A multi-blade wind turbine unit arrangement method, system, device and medium

Abstract

The application discloses a kind of multi-blade wind turbine layout method, system, equipment and medium, it is related to wind power generation field, the method includes: obtaining the vehicle data and wind speed data of target site on highway;Based on vehicle data and wind speed data regression function is constructed, and according to wind speed data and the time proportion of vehicle flow when there is vehicle, the average annual wind speed is calculated;Based on the width of highway green belt, the first geometric parameter of wind turbine is determined;Based on the average annual wind speed, the second geometric parameter of wind turbine is determined using computational fluid dynamics method;According to the first geometric parameter and the second geometric parameter, the flow field of wind turbine with different installation combinations is calculated using computational fluid dynamics method, and the optimal installation parameter is determined according to the flow field calculation result and regression function, and the wind turbine is laid according to the first geometric parameter, the second geometric parameter and the optimal installation parameter.The application is suitable for highway wake wind resources.

Description

Technical Field

[0001] This invention relates to the field of wind power generation, and in particular to a method, system, equipment and medium for the layout of multi-blade wind turbine units. Background Technology

[0002] Currently, vigorously developing renewable energy has become a major strategic direction for global energy transition and addressing climate change. Wind energy is a clean and renewable energy source, and the utilization of wind energy from the natural environment has a long history. A wind turbine is an energy conversion device that can convert wind energy into mechanical energy, electrical energy, and thermal energy. This paper proposes the idea of ​​installing small wind turbines in the median strip of highways to utilize wind energy generated by vehicles.

[0003] In wind turbine selection, horizontal axis wind turbines require yaw devices, while vertical axis wind turbines can accept wind from all directions without the need for yaw control, resulting in a simpler structure and control. Vertical axis wind turbines can be divided into lift-type and drag-type turbines. Lift-type turbines have higher aerodynamic efficiency than drag-type turbines, but they suffer from starting difficulties at low wind speeds, while drag-type vertical axis turbines have issues such as starting dead zones and complex structures. Currently, existing vertical axis wind turbines on the market are designed for medium to high wind speeds, but wind speeds are low in highway medians, making existing vertical axis turbines unsuitable for the wind resource characteristics of highways. Summary of the Invention

[0004] Based on this, embodiments of the present invention provide a method, system, equipment and medium for the deployment of multi-blade wind turbines, which are suitable for the wake wind resources of highways.

[0005] To achieve the above objectives, embodiments of the present invention provide the following solutions:

[0006] A method for arranging multi-bladed wind turbine units includes:

[0007] Acquire vehicle data and wind speed data at a target location on a highway; the vehicle data includes traffic flow and average vehicle speed; the wind speed data includes: first wind speed data and second wind speed data measured at predetermined intervals from the vertical direction at the target location within a predetermined height range; the first wind speed is the average wind speed of natural wind when there are no vehicles; the second wind speed is the average wind speed of natural wind when there are vehicles.

[0008] A regression function is constructed based on the vehicle data and the wind speed data, and the annual average wind speed is calculated according to the wind speed data and the time proportion of traffic flow when there are vehicles; the regression function represents the relationship between vehicle data and average wind speed.

[0009] Based on the width of the green belt along the highway, the first geometric parameters of the wind turbine are determined; the first geometric parameters include: radial blade width, distance from the center to the radial blade, blade radius, and radius of the circle in which the blade center is located;

[0010] Based on the annual average wind speed, the second geometric parameters of the wind turbine are determined using computational fluid dynamics; the second geometric parameters include: the number of blades, blade camber, blade height, and rotational speed of the wind turbine.

[0011] Based on the first and second geometric parameters, computational fluid dynamics is used to calculate the flow field of wind turbines with different installation combinations. The optimal installation parameters are determined based on the flow field calculation results and the regression function. The installation parameters include the number of wind turbines and the installation distance between them. The number of wind turbines and / or the installation distance between them differ in different installation combinations. The optimal installation parameters have the minimum payback period.

[0012] The wind turbine units are deployed according to the first geometric parameters, the second geometric parameters, and the optimal installation parameters.

[0013] Optionally, based on the first and second geometric parameters, computational fluid dynamics methods are used to calculate the flow field of wind turbines with different installation combinations, and the optimal installation parameters are determined based on the flow field calculation results and the regression function, specifically including:

[0014] Based on the first and second geometric parameters, a two-dimensional geometric model of the blade is established using CAD tools, and a computational mesh is generated using computational fluid dynamics. Based on the computational mesh, flow field calculations are performed on wind turbine units with different installation combinations to obtain flow field calculation results. The flow field calculation results include the torque of the wind turbine rotor in the wind turbine unit.

[0015] The power of the wind turbine is calculated based on the torque and the rotational speed, and the wind energy conversion efficiency of the wind turbine is calculated based on the power.

[0016] A linear function of power generation and installation distance is established based on the power and wind energy conversion efficiency.

[0017] The daily power generation per kilometer for different installation combinations is calculated based on the linear function and the regression function.

[0018] The payback period for different installation combinations is determined based on the daily power generation per kilometer, and the installation parameters corresponding to the minimum payback period are determined as the optimal installation parameters.

[0019] Optionally, the first geometric parameters of the wind turbine are determined based on the width of the green belt along the highway, specifically including:

[0020] The maximum diameter of the impeller is obtained by subtracting the set width value from the width of the green belt on the highway.

[0021] The first geometric parameters of the wind turbine are determined based on the maximum diameter of the impeller.

[0022] Optionally, based on the annual average wind speed, the second geometric parameters of the wind turbine are determined using computational fluid dynamics methods, specifically including:

[0023] Geometric models of wind turbines with different numbers of blades, blade camber, blade height, and rotational speed are performed. Simulation boundary conditions are set according to the annual average wind speed. Computational fluid dynamics is used to perform performance calculations. The second geometric parameters of the wind turbines are determined based on the performance calculation results.

[0024] Optionally, a regression function is constructed based on the vehicle data and the wind speed data, specifically including:

[0025] Based on the vehicle data and the second wind speed in the wind speed data, an initial regression function is constructed using a quadratic polynomial;

[0026] The regression function is corrected using the first wind speed from the wind speed data to obtain the final regression function.

[0027] Optionally, the formula for calculating the first geometric parameter is:

[0028] 2a + D2 = D1

[0029]

[0030]

[0031]

[0032] Where D1 is the maximum impeller diameter; β1 is the angle between the outlet wind speed and the turbine tangent; β2 is the angle between the relative velocity from the average wind speed to the turbine blade tip and the turbine tangent; a is the radial blade width; D2 is the distance from the center of the circle to the radial blade; R b R is the blade radius; c It is the radius of the circle in which the center of the blade lies.

[0033] Optionally, the expression for the regression function is:

[0034] v = ax 2 +bxy+cy 2 +dx+ey+f

[0035] Where x represents traffic flow; y represents average vehicle speed; v represents average wind speed; and a, b, c, d, e, and f are all fitting coefficients.

[0036] This invention also discloses a multi-blade wind turbine deployment system, comprising:

[0037] The data acquisition module is used to acquire vehicle data and wind speed data at a target location on the highway. The vehicle data includes traffic flow and average vehicle speed. The wind speed data includes a first wind speed and a second wind speed measured at predetermined intervals from the vertical direction at the target location within a set height range. The first wind speed is the average wind speed of natural wind when there are no vehicles. The second wind speed is the average wind speed of natural wind when there are vehicles.

[0038] The regression function construction module is used to construct a regression function based on the vehicle data and the wind speed data, and to calculate the annual average wind speed according to the wind speed data and the time proportion of traffic flow when there are vehicles; the regression function represents the relationship between vehicle data and average wind speed.

[0039] The first geometric parameter calculation module is used to determine the first geometric parameters of the wind turbine based on the width of the green belt of the highway; the first geometric parameters include: radial blade width, distance from the center to the radial blade, blade radius, and radius of the circle in which the blade center is located;

[0040] The second geometric parameter calculation module is used to determine the second geometric parameters of the wind turbine based on the annual average wind speed using computational fluid dynamics methods; the second geometric parameters include: the number of blades, blade camber, blade height, and rotational speed of the wind turbine.

[0041] The installation parameter calculation module is used to perform flow field calculations on wind turbines with different installation combinations based on the first geometric parameters and the second geometric parameters using computational fluid dynamics methods, and to determine the optimal installation parameters based on the flow field calculation results and the regression function. The installation parameters include: the number of wind turbines and the installation distance between wind turbines; the number of wind turbines and / or the installation distance between wind turbines are different in different installation combinations; the optimal installation parameters have the minimum payback period.

[0042] The wind turbine deployment module is used to deploy wind turbine units according to the first geometric parameters, the second geometric parameters, and the optimal installation parameters.

[0043] The present invention also discloses an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the above-described multi-blade wind turbine deployment method.

[0044] The present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described multi-blade wind turbine deployment method.

[0045] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0046] This invention constructs a regression function based on vehicle and wind speed data, and calculates the annual average wind speed based on wind speed data and the time proportion of traffic flow when vehicles are present. It determines the first geometric parameter of the wind turbine based on the width of the green belt along the highway. Based on the annual average wind speed, it uses computational fluid dynamics to determine the second geometric parameter of the wind turbine. Based on the first and second geometric parameters, it uses computational fluid dynamics to perform flow field calculations on wind turbines with different installation combinations, and determines the optimal installation parameters based on the flow field calculation results and the regression function. By deploying wind turbines according to the first, second, and optimal geometric parameters, the multi-blade design is more suitable for highway wake wind resources. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 A flowchart illustrating the multi-blade wind turbine layout method provided in this embodiment of the invention;

[0049] Figure 2 A schematic diagram illustrating the calculation principle of the first geometric parameter provided in an embodiment of the present invention;

[0050] Figure 3 A schematic diagram illustrating the calculation principle of wind energy conversion efficiency provided in an embodiment of the present invention;

[0051] Figure 4 This is a schematic diagram of the installation distance calculation model provided in an embodiment of the present invention;

[0052] Figure 5 This is a model diagram of a multi-bladed vertical axis wind turbine provided in an embodiment of the present invention;

[0053] Figure 6 This is a diagram showing the relative power coefficient distribution of wind turbines at different spacings, provided in an embodiment of the present invention.

[0054] Figure 7 This is a structural diagram of a multi-blade wind turbine deployment system provided in an embodiment of the present invention. Detailed Implementation

[0055] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention.

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Example 1

[0058] See Figure 1 The multi-blade wind turbine layout method of this embodiment includes:

[0059] Step 101: Obtain vehicle data and wind speed data at the target location on the highway.

[0060] The vehicle data includes: traffic flow and average vehicle speed; the wind speed data includes: first wind speed data and second wind speed data measured at predetermined intervals from the direction perpendicular to the ground within a predetermined height range at the target location; the first wind speed is the average wind speed of natural wind when there are no vehicles; the second wind speed is the average wind speed of natural wind when there are vehicles.

[0061] Step 102: Construct a regression function based on the vehicle data and the wind speed data, and calculate the annual average wind speed according to the wind speed data and the time proportion of traffic flow when there are vehicles.

[0062] The regression function characterizes the relationship between vehicle data and average wind speed.

[0063] Specifically, the regression function constructed based on the vehicle data and the wind speed data includes:

[0064] Based on the vehicle data and the second wind speed in the wind speed data, an initial regression function is constructed using a quadratic polynomial; the regression function is then corrected using the first wind speed in the wind speed data to obtain the final regression function.

[0065] The expression for the regression function is:

[0066] v = ax 2 +bxy+cy 2 +dx+ey+f

[0067] Where x represents traffic flow; y represents average vehicle speed; v represents average wind speed; and a, b, c, d, e, and f are all fitting coefficients.

[0068] Step 103: Based on the width of the green belt of the highway, determine the first geometric parameters of the wind turbine; the first geometric parameters include: radial blade width, distance from the center to the radial blade, blade radius, and radius of the circle in which the blade center is located.

[0069] Step 103 specifically includes:

[0070] The maximum diameter of the impeller is obtained by subtracting the set width value from the width of the green belt of the highway; the first geometric parameters of the wind turbine are determined based on the maximum diameter of the impeller.

[0071] See Figure 2 The formula for calculating the first geometric parameter is:

[0072] 2a + D2 = D1

[0073]

[0074]

[0075]

[0076] Where D1 is the maximum impeller diameter; β1 is the angle between the outlet wind speed and the turbine tangent; β2 is the angle between the relative velocity from the average wind speed to the turbine blade tip and the turbine tangent; a is the radial blade width; D2 is the distance from the center of the circle to the radial blade; R b R is the blade radius; c It is the radius of the circle in which the center of the blade lies. Figure 2 θ of d b The angle that each blade traverses.

[0077] Step 104: Based on the annual average wind speed, determine the second geometric parameters of the wind turbine using computational fluid dynamics (CFD) methods.

[0078] The second geometric parameters include: the number of blades, blade camber, blade height, and rotational speed of the wind turbine.

[0079] Step 104 specifically includes:

[0080] Geometric models of wind turbines with different numbers of blades, blade camber, blade height, and rotational speed are performed. Simulation boundary conditions are set according to the annual average wind speed. Computational fluid dynamics is used to perform performance calculations. The second geometric parameters of the wind turbines are determined based on the performance calculation results.

[0081] Step 105: Based on the first geometric parameters and the second geometric parameters, use computational fluid dynamics to perform flow field calculations on wind turbines with different installation combinations, and determine the optimal installation parameters based on the flow field calculation results and the regression function.

[0082] The installation parameters include: the number of wind turbines and the installation distance between them; the number of wind turbines and / or the installation distance between them differ in different installation combinations; the optimal installation parameters have the minimum payback period.

[0083] Step 106: Deploy the wind turbine units according to the first geometric parameters, the second geometric parameters, and the optimal installation parameters.

[0084] Step 106 specifically includes:

[0085] 1) Based on the first geometric parameters and the second geometric parameters, a two-dimensional geometric model of the blade is established using CAD tools, and a computational mesh is generated using computational fluid dynamics. The flow field is calculated for wind turbine units with different installation combinations based on the computational mesh to obtain the flow field calculation results. The flow field calculation results include the torque of the wind turbine rotor in the wind turbine unit.

[0086] 2) Calculate the power of the wind turbine based on the torque and the rotational speed, and calculate the wind energy conversion efficiency of the wind turbine based on the power.

[0087] like Figure 3 As shown, the formula for calculating power is: P = ωT; ω represents rotational speed, T represents torque, and P represents power.

[0088] The formula for calculating wind energy conversion efficiency is:

[0089]

[0090] Among them, C p The value represents the wind energy conversion efficiency; ρ is the air density; A is the projected area of ​​the wind turbine, which is taken as D1×h; h is the blade height, which is determined according to the height requirements of the highway median strip.

[0091] 3) Establish a linear function of power generation and installation distance based on the power and wind energy conversion efficiency.

[0092] 4) Calculate the daily power generation per kilometer for different installation combinations based on the linear function and the regression function.

[0093] 5) Determine the payback period for different installation combinations based on the daily power generation per kilometer, and determine the installation parameters corresponding to the minimum payback period as the optimal installation parameters.

[0094] The formula for calculating the cost recovery time is: T1=M / (Q×p); T1 represents the cost recovery time; M is the total investment cost; p is the electricity price from the grid; Q is the daily power generation per kilometer.

[0095] The multi-blade wind turbine layout method of the above embodiments will be further described in detail below.

[0096] This embodiment, based on practical feasibility and considering the reliability of the results, proposes a method for deploying multi-blade wind turbines suitable for highway wake wind resources, including:

[0097] Step 1: Survey of traffic flow and speed conditions.

[0098] Step 2, survey of natural wind resources.

[0099] Step 3: Design and performance calculation of the new vertical axis wind turbine.

[0100] Step 4, calculate the installation distance.

[0101] Step 5, calculate power generation.

[0102] Step 6, economic analysis.

[0103] Step 1 includes: surveying the traffic flow and average speed of the macro-selected site to obtain daily traffic flow, hourly traffic flow, daily average speed, and hourly average speed data.

[0104] Step 2 includes: surveying the natural wind resources of the macroscopic site selection location, measuring the average wind speed when there are no vehicles and the average wind speed when there is traffic, obtaining the wind speed every 50 centimeters from the ground to a height of 3 meters above the ground. Based on the average wind speed when there are vehicles and the traffic flow and average vehicle speed in Step 1, a regression function is established using a quadratic polynomial to correlate hourly traffic flow, average vehicle speed, and average wind speed, as shown in the following formula, where x represents traffic flow, y represents average vehicle speed, v represents average wind speed, and a, b, c, d, e, and f are fitting coefficients. When the traffic flow x is 0, the average vehicle speed y is also 0, so the average wind speed when there is no traffic can be deduced, i.e., the constant term f. The deduced average wind speed when there is no traffic is compared with the measured average wind speed when there is no traffic. The fitting accuracy is judged by the error between the two. If the error exceeds the error range, the measured average wind speed when there is no traffic is used to replace the deduced average wind speed when there is no traffic, and the regression function is corrected.

[0105] v = ax 2 +bxy+cy 2 +dx+ey+f

[0106] The annual average wind speed is calculated based on the average wind speed when there is traffic, the average wind speed when there is no traffic, and the percentage of time with traffic.

[0107] Step 3 includes: Based on the limitation of the highway green belt width, the maximum impeller diameter D1 is taken as the green belt width minus 20cm. In this design, β1 is taken as 90 degrees, and β2 is the angle between the relative velocity v to the turbine blade tip and the turbine tangent. The values ​​of the first geometric parameters are obtained according to the calculation formula of the first geometric parameter (the specific formula is not elaborated here). Calculations show that in this embodiment, the radial blade width a = 0.18D1, and the blade radius R... b=0.156D1, the radius R of the circle containing the center of the blade. c =0.382D1.

[0108] Geometric models were created for wind turbine units with different parameters such as number of blades, blade camber, blade height, and rotational speed. Boundary conditions for CFD simulation were set accordingly based on the annual average wind speed characteristics obtained in step 2, and performance calculations were performed using computational fluid dynamics. Taking into account the requirements for power coefficient and starting performance, a uniform design method was adopted to finally determine the second geometric parameters (i.e., number of wind turbine blades, blade camber, blade height, and rotational speed) suitable for this scheme.

[0109] Step 4 includes: based on the geometric parameters from step 3, using CAD tools to establish the two-dimensional geometry of the blades, using CFD software to generate a computational mesh, performing flow field calculations on five horizontally placed wind turbines with different installation distances (i.e., spacing) to obtain the torque T of each rotor. The power of each rotor is calculated using the power calculation formula, and the wind energy conversion efficiency of the rotor is calculated using the wind energy conversion efficiency calculation formula.

[0110] Adjust the spacing between the wind turbines and repeat the above calculations. Establish a linear function of total power generation (number of wind turbines × wind energy conversion efficiency × turbine power) versus installation distance. Subsequently, use wind energy conversion efficiency as a guide to maximize power generation per kilometer. A schematic diagram of the calculation model is shown below. Figure 4 As shown.

[0111] Step 5 includes: calculating the number N of wind turbines that can be installed per kilometer based on the installation distance determined in step 4. Based on the relationship between traffic flow and wind speed obtained in step 2, and the hourly traffic flow data at the macroscopic site selection point, calculating the hourly average wind speed and single-unit power at different times of the day, calculating the daily electricity generation per unit, and multiplying this by the number N of wind turbines installed per kilometer to obtain the daily power generation Q per kilometer.

[0112] Step 6 includes: calculating the relationship between the time to recover costs and the minimum daily power generation to obtain the time to recover costs T1.

[0113] Establish the relationship between the cost recovery time and the daily traffic volume required for that road segment. Based on minimizing the cost recovery time, repeat steps 4-6 to obtain the optimal installation parameters (i.e., the number of wind turbines and the installation distance between them).

[0114] A specific example of the above embodiments in practical application is as follows:

[0115] The first step is the design of a novel vertical axis wind turbine. Traditional vertical axis wind turbines cannot fully meet the needs of high-speed wind energy utilization. The wind turbine designed in this example is a multi-bladed vertical axis wind turbine. The torque coefficient of the wind turbine fluctuates less with the azimuth angle and there is no case where the torque coefficient is negative. It also maintains good self-starting performance under low wind speed conditions.

[0116] The second step is performance calculation. Taking into account factors such as power coefficient, starting performance, and torque stability, the appropriate number of blades for the wind turbine unit in the example is ultimately determined.

[0117] The third step is to calculate the installation distance. During the rotation of the wind turbine, the wake will return to the mainstream speed near 5D. However, the incoming flow generally has an angle. The airflow driven by the vehicle has a smaller angle. Therefore, the angle of the incoming flow in the numerical simulation process should not be too large (set to 15°). Using numerical simulation, calculations are performed on five horizontally placed wind turbines with different spacing to obtain a suitable installation distance.

[0118] The fourth step is to calculate the power generation. If the wind turbine model is installed at a distance of 2.5D, then 235 wind turbines can be installed per kilometer. Table 1 shows the power generation corresponding to the average wind speed.

[0119] Table 1. Power Generation Corresponding to Average Wind Speed

[0120]

[0121] Step 5: Economic Analysis. Taking a highway in Beijing, China as an example, 235 wind turbines are installed per kilometer. The cost of each wind turbine and its generator is approximately 900 yuan. The cost of the power line per kilometer is approximately 20,000 yuan. Therefore, the price of a one-kilometer wind power generation system is approximately 231,500 yuan. If the payback period is 5 years, then each wind turbine needs to generate an average of 271.744 kWh of electricity per year, or 0.744 kWh per day.

[0122] Step 6: Wind Resource Requirements. If the cost recovery period is 5 years, then the required average annual wind speed is 3.1 m / s. It should be noted that this average wind speed calculation assumes that traffic flow remains relatively constant throughout the day, meaning that the wind turbines generate electricity for 24 hours a day.

[0123] Step 7: Traffic Demand Analysis. If the cost recovery period is 5 years, then the daily traffic volume for this road segment needs to be 745,000 vehicles.

[0124] Step 8: Vehicle speed demand analysis. Table 2 shows the relationship between the recovery cost time and various parameters.

[0125] Table 2 Relationship between cost recovery time and various parameters

[0126]

[0127]

[0128] This embodiment proposes a multi-blade design in the turbine impeller. Wind turbines using this design feature low starting wind speed and low rotational speed, making them suitable for the wind energy characteristics of highways. The advantages of this embodiment are as follows:

[0129] (1) A method for determining the blade design of multi-blade vertical axis wind turbines suitable for wind resource utilization along highways was proposed, such as... Figure 5 As shown, the torque coefficient of the drag-type vertical axis wind turbine fluctuates less with changes in azimuth angle, and there are no cases where the torque coefficient is negative. It also maintains good self-starting performance under low wind speed conditions.

[0130] (2) A method for determining the deployment scheme of multi-bladed vertical axis wind turbines suitable for wind resource utilization along highways is proposed, and it is suggested that when the installation distance is 2.5D, the power coefficient of the downstream wind turbine is close to 90% of that of a single unit. Figure 6 As shown.

[0131] (3) The ideal environment for power generation on highways was determined based on economic analysis, wind resource demand, traffic flow demand, and vehicle speed demand.

[0132] Example 2

[0133] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a multi-blade wind turbine deployment system is provided below.

[0134] See Figure 7 The system includes:

[0135] The data acquisition module 201 is used to acquire vehicle data and wind speed data at a target location on a highway; the vehicle data includes traffic flow and average vehicle speed; the wind speed data includes: first wind speed data and second wind speed data measured at predetermined intervals from the direction perpendicular to the ground within a set height range at the target location; the first wind speed is the average wind speed of natural wind when there are no vehicles; the second wind speed is the average wind speed of natural wind when there are vehicles.

[0136] The regression function construction module 202 is used to construct a regression function based on the vehicle data and the wind speed data, and to calculate the annual average wind speed according to the wind speed data and the time proportion of traffic flow when there are vehicles; the regression function represents the relationship between vehicle data and average wind speed.

[0137] The first geometric parameter calculation module 203 is used to determine the first geometric parameters of the wind turbine based on the width of the green belt of the highway; the first geometric parameters include: radial blade width, distance from the center to the radial blade, blade radius, and radius of the circle in which the blade center is located.

[0138] The second geometric parameter calculation module 204 is used to determine the second geometric parameters of the wind turbine based on the annual average wind speed using computational fluid dynamics methods; the second geometric parameters include: the number of blades, blade camber, blade height and rotational speed of the wind turbine.

[0139] The installation parameter calculation module 205 is used to perform flow field calculations on wind turbines with different installation combinations based on the first geometric parameters and the second geometric parameters using computational fluid dynamics methods, and to determine the optimal installation parameters based on the flow field calculation results and the regression function; the installation parameters include: the number of wind turbines and the installation distance between wind turbines; the number of wind turbines and / or the installation distance between wind turbines are different in different installation combinations; the optimal installation parameters have the minimum payback period.

[0140] The wind turbine deployment module 206 is used to deploy the wind turbine unit according to the first geometric parameters, the second geometric parameters and the optimal installation parameters.

[0141] Example 3

[0142] This embodiment provides an electronic device, including a memory and a processor. The memory is used to store computer programs, and the processor runs the computer programs to enable the electronic device to execute the multi-blade wind turbine deployment method of Embodiment 1.

[0143] Alternatively, the aforementioned electronic device may be a server.

[0144] In addition, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the multi-blade wind turbine deployment method of Embodiment 1.

[0145] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0146] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for arranging multi-blade wind turbine units, characterized in that, include: Acquire vehicle and wind speed data at target locations on highways; The vehicle data includes: traffic flow and average vehicle speed; the wind speed data includes: first wind speed data and second wind speed data measured at predetermined intervals from the direction perpendicular to the ground within a predetermined height range at the target location; the first wind speed is the average wind speed of natural wind when there are no vehicles; the second wind speed is the average wind speed of natural wind when there are vehicles. A regression function is constructed based on the vehicle data and the wind speed data, and the annual average wind speed is calculated according to the wind speed data and the time proportion of traffic flow when there are vehicles; the regression function represents the relationship between vehicle data and average wind speed. Based on the width of the green belt along the highway, the first geometric parameters of the wind turbine are determined; the first geometric parameters include: radial blade width, distance from the center to the radial blade, blade radius, and radius of the circle in which the blade center is located; Based on the annual average wind speed, the second geometric parameters of the wind turbine are determined using computational fluid dynamics; the second geometric parameters include: the number of blades, blade camber, blade height, and rotational speed of the wind turbine. Based on the first and second geometric parameters, computational fluid dynamics is used to calculate the flow field of wind turbines with different installation combinations. The optimal installation parameters are determined based on the flow field calculation results and the regression function. The installation parameters include the number of wind turbines and the installation distance between them. The number of wind turbines and / or the installation distance between them differ in different installation combinations. The optimal installation parameters have the minimum payback period. The wind turbines are deployed according to the first geometric parameters, the second geometric parameters, and the optimal installation parameters; Based on the first and second geometric parameters, computational fluid dynamics methods are used to calculate the flow field of wind turbines with different installation combinations. The optimal installation parameters are then determined based on the flow field calculation results and the regression function. Specifically, this includes: Based on the first and second geometric parameters, a two-dimensional geometric model of the blade is established using CAD tools, and a computational mesh is generated using computational fluid dynamics. Based on the computational mesh, flow field calculations are performed on wind turbine units with different installation combinations to obtain flow field calculation results. The flow field calculation results include the torque of the wind turbine rotor in the wind turbine unit. The power of the wind turbine is calculated based on the torque and the rotational speed, and the wind energy conversion efficiency of the wind turbine is calculated based on the power. A linear function of power generation and installation distance is established based on the power and wind energy conversion efficiency. The daily power generation per kilometer for different installation combinations is calculated based on the linear function and the regression function. The payback period for different installation combinations is determined based on the daily power generation per kilometer, and the installation parameters corresponding to the minimum payback period are determined as the optimal installation parameters. Based on the width of the green belt along the highway, the first geometric parameters of the wind turbine are determined, specifically including: The maximum diameter of the impeller is obtained by subtracting the set width value from the width of the green belt on the highway. The first geometric parameters of the wind turbine are determined based on the maximum diameter of the impeller. The formula for calculating the first geometric parameter is: in, D 1 represents the maximum diameter of the impeller; The angle between the wind speed at the outlet and the turbine tangent; The angle between the average wind speed and the relative velocity at the turbine blade tip and the turbine tangent; a Radial blade width; D 2 represents the distance from the center of the circle to the radial blade; R b Where is the blade radius; R c It is the radius of the circle in which the center of the blade lies.

2. The multi-blade wind turbine layout method according to claim 1, characterized in that, Based on the aforementioned annual average wind speed, the second geometric parameters of the wind turbine are determined using computational fluid dynamics methods, specifically including: Geometric models of wind turbines with different numbers of blades, blade camber, blade height, and rotational speed are performed. Simulation boundary conditions are set according to the annual average wind speed. Computational fluid dynamics is used to perform performance calculations. The second geometric parameters of the wind turbines are determined based on the performance calculation results.

3. The multi-blade wind turbine layout method according to claim 1, characterized in that, A regression function is constructed based on the vehicle data and the wind speed data, specifically including: Based on the vehicle data and the second wind speed in the wind speed data, an initial regression function is constructed using a quadratic polynomial; The regression function is corrected using the first wind speed from the wind speed data to obtain the final regression function.

4. The multi-blade wind turbine layout method according to claim 3, characterized in that, The expression for the regression function is: Where x represents traffic flow; y represents average vehicle speed; v represents average wind speed; and a, b, c, d, e, and f are all fitting coefficients.

5. A multi-blade wind turbine deployment system, characterized in that, The multi-blade wind turbine deployment system adopts the multi-blade wind turbine deployment method according to any one of claims 1-4, and the multi-blade wind turbine deployment system includes: The data acquisition module is used to acquire vehicle data and wind speed data at a target location on the highway. The vehicle data includes traffic flow and average vehicle speed. The wind speed data includes a first wind speed and a second wind speed measured at predetermined intervals from the vertical direction at the target location within a set height range. The first wind speed is the average wind speed of natural wind when there are no vehicles. The second wind speed is the average wind speed of natural wind when there are vehicles. The regression function construction module is used to construct a regression function based on the vehicle data and the wind speed data, and to calculate the annual average wind speed according to the wind speed data and the time proportion of traffic flow when there are vehicles; the regression function represents the relationship between vehicle data and average wind speed. The first geometric parameter calculation module is used to determine the first geometric parameters of the wind turbine based on the width of the green belt of the highway; the first geometric parameters include: radial blade width, distance from the center to the radial blade, blade radius, and radius of the circle in which the blade center is located; The second geometric parameter calculation module is used to determine the second geometric parameters of the wind turbine based on the annual average wind speed using computational fluid dynamics methods; the second geometric parameters include: the number of blades, blade camber, blade height, and rotational speed of the wind turbine. The installation parameter calculation module is used to perform flow field calculations on wind turbines with different installation combinations based on the first geometric parameters and the second geometric parameters using computational fluid dynamics methods, and to determine the optimal installation parameters based on the flow field calculation results and the regression function. The installation parameters include: the number of wind turbines and the installation distance between wind turbines; the number of wind turbines and / or the installation distance between wind turbines are different in different installation combinations; the optimal installation parameters have the minimum payback period. The wind turbine deployment module is used to deploy wind turbine units according to the first geometric parameters, the second geometric parameters, and the optimal installation parameters.

6. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program and the processor runs the computer program to enable the electronic device to perform the multi-blade wind turbine deployment method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the multi-blade wind turbine deployment method as described in any one of claims 1 to 4.

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