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Site Suitability Assessment for Particular Wind Turbines in Furillen, Gotland Island, Sweden

Submitted to Bahri Uzunoglu

as part Wind Turbine Concepts and Applications course

Uppsala University

Dept. of Earth Sciences, Campus Gotland

Orkhan Baghirli

11/14/2014

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Project Summary

In this assignment, site suitability checks are performed for the given wind turbines in Gotland

Island, Sweden. For this purpose, several procedures are followed:

1. Downloading wind statistics data from available online sources throughout the whole

island.

2. Performing correlation between these wind resources and defining those which show

decent linear relationship and generating Wind Rose Map.

3. Defining borders of Gotland pertaining to roughness and height contour coverages.

4. Pairing downloaded wind resources as long-term reference sources and short-term mast

data all over the island

5. Performing “Measure/Correlate/Predict” (MCP) for each of these pairs to correct the

shot-term site data to long-term reference data using Linear Regression method and Wind

Index method.

6. Performing site compliance checks for the given turbines using the best pair of wind

statistics data that represent the similar climatic properties with that of wind turbine

locations.

7. Delivering WindPro project file together with supplementing reports.

All of these points mentioned above are completed and delivered as a single file upon request.

1.0 Introduction

Main objective of this report is to assess the site suitability for the given wind turbines. WindPro

coupled with WAsP calculations are chosen as a working environment upon availability of the

software. During the different stages of the project, Google Earth became handy to define the

boundaries of Island, to convert between different coordinate systems and to verify the location

of wind turbines on their natural habitat. Rest of this paper will guide you step by step to the final

stage of the project. This project is a part of teamwork and individual work plan.

1.1 Map of Gotland

Map of Gotland is chosen as it is shown below using Google Earth.

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1.2 WindPro Coordinate system

WindPro coordinate system is configured as shown below.

1.3 Site Center

Site Center data is defined as following.

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2.0 Wind Statistics

All of the wind statistics data available online for Gotland have been downloaded. Total number

of 40 meteo objects used are shown below.

2.1 Downloaded Meteo Objects

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2.2 Meteo Objects on map

3.0 Correlation matrix and Wind Rose map

Correlation matrix and wind rose maps play essential role in decision making procedure to assess

the accuracy of wind measurement taken at one site compared to others. Using 40 different wind

resources, 40x40 correlation matrix is generated and wind roses are attached on the map

correspondingly. Matlab is used for this purpose.

3.1 Correlation matrix

Due to the size of the matrix, readable version will be submitted as a separate file.

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3.2 Wind Rose Map

Wind Rose maps show the dominant direction of the wind over the whole island and is also used

to choose the suitable reference wind data for short term correction.

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4.0 Long term reference and short term site data preferences

Wind data resources are narrowed down to 10+1 pairs of long and short term data. Objective is

to identify pairs of data with decent correlation coefficients, existence of concurrent data, nice

alignment of wind roses, good coverage of different regions of the island. Extra one pair is added

to help me to express my reasoning better for the individual part of this assignment.

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5.0 Roughness and Height contour coverage maps

Roughness and height contour maps together with paired wind statistics data define the

boundaries of Gotland. Both maps are downloaded online using “Insert Line data” and “Insert

Area data” WindPro objects.

5.1 Roughness map

Roughness map covers all of the island with the parameters of 150,000 m length, 150,000 width

and 0.1 background roughness as an offset value.

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5.2 Height contour map

In the same manner, also the height contour map covers all of the island. Same length and width

parameters in roughness map are used.

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6.0 MCP

MCP is the abbreviation for Measure-Correlate-Predict techniques, which is widely in use for

establishing a long-term wind statistic using limited wind data from the local site and long-term

data from a nearby site. The task of any estimation of a long-term wind statistic is to establish a

transfer model between the available short-term local data and the concurrent data from a long-

term reference data set (WindPRO Manual, 2013).

6.1 Linear Regression method

The Linear Regression tool enables the user to inspect the fit directly through an animated graph.

If the fit is not satisfactory, a wide range of parameters may be fine-tuned to provide a better fit.

The regression tool is not limited to linear regression, but also higher order polynomials may be

used in modeling wind speeds and wind veer (WindPRO Manual, 2013).

6.2 Wind Index Method

The index correlation method is a method typically making the MCP analysis by using monthly

averages of the energy yield, thus disregarding the directional distribution of the winds. Even

though this method may seem rather crude and primitive when comparing to other more

advanced MCP methods, it has its advantages in stability and performance even in the cases

where other MCP methods seem to fail. The Wind Index MCP method in WindPRO offers the

opportunity to calculate the wind indexes using real power curves from the wind turbines

included in the wind turbine catalogue in WindPRO (WindPRO Manual, 2013).

6.3 Best Qualified Pair

MCP is performed for all of the 11 pre-identified long-term and short-term data. For the

convenience, figures only for the best qualified set of data are presented here. For the rest of

MCP calculations refer to Appendix A: MCP.

Selection of the best qualified pair out of 11 different combinations are done based on the criteria

such as correlation coefficients, wind roses, proximity of the met masts. Best correlation is

observed in the 7th pair: CFSR E18.75 N57.606 and CFSR E18.50 N58. Corresponding cofactors

are: Linear Regression Method: 0.9918, Wind Index Method: 0.9914. However, there is another

pair that represents the strategic location of wind turbines better than this option. Therefore, best

qualified option used in this project is the 11th pair: VisbyFlygplats_METAR_N57.670_E18.350

(long term)/ CFSR2_E18.818_N57.751 (short term) with the correlation coefficient parameters

of: Linear Regression Method: 0.9386, Wind Index Method: 0.9207.

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Qualified Long term and long term data

As it is shown from the figure, VisbyFlygplats_METAR_N57.670_E18.350 (leftmost black

circle) represents the similar boundary layer conditions to that of wind turbines in terms of their

proximity to the sea. Both VisbyFlygplats_METAR_N57.670_E18.350 (long term) and turbine

location are affected by the wind coming from the sea, so they are expected to have the similar

wind profiles. Furthermore, as a short term site mast, CFSR2_E18.818_N57.751 (middle black

circle) is chosen due to its proximity to the turbines. Results of MCP for this pair:

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Visby_Flygplats_METAR_N57.670_E18.350 / CFSR2_E18.818_N57.751

Initial Correlation

Linear Regression method

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Corrected and Predicted short term data

Wind Index Method

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7.0 Site Suitability

Purpose of this module is to check if a particular turbine class complies with the site and layout

conditions. Site compliance module calculates the seven main checks required for site

assessments: Terrain Complexity, Extreme Wind, Effective Turbulence, Wind Distribution,

Wind Shear Flow Inclination, Air density. Three additional checks are also included:

Temperature range, Seismic hazard, Lightning rate.

To start with the calculations, main panel is set as shown below:

Then, long term and short term data are chosen in the “Mast data” complying with best qualified

pair. In the “WTG” panel, assigned two wind turbines are chosen (Slitevind 11 and 12, both

Enercon E40/500, power 500kW, diameter 40m, hub height 50m). Following this, mast data for

each WTG is chosen. Long term corrected short term data (closest one) is chosen by default.

Afterwards WAsP calculations are performed. A screenshot from site compliance report is given

below (full version is available separately in a submitted file or exported project file from

WindPro).

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Snapshot from Site Suitability report

As it seen from the site suitability checks, there are no observed critical issues. Some

adjustments are done on extreme wind calculations. Default mode extreme wind calculations use

POT-N/Gumbel fit method. Results from this run is overestimated. According to this method,

over 50 m/s wind gusts are expected, however maximum wind speed observed in a long term

data doesn’t exceed 20 m/s. Therefore, to represent better and more realistic approach, Weibull

method is used for extreme wind calculations. As a result of these calculations, it can be

concluded that site is suitable for the given wind turbines.

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Appendices

Appendix A: MCP Calculations

1. CFSR E19.00 N57.5 and CFSR2 E18.614 N57.457 [S11 & S14]

Correlation

Linear Regression

Corrected short term data

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Wind Index method

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2. CFSR E18.5 N58 and CFSR2 E18.614 N57.751 [S8 & S15]

Correlation

Linear Regression method

Corrected short term data

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Wind Index method

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3. SYNOP02-589 E18.430 N57.730 and CFRS2 E18.614 N57.751 [S37 & S15]

Correlation

Linear Regression method

Corrected short term data

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Wind Index method

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4. SYNOP02-595 E18.470 N57.480 and CFSR E18.5 N57.5 [S38 & S7]

Correlation

Linear Regression method

Corrected short term data

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Wind Index method

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5. EmdERA E18.278 N57.193 and SYNOP02-595 E18.470 N57.480 [S17 & S38]

Correlation

Linear Regression method

Corrected short term data

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Wind Index method

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6. VISBY FLYPLATS E18.35 N57.67 and CFSR2 E18.614 N57.751 [S40 & S16]

Correlation

Linear Regression method

Corrected short term data

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Wind Index method

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7. CFSR E18.75 N57.606 and CFSR E18.50 N58 [S9 & S16]

Correlation

Linear Regression method

Corrected short term data

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Wind Index method

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8. MERRA BASIC E18.668 N57.5 and CFSR2 E18.614 N57.547 [S23 & S14]

Correlation

Linear Regression

Corrected short term data

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Wind Index method

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9. SYNOP02-587 E18.950 N57.920 and CFSR E19 N58 [S36 & S12]

Correlation

Linear Regression method

Corrected short term data

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Wind Index method

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10. SNOP 02-589 N57.730 E18.430 and NSCAR Basic N60 E17.50

Correlation

Linear Regression

Corrected short term data

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Wind Index method

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11. Visby_Flygplats_METAR_N57.670_E18.350 / CFSR2_E18.818_N57.751

Correlation

Linear Regression method

Corrected short term data

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Wind Index Method