CN104268429A - Satellite-borne SAR (Synthetic Aperture Radar) based offshore wind energy resource remote sensing method and system - Google Patents

Abstract

The present invention provides a remote sensing method for near-coast offshore wind energy resources based on spaceborne SAR, comprising: S1. Obtaining the long-term sequence SAR sea surface wind speed value in each grid and the sea surface wind direction value in each grid after gridding processing ;Generate a wind rose diagram; S2. Obtain sea surface wind speed values at different altitudes, and estimate the scale factor A and shape factor k in each grid; calculate the average wind speed value representing wind energy characteristics at different altitudes; S3. Obtain wind energy density S4. Generate the distribution map of remote sensing results of SAR nearshore offshore wind energy resources at different altitudes, and obtain the macroscopic and microcosmic temporal and spatial characteristics of wind energy resources in the coastal offshore area according to the distribution map of remote sensing results, and give potential wind farms and potential wind farms. Microcosmic temporal and spatial characteristics of wind energy resources in wind farms; S5. Give the technical parameters of wind turbine space layout in potential wind farms, and combine the microscopic research results of wind energy resources in potential wind farms to give the layout of wind turbines in potential wind farms.

Description

Remote sensing method and system for offshore wind energy resources based on spaceborne SAR

technical field

The invention relates to the technical field of remote sensing of wind energy resources, in particular to a method and system for remote sensing of offshore wind energy resources based on spaceborne SAR.

Background technique

Offshore offshore wind energy resources do not occupy land resources, have a large space for location selection, are conducive to site selection, are less restricted by the environment, have higher wind speeds, more abundant wind energy resources, superior transportation and hoisting conditions, and larger single-machine capacity of wind turbines. Countries around the world are developing their own offshore wind power industries one after another due to the advantages of higher annual utilization hours.

The development of offshore wind energy resources near the coast must first understand the location, storage and coverage area of available wind energy resources, and the preliminary construction of wind farms needs to know the temporal and spatial distribution characteristics of wind energy resources within the construction area and the technical parameters of the spatial layout of wind turbines in potential wind farms , to provide macro and micro basis for the layout of wind turbines. Therefore, the research work on offshore wind energy resources in the early stage of wind farm erection directly affects the economics of project construction.

For the study of offshore wind energy resources near the coast, the traditional method of extrapolation based on the statistical results of wind measurement data from land meteorological stations cannot obtain wind field data with high spatial coverage density near the coast, and the accuracy and resolution of numerical simulation techniques are relatively low. Low. With the development of satellite remote sensing technology, scatterometers provide new technical means for the study of offshore wind energy resources, but their resolution is too low, and due to the influence of land echoes, it is impossible to obtain effective wind field information near the coast. Coastal high-resolution offshore wind resource research has lost its effective value and significance.

The working wavelength of Synthetic Aperture Radar (SAR) is centimeters long, and it can not be affected by factors such as clouds and weather. It has the advantages of all-weather, all-time, high resolution, and high spatial coverage density. With the successful launch of more and more satellites carrying SAR microwave sensors and the maturing of SAR high-resolution sea surface wind field retrieval technology, the use of high-resolution wind field data retrieved by SAR for remote sensing research on wind energy resources has become an important aspect of offshore wind energy resources. New technological means of research.

The existing SAR image-based remote sensing technology for wind energy resources only studies the macroscopic spatiotemporal and spatial characteristics of offshore wind energy resources, and only gives some qualitative descriptions of the wind field characteristics in the wake area of wind turbines. The microstructure changes of regional wind energy resources and the spatial quantitative change characteristic data of small-scale wind fields in the wake area of wind turbines provide relevant technical parameters for the spatial layout of wind turbines for potential wind farms. Therefore, the microscopic analysis of wind energy resources is not accurate and is not conducive to wind power generation. Field fan layout.

Contents of the invention

The present invention provides a method and system for remote sensing of offshore wind energy resources based on spaceborne SAR, which can quantitatively analyze the spatial quantitative change characteristics of small-scale wind fields in the wake region of wind turbines and the fine structure changes of wind energy resources.

A remote sensing method for near-coast offshore wind energy resources based on spaceborne SAR, comprising the following steps:

S1. Perform long-term sequence SAR sea surface wind speed inversion processing on the acquired SAR remote sensing image to obtain the long-time sequence SAR sea surface wind speed value in each grid and the sea surface wind direction value in each grid after grid processing; The sea surface wind direction value in each grid generates a wind direction rose diagram;

S2. Obtain the sea surface wind speed values at different altitudes through the long-term sequence SAR sea surface wind speed values in each grid, and estimate the scale factor A and shape factor k in the Weibull distribution model in each grid by the maximum likelihood estimation method ; Calculate the average wind speed value representing wind energy characteristics at different altitudes through the obtained scale factor A and shape factor k;

S3. Calculating air density values at different altitudes; obtaining wind energy density values through calculation of average wind speed values and air density values at different altitudes;

S4. According to the average wind speed value, wind energy density value, shape factor k, scale factor A and wind direction rose diagram, generate the distribution map of remote sensing results of SAR offshore wind energy resources at different altitudes (average wind speed map, wind energy density map, shape factor k map, scale factor A map, and wind direction rose map), and according to the distribution map of remote sensing results to obtain the macroscopic and microscopic spatio-temporal characteristics of wind energy resources in the coastal offshore area, the potential wind farms and the microscopic spatio-temporal characteristics of wind energy resources in potential wind farms are given. characteristic;

S5. Analyze the multi-temporal and high-resolution SAR spatial variation characteristics of a single wind turbine in a completed wind power plant and the subtle structural changes in the wind energy resources of a potential wind farm, give the technical parameters of the spatial layout of the wind turbines in the potential wind farm, and combine the wind energy of the potential wind farm The results of the microscopic study of the resource give the layout of the wind turbines of the potential wind farm.

A remote sensing system for offshore wind energy resources based on spaceborne SAR, which includes the following modules:

The inversion module is used to perform long-time series SAR sea surface wind speed inversion processing on the acquired SAR remote sensing images, so as to obtain the long-time series SAR sea surface wind speed values in each grid and the sea surface in each grid after grid processing Wind direction value; and used to generate a wind direction rose diagram according to the sea surface wind direction value in each grid;

The parameter determination module is used to obtain the sea surface wind speed values at different altitudes through the long-term sequence SAR sea surface wind speed values in each grid, and to estimate the Weibull distribution at different altitudes in each grid by the maximum likelihood estimation method The scale factor A and shape factor k in the model; it is also used to calculate the average wind speed value representing wind energy characteristics at different altitudes through the obtained scale factor A and shape factor k;

The wind energy density determination module is used to calculate the air density value at different altitudes; and is used to obtain the wind energy density value by calculating the average wind speed value and the air density value at different altitudes;

The analysis module is used to generate the remote sensing results distribution map of SAR offshore wind energy resources near the coast at different altitudes according to the average wind speed value, wind energy density value, shape factor k, scale factor A and wind direction rose diagram, and is used to generate the distribution map according to the remote sensing results Obtain the macroscopic and microscopic spatio-temporal characteristics of coastal wind energy resource areas, and give potential wind farms; used for the multi-temporal and high-resolution SAR wind field spatial variation characteristics of individual wind turbines in the built wind farms and the fine structure of wind energy resources in potential wind farms The technical parameters of the spatial layout of wind turbines in potential wind farms are given, combined with the microscopic research results of wind energy resources in potential wind farms, the layout of wind turbines in potential wind farms is given.

The remote sensing method and system for offshore wind energy resources based on spaceborne SAR provided by the present invention generate SAR near-coast offshore wind energy resources at different altitudes based on the average wind speed value, wind energy density value, shape factor k, scale factor A, and wind direction rose diagram. The distribution map of remote sensing results of wind energy resources, and the macro and micro spatio-temporal characteristics of coastal wind energy resource regions can be obtained according to the distribution map of remote sensing results. It can quantitatively analyze the spatial variation characteristics of small-scale wind fields in the wake area of wind turbines and the subtle structural changes of wind energy resources in potential wind farms, and provide technical parameters for the spatial layout of wind turbines in potential wind farms, which is conducive to the rational layout of wind turbine spaces.

Description of drawings

Fig. 1 is a flow chart of a remote sensing method for near-coast offshore wind energy resources based on spaceborne SAR provided by an embodiment of the present invention;

Fig. 2 is the sub-flow chart of step S1 in Fig. 1;

FIG. 3 is a structural diagram of a remote sensing system for offshore wind energy resources based on spaceborne SAR provided by an embodiment of the present invention;

Fig. 4 is a substructure block diagram of the inversion module in Fig. 3 .

Detailed ways

As shown in Figure 1, the embodiment of the present invention provides a method for remote sensing of offshore wind energy resources based on spaceborne SAR, which includes the following steps:

S1. Perform long-term sequence SAR sea surface wind speed inversion processing on the acquired SAR remote sensing images to obtain long-time sequence SAR sea surface wind speed values in each grid and sea surface wind direction values in each grid after grid processing. A wind rose diagram is generated according to the sea surface wind direction value in each grid.

Wind direction rose diagram (referred to as wind rose diagram) is also called wind direction frequency rose diagram. It is based on the percentage value of each wind direction and wind speed in a certain area for many years, and is drawn in a certain proportion. Generally, it is represented by 8 or 16 compass positions. .

Optionally, as shown in Figure 2, the step S1 includes the following sub-steps:

S11. Inverting the long-term sequence of SAR remote sensing images to obtain inversion wind speed data.

SAR remote sensing images can be purchased through the Satellite Remote Sensing Ground Receiving Station of the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. The C-band SAR sea surface wind speed retrieval model and the polarizability model commonly used in the world are used to retrieve the SAR sea surface wind speed.

The commonly used C-band SAR sea surface wind speed retrieval models in the world mainly include CMOD4, CMOD5 and CMOD-IFR2, and their different polarizability models for HH polarization and cross-polarization conversion.

S12. Perform a correlation analysis between the long-time series SAR sea surface wind speed values and the long-time series model data, remove the typhoon data, and retain the relevant long-time series SAR retrieval wind speed data.

Because the wind direction information directly extracted from SAR images will lead to large errors, the Kriging interpolation method can be used to analyze the long-term sequence wind direction data (NOGAPS) (spatial resolution 1°×1° , with a temporal resolution of 6 hours) or the long-term series reanalysis wind direction data (NCEP-NCAR) jointly launched by the National Center for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) (spatial resolution of 2.5°×2.5° , with a temporal resolution of 6 hours) for spatial interpolation to obtain sea surface wind direction information that is aligned with the spatial position of the SAR pixel. The model data can be obtained through the MM5 model. MM5 (Mesoscale Model5) has the ability of multiple nesting, non-hydrostatic dynamic model and four-dimensional assimilation, and can run on a computer platform to simulate or forecast mesoscale and regional scale atmospheric circulation .

Among them, the spatial coverage of the long-term sequence of SAR remote sensing images needs to reach 165 scenes or more; the long-term sequence of NOGAPS wind direction data and NCEP-NCAR reanalysis wind direction data can be downloaded through the US National Center for Environmental Prediction.

S13. Carry out grid processing on the retained long-time sequence inversion wind speed data, and obtain the long-time sequence SAR sea surface wind speed value in each grid and the sea surface wind direction value in each grid after grid processing. And generate a wind direction rose diagram according to the sea surface wind direction value in each grid.

The grid size is set to different resolution levels not greater than 1000 meters × 1000 meters, and the grids can be divided into 1000, 900, 800, 700, 600, 500, 400, 300, 200 and 100 meters, etc., to get each grid The long-term series SAR sea surface wind speed value in the point and the sea surface wind direction value in each grid. The gridded data complies with the WGS84 standard and is displayed in the projection mode of latitude and longitude grids.

S2. Obtain the sea surface wind speed values at different altitudes through the long-term sequence SAR sea surface wind speed values in each grid, and estimate the scale factor A in the Weibull distribution model at different altitudes in each grid by the maximum likelihood estimation method and form factor k. The average wind speed value representing the wind energy characteristics at different altitudes in each grid is calculated by the obtained scale factor A and shape factor k.

When the occurrence probabilities of various grades of wind speed are very different, the calculated wind energy will be very different. Therefore, it is necessary to use the wind speed probability distribution model to calculate the wind energy density. The research shows that the two-parameter Weibull wind speed distribution model including scale factor A (m/s) and shape parameter k (dimensionless) is an ideal model for wind energy calculation. The following formula gives the Weibull wind speed distribution function of 0~V integral, and as long as the parameters A and k are known, the distribution characteristics of wind speed can be determined.

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; exp</span>}<span\; class="notranslate">\; [</span><span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; V</span>}}{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; ]</span>$

Optionally, in the step S2, the formula for obtaining the sea surface wind speed values at different altitudes through the long-term sequence SAR sea surface wind speed values in each grid is as follows:

Among them, V _n and V ₁ are the average wind speed at the heights of Z _n and Z ₁ respectively, α is the shear exponent of the average wind speed changing with height, and the value of α can be selected as 0.09.

In wind power development, the height of the wind turbine hub is generally built at a height of 70m or higher from the sea surface. The SAR inversion obtains the wind speed data at a height of 10m. To obtain a more comprehensive distribution of wind energy resources at different heights at sea, it is necessary to convert the wind speed data obtained by SAR inversion at a height of 10m to different altitudes to calculate the different The high sea surface wind speed can be obtained by the above formula To describe the exponential wind profile of the wind speed vertical shear characteristics, to approximate the wind speed vertical shear edge change characteristics.

The formula for calculating the average wind speed value is as follows:

Where Γ is the gamma function, k is the shape factor (dimensionless), and A is the scale factor (m/s).

S3. Calculating air density values at different altitudes. And the wind energy density value is obtained by calculating the average wind speed value and the air density value at different altitudes.

Optionally, the calculation formula of the air density values at different altitudes in the step S3 is as follows:

ρ=(P ₀ /(Rt))e ^(-gz/(Rt)) , where P ₀ is the standard plane atmospheric pressure (101.325kPa), g is the acceleration of gravity (9.8m/s2), z is the site height (m) , R is the gas constant J/(Kg K)), t is the temperature value, optionally, the temperature value t can be changed by the relational expression dt/dz=-1/100 ℃/m of atmospheric temperature with the change of height to calculate.

The air temperature and pressure at sea level commonly used in international standards are 288.15k and 101.325kPa respectively, so the air density at standard sea level is 1.225kg/m3, and the air density at different altitudes in each grid can also be approximated. .

The calculation formula of wind energy density value is as follows:

$\stackrel{\&OverBar;}{W}=\frac{1}{2}\mathrm{\&rho;}{\&Integral;}_0^{\&infin;}{V}^{3}f\left(V\right)\mathrm{dV}=0.5\mathrm{\&rho;}{A}^3\mathrm{\&Gamma;}(\frac{3}{k}+1),$ where ρ is the air density value.

S4. According to the average wind speed value, wind energy density value, shape factor k, scale factor A and wind direction rose diagram in each grid, generate the wind direction rose diagram of the entire coastal sea area and the remote sensing results of SAR offshore wind energy resources at different altitudes Spatial distribution map (average wind speed map, wind energy density map, shape factor k map and scale factor A map), and obtain the temporal and spatial characteristics of coastal wind energy resource areas according to the distribution map of remote sensing results.

Optionally, the spatial resolutions of the spatial distribution map of the remote sensing results of offshore wind energy resources in the step S4 are 1000, 900, 800, 700, 600, 500, 400, 300, 200 and 100 meters respectively; The temporal and spatial characteristics of the region include the horizontal spatial distribution of offshore wind energy resources, the vertical spatial distribution of different altitudes, seasonal distribution, and monthly distribution characteristics.

The distribution map of remote sensing results of SAR offshore wind energy resources near the coast (the grid size is set to not less than 500m×500m, including 1000, 900, 800, 700, 600 and 500m) results, which can macroscopically analyze the offshore wind energy near the coast Macroscopic research on the distribution of resources mainly includes research and analysis on the horizontal spatial distribution of offshore wind energy resources near the coast, the vertical spatial distribution of different altitudes, seasonal changes and monthly changes, and find out areas rich in offshore wind energy resources near the coast.

The distribution map of remote sensing results of SAR offshore wind energy resources in areas rich in offshore wind energy resources near the coast (the grid size is set to no more than 500 meters × 500 meters, including 400, 300, 200 and 100 meters). Conduct microscopic analysis and research, mainly including the research and analysis of horizontal spatial changes in areas rich in wind energy resources, vertical space changes at different altitudes, seasonal changes and monthly changes, and give the characteristics of small-scale microstructure changes in wind energy resource-rich areas .

Based on the spaceborne SAR remote sensing technology and research process of offshore wind energy resources, the remote sensing of wind energy in the offshore coastal research area is obtained through the long-term average wind speed value, wind energy density value, shape factor k and scale factor A of each grid point The result distribution diagram, and the grid edge burrs are eliminated by 3×3 or 5×5 median filtering, and the smoothed offshore SAR remote sensing distribution results of offshore wind energy resources are obtained. Furthermore, based on the high-resolution SAR remote sensing distribution map of offshore wind energy resources near the coast, a macroscopic study is carried out on the horizontal spatial variation of offshore wind energy resources, the vertical spatial variation, seasonal variation and monthly variation of different altitudes, and find out the near-coastal offshore wind energy resources. It can enrich the area and provide a reference basis for the planning of possible candidate wind farms; at the same time, carry out microscopic research on the horizontal spatial changes in wind energy resource-rich areas, the vertical spatial changes at different altitudes, seasonal changes and monthly changes, so as to provide potential wind farms The reasonable layout of fan space lays the foundation.

S5. Through the long-term SAR sea surface wind field, analyze the small-scale spatial variation characteristics of the long-term and directional wind field of a single wind turbine in the completed wind farm, and the wind energy resources of the potential wind farm (average wind speed, wind energy density, shape factor k, scale factor A and wind direction), the technical parameters of the spatial layout of wind turbines in potential wind farms are given, combined with the microscopic research results of wind energy resources in potential wind farms, the layout of wind turbines in potential wind farms is given.

Analyze the spatial variation characteristics of a single fan (fan blade height is clear) in different wind directions and different wind speeds in small-scale wind fields. Optionally, the distance between the fans along the wind direction is restored to the original wind speed or more than 75% of the original wind speed. The layout spacing of wind turbines along the direction perpendicular to the wind direction is also the distance to restore the original wind speed or more than 75% of the original wind speed. In this way, combined with the microscopic research results of wind energy resources in the sea area of potential wind farms, reasonable technical parameters for the spatial layout of wind turbines can be given.

In view of the technical parameters of wind turbine spatial layout of potential wind farms in my country, we can firstly take Shanghai Donghai Bridge Wind Farm as an example to develop the small-scale spatial variation characteristics of wind farms in the wake area of wind farms with different wind speeds and different wind directions, and give the potential wind farms. Fan space layout technical parameters. Among them, the characteristics of wind speed variation with distance in the wind turbine wake area mainly include the variation range and intensity of wind speed along the wind direction and perpendicular to the wind direction, and give the small-scale spatial variation characteristics of the wind field around the wind farm wind turbine. The change distance of wind speed is generally based on returning to the original wind speed or more than 75% of the original wind speed. The change intensity of wind speed is defined as ((original wind speed-current wind speed)/(original wind speed)), if there is no change in wind speed, it is 0%, In this way, through the analysis of the spatial characteristics of the wind field with varying intensity, the small-scale spatial characteristics of the wind field in the wake area of a single wind turbine are given.

For the microscopic study of wind energy resources in potential wind farms, through the research and analysis of horizontal spatial changes in wind energy resource-rich areas, vertical space changes at different altitudes, seasonal changes, and monthly changes, the small-scale wind energy resource-rich areas are given. The microstructure change characteristics mainly include the most concentrated points of wind energy resources in potential wind farms, the wind direction, average wind speed, wind energy density, shape factor k and scale factor A of the potential wind farm sea area.

Optionally, in the potential wind farm area, by referring to the latitude, longitude and height information of the most concentrated point of wind energy resources as the base point, through the subtle structural changes of wind direction, average wind speed, wind energy density, shape factor k and scale factor A in the sea area of the potential wind farm Combined with the analysis of the small-scale wind field spatial characteristics in the wake area of a single wind turbine in the completed wind field, the technical parameters of the spatial layout of potential wind turbines are given, which is conducive to the rational layout of the wind turbine space.

The remote sensing method for offshore wind energy resources based on space-borne SAR provided by the present invention, through the The average wind speed value, wind energy density value, shape factor k, scale factor A and wind direction generate the distribution map of remote sensing results of SAR offshore wind energy resources near the coast at different altitudes, and can obtain the macroscopic characteristics of coastal wind energy resource areas according to the distribution map of remote sensing results , to provide possible microscopic candidate sites for wind farm planning. At the same time, microscopic analysis of potential wind farms can be carried out according to the distribution map of remote sensing results, and the spatial variation characteristics of small-scale wind fields in the wake area of completed wind turbines can be quantitatively analyzed, and reference technical parameters for the spatial layout of wind turbines in potential wind farms can be given, which is beneficial to wind power. Reasonable layout of fan space in the field.

As shown in Figure 3, the embodiment of the present invention also provides a remote sensing system for offshore wind energy resources based on spaceborne SAR, which includes the following modules:

The inversion module 10 is used to perform SAR sea surface wind speed inversion processing on the acquired long-term SAR remote sensing images, so as to obtain the long-term SAR sea surface wind speed values in each grid and the Inland sea surface wind direction value. And it is used to generate a wind rose diagram according to the sea surface wind direction value in each grid.

Preferably, as shown in Figure 4, the inversion module 10 includes the following units:

The inverted wind speed data acquisition unit 11 is used to invert the long-time series of SAR remote sensing images to obtain the inverted wind speed data.

The verification unit 12 is used to perform correlation analysis between the long-time series SAR sea surface wind speed value and the long-time series model data, remove the typhoon data, retain the remaining long-time series SAR retrieval wind speed data, and retain the long-time series SAR The spatial coverage of remote sensing images needs to reach 165 scenes or more.

The grid processing unit 13 is used to perform grid processing on the retained long-time series SAR inversion wind speed data, and obtain the long-time series SAR sea surface wind speed values in each grid after the grid processing and each grid Inland sea surface wind direction value. The grid processing unit 13 is further configured to generate a wind rose diagram according to the sea surface wind direction value in each grid.

The parameter determination module 20 is used to obtain the sea surface wind speed values at different altitudes through the long-term sequence SAR sea surface wind speed values in each grid, and is used to estimate the scale factor A and the shape factor in the Weibull distribution model by the maximum likelihood estimation method k. The parameter determination module 20 is also used to calculate the average wind speed value representing the characteristic value of wind energy through the obtained scale factor A and shape factor k at different altitudes.

Preferably, the formula for obtaining the sea surface wind speed values at different altitudes through the long-term sequence SAR sea surface wind speed values in each grid in the parameter determination module 20 is as follows:

Among them, V _n and V ₁ are the average wind speed at the heights of Z _n and Z ₁ respectively, α is the shear exponent of the average wind speed changing with height, and the value of α can be selected as 0.09;

The formula for calculating the average wind speed value is as follows:

where Γ is the gamma function, k is the shape factor, and A is the scale factor.

The wind energy density determination module 30 is used to calculate air density values at different altitudes. And it is used to obtain the wind energy density value by calculating the average wind speed value and the air density value at different altitudes.

Optionally, the formula for calculating the air density values at different altitudes in the wind energy density determination module 30 is as follows:

ρ=(P ₀ /(Rt))e ^(-gz/(Rt)) , where P ₀ is the standard plane atmospheric pressure, g is the acceleration of gravity, z is the field height, R is the gas constant, t is the temperature value, optional Specifically, the temperature value t can be calculated by the relational expression dt/dz=-1/100°C/m that the atmospheric temperature changes with the altitude.

The calculation formula of wind energy density value is as follows:

$\stackrel{\&OverBar;}{W}=\frac{1}{2}\mathrm{\&rho;}{\&Integral;}_0^{\&infin;}{V}^{3}f\left(V\right)\mathrm{dV}=0.5\mathrm{\&rho;}{A}^3\mathrm{\&Gamma;}(\frac{3}{k}+1),$ where ρ is the air density value.

The analysis module 40 is used to generate SAR images covering sea areas with different altitudes, different spatial resolutions, and different time resolutions of SAR near-coast offshore wind energy according to the average wind speed value, wind energy density value, shape factor k, scale factor A, and wind direction rose diagram. The distribution map of remote sensing results of resources, the analysis module 40 is also used to obtain the macroscopic and micro temporal and spatial characteristics of the coastal wind energy resource area according to the distribution map of the remote sensing results, find out the area rich in offshore wind energy resources near the coast, and can be a possible candidate wind farm Planning provides a reference basis;

It is used to give the characteristics of small-scale microstructure changes in areas rich in wind energy resources through the research and analysis of horizontal spatial changes of offshore wind energy resources in sea areas of potential wind farms, vertical spatial changes at different altitudes, seasonal changes and monthly changes;

It is used to analyze the small-scale spatial variation characteristics of a single wind turbine in a built wind farm (the height of the blade of the wind turbine) in different wind directions and different wind speeds through long-term sequence SAR sea surface wind speed. Optionally, the layout spacing of wind turbines along the wind direction is Return to the original wind speed or the distance of more than 75% of the original wind speed, and the distance between the layout of the fans along the direction perpendicular to the wind direction is also the distance to return to the original wind speed or more than 75% of the original wind speed, and the technical parameters of the fan space layout are given;

It is used to give the technical parameters of the spatial layout of potential wind farms and carry out the layout of wind turbines in the wind farm through the technical parameters of the spatial layout of the wind turbines obtained from the analysis of the individual wind turbines in the completed wind farms and the microscopic research results of the wind energy resources of the potential wind farms.

Optionally, the characteristics of the offshore wind energy resource area in the analysis module 40 include the horizontal spatial variation of offshore wind energy resources, the vertical spatial variation at different altitudes, the macroscopic characteristics of seasonal variation and monthly variation, and also include Horizontal spatial variation of wind energy resources (most concentrated point of wind energy resources, wind direction, average wind speed, wind energy density, shape factor k and scale factor A) in areas rich in offshore wind energy resources, vertical spatial variation, seasonal variation and monthly variation at different altitudes Microscopic properties at small scales.

Optionally, the technical parameters of the spatial layout of the wind turbines in the analysis module 40 include the characteristics of the wind speed variation with the distance of the wind speed in the wake area of the established single wind turbine, which mainly includes the range of the wind speed along the direction of the wind direction and the distance perpendicular to the direction of the wind direction. Wind speed varies in intensity.

Each embodiment in this specification is described in a progressive manner, each embodiment focuses on the difference from other embodiments, and the same and similar parts of each embodiment can be referred to each other.

Professionals can further realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software or a combination of the two. In order to clearly illustrate the possible For interchangeability, in the above description, the composition and steps of each example have been generally described in terms of functionality. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not exceed the scope of the present invention.

The steps of the methods or algorithms described in connection with the embodiments disclosed herein may be directly implemented by hardware, software modules executed by a processor, or a combination of both. Software modules can be placed in random access memory, internal memory, read-only memory, electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form known in the technical field in the storage medium.

It can be understood that those skilled in the art can make various other corresponding changes and modifications according to the technical concept of the present invention, and all these changes and modifications should belong to the protection scope of the claims of the present invention.

Claims (11)

1. A method for remote sensing of offshore wind energy resources on the coast based on spaceborne SAR, characterized in that it comprises the following steps: S1. Perform long-term sequence SAR sea surface wind speed inversion processing on the acquired SAR remote sensing image to obtain the long-time sequence SAR sea surface wind speed value in each grid and the sea surface wind direction value in each grid after grid processing; The sea surface wind direction value in each grid generates a wind direction rose diagram; S2. Obtain the sea surface wind speed values at different altitudes through the long-term sequence SAR sea surface wind speed values in each grid, and estimate the scale factor A and shape factor k in the Weibull distribution model in each grid by the maximum likelihood estimation method ; Calculate the average wind speed value representing wind energy characteristics at different altitudes through the obtained scale factor A and shape factor k; S3. Calculating air density values at different altitudes; obtaining wind energy density values through calculation of average wind speed values and air density values at different altitudes; S4. According to the average wind speed value, wind energy density value, shape factor k, scale factor A and wind direction rose diagram, generate the distribution map of remote sensing results of SAR offshore wind energy resources at different altitudes. The distribution map of remote sensing results includes average wind speed map, wind energy density map, shape factor k map, scale factor A map, and wind direction rose map, and obtain the horizontal spatial distribution of wind energy resources in coastal areas, vertical spatial distribution at different altitudes, seasonal distribution, monthly distribution characteristics, etc. based on the distribution map of remote sensing results Macroscopic and microscopic spatiotemporal and spatial characteristics, giving the microscopic spatiotemporal and spatial characteristics of potential wind farms and wind energy resources of potential wind farms; S5. Analyze the multi-temporal and high-resolution SAR spatial variation characteristics of a single wind turbine in a completed wind power plant and the subtle structural changes in the wind energy resources of a potential wind farm, give the technical parameters of the spatial layout of the wind turbines in the potential wind farm, and combine the wind energy of the potential wind farm The results of the microscopic study of the resource give the layout of the wind turbines of the potential wind farm. 2. The method for remote sensing of offshore wind energy resources based on spaceborne SAR as claimed in claim 1, wherein said step S1 comprises the following sub-steps: S11. Inverting the long-term sequence of SAR remote sensing images to obtain inversion wind speed data; S12. Carry out a correlation analysis between the long-time series SAR sea surface wind speed value and the long-time series model data, remove the typhoon data, and retain the remaining inversion wind speed data; S13. Carry out grid processing on the retained inversion wind speed data, and obtain the long-term sequence SAR sea surface wind speed value in each grid and the sea surface wind direction value in each grid after grid processing; and according to each grid The inland sea surface wind direction value generates a wind rose diagram. 3. The remote sensing method for offshore wind energy resources based on spaceborne SAR as claimed in claim 2, wherein in the step S2, the sea surface at different altitudes is obtained by the long-time sequence SAR sea surface wind speed value in each grid The formula for the wind speed value is as follows: Among them, V _n and V ₁ are the average wind speed at the heights of Z _n and Z ₁ respectively, α is the shear exponent of the average wind speed changing with height, and the value of α can be selected as 0.09; The formula for calculating the average wind speed value is as follows: where Γ is the gamma function, k is the shape factor, and A is the scale factor. 4. the remote sensing method for offshore wind energy resources based on spaceborne SAR as claimed in claim 3, characterized in that, The calculation formula of the air density values at different altitudes in the step S3 is as follows: ρ=(P ₀ /(Rt))e ^(-gz/(Rt)) where P ₀ is the standard plane atmospheric pressure, g is the acceleration of gravity, z is the field height, R is the gas constant, and t is the temperature value; The calculation formula of wind energy density value is as follows: $\stackrel{\&OverBar;}{W}=\frac{1}{2}\mathrm{\&rho;}{\&Integral;}_0^{\&infin;}{V}^{3}f\left(V\right)\mathrm{dV}=0.5\mathrm{\&rho;}{A}^3\mathrm{\&Gamma;}(\frac{3}{k}+1),$ where ρ is the air density value. 5. The remote sensing method for offshore wind energy resources based on spaceborne SAR as claimed in claim 4, characterized in that, the characteristics of the offshore wind energy resource area in the step S4 include the horizontal spatial variation of the offshore wind energy resources, different The macroscopic characteristics of the vertical spatial variation, seasonal variation and monthly variation of altitude, and the horizontal spatial variation of wind energy resources in regions rich in wind energy resources, and the microscopic characteristics of vertical spatial variation, seasonal variation and monthly variation of different altitudes. 6. The remote sensing method for offshore wind energy resources based on spaceborne SAR as claimed in claim 5, wherein the technical parameters of the spatial layout of wind turbines in the potential wind farms in the step S5 include the wind speed in the wake area of a single wind turbine in a wind farm The characteristics of wind speed change with distance mainly include the range of wind speed change along the wind direction and the distance perpendicular to the wind direction and the change intensity of wind speed. The change intensity is selected to be 25% or less, and the change distance is selected to be along the wind direction The layout distance of the wind turbines in the direction is the space distance where the wind speed is restored to the original wind speed or more than 75% of the original wind speed, and the wind turbine layout perpendicular to the wind direction is also the space distance to restore the original wind speed to more than 75% of the original wind speed. The calculation is given based on the long-term Sequential SAR sea surface wind speed and wind turbine spatial layout technical parameters under different wind speeds and wind directions, combined with the microscopic research results of wind energy resources in potential wind farms, the microscopic research results include the most concentrated points of wind energy resources, wind direction rose diagram and average wind speed, wind energy density, The horizontal spatial variation of the shape factor k and the scale factor A, the vertical spatial variation of different altitudes, the microscopic characteristics of seasonal variation and monthly variation, and the wind turbine layout of potential wind farms. 7. A remote sensing system for offshore wind energy resources based on spaceborne SAR, characterized in that it includes the following modules; The inversion module is used to perform long-time series SAR sea surface wind speed inversion processing on the acquired SAR remote sensing images, so as to obtain the long-time series SAR sea surface wind speed values in each grid and the sea surface in each grid after grid processing Wind direction value; and used to generate a wind direction rose diagram according to the sea surface wind direction value in each grid; The parameter determination module is used to obtain the sea surface wind speed values at different altitudes through the long-term sequence SAR sea surface wind speed values retained in each grid, and is used to estimate the Weibull distribution model in each grid by the maximum likelihood estimation method Scale factor A and shape factor k; also used to calculate the average wind speed value representing wind energy characteristics at different altitudes through the obtained scale factor A and shape factor k; The wind energy density determination module is used to calculate the air density value at different altitudes; and is used to obtain the wind energy density value by calculating the average wind speed value and the air density value at different altitudes; The analysis module is used to generate the distribution map of remote sensing results of SAR near-coast offshore wind energy resources at different sea heights according to the average wind speed value, wind energy density value, shape factor k, scale factor A and wind direction rose diagram in each grid, and use it for According to the distribution map of remote sensing results, the macroscopic temporal and spatial characteristics of coastal wind energy resource areas are obtained, and potential wind farms with rich wind energy resources are given; it is used to generate high-resolution remote sensing results distribution maps of wind energy resources in areas rich in wind energy resources, and is used to Analyze the microscopic spatio-temporal and fine-structure change characteristics of the potential wind field area through the distribution map of high-rate remote sensing results; it is used to give a reasonable spatial layout of wind turbines based on the multi-temporal and high-resolution SAR wind field spatial variation characteristics of a single wind turbine in the completed wind field Technical parameters: It is used to give the technical parameters of the spatial layout of wind turbines based on the analysis of individual wind turbines in the completed wind farm, combined with the analysis results of the subtle structural changes of wind energy resources in potential wind farms, to give the technical parameters of the spatial layout of wind turbines in potential wind farms, and to provide a basis for potential wind farms. The layout of wind turbines in wind farms provides the basis for spatial layout. 8. The remote sensing system for offshore wind energy resources near the coast based on spaceborne SAR as claimed in claim 7, wherein the inversion module comprises the following units: The inversion wind speed data acquisition unit is used to invert long-term multi-view SAR remote sensing images to obtain inversion wind speed data; The verification unit is used for correlation analysis between the long-term SAR sea surface wind speed value and the long-time series model data, removes the typhoon data, retains the remaining SAR retrieval wind speed data, and preserves the space coverage of the long-term SAR remote sensing image The rate needs to reach 165 scenes or more; The grid processing unit is used to perform grid processing on the retained SAR retrieval wind speed data, and obtain the long-term SAR sea surface wind speed value in each grid and the sea surface wind direction value in each grid after grid processing ; and used to generate a wind rose diagram according to the sea surface wind direction value in each grid. 9. The remote sensing system for offshore wind energy resources on the coast based on spaceborne SAR as claimed in claim 8, wherein, in the parameter determination module, the values of different altitudes are obtained by long-term sequence SAR sea surface wind speed values in each grid. The formula for sea surface wind speed is as follows: Among them, V _n and V ₁ are the average wind speed at the heights of Z _n and Z ₁ respectively, α is the trimming index of the average wind speed changing with height, and the value of α can be selected as 0.09; The formula for calculating the average wind speed value is as follows: where Γ is the gamma function, k is the shape factor, and A is the scale factor. 10. The remote sensing system for offshore wind energy resources based on spaceborne SAR as claimed in claim 9, wherein The calculation formula of the air density values at different altitudes in the wind energy density determination module is as follows: ρ=(P ₀ /(Rt))e ^(-gz/(Rt)) , where P ₀ is the standard plane atmospheric pressure, g is the acceleration of gravity, z is the field height, R is the gas constant, and t is the temperature value; The calculation formula of wind energy density value is as follows: $\stackrel{\&OverBar;}{W}=\frac{1}{2}\mathrm{\&rho;}{\&Integral;}_0^{\&infin;}{V}^{3}f\left(V\right)\mathrm{dV}=0.5\mathrm{\&rho;}{A}^3\mathrm{\&Gamma;}(\frac{3}{k}+1),$ where ρ is the air density value. 11. The space-borne SAR-based remote sensing system for near-coast offshore wind energy resources as claimed in claim 10, characterized in that, the characteristics of the coastal wind energy resource region in the analysis module include the horizontal spatial variation of the near-coast offshore wind energy resources, different The macroscopic characteristics of vertical spatial variation, seasonal variation and monthly variation of altitude; at the same time, including the horizontal spatial variation of high-resolution wind energy resources in areas rich in wind energy resources, vertical spatial variation, seasonal variation and monthly variation of different altitudes Features; at the same time, it includes the multi-temporal and high-resolution SAR wind field spatial variation characteristics of a single wind turbine in the completed wind farm, and gives reasonable technical parameters for the spatial layout of the wind turbine; it also includes the spatial layout technology of the wind turbine based on the analysis of the single wind turbine in the completed wind farm Parameters, combined with the analysis results of the fine structure changes of wind energy resources in potential wind farms, the technical parameters of the spatial layout of wind turbines in potential wind farms are given, which provides a basis for the spatial layout of wind turbines in potential wind farms.