CN116050288A - Wake loss calculation method and device in far and near wake regions - Google Patents
Wake loss calculation method and device in far and near wake regions Download PDFInfo
- Publication number
- CN116050288A CN116050288A CN202211591562.9A CN202211591562A CN116050288A CN 116050288 A CN116050288 A CN 116050288A CN 202211591562 A CN202211591562 A CN 202211591562A CN 116050288 A CN116050288 A CN 116050288A
- Authority
- CN
- China
- Prior art keywords
- wake
- wind turbine
- direction function
- flow direction
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/06—Wind turbines or wind farms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Fluid Mechanics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Computing Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Wind Motors (AREA)
Abstract
The application discloses a wake loss calculation method and device in a near-far wake zone. According to the method, the corresponding flow direction function is obtained by acquiring basic data of wind turbine set parameters and wind turbine running states, then a correction term is determined, the flow direction function is corrected, then a cross-direction function is corrected, and finally an improved double Gaussian wake model is obtained and wake loss is calculated. According to the wake loss calculation method in the near-far wake zone, aiming at the complex wake effect of the wind power plant, a double Gaussian wake model which is commonly used for speed loss calculation in the near-far wake zone is established, so that calculation results in the near wake zone and the far wake zone are high in accuracy, the problem of large error in simulation in the near wake zone is avoided, more accurate wind speed loss estimation and more accurate simulation results can be obtained, and further optimization arrangement research of the wind power plant is facilitated.
Description
Technical Field
The application relates to the technical field of wind power generation, in particular to a wake loss calculation method and device in a near-far wake zone.
Background
In a wind farm, when a stable air flow blows over a fan blade to drive a wind turbine to rotate, the wind turbine obtains energy from wind and forms a wake zone with reduced wind speed at the downstream of the wind turbine. In the wake zone, wake effects can occur downwind of the wind turbine due to the rotation of the wind turbine and the blocking action of the blades. The wake effect causes uneven wind speed distribution in the wind power plant, influences the running condition of each wind turbine in the wind power plant, and further influences the running condition and output of the wind power plant. In recent years, research on wake models is deepened, and speed loss of wake areas is currently calculated mainly by adopting a fan analytic wake model based on Gaussian distribution, namely an Ishihara wake model.
However, multiple fan wake flows in a wind power plant tend to overlap, and the turbulence structure of the wake flow overlapping region is complex, so that in addition to precisely calculating a single wake flow, modeling calculation is also required for speed loss and turbulence change of different wake flow overlapping regions. Therefore, when such a model is used to calculate wake zone wind speed loss, the following problems exist: (1) The existing model is only suitable for a far wake region, and the influence of fan aerodynamics is weakened due to the fact that the environmental turbulence intensity of a near wake region is too high and the thrust coefficient is too large, so that the calculation result is inaccurate. (2) existing models have asymmetry in the near wake region. For example, at 2 rotor diameters and 4 rotor diameters in the flow direction, the Ishihara wake model calculation results and LES calculation results are found to have large errors from simulation.
Disclosure of Invention
The object of the present application is to solve at least to some extent one of the technical problems described above.
Therefore, a first object of the present invention is to provide a wake loss calculation method in a near-far wake region, so that the improved dual-gaussian wake model has higher versatility and flexibility, and the calculation results in the near wake region and the far wake region have higher accuracy, so that the problem of larger error in simulation in the near wake region is avoided, and more accurate wind speed loss estimation and more accurate simulation results can be obtained.
A second object of the present application is to propose wake loss calculation means in the near-far wake zone.
To achieve the above object, an embodiment of a first aspect of the present application provides a wake loss calculation method in a near-far wake region, including
Basic data of wind turbine set parameters and wind turbine running states are obtained, wherein the wind turbine set parameters comprise a wind turbine thrust coefficient C T The rotor diameter D, the axial distance x from the rotor center, the radial distance r from the wake center, the basic data includes the environmental vortex intensity I a Standard deviation σ of the average velocity loss distribution in the span direction on each cross section;
obtaining a corresponding flow direction function according to the wind turbine generator system parameters and the basic data;
determining a correction term according to the wind turbine generator system parameters and the basic data, and correcting the flow direction function based on the correction term to obtain a corrected flow direction function;
obtaining a corrected cross function according to the wind turbine generator system parameters and the basic data;
an improved double Gaussian wake model is obtained based on the corrected cross-direction function and the corrected flow direction function;
wake losses are calculated using an improved dual gaussian wake model.
Optionally, obtaining a corresponding flow direction function according to the wind turbine generator system parameters and the basic data includes:
determining a first parameter a and a second parameter b in the flow direction function, wherein a=0.93c T -0.75 I a 0.17 ，b＝0.42C T 0.60 I a 0.20 ；
Obtaining a corresponding flow direction function according to the first parameter a, the second parameter b, the rotor diameter D and the axial distance x from the rotor center,
optionally, determining a correction term according to the wind turbine generator system parameter and the basic data, and correcting the flow direction function based on the correction term to obtain a corrected flow direction function, including:
according to the thrust coefficient C of the wind turbine T Rotor diameter D, axial distance x from rotor center, environmental vortex intensity I a Determining correction term p, p-C T - αI a -β (1-x/D) -2 ；
Adding the correction term p to the flow function, and generating a corrected flow function,
optionally, obtaining the modified cross-direction function according to the wind turbine generator system parameters and the basic data includes:
according to the thrust coefficient C of the wind turbine T Rotor diameter D, axial distance x from rotor center, environmental vortex intensity I a A third parameter m is determined which,
a fourth parameter k is determined from the rotor diameter D and the axial distance x from the rotor center,
generating a modified cross-direction function from the radial distance r from the wake center, the standard deviation sigma of the average velocity loss distribution in the span direction over each cross-section, the third parameter m and the fourth parameter k,
optionally, the dual gaussian wake model improved based on the modified cross-direction function and the modified flow direction function comprises:
multiplying the corrected cross-direction function and the corrected flow direction function to obtain a wind speed loss calculation formula of the improved double-Gaussian wake model,
according to the wake loss calculation method in the near-far wake zone, through obtaining basic data of wind turbine parameters and wind turbine running states, a corresponding flow direction function is obtained, then a correction term is determined, the flow direction function is corrected, then a cross direction function is corrected, finally an improved double-Gaussian wake model is obtained, wake loss is calculated, the improved double-Gaussian wake model has higher universality and flexibility, calculation results in the near wake zone and the far wake zone are higher in accuracy, the problem of larger error in simulation in the near wake zone is avoided, and therefore more accurate wind speed loss estimation and more accurate simulation results can be obtained, and optimization arrangement research of a wind farm is facilitated.
To achieve the above object, an embodiment of a second aspect of the present application provides a wake loss calculation device in a near-far wake zone, including:
the acquisition module is used for acquiring basic data of wind turbine set parameters and wind turbine running states, wherein the wind turbine set parameters comprise a wind turbine thrust coefficient C T The rotor diameter D, the axial distance x from the rotor center, the radial distance r from the wake center, the basic data includes the environmental vortex intensity I a Standard deviation σ of the average velocity loss distribution in the span direction on each cross section;
the flow direction function calculation module is used for obtaining a corresponding flow direction function according to the wind turbine generator system parameters and the basic data;
the correction module is used for determining correction items according to the wind turbine generator system parameters and the basic data, correcting the flow direction function based on the correction items, and obtaining a corrected flow direction function;
the cross-direction function calculation module is used for obtaining a corrected cross-direction function according to the wind turbine generator system parameters and the basic data;
the double Gaussian computing module is used for obtaining an improved double Gaussian wake model based on the corrected cross-direction function and the corrected flow direction function;
and the wake loss calculation module is used for calculating wake loss by using the improved double-Gaussian wake model.
Optionally, the flow direction function calculation module is configured to:
determining a first parameter a and a second parameter b in the flow direction function, wherein a=0.93c T -0.75 I a 0.17 ，b＝0.42C T 0.60 I a 0.20 ；
Obtaining a corresponding flow direction function according to the first parameter a, the second parameter b, the rotor diameter D and the axial distance x from the rotor center,
optionally, the correction module is configured to:
according to the thrust coefficient C of the wind turbine T Rotor diameter D, axial distance x from rotor center, environmental vortex intensity I a Determining correction term p, p-C T -α I a -β (1-x/D) -2 ；
Adding the correction term p to the flow function, and generating a corrected flow function,
optionally, the cross-direction function calculation module is configured to:
according to the thrust coefficient C of the wind turbine T Rotor diameter D, axial distance x from rotor center, environmental vortex intensity I a A third parameter m is determined which,
a fourth parameter k is determined from the rotor diameter D and the axial distance x from the rotor center,
generating a modified cross-direction function from the radial distance r from the wake center, the standard deviation sigma of the average velocity loss distribution in the span direction over each cross-section, the third parameter m and the fourth parameter k,
optionally, the double-gaussian computing module is configured to:
multiplying the corrected cross-direction function and the corrected flow direction function to obtain a wind speed loss calculation formula of the improved double-Gaussian wake model,
according to the wake loss calculation device in the near-far wake zone, through obtaining the basic data of the wind turbine parameters and the running state of the wind turbine, the corresponding flow direction function is obtained, the correction item is determined, the flow direction function is corrected, the cross direction function is corrected, the improved double-Gaussian wake model is finally obtained, the wake loss is calculated, the improved double-Gaussian wake model has higher universality and flexibility, the calculation results in the near wake zone and the far wake zone are higher in accuracy, the problem of larger error in simulation in the near wake zone is avoided, and therefore more accurate wind speed loss estimation and more accurate simulation results can be obtained, and the optimization arrangement research of a wind farm is facilitated.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 illustrates a flow chart of a wake loss calculation method in the near-far wake region of one embodiment;
FIG. 2 illustrates a flow diagram of a correction cross-direction function of one embodiment;
FIG. 3 illustrates a flow chart of a comparison of wind speed loss calculations for an Ishihara wake model and an improved double Gaussian wake model of one embodiment;
FIG. 4 shows a schematic diagram of an Ishihara wake model of an embodiment;
FIG. 5 illustrates a schematic diagram of an improved dual Gaussian wake model of a particular embodiment;
FIG. 6 shows a graph of the results of average wind speed loss in the z-direction for the Ishihara wake model in one embodiment;
FIG. 7 is a graph showing the results of an improved dual Gaussian wake model with average wind speed in the z-direction in one embodiment;
FIG. 8 is a graph showing the results of an Ishihara wake model in one embodiment for average wind speed loss in the y-direction;
FIG. 9 is a graph showing the results of an improved dual Gaussian wake model with average wind speed in the y-direction in one embodiment;
FIG. 10 shows a schematic diagram of the wake loss calculation device in the near-far wake zone of one embodiment.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
The invention is described in further detail below in connection with specific examples which are not to be construed as limiting the scope of the invention as claimed.
The following describes a wake loss calculation method and apparatus in a near-far wake zone according to an embodiment of the present application with reference to the accompanying drawings.
FIG. 1 is a flow chart of a wake loss calculation method in a near-far wake zone according to one embodiment of the present application, the method specifically comprising the steps of:
s1, acquiring basic data of wind turbine set parameters and wind turbine running states.
In one embodiment, the wind turbine parameters may include a wind turbine thrust coefficient C T The rotor diameter D, the axial distance x from the rotor center, the radial distance r from the wake center, the basic data may include the ambient vortex intensity I a Standard deviation σ of the average velocity loss distribution in the span direction over each cross section.
S2, obtaining a corresponding flow direction function according to the wind turbine generator system parameters and the basic data.
Specifically, a first parameter a and a second parameter b in the flow direction function are determined, wherein a=0.93c T -0.75 I a 0.17 ，b＝0.42C T 0.60 I a 0.20 . Then, according to the first parameter a, the second parameter b, the rotor diameter D and the axial distance x from the rotor center, a corresponding flow direction function is obtained,
s3, determining a correction term according to the wind turbine generator system parameters and the basic data, and correcting the flow direction function based on the correction term to obtain a corrected flow direction function.
In particular according to wind turbine propulsionForce coefficient C T Rotor diameter D, axial distance x from rotor center, environmental vortex intensity I a Determining correction term p, p-C T -α I a -β (1-x/D) -2 . Then, the correction term p is added to the flow direction function, and a corrected flow direction function is generated,
in a specific embodiment, the correction term p is first determined qualitatively. Because the correction term p should decrease with increasing downwind distance from the fan in the near-wake region, i.e., p+_1+x/D -2 . However, since the influence of the fan aerodynamics is weakened by the excessive environmental turbulence intensity and the excessive thrust coefficient, the correction term is determined as p T -α I a -β (1-x/D) -2 . Then, the correction term p is quantitatively determined. The parameters alpha and beta are obtained through fitting, so that the expression of the correction term p is determined: p.alpha.0.15C T --0.25 I a -0.7 (1-x/D) -2 。
According to the correction process, the situation that the environmental turbulence intensity of the near wake area is too high and the thrust coefficient is too large is considered, the value formula of the correction term p is determined, and the problem that the calculation result is affected due to the fact that the influence of fan aerodynamics is weakened in the near wake area is avoided, so that the accuracy of the calculation result in the near wake area is improved to a higher level while the accuracy of the calculation result in the far wake area is ensured to be higher.
S4, obtaining a corrected cross function according to the wind turbine generator system parameters and the basic data.
As shown in fig. 2, S4 may be specifically divided into the following steps:
s41, according to the thrust coefficient C of the wind turbine T Rotor diameter D, axial distance x from rotor center, environmental vortex intensity I a A third parameter m is determined which,
s42, determining a fourth parameter k according to the rotor diameter D and the axial distance x from the center of the rotor,
s43, generating a corrected cross-direction function according to the radial distance r from the center of the wake, the standard deviation sigma of the average speed loss distribution along the span direction on each cross section, the third parameter m and the fourth parameter k,
according to the correction process, the complex change of the environmental turbulence of the near wake zone is considered, and the expression of the cross function is corrected based on various parameter values, so that the accuracy of the calculation result of the near wake zone is improved to a higher level while the accuracy of the calculation result of the far wake zone is ensured.
S5, an improved double Gaussian wake model is obtained based on the corrected cross-direction function and the corrected flow direction function.
Specifically, the wind speed loss calculation formula of the improved double Gaussian wake model is obtained by multiplying the corrected cross-direction function and the corrected flow direction function,
S6, calculating wake loss by using the improved double Gaussian wake model.
The improved double Gaussian wake model is applied to actual calculation of wake loss, so that the overall simulation of the wind power plant is closer to the real situation in a near-far wake area, the optimal arrangement of the wind power plant is facilitated, and the influence of wake effects on uneven wind speed distribution in the wind power plant is effectively reduced.
According to the wake loss calculation method in the near-far wake zone, through obtaining basic data of wind turbine parameters and wind turbine running states, a corresponding flow direction function is obtained, then a correction term is determined, the flow direction function is corrected, then a cross direction function is corrected, finally an improved double-Gaussian wake model is obtained, wake loss is calculated, the improved double-Gaussian wake model has higher universality and flexibility, calculation results in the near wake zone and the far wake zone are higher in accuracy, the problem of larger error in simulation in the near wake zone is avoided, and therefore more accurate wind speed loss estimation and more accurate simulation results can be obtained, and optimization arrangement research of a wind farm is facilitated.
In addition, the improved dual gaussian wake model of the present application can also be validated.
In one particular embodiment, the wind speed losses for different fans may be calculated using the existing Ishihara wake model and the modified double Gaussian wake model of the present application, respectively, and the calculation compared to LES simulation (Large Eddy Simulation, large vortex simulation) calculation. Wherein, fan parameters calculated by using the Ishihara wake model are I a1 ＝3.5％，C T1 =0.37, fan parameter calculated using the modified double gaussian wake model is I a2 ＝3.5％，C T2 ＝0.81。
As shown in fig. 3, the comparison of the wind speed loss calculation results of the two models is as follows:
s301, acquiring basic data of wind turbine parameters and wind turbine running states.
Wherein the wind turbine group parameters comprise a wind turbine thrust coefficient C T The basic data of the running state of the wind turbine comprises the environmental vortex intensity I, wherein the rotor diameter D, the axial distance from the rotor center is x, and the radial distance from the wake center is r a Standard deviation σ of the average velocity loss distribution in the span direction over each cross section.
S302, the obtained actual parameter values are brought into an Ishihara wake model.
Wherein, as shown in fig. 4, a schematic diagram of the Ishihara wake model is shown.
S303, carrying the obtained actual parameter values into an improved double Gaussian wake model for calculation.
Wherein, fig. 5 is a schematic diagram of an improved dual gaussian wake model.
Specifically, the improved double gaussian wake model formula is as follows:
S304, comparing average wind speed loss of the fan in y and z directions under the Ishihara wake model and the improved double Gaussian wake model with LES results.
Specifically, as shown in fig. 6-9, the fan is shown in a visual image comparing average wind speed loss in y and z directions with LES simulation results under the Ishihara wake model and the modified double-gaussian wake model, wherein a solid line is the calculation result of the Ishihara wake model, a hollow circle is the LES result, and a dotted line is the calculation result of the modified double-gaussian wake model.
The result of the average wind speed loss in the z direction obtained by the Ishihara wake model is shown in fig. 6, the result of the average wind speed in the z direction obtained by the modified double-gaussian wake model is shown in fig. 7, the result of the average wind speed loss in the y direction obtained by the Ishihara wake model is shown in fig. 8, and the result of the average wind speed in the y direction obtained by the modified double-gaussian wake model is shown in fig. 9.
According to the embodiment, compared with the Ishihara wake model, the improved double-Gaussian wake model can more accurately calculate the wind speed loss of the near wake area of the fan, the problem that the error is large when the existing wake model is used for simulation in the near wake area is solved, the calculation result can more accurately fit LES experimental data of the near wake area, and the overall simulation of the wind power plant is more similar to the real situation.
In another embodiment, when fans in two actual offshore wind farms (offshore wind farm No. 1 and offshore wind farm No. 2) are in multiple wake stacks, multiple models may be used to calculate their wind speed loss and capacity of the entire wind farm. Among the various models may include a Jensen wake model, a uniform turbulence model (BPA model), a non-uniform turbulence model (Ishihara wake model), and a modified double gaussian wake model in the present application. Then, as shown in table 1, the calculation results of the respective models are compared and analyzed with the actually measured wind farm SCADA data.
TABLE 1 offshore wind farm calculation results
From table 1, it can be seen that, no matter the offshore wind farm No. 1 with smaller fan spacing or the offshore wind farm No. 2 with larger spacing, the improved double-gauss wake model can better predict wake effects of the wind farm, and the error is about 5%. Meanwhile, compared with other models, the improved double-Gaussian wake model calculates the wind speed loss of a near wake area of the fan more accurately. For example, when the wind farm pitch is small (No. 1 offshore wind farm is at 300 ° wind direction, the fan pitch is 3.3D), the calculation accuracy is higher. Therefore, the improved double Gaussian wake model can realize accurate calculation of wake loss of the fan when the phenomenon that wake is quickly recovered and wind speed loss is reduced due to the influence of additional turbulence intensity on the downstream fan, and has higher precision when calculating a wind power plant with smaller fan spacing.
In order to implement the above embodiment, the present application also proposes wake loss calculation means in the near-far wake zone.
FIG. 10 is a schematic diagram of a wake loss calculation device in the near-far wake zone according to one embodiment of the present application.
As shown in fig. 10, the wake loss calculation device in the far and near wake region includes an acquisition module 110, a flow direction function calculation module 120, a correction module 130, a cross direction function calculation module 140, a double gaussian calculation module 150, and a wake loss calculation module 160.
An acquisition module 110 for acquiring basic data of wind turbine parameters and wind turbine operation states, wherein the wind turbine parameters include a wind turbine thrust coefficient C T The rotor diameter D, the axial distance x from the rotor center, the radial distance r from the wake center, the basic data includes the environmental vortex intensity I a Standard deviation σ of the average velocity loss distribution in the span direction over each cross section.
The flow direction function calculation module 120 is configured to obtain a corresponding flow direction function according to the wind turbine generator system parameters and the basic data.
The flow direction function calculation module 120 is specifically configured to: determining a first parameter a and a second parameter b in the flow direction function, wherein a=0.93c T -0.75 I a 0.17 ，b＝0.42C T 0.60 I a 0.20 The method comprises the steps of carrying out a first treatment on the surface of the Obtaining a corresponding flow direction function according to the first parameter a, the second parameter b, the rotor diameter D and the axial distance x from the rotor center,
the correction module 130 is configured to determine a correction term according to the wind turbine generator system parameter and the basic data, and correct the flow direction function based on the correction term, so as to obtain a corrected flow direction function.
The correction module 130 is specifically configured to: according to the thrust coefficient C of the wind turbine T Rotor diameter D, axial distance x from rotor center, environmental vortex intensity I a Determining correction term p, p-C T - αI a -β (1-x/D) -2 The method comprises the steps of carrying out a first treatment on the surface of the Adding correction term p to flow functionAnd generates a modified flow direction function,
and the cross-direction function calculation module 140 is used for obtaining a corrected cross-direction function according to the wind turbine generator system parameters and the basic data.
The cross-direction function calculation module 140 is specifically configured to: according to the thrust coefficient C of the wind turbine T Rotor diameter D, axial distance x from rotor center, environmental vortex intensity I a A third parameter m is determined which,
the dual-gaussian computation module 150 is configured to obtain an improved dual-gaussian wake model based on the modified cross-direction function and the modified flow direction function.
The double gaussian calculation module 150 is specifically configured to: and multiplying the corrected cross-direction function and the corrected flow direction function to obtain an improved wind speed loss calculation formula of the double-Gaussian wake model.
A wake loss calculation module 160 for calculating wake loss using the modified double gaussian wake model.
It should be understood that the wake loss calculation device in the far and near wake regions is consistent with the corresponding embodiment of the wake loss calculation method in the far and near wake regions, so that the description is omitted in this embodiment.
According to the wake loss calculation device in the near-far wake zone, through obtaining the basic data of the wind turbine parameters and the running state of the wind turbine, the corresponding flow direction function is obtained, the correction item is determined, the flow direction function is corrected, the cross direction function is corrected, the improved double-Gaussian wake model is finally obtained, the wake loss is calculated, the improved double-Gaussian wake model has higher universality and flexibility, the calculation results in the near wake zone and the far wake zone are higher in accuracy, the problem of larger error in simulation in the near wake zone is avoided, and therefore more accurate wind speed loss estimation and more accurate simulation results can be obtained, and the optimization arrangement research of a wind farm is facilitated.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
It should be noted that in the description of the present specification, descriptions of terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Claims (10)
1. A wake loss calculation method in a near-far wake zone, comprising:
basic data of wind turbine set parameters and running states of a wind turbine are obtained, wherein the wind turbine set parameters comprise a wind turbine thrust coefficient C T The rotor diameter D, the axial distance x from the rotor center, the radial distance r from the wake center, the basic data including the ambient vortex intensity I a Standard deviation σ of the average velocity loss distribution in the span direction on each cross section;
obtaining a corresponding flow direction function according to the wind turbine generator system parameters and the basic data;
determining a correction term according to the wind turbine generator system parameters and the basic data, and correcting the flow direction function based on the correction term to obtain a corrected flow direction function;
obtaining a corrected cross function according to the wind turbine generator system parameters and the basic data;
an improved double Gaussian wake model is obtained based on the corrected cross-direction function and the corrected flow direction function;
wake losses are calculated using an improved dual gaussian wake model.
2. The method of claim 1, wherein deriving a corresponding flow direction function from the wind turbine parameters and the base data comprises:
determining a first parameter a and a second parameter b in the flow direction function, wherein,
a＝0.93C T -0.75 I a 0.17 ，b＝0.42C T 0.60 I a 0.20 ；
obtaining a corresponding flow direction function according to the first parameter a, the second parameter b, the rotor diameter D and the axial distance x from the rotor center,
3. the method of claim 1, wherein determining a correction term from the wind turbine parameters and the base data and correcting the flow function based on the correction term to obtain a corrected flow function, comprising:
according to the thrust coefficient C of the wind turbine T The rotor diameter D, the axial distance x from the rotor center, the ambient vortex strength I a Determining the correction term p, p T -α I a -β (1-x/D) -2 ；
Adding the correction term p to the flow function and generating the corrected flow function,
4. the method of claim 1, wherein deriving a modified cross-direction function based on the wind turbine parameters and the base data comprises:
according to the thrust coefficient C of the wind turbine T The rotor diameter D, the axial distance x from the rotor center, the ambient vortex strength I a A third parameter m is determined which,
a fourth parameter k is determined from said rotor diameter D and said axial distance x from the rotor centre,
generating a modified cross-direction function according to the radial distance r from the center of the wake, the standard deviation sigma of the average speed loss distribution along the span direction on each cross section, the third parameter m and the fourth parameter k,
5. the method of claim 1, wherein the modified dual gaussian wake model based on the modified cross-direction function and the modified flow direction function comprises:
the corrected cross-direction function and the corrected flow direction function are multiplied to obtain an improved wind speed loss calculation formula of the double-Gaussian wake model,
6. a wake loss calculation apparatus in a near-far wake zone, comprising:
the acquisition module is used for acquiring basic data of wind turbine set parameters and wind turbine running states, wherein the wind turbine set parameters comprise a wind turbine thrust coefficient C T The rotor diameter D, the axial distance x from the rotor center, the radial distance r from the wake center, the basic data including the ambient vortex intensity I a Standard deviation σ of the average velocity loss distribution in the span direction on each cross section;
the flow direction function calculation module is used for obtaining a corresponding flow direction function according to the wind turbine generator system parameters and the basic data;
the correction module is used for determining a correction term according to the wind turbine generator system parameters and the basic data, correcting the flow direction function based on the correction term and obtaining a corrected flow direction function;
the cross-direction function calculation module is used for obtaining a corrected cross-direction function according to the wind turbine generator system parameters and the basic data;
the double Gaussian computing module is used for obtaining an improved double Gaussian wake model based on the corrected cross-direction function and the corrected flow direction function;
and the wake loss calculation module is used for calculating wake loss by using the improved double-Gaussian wake model.
7. The apparatus of claim 6, wherein the flow direction function calculation module is configured to:
determining a first parameter a and a second parameter b in the flow direction function, wherein a=0.93c T -0.75 I a 0.17 ，b＝0.42C T 0.60 I a 0.20 ；
Obtaining a corresponding flow direction function according to the first parameter a, the second parameter b, the rotor diameter D and the axial distance x from the rotor center,
8. the apparatus of claim 6, wherein the correction module is to:
according to the thrust coefficient C of the wind turbine T The rotor diameter D, the axial distance x from the rotor center, the ambient vortex strength I a Determining the correction term p, p T -α I a -β (1-x/D) -2 ；
Adding the correction term p to the flow function and generating the corrected flow function,
9. the apparatus of claim 6, wherein the cross-direction function calculation module is to:
according to the thrust coefficient C of the wind turbine T The rotor diameter D, the axial distance x from the rotor center, the ambient vortex strength I a A third parameter m is determined which,
a fourth parameter k is determined from said rotor diameter D and said axial distance x from the rotor centre,
generating a modified cross-direction function according to the radial distance r from the center of the wake, the standard deviation sigma of the average speed loss distribution along the span direction on each cross section, the third parameter m and the fourth parameter k,
10. the apparatus of claim 6, wherein a double gaussian calculation module is configured to:
the corrected cross-direction function and the corrected flow direction function are multiplied to obtain an improved wind speed loss calculation formula of the double-Gaussian wake model,
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211591562.9A CN116050288A (en) | 2022-12-12 | 2022-12-12 | Wake loss calculation method and device in far and near wake regions |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211591562.9A CN116050288A (en) | 2022-12-12 | 2022-12-12 | Wake loss calculation method and device in far and near wake regions |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116050288A true CN116050288A (en) | 2023-05-02 |
Family
ID=86117204
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211591562.9A Pending CN116050288A (en) | 2022-12-12 | 2022-12-12 | Wake loss calculation method and device in far and near wake regions |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116050288A (en) |
-
2022
- 2022-12-12 CN CN202211591562.9A patent/CN116050288A/en active Pending
Non-Patent Citations (1)
| Title |
|---|
| 彭杰: "风机尾流解析模型及风电场多策略优化排布研究", 《万方硕士论文》, pages 1 - 93 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| van der Laan et al. | Power curve and wake analyses of the Vestas multi-rotor demonstrator | |
| EP3364324B1 (en) | Method and device for detecting equivalent load of wind turbine generator system | |
| JP5033033B2 (en) | Method for measuring turbulence intensity of horizontal axis wind turbine | |
| CN106548414B (en) | Method for calculating power generation capacity of offshore wind farm | |
| US20120211986A1 (en) | Wind sensor system using blade signals | |
| US10724499B2 (en) | Controlling wind turbines according to reliability estimates | |
| CN109779848B (en) | Method and device for obtaining full-field wind speed correction function and wind power plant | |
| CN107832899B (en) | Wind power plant output optimization method and device and implementation device | |
| CN103061980A (en) | Feed-forward control system and feed-forward control method for wind generating set based on laser wind finding radar | |
| CN116050287B (en) | Modeling method and device for wake flow analysis of offshore floating fan | |
| CN112784509A (en) | Wind power generator power calculation method based on improved Gaussian distribution wake flow model | |
| CN110809672B (en) | Determining a wind speed value | |
| Huang et al. | Optimal design of protuberant blades for small variable-speed horizontal axis wind turbine-experiments and simulations | |
| Kecskemety et al. | Influence of wake dynamics on the performance and aeroelasticity of wind turbines | |
| JP7009237B2 (en) | Wind power generation equipment and wind power generation system | |
| CN116484652B (en) | Wake flow interference detection method in wind power plant based on blade root load | |
| CN116050288A (en) | Wake loss calculation method and device in far and near wake regions | |
| CN109766611B (en) | Wind farm power simplified prediction method considering terrain gradient | |
| Bertelè et al. | Brief communication: Wind inflow observation from load harmonics–wind tunnel validation of the rotationally symmetric formulation | |
| CN116245039A (en) | Wake flow assessment method and system for offshore wind power generation field group | |
| Laino et al. | Current efforts toward improved aerodynamic modeling using the AeroDyn subroutines | |
| CN112283051A (en) | Vibration signal characteristic optimization method and system based on lift line model | |
| CN114076065A (en) | Method and device for identifying blade stall of wind generating set | |
| Potentier et al. | High-Reynolds-number wind turbine blade equipped with root spoilers–Part 2: Impact on energy production and turbine lifetime | |
| CN110968942A (en) | Performance evaluation method of wind turbine generator based on surrounding environment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |