Offshore Windfarm Site Establishment – Using Remote Sensing Techniques and GIS

Offshore Windfarm Site Establishment – Using Remote Sensing Techniques and GIS

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Saranya.K & Hari Prasath.P
Student
B.E. Geo-Informatics
College Of Engineering
Anna University, Chennai-25
Mail id: [email protected]

ABSTRACT:
The intension is to situate the best fitting site for offshore wind farm establishment in the Indian coastline. For the planning of offshore wind farms the offshore wind resources is a key parameter. The main constituents of offshore wind resource is wind direction, wind velocity, wind intensity, monsoons, ocean topography, etc, The periodical variations in the above said constituents are identified and studied at assorted zones by acquiring wind field maps extracted from satellite imageries (SAR and other microwave sensors, LIDAR and other optical sensors) using remote sensing techniques and applying some suitable algorithms in it. And by incorporating GIS with the output. The reliability and accuracy of the selected site is analyzed using GIS software and the best fitting site is proposed. Further this methodology can also be extended for finding the site accuracy of the selected site of the currently working models of offshore wind farms and also the establishment of onshore wind farms.

INTRODUCTION:
In the modern era, the development and innovations in all the fields are tremendously increasing. It is much obvious that the usage of electricity is inevitable. Though the current stage of production of electricity is more than its consumption, this saturated stage will not prevail in near future because of the extinction of non-renewable resources (which includes coal and water).

This can be compensated by using renewable resources like “WIND”. It is a well known fact that, in thermal method of generating electricity, large amount of green house gases like co2 are emitted, which results in global warming, and it is the greatest threat for the cornered human beings.

Hence wind resource gives us a safe and secure source for electricity. Also it does not bet the safety of human lives like nuclear energy production which involves the use of radio active materials.

Thus on the whole wind provides us a safe and reliable source of electricity requirements fro the progress in all fields.

WHY OFFSHORE?
Offshore refers to the place which is a few kilometer away from the land shore into the sea. As there is no obstructions to the flow of wind, the wind density can be maintained at the maximum potential level. Also, the offshore sites of windfarms helps us in the effective usage of lands on shore.

As the wind energy is the only raw material used in this kind of energy production the cost of production remains constant. And it includes one time investment, that is the construction alone. Thus wind energy has its advantage in its cost effective nature too.

CITING METHODS:
The suitable site for the construction of wind farms includes the place which consists of sustainable wind potential. These sites can be cited in a number of ways.

  • WaSP
  • Meteorological mast
  • Satellite methods

WaSP:
It is the acronym for wind analysis and application program. It provides us the local wind climate and potential power.

METEOROLOGICAL MAST:
It is a tower which records the wind potential using anemometer and wind vanes at differential heights over a particular area. The observed values are reliable and can be extrapolated for about 20sq.km around the mast.

SATELLITE METHOD:
In this method, wind density is estimated using the satellite imageries taken over that area. The advantages of this method is the coverage of large area and the spatial variability of wind density can be located easily. It can be used in places where In-situ measurements are not possible.

In this paper, the methodology for selecting the suitable sites for the construction of offshore wind farms using satellite imageries are discussed.

REQUIRED DATA:

  • SAR data (vv polarised) – for wind field mapping.
  • High resolution optical data- for land use mapping.
  • Elevation data – for DEM generation.
  • Attribute data- for ranking of features.

NOTE:
As SAR data for the Indian coast lines are not available freely, we have explained the methodology with the help of SAR data of Denmark coastline.

METHODOLOGY:
The data which we get from the satellite which has synthetic aperture radar is basically a raw data which has to be calibrated and processed accordingly to retrieve the wind field maps. The processes are explained briefly as follows,

IMAGE CALIBERATION:
The image calibration is done using the BEST (Basic Envisat SAR Toolbox) software in the following methods,

  • Header analysis
  • Full resolution extraction
  • Linear to DB conversion
  • Back scattering image generation
  • Data export

HEADER ANALYSIS:
Before any processing can be performed on data using BEST (including quick look generation or data extraction), the header analysis module must be run to extract into an internal format file the header information contained in the product or accompanying file. The header analysis function decodes all the header parameters from a product on tape, CD-ROM or hard disk. This information is extracted and stored in a plain ASCII file (extension.txt) and in a file in the Toolbox internal format (extension .HAN). The ASCII file can be examined using a standard text editor to provide useful information about the data.

FULL RESOLUTION EXTRACTION:
The Full Resolution Extraction function is used to extract a full resolution image portion from a product on tape, CD-ROM or hard disk. The resulting image file will be in the BEST internal format and will contain the image pixels plus the various header fields (i.e. the image ancillary data) already obtained with the HEADER ANALYSIS operation.

LINEAR TO DB CONVERSION:
The linear to dB conversion function is used to convert an image (amplitude or an intensity image) from the linear scale to a dB scale. The AOI which are permitted are the rectangular AOI (with corners expressed in row, col system or in lat, long) or even the polygonal AOI (in this case the surrounding rectangular AOI is used).No further parameter is needed. Note that to convert a complex image into dB, a modulus extraction shall be executed.

BACK SCATTER IMAGE GENERATION:
The Backscattering Image Generation tool converts a power image into a backscatter image. The following radiometric effects are corrected:

  1. Incidence angle
  2. Absolute calibration constant
  3. Range spreading loss
  4. Antenna pattern

Only the first two need be applied to detected ground range products. For slant-range complex products, the last two must also be compensated. This differentiation is automatically performed by the system. The output image may have either a linear or dB scale.

EXTRACTION OF SUB IMAGE:
The ERS-2 satellite operated by the European Space Agency (ESA) passes over Denmark coast line three times per month at an altitude of 785 km. On board the satellite is a SAR instrument which scans the earth surface at incidence angles of 20-26 degrees in 100 km wide bands. Each of the SAR scenes covers 100 km by 100 km with a spatial resolution of 25 m. For the analysis, subimages of 50 km by 50 km are extracted.

The calibration procedure includes pixel averaging to a pixel size of 100 m; this is to reduce the random noise always found in SAR images (i.e. speckle). Before the CMOD-4 algorithm is applied to the backscatter images, pixels are further averaged up to 400 m to match the algorithm, which has been developed for pixel sizes in the order of 500 m.

STEPS INVOLVED:
The parameters for the offshore wind field map is described as follows,

  • Wind speed
  • Wind direction
  • Incidence angle
  • Backscatter coefficient
  • Temperature
  • Pressure
  • Wind density


Figure 1: extraction of wind field from SAR image.

CMOD4:
It is an algorithm which is developed by Stoffelen & Anderson in 1993 for retrieving wind speed from Scatterometer data. This is then modeled to apply for SAR data. This model while applying in SAR data can be able to give wind speed with wind direction as input and wind direction with wind speed as input.

The inputs for CMOD4 algorithm includes,

  • Backscatter coefficient.
  • Incidence angle.
  • Wind speed / wind direction depending on the required output.

BACKSCATTER COEFFICIENT:
It is the ratio of the fraction of signal received by the sensor after reflection from the target to that which is emitted by the sensor.

INCIDENCE ANGLE:
It is defined as the angle at which the signal from the sensor gets incident on the target. In general, for ERS-2 satellite, these angle various from 20 – 26 degrees.

WIND DIRECTION:
When our output need is basically the wind speed, it is mandatory that wind direction should be given as input. This wind direction over a particular area can be determined by following methods,

  1. By In-situ measurements(meteorological tower )
  2. Fast Fourier Transformation (FFT).
  3. Streaks or wavelet analysis.

Here we are not interested in Insitu measurement; we use FFT to calculate the wind speed by applying the algorithm. Which is

Where U10 is the wind speed at 10 m above the surface and c is the azimuth cut-off wave length (back scatter co-eff).

CMOD is capable of providing wind speed from2 -26 m/s with an overall accuracy of +/- 2m/s. The obtained speed is the speed of wind at a height of 10m from the surface on earth.

Alternatively the wind direction can be calculated from the algorithm by providing wind speed details. Wind speed can be obtained by,

  • Insitu measurements.
  • Scatterometer data.

CMOD4 model provides s0 values as a function of relative wind direction (=0 for a wind blowing toward the radar), wind speed and incidence angle, expressed as

The coefficients B0, B1 and B2 depend on the local incidence angle of the radar beam and wind speed. The accuracy in the model is 20° in relative wind direction.

# The output thus obtained gives the wind field at that area i.e., wind field is the combination of wind direction and wind speed at a height of 10m from surface.

# This is then extrapolated to find the wind speed at varying heights by applying the logic that wind speed various logarithmically with respect to height

Thus, obtaining a wind field map.


Figure2: wind speed map over that area.

OPTICAL IMAGE PROCESSING: The optical image is mainly used for the interpretation of the elements. Here, it is done by using supervised & unsupervised classification. The typical land features are marked and the suitability of the land is analyzed. The processes involved are

  • Image interpretation.
  • Classification (supervised & unsupervised).
  • Suitability studies.

GIS PROCESSING:

1)Creation of DEM:
The DEM can be generated by using contour map of that area in ArcGIS. From the DEM, the terrain modeling is done & the hub height are calculated as follows,

Hub height above MSL = optimal wind potential height – terrain height. (above MSL)

The process involved in this are,

  • Creating TIN (triangular irregular network) for the contour map of that area.
  • Creating DEM from the TIN.
  • Studying the characteristics of terrain with respect to wind parameters.

LAND EFFECTIVENESS ANALYSIS:
This step involves the analysis of various attributes like soil nature, coral reefs, ocean currents, earthquakes, etc., in case of offshore. And while considering onshore the attributes include productivity, land usage, soil nature, and mainly cost of the land depending on the above said parameters.

All such parameters are considered and effective sites are ranked accordingly. This attribute table when linked to the attributes table developed for the wind potential map provides a overall statistics. By selecting the zones of our requirements, typical wind potential zones can be extracted.

OUTPUT image:


Figure 3: this the final map extracted from the process showing the wind potential over the Denmark seas.

OTHERS:

1) WAKE ANALYSIS:
1 Wind maps retrieved from airborne and spaceborne SAR were used to quantify the wake effect downstream of large offshore wind farms. Information on the magnitude and spatial extend of wind wakes is important for the siting of wind farms in clusters. Moreover, the information is needed in environmental impact studies. This can be seen by analysing the following image,

2)BATHYMETRY:
As the sub surface of sea affects the back scatter due to the effect of corral reef, it should be studied and treated accordingly.

3)CONTINUOUS MONITORING:
It is a well known fact that the satellite which is used to select the site will revisit the site at regular period of intervals and thus there is no need of special monitoring agent. Also this methodology can be again used to predict the current status of the site.

CONCLUSION:
The accuracy on SAR-derived wind speed maps by careful processing is less than the ±2ms-1 and the accuracy on wind direction retrieval is around ±20°. The number of satellite scenes (samples) that would give an estimation of the mean wind speed within ±10% of the true value at the 90% confidence level (assuming no error in the SAR wind speed maps) is around 60-70 scenes. The uncertainty in other parameter estimates is larger and hence caution must be used in use of SAR for offshore wind resource feasibility studies. Nevertheless many technical issues have been resolved with respect to the accuracy of wind speeds from analysis of individual scenes and the future may yield important advances with respect to asymmetric errors associated with SAR wind speed retrievals.

REFERENCE:

  • www.esa.int
  • Introduction to digital image processing – by Jensen
  • Introduction to concepts of GIS processing – by C.P. Lou
  • Google