WO2018161626A1 - 用于计算风电场发电量的方法和设备 - Google Patents

用于计算风电场发电量的方法和设备 Download PDF

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Publication number
WO2018161626A1
WO2018161626A1 PCT/CN2017/109513 CN2017109513W WO2018161626A1 WO 2018161626 A1 WO2018161626 A1 WO 2018161626A1 CN 2017109513 W CN2017109513 W CN 2017109513W WO 2018161626 A1 WO2018161626 A1 WO 2018161626A1
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Prior art keywords
wind
data
wind tower
numerical simulation
wind farm
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PCT/CN2017/109513
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English (en)
French (fr)
Inventor
敖娟
刘钊
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新疆金风科技股份有限公司
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Priority to EP17868504.6A priority Critical patent/EP3396571A4/en
Priority to AU2017352549A priority patent/AU2017352549B2/en
Priority to US15/776,294 priority patent/US11168667B2/en
Publication of WO2018161626A1 publication Critical patent/WO2018161626A1/zh

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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • 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
    • 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
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/84—Modelling or simulation
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/32—Wind speeds
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/335—Output power or torque
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • 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

Definitions

  • the present application relates to the field of wind power generation and, more particularly, to a method and apparatus for calculating the amount of power generated by a wind farm.
  • the construction scale, economic benefits and risk degree of wind farms depend on the calculation of power generation of early wind farms, and the calculation of wind farm power generation depends on the actual situation of wind resource distribution in each region, and also depends on the calculation method of power generation. Adaptability and accuracy. Complex terrain and meteorological conditions have brought about huge errors in the simulation of wind farm wind conditions and the calculation of unit selection and power generation.
  • the present application provides a method and apparatus for calculating a wind farm power generation amount, the method and apparatus capable of being in a situation where the terrain of the wind farm field is complex and the wind tower data in the wind farm field is underrepresented.
  • Mesoscale numerical simulation of meteorological variables in the wind farm field is carried out by using the WRF (Weather Research and Forecasting Model), and the mesoscale numerical simulation data is extracted as the virtual wind tower data, and the wind power generation is calculated.
  • WRF Weight Research and Forecasting Model
  • a method for calculating a wind farm power generation amount comprising: determining whether a terrain complexity of a wind farm field exceeds a predetermined complexity; if the terrain complexity exceeds a predetermined complexity , to determine the representativeness of the wind tower data in the wind farm field; if the wind tower data is insufficiently representative, perform mesoscale numerical simulation of the meteorological variables in the wind farm field; extract mesoscale numerical simulation data as a virtual wind Tower data; calculation of wind farm power generation by using virtual wind tower data.
  • an apparatus for calculating a power generation amount of a wind farm comprises: a terrain complexity determining unit configured to determine whether the terrain complexity of the wind farm field exceeds a predetermined complexity; the wind tower data representative determining unit is configured to be in a situation where the terrain complexity exceeds a predetermined complexity The representative of the wind tower data in the wind farm field is judged; the mesoscale numerical simulation unit is configured to perform mesoscale numerical simulation of the meteorological variables of the wind farm field in the case of insufficient representativeness of the wind tower data; The scale numerical simulation data extracting unit is configured to extract the mesoscale numerical simulation data as the virtual wind tower data; the wind farm power generation amount calculation unit is configured to calculate the wind farm power generation amount by using the virtual wind tower data.
  • a computer storage medium having recorded thereon a program for performing a method comprising the steps of: determining whether a terrain complexity of a wind farm field exceeds a predetermined complexity If the terrain complexity exceeds the predetermined complexity, the wind tower data in the wind farm field is judged to be representative; if the wind tower data is insufficiently representative, the mesoscale numerical simulation of the meteorological variables in the wind farm field is performed; Mesoscale numerical simulation data is used as virtual wind tower data; wind power generation is calculated by using virtual wind tower data.
  • an apparatus for calculating a wind farm power generation amount comprising: a memory configured to store a program for performing a method of determining whether a terrain complexity of a wind farm field exceeds Predetermined complexity; if the terrain complexity exceeds the predetermined complexity, it is judged that the wind tower data in the wind farm field is representative; if the wind tower data is insufficiently representative, the mesoscale value of the meteorological variables in the wind farm field is Simulation; extracting mesoscale numerical simulation data as virtual wind tower data; calculating wind farm power generation by using virtual wind tower data; and a processor configured to execute the program.
  • the present application can effectively improve the calculation accuracy of the wind farm power generation by introducing the virtual wind tower data through mesoscale numerical simulation, and can verify and correct the reliability by using the mesoscale numerical simulation data. Further improve the calculation accuracy of increasing the amount of power generated by the wind farm, so that even in the case of complex terrain and insufficient representativeness of the wind tower data, the calculation error of the wind farm power generation amount can be reduced, and a more accurate calculation result can be provided, and at the same time, It also saves wind tower installation and maintenance costs.
  • FIG. 1 is a flowchart illustrating a method for calculating a wind farm power generation amount according to an exemplary embodiment of the present application
  • FIG. 2 is a flow chart showing a method for calculating a wind farm power generation amount according to another exemplary embodiment of the present application
  • FIG. 3 is a block diagram showing an apparatus for calculating a wind farm power generation amount according to an exemplary embodiment of the present application
  • FIG. 4 is a block diagram illustrating an apparatus for calculating a wind farm power generation amount according to another exemplary embodiment of the present application.
  • the “Mesoscale Numerical Model” (also known as the “WRF Model”) is a new generation open source meteorological model jointly developed by the US Environmental Prediction Center and the US National Center for Atmospheric Research.
  • the module group included in the WRF mode can be used as a theoretical basis for the discussion of basic physical processes. It can also simulate the real weather case.
  • the horizontal resolution, vertical direction level, integral area and various physical processes of the WRF mode can be adjusted according to user needs. It is extremely easy to use and is widely recognized and used worldwide. Accordingly, “mesoscale numerical simulation” refers to numerical simulation using "WRF mode”.
  • FIG. 1 is a flowchart illustrating a method for calculating a wind farm power generation amount according to an exemplary embodiment of the present application.
  • step 101 it is determined whether the terrain complexity of the wind farm field exceeds a predetermined complexity.
  • the difference in terrain complexity is one of the main reasons for choosing different simulation methods.
  • linear wind flow models are often used (for example, WAsP) Satisfactory simulation results can be obtained; in complex mountains, because the linear model overestimates the acceleration effect of the terrain, the flow field is usually simulated using three-dimensional fluid simulation software (for example, WindSim, Meteodyn WT); in special complex mountain terrain, As in the deep valleys of the mountains, almost any model of the wind flow cannot give satisfactory results.
  • the method for calculating the wind farm power generation amount proposed by the present application is mainly directed to a complex terrain or an extremely complicated terrain, the method for calculating the wind farm power generation amount according to an exemplary embodiment of the present application first determines the wind farm field area.
  • Terrain complexity In some embodiments, a Ruggedness Index (RIX) can be used to determine whether the terrain complexity of the wind farm field exceeds a predetermined complexity.
  • the RIX can be calculated by the following method: In a polar coordinate with a radius of R at a certain point, each radius line may intersect the contour line, and the intersection point divides the radius line into several line segments.
  • the rugged index RIX value is obtained by dividing the sum of the line segments whose terrain slope exceeds the critical slope ⁇ by the sum of all the line segments (ie, the sum of the radius lines R). For a particular point, its RIX value depends on three parameters: the calculated radius R, the critical slope ⁇ , and the number of radius lines N. In some embodiments, the calculation radius R may take 3 to 5 km, and the specific value may be determined by the range of the testable wind farm. The default is to take R to 3.5 km, and the critical slope ⁇ to be 0.3 rad (about 17°). The number N is 72. If the RIX is equal to 0%, the slope of the terrain is less than 0.3 rad. If the RIX is greater than 0%, the slope of some areas is greater than 0.3 rad.
  • the ruggedness index is greater than or equal to a predetermined first rugged index, it is determined that the terrain complexity of the wind farm field exceeds a predetermined complexity.
  • the terrain complexity may be determined to be complex. If the rugged index is greater than or equal to the predetermined second rugged index, the terrain complexity may be determined as Extremely complex, where the second rugged index is greater than the first rugged index. For example, the first rugged index can be 30%, and the second rugged index can be 50%. If the RIX is greater than 30%, the terrain complexity is judged to be complicated. If the RIX is greater than 50%, the terrain complexity is extremely complicated.
  • the calculation method for calculating the amount of wind farm power generation may further include: outputting a warning when determining that the terrain complexity of the wind farm field is extremely complicated to alert the computing personnel to pay special attention.
  • step 102 the wind tower data representative in the wind farm field is determined.
  • the wind tower data representative is used to measure whether the wind tower data can represent the local area of the wind farm field. climate.
  • the wind tower data representativeness can be judged by the wind tower density, the altitude similarity, or the topographical similarity in the wind farm field.
  • the wind tower data is indicated. Has a certain representativeness. Otherwise, it indicates that the wind tower data is under-represented, that is, the wind tower data is not representative and is not sufficient to represent the local climate of the wind farm.
  • the wind tower data representation may be determined by the wind tower density within the wind farm field.
  • the wind tower density can be characterized by the ratio of the distance between the diagonal of the wind farm field and the number of wind towers in the wind farm field. If the density of the wind tower is less than the predetermined density, and the circle is rounded at the predetermined density as the center of each wind tower, the sum of the areas of all the circles (the area where the circle and the circle coincide with each other when calculating the sum of the areas) Calculate only once) If the proportion of the area of the wind farm field reaches a predetermined ratio or more, the data of the wind tower is judged to be representative.
  • the predetermined density may be 3 km, and the predetermined ratio may be 80%, but the present application is not limited thereto, and a more relaxed or stricter judgment threshold may be selected according to actual requirements.
  • the wind tower data representation can be judged by altitude similarity, wherein if the difference between the highest altitude and the lowest altitude within the jurisdiction of each wind tower is less than the predetermined altitude difference, then the jurisdiction is indicated The altitude inside is similar, and the data of the wind tower is judged to be representative. Otherwise, the data of the wind tower is judged to be insufficient, and the jurisdiction of the wind tower is determined as the area where the wind tower data is insufficiently representative.
  • the jurisdiction of each wind tower may be characterized by a circle centered on the wind tower and having a radius of half the distance between two adjacent wind towers.
  • the above definitions of the jurisdiction of each wind tower are only examples, and other methods can be used to define the jurisdiction of the wind tower.
  • the jurisdiction of each wind tower can be determined by the wind tower. It is characterized by a square centered on the side of the distance between two adjacent wind towers.
  • the predetermined altitude difference may be 150 m, but is not limited thereto, and for example, the predetermined altitude difference may be a value smaller or larger than 150 m.
  • the wind tower data representation can be determined by topographical similarity. If the difference between the maximum terrain roughness and the minimum terrain roughness within the jurisdiction of each wind tower is less than the predetermined roughness difference, it indicates that the topography within the jurisdiction has similarities, and the wind tower data is representative. Otherwise, it is judged that the wind tower data is insufficiently representative, and the wind tower tube is The area under the jurisdiction is determined as an area where the wind tower data is under-represented.
  • the predetermined roughness difference may be 0.1, or a value smaller than 0.1 or greater may be selected depending on actual calculations.
  • step S102 If it is determined in step S102 that the wind tower data is insufficiently representative, the process proceeds to step 103. Otherwise, if the wind tower data is representative, the wind farm power generation amount calculation is directly performed by using the wind tower data by the prior art method.
  • step 103 a mesoscale numerical simulation of the meteorological variables of the wind farm field is performed. As a supplement to the wind measurement data, the simulation accuracy of the mesoscale data needs to reach a higher level. Otherwise, the mesoscale simulation that does not meet the required simulation results will introduce new errors, which will cause more errors in calculating the power generation.
  • the corresponding parameterization scheme may be selected according to the topographical features, climate characteristics, and/or historical simulation results of the wind power location.
  • the meteorological variable can include at least one of wind speed, wind direction, temperature, humidity, turbulence, and air pressure.
  • the wind speed and direction of the wind farm field can be selected according to the topographical features, climate characteristics and/or historical simulation results of the area where the wind power is located.
  • Mesoscale numerical simulations were performed on multiple meteorological variables.
  • a suitable parameterization scheme for a wind farm field it is first necessary to consider the weather characteristics of the region where the wind farm is located. In areas with strong convective activity, the cumulus convective parameterization scheme is more important; in areas with intense atmospheric activity in the boundary layer, the parametric scheme of the planetary boundary layer is more important; in areas with large differences in surface water and land distribution, the surface parameterization scheme is more important. In addition, microphysics, turbulence, diffusion, long-wave radiation, short-wave radiation, etc. are also important parametric options. Since there are many parameterization options inside WRF, we will not repeat them here.
  • the YSU scheme is a first-order non-local closure scheme based on the K-diffusion mode, and the heat exchange caused by the entrainment in the inversion layer is considered in detail, and The inverse gradient transport term in the turbulent diffusion equation is considered;
  • the ACM2 scheme fuses the vortex diffusion into the non-local diffusion scheme, which can describe the turbulent transport process at the super-grid scale and sub-grid scale in the convective boundary layer;
  • MYNN3 scheme It is a parameterization scheme for turbulent flow energy that predicts turbulent flow energy and other secondary fluxes.
  • MYJ ⁇ flow energy scheme is planetary boundary layer and freedom
  • the turbulent parameterization in the atmosphere is a non-singular scheme, and the upper limit of the main length scale is based on the turbulent growth condition.
  • the condition that the kinetic energy produces a non-singularity is derived from the shear flow of the turbulent flow energy, buoyancy, and driving flow.
  • the mode in the mesoscale numerical simulation of the meteorological variables of the wind farm field is The resolution should not be lower than the predetermined resolution.
  • the predetermined resolution is preferably 3 km and 1 km, so that it is convenient to ensure that the mesoscale numerical simulation data is used as supplementary virtual wind tower data after the wind tower in the wind farm field.
  • the data is representative.
  • the mesoscale numerical simulation data is extracted as the virtual wind tower data.
  • the mesoscale numerical simulation data of the area where the wind tower data in the wind farm field is insufficiently representative may be used as a supplementary virtual test of the area.
  • Wind tower data If there is no wind tower in the wind farm field, the mesoscale numerical simulation data of the wind farm field can be used as the virtual wind tower data of the wind farm field.
  • the wind farm power generation amount can be calculated by using the virtual wind tower data.
  • the measured data of the wind tower in the wind farm field can be combined with the virtual wind tower data supplemented in step 103 to calculate the wind farm power generation. the amount. If there is no wind tower in the wind farm field, the virtual wind tower data obtained in step 103 can be directly used to calculate the wind farm power generation.
  • the representativeness of all the wind tower data including the virtual wind tower data in the wind farm field may be judged again, wherein, if the determination is made The wind tower data is representative, and a step 105 of calculating the wind farm power generation amount by using the virtual wind tower data is performed.
  • At step 105 at least one of the WT, WindSim, WAsP, and WindPro software can be employed as a computing tool to calculate wind farm power generation. If there is a wind tower in the wind farm field, the measured data of the existing wind tower is input into at least one of WT, WindSim, WAsP and WindPro software together with the supplementary virtual wind tower data to calculate the power generation of the wind farm. the amount. If there is no wind tower in the wind farm field, the obtained virtual wind tower data is directly input into at least one of WT, WindSim, WAsP and WindPro software to calculate the power generation amount of the wind farm.
  • the corresponding relationship between the wind acceleration factors at the points of the whole field and the wind tower can be obtained according to the linear or nonlinear relationship.
  • the parameters of wind speed and turbulence at all points of the whole field are derived.
  • the wind speed, turbulence and other parameters are selected to suit the wind condition, and the actual power curve of the model and the wind speed at the fan point are used to calculate the power generation of each wind turbine, and then the entire wind farm is obtained. The amount of electricity generated.
  • the virtual wind tower can solve the problem of insufficient number of wind towers in the wind farm, combining the virtual wind tower data with the measured data of the wind tower can calculate the power generation of the wind farm more accurately.
  • the method for calculating wind power generation amount is introduced into the virtual wind tower data by mesoscale numerical simulation of meteorological variables in the wind farm field, so that the terrain complexity exceeds the predetermined complexity and the wind farm field.
  • the accuracy of calculation of wind farm power generation is improved.
  • FIG. 2 is a flowchart illustrating a method for calculating a wind farm power generation amount according to another exemplary embodiment of the present application.
  • Steps 201, 202, and 203 in FIG. 2 are identical to steps 101 to 103 in FIG. 1, and therefore, the descriptions of steps 101 through 103 are equally applicable to steps 201 through 203, and details are not described herein.
  • FIG. 2 is used to calculate the wind farm according to another exemplary embodiment of the present application.
  • the method of generating power adds a step of determining the reliability of the mesoscale numerical simulation data and a step of correcting the mesoscale numerical simulation data after step 203 to ensure that the accuracy of the mesoscale numerical simulation data meets the requirements, thereby increasing the complexity or Accuracy of calculation of wind farm power generation under extremely complex terrain.
  • Step 204 and step 205 are described in detail below.
  • the reliability of the mesoscale numerical simulation data is verified.
  • the reliability of the mesoscale numerical simulation data can be verified by using the correlation coefficient between the existing wind tower data and the mesoscale numerical simulation data.
  • the measured data of the wind tower in the wind farm field can be used to verify the reliability of the mesoscale numerical simulation data. If the correlation coefficient between the measured data of the wind tower and the mesoscale analog data in the wind farm field is greater than a predetermined correlation coefficient, the mesoscale numerical simulation data can be verified to be reliable, otherwise, the verification rule is The numerical simulation data is unreliable.
  • the mesoscale numerical simulation data of the simulation It is optional to discard the mesoscale numerical simulation data of the simulation and re-select the combination of the mode resolution and the parameterization scheme for the mesoscale numerical simulation to re-simulate until the mesoscale numerical simulation.
  • the data was verified to be reliable.
  • the reliability of the mesoscale numerical simulation data can be verified by using the measured data of the wind tower of the adjacent wind farm field. In some embodiments, if the measured data of the wind tower adjacent to the wind farm field is related to the mesoscale numerical simulation data of the vicinity of the wind tower (in the mesoscale grid where the wind tower is located) If the coefficient is greater than the predetermined correlation coefficient, then verifying the mesoscale numerical simulation data is reliable.
  • the predetermined correlation coefficient may be 0.8, but is not limited thereto, but a higher or lower predetermined correlation coefficient may be selected according to the requirements for the mesoscale numerical simulation data reliability standard. For example, if there is a wind tower in the wind farm field, the mesoscale numerical simulation data can be considered reliable when the correlation coefficient between the measured wind speed of the wind tower and the simulated wind speed obtained by the mesoscale numerical simulation reaches 0.8 or more. . If there is no wind tower in the wind farm field, when the correlation coefficient between the measured wind speed of the wind tower adjacent to the wind farm and the simulated wind speed in the adjacent area reaches 0.8 or more, it can be considered that the mesoscale numerical simulation data is reliable. of.
  • the mesoscale numerical simulation data that is verified to be reliable is corrected.
  • statistically validated mesoscale numerical simulation data that is verified to be reliable by using the wind farm or measured data or radar data of a wind tower in a wind farm adjacent to the wind farm .
  • the radar data may be wind data measured by a laser radar or a sodar or the like.
  • a multivariate regression statistical method can be used to verify the mesoscale numerical simulation data that is verified to be reliable.
  • the influence factor that is better correlated with the measured wind speed from the mesoscale numerical simulation data for example, it can be Temperature, humidity, pressure, etc.
  • the regression equation the correction relationship of a single point can be pushed to the entire wind farm field, thereby the mesoscale of the entire wind farm field.
  • the simulation data is corrected.
  • machine learning algorithms such as neural networks or support vector machines can also be used for correction. These algorithm models will find the relationship between the influence factor and the actual wind speed. This relationship is generally a nonlinear relationship, and then the mesoscale The simulation data is corrected. It should be understood that only an example of a statistical method for correcting mesoscale numerical simulation data is given herein, and the statistical method for correcting the mesoscale numerical simulation data according to the present application is not limited thereto.
  • Steps 206 and 207 correspond to steps 104 and 105 of FIG. 1, respectively, and therefore, here is not Again, the only difference is that step 205 is performed after the mesoscale numerical simulation data is verified to be reliable and corrected, and prior to step 104, the operation of verifying and correcting the mesoscale numerical simulation data is not performed.
  • step 205 is performed after the mesoscale numerical simulation data is verified to be reliable and corrected, and prior to step 104, the operation of verifying and correcting the mesoscale numerical simulation data is not performed.
  • the description of the other contents in FIG. 1 is also applicable to FIG. 2, and for brevity, no further description is made here.
  • the reliability of the mesoscale numerical simulation data is further verified and the mesoscale numerical simulation data is performed on the basis of the calculation method of the wind farm power generation amount of FIG.
  • the correction is performed, so the accuracy of the mesoscale numerical simulation results can be further improved, thereby improving the calculation accuracy of the wind farm power generation amount.
  • FIG. 3 is a block diagram showing an apparatus 300 for calculating a wind farm power generation amount according to an exemplary embodiment of the present application.
  • the apparatus 300 may include a terrain complexity determination unit 301, a wind tower data representative determination unit 302, a mesoscale numerical simulation unit 303, a mesoscale numerical simulation data extraction unit 304, and a wind farm power generation amount calculation unit 305.
  • the terrain complexity determination unit 301 can determine whether the terrain complexity of the wind farm field exceeds a predetermined complexity.
  • the wind tower data representative determining unit 302 can determine the representativeness of the wind tower data in the wind farm field area if the terrain complexity of the wind farm field exceeds a predetermined complexity.
  • the mesoscale numerical simulation unit 303 can perform mesoscale numerical simulation of the meteorological variables of the wind farm field in the case where the wind tower data is under-represented.
  • the mesoscale numerical simulation data extracting unit 304 can extract the mesoscale numerical simulation data as the virtual wind tower data.
  • the wind farm power generation amount calculation unit 305 can calculate the wind farm power generation amount by using the virtual wind tower data.
  • the terrain complexity determination unit 301 may employ a rugged index to determine whether the terrain complexity of the wind farm field exceeds a predetermined complexity. If the rugged index is greater than or equal to the predetermined first rugged index, determining that the terrain complexity of the wind farm field exceeds a predetermined complexity, in some embodiments, if the rugged index is greater than or equal to the predetermined first rugged index, then The terrain complexity determining unit 301 determines that the terrain complexity is complex. If the rugged index is greater than or equal to the predetermined second rugged index, the terrain complexity determining unit 301 determines that the terrain complexity is extremely complicated, wherein the second rugged index is greater than the first Rugged index.
  • the operation performed by the terrain complexity determination unit 301 corresponds to the step 101 shown in FIG. 1. Therefore, the related description about the step 101 is equally applicable to the terrain complexity determination unit 301, and details are not described herein again.
  • the wind tower data representative determining unit 302 may determine the wind tower data representative by the wind tower density, the altitude similarity, or the topographical similarity in the wind farm field.
  • the wind tower data representative judging unit 302 judges the wind tower data representative by the wind tower density in the wind farm field, if the wind tower density is less than the predetermined density, and the wind tower is centered and The predetermined density is rounded as a diameter. If the sum of the areas of all the circular areas occupies a predetermined ratio of the area of the wind farm field, the wind tower data representative determining unit 302 can determine that the wind tower data is representative.
  • the wind tower density may be characterized by a ratio of the distance of the diagonal of the wind farm field to the number of wind towers in the wind farm field, but is not limited thereto.
  • the wind tower data is determined to be insufficiently representative, and the wind tower jurisdiction can be determined as An area where the wind tower data is under-represented.
  • the wind tower data representative determining unit 302 determines the representativeness of the wind tower data by the topographical similarity, if the difference between the maximum terrain roughness and the minimum terrain roughness in the jurisdiction of each wind tower is less than the predetermined roughness The degree difference indicates that the topography of the jurisdiction has similarity, and the wind tower data representative determining unit 302 can determine that the wind tower data is representative; otherwise, the wind tower data is insufficiently representative, and the The wind tower jurisdiction is identified as an area where the wind tower data is under-represented.
  • the operation performed by the wind tower data representative determination unit 302 corresponds to the step 102 shown in FIG.
  • the step 102 with respect to the relevant description of the step 102 (for example, regarding the jurisdiction, the predetermined ratio, the predetermined density, the predetermined altitude difference, and the predetermined roughness).
  • the description of the difference or the like is also applicable to the wind tower data representative judgment unit 302, and therefore, it will not be described again here.
  • the mesoscale numerical simulation unit 303 can select a corresponding parameterization scheme combination according to the topographical features, climatic characteristics and/or historical simulation results of the wind power location to perform mesoscale numerical simulation of the meteorological variables of the wind farm field.
  • the weather variable may include at least one of wind speed, wind direction, temperature, humidity, turbulence, and air pressure, but is not limited thereto. Since the present application calculates the wind farm power generation amount for the wind farm field with complex terrain or extremely complicated terrain, the mode resolution of the mesoscale numerical simulation unit 303 for the mesoscale numerical simulation of the meteorological variables of the wind farm field is preferably not Below the predetermined resolution, 3km and 1km are preferred.
  • the operation performed by the mesoscale numerical simulation unit 303 corresponds to the step 103 shown in FIG. 1, and thus, a related description about the step 103 (eg, a parameterization scheme) The related description) is equally applicable to the mesoscale numerical simulation unit 303, and will not be described again here.
  • the mesoscale numerical simulation data extracting unit 304 may extract the mesoscale numerical simulation data as the virtual wind tower data. According to an exemplary embodiment, if there is a wind tower in the wind farm field, the mesoscale numerical simulation data extracting unit 304 may use the mesoscale numerical simulation data of the region where the wind tower data in the wind farm field is insufficiently representative as the Supplementary virtual wind tower data for the area. If there is no wind tower in the wind farm field, the mesoscale numerical simulation data extracting unit 304 may use the mesoscale numerical simulation data of the wind farm field as the virtual wind tower data of the wind farm field.
  • the operation performed by the mesoscale numerical simulation data extracting unit 304 corresponds to the step 104 shown in FIG. 1. Therefore, the related description about the step 104 is equally applicable to the mesoscale numerical simulation data extracting unit 304, and details are not described herein again.
  • the wind farm power generation amount calculation unit 305 can calculate the wind farm power generation amount by using the virtual wind tower data. In some embodiments, if there is a wind tower in the wind farm field, the measured data of the wind tower in the wind farm field can be combined with the supplementary virtual wind tower data to calculate the wind farm power generation. If there is no wind tower in the wind farm field, the virtual wind tower data can be directly used to calculate the wind farm power generation. According to an exemplary embodiment, after the mesoscale numerical simulation data is extracted as the virtual wind tower data, the wind tower data representative determination unit 302 may again determine the representativeness of all the wind tower data including the virtual wind tower data.
  • the wind farm power generation amount calculation unit 305 calculates the wind farm power generation amount by using the virtual wind tower data.
  • the wind farm power generation amount calculation unit 305 may calculate the wind farm power generation amount using at least one of the WT, WindSim, WAsP, and WindPro software as a calculation tool.
  • the operation performed by the wind farm power generation amount calculation unit 305 corresponds to the step 105 shown in FIG. 1, and therefore, the related description about the step 105 is equally applicable to the wind farm power generation amount calculation unit 305, and details are not described herein again.
  • the device for calculating the amount of wind farm power generation introduces virtual wind tower data by mesoscale numerical simulation of meteorological variables in the wind farm field, which can be complex or extremely complex in terrain and measured in the wind farm field.
  • the accuracy of calculation of wind farm power generation is improved.
  • FIG. 4 is a block diagram showing an apparatus 400 for calculating a wind farm power generation amount according to another exemplary embodiment of the present application.
  • the device 400 may include a terrain complexity determining unit 401 and representative wind tower data.
  • the terrain complexity determination unit 401, the wind tower data representative judgment unit 402, the mesoscale numerical simulation unit 403, the mesoscale numerical simulation data extraction unit 406, and the wind farm power generation amount calculation unit 407 respectively have the terrain complexity described with reference to FIG.
  • the judging unit 301, the wind tower data representative judging unit 302, the mesoscale numerical simulation unit 303, the mesoscale numerical simulation data extracting unit 304, and the wind farm electric power generation amount calculating unit 305 are the same, and therefore, the description thereof will not be repeated here.
  • the apparatus 400 adds a reliability verification unit 404 and a correction unit 405 to the apparatus 300 to ensure that the accuracy of the mesoscale numerical simulation data meets the requirements, thereby Further improve the calculation accuracy of wind farm power generation under complex or extremely complex terrain.
  • the reliability verification unit 404 and the correction unit 405 will be described in detail.
  • the reliability verification unit 404 can verify the reliability of the mesoscale numerical simulation data after the mesoscale numerical simulation unit 403 performs the mesoscale numerical simulation on the meteorological variables of the wind farm field. For example, the reliability verification unit 404 can verify the reliability of the mesoscale numerical simulation data by using the correlation coefficient between the existing wind tower data and the mesoscale numerical simulation data. If there is a wind tower in the wind farm field, the reliability verification unit 404 can verify the reliability of the mesoscale numerical simulation data by using the measured data of the wind tower in the wind farm field.
  • the reliability verification unit 404 verifies that the mesoscale numerical simulation data is reliable. If there is no wind tower in the wind farm field, the reliability verification unit 404 can verify the reliability of the mesoscale numerical simulation data by using the measured data of the wind tower of the adjacent wind farm field, wherein if the wind farm is adjacent to the wind farm The correlation coefficient between the measured data of the wind tower of the field and the mesoscale numerical simulation data of the vicinity of the wind tower is greater than the predetermined correlation coefficient, and the reliability verification unit 404 can verify that the mesoscale numerical simulation data is reliable. .
  • the operation performed by the reliability verification unit 404 corresponds to the step 204 shown in FIG. 2, and therefore, the related description about the step 204 is also applicable to the reliability. Verification unit 404, which will not be described again here.
  • Correction unit 405 can correct the mesoscale numerical simulation data that is verified to be reliable.
  • the correction unit 405 may use a statistical method to verify a reliable mesoscale numerical simulation by using the wind farm or the measured data or radar data of the wind tower in the adjacent wind farm of the wind farm. The data is corrected.
  • the operation performed by the correction unit 405 corresponds to that shown in FIG. Step 205, therefore, the related description of step 205 (for example, a description about statistical methods and the like) is equally applicable to the reliability verification unit 404, and details are not described herein again.
  • the mesoscale numerical simulation data extraction unit 406 and the wind farm power generation amount calculation unit 407 may perform subsequent operations as described in FIG.
  • the mesoscale numerical simulation result can be further improved. Accuracy, thereby improving the calculation accuracy of wind farm power generation.
  • the present application has been described in detail above with reference to FIGS. 1 to 4.
  • the wind farm can improve the calculation accuracy of the wind farm field by supplementing the virtual wind tower, saving the wind tower installation, maintenance cost and data acquisition time; even in the wind farm without the wind tower, if the nearby wind farm has The measured data of the wind tower can still obtain relatively reliable virtual wind tower data through the method of the present application, which saves the installation of the wind tower, data collection time, and accelerates the development progress of the wind farm project.
  • mesoscale numerical simulation of multiple wind farm fields it is possible to accumulate experience of parameterization schemes for numerical simulation of different regions.
  • the term "comprises” or any other variant is intended to encompass a non-exclusive inclusion, such that a method or apparatus comprising a plurality of elements includes not only those elements but also those not explicitly listed. Other elements, or elements that are inherent to such a method or device.
  • the elements defined by the phrase "comprising”, without limitation, do not exclude the presence of additional equivalent elements in the method or device including the above.
  • the respective components of the apparatus for calculating the amount of wind farm power generation may be implemented as hardware components or software components, and may be combined as needed. Additionally, those skilled in the art can implement various components using, for example, a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC), depending on the processing performed by the various components.
  • FPGA field programmable gate array
  • ASIC application specific integrated circuit
  • a method for calculating a wind farm power generation amount according to the present application may be recorded in a computer readable medium including program instructions that perform various operations implemented by a computer.
  • Examples of computer readable media include magnetic media (such as hard disks, floppy disks, and magnetic tapes); optical media (such as CD-ROMs and DVDs); magneto-optical media (e.g., optical disks); and hardware specially configured for storing and executing program instructions A device (eg, read only memory (ROM), random access memory (RAM), flash memory, etc.).
  • Examples of program instructions include, for example, machine code produced by a compiler and files containing higher level code that can be executed by a computer using an interpreter.

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Abstract

一种用于计算风电场发电量的方法和设备。所述方法包括：判断风电场场区的地形复杂度是否超过预定复杂度（101）；如果地形复杂度超过预定复杂度，则判断风电场场区内的测风塔数据代表性（102）；如果测风塔数据代表性不足，则对风电场场区的气象变量进行中尺度数值模拟（103）；提取中尺度数值模拟数据作为虚拟测风塔数据（104）；通过使用虚拟测风塔数据来计算风电场发电量（105）。

Description

用于计算风电场发电量的方法和设备 技术领域
本申请涉及风力发电领域，更具体地，涉及一种用于计算风电场发电量的方法和设备。
背景技术
风电场的建设规模、经济效益以及风险程度取决于早期风电场发电量的计算，而风电场发电量计算的多寡除了取决于各地区风资源分布的实际情况之外，还取决于发电量计算方法的适应性及精确性。复杂的地形和气象条件给风电场风况的仿真，以及由此进行的机组选型和发电量的计算带来了巨大的误差。
鉴于此，需要能够减小风电场发电量计算误差的风电场发电量计算方法，从而提高风电场发电量计算准确性。
发明内容
本申请提供了一种用于计算风电场发电量的方法和设备，所述方法和设备能够在风电场场区的地形复杂以及风电场场区内的测风塔数据代表性不足的情况下，通过采用中尺度数值模式WRF(Weather Research and Forecasting Model)对风电场场区的气象变量进行中尺度数值模拟，提取中尺度数值模拟数据作为虚拟测风塔数据，并通过在计算风电场发电量时使用虚拟测风塔数据来提高风电场发电量的计算准确性。
根据本申请的一方面，提供了一种用于计算风电场发电量的方法，所述方法可包括：判断风电场场区的地形复杂度是否超过预定复杂度；如果地形复杂度超过预定复杂度，则判断风电场场区内的测风塔数据代表性；如果测风塔数据代表性不足，则对风电场场区的气象变量进行中尺度数值模拟；提取中尺度数值模拟数据作为虚拟测风塔数据；通过使用虚拟测风塔数据来计算风电场发电量。
根据本申请的另一方面，提供了一种用于计算风电场发电量的设备，所 述设备包括：地形复杂度判断单元，被配置为判断风电场场区的地形复杂度是否超过预定复杂度；测风塔数据代表性判断单元，被配置为在地形复杂度超过预定复杂度的情况下判断风电场场区内的测风塔数据代表性；中尺度数值模拟单元，被配置为在测风塔数据代表性不足的情况下对风电场场区的气象变量进行中尺度数值模拟；中尺度数值模拟数据提取单元，被配置为提取中尺度数值模拟数据作为虚拟测风塔数据；风电场发电量计算单元，被配置为通过使用虚拟测风塔数据来计算风电场发电量。
根据本申请的一方面，提供了一种计算机存储介质，其中，所述计算机可读介质上记录有用于执行包括以下步骤的方法的程序：判断风电场场区的地形复杂度是否超过预定复杂度；如果地形复杂度超过预定复杂度，则判断风电场场区内的测风塔数据代表性；如果测风塔数据代表性不足，则对风电场场区的气象变量进行中尺度数值模拟；提取中尺度数值模拟数据作为虚拟测风塔数据；通过使用虚拟测风塔数据来计算风电场发电量。
根据本申请的另一方面，提供了一种用于计算风电场发电量的设备，所述设备包括：存储器，被配置为存储执行以下方法的程序：判断风电场场区的地形复杂度是否超过预定复杂度；如果地形复杂度超过预定复杂度，则判断风电场场区内的测风塔数据代表性；如果测风塔数据代表性不足，则对风电场场区的气象变量进行中尺度数值模拟；提取中尺度数值模拟数据作为虚拟测风塔数据；通过使用虚拟测风塔数据来计算风电场发电量；以及处理器，被配置为执行所述程序。
根据所述方法和设备，本申请可通过中尺度数值模拟引入虚拟测风塔数据来有效地提高风电场发电量的计算准确性，并且可通过对中尺度数值模拟数据进行可靠性验证和校正，进一步提高提高风电场发电量的计算准确性，从而使得即使在复杂地形且测风塔数据代表性不足的情况下，也能够减少风电场发电量的计算误差，提供更加准确的计算结果，同时，也节省了测风塔安装和维护成本。
附图说明
为了更清楚地说明本申请的实施例，下面将对实施例的描述中所需要使用的附图作简单地介绍，然而，下面描述中的附图仅仅是本申请的实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据 提供的附图获得其他的附图。
图1是示出根据本申请的示例性实施例的用于计算风电场发电量的方法的流程图；
图2是示出根据本申请的另一示例性实施例的用于计算风电场发电量的方法的流程图；
图3是示出根据本申请的示例性实施例的用于计算风电场发电量的设备的框图；
图4是示出根据本申请的另一示例性实施例的用于计算风电场发电量的设备的框图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
为了便于理解本申请，这里首先对本申请中所使用的部分术语和与这些术语相关的知识进行解释和说明。“中尺度数值模式”(也称为“WRF模式”)是由美国环境预测中心以及美国国家大气研究中心等美国的科研机构联合开发的新一代开源气象模式。WRF模式包含的模块组可以作为基本物理过程探讨的理论根据，还可以对真实天气的案例进行模拟，WRF模式的水平分辨率、垂直方向层次、积分区域及各种物理过程都可根据用户需求调整，使用极为便捷，目前在世界范围内被广泛认可和使用。相应地，“中尺度数值模拟”指利用“WRF模式”进行数值模拟。在利用“WRF模式”对气象变量进行中尺度数值模拟的过程中，由于模式分辨率不足等原因，模式对次网格尺度的物理过程不能很好地描述，需要诸如辐射、边界层、微物理等物理过程参数化来完善模拟效果。不同参数化方案的组合对数值模拟的精度有重要的影响。
图1是示出根据本申请的示例性实施例的用于计算风电场发电量的方法的流程图。
参照图1，在步骤101，判断风电场场区的地形复杂度是否超过预定复杂度。风电场流场仿真有多种方法，地形复杂度的差异是决定选用不同仿真方法的一个主要原因。在平坦的简单地形，常使用线性风流模型(例如，WAsP) 即可得到满意的模拟效果；在复杂山地，由于线性模型高估了地形的加速效应，因此通常使用三维流体仿真软件(例如，WindSim、Meteodyn WT)对流场进行模拟；在特殊复杂山地地形，如高山深谷，几乎任何风流模型都不能给出满意的模拟的结果。由于本申请所提出的用于计算风电场发电量的方法主要针对复杂地形或极端复杂地形，因此，根据本申请的示例性实施例的用于计算风电场发电量的方法首先判断风电场场区的地形复杂度。在一些实施例中，可采用崎岖指数(Ruggedness Index，RIX)来判断风电场场区的地形复杂度是否超过预定复杂度。可通过以下方法计算RIX：在某点以R为半径的极坐标中，每条半径线都可能与等高线相交，交点则把半径线分为若干线段。用地形坡度超过关键坡度θ的线段总和，除以全部线段总和(即，半径线R的总和)，就可得到崎岖指数RIX值。对于某一特定点来说，其RIX值取决于三个参数：计算半径R、关键坡度θ、半径线数目N。在一些实施例中：计算半径R可取3～5km，具体取值可试风电场场区范围而定，默认可令R取3.5km，关键坡度θ取0.3rad(约为17°)，半径线数目N取72。如果RIX等于0％，则说明地形坡度都小于0.3rad，如果RIX大于0％，则说明部分地区的坡度大于0.3rad。
根据本申请的示例性实施例，如果崎岖指数大于或等于预定的第一崎岖指数，则判断风电场场区的地形复杂度超过预定复杂度。在一些实施例中，如果崎岖指数大于或等于所述预定的第一崎岖指数，则可判断地形复杂度为复杂，如果崎岖指数大于或等于预定的第二崎岖指数，则可判断地形复杂度为极端复杂，其中，第二崎岖指数大于第一崎岖指数。例如：第一崎岖指数可以是30％，第二崎岖指数可以是50％，如果RIX大于30％，则判断地形复杂度为复杂，如果RIX大于50％，则判断地形复杂度为极端复杂。可以理解的是，第一崎岖指数和第二崎岖指数的取值不限于以上示例，而是可根据具体实际和需求选取合适的判断阈值。由于极端复杂地形的仿真结果偏差会非常大，需要引起特别关注。因此，在一些实施例中，用于计算风电场发电量的计算方法还可包括：在判断风电场场区的地形复杂度为极端复杂时输出警告，以提醒计算人员进行特别关注。
如果在步骤101判断地形复杂度超过预定复杂度，则进入步骤102，否则，结束该方法。在步骤102，判断风电场场区内的测风塔数据代表性。其中，测风塔数据代表性用于衡量测风塔数据是否能够代表风电场场区的局地 气候。根据本申请示例性实施例，可通过风电场场区内的测风塔密度、海拔相似性或地形地貌相似性来判断测风塔数据代表性。在一些实施例中，在地形复杂或极端复杂的区域内，当测风塔密度足够高，每座测风塔管辖范围内海拔差异较小，并且地形粗糙度差异较小时，说明测风塔数据具有一定的代表性。否则，则说明测风塔数据代表性不足，即，测风塔数据不具有代表性，不足以代表风电场场区的局地气候。
根据示例性实施例，可通过风电场场区内的测风塔密度来判断测风塔数据代表性。其中，测风塔密度可由风电场场区对角线的距离与风电场场区内测风塔的个数的比值来表征。如果测风塔密度小于预定密度，并且以各测风塔为圆心以所述预定密度作为直径做圆，所有圆形的面积之和(圆形与圆形之间重合的面积在计算面积总和时仅计算一次)占风电场场区的面积比例达到预定比例以上，则判断测风塔数据具有代表性，否则，判断测风塔数据代表性不足，并将圆形未曾覆盖的区域判断为测风塔数据代表性不足的区域。根据示例性实施例，所述预定密度可以是3km，所述预定比例可以是80％，但是本申请不限于此，而是可根据实际要求选择更加宽松或更加严格的判断阈值。
在一些实施例中，可通过海拔相似性来判断测风塔数据代表性，其中，如果在每一座测风塔的管辖范围内最高海拔与最低海拔之差小于预定海拔差，则表明该管辖范围内的海拔具有相似性，判断测风塔数据具有代表性，否则，判断测风塔数据代表性不足，并将该测风塔管辖区域确定为测风塔数据代表性不足的区域。根据本申请的示例性实施例，每一座测风塔的管辖范围可由以测风塔为圆心并以相邻两座测风塔之间的距离的一半为半径所做的圆来表征。可以理解的是，以上关于每一座测风塔的管辖范围的定义仅是示例，还可采用其他方式来定义测风塔的管辖范围，例如，每一座测风塔的管辖范围可由以测风塔为中心并以相邻两座测风塔之间的距离为边长的正方形来表征。根据示例性实施例，所述预定海拔差可以是150m，但是不限于此，例如，所述预定海拔差可以是比150m更小或更大的数值。
另外，在一些实施例中，可通过地形地貌相似性来判断测风塔数据代表性。如果在每一座测风塔的管辖范围内最大地形粗糙度与最小地形粗糙度之差小于预定粗糙度差，则表明该管辖范围内的地形地貌具有相似性，判断测风塔数据具有代表性，否则，判断测风塔数据代表性不足，并将该测风塔管 辖区域确定为测风塔数据代表性不足的区域。在一些实施例中，所述预定粗糙度差可以是0.1，或者还可以根据实际计算需要选择比0.1更小或更大的数值。
如果在步骤S102判断测风塔数据代表性不足，则进入步骤103，否则如果测风塔数据具有代表性，则直接采用现有技术的方法利用测风塔数据进行风电场发电量计算。在步骤103，对风电场场区的气象变量进行中尺度数值模拟。作为测风数据的补充，中尺度数据的模拟精度需要达到较高的水平，否则模拟效果达不到要求的中尺度模拟会引入新的误差，进而在计算发电量时造成更大的误差。根据示例性实施例，为了提高中尺度数值模拟数据的精度，提高风电场发电量的计算准确性，可根据风电场所处地区的地形特征、气候特征和/或历史模拟结果选择相应的参数化方案组合来对风电场场区的气象变量进行中尺度数值模拟。所述气象变量可包括风速、风向、温度、湿度、湍流和气压中的至少一个。如上所述，在利用“WRF模式”对气象变量进行中尺度数值模拟的过程中，不同参数化方案的组合对数值模拟的精度有重要的影响。因此，为了确保中尺度模拟数据的局地适应性，可根据风电场所处地区的地形特征、气候特征和/或历史模拟结果选择合适的参数化方案组合来对风电场场区的风速、风向以及多个气象变量进行中尺度数值模拟。
针对某一风电场场区的合适的参数化方案，首先需要考虑风电场场区所在地区的天气特征来进行选择。在对流活动旺盛的地区，积云对流参数化方案比较重要；在边界层大气活动剧烈的地区，行星边界层参数化方案比较重要；在地表水陆分布差异较大的地区，地表参数化方案比较重要；除此之外，微物理、湍流、扩散、长波辐射、短波辐射等也是比较重要的参数化方案选项。由于WRF内部的参数化选项非常多，在此不一一赘述。
如果以对地面风速影响较大的边界层参数化方案为例，YSU方案是一种基于K扩散模式的一阶非局部闭合方案，详细考虑了在逆温层中夹卷造成的热量交换，并且考虑了湍流扩散方程中的反向梯度输送项；ACM2方案则将涡旋扩散融合到非局地扩散方案中，能够描述对流边界层中超网格尺度和次网格尺度的湍流输送过程；MYNN3方案是一个预报湍流动能及其他二级通量的湍流动能参数化方案，融入了凝结物理过程，减小了雾形成时间的预报偏差与消散时间预报的偏差；MYJ湍流动能方案为行星边界层和自由大气中的湍流参数化非奇异方案，主长度尺度的上限根据在湍流增长的条件下，扰 动动能产生项非奇异这一条件推导而得，其依赖于湍流动能、浮力、驱动流气流的切变。以某风电场为例，采用以上四种不同的边界层参数化方案，对近地层风速的模拟效果就有不同，根据该风电场的实测数据评估的效果是MYNN3>YSU>ACM2>MYJ。
可根据以上挑选参数化方案的原则，并结合该地区的历史模拟经验，针对不同地区的风电场，采取不同的参数化方案组合进行模拟。另外，由于本申请关注在地形复杂且测风塔数据代表性不足时的风电量计算，因此，根据本申请示例性实施例，在对风电场场区的气象变量进行中尺度数值模拟时的模式分辨率不应低于预定分辨率。例如，在地形复杂或极端复杂的地区，所述预定分辨率以为3km和1km为宜，从而便于确保将中尺度数值模拟数据作为补充的虚拟测风塔数据之后风电场场区内的测风塔数据具有代表性。
在步骤103对风电场场区的气象变量进行中尺度数值模拟之后，在步骤104，提取中尺度数值模拟数据作为虚拟测风塔数据。实际中，风电场场区内可能设置有测风塔，也可能没有设置测风塔。根据示例性实施例，如果所述风电场场区内有测风塔，则可将风电场场区内测风塔数据代表性不足的区域的中尺度数值模拟数据作为该区域的补充的虚拟测风塔数据。而如果所述风电场场区内无测风塔，则可将风电场场区的中尺度数值模拟数据作为该风电场场区的虚拟测风塔数据。
在获得了虚拟测风塔数据之后，在步骤105，可通过使用虚拟测风塔数据来计算风电场发电量。在一些实施例中，如果风电场场区内有测风塔，则可将该风电场场区内的测风塔的实测数据与在步骤103补充的虚拟测风塔数据结合来计算风电场发电量。如果风电场场区内无测风塔，则可直接使用在步骤103获得的虚拟测风塔数据来计算风电场发电量。根据示例性实施例，在提取中尺度数值模拟数据作为虚拟测风塔数据之后，可再次判断风电场场区内包括虚拟测风塔数据的全部测风塔数据的代表性，其中，如果判断测风塔数据具有代表性，则执行通过使用虚拟测风塔数据来计算风电场发电量的步骤105。
在一些实施例中，在步骤105，可采用WT、WindSim、WAsP和WindPro软件中的至少一个作为计算工具来计算风电场发电量。如果风电场场区内有测风塔，则将已有的测风塔的实测数据与补充的虚拟测风塔数据一起输入WT、WindSim、WAsP和WindPro软件中的至少一个来计算风电场的发电量。 如果风电场场区内没有测风塔，则直接将获得的虚拟测风塔数据输入WT、WindSim、WAsP和WindPro软件中的至少一个来计算风电场的发电量。
在一些实施例中，通过上述软件，可先根据风电场区域内地形及局地气候的差异，依据线性或非线性关系，得到全场各点与测风塔点位处风加速因子的对应关系，再根据测风塔处实际的风速、风向、湍流等值，同时结合风速、湍流与风加速因子之间的正、反比关系，推得全场各点的风速及湍流等参数。然后，再由风速、湍流等参数选择适合该风况的机型，结合该机型的实际功率曲线和风机点位处的风速，计算得到风电场各台风机的发电量，进而得到整个风电场的发电量。
由于虚拟测风塔能够解决风电场内测风塔数量不足的问题，所以将虚拟测风塔数据与测风塔实测数据结合能更精确地计算风电场的发电量。参照图1所述的用于计算风电场发电量的方法通过对风电场场区的气象变量进行中尺度数值模拟引入虚拟测风塔数据，从而在地形复杂度超过预定复杂度以及风电场场区内测风塔数量不足的情况下，提高风电场发电量计算的准确性。
图2是示出根据本申请的另一示例性实施例的用于计算风电场发电量的方法的流程图。
图2中的步骤201、202和203与图1中的步骤101至103完全相同，因此，关于步骤101至步骤103的描述同样适用于步骤201至步骤203，这里不再进行赘述。与图1的用于计算风电场发电量的方法不同，为确保中尺度数值模拟数据的模拟精度达到更好的水平，图2中根据本申请的另一示例性实施例的用于计算风电场发电量的方法在步骤203之后新添加了判断中尺度数值模拟数据的可靠性的步骤以及对中尺度数值模拟数据进行校正的步骤，以确保中尺度数值模拟数据的精度达到要求，从而提高复杂或极端复杂地形下风电场发电量的计算准确性。
以下对步骤204和步骤205进行详细描述。在步骤204，对中尺度数值模拟数据的可靠性进行验证。根据示例性实施例，可通过使用已有的测风塔数据与中尺度数值模拟数据之间的相关系数来对中尺度数值模拟数据的可靠性进行验证。在一些实施例中，如果风电场场区内有测风塔，则可采用该风电场场区内的测风塔的实测数据对中尺度数值模拟数据的可靠性进行验证。如果所述风电场场区的测风塔的实测数据与中尺度模拟数据的相关系数大于预定相关系数，则可验证中尺度数值模拟数据是可靠的，否则，则验证中尺 度数值模拟数据是不可靠的，可选择丢弃此次模拟的中尺度数值模拟数据，并重新选择进行中尺度数值模拟时的模式分辨率和参数化方案的组合进行再次模拟，直至中尺度数值模拟数据被验证为是可靠的。作为另一种情况，如果所述风电场场区内没有测风塔，则可采用相邻风电场场区的测风塔的实测数据对中尺度数值模拟数据的可靠性进行验证。在一些实施例中，如果邻近风电场场区的测风塔的实测数据与该测风塔邻近区域(该测风塔位置所在的中尺度网格内)的中尺度数值模拟数据之间的相关系数大于预定相关系数，则验证中尺度数值模拟数据是可靠的。在一些实施例中，所述预定相关系数可以是0.8，但不限于此，而是可根据对中尺度数值模拟数据可靠性标准的要求选择更高或更低的预定相关系数。例如，如果风电场场区内有测风塔，则当测风塔的实测风速与经过中尺度数值模拟所得到的模拟风速的相关系数达到0.8以上时，可以认为中尺度数值模拟数据是可靠的。如果风电场场区内没有测风塔，则当该风电场邻近场区内的测风塔的实测风速与其邻近区域的模拟风速的相关系数达到0.8以上时，可以认为中尺度数值模拟数据时可靠的。
为了进一步确保中尺度数值模拟数据的精度达到要求，提高风电场发电量的计算准确性，在步骤205，对被验证为可靠的中尺度数值模拟数据进行校正。在一些实施例中，可通过采用所述风电场或所述风电场的邻近风电场内的测风塔的实测数据或雷达数据，利用统计方法对被验证为可靠的中尺度数值模拟数据进行校正。根据示例性实施例，雷达数据可以是用激光雷达或声雷达等进行测量得到的风数据。例如，可采取多元回归的统计方法来对被验证为可靠的中尺度数值模拟数据进行验证，首先，需要从中尺度数值模拟数据中挑选出与实测风速相关性比较好的影响因子(例如，可以是温度、湿度、气压等)，然后据此建立实测风速与影响因子的回归方程，根据该回归方程可以将单一点的校正关系推至整个风电场场区，由此整个风电场场区的中尺度模拟数据得以校正。例如，还可采取神经网络或支持向量机等机器学习算法进行校正，这些算法模型会找出影响因子与实际风速之间存在的关系，这种关系一般为非线性关系，再据此对中尺度模拟数据进行校正。应理解，这里仅给出了对中尺度数值模拟数据进行校正的统计方法的示例，根据本申请的对中尺度数值模拟数据进行校正的统计方法不限于此。
步骤206和步骤207分别与图1的步骤104和105相应，因此，这里不 再进行赘述，不同之处仅在于，步骤205是在中尺度数值模拟数据被验证为可靠并且被校正之后执行的，而在步骤104之前，未进行对中尺度数值模拟数据验证和校正的操作。另外，图1中其他内容的描述也同样适用于图2，为了简洁，这里也不再进行赘述。
根据参照图2所述的用于计算风电场发电量的方法，由于在图1的风电场发电量计算方法的基础上进一步进行了中尺度数值模拟数据可靠性的验证并对中尺度数值模拟数据进行了校正，因此，可进一步提高中尺度数值模拟结果的精度，从而提高风电场发电量的计算准确性。
以下，将参照图3和图4描述根据示例性实施例的用于计算风电场发电量的设备。
图3是示出根据本申请的示例性实施例的用于计算风电场发电量的设备300的框图。
参照图3，设备300可包括地形复杂度判断单元301、测风塔数据代表性判断单元302、中尺度数值模拟单元303、中尺度数值模拟数据提取单元304和风电场发电量计算单元305。地形复杂度判断单元301可判断风电场场区的地形复杂度是否超过预定复杂度。测风塔数据代表性判断单元302可在风电场场区的地形复杂度超过预定复杂度的情况下判断风电场场区内的测风塔数据代表性。中尺度数值模拟单元303可在测风塔数据代表性不足的情况下对风电场场区的气象变量进行中尺度数值模拟。中尺度数值模拟数据提取单元304可提取中尺度数值模拟数据作为虚拟测风塔数据。风电场发电量计算单元305可通过使用虚拟测风塔数据来计算风电场发电量。
以下，对图3中示出的上述单元进行更加详细的描述。根据示例性实施例，地形复杂度判断单元301可采用崎岖指数来判断风电场场区的地形复杂度是否超过预定复杂度。如果崎岖指数大于或等于预定的第一崎岖指数，则判断风电场场区的地形复杂度超过预定复杂度，在一些实施例中，如果崎岖指数大于或等于所述预定的第一崎岖指数，则地形复杂度判断单元301判断地形复杂度为复杂，如果崎岖指数大于或等于预定的第二崎岖指数，则地形复杂度判断单元301判断地形复杂度为极端复杂，其中，第二崎岖指数大于第一崎岖指数。地形复杂度判断单元301所执行的操作对应于图1中示出的步骤101，因此，关于步骤101的相关描述同样适用于地形复杂度判断单元301，这里不再赘述。
根据示例性实施例，测风塔数据代表性判断单元302可通过风电场场区内的测风塔密度、海拔相似性或地形地貌相似性来判断测风塔数据代表性。当测风塔数据代表性判断单元302通过风电场场区内的测风塔密度来判断测风塔数据代表性时，如果测风塔密度小于预定密度，并且以各测风塔为圆心以及以所述预定密度作为直径做圆，如果所有圆形的面积之和占风电场场区的面积比例达到预定比例以上，则测风塔数据代表性判断单元302可判断测风塔数据具有代表性，否则，判断测风塔数据代表性不足，并将圆形未曾覆盖的区域判断为测风塔数据代表性不足的区域。在一些实施例中，测风塔密度可由风电场场区对角线的距离与风电场场区内测风塔的个数的比值来表征，但不限于此。当测风塔数据代表性判断单元302通过海拔相似性来判断测风塔数据代表性时，如果在每一座测风塔的管辖范围内最高海拔与最低海拔之差小于预定海拔差，则表明该管辖范围内的海拔具有相似性，测风塔数据代表性判断单元302可判断测风塔数据具有代表性，否则，判断测风塔数据代表性不足，并可将该测风塔管辖区域确定为测风塔数据代表性不足的区域。当测风塔数据代表性判断单元302通过地形地貌相似性来判断测风塔数据代表性时，如果在每一座测风塔的管辖范围内最大地形粗糙度与最小地形粗糙度之差小于预定粗糙度差，则表明该管辖范围内的地形地貌具有相似性，测风塔数据代表性判断单元302可判断测风塔数据具有代表性，否则，判断测风塔数据代表性不足，并可将该测风塔管辖区域确定为测风塔数据代表性不足的区域。测风塔数据代表性判断单元302所执行的操作对应于图1中示出的步骤102，关于步骤102的相关描述(例如，关于管辖范围、预定比例、预定密度、预定海拔差和预定粗糙度差等的描述)同样适用于测风塔数据代表性判断单元302，因此，这里不再赘述。
中尺度数值模拟单元303可根据风电场所处地区的地形特征、气候特征和/或历史模拟结果选择相应的参数化方案组合来对风电场场区的气象变量进行中尺度数值模拟。所述气象变量可包括风速、风向、温度、湿度、湍流和气压中的至少一个，但不限于此。由于，本申请针对地形复杂或极端复杂的风电场场区计算风电场发电量，因此，中尺度数值模拟单元303对风电场场区的气象变量进行中尺度数值模拟时的模式分辨率最好不低于预定分辨率，以为3km和1km为宜。中尺度数值模拟单元303所执行的操作对应于图1中示出的步骤103，因此，关于步骤103的相关描述(例如，参数化方案的 相关描述)同样适用于中尺度数值模拟单元303，这里不再赘述。
在中尺度数值模拟单元304对风电场场区的气象变量进行中尺度数值模拟之后，中尺度数值模拟数据提取单元304可提取中尺度数值模拟数据作为虚拟测风塔数据。根据示例性实施例，如果风电场场区内有测风塔，则中尺度数值模拟数据提取单元304可将风电场场区内测风塔数据代表性不足的区域的中尺度数值模拟数据作为该区域的补充的虚拟测风塔数据。如果风电场场区内无测风塔，则中尺度数值模拟数据提取单元304可将风电场场区的中尺度数值模拟数据作为该风电场场区的虚拟测风塔数据。中尺度数值模拟数据提取单元304所执行的操作对应于图1中示出的步骤104，因此，关于步骤104的相关描述同样适用于中尺度数值模拟数据提取单元304，这里不再赘述。
在获得了虚拟测风塔数据之后，风电场发电量计算单元305可通过使用虚拟测风塔数据来计算风电场发电量。在一些实施例中，如果风电场场区内有测风塔，则可将该风电场场区内的测风塔的实测数据与补充的虚拟测风塔数据结合来计算风电场发电量。如果该风电场场区内无测风塔，则可直接使用虚拟测风塔数据来计算风电场发电量。根据示例性实施例，在提取中尺度数值模拟数据作为虚拟测风塔数据之后，测风塔数据代表性判断单元302可再次判断包括虚拟测风塔数据的全部测风塔数据的代表性。如果判断测风塔数据具有代表性，则风电场发电量计算单元305通过使用虚拟测风塔数据来计算风电场发电量。例如，风电场发电量计算单元305可采用WT、WindSim、WAsP和WindPro软件中的至少一个作为计算工具来计算风电场发电量。风电场发电量计算单元305所执行的操作对应于图1中示出的步骤105，因此，关于步骤105的相关描述同样适用于风电场发电量计算单元305，这里不再赘述。
参照图3所述的用于计算风电场发电量的设备通过对风电场场区的气象变量进行中尺度数值模拟引入虚拟测风塔数据，能够在地形复杂或极端复杂并且风电场场区内测风塔数量不足的情况下，提高风电场发电量计算的准确性。
图4是示出根据本申请的另一示例性实施例的用于计算风电场发电量的设备400的框图。
参照图4，设备400可包括地形复杂度判断单元401、测风塔数据代表性 判断单元402、中尺度数值模拟单元403、可靠性验证单元404、校正单元405、中尺度数值模拟数据提取单元406和风电场发电量计算单元407。地形复杂度判断单元401、测风塔数据代表性判断单元402、中尺度数值模拟单元403、中尺度数值模拟数据提取单元406和风电场发电量计算单元407分别与参照图3描述的地形复杂度判断单元301、测风塔数据代表性判断单元302、中尺度数值模拟单元303、中尺度数值模拟数据提取单元304和风电场发电量计算单元305相同，因此，这里不再对其进行赘述。
与图3的用于计算风电场发电量的设备300不同，设备400在设备300的基础上新增了可靠性验证单元404和校正单元405，以确保中尺度数值模拟数据的精度达到要求，从而进一步提高复杂或极端复杂地形下风电场发电量的计算准确性。以下，将对可靠性验证单元404和校正单元405进行详细描述。
可靠性验证单元404可在中尺度数值模拟单元403对风电场场区的气象变量进行中尺度数值模拟之后，对中尺度数值模拟数据的可靠性进行验证。例如，可靠性验证单元404可通过使用已有的测风塔数据与中尺度数值模拟数据之间的相关系数来验证中尺度数值模拟数据的可靠性。如果风电场场区内有测风塔，则可靠性验证单元404可采用所述风电场场区内的测风塔的实测数据对中尺度数值模拟数据的可靠性进行验证。这里，如果风电场场区的测风塔的实测数据与中尺度模拟数据的相关系数大于预定相关系数，则可靠性验证单元404验证中尺度数值模拟数据是可靠的。如果风电场场区内没有测风塔，则可靠性验证单元404可采用相邻风电场场区的测风塔的实测数据对中尺度数值模拟数据的可靠性进行验证，其中，如果邻近风电场场区的测风塔的实测数据与该测风塔邻近区域的中尺度数值模拟数据之间的相关系数大于所述预定相关系数，则可靠性验证单元404可验证中尺度数值模拟数据是可靠的。由于设备400与图2所示的风电场发电量计算方法相应，可靠性验证单元404所执行的操作对应于图2中示出的步骤204，因此，关于步骤204的相关描述同样适用于可靠性验证单元404，这里不再赘述。
校正单元405可对被验证为可靠的中尺度数值模拟数据进行校正。根据示例性实施例，校正单元405可通过采用所述风电场或所述风电场的邻近风电场内的测风塔的实测数据或雷达数据，利用统计方法对被验证为可靠的中尺度数值模拟数据进行校正。校正单元405所执行的操作对应于图2中示出 的步骤205，因此，关于步骤205的相关描述(例如，关于统计方法等的描述)同样适用于可靠性验证单元404，这里不再赘述。
在中尺度数值模拟数据被验证为可靠并且被校正之后，中尺度数值模拟数据提取单元406和风电场发电量计算单元407可如图3所描述的进行后续操作。
参照图4所述的用于计算风电场发电量的设备，由于进行了中尺度数值模拟数据可靠性的验证并对中尺度数值模拟数据进行了校正，因此，可进一步提高中尺度数值模拟结果的精度，从而提高风电场发电量的计算准确性。
以上已参照附图1至图4对本申请进行了详细说明，如上所述，根据本申请，可提高地形复杂或极端复杂的风电场场区的发电量计算的准确性；对于测风塔不足的风电场，通过补充虚拟测风塔可提高风电场场区的计算准确性，节省测风塔安装、维护成本以及数据采集的时间；即使在没有测风塔的风电场，如果临近的风电场有测风塔实测数据，依然可以通过本申请的方法，得到相对可靠的虚拟测风塔数据，节省了测风塔安装、数据采集时间，加快风电场项目开发进度。另外，在进行多个风电场场区的中尺度数值模拟时，可以积累针对不同地区数值模拟的参数化方案经验，这对风电场风速预报准确率的提高以及各个地区中尺度数据库的建立有很大的帮助。此外，在进行多个风电场的中尺度数值模拟结果校正时，同时可以得到各个地区的、与实测风速相关性较好的影响因子库，该因子库的积累对风电场发电量计算的准确性有较大的帮助。
需要说明的是，以上实施例的描述中重点说明的都是与其他实施例的不同之处，各个实施例之间相同或相似的部分互相参见即可。
还需要说明的是，在本文中，术语“包括”或者任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的方法或设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种方法或设备所固有的要素。在没有更多限制的情况下，由语句“包括……”限定的要素，并不排除在包括上述要素的方法或设备中还存在另外的相同要素。
此外，根据本申请的用于计算风电场发电量的设备的各个组件可被实现为硬件组件或软件组件，并且可根据需要进行组合。另外，本领域技术人员可根据各个组件所执行的处理，使用例如现场可编程门阵列(FPGA)或专用集成电路(ASIC)来实现各个组件。
根据本申请的用于计算风电场发电量的方法可被记录在包括执行由计算机实现的各种操作的程序指令的计算机可读介质中。计算机可读介质的示例包括磁介质(例如硬盘、软盘和磁带)；光学介质(例如CD-ROM和DVD)；磁光介质(例如，光盘)；以及特别配制用于存储并执行程序指令的硬件装置(例如，只读存储器(ROM)、随机存取存储器(RAM)、闪存等)。程序指令的示例包括例如由编译器产生的机器码和包含可使用解释器由计算机执行的高级代码的文件。
尽管已经参照本申请的示例性实施例具体显示和描述了本申请，但是本领域的技术人员应该理解，在不脱离由权利要求限定的本申请的精神和范围的情况下，可以对其进行形式和细节上的各种改变。

Claims (24)

  1. 一种用于计算风电场发电量的方法，所述方法包括：
    判断风电场场区的地形复杂度是否超过预定复杂度；
    如果地形复杂度超过预定复杂度，则判断风电场场区内的测风塔数据代表性；
    如果测风塔数据代表性不足，则对风电场场区的气象变量进行中尺度数值模拟；
    提取中尺度数值模拟数据作为虚拟测风塔数据；
    通过使用虚拟测风塔数据来计算风电场发电量。
  2. 根据权利要求1所述的方法，其中，所述方法还包括：在对风电场场区的气象变量进行中尺度数值模拟之后且在提取中尺度数值模拟数据之前，对中尺度数值模拟数据的可靠性进行验证，并对被验证为可靠的中尺度数值模拟数据进行校正，
    其中，提取中尺度数值模拟数据的步骤包括：提取被校正后的中尺度数值模拟数据作为虚拟测风塔数据。
  3. 根据权利要求1所述的方法，其中，通过以下方式之一来判断风电场场区内的测风塔数据代表性：
    通过风电场场区内的测风塔密度来判断测风塔数据代表性，其中，测风塔密度由风电场场区对角线的距离与风电场场区内测风塔的个数的比值来表征，如果测风塔密度小于预定密度，并且以各测风塔为圆心以及以所述预定密度作为直径做圆，如果所有圆形的面积之和占风电场场区的面积比例达到预定比例以上，则判断测风塔数据具有代表性，否则，判断测风塔数据代表性不足，并将圆形未曾覆盖的区域判断为测风塔数据代表性不足的区域；或者
    通过海拔相似性来判断测风塔数据代表性，其中，如果在每一座测风塔的管辖范围内最高海拔与最低海拔之差小于预定海拔差，则表明该管辖范围内的海拔具有相似性，判断测风塔数据具有代表性，否则，判断测风塔数据代表性不足，并将该测风塔管辖区域确定为测风塔数据代表性不足的区域；或者
    通过地形地貌相似性来判断测风塔数据代表性，其中，如果在每一座测 风塔的管辖范围内最大地形粗糙度与最小地形粗糙度之差小于预定粗糙度差，则表明该管辖范围内的地形地貌具有相似性，判断测风塔数据具有代表性，否则，判断测风塔数据代表性不足，并将该测风塔管辖区域确定为测风塔数据代表性不足的区域。
  4. 根据权利要求1或2所述的方法，其中，对风电场场区的气象变量进行中尺度数值模拟的步骤包括：根据风电场所处地区的地形特征、气候特征和/或历史模拟结果选择相应的参数化方案组合来对风电场场区的气象变量进行中尺度数值模拟。
  5. 根据权利要求4所述的方法，其中，对风电场场区的气象变量进行中尺度数值模拟时的模式分辨率不低于预定分辨率。
  6. 根据权利要求2所述的方法，其中，对中尺度数值模拟数据的可靠性进行验证的步骤包括：通过使用已有的测风塔数据与中尺度数值模拟数据之间的相关系数来验证中尺度数值模拟数据的可靠性。
  7. 根据权利要求6所述的方法，其中，
    如果所述风电场场区内有测风塔，则采用所述风电场场区内的测风塔的实测数据对中尺度数值模拟数据的可靠性进行验证，其中，如果所述风电场场区的测风塔的实测数据与中尺度模拟数据的相关系数大于预定相关系数，则验证中尺度数值模拟数据是可靠的；
    如果所述风电场场区内没有测风塔，则采用相邻风电场场区的测风塔的实测数据对中尺度数值模拟数据的可靠性进行验证，其中，如果邻近风电场场区的测风塔的实测数据与该测风塔邻近区域的中尺度数值模拟数据之间的相关系数大于所述预定相关系数，则验证中尺度数值模拟数据是可靠的。
  8. 根据权利要求2所述的方法，其中，对被验证为可靠的中尺度数值模拟数据进行校正的步骤包括：通过采用所述风电场或所述风电场的邻近风电场内的测风塔的实测数据或雷达数据，利用统计方法对被验证为可靠的中尺度数值模拟数据进行校正。
  9. 根据权利要求1或2所述的方法，其中，如果所述风电场场区内有测风塔，则：
    将所述风电场场区内测风塔数据代表性不足的区域的中尺度数值模拟数据作为该区域的补充的虚拟测风塔数据；
    通过使用虚拟测风塔数据来计算风电场发电量的步骤包括：
    将所述风电场场区内的测风塔的实测数据与补充的虚拟测风塔数据结合来计算风电场发电量。
  10. 根据权利要求1或2所述的方法，其中，如果所述风电场场区内无测风塔，则：
    将所述风电场场区的中尺度数值模拟数据作为该风电场场区的虚拟测风塔数据；
    通过使用虚拟测风塔数据来计算风电场发电量的步骤包括：直接使用虚拟测风塔数据来计算风电场发电量。
  11. 根据权利要求1或2所述的方法，其中，所述方法还包括：在提取中尺度数值模拟数据作为虚拟测风塔数据之后，再次判断包括虚拟测风塔数据的全部测风塔数据的代表性，其中，如果判断测风塔数据具有代表性，则执行通过使用虚拟测风塔数据来计算风电场发电量的步骤。
  12. 一种用于计算风电场发电量的设备，所述设备包括：
    地形复杂度判断单元，被配置为判断风电场场区的地形复杂度是否超过预定复杂度；
    测风塔数据代表性判断单元，被配置为在地形复杂度超过预定复杂度的情况下判断风电场场区内的测风塔数据代表性；
    中尺度数值模拟单元，被配置为在测风塔数据代表性不足的情况下对风电场场区的气象变量进行中尺度数值模拟；
    中尺度数值模拟数据提取单元，被配置为提取中尺度数值模拟数据作为虚拟测风塔数据；
    风电场发电量计算单元，被配置为通过使用虚拟测风塔数据来计算风电场发电量。
  13. 如权利要求12所述的设备，其中，所述设备还包括：
    可靠性验证单元，被配置为在中尺度数值模拟单元对风电场场区的气象变量进行中尺度数值模拟之后且在中尺度数值模拟数据提取单元提取中尺度数值模拟数据之前，对中尺度数值模拟数据的可靠性进行验证；
    数据校正单元，被配置为对被验证为可靠的中尺度数值模拟数据进行校正，
    其中，中尺度数值模拟单元被配置为提取被校正后的中尺度数值模拟数据作为虚拟测风塔数据。
  14. 根据权利要求12所述的设备，其中，测风塔数据代表性判断单元通过以下方式之一来判断风电场场区内的测风塔数据代表性：
    通过风电场场区内的测风塔密度来判断测风塔数据代表性，其中，测风塔密度由风电场场区对角线的距离与风电场场区内测风塔的个数的比值来表征，如果测风塔密度小于预定密度，并且以各测风塔为圆心以及以所述预定密度作为直径做圆，如果所有圆形的面积之和占风电场场区的面积比例达到预定比例以上，则判断测风塔数据具有代表性，否则，判断测风塔数据代表性不足，并将圆形未曾覆盖的区域判断为测风塔数据代表性不足的区域；或者
    通过海拔相似性来判断测风塔数据代表性，其中，如果在每一座测风塔的管辖范围内最高海拔与最低海拔之差小于预定海拔差，则表明该管辖范围内的海拔具有相似性，判断测风塔数据具有代表性，否则，判断测风塔数据代表性不足，并将该测风塔管辖区域确定为测风塔数据代表性不足的区域；或者
    通过地形地貌相似性来判断测风塔数据代表性，其中，如果在每一座测风塔的管辖范围内最大地形粗糙度与最小地形粗糙度之差小于预定粗糙度差，则表明该管辖范围内的地形地貌具有相似性，判断测风塔数据具有代表性，否则，判断测风塔数据代表性不足，并将该测风塔管辖区域确定为测风塔数据代表性不足的区域。
  15. 根据权利要求12或13所述的设备，其中，中尺度数值模拟单元根据风电场所处地区的地形特征、气候特征和/或历史模拟结果选择相应的参数化方案组合来对风电场场区的气象变量进行中尺度数值模拟。
  16. 根据权利要求15所述的设备，其中，对风电场场区的气象变量进行中尺度数值模拟时的模式分辨率不低于预定分辨率。
  17. 根据权利要求13所述的设备，其中，可靠性验证单元通过使用已有的测风塔数据与中尺度数值模拟数据之间的相关系数来验证中尺度数值模拟数据的可靠性。
  18. 根据权利要求17所述的设备，其中，
    如果所述风电场场区内有测风塔，则可靠性验证单元采用所述风电场场区内的测风塔的实测数据对中尺度数值模拟数据的可靠性进行验证，其中，如果所述风电场场区的测风塔的实测数据与中尺度模拟数据的相关系数大于 预定相关系数，则验证中尺度数值模拟数据是可靠的；
    如果所述风电场场区内没有测风塔，则可靠性验证单元采用相邻风电场场区的测风塔的实测数据对中尺度数值模拟数据的可靠性进行验证，其中，如果邻近风电场场区的测风塔的实测数据与该测风塔邻近区域的中尺度数值模拟数据之间的相关系数大于所述预定相关系数，则验证中尺度数值模拟数据是可靠的。
  19. 根据权利要求13所述的设备，其中，校正单元通过采用所述风电场或所述风电场的邻近风电场内的测风塔的实测数据或雷达数据，利用统计方法对被验证为可靠的中尺度数值模拟数据进行校正。
  20. 根据权利要求12或13所述的设备，其中，如果所述风电场场区内有测风塔，则：
    中尺度数值模拟数据提取单元将所述风电场场区内测风塔数据代表性不足的区域的中尺度数值模拟数据作为该区域的补充的虚拟测风塔数据；并且
    风电场发电量计算单元将所述风电场场区内的测风塔的实测数据与补充的虚拟测风塔数据结合来计算风电场发电量。
  21. 根据权利要求12或13所述的设备，其中，如果所述风电场场区内无测风塔，则：
    中尺度数值模拟数据提取单元将所述风电场场区的中尺度数值模拟数据作为该风电场场区的虚拟测风塔数据；并且
    风电场发电量计算单元直接使用补充的虚拟测风塔数据来计算风电场发电量。
  22. 根据权利要求12或13所述的设备，其中，在中尺度数值模拟数据提取单元提取中尺度数值模拟数据作为虚拟测风塔数据之后，测风塔数据代表性判断单元再次判断包括虚拟测风塔数据的全部测风塔数据的代表性，其中，如果判断测风塔数据具有代表性，则风电场发电量计算单元通过使用虚拟测风塔数据来计算风电场发电量。
  23. 一种计算机可读介质，其中，所述计算机可读介质上记录有用于执行根据权利要求1至权利要求11中的任何一个的方法的程序。
  24. 一种用于计算风电场发电量的设备，所述设备包括：
    存储器，被配置为存储执行以下方法的程序：
    判断风电场场区的地形复杂度是否超过预定复杂度；
    如果地形复杂度超过预定复杂度，则判断风电场场区内的测风塔数据代表性；
    如果测风塔数据代表性不足，则对风电场场区的气象变量进行中尺度数值模拟；
    提取中尺度数值模拟数据作为虚拟测风塔数据；
    通过使用虚拟测风塔数据来计算风电场发电量；
    处理器，被配置为执行所述程序。
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