A wind turbine layout optimization method based on diamond constraints

Abstract

This application discloses a wind turbine layout optimization method based on diamond constraints. It uses a more accurate wake analysis model, the Ishihara‑Qian wake model, to evaluate the power generation of a wind farm. This model can efficiently and accurately predict the wind speed loss and additional turbulence intensity in the wind turbine wake area. At the same time, this application adopts a diamond constraint strategy to represent the wind turbine spacing constraint, thereby constructing the constraint of the objective function. For wind farms with dominant risks, the diamond constraint can make full use of the internal space of the wind farm, improving energy output efficiency while maintaining safety and maintenance convenience.

Description

Technical Field

[0001] The present invention relates to wind power generation layout technology, and in particular to a wind turbine layout optimization method based on diamond constraints. Background Art

[0002] Wind energy is a key driver of sustainable energy. As a clean, renewable energy source, it plays a vital role in the transformation of the global energy structure. With technological advancements and the realization of economies of scale, wind power has become one of the most cost-effective renewable energy sources.

[0003] Optimizing wind farm layout not only impacts the economic benefits of wind farms but also directly impacts the effective utilization of wind energy resources and environmental protection. Precise layout design maximizes wind energy capture, minimizes interference between turbines, and optimizes the wind farm's adaptability to terrain and environment, ultimately achieving sustainable power generation.

[0004] In recent years, some research has begun exploring layout optimization strategies that employ specific geometric constraints. These methods limit the relative positions of wind turbines and reduce the wake effect, thereby improving the overall power generation efficiency of wind farms. However, these strategies have limited adaptability and flexibility when dealing with specific types of wind farms, such as those with concentrated wind directions. They rely on fixed or dynamic grid systems to determine wind turbine locations and do not fully consider the wake effect, resulting in suboptimal overall wind farm efficiency. Summary of the Invention

[0005] Based on this, the present invention aims to propose a wind turbine layout optimization method based on diamond constraints, use an improved three-dimensional wake analytical model to simulate the wake effect of the wind farm, and propose a diamond constraint model that adapts to the main wind direction to effectively utilize the wind farm space.

[0006] In a first aspect, the present invention proposes a wind turbine layout optimization method based on diamond constraints, comprising:

[0007] Obtain target wind farm information;

[0008] The Ishihara-Qian wake model is used to describe the total power generation of the wind farm based on the target wind farm information;

[0009] Determine the boundary of the target wind farm and construct the boundary constraints of the target wind farm so that the wind turbine positions solved in the target wind farm are all located within the boundary of the target wind farm;

[0010] With a single wind turbine as the center of the rhombus, and with the long diagonal of each rhombus parallel to the main wind direction of the target wind farm, and the rhombuses corresponding to each wind turbine congruent, the wind turbine spacing constraints of the target wind farm are constructed so that the layout solution contains exactly one wind turbine inside each rhombus.

[0011] Taking the maximum total power generation of the wind farm as the optimization goal, an optimization model is established with boundary constraints and wind turbine spacing constraints as constraints of the optimization goal. The genetic algorithm is used to solve the optimization model to obtain the wind turbine layout plan.

[0012] Furthermore, the boundary of the target wind farm is determined, and the boundary constraints of the target wind farm are constructed so that the solutions of the layout scheme are all located within the boundary of the target wind farm. The following are included:

[0013] Use polygons to describe the boundaries of the target wind farm and determine the coordinates of the boundary points;

[0014] According to the coordinates of the boundary points, the straight line equation of each side on the boundary is expressed as , where i represents the edge number, 、 、 are the coefficients of the equation calculated based on the coordinates of the edge endpoints;

[0015] Let the position coordinates of each wind turbine in the solution of the layout scheme be are all located within the boundary of the target wind farm, that is, the positions of all wind turbines in the target wind farm simultaneously satisfy the inequality , Q represents the number of edges on the boundary.

[0016] Furthermore, the construction process of wind turbine spacing constraints includes:

[0017] The main direction factor and the secondary direction factor are defined for the long diagonal and the short diagonal of the rhombus respectively. The main direction factor and the secondary direction factor are calculated according to the geometric parameters of the rhombus.

[0018] The projection distance of the fan in the diagonal direction of the diamond is calculated using the main direction factor and the secondary direction factor;

[0019] The projection distance of each wind turbine is used as the constraint expression of the wind turbine spacing constraint.

[0020] Furthermore, the main directional factor and the secondary directional factor are calculated based on the geometric parameters of the rhombus, including:

[0021] Assume that a diamond-shaped central target is , the coordinates of any point in the target wind farm are expressed as , principal direction factor and secondary directional factors They are represented as follows:

[0022]

[0023] ,

[0024] in, represents the angle between the long diagonal of the rhombus and the x-axis, and are the lengths of the long and short diagonals of the rhombus, respectively.

[0025] Furthermore, the projection distance is expressed as follows

[0026]

[0027] in, represents the main direction factor, represents the secondary direction factor, represents the expansion factor, which is related to the number of iterations of the optimization process, that is, , represents the initial expansion factor, represents the number of iterations, represents the attenuation coefficient;

[0028] The fan spacing constraint is expressed as .

[0029] Furthermore, taking the maximum total power generation of the wind farm as the optimization objective, and taking the boundary constraint and the wind turbine spacing constraint as the constraint conditions of the optimization objective, an optimization model is established, including:

[0030] For a given wind farm layout, the number m of coordinates in the target wind farm that do not meet the boundary constraints is calculated, and the penalty factor M is determined according to the rated power of the wind turbine. The optimization model is expressed as follows:

[0031]

[0032] in, The overall violation degree coefficient indicating that the spacing between all wind turbines in the wind farm does not meet the distance constraint, Indicates the total power generation of the wind farm.

[0033] Furthermore, the total power generation of the wind farm is described based on the target wind farm information using the Ishihara-Qian wake model, including:

[0034] Convert the initial wind turbine layout of the wind farm to the prevailing wind direction according to the given wind direction;

[0035] Calculate the wind speed loss and additional turbulence generated by the upstream wind turbine of the nth wind turbine in the wind farm at the nth wind turbine according to the unit specification parameters along the wind direction;

[0036] The average wind speed at the rotor plane of the nth wind turbine is calculated based on the wind speed loss, and the average turbulence at the nth wind turbine is calculated based on the additional turbulence;

[0037] The power generation of the nth wind turbine is calculated based on the average wind speed and average turbulence on the rotor plane, and the total power generation of the wind farm is calculated based on the power generation of each wind turbine.

[0038] Furthermore, calculating the average wind speed on the rotor plane at the nth wind turbine based on the wind speed deficit includes:

[0039] Considering the wind speed deficit caused by the upstream wind turbine of the nth wind turbine at the nth wind turbine, the total wind speed deficit at the nth wind turbine is calculated as follows:

[0040] ,

[0041] in, represents the wind speed loss caused by the mth upstream wind turbine at the nth wind turbine, Indicates the background wind speed loss, is the downwind position of the wind turbine hub center in the coordinate system, and are the coordinates of the crosswind direction and the vertical direction in the coordinate system respectively;

[0042] The average wind speed at the rotor plane is calculated based on the total wind speed loss at the nth wind turbine as follows:

[0043] ,

[0044] in, is the effective wind speed of the rotor surface of the nth wind turbine, is the blade swept area of ​​the nth fan, represents the integral over the rotor plane.

[0045] Furthermore, calculating the average turbulence at the nth fan according to the additional turbulence includes:

[0046] The turbulence superposition correction term is introduced into the additional turbulence as follows:

[0047]

[0048]

[0049]

[0050] in, It represents the distance from any point in the yz plane to the hub center of the mth upstream fan of the nth fan, , H is the height of the fan hub center, represents the y-axis coordinate of the mth upstream wind turbine; is the secant length of the intersection area between the tail flow of the mth upstream wind turbine and wind turbine l, , wind turbine l is the wind turbine closest to the mth upstream wind turbine in the wind farm, represents the fan wake width, .

[0051] Furthermore, the power generation of the nth wind turbine can be calculated based on the average wind speed and average turbulence on the rotor plane as follows:

[0052] ,

[0053] in, is the air density, is the blade swept area of ​​the nth fan, is the effective wind speed of the rotor surface of the nth wind turbine, is the power generation efficiency of the nth wind turbine, is the fan power coefficient.

[0054] Furthermore, the total power generation of the wind farm is calculated based on the power generation of each wind turbine, including:

[0055] The given wind farm layout is calculated based on the power generation of each wind turbine as follows: At ambient wind speed ,wind direction and turbulence The overall power generation under:

[0056]

[0057] in, represents the power generation of the nth wind turbine, i and j represent the wind speed and wind direction conditions respectively, and N represents the number of wind turbines;

[0058] Considering different wind direction and wind speed distribution frequencies, the total power generation of the wind farm is calculated as follows:

[0059]

[0060] Where I and J represent the total number of wind speed conditions and wind direction conditions, respectively. Indicates the frequency of wind direction and speed distribution.

[0061] In a second aspect, the present invention provides a wind turbine layout optimization device, comprising:

[0062] A wind farm information acquisition module is used to acquire target wind farm information;

[0063] Power generation calculation module, used to describe the total power generation of the wind farm based on the target wind farm information using the Ishihara-Qian wake model;

[0064] A first constraint construction module is used to determine the boundary of the target wind farm and construct the boundary constraints of the target wind farm so that the wind turbine positions solved in the target wind farm are all located within the boundary of the target wind farm;

[0065] The second constraint construction module is used to construct the wind turbine spacing constraints for the target wind farm, with a single wind turbine as the center of the rhombus. The long diagonal of each rhombus is parallel to the main wind direction of the target wind farm, and the rhombuses corresponding to each wind turbine are congruent. This ensures that the layout solution contains only one wind turbine inside each rhombus.

[0066] The layout optimization module is used to establish an optimization model with the maximum total power generation of the wind farm as the optimization goal, using boundary constraints and wind turbine spacing constraints as constraint conditions of the optimization goal, and using genetic algorithms to solve the optimization model to obtain the wind turbine layout plan.

[0067] In a third aspect, the present invention provides an electronic device comprising a memory storing computer-executable instructions and a processor, wherein when the computer-executable instructions are executed by the processor, the device executes the various steps of the wind turbine layout optimization method based on diamond constraints provided in the first aspect.

[0068] In a fourth aspect, the present invention provides a readable storage medium storing a computer executable program, which, when executed, can implement the various steps of the wind turbine layout optimization method based on diamond constraints provided in the first aspect.

[0069] It can be seen from the above technical solutions that the present invention has the following beneficial effects:

[0070] The present invention provides a wind turbine layout optimization method based on diamond constraints. It uses a more accurate wake analytical model, the Ishihara-Qian wake model, to evaluate the power generation of a wind farm. This model can efficiently and accurately predict the wind speed loss and additional turbulence intensity in the wind turbine wake area. At the same time, the present invention adopts a diamond constraint strategy to construct the constraints of the objective function. For wind farms with dominant risks, the diamond constraint can make full use of the internal space of the wind farm, improving energy output efficiency while maintaining safety and maintenance convenience. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0072] Figure 1This is a flow chart of a wind turbine layout optimization method based on diamond constraints provided by an embodiment of the present invention;

[0073] Figure 2 is a schematic diagram of boundary constraints and wind turbine spacing constraints provided by an embodiment of the present invention;

[0074] Figure 3 This is a schematic diagram of wind turbine spacing constraint calculation provided by an embodiment of the present invention;

[0075] Figure 4 This is a wind resource map provided by an embodiment of the present invention;

[0076] Figure 5 Schematic diagram of wind turbine layout after rhombus constraint optimization provided by an embodiment of the present invention;

[0077] Figure 6 Schematic diagram of the wind turbine wake after rhombus constraint optimization according to an embodiment of the present invention;

[0078] Figure 7 Schematic diagram of wind turbine layout after circular constraint optimization provided by an embodiment of the present invention;

[0079] Figure 8 Schematic diagram of the wind turbine wake after circular constraint optimization according to an embodiment of the present invention;

[0080] Figure 9 This is a comparison chart showing how the fitness of layout optimization based on diamond constraints and circular constraints changes with the number of algorithm iterations provided by an embodiment of the present invention;

[0081] Figure 10 Schematic diagram of the structure of a wind turbine layout optimization device provided by an embodiment of the present invention;

[0082] Figure 11 This is a diagram of the electronic device architecture provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0083] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0084] like Figure 1 As shown, an embodiment of the present invention provides a wind turbine layout optimization method based on diamond constraints, comprising the following steps:

[0085] Step S110: Acquire target wind farm information.

[0086] In this step, the target wind farm information obtained includes wind resource data and unit specification parameters, and the boundary of the wind farm is fitted into a polygon to obtain the boundary points of the site.

[0087] A further implementation involves gridding the target wind farm and turbine locations. For safety reasons, the turbine spacing d can be set to no less than 3D, where D is the diameter of the turbine rotor. Specifically, the site is divided into K numbered grids, with the center of each grid representing a possible installation location for a wind turbine. Let N be the number of wind turbines to be optimized. The wind farm layout can be represented by an array of N real numbers.

[0088] Step S120: Using the Ishihara-Qian wake model to describe the total power generation of the wind farm based on the target wind farm information.

[0089] Step S130: Determine the boundary of the target wind farm, and construct boundary constraints of the target wind farm so that all wind turbine positions solved in the target wind farm are located within the boundary of the target wind farm.

[0090] Step S140. With a single wind turbine as the center of the rhombus, and with the long diagonal of each rhombus parallel to the main wind direction of the target wind farm, and with the rhombuses corresponding to each wind turbine being congruent, construct the wind turbine spacing constraints of the target wind farm so that the solution to the layout plan has one and only one wind turbine inside each rhombus.

[0091] Step S150: Taking the maximum total power generation of the wind farm as the optimization goal, establishing an optimization model with boundary constraints and wind turbine spacing constraints as constraints of the optimization goal, and using a genetic algorithm to solve the optimization model to obtain a wind turbine layout plan.

[0092] In a further embodiment, step S120 includes the following steps:

[0093] Step S121: Convert the initial wind turbine layout of the wind farm to the main wind direction according to the given wind direction.

[0094] Step S122: Calculate the wind speed deficit and additional turbulence generated by the upstream wind turbine of the nth wind turbine in the wind farm at the nth wind turbine according to the wind turbine unit specification parameters along the wind direction.

[0095] Step S123. Calculate the average wind speed at the rotor plane of the n-th wind turbine based on the wind speed deficit, and calculate the average turbulence at the n-th wind turbine based on the additional turbulence.

[0096] Step S124: Calculate the power generation of the nth wind turbine based on the average wind speed and the average turbulence on the wind turbine plane, and calculate the total power generation of the wind farm based on the power generation of each wind turbine.

[0097] The embodiment of the present invention uses the more accurate Ishihara-Qian wake model to evaluate the overall power generation of a wind farm under given information. In the process of analyzing the model, the given parameters required include the given wind turbine layout X of the wind farm, the wind turbine hub height H, the ambient wind speed U i ,wind direction and turbulence .

[0098] Before introducing the Ishihara-Qian wake model to analyze the wind farm simulation, the following definitions are made:

[0099] For a given wind farm with N wind turbines, the wind turbine layout When calculating the power generation in each wind direction, it is necessary to transform its coordinates so that the future flow direction is aligned with the direction of the wind farm. It should be noted that the wind turbine n increases in sequence along the x direction in any wind direction. For any wind turbine in the wind farm, its power generation It can be calculated by the following formula:

[0100] ,

[0101] in, is the air density, is the blade swept area of ​​the nth fan, is the effective wind speed of the rotor surface of the nth wind turbine, is the power generation efficiency of the nth wind turbine, is the fan power coefficient, It can be calculated by the following formula:

[0102]

[0103] in, Indicates wind direction The wind speed distribution on the rotor surface of the next n-th wind turbine, is the blade swept area of ​​the nth fan, represents the integral over the rotor plane.

[0104] For a given model of wind turbine, due to its rotor area , power generation efficiency , and power factor curves It is known that we only need to obtain the effective inflow velocity of the fan: The power generation can be predicted. In order to accurately reflect the overall power generation of the wind farm, the embodiment of the present invention takes into account the wake effect. The Ishihara-Qian model is used to accurately predict the effect of the wind turbine wake on the plane speed of each wind turbine rotor. impact.

[0105] Specifically, the wind speed is divided into I intervals from the cut-in wind speed to the cut-out wind speed according to the set interval (the wind speed corresponding to each interval is recorded as U i ), the wind direction is divided into J intervals according to the set interval (the wind direction corresponding to each interval is recorded as ), and statistically analyze the probability distribution of different wind speed and wind direction intervals, as well as the turbulence at different wind speeds In a more preferred embodiment, if the turbulence at different wind speeds No statistical data is available, but it can be calculated based on the International Electrotechnical Commission standard IEC 61400-1.

[0106] The Ishihara-Qian wake model also incorporates the effect of turbulence on the wake wind speed loss. Therefore, it is necessary to introduce the effective inflow turbulence of the wind turbine rotor surface into each wind turbine. , the specific calculation method is as follows:

[0107]

[0108] in, Represents the distribution of the standard deviation of the pulsating wind speed on the rotor surface of the nth wind turbine.

[0109] In the Ishihara-Qian wake model, for any wind turbine n in the wind farm, the wind speed loss in its wake is and the additional turbulence generated It can be calculated as follows:

[0110]

[0111]

[0112]

[0113]

[0114] in, 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 wind wheel diameter, represents the fan wake width, as well as These are the parameters of the Ishihara-Qian wake model, as shown in Table 1 below:

[0115] Table 1

[0116]

[0117] in, It represents the thrust coefficient of the fan, which is about Function of thrust coefficient curve It is determined by the aerodynamic characteristics of the fan and is generally provided by the designer of the specific model of fan.

[0118] For wind direction Wind speed distribution on the rotor surface of the next nth wind turbine , linear superposition is used to consider the wind speed loss caused by the n-1 wind turbines upstream at the n-th wind turbine, and the total wind speed loss at the n-th wind turbine is calculated as follows:

[0119] ,

[0120] in, represents the wind speed loss caused by the mth upstream wind turbine at the nth wind turbine, Indicates the background wind speed loss, is the downwind position of the wind turbine hub center in the coordinate system, and are the crosswind and vertical coordinates in the coordinate system respectively.

[0121] The average wind speed at the rotor plane of the nth wind turbine is calculated based on the total wind speed loss calculated above as follows:

[0122] ,

[0123] in, is the effective wind speed of the rotor surface of the nth wind turbine, is the blade swept area of ​​the nth fan, represents the integral over the rotor plane.

[0124] In addition, the Ishihara-Qian wake model also considers the superposition effect of additional turbulence. Directly superimposing the additional turbulence intensities of two wind turbines using the linear square sum principle cannot reproduce the turbulence superposition results in the actual flow field. 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 additional turbulence The calculation expression of the corrected standard deviation of the pulsating wind speed on the wind wheel surface of the nth wind turbine is It can be calculated as follows:

[0125]

[0126]

[0127]

[0128] in, It represents the distance from any point in the yz plane to the hub center of the mth upstream fan of the nth fan, , H is the height of the fan hub center, represents the y-axis coordinate of the mth upstream wind turbine; is the secant length of the intersection area between the tail flow of the mth upstream wind turbine and wind turbine l, , wind turbine l is the wind turbine closest to the mth upstream wind turbine in the wind farm, represents the fan wake width, .

[0129] The calculated Substitute the aforementioned inflow turbulence into The calculation expression is the average turbulence at the nth wind turbine. From the above calculation process, it can be seen that turbulence is directly related to the standard deviation of wind speed, which affects the mean wind speed of the wind turbine, thereby indirectly affecting the power generation of each wind turbine. Considering the turbulence superposition, according to the above power generation The calculation expression can be used to calculate the power generation at the nth wind turbine.

[0130] By performing the above calculation for each wind turbine, we can describe the performance of a given wind turbine layout X at an ambient wind speed U. i ,wind direction and turbulence The overall power generation under:

[0131]

[0132] in, represents the power generation of the nth wind turbine, i and j represent the wind speed and wind direction conditions respectively, and n represents the number of wind turbines.

[0133] Frequency distribution of wind speed in known wind direction In this case, the total power generation of the wind farm can be calculated, which is also the initial optimization model for layout optimization:

[0134]

[0135] Where I and J represent the total number of wind speed conditions and the total number of wind direction conditions, respectively.

[0136] In a further embodiment, considering that the boundary of a wind farm may present a complex polygonal geometric shape, and that the gridded wind farm layout often ignores potential feasible solutions and reduces space utilization, a further embodiment of the present invention constructs boundary constraints and wind turbine spacing constraints for the above-mentioned initial optimization model.

[0137] In a more preferred embodiment, step S130 includes the following steps:

[0138] Step S131. Describe the boundary of the target wind farm with a polygon and determine the coordinates of the boundary points;

[0139] Step S132. Establish the straight line equation of each edge on the boundary according to the coordinates of the boundary points. , where i represents the edge number, 、 、 are the coefficients of the equation calculated based on the coordinates of the edge endpoints;

[0140] Step S133. Let the position coordinates of each wind turbine in the solution of the layout plan be are all located within the boundary of the target wind farm, that is, the positions of all wind turbines in the target wind farm simultaneously satisfy the inequality , Q represents the number of edges on the boundary.

[0141] Specifically, an arbitrary polygon is used for the boundary of the wind farm, and its boundary points can be expressed as , for the edges on the polygon , based on the endpoints of the edge and The equation of the straight line is expressed as , determine the direction of the inner side of the polygon relative to the edge, establish a linear inequality for each edge, so that all wind turbine positions are on one side of the straight line to ensure that the wind turbine is inside the polygon. Then the position of each wind turbine in the target wind farm must satisfy the inequality at the same time. .

[0142] In a more preferred embodiment, step S140 constructs a diamond constraint based on the main wind direction. Compared with the traditional non-uniform constraint conditions used to describe wind turbines in different wind directions, the diamond constraint proposed in the embodiment of the present invention constructs a diamond area for each wind turbine, with the wind turbine as the center of the diamond. The long diagonal of each diamond is parallel to the main wind direction of the target wind farm, and the diamonds corresponding to each wind turbine are congruent, so that the solution of the layout plan has only one wind turbine inside each diamond. The constraint is given by Figure 2 gesture.

[0143] The construction process of diamond constraints is specifically described below.

[0144] Assume that a diamond-shaped central target is , the coordinates of any point in the target wind farm are expressed as ,by The lengths of the long and short diagonals of the rhombus centered are and , the angles between the two diagonals and the x-axis are and , then the coordinates in space The positional relationship with the rhombus can be expressed as follows:

[0145]

[0146] The above formulas represent the spatial coordinates respectively Located inside, outside, and on the borders of the rhombus.

[0147] The embodiment of the present invention takes into account the arbitrariness of the rhombus angle and diagonal length. Since the rhombus is rotated, the coordinates need to be considered. The projection distance in the diagonal direction of the rhombus, for this purpose, the parameter To adjust the weight of the point in two directions.

[0148] For the long diagonal of a rhombus, the principal direction factor is defined as:

[0149]

[0150] For the short diagonal, the secondary direction factor is defined as:

[0151]

[0152] The error tolerance of the algorithm is increased by using the expansion factor, and the corrected projection distance is expressed as follows

[0153]

[0154] in, represents the expansion factor, which is related to the number of iterations of the optimization process, that is, , represents the initial expansion factor, represents the number of iterations, represents the attenuation coefficient, Take a constant to control the decay rate of the expansion factor as the number of iterations increases.

[0155] In order to ensure that there is only one fan inside each rhombus, that is, the spatial coordinates Not in In the diamond centered on ,Right now

[0156]

[0157] The fan spacing constraint obtained above can be obtained by Figure 3 gesture.

[0158] The above steps have resulted in an initial optimization model For a given wind farm layout, calculate the number of coordinates m that do not meet the boundary constraints within the target wind farm. Secondly, calculate the degree of violation of the distance constraint for each wind turbine spacing according to the following formula (defined as a real number between 0 and 1, the closer the distance between wind turbines, the more serious the violation of the constraint). Then sum the violation degree coefficients of all wind turbines to obtain the overall violation coefficient c dist .

[0159]

[0160] The penalty factor M is determined based on the rated power of the wind turbine. The penalty factor is usually taken as the rated power of a single wind turbine to be optimized. The final optimization model is expressed as follows:

[0161]

[0162] The optimization model aims to maximize the total power generation of the wind farm while simultaneously considering the wind farm's boundary constraints and turbine spacing constraints. In the early stages of the iterations, when the number of iterations g is small, the factor q is large, indicating a strong penalty for turbine spacing. Later in the algorithm's run, the penalty gradually decreases, allowing the algorithm to focus on minimizing the wake effect and thus increasing power generation.

[0163] With the above optimization target model As individuals in the genetic algorithm, in a more preferred embodiment, considering that higher individual fitness increases the probability of selection, embodiments of the present invention employ a tournament selection method. When the genetic algorithm reaches a set number of iterations or converges to the target solution, the optimized wind farm layout is output, along with the decoded position coordinates of each wind turbine.

[0164] A further embodiment of the genetic algorithm may use a single point crossover strategy.

[0165] In order to verify the effectiveness of the layout optimization method proposed in the present invention, an experimental example is given below.

[0166] The Horns Rev 2 offshore wind farm is used as the research object to calculate the optimization scheme. The Horns Rev 2 offshore wind farm is located in the northeastern waters of Denmark, about 30 km offshore. It consists of 91 Siemens SWT2.3-93 wind turbines with a total installed capacity of 209.3MW. The wind farm occupies an area of ​​about 33 km offshore. 2 , the site boundary is roughly fan-shaped. 16 sets of wind directions and certain wind speed probabilities are selected as input wind conditions, and the wind resource map is as follows Figure 4 shown.

[0167] Turbulence is calculated according to the International Electrotechnical Commission standard IEC 61400-1:

[0168]

[0169] in, is the reference turbulence when the wind speed is 15m / s, taken as 0.12, b=5.6m / s, is the wind speed passing through the fan.

[0170] The specifications of the wind turbines installed in the wind farm are shown in Table 2.

[0171]

[0172] The rated power of the wind turbine is 2.4MW. To ensure that the total power is consistent with the original power plant, the number of wind turbines is selected as 80.

[0173] When considering diamond constraints, the algorithm parameters are shown in Table 3. When considering circular constraints, to ensure the safety of the wind turbine cluster, the distance between wind turbines in all directions must be less than 4 times the wind turbine diameter (4D).

[0174] Table 3

[0175]

[0176] The optimal layout scheme of wind farms considering diamond constraints is as follows: Figure 5 The wake diagram is shown in Figure 6 ; The optimal layout scheme of the wind farm considering the circular constraint is as follows Figure 7 The wake diagram is shown in Figure 8 The fitness curves corresponding to two different constraints are as follows: Figure 9 The optimization results are shown in Table 4.

[0177]

[0178] from Figures 5 to 9 As can be seen from Table 4, compared with the initial scheme, the optimized power generation is greatly improved and the wake loss is controlled within 15%. Compared with the traditional circular constraint strategy, the output power of the wind farm under the diamond constraint based on the main wind direction is significantly improved.

[0179] When optimizing a wind turbine array, the diamond constraint model resulted in higher power generation than the circular constraint model. In this example, power generation increased by 1.06%. Figure 5 and Figure 7 The optimized layout diagram shows that when considering the diamond constraint, because the area of ​​a diamond is smaller than that of a circle with the same diameter and major axis, the impact area of ​​a single wind turbine layout is smaller, so more wind turbines can be deployed in the same area. This is particularly important for wind farms with limited area and concentrated wind direction, because increasing the number of wind turbines deployed will significantly increase the wind farm's power generation.

[0180] The above embodiment describes a wind turbine layout optimization method based on diamond constraints, which uses a higher-precision wake analytical model, the Ishihara-Qian wake model, to evaluate the power generation of a wind farm. This model can efficiently and accurately predict wind speed losses and additional turbulence intensity. At the same time, the present invention adopts a diamond constraint strategy to construct the constraints of the objective function. For wind farms with dominant risks, the diamond constraint can make full use of the internal space of the wind farm, improving energy output efficiency while maintaining safety and maintenance convenience.

[0181] The above-disclosed method can be implemented using various types of equipment. Therefore, the present invention also discloses a wind turbine layout optimization device corresponding to the above-disclosed method, which will be described in detail with specific embodiments given below.

[0182] like Figure 10 As shown, one embodiment of the present invention provides a wind turbine layout optimization device, comprising:

[0183] The wind farm information acquisition module 1002 is used to acquire target wind farm information;

[0184] A power generation calculation module 1004 is used to describe the total power generation of the wind farm based on the target wind farm information using the Ishihara-Qian wake model;

[0185] A first constraint building module 1006 is used to determine the boundary of the target wind farm and build boundary constraints of the target wind farm so that the wind turbine positions solved in the target wind farm are all located within the boundary of the target wind farm;

[0186] The second constraint construction module 1008 is used to construct wind turbine spacing constraints for the target wind farm, with a single wind turbine as the center of the rhombus, and to ensure that the long diagonal of each rhombus is parallel to the main wind direction of the target wind farm, and the rhombuses corresponding to each wind turbine are congruent, so that the layout solution contains only one wind turbine inside each rhombus;

[0187] The layout optimization module 1010 is used to establish an optimization model with the maximum total power generation of the wind farm as the optimization goal, using boundary constraints and wind turbine spacing constraints as constraints of the optimization goal, and using a genetic algorithm to solve the optimization model to obtain a wind farm layout plan.

[0188] The device provided in the embodiment of the present application has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment.

[0189] The methods and related devices mentioned in the above embodiments are described with reference to the method flow charts and / or structural diagrams provided in the embodiments of the present application. Specifically, each process and / or block in the method flow charts and / or structural diagrams, as well as the combination of processes and / or blocks in the flow charts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 Schematic diagram of one or more processes and / or structures Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 Schematic diagram of one or more processes and / or structures Figure 1 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 produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 The flow or flows and / or structures illustrate the steps of the functions specified in one block or multiple blocks.

[0190] The following embodiments illustrate this method using a computer device as an example. It is understood that the computer device may be any device with computing and processing capabilities, including, but not limited to, a server or a personal laptop. In one embodiment, the computer device may be an application server, which may be a server for running the application under test.

[0191] See Figure 11 , which shows a hardware block diagram of an electronic device, which 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 may also represent various forms of mobile devices, such as personal digital assistants, 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.

[0192] like Figure 11As 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;

[0193] 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 communicate with each other through the communication bus 4;

[0194] The processor 1 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention;

[0195] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory;

[0196] The memory stores a program, and the processor can call the program stored in the memory, wherein the program is used to implement various processing flows of the aforementioned wind turbine layout optimization solution based on diamond constraints.

[0197] An embodiment of the present invention also provides a readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the various processing flows of the wind turbine layout optimization solution based on diamond constraints provided in the above embodiment and / or any possible implementation method in combination with the embodiment.

[0198] The above embodiments have described the invention in particular detail with respect to possible scenarios, and those skilled in the art will recognize that the invention can be practiced through other embodiments. The specific naming of components, capitalization of terms, attributes, data structures, or any other programming or structural aspects are not mandatory or important, and the mechanisms or features of the invention may have different names, forms, or procedures. The system may 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 between the various system components described herein is exemplary only and not mandatory; rather, the functions performed by a single system component may be performed by multiple components, or the functions performed by multiple components may be performed by a single component.

[0199] Those skilled in the art will appreciate that the various steps of the method disclosed above can be implemented by a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the embodiments disclosed herein are not limited to any specific combination of hardware and software.

[0200] The programs executable by these computing devices (also referred to as programs, software, software applications, or code) include machine instructions for programmable processors 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, apparatus, and / or device (e.g., a magnetic 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.

[0201] Certain aspects of the present invention include the process steps and instructions described herein 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 in software, they can be downloaded, stored on different platforms used by various operating systems, and operated from the platforms.

[0202] Those skilled in the art will understand that the structures shown in the accompanying drawings are merely block diagrams of partial structures related to the scheme of the present application, and do not constitute a limitation on the terminal device to which the scheme of the present application is applied. The specific terminal device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0203] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "possible design" means that the specific features, structures, materials or characteristics described in conjunction 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 may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and features of different embodiments or examples, unless they are mutually inconsistent.

[0204] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0205] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wind turbine layout optimization method based on diamond constraints, characterized in that: include: Obtain target wind farm information; The Ishihara-Qian wake model is used to describe the total power generation of the wind farm based on the target wind farm information; Determine the boundary of the target wind farm and construct the boundary constraints of the target wind farm so that the wind turbine positions solved in the target wind farm are all located within the boundary of the target wind farm; With a single wind turbine as the center of the rhombus, and with the long diagonal of each rhombus parallel to the main wind direction of the target wind farm, and the rhombuses corresponding to each wind turbine congruent, the wind turbine spacing constraints of the target wind farm are constructed so that the layout solution contains exactly one wind turbine inside each rhombus. The process of constructing the wind turbine spacing constraint includes: The main direction factor and the secondary direction factor are defined for the long diagonal and the short diagonal of the rhombus respectively. The main direction factor and the secondary direction factor are calculated according to the geometric parameters of the rhombus. The projection distance of the wind turbine in the diagonal direction of the diamond is calculated using the main direction factor and the secondary direction factor. The projection distance of each wind turbine is used as the constraint expression of the wind turbine spacing constraint. Specifically, assuming that the center target of a diamond is , the coordinates of any point in the target wind farm are expressed as , principal direction factor and secondary directional factors They are represented as follows: ； ， in, represents the angle between the long diagonal of the rhombus and the x-axis, and are the lengths of the long and short diagonals of the rhombus, respectively; The projection distance is expressed as follows ； in, represents the main direction factor, represents the secondary direction factor, represents the expansion factor, which is related to the number of iterations of the optimization process, that is, , represents the initial expansion factor, represents the number of iterations, represents the attenuation coefficient; The fan spacing constraint is expressed as ; Taking the maximum total power generation of the wind farm as the optimization goal, an optimization model is established with boundary constraints and wind turbine spacing constraints as constraints of the optimization goal. The genetic algorithm is used to solve the optimization model to obtain the wind turbine layout plan.

2. The method according to claim 1, characterized in that Determining the boundary of the target wind farm and constructing boundary constraints of the target wind farm so that all solutions of the layout scheme are located within the boundary of the target wind farm includes: Use polygons to describe the boundaries of the target wind farm and determine the coordinates of the boundary points; According to the coordinates of the boundary points, the straight line equation of each side on the boundary is expressed as , where i represents the edge number, 、 、 are the coefficients of the equation calculated based on the coordinates of the edge endpoints; Let the position coordinates of each wind turbine in the solution of the layout scheme be are all located within the boundary of the target wind farm, that is, the positions of all wind turbines in the target wind farm simultaneously satisfy the inequality , Q represents the number of edges on the boundary.

3. The method according to claim 1, characterized in that The optimization model is established by taking the maximum total power generation of the wind farm as the optimization objective and taking the boundary constraint and the wind turbine spacing constraint as the constraint conditions of the optimization objective, including: For a given wind farm layout, the number m of coordinates that do not meet the boundary constraints in the target wind farm is calculated, and the penalty factor M is determined according to the rated power of the wind turbine. The optimization model is expressed as follows: ； in, The overall violation degree coefficient indicating that the spacing between all wind turbines in the wind farm does not meet the distance constraint, Indicates the total power generation of the wind farm.

4. The method according to claim 1, wherein The method of using 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 prevailing wind direction according to the given wind direction; Calculate the wind speed loss and additional turbulence generated by the upstream wind turbine of the nth wind turbine in the wind farm at the nth wind turbine according to the unit specification parameters along the wind direction; The average wind speed at the rotor plane of the nth wind turbine is calculated based on the wind speed loss, and the average turbulence at the nth wind turbine is calculated based on the additional turbulence; The power generation of the nth wind turbine is calculated based on the average wind speed and average turbulence on the rotor plane, and the total power generation of the wind farm is calculated based on the power generation of each wind turbine.

5. The method according to claim 4, characterized in that Calculating the average wind speed on the rotor plane of the nth wind turbine according to the wind speed loss includes: Considering the wind speed deficit caused by the upstream wind turbine of the nth wind turbine at the nth wind turbine, the total wind speed deficit at the nth wind turbine is calculated as follows: ， in, represents the wind speed loss caused by the mth upstream wind turbine at the nth wind turbine, Indicates the background wind speed loss, is the position of the center of the wind turbine hub along the wind direction in the coordinate system, and are the coordinates of the crosswind direction and the vertical direction in the coordinate system respectively; The average wind speed at the rotor plane is calculated based on the total wind speed loss at the nth wind turbine as follows: ， in, is the effective wind speed of the rotor surface of the nth wind turbine, is the blade swept area of ​​the nth fan, represents the integral over the rotor plane.

6. A wind turbine layout optimization device, characterized in that: include: A wind farm information acquisition module is used to acquire target wind farm information; Power generation calculation module, used to describe the total power generation of the wind farm based on the target wind farm information using the Ishihara-Qian wake model; A first constraint construction module is used to determine the boundary of the target wind farm and construct the boundary constraints of the target wind farm so that the wind turbine positions solved in the target wind farm are all located within the boundary of the target wind farm; The second constraint construction module is used to construct the wind turbine spacing constraints for the target wind farm, with a single wind turbine as the center of the rhombus. The long diagonal of each rhombus is parallel to the main wind direction of the target wind farm, and the rhombuses corresponding to each wind turbine are congruent. This ensures that the layout solution contains only one wind turbine inside each rhombus. When the second constraint building module builds the wind turbine spacing constraint, the following process is involved: The main direction factor and the secondary direction factor are defined for the long diagonal and the short diagonal of the rhombus respectively. The main direction factor and the secondary direction factor are calculated according to the geometric parameters of the rhombus. The projection distance of the wind turbine in the diagonal direction of the diamond is calculated using the main direction factor and the secondary direction factor. The projection distance of each wind turbine is used as the constraint expression of the wind turbine spacing constraint. Specifically, assuming that the center target of a diamond is , the coordinates of any point in the target wind farm are expressed as , principal direction factor and secondary directional factors They are represented as follows: ； ， in, represents the angle between the long diagonal of the rhombus and the x-axis, and are the lengths of the long and short diagonals of the rhombus, respectively; The projection distance is expressed as follows ； in, represents the main direction factor, represents the secondary direction factor, represents the expansion factor, which is related to the number of iterations of the optimization process, that is, , represents the initial expansion factor, represents the number of iterations, represents the attenuation coefficient; The fan spacing constraint is expressed as ; The layout optimization module is used to establish an optimization model with the maximum total power generation of the wind farm as the optimization goal, using boundary constraints and wind turbine spacing constraints as constraint conditions of the optimization goal, and using genetic algorithms to solve the optimization model to obtain the wind turbine layout plan.

7. An electronic device, characterized in that: The device comprises a memory storing computer executable instructions and a processor, and when the computer executable instructions are executed by the processor, the device executes the wind turbine layout optimization method based on diamond constraints as described in any one of claims 1 to 5.

8. A readable storage medium, characterized in that: A computer executable program is stored, and when the program is executed, the wind turbine layout optimization method based on diamond constraints as described in any one of claims 1 to 5 can be implemented.

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