Improved Gaussian wake flow distribution calculation method and system for wind power plant and storage medium
By introducing atmospheric thermal stability and wind farm equivalent roughness parameters to modify the Gaussian wake model, the problem of insufficient accuracy in wake loss assessment of large wind farms is solved, and more accurate wake loss rate calculation and power generation assessment are achieved.
Patent Information
- Application Number
- CN202511627082.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-07
- Publication Date
- 2026-02-10
AI Technical Summary
Existing wake loss assessment methods lack sufficient accuracy in large-scale wind farms and fail to adequately consider the impact of atmospheric stability and wind farm clusters on the boundary layer, leading to overestimation or underestimation of power generation losses.
By introducing atmospheric thermal stability and wind farm equivalent roughness parameters, the wake dissipation rate in the classic Gaussian wake model is corrected. By calculating the wake non-uniformity coefficient and equivalent roughness, and combining the square sum superposition model, the wake loss rate is accurately calculated.
It significantly improves the accuracy of wake loss rate calculation for large wind farms, provides more accurate power generation assessment and planning tools, and is suitable for macro-planning and micro-site selection of large wind farms.
Smart Images
Figure CN121503322A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wind power generation technology, and in particular to a method, system and storage medium for calculating improved Gaussian wake distribution in wind farms. Background Technology
[0002] With the trend towards clustering and large-scale development of wind farms, the increasing size of the farm area and the decreasing spacing between them have led to a more pronounced wake effect between farms. Wind farms with an installed capacity of 200MW or more are considered large-scale wind farms. In large-scale wind farms, the wake effect causes a decrease in wind speed and an increase in turbulence at the downstream wind farm inlet, resulting in significant power generation losses. However, current wake loss assessment methods often underestimate the severity of this problem, leading to biases in the assessment of the overall planning benefits of wind farms and the grid's absorption capacity.
[0003] Currently, engineering wake models, such as the Jensen model, Larsen model, and Ishihara model, are commonly used to assess wake deficit in large-scale wind farms. However, these traditional models have significant shortcomings when applied to farm-level wake calculations for large-scale wind farms: they are mostly based on the superposition of individual turbine wakes and fail to fully consider the changes in the boundary layer caused by atmospheric stability and the overall wind farm cluster (i.e., the equivalent roughness effect). This leads to overly optimistic predictions of wake deficit at the inlet of the downstream wind farm (target wind farm), excessively fast wake recovery rates, and insufficient calculation accuracy.
[0004] Although the Gaussian model is better than the traditional model, its wake dissipation rate parameter is usually only related to the intensity of environmental turbulence and does not fully consider the influence of atmospheric thermal stratification and wind farm as a whole rough element. Therefore, there are still biases when predicting field-level wakes that are several kilometers to tens of kilometers long.
[0005] Therefore, there is an urgent need in this field for a calculation method that can more accurately and physically predict the impact of wake loss between large wind farms, providing a reliable tool for the planning of capacity density, site spacing (wind speed recovery zone) design, and accurate assessment of power generation of large wind farms. Summary of the Invention
[0006] The purpose of this invention is to provide an improved method, system, and storage medium for calculating Gaussian wake distribution in wind farms. This method significantly improves the accuracy of wake loss rate calculation among large wind farms by introducing atmospheric thermal stability and equivalent roughness of the wind farm to physically correct the wake dissipation rate in the classical Gaussian model.
[0007] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a method for calculating an improved Gaussian wake distribution in a wind farm, comprising: Acquire multidimensional data from wind farms; Based on multidimensional data of wind farms, the wake non-uniformity coefficient of wind farms is calculated. The equivalent roughness of the wind farm is calculated based on the wake non-uniformity coefficient of the wind farm. The wake dissipation rate is obtained based on the equivalent roughness of the wind farm. Input the wake dissipation rate into the improved Gaussian wake model and calculate the wake loss rate of each upstream wind turbine in the wind farm to the target wind turbine at the spatial point. The wake loss rate of the target wind turbine at the spatial point is obtained by superimposing the wake loss rates of all upstream wind turbines in the wind farm at the spatial point using the sum of squares superposition model. Based on the comprehensive wake loss rate of the target wind turbine at a spatial point, the wake distribution of the target wind turbine is obtained.
[0008] Optionally, the multidimensional data of the wind farm includes wind turbine parameters, atmospheric thermal stability parameters, environmental turbulence intensity, and surface roughness; the turbine parameters include the total number of wind turbines, rotor diameter, rotor rotation area, wind turbine thrust coefficient, hub height, wind turbine coordinates, as well as wind farm capacity and wind farm capacity density; the atmospheric thermal stability parameters include the Moning length.
[0009] Optionally, based on multidimensional data from the wind farm, the formula for calculating the wake non-uniformity coefficient of the wind farm is expressed as follows: ; In the formula, This represents the wake non-uniformity coefficient of a wind farm; This indicates the inflow wind speed at the wind farm; This indicates the wind speed at the hub height of the target wind turbine in the wind farm. Indicates the length of Morning; h Indicates the wheel hub height; Indicates altitude range and mixed length The ratio; the mixing length ; express The stability function; Represents the von Kármán constant; Indicates the intensity of environmental turbulence; Indicates the effective drag coefficient; in The formula is calculated using the wind turbine drag coefficient and the surface roughness drag coefficient, and is expressed as follows: ; ; ; In the formula, This represents the sum of the drag coefficients of all wind turbines in the wind farm; This represents the surface roughness resistance coefficient; N This indicates the total number of wind turbines in a wind farm. D i Indicates the first i Typhoon turbine rotor diameter; A i Indicates the first i Rotational area of the wind turbine rotor in a typhoon generator; Indicates the first i typhoon turbine thrust coefficient; z0 represents surface roughness; express The integral stability function; in, ; ; ; ; In the formula, a and b All are preset coefficients; Indicates an intermediate variable.
[0010] Optionally, the formula for calculating the equivalent roughness of a wind farm is expressed as follows: ; In the formula: This represents the equivalent roughness of a wind farm. D This represents the average rotor diameter of all wind turbines in the wind farm. This represents the additional eddy viscosity coefficient of the wake. ; This represents the corrected thrust coefficient.
[0011] Optionally, the formula for the modified thrust coefficient is expressed as follows: ; In the formula: C Indicates the capacity of the wind farm; Indicates the first i Thrust coefficient of typhoon generator unit; n This indicates the total number of upstream wind turbine units in a wind farm; This indicates the capacity density of a wind farm.
[0012] Optionally, the wake dissipation rate is obtained based on the equivalent roughness of the wind farm, and the calculation formula for the wake dissipation rate is as follows: ; In the formula: Indicates the wake dissipation rate; This indicates the intensity of environmental turbulence.
[0013] Optionally, the improved Gaussian wake model is expressed as follows: ; In the formula: express( x , y , z ) j The wake loss rate of the upstream wind turbine to the target wind turbine; express( x , y , z The inflow velocity at point ) and the first j The difference in wake wind speed between the upstream wind turbine and the target wind turbine; x , y , z These are the coordinates of the target wind turbine in the following, crosswind, and vertical directions, respectively, with the ground of the target wind turbine as the origin. The crosswind direction is perpendicular to the following direction. This represents the average thrust coefficient of all wind turbines in the wind farm. This indicates when x approaches 0. The value, , The standard deviation of the Gaussian-like velocity deficit distribution at each point is expressed by the following formula: ; In the formula: It is an intermediate variable.
[0014] Optionally, the sum of squares superposition model is expressed as: ; In the formula: express( x , y , z The wake loss rate of all upstream wind turbines relative to the target wind turbine; j Indicates the sequence number.
[0015] Secondly, the present invention provides a calculation system for improved Gaussian wake distribution in wind farms, comprising: The data acquisition module is used to acquire multidimensional data from wind farms; The non-uniformity coefficient calculation module is used to calculate the wake non-uniformity coefficient of a wind farm based on multi-dimensional data of the wind farm. The equivalent roughness calculation module is used to calculate the equivalent roughness of the wind farm based on the wake non-uniformity coefficient of the wind farm. The wake dissipation rate calculation module is used to obtain the wake dissipation rate based on the equivalent roughness of the wind farm. The single-effect wake calculation module is used to input the wake dissipation rate into the improved Gaussian wake model and calculate the wake loss rate of each upstream wind turbine in the wind farm to the target wind turbine at the spatial point. The wake superposition module is used to superimpose the wake loss rates of all upstream wind turbines in the wind farm at a spatial point to the target wind turbine using a square sum superposition model, so as to obtain the comprehensive wake loss rate of the target wind turbine at the spatial point. The spatial combination module is used to obtain the wake distribution of the target wind turbine based on the comprehensive wake loss rate of the target wind turbine at a spatial point.
[0016] Thirdly, the present invention provides a computer-readable storage medium storing a computer program that, when executed, implements the wind farm improved Gaussian wake distribution calculation method described in the first aspect.
[0017] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: This invention provides an improved Gaussian wake distribution calculation method, system, and storage medium for wind farms. The method calculates the wake non-uniformity coefficient based on wind farm turbine parameters, environmental turbulence intensity, and surface roughness, while also incorporating atmospheric thermal stability parameters. It then calculates the equivalent roughness of the wind farm based on this coefficient, and uses this equivalent roughness to correct the wake dissipation rate in the classic Gaussian wake model, resulting in an improved Gaussian wake model. Finally, the wake dissipation rate is input into the improved Gaussian wake model to calculate the impact of each upstream wind turbine on the target wind turbine at a spatial point. The wake deficit rate of a downstream wind turbine is calculated, and the wake deficit rates of all upstream wind turbines at a spatial point are superimposed on the target wind turbine (a downstream wind turbine) using a square sum superposition model to obtain the comprehensive wake deficit rate of the target wind turbine at the spatial point. Finally, the wake distribution of the target wind turbine is obtained through the comprehensive wake deficit rate of the target wind turbine at the spatial point. This method improves the core parameters of the Gaussian wake model by introducing two key physical factors: atmospheric stability and the equivalent roughness of the surrounding wind farm, making it more consistent with the physical mechanism of the atmospheric boundary layer and significantly improving its accuracy.
[0018] This invention provides an improved method, system, and storage medium for calculating Gaussian wake distribution in wind farms. This method utilizes equivalent roughness to correct (calculate) the wake dissipation rate, which has a clear physical meaning. Equivalent roughness characterizes the overall frictional effect of the wind farm cluster on the atmospheric boundary layer. Atmospheric stability affects the efficiency of turbulent mixing and vertical momentum transport. This invention dynamically correlates these two factors with the wake dissipation rate, improving the accuracy of wake calculation. While maintaining the high efficiency of wake loss rate calculation, it significantly improves accuracy, making it highly suitable for large-scale application in engineering practices such as macro-planning, micro-site selection, and power generation assessment of large wind farms; it provides a reliable tool for the optimized design of various large-scale wind power projects. Attached Figure Description
[0019] Figure 1 The diagram shown is a flowchart of the improved Gaussian wake calculation in one embodiment of the present invention. Figure 2 The figure shown is a diagram of the wake effect calculated by the Gaussian wake model under a 278° wind direction in one embodiment of the present invention. Figure 3 The figure shown is a diagram of the wake effect calculated by the improved Gaussian wake model under a 278° wind direction in one embodiment of the present invention. Figure 4 The figure shown is a diagram of the wake effect calculated by the Emeis model under a 278° wind direction in one embodiment of the present invention. Figure 5 The figure shown is a diagram of the wake effect calculated by the TurbOPark model under a 278° wind direction in one embodiment of the present invention. Figure 6 The figure shown is a comparison of the calculated values of three field-level wake models and SCADA data under a 278° wind direction in one embodiment of the present invention. Figure 7 The figure shown is a diagram of the wake effect calculated by the improved Gaussian wake model under 98° wind direction in one embodiment of the present invention. Figure 8 The figure shown is a diagram of the wake effect calculated by the Emeis model under a 98° wind direction in one embodiment of the present invention. Figure 9 The figure shown is a diagram of the wake effect calculated by the TurbOPark model under a 98° wind direction in one embodiment of the present invention. Figure 10 The figure shown is a comparison chart of the calculated values of three models and SCADA data under a 98° wind direction in one embodiment of the present invention; Figure 11 The figure shown is a comparison chart of RMSE calculated by two models under two wind directions in one embodiment of the present invention; Figure 12The figure shown is a comparison chart of the RMSE of three model calculation values under two wind directions and SCADA data in one embodiment of the present invention. Detailed Implementation
[0020] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.
[0021] Example 1
[0022] like Figure 1 As shown in the figure, this invention provides an improved method for calculating the Gaussian wake distribution in wind farms, comprising the following steps: S01: Acquire multi-dimensional data of wind farms; S02: Calculate the wake non-uniformity coefficient of the wind farm based on multi-dimensional data of the wind farm; S03: Calculate the equivalent roughness of the wind farm based on the wake non-uniformity coefficient of the wind farm; S04: Based on the equivalent roughness of the wind farm, the wake dissipation rate is obtained; S05: Input the wake dissipation rate into the improved Gaussian wake model and calculate the wake loss rate of each upstream wind turbine in the wind farm at the spatial point to the target wind turbine. S06: Using the sum of squares superposition model, the wake loss rates of all upstream wind turbines in the wind farm at the spatial point to the target wind turbine are superimposed to obtain the comprehensive wake loss rate of the target wind turbine at the spatial point. S07: Based on the comprehensive wake loss rate of the target wind turbine at a spatial point, the wake distribution of the target wind turbine is obtained.
[0023] This invention's method, based on wind farm turbine parameters, environmental turbulence intensity, and surface roughness, introduces atmospheric thermal stability parameters to calculate the wake non-uniformity coefficient, thereby calculating the equivalent roughness of the wind farm. It then uses this equivalent roughness to correct the wake dissipation rate in the classical Gaussian wake model, resulting in an improved Gaussian wake model. Finally, based on the improved Gaussian wake model, it calculates the wake deficit rate of each upstream wind turbine at a spatial point relative to the target wind turbine (a downstream wind turbine), and superimposes these values using a sum-of-squares superposition model to obtain the comprehensive wake deficit rate of the target wind turbine. This method significantly improves the accuracy of large-scale wind farm-level wake calculations by physically correcting the wake dissipation rate of the classical Gaussian model through the introduction of atmospheric thermal stability and the equivalent roughness of the wind farm.
[0024] In this embodiment, the multidimensional data of the wind farm in step S01 includes wind turbine parameters, atmospheric thermal stability parameters, environmental turbulence intensity, and surface roughness, as detailed below: The turbine parameters include the total number of wind turbines, rotor diameter, rotor rotation area, wind turbine thrust coefficient, hub height, wind turbine coordinates, as well as wind farm capacity and wind farm capacity density. Among these, rotor diameter, rotor rotation area, and hub height can be obtained from the technical specifications or product data sheets provided by the wind turbine manufacturer; the wind turbine thrust coefficient can be obtained from the wind turbine manufacturer through a certified thrust coefficient curve data table that matches the specific turbine model.
[0025] Atmospheric thermal stability parameters include the Morning (Morning-Obukhov length) length. 、 Virtual air temperature The gravitational acceleration g at the Earth's surface, and the surface turbulent heat flux. The atmospheric thermal stability parameters were obtained from numerical weather prediction models or CFD (Computational Fluid Dynamics) simulations.
[0026] The environmental turbulence intensity is calculated based on the CFD simulation output. The wind speed and turbulent kinetic energy at the hub height are extracted from the CFD simulation output and estimated using a formula. This formula is common knowledge to those skilled in the art and will not be described in detail here. The surface roughness length is obtained by fitting wind profiles using anemometer data.
[0027] In this embodiment, step S02 calculates the wake non-uniformity coefficient of the wind farm based on multi-dimensional data of the wind farm. It introduces atmospheric thermal stability and environmental turbulence to calculate the wake non-uniformity coefficient of the wind farm, and calculates it through equations (1) to (9). Then, substitute the formula (10) to calculate the wake non-uniformity coefficient. The process is as follows: The ratio of wind speed at the hub height of the downstream (target) wind turbine to the upstream free-flow wind speed (inflow wind speed) of the wind farm. It can be represented as: (3) (4) (5) (6) (7) (8) (9) (10) In the formula, This represents the wake non-uniformity coefficient of a wind farm; This indicates the inflow wind speed at the wind farm; This indicates the wind speed at the hub height of the target wind turbine in the wind farm. Indicates altitude range and mixed length The ratio; the mixing length ; Indicates the intensity of environmental turbulence; express The stability function; Represents the von Kármán constant; Indicates the intensity of environmental turbulence; This represents the effective drag coefficient, which is calculated using the wind turbine drag coefficient and the surface roughness drag coefficient. This represents the sum of the drag coefficients of all wind turbines in the wind farm; This represents the surface roughness resistance coefficient; N This indicates the total number of wind turbines in a wind farm. D i Indicates the first i Typhoon turbine rotor diameter; A i Indicates the first i Rotational area of the wind turbine rotor in a typhoon generator; Indicates the first i Thrust coefficient of typhoon generator unit; h Indicates the wheel hub height; z0 represents the Moening length; z0 represents the surface roughness. express The integral stability function; a and b All are preset coefficients, and in this embodiment, the formula is: , ; Indicates intermediate variables; This is a highly independent frictional velocity in the stratosphere, located between the surface and hub heights; It is a stability parameter. This is the virtual air temperature, and g is the gravitational acceleration at the Earth's surface. It is the surface kinematic heat flux. It is the surface turbulent heat flux. It is air density. It is the specific heat capacity of air at constant pressure; Used to characterize atmospheric stability Indicates a highly unstable state; Indicates a neutral state; This indicates a stable stratified state.
[0028] In this embodiment, step S03 calculates the equivalent roughness of the wind farm based on the wake non-uniformity coefficient. Specifically, assuming the wind farm boundary layer is divided into three stress layers, and considering the non-uniformity of the wind farm flow, the equivalent roughness of the wind farm is calculated when the wind farm is in a fully developed state. Based on the non-uniformity coefficient calculated in step S02, the equivalent roughness of the wind farm can be calculated by substituting it into formula (11), as follows: (11) In the formula: This represents the equivalent roughness of a wind farm. D This represents the average rotor diameter of all wind turbines in the wind farm. This represents the additional eddy viscosity coefficient of the wake. ; The corrected thrust coefficient is expressed by the formula: (12) In the formula: C represents the wind farm capacity, in MW; Indicates the first i Thrust coefficient of typhoon turbines; n represents the total number of upstream wind turbines in the wind farm; This represents the wind farm capacity density, in MW / km². 2 ; i Indicates the sequence number.
[0029] In this embodiment, the formula for calculating the wake dissipation rate in step S04 is as follows: (13) In the formula: This represents the wake dissipation rate.
[0030] In this embodiment, step S05 inputs the wake dissipation rate into the improved Gaussian wake model to calculate the wake loss rate of each upstream wind turbine in the wind farm at the spatial point to the target wind turbine. The improved Gaussian wake model is expressed as follows:
[0031] ; In the formula: express( x , y , z ) j The wake loss rate of the upstream wind turbine to the target wind turbine; express( x , y ,z The inflow velocity at point ) and the first j The difference in wake wind speed between the upstream wind turbine and the target wind turbine; x , y , z These are the coordinates of the target wind turbine in the following, crosswind, and vertical directions, respectively, with the ground of the target wind turbine as the origin. The crosswind direction is perpendicular to the following direction. This represents the average thrust coefficient of all wind turbines in the wind farm. This indicates when x approaches 0. The value, , The standard deviation of the Gaussian-like velocity deficit distribution at each point is expressed by the following formula: ; In the formula: As an intermediate variable, it represents The function.
[0032] In the specific formula The wake dissipation rate is related to the Jensen model. different, It is the standard deviation that characterizes the velocity reduction of the Gaussian distribution. It changes with the increase of (downstream) target distance.
[0033] In this embodiment, step S06: The wake loss rates of all upstream wind turbines in the wind farm at the spatial point to the target wind turbine are superimposed using a sum-of-squares superposition model to obtain the comprehensive wake loss rate of the target wind turbine at the spatial point; wherein the sum-of-squares superposition model is expressed as: (15) In the formula: express( x , y , z The wake loss rate of all upstream wind turbines relative to the target wind turbine; j Indicates the sequence number.
[0034] Example 2
[0035] Based on the improved Gaussian wake distribution calculation method for wind farms provided in Example 1, this example verifies the method of the present invention in conjunction with specific embodiments: This embodiment compares and analyzes the SCADA (Supervisory Control and Data Acquisition) data of the Rødsand II wind farm and the Nysted wind farm under two wind direction conditions. It compares the changes in the accuracy of wake prediction before and after the improvement of the wake dissipation rate algorithm, as well as the TurbOPark model and Emies model, which are among the few acceptable accuracy models in current wind farm-level wake research.
[0036] Specifically, the TurbOPark and Emeis models were used to calculate the Rødsand II and Nysted wind farms under two operating conditions with wind directions of 278° and 98° respectively. The calculation results of the improved Gaussian wake model were compared with the calculation results of the two models and the Gaussian wake model respectively.
[0037] The working conditions for model validation are shown in Table 1: Table 1 Parameters for Wind Farm Model Validation
[0038] (a) The calculation results of the four models under a 278° wind direction are as follows: Figures 2-5 As shown: like Figure 3 and Figure 2 As shown, the location where the upstream wind farm wake recovers to 90% as calculated by the improved Gaussian model is compared to the location before the improvement. Figure 2 The wind turbine extended downstream by about 1 kilometer, and the wake loss at the Nysted inlet of the downstream wind farm increased by 4% compared to before the improvement. The predicted power is now closer to the SCADA data of the wind farm.
[0039] like Figure 4 and Figure 5 The diagram shows the wake effect calculated by the Emies and TurbOPark models under a 278° wind direction. The accuracy of the predictions by the Emies and TurbOPark models is acceptable.
[0040] Combination such as Figure 6 As shown in the figure, the comparison between the calculated values of the three field-level wake models and SCADA data under a 278° wind direction shows that at the downstream wind farm inlet, the predicted values of the improved Gaussian wake model and the TurbOPark model are closest to the SCADA data.
[0041] (II) The calculation results of the three models (TurbOPark model, Emies model, and improved Gaussian wake model) under 98° wind direction are as follows: Figures 7-9 As shown.
[0042] like Figures 7-9 As shown, under a 98° wind direction, the wake influence range of the upstream and downstream wind farms exceeds 8 kilometers. Due to the influence of the wake generated by the upstream wind farm Nysted, its wake wind speed only recovers to 90% at the middle position of the downstream wind farm. In the wake influence effect diagram predicted by the improved Gaussian model, the influence range predicted by the improved model increases by about 1200 meters. At the inlet of the downstream wind farm Rødsand II, the power predicted by the improved Gaussian model is 2% lower than that before the improvement, and is closer to the SCADA data value.
[0043] Combined in Figure 10 The comparison chart of the calculated values of the three models and SCADA data under a 98° wind direction shows that the prediction trends of the three models are largely consistent with the SCADA data in the upstream wind farm. However, in the latter half of the downstream wind farm, the power predicted by the three models fluctuates and does not rise steadily like in the SCADA data, especially the Emies model and the TurbOPark model.
[0044] Finally, from Figure 11 and Figure 12 The comparison of RMSE shows that the RMSE predicted by the improved Gaussian wake model decreased from (0.08167, 0.07516) to (0.01874, 0.01754), and the prediction accuracy was significantly improved.
[0045] Specifically, Figures 2-5 , Figures 7-9 The diagrams show the effects of wake erosion. In the diagrams, X [m] and Y [m] represent coordinates on the map (unit: meters). The numbers on the horizontal and vertical axes are coordinate values in projected coordinate systems such as UTM. The right vertical axis represents the speed loss rate. The color bars at the bottom of the diagrams represent the intensity of the wind speed loss (wake loss). Warm colors tend to be 1, indicating greater wake dissipation, while cool colors tend to be less, indicating less loss.
[0046] Example 3
[0047] This invention provides an improved Gaussian wake distribution calculation system for wind farms, comprising: The data acquisition module is used to acquire multidimensional data from wind farms; The non-uniformity coefficient calculation module is used to calculate the wake non-uniformity coefficient of a wind farm based on multi-dimensional data of the wind farm. The equivalent roughness calculation module is used to calculate the equivalent roughness of the wind farm based on the wake non-uniformity coefficient of the wind farm. The wake dissipation rate calculation module is used to obtain the wake dissipation rate based on the equivalent roughness of the wind farm. The single-effect wake calculation module is used to input the wake dissipation rate into the improved Gaussian wake model and calculate the wake loss rate of each upstream wind turbine in the wind farm to the target wind turbine at the spatial point. The wake superposition module is used to superimpose the wake loss rates of all upstream wind turbines in the wind farm at a spatial point to the target wind turbine using a square sum superposition model, so as to obtain the comprehensive wake loss rate of the target wind turbine at the spatial point. The spatial combination module is used to obtain the wake distribution of the target wind turbine based on the comprehensive wake loss rate of the target wind turbine at a spatial point.
[0048] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.
[0049] Example 4
[0050] This embodiment provides a computer-readable storage medium storing a computer program that, when executed, implements the wind farm improved Gaussian wake distribution calculation method described in Embodiment 1.
[0051] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0052] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0053] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0054] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.
Claims
1. A method for calculating the improved Gaussian wake distribution in a wind farm, characterized in that, include: Acquire multidimensional data from wind farms; Based on multidimensional data of wind farms, the wake non-uniformity coefficient of wind farms is calculated. The equivalent roughness of the wind farm is calculated based on the wake non-uniformity coefficient of the wind farm. The wake dissipation rate is obtained based on the equivalent roughness of the wind farm. Input the wake dissipation rate into the improved Gaussian wake model and calculate the wake loss rate of each upstream wind turbine in the wind farm to the target wind turbine at the spatial point. The wake loss rate of the target wind turbine at the spatial point is obtained by superimposing the wake loss rates of all upstream wind turbines in the wind farm at the spatial point using the sum of squares superposition model. Based on the comprehensive wake loss rate of the target wind turbine at a spatial point, the wake distribution of the target wind turbine is obtained.
2. The method for calculating the improved Gaussian wake distribution in wind farms according to claim 1, characterized in that, The multidimensional data of the wind farm includes wind turbine parameters, atmospheric thermal stability parameters, environmental turbulence intensity, and surface roughness; the turbine parameters include the total number of wind turbines, rotor diameter, rotor rotation area, wind turbine thrust coefficient, hub height, wind turbine coordinates, as well as wind farm capacity and wind farm capacity density; the atmospheric thermal stability parameters include the Moning length.
3. The method for calculating the improved Gaussian wake distribution in wind farms according to claim 2, characterized in that, Based on multidimensional data from wind farms, the formula for calculating the wake non-uniformity coefficient of wind farms is expressed as follows: ； In the formula, This represents the wake non-uniformity coefficient of a wind farm; Indicates the inflow wind speed of the wind farm; This indicates the wind speed at the hub height of the target wind turbine in the wind farm. Indicates the length of Morning; h Indicates the wheel hub height; Indicates altitude range and mixed length The ratio; the mixing length ; express The stability function; Represents the von Kármán constant; Indicates the intensity of environmental turbulence; Indicates the effective drag coefficient; in The formula is calculated using the wind turbine drag coefficient and the surface roughness drag coefficient, and is expressed as follows: ； ； ； In the formula, This represents the sum of the drag coefficients of all wind turbines in the wind farm; This represents the surface roughness resistance coefficient; N This indicates the total number of wind turbines in a wind farm. D i Indicates the first i Typhoon turbine rotor diameter; A i Indicates the first i Rotational area of the wind turbine rotor in a typhoon generator; Indicates the first i typhoon turbine thrust coefficient; z0 represents surface roughness; express The integral stability function; in, ; ； ； ； In the formula, a and b All are preset coefficients; Indicates an intermediate variable.
4. The method for calculating the improved Gaussian wake distribution in a wind farm according to claim 3, characterized in that, The formula for calculating the equivalent roughness of a wind farm is expressed as: ; In the formula: This represents the equivalent roughness of a wind farm. D This represents the average rotor diameter of all wind turbines in the wind farm. This represents the additional eddy viscosity coefficient of the wake. ; This represents the corrected thrust coefficient.
5. The method for calculating the improved Gaussian wake distribution in a wind farm according to claim 4, characterized in that, The formula for the corrected thrust coefficient is expressed as follows: ； In the formula: C Indicates the capacity of the wind farm; Indicates the first i Thrust coefficient of typhoon generator unit; n This indicates the total number of upstream wind turbine units in a wind farm; This indicates the capacity density of a wind farm.
6. The method for calculating the improved Gaussian wake distribution in a wind farm according to claim 5, characterized in that, The wake dissipation rate is obtained based on the equivalent roughness of the wind farm, and the formula for calculating the wake dissipation rate is as follows: ； In the formula: Indicates the wake dissipation rate; This indicates the intensity of environmental turbulence.
7. The method for calculating the improved Gaussian wake distribution in a wind farm according to claim 6, characterized in that, The improved Gaussian wake model is expressed as follows: ； In the formula: express( x , y , z ) j The wake loss rate of the upstream wind turbine to the target wind turbine; express( x , y , z The inflow velocity at point ) and the first j The difference in wake wind speed between the upstream wind turbine and the target wind turbine; x , y , z These are the coordinates of the target wind turbine in the following, crosswind, and vertical directions, with the ground surface as the origin. The crosswind direction is perpendicular to the following direction. This represents the average thrust coefficient of all wind turbines in the wind farm. This indicates when x approaches 0. The value, , The standard deviation of the Gaussian-like velocity deficit distribution at each point is expressed by the following formula: ； In the formula: It is an intermediate variable.
8. The method for calculating the improved Gaussian wake distribution in a wind farm according to claim 7, characterized in that, The sum-of-squares superposition model is expressed as follows: ； In the formula: express( x , y , z The wake loss rate of all upstream wind turbines relative to the target wind turbine; j Indicates the sequence number.
9. A calculation system for improved Gaussian wake distribution in wind farms, characterized in that, include: The data acquisition module is used to acquire multidimensional data from wind farms; The non-uniformity coefficient calculation module is used to calculate the wake non-uniformity coefficient of a wind farm based on multi-dimensional data of the wind farm. The equivalent roughness calculation module is used to calculate the equivalent roughness of the wind farm based on the wake non-uniformity coefficient of the wind farm. The wake dissipation rate calculation module is used to obtain the wake dissipation rate based on the equivalent roughness of the wind farm. The single-effect wake calculation module is used to input the wake dissipation rate into the improved Gaussian wake model and calculate the wake loss rate of each upstream wind turbine in the wind farm to the target wind turbine at the spatial point. The wake superposition module is used to superimpose the wake loss rates of all upstream wind turbines in the wind farm at a spatial point to the target wind turbine using a square sum superposition model, so as to obtain the comprehensive wake loss rate of the target wind turbine at the spatial point. The spatial combination module is used to obtain the wake distribution of the target wind turbine based on the comprehensive wake loss rate of the target wind turbine at a spatial point.
10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed, implements the wind farm improved Gaussian wake distribution calculation method according to any one of claims 1-8.