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Wind characteristics and wind energy assessment in the Barents Sea based on ERA-Interim reanalysis Cover

Wind characteristics and wind energy assessment in the Barents Sea based on ERA-Interim reanalysis

Open Access
|Dec 2018

Figures & Tables

Figure 1

Geographical location of the Barents Sea and the sea area within the inner sector is the selected domain for the statistical study
Geographical location of the Barents Sea and the sea area within the inner sector is the selected domain for the statistical study

Figure 2

Annual average, mean monthly maximum (April), minimum (September) distributions of the sea ice concentration and the ice zone partitions. In the ice-free zone, the points labeled from P1 to P1 are selected for further wind climate and energy analysis.
Annual average, mean monthly maximum (April), minimum (September) distributions of the sea ice concentration and the ice zone partitions. In the ice-free zone, the points labeled from P1 to P1 are selected for further wind climate and energy analysis.

Figure 3

Spatial distributions of annual average wind speed at 10 m
Spatial distributions of annual average wind speed at 10 m

Figure 4

Spatial distributions of mean seasonal wind speed at 10 m from winter to autumn
Spatial distributions of mean seasonal wind speed at 10 m from winter to autumn

Figure 5

Wind roses from P1 to P7 in the Barents Sea. The color scale represents wind speed classification
Wind roses from P1 to P7 in the Barents Sea. The color scale represents wind speed classification

Figure 6

Spatial distributions of annual average wind speed and power density at 100 m
Spatial distributions of annual average wind speed and power density at 100 m

Figure 7

Spatial distributions of mean seasonal wind speed at 100 m from winter to autumn
Spatial distributions of mean seasonal wind speed at 100 m from winter to autumn

Figure 8

Spatial distributions of mean seasonal wind power density at 100 m from winter to autumn
Spatial distributions of mean seasonal wind power density at 100 m from winter to autumn

Figure 9

Interannual variations of annual average wind power density at 100 m from P1 to P7 in 1996–2015
Interannual variations of annual average wind power density at 100 m from P1 to P7 in 1996–2015

Figure 10

Power output curves for wind turbine generators
Power output curves for wind turbine generators

Figure 11

A. Fitting probability density curves and cumulative probability density curves of the annual maximum wind speed series from P1 to P4; B. Fitting probability density curves and cumulative probability density curves of the annual maximum wind speed series from P5 to P7
A. Fitting probability density curves and cumulative probability density curves of the annual maximum wind speed series from P1 to P4; B. Fitting probability density curves and cumulative probability density curves of the annual maximum wind speed series from P5 to P7

Figure 12

Spatial distributions of extreme wind speeds in 50-year and 100-year return periods based on the P-III distribution function
Spatial distributions of extreme wind speeds in 50-year and 100-year return periods based on the P-III distribution function

Four extreme value distribution functions

Distribution typeProbability density distribution function f(x)Parameters
GEV 1α[ 1(vμα) ]1/β-1exp{ -[ 1(vμα) ]1/β } $\frac{1}{\alpha }{{\left[ 1\text{- }\!\!\beta\!\!\text{ }\left( \frac{v-\mu }{\alpha } \right) \right]}^{{1}/{\text{ }\!\!\beta\!\!\text{ -1}}\;}}\exp \left\{ \text{-}{{\left[ 1\text{- }\!\!\beta\!\!\text{ }\left( \frac{v-\mu }{\alpha } \right) \right]}^{{1}/{\text{ }\!\!\beta\!\!\text{ }}\;}} \right\}$μ-location parameter α-scale parameter β-shape parameter
Gumbel 1αexpμ[ (vα)expμ(vα) ] $\frac{1}{\text{ }\!\!\alpha\!\!\text{ }}\overset{\text{ }\!\!\mu\!\!\text{ }}{\mathop{\exp }}\,\left[ -\left( \frac{v-}{\text{ }\!\!\alpha\!\!\text{ }} \right)\overset{\text{ }\!\!\mu\!\!\text{ }}{\mathop{-\exp }}\,\left( -\frac{v-}{\text{ }\!\!\alpha\!\!\text{ }} \right) \right]$μ-location parameter α-scale parameter
Weibull αβ(vμ)α1exp[ (vμ)αβ ]xμ $\frac{\text{ }\!\!\alpha\!\!\text{ }}{\text{ }\!\!\beta\!\!\text{ }}{{\left( v-\text{ }\!\!\mu\!\!\text{ } \right)}^{\text{ }\!\!\alpha\!\!\text{ }-1}}\exp \left[ -\frac{{{\left( v-\text{ }\!\!\mu\!\!\text{ } \right)}^{\text{ }\!\!\alpha\!\!\text{ }}}}{\text{ }\!\!\beta\!\!\text{ }} \right]\,\,\,\,\,\,\,\,\,\,\,\,\,\,x\ge \text{ }\!\!\mu\!\!\text{ }$μ-location parameter β-shape parameter α-scale parameter
P-III βαΓ(α)(v- μ)α-1e(xμ)v3μ,α>0 $\frac{{{\text{ }\!\!\beta\!\!\text{ }}^{\text{ }\!\!\alpha\!\!\text{ }}}}{\Gamma \left( \text{ }\!\!\alpha\!\!\text{ } \right)}{{\left( v\,\text{- }\!\!\mu\!\!\text{ } \right)}^{\text{ }\!\!\alpha\!\!\text{ -1}}}{{e}^{\text{- }\!\!\beta\!\!\text{ }\left( x-\text{ }\!\!\mu\!\!\text{ } \right)}}\,\,\,{{v}^{3}}\text{ }\!\!\mu\!\!\text{ , }\!\!\alpha\!\!\text{ }\,\text{}\,\text{0}$μ-location parameter β-shape parameter α-scale parameter

Comparisons of the K-S test and RMSE between GEV, Gumbel, Weibull and P-III distributions

DistributionsK-S testMeanMaximumMinimum
RMSERMSERMSE
GEV99.97%0.04840.17000.0210
Gumbel99.96%0.05910.13530.0174
Weibull98.56%0.04730.29570.0191
P-III100%*0.0434*0.1061*0.0153*

Different return values of extreme wind speeds (m s−1) based on Gumbel and P-III distributions from P1 to P7 for 50-year and 100-year return periods; higher extreme speeds are marked with *

PointsDistribution5-year10-year25-year50-year100-year
P1Gumbel P-III22.62 22.5923.17 23.3924.32 24.2725.18* 24.8526.03* 25.38
P2Gumbel P-III22.35 22.4723.41 23.2724.75 24.1525.74* 24.7326.73* 25.26
P3Gumbel P-III22.43 22.8623.33 23.7324.48 24.6825.33* 25.3026.17* 25.88
P4Gumbel P-III21.89 22.1622.73 22.9723.78 23.8724.57* 24.4925.34* 25.06
P5Gumbel P-III21.57 21.7822.45 22.5823.56 23.4924.38* 24.1125.20* 24.68
P6Gumbel P-III21.65 21.7622.62 22.5823.85 23.5224.76* 24.1625.67* 24.76
P7Gumbel P-III22.26 22.7123.26 23.6624.52 24.7125.46* 25.4026.39* 26.03

Locations of the selected points and 100 m annual average wind speeds, wind power density (WPD) and net electric energy output from P1 to P7

PointsLongitude (N)Latitude (E)Mean speed (m s−1)WPD (W m−2)Electric energy (MWhy)
CCWE3000DSL500
P1227410.23110814 38423 534
P222729.99104114 00222 855
P3327410.26109714 53523 776
P4357210.10104614 31223 390
P542739.9198514 01122 873
P642719.9599114 12123 064
P737709.96100214 11823 079

Parameters of model wind turbine generators for CCWE3000D and SL500

WTGsRated power (kW)Hub height (m)Rotor diameter (m)Cut-in wind speed (m s−1)Rated wind speed (m s−1)Cut-out wind speed (m s−1)
CCWE3000D300010010331225
SL50050001001283.512.525
DOI: https://doi.org/10.1515/ohs-2018-0039 | Journal eISSN: 1897-3191 | Journal ISSN: 1730-413X
Language: English
Page range: 415 - 428
Submitted on: Feb 2, 2018
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Accepted on: Apr 26, 2018
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Published on: Dec 3, 2018
Published by: University of Gdańsk
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2018 Chenglin Duan, Zhifeng Wang, Sheng Dong, Liao Zhenkun, published by University of Gdańsk
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.