coordinate constraint, randomly generating the coordinates of each wind turbine within the area of ​​the wind farm, and establishing an initial population for a genetic algorithm based on these coordinates.

[0057] In one specific implementation, under the constraint of turbine spacing, the initial coordinates (x, y) of wind turbines can be randomly generated within the target wind farm area. The turbine coordinates are encoded using real numbers to establish an initial population. The initial population contains multiple individuals, each of which is a row vector containing 2N variables. Each individual represents a wind farm turbine layout scheme, where N is the number of wind turbines. The row vector of each individual consists of the x and y coordinates of each turbine, where the first to Nth variables are the x-coordinates and the (N+1)th to 2Nth variables are the y-coordinates. Real number encoding refers to representing each gene value of an individual using a floating-point number within a certain range.

[0058] For example, if the planned target wind farm is a 3000m × 3000m area, and the turbine spacing is limited to 4D, then the coordinate constraints for the wind turbines are:

[0059]

[0060] x∈(0,3000); y∈(0,3000)

[0061] Where, x i and x j The x-coordinates of the i-th and j-th wind turbines in the wind farm are respectively; y i and y j Let be the ordinates of the i-th and j-th wind turbines in the wind farm, respectively; D is the rotor diameter; d ij Let be the distance between the i-th and j-th generator units.

[0062] Specifically, based on the determined number of wind turbine units N, N coordinate positions are randomly generated within the target wind farm area, and the spacing between any two wind turbine units must meet the requirements.

[0063] Step S13: Based on the turbine parameter information, wind resource distribution information and preset wind turbine wake model of the surrounding wind farm, determine the wake velocity loss generated by the surrounding wind farm, and determine the wind speed in front of the hub of the wind turbine in the target wind farm based on the wake velocity loss.

[0064] In this embodiment, the preset wind turbine wake model adopts the Gaussian wake model.

[0065] Step S14: Based on the wind speed in front of the hub of the wind turbine and the wind energy efficiency curve of the wind turbine in the target wind farm, determine the total power generation of the wind farm corresponding to the individual in the initial population. With the maximum total power generation of the wind farm as the optimization objective, use a genetic algorithm to optimize the wind farm turbine layout scheme to obtain the wind turbine layout scheme of the target wind farm.

[0066] In this embodiment, this step may specifically include: determining the output power of the current wind turbine under preset wind conditions based on the wind speed and wind energy efficiency curve in front of the wind turbine hub, wherein the wind energy efficiency curve is the correspondence between wind speed and wind turbine power; and determining the total power generation of the wind farm based on the current output power of the wind turbine according to the following formula (1):

[0067]

[0068] Among them, P total W represents the total power generation of the wind farm. jk Indicates wind speed as u j Wind direction is θ k Wind conditions; P i For unit i in wind condition W jk Output power at f(W) jk (W) is the wind condition. jk Frequency of occurrence; N θ For wind direction quantity; N u N represents the number of wind speed ranges taken under a single wind direction; N represents the number of wind turbine units in the wind farm.

[0069] Based on steps S11 to S14 above, the total output power of the wind farm under the wind farm layout scheme corresponding to each individual is calculated as the optimization objective. Starting from the initial population, the optimization objective is maximized as the convergence condition. The layout scheme of the wind turbine units in the wind farm is optimized and iterated based on the genetic algorithm until the optimization objective reaches convergence, and a specific layout scheme of the wind farm is obtained, which includes the coordinate position of each wind turbine unit in the target wind farm.

[0070] The following provides further explanation of steps S11 and S13.

[0071] In step S11 above, the target wind farm refers to a planned wind farm to be constructed, and the surrounding wind farms refer to wind farms already built relative to the area where the target wind farm is located. For example, the area of ​​the target wind farm can be 3000m × 3000m, the number of wind turbines is N, for example N = 35, and the turbine model is Vestas-V80. The wind resource distribution information mainly includes wind resource measurement data and data calculated based on the wind resource measurement data, such as wind direction and speed, wind angle, frequency of wind direction and speed changes, ambient incoming wind speed, and ambient turbulence intensity. It can be understood that a wind rose diagram can be drawn based on the wind resource measurement data, and a thrust curve and wind energy efficiency curve can be drawn based on wind speed, wind turbine thrust coefficient, and wind turbine power. Figure 2 As shown, (a) is the rose wind diagram used in the embodiment of this application, and (b) is a schematic diagram of the thrust curve and wind energy efficiency curve used in the embodiment of this application. The environmental turbulence intensity is set to 0.12. Table 1 below shows the parameters of the hub height and rotor diameter of the wind turbine (hereinafter referred to as wind turbine) used in the embodiment of this application:

[0072] parameter size unit <![CDATA[Hub height z of the fan h > 70 m Wind turbine rotor diameter D 80 m

[0073] In step S13 above, this embodiment mainly calculates the impact of the wake of the surrounding wind farm within the planned wind farm based on the wind turbine parameter information and incoming flow information, through the additional turbulence intensity of a single wind turbine and the superposition method. Specifically: first, the operating status (thrust coefficient) of the first row of wind turbines is determined based on the inflow wind speed of the wind farm. The wake width of the wind farm is calculated through turbulence intensity. Then, the wake velocity distribution and additional turbulence intensity of the first row of wind turbines are calculated, which also yields the inflow velocity and turbulence intensity of the second row of wind turbines. And so on, the calculation is performed from front to back. The inflow velocity of the downstream unit is the result of the superposition of multiple upstream units.

[0074] The following is in conjunction with the appendix Figure 3 The above step S13 will be explained in detail.

[0075] In one specific implementation, the wind turbines within the surrounding wind farm are arranged according to... Figure 3 The steps shown are used to calculate the wake velocity loss generated by the surrounding wind farm, such as Figure 3 The process mainly includes the following steps S131 to S135.

[0076] Step S131: Obtain the inflow turbulence intensity of the wind turbine, determine the wake expansion rate and near-wake region length of the wind turbine based on the inflow turbulence intensity and wind turbine parameter information, and calculate the maximum wake loss distribution based on the near-wake region length;

[0077] Specifically, based on the inflow turbulence intensity I u Determining the wake expansion rate k of the wind turbine within 8D based on wind turbine parameters w The calculation formula is as follows (2):

[0078] k w =0.38I u +0.004 (2)

[0079] Based on the inflow turbulence intensity I u Determine the length x of the near-wake region of the wind turbine. NW The calculation formula is as follows (3):

[0080]

[0081] Where x0 = 1D, Sc t =0.5, σ e =0.18, S′=0.043, C T denoted as thrust coefficient, and D is the rotor diameter of the wind turbine.

[0082] Based on the near-wake region length x NW The maximum loss distribution of the wake is calculated using the following formula (4):

[0083]

[0084] Where x is the distance between the upstream and downstream wind turbine units.

[0085] Step S132: Determine the width of the wake region based on the wake expansion rate of the wind turbine and the maximum loss distribution of the wake;

[0086] Specifically, based on the wake expansion rate k of the wind turbine... w and the maximum loss distribution of the wake Determine the width of the wake region The following calculation formula (5) is used:

[0087]

[0088] in,

[0089]

[0090] Step S133: Based on the wake width, the wind turbine parameter information, and the wind resource distribution information, solve the wake velocity deficit of the wind turbine using a preset wind turbine wake model.

[0091] Specifically, the formula for calculating the wake velocity deficit ΔU of a single wind turbine unit based on the Gaussian wake model is as follows:

[0092]

[0093] Where ΔU represents the wake velocity deficit of the wind turbine, U ∞ For the ambient airflow velocity. The wake width is given. The wind turbine parameters in the formula are as follows: y is the radial coordinate of the wind turbine, z is the vertical coordinate, and z... h The hub height of the wind turbine, y h For the wind turbine unit extending towards the center, C T denoted as thrust coefficient, and D is the rotor diameter of the wind turbine.

[0094] Step S134: Calculate the inflow velocity loss at multiple points on the rotor of the current wind turbine based on the sum of the wake velocity loss of the upstream wind turbine on the current wind turbine.

[0095] Specifically, for each wind turbine in the target wind farm, the inflow velocity loss at multiple points on the rotor of the current wind turbine can be determined by superposition according to the following formula (7) based on the wake velocity loss of the upstream wind turbine at the current location of the wind turbine.

[0096]

[0097] Wherein, ΔU i (x,y,z) represents the inflow velocity deficit at point (x,y,z) on the rotor of the i-th unit; ΔU ij The wake velocity deficit of the j-th unit at the point (x,y,z) on the rotor of the i-th unit; q ij Let q be a binary variable, and let q be a variable that is downstream of the i-th unit if and only if the i-th unit is downstream of the j-th unit. ij =1, otherwise q ij =0; N is the number of wind turbines in the wind farm.

[0098] Step S135: Determine the wind speed in front of the hub of the current wind turbine based on the inflow velocity deficit at multiple points on the current wind turbine rotor:

[0099] Specifically, based on the average of the ambient incoming wind speed and the inflow velocity deficit at multiple points on the wind turbine rotor, the wind speed in front of the current wind turbine hub is calculated according to the following formula (8).

[0100]

[0101] Among them, U i U represents the wind speed in front of the hub of the i-th wind turbine under preset wind conditions; ∞ For the ambient airflow velocity; ΔU i (x,y,z) represents the inflow velocity deficit at multiple points on the rotor of the current i-th unit.

[0102] Furthermore, the method for obtaining the inflow turbulence intensity of the wind turbine in step S131 above will be described in detail below.

[0103] First, based on the preset additional turbulence intensity model, the additional turbulence intensity in the upstream wind turbine at multiple points on the rotor of the current wind turbine can be calculated.

[0104] The preset additional turbulence intensity model is determined according to the following formulas (9)-(15):

[0105] r 1 / 2 =1.18(k) w x+εD) (9)

[0106]

[0107] k w =0.38I b0 +0.004 (11)

[0108]

[0109] Then, for each wind turbine in the target wind farm, based on the additional turbulent intensity of the upstream wind turbine at multiple points on the rotor of the current wind turbine, the additional turbulent intensity of the current wind turbine at multiple points is obtained by superposition according to the following formula (16):

[0110]

[0111] Wherein, ΔIu i (x,y,z) represents the additional turbulence intensity in the flow direction at point (x,y,z) on the rotor of the current i-th unit; ΔIu jiq represents the additional directional turbulence intensity of the j-th unit at point (x,y,z) on the rotor of the i-th unit; ij Let q be a binary variable, and let q be a variable that is downstream of the i-th unit if and only if the i-th unit is downstream of the j-th unit. ij =1, otherwise q ij =0; N is the number of wind turbines in the wind farm.

[0112] Based on the ambient incoming turbulence intensity and the additional flow-direction turbulence intensity, the inflow turbulence intensity of the current wind turbine is obtained according to the following equation (17):

[0113]

[0114] Where Iu represents the inflow turbulence intensity of the wind turbine, ΔIu represents the additional flow-direction turbulence intensity, and I b0 δ(r) represents the intensity of the incoming turbulence, δ(r) represents the ground correction function, α represents the azimuth angle (0 degrees in the y-direction) corresponding to the radial peak position of the correction, and r 1 / 2 For the wake velocity deficit half-width, σ T This represents the standard deviation of the Gaussian distribution of ΔIu in the wake edge region.

[0115] For example, such as Figure 4 and Figure 5 As shown, Figure 4 This is a schematic diagram of an application scenario of the present invention, wherein wind farm A and wind farm B are the surrounding wind farms of the wind farm construction area, i.e., the target wind farm, as shown below. Figure 5 The diagram shows a specific wind farm layout scheme obtained using the wind farm layout optimization method considering the influence of surrounding wind farms provided by this invention. In optimizing the layout of wind turbines in a wind farm, this invention comprehensively considers the wake influence of surrounding wind farms on the target wind farm under construction. While ensuring the total output power of the wind farm, the wind farm layout scheme designed based on the method of this invention can effectively extend the operational life of the wind farm and increase its power generation throughout its entire life cycle.

[0116] It should be understood that the descriptions of orientations in the specification, claims, and drawings of this application, such as "up," "down," "left," and "right," indicating orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. The terms "first," "second," etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein.

[0117] 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, and these variations are all within the scope of protection of this application.

[0118] Those skilled in the art will understand that all or part of the processes in the method of the above embodiment of the present invention 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. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0119] Furthermore, the present invention also provides an electronic device. In one embodiment of the electronic device according to the present invention, the electronic device includes at least one processor and at least one memory. The memory can be configured to store a program for executing the wind farm layout optimization method considering the influence of surrounding wind farms as described in the above-described method embodiments. The processor can be configured to execute the program stored in the memory, which includes, but is not limited to, the program for executing the wind farm layout optimization method considering the influence of surrounding wind farms as described in the above-described method embodiments. For ease of explanation, only the parts related to the embodiments of the present invention are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of the present invention.

[0120] In the embodiments of this application, the electronic device may be a control device comprising various devices. In some possible implementations, the electronic device may include multiple memories and multiple processors. The program executing the wind farm layout optimization method considering the influence of surrounding wind farms in the above-described method embodiments can be divided into multiple subroutines. Each subroutine can be loaded and run by a processor to execute different steps of the wind farm layout optimization method considering the influence of surrounding wind farms in the above-described method embodiments. Specifically, each subroutine can be stored in different memories, and each processor can be configured to execute programs in one or more memories to jointly implement the wind farm layout optimization method considering the influence of surrounding wind farms in the above-described method embodiments. That is, each processor executes different steps of the wind farm layout optimization method considering the influence of surrounding wind farms in the above-described method embodiments to jointly implement the wind farm layout optimization method considering the influence of surrounding wind farms in the above-described method embodiments.

[0121] The aforementioned multiple processors can be processors deployed on the same device. For example, the aforementioned electronic device can be a high-performance device composed of multiple processors, and the aforementioned multiple processors can be processors configured on that high-performance device. Alternatively, the aforementioned multiple processors can also be processors deployed on different devices. For example, the aforementioned electronic device can be a server cluster, and the aforementioned multiple processors can be processors on different servers within the server cluster.

[0122] Furthermore, the present invention also provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to the present invention, the computer-readable storage medium can be configured to store a program that performs the wind farm layout optimization method considering the influence of surrounding wind farms in the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described wind farm layout optimization method considering the influence of surrounding wind farms. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The computer-readable storage medium can be a storage device device comprising various electronic devices. Optionally, in the embodiments of the present invention, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0123] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for wind farm layout optimization considering the influence of surrounding wind farms, characterized in that, The method comprises: acquiring the area range of a target wind farm and surrounding wind farms, wind turbine parameter information and wind resource distribution information; establishing an initial population of a genetic algorithm based on the area range and wind turbine parameter information of the target wind farm, wherein the initial population contains multiple individuals, each individual being a wind farm turbine arrangement scheme; determining the wake velocity deficit generated by the surrounding wind farms based on the wind turbine parameter information, wind resource distribution information and preset wind turbine wake model of the surrounding wind farms, and determining the wind speed in front of the hub of the wind turbine in the target wind farm based on the wake velocity deficit; determining the total power generation of the wind farm corresponding to the individual in the initial population based on the wind speed in front of the hub of the wind turbine, taking the maximum total power generation of the wind farm as the optimization target, and optimizing the wind farm turbine arrangement scheme by using the genetic algorithm to obtain the wind farm turbine arrangement scheme of the target wind farm; the determination of the wind speed in front of the hub of the wind turbine in the target wind farm based on the wake velocity deficit comprises: calculating the inflow velocity deficit at multiple points on the wind wheel of the current wind turbine based on the wake velocity deficit of the upstream wind turbine on the current wind turbine; determining the wind speed in front of the hub of the current wind turbine after averaging based on the inflow velocity deficit at multiple points on the wind wheel of the current wind turbine according to the following formula: wherein, U i is the wind speed in front of the hub of the current i-th wind turbine under the preset wind condition; U ∞ is the ambient incoming flow wind speed; ΔU i (x, y, z) is the incoming flow speed deficit at multiple points on the current i-th wind turbine rotor. the determination of the total power generation of the wind farm corresponding to the individual in the initial population based on the wind speed in front of the hub of the wind turbine and the wind energy efficiency curve of the wind turbine in the target wind farm comprises: determining the output power of the current wind turbine under a preset wind condition based on the wind speed in front of the hub of the wind turbine and the wind energy efficiency curve, wherein the wind energy efficiency curve is the corresponding relationship between wind speed and wind turbine power; determining the total power generation of the wind farm based on the output power of the current wind turbine according to the following formula: where P total is the total power output of the wind farm, W jk represents the wind condition with wind speed u j and wind direction θ k ; P i is the output power of unit i under wind condition W jk ; f(W jk ) is the frequency of wind condition W jk ; N θ is the number of wind directions; N u is the number of wind speed segments under a single wind direction; and N is the number of wind turbines in the wind farm.

2. The method of claim 1, wherein, The wind turbine parameter information includes the number of wind turbines, and the establishment of the initial population of the genetic algorithm based on the area range and wind turbine parameter information of the target wind farm comprises: setting the wind turbine coordinate constraint condition as the distance between any two wind turbines in the wind farm being greater than n times the diameter of the wind wheel, wherein n is a positive integer greater than 1; based on the number of wind turbines and the wind turbine coordinate constraint condition, randomly generating the coordinates of each wind turbine within the area range of the wind farm, and establishing the initial population of the genetic algorithm based on the coordinates.

3. The method of claim 1, wherein, The determination of the wake velocity deficit generated by the surrounding wind farms based on the wind turbine parameter information, wind resource distribution information and preset wind turbine wake model of the surrounding wind farms comprises: acquiring the inflow turbulence intensity of the wind turbine, determining the wind turbine wake expansion rate and near-wake zone length based on the inflow turbulence intensity and the wind turbine parameter information, and calculating the maximum wake deficit distribution based on the near-wake zone length; determining the wake zone width based on the wind turbine wake expansion rate and the maximum wake deficit distribution; Based on the wake zone width, the wind turbine parameter information, and the wind resource distribution information, a preset wind turbine wake model is used to solve a wake velocity deficit of the wind turbine.

4. The method of claim 3, wherein, The preset wind turbine wake model adopts a Gaussian wake model, and the Gaussian wake model is determined by the following formula: where ΔU represents the wake velocity deficit of the wind turbine, U ∞ is the ambient inflow wind speed, is the wake zone width, y is the radial coordinate of the wind turbine, z is the vertical direction coordinate, z h is the hub height of the wind turbine, y h is the spanwise center position of the wind turbine, C T is the thrust coefficient, D is the rotor diameter of the wind turbine.

5. The method of claim 3, wherein, The obtaining of the inflow turbulence intensity of the wind turbine includes: For each wind turbine in the target wind farm, based on the additional flow direction turbulence intensity of the upstream wind turbine at multiple points on the current wind turbine rotor, the additional flow direction turbulence intensity of the multiple points on the current wind turbine rotor is obtained according to the following formula by superposition: wherein, ΔIu i (x,y,z) is the additional flow direction turbulence intensity at point (x,y,z) on the wind wheel of the current i th unit; ΔIu ji is the additional flow direction turbulence intensity at point (x,y,z) on the wind wheel of the i th unit for the j th unit; q ij is a binary variable, q ij = 1 only when the current i th unit is downstream of the j th unit, and q ij = 0 otherwise; N is the number of wind turbines in the wind farm. Based on the environmental inflow turbulence intensity and the additional flow direction turbulence intensity, the inflow turbulence intensity of the current wind turbine is obtained according to the following formula: where Iu represents the inflow turbulence intensity of the wind turbine, ΔIu represents the additional flow direction turbulence intensity, I b0 represents the ambient inflow turbulence intensity.

6. The method of claim 1, wherein, The superposition calculation of the inflow velocity deficit at the multiple points on the current wind turbine rotor based on the wake velocity deficit of the upstream wind turbine at the current wind turbine includes: For each wind turbine in the target wind farm, based on the wake velocity deficit of the upstream wind turbine at the current wind turbine, the inflow velocity deficit at the multiple points on the current wind turbine rotor is determined according to the following formula by superposition: Where, ΔU i (x,y,z) represents the inflow velocity deficit at point (x,y,z) on the rotor of the i-th unit; ΔU ij The wake velocity deficit of the j-th unit at the point (x,y,z) on the rotor of the i-th unit; q ij Let q be a binary variable, and let q be a variable that is downstream of the i-th unit if and only if the i-th unit is downstream of the j-th unit. ij =1, otherwise q ij =0; N is the number of wind turbines in the wind farm.

7. An electronic device comprising at least one processor and at least one memory adapted to store a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by the processor to execute the wind farm layout optimization method considering the influence of surrounding wind farms according to any one of claims 1 to 6.

8. A computer readable storage medium having stored therein a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by the processor to execute the wind farm layout optimization method considering the influence of surrounding wind farms according to any one of claims 1 to 6.

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