CN115048790A - Method and system for predicting rapid downscaling of wind power - Google Patents
Method and system for predicting rapid downscaling of wind power Download PDFInfo
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Abstract
The invention provides a method and a system for predicting a fast downscaling of wind power. The method comprises the following steps: carrying out directional calculation on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain a full-field wind acceleration factor and a wind direction deflection angle of the wind power plant, and using the wind acceleration factor and the wind direction deflection angle as key parameters of a wind power prediction basic database to generate a gridding file; establishing association between the average state of the meteorological forecast data on the region represented by the resolution ratio of the meteorological forecast data and a three-dimensional space wind flow field in the micro-scale model; according to different wind directions and different atmospheric stabilities in the weather forecast result, linear interpolation is carried out on the wind direction and the thermal stability in the wind power prediction basic database by applying a gridding file; and calculating a wind power prediction result according to the linear interpolation result. The method has the advantages that the influence of meteorological parameters such as different atmospheric thermal stability and wind direction on the wind power is reflected by establishing the wind power prediction basic database, and meanwhile, the fast wind power prediction is carried out through interpolation, so that the method can meet the requirements of business operation.
Description
Technical Field
The invention belongs to the field of wind power plant weather, and particularly relates to a method and a system for predicting a rapid downscaling of wind power.
Background
The method finds out through searching domestic and foreign papers, academic conferences, scientific and technical documents, patents and other databases:
denmark (Denmark)
The national laboratory develops a Prediktor wind power prediction system based on a numerical weather forecast + WAsP + power curve, and the Prediktor wind power prediction system is applied to the Danish east in 1993. 1994
A Zephyr wind power prediction system combining Prediktor and WPPT is cooperatively developed by the technical university of Denmark in the laboratory, is applied nationwide in Denmark, and is popularized to Spain, Ireland, America, Japan and other countries and regions. The SIPTOLICO wind power prediction system of Spanish power grid company adopts Spanish weather bureau numerical forecast products and European middle-term weather forecast center numerical weather forecast products to carry out wind power collective forecast through 8 wind power prediction models, and finally obtains wind power predictions of 48 hours and 10 days in the future by combining with wind power prediction products of the Netherlands AEOLIS prediction service company, the Spanish engineering and technology institute (IIC) and Spanish METEOLOGICA professional wind power forecast and wind power prediction company. The wind power prediction work in China starts late, in 2008 in 11 months, and the Chinese Power science research institute develops a first wind power prediction system WPFS with independent intellectual property rights in China. At present, the methodThe China institute of Electrical science and the American atmospheric sciences research center (NCAR) develop an electrical numerical weather forecasting system with real-time and rapid updating assimilation and ensemble forecasting technologies, and provide basic numerical weather forecasting for wind power prediction of a wind power plant.
The wind power prediction methods generally adopted in China mainly comprise two types, one is a statistical analysis prediction method based on a wind power historical time sequence, and short-term wind power prediction is carried out; the other method is to simulate the neural network technology as a black box to forecast the wind power by combining a mesoscale weather forecast mode and measured data of a wind field, and the influence of meteorological parameters such as atmospheric thermal stability, wind direction and the like on the wind power cannot be considered at the same time. Particularly, for complex terrain and coastal wind power plants in China, because the atmosphere of the near stratum makes strong vertical motion, the thermal stability is more stable and unstable except neutral, and the wind power prediction precision in China is poor due to great influence on wind profile, wind shear index and wind speed distribution. Therefore, the mature foreign system cannot be directly applied to the wind farm in China, and a wind power prediction rapid power downscaling method suitable for the characteristics of the wind farm in China is needed.
The prior art has the following disadvantages: in the wind power prediction process, the scale of the weather forecast result is directly reduced to the point position of the fan through model calculation, and then wind-to-wind power conversion is carried out, so that the calculation efficiency is low; the neural network technology is simulated as a black box to forecast the wind power, and the influence of meteorological parameters such as atmospheric thermal stability, wind direction and the like on the wind power is difficult to consider at the same time.
Disclosure of Invention
In order to solve the technical problems, the invention provides a technical scheme of a method for rapidly reducing the wind power prediction scale so as to solve the technical problems.
The invention discloses a method for predicting a rapid downscaling of wind power in a first aspect, which comprises the following steps:
s1, collecting and mapping terrain elevation and roughness data of the wind power plant and a certain peripheral area range of the wind power plant, and establishing a micro-scale wind flow field model capable of representing local effect of the wind power plant and the peripheral area range of the wind power plant;
step S2, performing directional calculation on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain a full-field wind acceleration factor and a wind direction deflection angle of the wind power plant;
step S3, generating a gridded file by taking the wind acceleration factor and the wind direction drift angle of the whole wind farm as key parameters of a wind power prediction basic database;
s4, establishing association between the average state of the meteorological forecast data in the area represented by the resolution ratio of the meteorological forecast data and a three-dimensional space wind flow field in the microscale wind flow field model, and carrying out extrapolation simulation;
step S5, according to different wind directions and different atmospheric stabilities in weather forecast results, the gridded file is applied to carry out linear interpolation on the wind directions and the thermal stabilities in the wind power prediction basic database through the correlation;
step S6, obtaining wind direction, wind direction deflection angle of the wind power plant full field corresponding to the atmospheric stability state in the weather forecast data of each forecast time point according to the proportion of the linear interpolation;
and step S7, calculating a wind power prediction result according to the wind speed and wind direction information in the meteorological forecast data and the wind power plant full-field wind acceleration factor and the wind direction deflection angle at the corresponding time.
According to the method of the first aspect of the present invention, in step S1, the method for establishing a micro-scale wind flow field model capable of characterizing the local effect of the wind farm and its surrounding area includes:
and establishing a micro-scale wind flow field model capable of representing the local effect of the wind power plant and the surrounding area thereof based on a constant, adiabatic and incompressible Reynolds average Navier-Stokes equation.
According to the method of the first aspect of the present invention, in step S2, the method for calculating the full wind acceleration factor Δ k of the wind farm includes:
whereinU (z) represents the wind speed at a height z above the ground of the mountain land, U 0 (z) represents the wind speed at a height z above the flat ground.
According to the method of the first aspect of the present invention, in the step S4, the method further includes:
selecting a height position at which the weather forecast data is coupled to the microscale wind-flow-field model.
According to the method of the first aspect of the present invention, in step S7, the method for calculating the wind power prediction result by the wind speed and wind direction information in the weather forecast data and the wind farm full-scale wind acceleration factor and the wind direction drift angle at the corresponding time includes:
calculating a wind power plant wind parameter downscaling forecasting result according to wind speed and wind direction information in the meteorological forecasting data, a wind power plant full-field wind acceleration factor and a wind direction deflection angle at corresponding time;
and according to the downscaling prediction result, combining the coordinates and the power curve of the wind power plant machine position point to obtain a wind power prediction result of each fan.
According to the method of the first aspect of the present invention, in the step S7, the wind power and the wind speed at the corresponding time are calculated as:
wherein P (V) is wind power, V is wind speed, and V is Cutting into For wind turbines to cut into wind speed, V Rated value Rated wind speed V of the wind turbine Cutting out Cutting out wind speed for wind turbine, S (V) wind power output, P max Is the maximum power output.
According to the method of the first aspect of the present invention, in step S4, the specific method for associating includes: and (3) carrying out extrapolation simulation by taking the wind speed, the wind direction and other related meteorological parameters on the area represented by the resolution ratio of the downscaled meteorological forecast data as the input conditions of the three-dimensional space wind flow field in the microscale wind flow field model.
The second aspect of the invention discloses a system for predicting the fast downscaling of wind power, which comprises:
the first processing module is configured to collect and map terrain elevation and roughness data of the wind power plant and a certain area range around the wind power plant, and establish a micro-scale wind flow field model capable of representing a local effect of the wind power plant and a surrounding area;
the second processing module is configured to perform directional calculation on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain a full-field wind acceleration factor and a wind direction deflection angle of the wind power plant;
the third processing module is configured to generate a gridded file by taking the wind farm full-field wind acceleration factor and the wind direction deflection angle as key parameters of a wind power prediction basic database;
the fourth processing module is configured to correlate the average state of the weather forecast data in the area represented by the resolution ratio of the weather forecast data with the three-dimensional space wind flow field in the micro-scale model, and perform extrapolation simulation;
the fifth processing module is configured to apply the gridded file to perform linear interpolation on the wind direction and the thermal stability in the wind power prediction basic database according to different wind directions and different atmospheric stabilities in a weather forecast result through the correlation;
the sixth processing module is configured to obtain the wind direction in the meteorological forecast data at each forecast time point and the wind direction deflection angle of the corresponding wind power plant in the atmospheric stability state according to the proportion of the linear interpolation;
and the seventh processing module is configured to calculate a wind power prediction result through the wind speed and the wind direction information in the meteorological forecast data and the wind power plant full-field wind acceleration factor and the wind direction deflection angle at the corresponding time.
According to the system of the second aspect of the present invention, the first processing module is specifically configured to establish the micro-scale wind flow field model capable of characterizing the local effect of the wind farm and the surrounding area thereof, including:
and establishing a micro-scale wind flow field model capable of representing the local effect of the wind power plant and the surrounding area thereof based on a constant, adiabatic and incompressible Reynolds average Navier-Stokes equation.
According to the system of the second aspect of the present invention, the second processing module is specifically configured to calculate the wind farm full wind acceleration factor Δ k including:
wherein U (z) represents the wind speed at the height z above the ground of the mountain land, U 0 (z) represents the wind speed at a height z above the flat ground.
According to the system of the second aspect of the present invention, the fourth processing module is specifically configured to:
selecting a height position at which the weather forecast data is coupled to the microscale wind-flow-field model.
According to the system of the second aspect of the present invention, the seventh processing module is specifically configured to calculate the wind power prediction result by the wind speed and wind direction information in the weather forecast data and the wind direction drift angle and the wind acceleration factor of the wind farm at the corresponding time, and includes:
calculating a wind power plant wind parameter downscaling forecasting result according to wind speed and wind direction information in the meteorological forecasting data and a wind power plant full-field wind acceleration factor and a wind direction deflection angle at corresponding time;
and according to the downscaling prediction result, combining the coordinates and the power curve of the wind power plant machine position point to obtain a wind power prediction result of each fan.
According to the system of the second aspect of the present invention, the seventh processing module is specifically configured to calculate the wind power and the wind speed at the corresponding moment in time as follows:
wherein P (V) is wind power, V is wind speed, and V is Cutting into For wind turbines to cut into wind speed, V Rated value For rating wind of wind turbineSpeed, V Cutting out Cutting out wind speed for wind turbine, S (V) wind power output, P max Is the maximum power output.
According to the system of the second aspect of the present invention, the fourth processing module is specifically configured to associate including: and (3) carrying out extrapolation simulation by taking the wind speed, the wind direction and other related meteorological parameters on the area represented by the resolution ratio of the downscaled meteorological forecast data as the input conditions of the three-dimensional space wind flow field in the microscale wind flow field model.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of a method for wind power prediction of a fast downscaling according to any one of the first aspect of the present disclosure when executing the computer program.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps in a method of wind power prediction of a fast downscaling according to any one of the first aspect of the present disclosure.
According to the scheme provided by the invention, the wind power prediction basic database is established to reflect the influence of meteorological parameters such as different atmospheric thermal stability, wind direction and the like on the wind power, and meanwhile, the fast wind power prediction is carried out through interpolation, so that the requirements of business operation can be met.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method of wind power prediction for fast downscaling in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of a method of wind power prediction for fast downscaling according to an embodiment of the present invention;
FIG. 3 is a block diagram of a system for wind power prediction fast downscaling, according to an embodiment of the present invention;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention discloses a method for predicting a fast downscaling of wind power in a first aspect. Fig. 1 is a flowchart of a method for predicting a fast downscaling of wind power according to an embodiment of the present invention, as shown in fig. 1 and fig. 2, the method includes:
s1, collecting and mapping terrain elevation and roughness data of the wind power plant and a certain peripheral area range of the wind power plant, and establishing a micro-scale wind flow field model capable of representing local effect of the wind power plant and the peripheral area range of the wind power plant;
step S2, performing directional calculation on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain a full-field wind acceleration factor and a wind direction deflection angle of the wind power plant;
step S3, generating a gridded file by taking the wind farm full-field wind acceleration factor and the wind direction deflection angle as key parameters of a wind power prediction basic database;
s4, establishing association between the average state of the meteorological forecast data in the area represented by the resolution ratio of the meteorological forecast data and a three-dimensional space wind flow field in the microscale model, and carrying out extrapolation simulation;
step S5, according to different wind directions and different atmospheric stabilities in the weather forecast result, the gridded file is applied to carry out linear interpolation on the wind direction and the thermal stability in the wind power prediction basic database through the correlation;
step S6, obtaining wind direction, wind direction deflection angle of the wind power plant whole field corresponding to the atmospheric stability state in the weather forecast data of each forecast time point according to the linear interpolation proportion;
and step S7, calculating a wind power prediction result according to the wind speed and wind direction information in the meteorological forecast data and the wind direction drift angle and the wind acceleration factor of the whole wind farm at the corresponding time.
In step S1, the terrain elevation and roughness data of the wind farm and its surrounding area are collected and mapped, and a micro-scale wind flow field model that can characterize the local effect of the wind farm and its surrounding area is built.
In some embodiments, in step S1, the method for establishing a micro-scale wind flow field model capable of characterizing local effects of the wind farm and its surrounding area includes:
and establishing a micro-scale wind flow field model capable of representing the local effect of the wind power plant and the surrounding area thereof based on a constant, adiabatic and incompressible Reynolds average Navier-Stokes equation.
Wherein the Reynolds average Navier-Stokes equation:
where u is the fluid velocity, p is the fluid pressure, ρ is the fluid density, μ is the hydrodynamic viscosity, F i Are other forces.
Is referred to in the equation as the turbulent flux, also called the reynolds stress term. The reynolds stress term that appears in the momentum equation needs to be modeled:
v T ＝k 1/2 L T
in the formula, v T Is the turbulent viscosity. L is T Is a length scale, k is the Karman constant, z is the surface height, S m The Rif is solved based on the flux Rissen number, and the solution of the Rif is directly determined by the thermal stability. Rif takes a positive value when the atmospheric stability is in a steady state, takes a negative value when unstable, and takes 0 when in a neutral state.
Specifically, a weather forecast result of a certain wind power plant in Shanxi is selected to predict wind power, and input weather forecast data is analyzed to obtain the resolution of 9 km.
A micro-scale wind flow field model of a wind power plant area is established by adopting fluid mechanics simulation software, a self-surveyed wind power plant 100-meter precision terrain file and a self-surveyed wind power plant 300-meter precision roughness file are selected, and the terrain and landform characteristics of the wind power plant area are reflected.
In step S2, performing directional calculation on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain a wind acceleration factor and a wind deflection angle of the whole wind farm.
Specifically, 10-degree sector step length and 10 kinds of thermal stability of a wind power plant area are calculated, and the input and output characteristics of the micro-scale model under different conditions are described by the directional calculation results of the 360 models with the resolution of 100 meters in total.
In step S3, a grid file is generated by using the wind farm full wind acceleration factor and the wind direction declination as key parameters of a wind power prediction base database.
In some embodiments, in the step S3, where the acceleration effect of the mountain on the wind flow is usually quantitatively described by a wind acceleration factor, the method for calculating the wind farm full-field wind acceleration factor Δ k includes:
wherein U (z) represents the wind speed at the height z above the ground of the mountain land, U 0 (z) represents the wind speed at a height z above the flat ground.
Specifically, 360 wind farm full-field wind acceleration factors and wind direction declination obtained through directional calculation are used as wind power prediction base database key parameters, and a gridded txt file is generated and used as a subsequent wind power prediction interpolation base as shown in table 1.
TABLE 1
In step S4, the average state of the area represented by the weather forecast data based on its resolution is associated with the three-dimensional space wind flow field in the micro-scale model, and extrapolation simulation is performed to improve the accuracy and reduce the uncertainty.
In some embodiments, in step S4, the height position at which the weather forecast data is coupled to the micro-scale wind flow field model is selected to ensure that the height position is in a high altitude range, so as to provide effective weather forecast data, and reflect the contribution of the near-parameters to the micro-scale model.
The specific method for association comprises the following steps: and (3) carrying out extrapolation simulation by taking the wind speed, the wind direction and other related meteorological parameters on the area represented by the resolution ratio of the downscaled meteorological forecast data as the input conditions of the three-dimensional space wind flow field in the microscale wind flow field model.
Specifically, a weather forecast input unit is established based on the average state of a 9-kilometer area in a weather forecast result, and is associated with a three-dimensional space wind current field in a microscale model to perform extrapolation simulation setting; the height position at which the weather forecast data is coupled with the micro-scale model is selected to be 400 meters.
In step S5, according to the wind direction and the atmospheric stability in the weather forecast result, the grid file is applied to perform linear interpolation on the wind direction and the thermal stability in the wind power prediction base database through the correlation.
Specifically, the specific formula may refer to an inverse distance weighted interpolation formula:
wherein z is i Is the value of the attribute of the discrete point,
representation is made of sample points (x) i ，y i ) Distance to the interpolation point (x, y).
In step S6, the wind direction acceleration factor and the wind direction drift angle of the wind farm corresponding to the wind direction and the atmospheric stability in the weather forecast data at each forecast time point are obtained according to the linear interpolation ratio.
In step S7, a wind power prediction result is calculated according to the wind speed and wind direction information in the weather forecast data and the wind direction drift angle and the wind acceleration factor of the whole wind farm at the corresponding time.
In some embodiments, in step S7, the method for calculating the wind power prediction result by the wind speed, wind direction information and wind direction drift angle of the wind farm at the corresponding time in the weather forecast data includes:
calculating a wind power plant wind parameter downscaling forecasting result according to wind speed and wind direction information in the meteorological forecasting data and a wind power plant full-field wind acceleration factor and a wind direction deflection angle at corresponding time;
and according to the downscaling forecast result, combining the coordinates and the power curve of the wind power plant machine position point to obtain a wind power forecast result of each fan.
The calculation relationship between the wind power and the wind speed at the corresponding moment is,
wherein P (V) is wind power, V is wind speed, and V is Cutting into For wind turbines to cut into wind speed, V Rated value Rated wind speed V of the wind turbine Cutting out Cutting out wind speed for wind turbine, S (V) wind power output, P max Is the maximum power output.
In conclusion, the scheme provided by the invention can reflect the influence of meteorological parameters such as different atmospheric thermal stability, wind direction and the like on the wind power by establishing the wind power prediction basic database, and can carry out rapid wind power prediction by interpolation, so that the wind power prediction method can meet the requirements of business operation.
The invention discloses a system for quickly reducing the scale of wind power prediction in a second aspect. FIG. 3 is a block diagram of a system for wind power prediction fast downscaling, according to an embodiment of the present invention; as shown in fig. 3, the system 100 includes:
the first processing module 101 is configured to collect and map terrain elevation and roughness data of a wind power plant and a certain area range around the wind power plant, and establish a micro-scale wind flow field model capable of representing a local effect of the wind power plant and a surrounding area;
the second processing module 102 is configured to perform directional calculation on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain a full-field wind acceleration factor and a wind direction deflection angle of the wind power plant;
the third processing module 103 is configured to generate a gridded file by taking the wind farm full-field wind acceleration factor and the wind direction declination as key parameters of a wind power prediction basic database;
a fourth processing module 104 configured to correlate the weather forecast data with a three-dimensional space wind flow field in the micro-scale model based on an average state over an area represented by a resolution of the weather forecast data, and perform extrapolation simulation;
a fifth processing module 105, configured to apply the gridded file to perform linear interpolation on wind direction and thermal stability in the wind power prediction basic database according to different wind directions and different atmospheric stabilities in the weather forecast result through the correlation;
a sixth processing module 106, configured to obtain, according to the linear interpolation ratio, a wind direction in the weather forecast data at each forecast time point, and a wind direction deflection angle of the wind farm in the atmospheric stability state;
and the seventh processing module 107 is configured to calculate a wind power prediction result through the wind speed and the wind direction information in the meteorological forecast data and the wind direction drift angle and the wind farm full-field wind acceleration factor at the corresponding time.
According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured to establish the micro-scale wind flow field model capable of characterizing the local effect of the wind farm and its surrounding area, including:
and establishing a micro-scale wind flow field model capable of representing the local effect of the wind power plant and the surrounding area thereof based on a constant, adiabatic and incompressible Reynolds average Navier-Stokes equation.
According to the system of the second aspect of the present invention, the second processing module 102 is specifically configured to: the calculation of the wind power plant full wind acceleration factor Δ k comprises the following steps:
wherein U (z) represents the wind speed at the height z above the ground of the mountain land, U 0 (z) represents the wind speed at a height z above the flat ground.
According to the system of the second aspect of the present invention, the fourth processing module 104 is specifically configured to:
selecting a height position at which the weather forecast data is coupled to the microscale wind-flow-field model.
According to the system of the second aspect of the present invention, the seventh processing module 107 is specifically configured to calculate the wind power prediction result by the wind speed, the wind direction information and the wind farm full-field wind acceleration factor and the wind direction drift angle at the corresponding time in the weather forecast data, including:
calculating a wind power plant wind parameter downscaling forecasting result according to wind speed and wind direction information in the meteorological forecasting data and a wind power plant full-field wind acceleration factor and a wind direction deflection angle at corresponding time;
and according to the downscaling prediction result, combining the coordinates and the power curve of the wind power plant machine position point to obtain a wind power prediction result of each fan.
According to the system of the second aspect of the present invention, the seventh processing module 107 is specifically configured to calculate the wind power and the wind speed at the corresponding moment in time as follows:
wherein P (V) is wind power, V is wind speed, and V is Cutting into For wind turbines to cut into wind speed, V Rated value Rated wind speed V of the wind turbine Cutting out Cutting out wind speed for the wind turbine, S (V) wind power output, P max Is the maximum power output.
According to the system of the second aspect of the present invention, the fourth processing module 104 is specifically configured to associate including: and (3) carrying out extrapolation simulation by taking the wind speed, the wind direction and other related meteorological parameters on the area represented by the resolution ratio of the downscaled meteorological forecast data as the input conditions of the three-dimensional space wind flow field in the microscale wind flow field model.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for predicting the wind power fast down scale according to any one of the first aspect of the disclosure.
Fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device, which are connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic equipment comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the electronic device is used for communicating with an external terminal in a wired or wireless mode, and the wireless mode can be realized through WIFI, an operator network, Near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the electronic equipment, an external keyboard, a touch pad or a mouse and the like.
It will be understood by those skilled in the art that the structure shown in fig. 4 is only a partial block diagram related to the technical solution of the present disclosure, and does not constitute a limitation of the electronic device to which the solution of the present application is applied, and a specific electronic device may include more or less components than those shown in the drawings, or combine some components, or have a different arrangement of components.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of a method of wind power prediction of a fast downscaling according to any one of the first aspect of the present disclosure.
It should be noted that the technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, the scope of the present description should be considered. The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for fast downscaling of wind power predictions, the method comprising:
s1, collecting and mapping terrain elevation and roughness data of the wind power plant and a certain peripheral area range of the wind power plant, and establishing a micro-scale wind flow field model capable of representing local effect of the wind power plant and the peripheral area range of the wind power plant;
step S2, performing directional calculation on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain a full-field wind acceleration factor and a wind direction deflection angle of the wind power plant;
step S3, generating a gridded file by taking the wind farm full-field wind acceleration factor and the wind direction deflection angle as key parameters of a wind power prediction basic database;
s4, establishing association between the average state of the meteorological forecast data in the area represented by the resolution ratio of the meteorological forecast data and a three-dimensional space wind flow field in the microscale wind flow field model, and carrying out extrapolation simulation;
step S5, according to different wind directions and different atmospheric stabilities in the weather forecast result, the gridded file is applied to carry out linear interpolation on the wind direction and the thermal stability in the wind power prediction basic database through the correlation;
step S6, obtaining wind direction, wind direction deflection angle of the wind power plant full field corresponding to the atmospheric stability state in the weather forecast data of each forecast time point according to the proportion of the linear interpolation;
and step S7, calculating a wind power prediction result according to the wind speed and wind direction information in the meteorological forecast data and the wind power plant full-field wind acceleration factor and the wind direction deflection angle at the corresponding time.
2. The method for wind power prediction to rapidly downscale according to claim 1, wherein in the step S1, the method for establishing a micro-scale wind flow field model capable of characterizing local effects of the wind farm and the surrounding area comprises:
and establishing a micro-scale wind flow field model capable of representing the local effect of the wind power plant and the surrounding area thereof based on a constant, adiabatic and incompressible Reynolds average Navier-Stokes equation.
3. The method for wind power prediction to rapidly downscale according to claim 1, wherein in the step S2, the method for calculating the wind farm full wind acceleration factor Δ k comprises:
wherein U (z) represents the wind speed at the height z above the ground of the mountain land, U 0 (z) represents the wind speed at a height z above the flat ground.
4. The method for wind power prediction fast downscaling according to claim 1, wherein in the step S4, the method further includes:
selecting a height position at which the weather forecast data is coupled to the microscale wind-flow-field model.
5. The method for fast downscaling of wind power prediction according to claim 1, wherein in the step S7, the method for calculating the wind power prediction result through the wind speed, wind direction information and wind farm full-field wind acceleration factor and wind direction declination at the corresponding time in the meteorological forecast data comprises:
calculating a wind power plant wind parameter downscaling forecasting result according to wind speed and wind direction information in the meteorological forecasting data, a wind power plant full-field wind acceleration factor and a wind direction deflection angle at corresponding time;
and according to the downscaling prediction result, combining the coordinates and the power curve of the wind power plant machine position point to obtain a wind power prediction result of each fan.
6. The method for wind power prediction to rapidly downscale according to claim 5, wherein in the step S7, the wind power and the wind speed at the corresponding time are calculated as follows:
wherein P (V) is wind power, V is wind speed, and V is Cutting into For wind turbines to cut into wind speed, V Rated value Rated wind speed V of the wind turbine Cutting out Cutting out wind speed for wind turbine, S (V) wind power output, P max Is the maximum power output.
7. The method for wind power prediction fast downscaling according to claim 1, wherein in the step S4, the specific method for associating includes: and (3) carrying out extrapolation simulation by taking the wind speed, the wind direction and other related meteorological parameters on the area represented by the resolution ratio of the downscaled meteorological forecast data as the input conditions of the three-dimensional space wind flow field in the microscale wind flow field model.
8. A system for fast downscaling of wind power predictions, characterized in that the system comprises:
the first processing module is configured to collect and map terrain elevation and roughness data of the wind power plant and a certain area range around the wind power plant, and establish a micro-scale wind flow field model capable of representing a local effect of the wind power plant and a surrounding area;
the second processing module is configured to perform directional calculation on the micro-scale wind flow field model according to different wind directions and different thermal stabilities to obtain a full-field wind acceleration factor and a wind direction deflection angle of the wind power plant;
the third processing module is configured to generate a gridded file by taking the wind farm full-field wind acceleration factor and the wind direction deflection angle as key parameters of a wind power prediction basic database;
the fourth processing module is configured to correlate the weather forecast data with a three-dimensional space wind flow field in the micro-scale model based on the average state of the area represented by the resolution of the weather forecast data, and perform extrapolation simulation;
a fifth processing module, configured to apply the gridded file to perform linear interpolation on wind direction and thermal stability in a wind power prediction base database according to different wind directions and different atmospheric stabilities in a weather forecast result through the association;
the sixth processing module is configured to obtain the wind direction in the meteorological forecast data at each forecast time point and the wind direction deflection angle of the corresponding wind power plant in the atmospheric stability state according to the proportion of the linear interpolation;
and the seventh processing module is configured to calculate a wind power prediction result through the wind speed and the wind direction information in the meteorological forecast data and the wind power plant full-field wind acceleration factor and the wind direction deflection angle at the corresponding time.
9. An electronic device, characterized in that the electronic device comprises a memory and a processor, the memory stores a computer program, and the processor, when executing the computer program, implements the steps of a method for wind power prediction fast downscaling according to any one of claims 1-7.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of a method of wind power prediction of a fast droop scale of any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115600639A (en) * | 2022-09-30 | 2023-01-13 | 国网四川省电力公司眉山供电公司（Cn） | Wind speed sensor, power transmission line wind speed prediction method and early warning system |
| CN116881654B (en) * | 2023-07-03 | 2024-03-15 | 中国气象局地球系统数值预报中心 | Weather probability forecasting method and device for winter ice and snow sport service guarantee |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115600639A (en) * | 2022-09-30 | 2023-01-13 | 国网四川省电力公司眉山供电公司（Cn） | Wind speed sensor, power transmission line wind speed prediction method and early warning system |
| CN115600639B (en) * | 2022-09-30 | 2023-11-14 | 国网四川省电力公司眉山供电公司 | Wind speed sensor, wind speed prediction method of power transmission line and early warning system |
| CN116881654B (en) * | 2023-07-03 | 2024-03-15 | 中国气象局地球系统数值预报中心 | Weather probability forecasting method and device for winter ice and snow sport service guarantee |
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