Wind Farm Layout Optimization Method Based on Gaussian Wake Model and Improved Genetic Algorithm

Abstract

The present application discloses a method for optimizing the layout of a wind farm based on the Gaussian wake model and an improved genetic algorithm. The Ishihara-Qian wake model, a wake analysis model with higher accuracy, is used to evaluate the power generation of the wind farm. This model can efficiently and accurately predict the wind speed deficit and additional turbulence intensity in the wake area of the wind turbines. The present application also improves the mutation rate and crossover rate of the genetic algorithm according to the population fitness, enabling these two parameters to be dynamically adjusted according to the population fitness during each iteration. When the fitness difference in a population is small (the population is relatively uniform), the corresponding crossover rate or mutation rate increases, thereby increasing the diversity of the population and avoiding falling into a local optimal solution.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation layout, and particularly relates to a method for optimizing the layout of a wind farm based on a Gaussian wake model and an improved genetic algorithm. Background Art

[0002] As a renewable and clean energy source, the development and utilization of wind energy have become an important development trend. In the actual application of a wind farm, when the wind speed flows through a wind turbine, flow characteristics such as a decrease in the downstream wind speed and an increase in the turbulence intensity will occur. This phenomenon is called the wake effect. As the downstream distance increases, the influence of the wake effect will decrease. In a large-scale wind farm, to improve land utilization rate and economic benefits, it is necessary to install as many wind turbines as possible within a limited space to maximize power generation. Therefore, the position arrangement of wind turbines is particularly crucial. The wake effect is one of the key factors determining the distance and arrangement of wind turbines. How to reduce the influence of the wake effect under limited conditions and maximize power generation has become one of the research hotspots in this field.

[0003] Currently, many studies on the optimization scheme of wind turbine arrangement often use wake models such as Jensen's. The model is simple but has limited accuracy. In addition, as the most commonly used algorithm in the layout optimization of wind turbines, the genetic algorithm has low computational efficiency when dealing with multiple constraints such as boundary conditions, safe distance between turbines, and infeasible spaces. Under these constraints, minimizing the influence of the wake effect is a multi-variable non-convex problem. The number of potential arrangement schemes is huge and there are a large number of local optimal solutions, making it difficult to obtain the global optimal solution. Summary of the Invention

[0004] In view of this, the present invention aims to propose a method for optimizing the layout of a wind farm based on a Gaussian wake model and an improved genetic algorithm, which uses an improved three-dimensional wake analysis model and a genetic algorithm to minimize the wake effect of the wind farm and reduce the possibility of falling into local optimal solutions.

[0005] In a first aspect, the present invention proposes a method for optimizing the layout of a wind farm based on a Gaussian wake model and an improved genetic algorithm, including:

[0006] Obtaining the information of the target wind farm;

[0007] Describing the total power generation of the wind farm based on the target wind farm information using the Ishihara-Qian wake model;

[0008] Meshing the target wind farm, taking the maximum total power generation of the wind farm as the optimization objective, and using an improved genetic algorithm to optimize the grid positions of the wind turbines in the wind farm to obtain a wind farm layout scheme;

[0009] Wherein, the crossover rate and mutation rate of the improved genetic algorithm are calculated according to the population fitness.

[0010] Further, using the Ishihara-Qian wake model to describe the total power generation of a wind farm based on the information of the target wind farm includes:

[0011] Convert the initial layout of wind turbines in the wind farm to the main wind direction according to the given wind direction;

[0012] Calculate the wind speed deficit and additional turbulence intensity generated by the upstream wind turbines of the nth wind turbine in the wind farm at the location of the nth wind turbine along the wind direction according to the unit specification parameters;

[0013] Calculate the average wind speed at the wind turbine plane at the location of the nth wind turbine according to the wind speed deficit, and calculate the average turbulence intensity at the location of the nth wind turbine according to the additional turbulence intensity;

[0014] Calculate the power generation of the nth wind turbine based on the average wind speed at the wind turbine plane and the average turbulence intensity, and calculate the total power generation of the wind farm according to the power generation of each wind turbine.

[0015] Further, calculating the average wind speed at the wind turbine plane at the location of the nth wind turbine according to the wind speed deficit includes:

[0016] Considering the wind speed deficit generated by the upstream wind turbines of the nth wind turbine at the location of the nth wind turbine, calculate the total wind speed deficit at the location of the nth wind turbine as follows:

[0017] ,

[0018] Wherein, represents the wind speed deficit caused by the mth upstream wind turbine of the nth wind turbine at the location of the nth wind turbine, represents the background incoming flow wind speed deficit, is the position of the wind turbine hub center in the downwind direction in this coordinate system, and are the crosswind and vertical coordinates in the coordinate system respectively;

[0019] Calculate the average wind speed at the wind turbine plane at the location of the nth wind turbine according to the total wind speed deficit at the location of the nth wind turbine as follows:

[0020] ,

[0021] Wherein, is the effective wind speed at the wind turbine plane of the nth wind turbine, is the blade swept area of the nth wind turbine, represents the integration over the wind turbine plane area.

[0022] Further, calculating the average turbulence intensity at the location of the nth wind turbine according to the additional turbulence intensity includes:

[0023] Introduce a turbulence superposition correction term into the additional turbulence intensity as follows:

[0024]

[0025]

[0026]

[0027] Among them, represents the distance from any point in the y-z plane to the hub center of the m-th upstream wind turbine of the n-th wind turbine, and there is , where H is the height of the wind turbine hub center, represents the y-axis coordinate of the m-th upstream wind turbine; is the secant length of the intersection area between the m-th upstream wind turbine and the wake of wind turbine l, and there is , where wind turbine l is the wind turbine closest to the m-th upstream wind turbine in the wind farm, represents the width of the wind turbine wake, .

[0028] Furthermore, the power generation of the n-th wind turbine is calculated based on the average wind speed and average turbulence intensity in the wind wheel plane, and there is the following expression:

[0029] ,

[0030] Among them, is the air density, is the blade swept area of the n-th wind turbine, is the effective wind speed in the wind wheel plane of the n-th wind turbine, is the power generation efficiency of the n-th wind turbine, is the wind turbine power coefficient.

[0031] Furthermore, calculating the total power generation of the wind farm based on the power generation of each wind turbine includes:

[0032] Calculating the given wind farm layout according to the power generation of each wind turbine At the environmental wind speed , wind direction and turbulence intensity The overall power generation is as follows:

[0033]

[0034] Among them, represents the power generation of the n-th wind turbine, i and j respectively represent the wind speed conditions and wind direction conditions, and N represents the number of wind turbines;

[0035] Considering the different wind direction and wind speed distribution frequencies, the total power generation of the wind farm is calculated as follows:

[0036]

[0037] Wherein, I and J respectively represent the total number of wind speed conditions and the total number of wind direction conditions, represents the wind direction and wind speed distribution frequency.

[0038] Furthermore, the crossover rate and mutation rate of the improved genetic algorithm calculated according to the population fitness include:

[0039] When using the improved genetic algorithm to optimize the calculation of the unit grid positions in a wind farm, calculate the maximum fitness and average fitness of the population at the t-th iteration. Calculate the crossover rate at the t-th iteration according to the maximum fitness and average fitness of the population as follows and the mutation rate :

[0040]

[0041]

[0042] Wherein, represents the difference between the maximum fitness and the average fitness of the population at the t-th iteration, and represents the maximum recorded during the t iterations ,

[0043] That is , and are sensitivity factors characterizing the speed of probability change, is a constant.

[0044] In a second aspect, the present invention proposes a wind farm layout optimization device, including:

[0045] A wind farm information acquisition module for acquiring target wind farm information;

[0046] A power generation calculation module for describing the total power generation of the wind farm based on the Ishihara-Qian wake model and the target wind farm information;

[0047] A layout optimization module for meshing the target wind farm, taking the maximum total power generation of the wind farm as the optimization goal, and using the improved genetic algorithm to optimize the unit grid positions in the wind farm to obtain a wind farm layout plan; wherein, the crossover rate and mutation rate of the improved genetic algorithm are calculated according to the population fitness.

[0048] In a third aspect, the present invention provides an electronic device, including a memory storing computer-executable instructions and a processor, and when the computer-executable instructions are executed by the processor, the device executes each step of the wind farm layout optimization method provided in the first aspect based on the Gaussian wake model and the improved genetic algorithm.

[0049] Fourthly, the present invention provides a readable storage medium storing a computer-executable program, which can implement the steps of the wind farm layout optimization method based on the Gaussian wake model and the improved genetic algorithm provided in the first aspect when the program is executed.

[0050] As can be seen from the above technical solutions, the present invention has the following beneficial effects:

[0051] The present invention provides a wind farm layout optimization method based on the Gaussian wake model and the improved genetic algorithm, which uses a higher-precision wake analysis model - the Ishihara-Qian wake model to evaluate the power generation of the wind farm. This model can efficiently and accurately predict the wind speed deficit and additional turbulence intensity in the wake area of the wind turbine. The present invention also improves the mutation rate and crossover rate of the genetic algorithm according to the population fitness, so that these two parameters in each iteration can be dynamically adjusted according to the population fitness. When the fitness difference in a population is small (the population is relatively uniform), the corresponding crossover rate or mutation rate increases, thereby increasing the diversity of the population and avoiding falling into a local optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0053] Figure 1 is a flowchart of the implementation of the wind farm layout optimization method based on the Gaussian wake model and the improved genetic algorithm provided in the embodiment of the present invention;

[0054] Figure 2 is a schematic diagram of the crossover and mutation logic of the improved genetic algorithm provided in the embodiment of the present invention;

[0055] Figure 3 is a bar chart of the wind condition distribution probability provided in the embodiment of the present invention;

[0056] Figure 4 is a comparison chart of the fitness change with the algorithm iteration times of the improved genetic algorithm and the traditional genetic algorithm under wind condition 1 provided in the embodiment of the present invention;

[0057] Figure 5 is a comparison chart of the fitness change with the algorithm iteration times of the improved genetic algorithm and the traditional genetic algorithm under wind condition 2 provided in the embodiment of the present invention;

[0058] Figure 6It is a schematic diagram showing the layout comparison of the wind turbines after optimizing the wind farm layout by the improved genetic algorithm provided by the embodiment of the present invention and the traditional genetic algorithm under wind condition 1;

[0059] Figure 7 It is a schematic diagram showing the layout comparison of the wind turbines after optimizing the wind farm layout by the improved genetic algorithm provided by the embodiment of the present invention and the traditional genetic algorithm under wind condition 2;

[0060] Figure 8 It is a schematic diagram showing the wake comparison of the wind turbines after optimizing the wind farm layout by the improved genetic algorithm provided by the embodiment of the present invention and the traditional genetic algorithm under wind condition 1;

[0061] Figure 9 It is a schematic structural diagram of the wind farm layout optimization device provided by the embodiment of the present invention;

[0062] Figure 10 It is an architecture diagram of the electronic device provided by the embodiment of the present invention. Specific embodiments

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0064] As Figure 1 shown, an embodiment of the present invention provides a wind farm layout optimization method based on the Gaussian wake model and the improved genetic algorithm, including the following steps:

[0065] Step S110. Obtain the target wind farm information.

[0066] In this step, the obtained target wind farm information includes wind resource data, unit specification parameters, and the boundary of the wind farm is fitted into a polygon to obtain the boundary points of the site.

[0067] In a further implementation, the target wind farm and the positions of the wind turbines are gridified. For safety considerations, the distance d between the wind turbines can be set to not less than 3D, where D is the diameter of the wind turbine impeller. Specifically, the site is divided into K grids and the grids are numbered. The center of each grid is the possible installation position of a wind turbine. Let the number of wind turbines to be optimized be N, and the layout scheme of the wind farm can be represented by an array composed of N real numbers.

[0068] Step S120. Use the Ishihara-Qian wake model to describe the total power generation of the wind farm based on the target wind farm information.

[0069] Step S130. Grid the target wind farm. With the maximum total power generation of the wind farm as the optimization objective, use an improved genetic algorithm to optimize the grid positions of the wind turbines in the wind farm to obtain a wind farm layout plan. Among them, the crossover rate and mutation rate of the improved genetic algorithm are calculated according to the population fitness.

[0070] In a further implementation manner, step S120 includes the following steps:

[0071] Step S121. Convert the initial wind turbine layout of the wind farm to the main wind direction according to the given wind direction.

[0072] Step S122. Calculate the wind speed deficit and additional turbulence intensity generated by the upstream wind turbines of the nth wind turbine in the wind farm at the position of the nth wind turbine along the wind direction according to the unit specification parameters.

[0073] Step S123. Calculate the average wind speed of the wind wheel plane at the position of the nth wind turbine according to the wind speed deficit, and calculate the average turbulence intensity at the position of the nth wind turbine according to the additional turbulence intensity.

[0074] Step S124. Calculate the power generation of the nth wind turbine based on the average wind speed of the wind wheel plane and the average turbulence intensity, and calculate the total power generation of the wind farm according to the power generation of each wind turbine.

[0075] The embodiment of the present invention uses the more accurate Ishihara-Qian wake model to evaluate the overall power generation of the wind farm under the given information. In the analysis process of this model, the given parameters required include the given wind turbine layout X of the wind farm, the hub height H of the wind turbine, the ambient wind speed U i , wind direction and turbulence intensity .

[0076] Before introducing the Ishihara-Qian wake model to simulate and analyze the wind farm, the following definitions are made:

[0077] For the wind turbine layout of a wind farm with N given wind turbines , when calculating the power generation in each wind direction, it is necessary to convert its coordinates so that the incoming flow direction is aligned with the wind farm direction. It should be noted that the wind turbine n increases sequentially along the x direction in any wind direction. Then, for any wind turbine in the wind farm, its power generation can be calculated by the following formula:

[0078] ,

[0079] where is the air density, is the blade swept area of the nth wind turbine, is the effective wind speed of the wind wheel plane of the nth wind turbine, is the power generation efficiency of the nth wind turbine, is the power coefficient of the wind turbine, which can be calculated by the following formula:

[0080]

[0081] wherein, represents the wind direction and the wind speed distribution on the wind turbine rotor plane of the nth wind turbine, is the blade swept area of the nth wind turbine, represents the integral over the rotor plane area.

[0082] For a given type of wind turbine, since its rotor area , power generation efficiency , and power coefficient curve are known, only by obtaining the effective inflow wind speed of the wind turbine, that is can its power generation be predicted. In the embodiments of the present invention, considering the wake effect, in order to accurately reflect the overall power generation of the wind farm, the Ishihara-Qian model is used to accurately predict the influence of the wind turbine wake on the plane velocity of each wind turbine rotor, that is .

[0083] Specifically, the wind speed is divided into I intervals at set intervals from the cut-in wind speed to the cut-out wind speed (the wind speed corresponding to each interval is denoted as U i ), and the wind direction is divided into J intervals at set intervals (the wind direction corresponding to each interval is denoted as ), and the probability distribution under different wind speed and wind direction intervals is statistically analyzed, as well as the turbulence intensity at different wind speeds. In a more preferred embodiment, if there is no statistical data on the turbulence intensity at different wind speeds, it can be calculated according to the International Electrotechnical Commission standard IEC 61400-1.

[0084] The Ishihara-Qian wake model simultaneously incorporates the influence of turbulence intensity on the wake wind speed deficit. Therefore, the effective inflow turbulence intensity of the rotor plane needs to be introduced for each wind turbine, and the specific calculation method is as follows:

[0085]

[0086] wherein, represents the distribution of the standard deviation of the pulsating wind speed on the rotor plane of the nth wind turbine.

[0087] In the Ishihara-Qian wake model, for any wind turbine n in the wind farm, the wind speed deficit in its wake and the additional turbulence generated can be calculated as follows:

[0088]

[0089]

[0090]

[0091]

[0092] Among them, is the coordinate relative to the hub center of the nth wind turbine, is the height of the hub center of the nth wind turbine, is the rotor diameter, represents the wake width of the wind turbine, and are all Ishihara-Qian wake model parameters, as shown in Table 1 below:

[0093] Table 1

[0094]

[0095] Among them, represents the thrust coefficient of the wind turbine, which is a function of , and the thrust coefficient curve is determined by the aerodynamic characteristics of the wind turbine and is generally provided by the designer of a specific type of wind turbine.

[0096] For the wind speed distribution on the rotor plane of the nth wind turbine under the wind direction , the wind speed deficit generated by the upstream n - 1 wind turbines at the nth wind turbine is considered by linear superposition, and the total wind speed deficit at the nth wind turbine is calculated as follows:

[0097] ,

[0098] Among them, represents the wind speed deficit caused by the mth upstream wind turbine of the nth wind turbine at the nth wind turbine, represents the background incoming flow wind speed deficit, is the position of the hub center of the wind turbine in the downwind direction in this coordinate system, and are the crosswind and vertical coordinates in the coordinate system respectively.

[0099] The average wind speed on the rotor plane at the nth wind turbine is calculated according to the total wind speed deficit calculated above as follows:

[0100] ,

[0101] Among them, is the effective wind speed on the rotor plane of the nth wind turbine, is the blade swept area of the nth wind turbine, denotes the integration over the rotor plane area.

[0102] In addition, the Ishihara-Qian wake model also takes into account the superposition effect of additional turbulence. It is impossible to reproduce the turbulence superposition result in the actual flow field by directly superposing the additional turbulence intensities of two wind turbines using the principle of linear sum of squares. Therefore, in addition to considering the additional turbulence caused by the mth upstream wind turbine at the nth wind turbine, it is also necessary to introduce a correction term to consider the turbulence superposition effect brought by the wind turbine closest to the mth upstream wind turbine. According to the aforementioned calculation expression of additional turbulence, the standard deviation of the pulsating wind speed on the rotor plane of the corrected nth wind turbine can be calculated as follows:

[0103]

[0104]

[0105]

[0106] where, represents the distance from an arbitrary point in the y-z plane to the hub center of the mth upstream wind turbine of the nth wind turbine, and there is , H is the height of the wind turbine hub center, represents the y-axis coordinate of the mth upstream wind turbine; is the secant length of the intersection region between the wake of the mth upstream wind turbine and the wind turbine l, and there is , and the wind turbine l is the wind turbine closest to the mth upstream wind turbine in the wind farm, represents the wind turbine wake width, .

[0107] Substitute the calculated into the aforementioned calculation expression of the inflow turbulence intensity , which is the average turbulence intensity at the nth wind turbine. From the above calculation process, it can be seen that the turbulence intensity is directly related to the standard deviation of the wind speed, which affects the mean wind speed of the wind turbine and thus indirectly affects the power generation of each wind turbine. Under the condition of considering turbulence superposition, according to the aforementioned calculation expression of power generation , the power generation at the nth wind turbine can be calculated.

[0108] Performing the above calculations for each wind turbine can describe the overall power generation of a given wind turbine layout X under the environmental wind speed U i , wind direction and turbulence intensity :

[0109]

[0110] Among them, represents the power generation of the nth wind turbine. i and j represent the wind speed condition and the wind direction condition respectively, and n represents the number of wind turbines.

[0111] Given the distribution frequency of wind direction and wind speed , the total power generation of the wind farm can be calculated, which is also the optimization model for layout optimization:

[0112]

[0113] Among them, I and J represent the total number of wind speed conditions and the total number of wind direction conditions respectively.

[0114] Taking the above optimization objective function as an individual of the genetic algorithm, in a more preferred embodiment, considering that the higher the fitness of an individual, the greater the probability of its being selected, the tournament selection method is adopted in the embodiments of the present invention.

[0115] After meshing the target wind farm, the center of each grid represents the installation position of a wind turbine. Since the genetic algorithm coding may generate the same genes during the crossover process, that is, the same numbers appear in a certain layout array, resulting in two wind turbines appearing at one grid center point, the same genes can be extracted before individual crossover and crossover is performed on the different parts of the genes. The embodiments of the present invention improve the traditional genetic algorithm as follows:

[0116] Traverse all individuals in population C. For each pair of adjacent individuals Perform the following operations:

[0117]

[0118]

[0119]

[0120] Among them, represents the offspring individual after crossover, is the kth element of, m represents the latitude of S, that is, the number of genes shared by the parental individuals for crossover, and are both random numbers between [0, 1], represents the crossover rate of the tth iteration. The embodiments of the present invention calculate the crossover rate as follows:

[0121]

[0122] Among them, represents the difference between the maximum fitness and the average fitness of the population at the tth iteration, representing the maximum recorded during the tth iteration ,

[0123] Right now , It is a sensitivity factor that characterizes how fast the probability changes.

[0124] During the mutation operation, the mutation range should be non-existing genes. The embodiment of the present invention also considers dynamically adjusting the mutation rate based on the fitness difference of the population. for:

[0125]

[0126] in, is a sufficiently large number to ensure Within a reasonable range, parameters Same effect During mutation, a random gene of a parent individual mutates into another gene that is not present in the parent individual.

[0127] In summary, the logic of crossover and mutation of the improved genetic algorithm in the embodiment of the present invention is as follows: Figure 2 As shown, each round of iteration dynamically adjusts the crossover rate according to the fitness of the population. and mutation rate , so that when the fitness difference in a population is small (the population is relatively uniform), the corresponding crossover rate / mutation rate increases, thereby increasing the diversity of the population and avoiding the local optimal solution. On the contrary, when the fitness difference is large (there may be some excellent individuals in the population), the crossover rate and mutation rate decrease to achieve the purpose of inheriting excellent genes. When the number of iterations of the genetic algorithm reaches the set number of cycles or the target solution converges, the layout optimization results of the wind farm can be output, and the position coordinates of each wind turbine can be decoded and output.

[0128] In order to verify the effectiveness of the layout optimization method proposed in the present invention, an experimental example is given below.

[0129] Consider a square offshore wind farm site with a boundary length and width of 5 km and 40 wind turbines deployed in the wind farm. The wind farm site is gridded into 10*10 grid areas, where the center of each grid point is the potential deployment location of the wind turbine.

[0130] Consider two wind conditions, as follows:

[0131] Wind condition 1: constant wind direction and speed (225°, 8m / s)

[0132] Wind condition 2: 36 wind directions, constant wind speed (8m / s), wind direction from 0° to 360° with intervals of 10°, as follows Figure 3 As shown, the horizontal axis represents wind direction, and the vertical axis represents wind direction distribution probability.

[0133] The turbulence intensity is calculated according to the International Electrotechnical Commission (IEC) standard IEC 61400-1:

[0134]

[0135] where is the reference turbulence intensity at a wind speed of 15 m / s, taken as 0.12, b = 5.6 m / s, is the wind speed flowing through the wind turbine.

[0136] The specification parameters of the wind turbines installed in the wind farm are shown in Table 2 below.

[0137]

[0138] The algorithm parameters for the optimization solution based on the above improved genetic algorithm are shown in Table 3 below, and the algorithm parameters for the traditional genetic algorithm for comparison are shown in Table 4 below.

[0139] Table 3

[0140]

[0141] Table 4

[0142]

[0143] The Ishihara-Qian wake model is used to describe the total power generation of a wind farm under a certain layout , and the calculation results are as Figures 4 - 8 and shown in Table 5 below, where Table 5 shows the optimization results of different genetic algorithms under various wind conditions.

[0144] Table 5

[0145]

[0146] As can be seen from Table 5, compared with the traditional genetic algorithm, the improved genetic algorithm proposed in the present invention, which dynamically adjusts the crossover rate and mutation rate based on the population fitness difference, can significantly improve the drawback that the wind farm optimization is prone to falling into a local optimal solution, enhance the optimization ability of the algorithm in the wind farm optimization problem, and after the maximum number of loop iterations, the objective function value has been improved compared with the traditional genetic algorithm under each wind condition.

[0147] Figure 4 and Figure 5 respectively show the variation trends of the fitness of the traditional genetic algorithm (GA) and the improved genetic algorithm (improved GA) proposed in the present invention under wind condition 1 and wind condition 2 with the number of iterations. Under the same number of generations, the fitness optimized by the improved genetic algorithm in the embodiments of the present invention is higher than that of the traditional genetic algorithm, indicating that the ability of the algorithm to jump out of the local optimal solution is significantly improved.

[0148] Figure 6and 7 Figure 2 shows the comparison of the layouts of wind turbines after the optimization of the wind farm layout by the traditional genetic algorithm (GA) and the improved genetic algorithm (improved GA) proposed in the present invention. Figure 8 Figure 3 shows the wake schematic diagrams of the wind turbine layouts optimized by the two algorithms.

[0149] From the perspective of this experimental example, after the wind field simulation is carried out based on the Ishihara-Qian wake model, the improved genetic algorithm adapted to the main wind direction significantly improves the optimization degree of the algorithm under the same conditions. More importantly, the global optimization ability of its algorithm has been greatly improved. Under the same number of iterations, the fitness improvement of the improved genetic algorithm under both wind conditions is higher than that of the traditional genetic algorithm; at the same time, the fitness curve of the improved genetic algorithm is more tortuous and variable than that of the traditional algorithm, and it can often quickly find a better solution in a shorter number of iterations, significantly enhancing the global search ability and greatly avoiding premature convergence caused by falling into local optimal solutions. As the iteration progresses, the growth rate of the fitness slows down, but regardless of which wind condition, the final fitness of the improved genetic algorithm is higher than that of the traditional genetic algorithm, indicating that the improved genetic algorithm proposed in the present invention has the advantages of higher efficiency and accuracy compared with the traditional genetic algorithm.

[0150] The above embodiments describe a method for optimizing the layout of a wind farm based on the Gaussian wake model and the improved genetic algorithm. The Ishihara-Qian wake model, a wake analysis model with higher accuracy, is used to evaluate the power generation of the wind farm. This model can efficiently and accurately predict the wind speed deficit and additional turbulence intensity, and improve the mutation rate and crossover rate of the genetic algorithm according to the population fitness, so that these two parameters in each iteration can be dynamically adjusted according to the population fitness. When the fitness difference in a population is small (the population is relatively uniform), the corresponding crossover rate or mutation rate increases, thereby increasing the diversity of the population and avoiding falling into local optimal solutions.

[0151] The above disclosed method can be implemented by devices in various forms. Therefore, the present invention also discloses a device for optimizing the layout of a wind farm corresponding to the above method, and specific embodiments are given below for detailed description.

[0152] As Figure 9 shown, an embodiment of the present invention provides a device for optimizing the layout of a wind farm, including:

[0153] A wind farm information acquisition module 902, configured to acquire target wind farm information;

[0154] A power generation calculation module 904, configured to describe the total power generation of the wind farm based on the target wind farm information by using the Ishihara-Qian wake model;

[0155] A layout optimization module 906 is configured to grid the target wind farm, with the maximum total power generation of the wind farm as the optimization goal, and use an improved genetic algorithm to optimize the grid positions of the wind turbines in the wind farm to obtain a wind farm layout plan. Among them, the crossover rate and mutation rate of the improved genetic algorithm are calculated according to the population fitness.

[0156] For the device provided in the embodiments of the present application, the implementation principle and the technical effects produced are the same as those in the foregoing method embodiments. For a brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding contents in the foregoing method embodiments.

[0157] The methods and related devices mentioned in the above embodiments are described with reference to the method flowcharts and / or structural schematic diagrams provided in the embodiments of the present application. Specifically, they can be implemented by computer program instructions for each process and / or block in the method flowchart and / or structural schematic diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or structural schematic Figure 1 one block or multiple blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or structural schematic Figure 1 one block or multiple blocks. These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or structural schematic one block or multiple blocks.

[0158] In the following embodiments, the method is described by taking its application to a computer device as an example. It can be understood that the computer device can be any device with computing and processing functions, and can be, but is not limited to, a server or a personal laptop computer, etc. In one of the embodiments, the computer device can be an application server, and the application server can be a server for running a to-be-tested application program.

[0159] Refer to Figure 10, which shows a hardware block diagram of an electronic device. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0160] As Figure 10 shown, the electronic device includes: at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4;

[0161] In the embodiment of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 complete mutual communication through the communication bus 4;

[0162] The processor 1 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention, etc.;

[0163] The memory 3 may include high-speed RAM memory, and may also include non-volatile memory, etc., such as at least one disk memory;

[0164] Among them, the memory stores a program, and the processor can call the program stored in the memory. The program is used to: implement each processing flow of the aforementioned wind farm layout optimization scheme based on the Gaussian wake model and the improved genetic algorithm.

[0165] The embodiment of the present invention also provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements each processing flow of the wind farm layout optimization scheme based on the Gaussian wake model and the improved genetic algorithm provided by any possible implementation manner of the above embodiments and / or the combined embodiments.

[0166] The above embodiments have described the present invention in particular detail with respect to possible scenarios, and those skilled in the art will recognize that the present invention can be practiced through other embodiments. The specific naming of components, the case of terms, attributes, data structures, or any other programming or structural aspects are not mandatory or significant, and the mechanisms or features for implementing the present invention can have different names, forms, or procedures. The system can be implemented through a combination of hardware and software (as described), entirely through hardware elements, or entirely through software elements. The specific division of functions among the various system components described in the text is exemplary and not mandatory; on the contrary, the functions performed by a single system component can be performed by multiple components, or the functions performed by multiple components can be performed by a single component.

[0167] Those skilled in the art should understand that each step of the above-disclosed method can be implemented by a general-purpose computing device, which can be centralized on a single computing device or distributed over a network composed of multiple computing devices. Optionally, they can be implemented with program code executable by the computing device, so that they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. Thus, the disclosure of the embodiments of the present invention is not limited to any specific combination of hardware and software.

[0168] These programs executable by the computing device (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., a disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0169] Certain aspects of the present invention include the process steps and instructions described in the text in the form of algorithms. It should be noted that the process steps and instructions of the present invention can be implemented in software, firmware, and / or hardware, and when implemented by software, it can be downloaded and thus saved on different platforms used by various operating systems and operated from those platforms.

[0170] Those skilled in the art can understand that the structures shown in the drawings are merely block diagrams of some structures related to the solution of the present application, and do not constitute a limitation on the terminal devices to which the solution of the present application is applied. The specific terminal devices may include more or fewer components than those shown in the drawings, or combine some components, or have different component arrangements.

[0171] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "possible design", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0172] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wind farm layout optimization method based on the Gaussian wake model and the improved genetic algorithm, characterized in that Including: Obtain target wind farm information; Use the Ishihara-Qian wake model to describe the total power generation of the wind farm based on the target wind farm information; Grid the target wind farm, taking the maximum total power generation of the wind farm as the optimization goal, and use the improved genetic algorithm to optimize the grid positions of the wind turbines in the wind farm to obtain the wind farm layout plan; Among them, the crossover rate and mutation rate of the improved genetic algorithm are calculated according to the population fitness; The use of the Ishihara-Qian wake model to describe the total power generation of the wind farm based on the target wind farm information includes: Convert the initial wind turbine layout of the wind farm to the main wind direction according to the given wind direction; Calculate the wind speed deficit and additional turbulence intensity generated by the upstream wind turbines of the nth wind turbine in the wind farm at the nth wind turbine along the wind direction according to the unit specification parameters; Calculate the average wind speed at the wind wheel plane of the nth wind turbine according to the wind speed deficit, and calculate the average turbulence intensity at the nth wind turbine according to the additional turbulence intensity; Calculate the power generation of the nth wind turbine based on the average wind speed at the wind wheel plane and the average turbulence intensity, and calculate the total power generation of the wind farm according to the power generation of each wind turbine; Among them, the calculation of the average turbulence intensity at the nth wind turbine according to the additional turbulence intensity includes: Introduce a turbulence superposition correction term in the additional turbulence intensity as follows: where r m represents the distance from any point in the y-z plane to the hub center of the m-th upstream wind turbine of the n-th wind turbine, and there is H is the height of the hub center of the wind turbine, and y m represents the y-axis coordinate of the m-th upstream wind turbine; L w,ml is the secant length of the intersection area between the m-th upstream wind turbine and the wake of wind turbine l, and there is Wind turbine l is the wind turbine closest to the m-th upstream wind turbine in the wind farm, and D w,s represents the width of the wind turbine wake, where s = m, l.

2. The method according to claim 1, characterized in that, The calculation of the average wind speed at the wind wheel plane of the nth wind turbine according to the wind speed deficit includes: Considering the wind speed deficit generated by the upstream wind turbines of the nth wind turbine at the nth wind turbine, calculate the total wind speed deficit at the nth wind turbine as follows: Among them, ΔU mn represents the wind speed deficit caused by the m-th upstream wind turbine of the n-th wind turbine at the n-th wind turbine, and U0(x n , y, z) represents the background incoming flow wind speed deficit. x n is the position of the wind turbine hub center in the downwind direction in this coordinate system, and y and z are the crosswind and vertical coordinates in the coordinate system respectively; Calculate the average wind speed at the wind wheel plane according to the total wind speed deficit at the nth wind turbine as follows: Among them, U h,n is the effective wind speed on the wind wheel surface of the nth wind turbine, and A n is the blade swept area of the nth wind turbine, represents the integration over the wind wheel plane area.

3. The method according to claim 1, characterized in that The calculation of the power generation of the nth wind turbine based on the average wind speed at the wind wheel plane and the average turbulence intensity includes: Calculate the power generation of the nth wind turbine according to the average wind speed at the wind wheel plane by considering the influence of turbulence on the wind speed of the wind turbine according to the average turbulence intensity as follows: Among them, ρ is the air density, A n is the blade swept area of the nth fan, U h,n is the effective wind speed of the wind wheel surface of the nth fan, η n is the power generation efficiency of the nth fan, C p (U h,n ) is the fan power coefficient.

4. The method according to claim 1, characterized in that, The calculation of the total power generation of the wind farm according to the power generation of each wind turbine includes: The total power generation of a given wind farm layout X at ambient wind speed U i , wind direction θ j and turbulence intensity I a,i is calculated as follows: Among them, p n represents the power generation of the nth fan, i and j respectively represent the wind speed condition and the wind direction condition, and N represents the number of fans; Consider the different wind direction and wind speed distribution frequencies to calculate the total power generation of the wind farm as follows: where I and J represent the total number of wind speed conditions and the total number of wind direction conditions respectively, and f ij represents the wind direction and wind speed distribution frequency.

5. The method according to claim 1, wherein The calculation of the crossover rate and mutation rate of the improved genetic algorithm according to the population fitness includes: When using the improved genetic algorithm to optimize the calculation of the unit grid positions in a wind farm, calculate the maximum fitness and average fitness of the population at the \(t\)-th iteration, and calculate the crossover rate \(P_{c}(t)\) and mutation rate \(P_{m}(t)\) at the \(t\)-th iteration according to the maximum fitness and average fitness of the population as follows: C (t) and mutation rate \(P\) m (t): Among them, fitness d (t) represents the difference between the maximum fitness and the average fitness of the population at the t-th iteration, and represents the maximum fitness recorded during the t iterations d (t), i.e., fitness d,max f(t) = max[fitness d (1), fitness d (2), …, fitness d (t)], where α and γ are sensitivity factors characterizing the rate of change of probability, and λ is a constant.

6. An optimized device for wind farm layout, characterized in that Including: A wind farm information acquisition module for obtaining target wind farm information; A power generation calculation module for using the Ishihara-Qian wake model to describe the total power generation of the wind farm based on the target wind farm information; A layout optimization module for gridding the target wind farm, taking the maximum total power generation of the wind farm as the optimization goal, and using the improved genetic algorithm to optimize the grid positions of the wind turbines in the wind farm to obtain the wind farm layout plan; among them, the crossover rate and mutation rate of the improved genetic algorithm are calculated according to the population fitness; When the power generation calculation module executes using the Ishihara-Qian wake model to describe the total power generation of the wind farm based on the target wind farm information, it specifically executes the following process: The use of the Ishihara-Qian wake model to describe the total power generation of the wind farm based on the target wind farm information includes: Convert the initial wind turbine layout of the wind farm to the main wind direction according to the given wind direction; Calculate the wind speed deficit and additional turbulence intensity generated by the upstream wind turbines of the nth wind turbine in the wind farm at the nth wind turbine along the wind direction according to the unit specification parameters; Calculate the average wind speed at the rotor plane of the nth wind turbine according to the wind speed deficit, and calculate the average turbulence intensity at the nth wind turbine according to the additional turbulence intensity; Calculate the power generation of the nth wind turbine based on the average wind speed at the rotor plane and the average turbulence intensity, and calculate the total power generation of the wind farm according to the power generation of each wind turbine; Among them, the calculation of the average turbulence intensity at the nth wind turbine according to the additional turbulence intensity includes: Introduce a turbulence superposition correction term in the additional turbulence intensity as follows: where r m represents the distance from any point in the y-z plane to the hub center of the m-th upstream wind turbine of the n-th wind turbine, and there is H is the height of the hub center of the wind turbine, and y m represents the y-axis coordinate of the m-th upstream wind turbine; L w,ml is the secant length of the intersection area between the m-th upstream wind turbine and the wake of wind turbine l, and there is Wind turbine l is the wind turbine closest to the m-th upstream wind turbine in the wind farm, and D w,s represents the wake width of the wind turbine, where s = m, l.

7. An electronic device, characterized in that, It includes a memory and a processor storing computer-executable instructions, and when the computer-executable instructions are executed by the processor, the device is caused to execute the wind farm layout optimization method based on the Gaussian wake model and the improved genetic algorithm according to any one of claims 1 to 5.

8. A readable storage medium, characterized in that, Store a computer-executable program, and when the program is executed, the wind farm layout optimization method based on the Gaussian wake model and the improved genetic algorithm according to any one of claims 1 to 5 can be realized.

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