An assessment of wind energy potential at the demonstration offshore wind farm in Korea

Oh, Ki‐Yong; Kim, Ji Young; Lee, Jae Kwan; Ryu, Moo Sung; Lee, Jun Shin

doi:10.1016/j.energy.2012.07.056

Cited by 97 publications

(37 citation statements)

References 11 publications

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“…Turbulence can be considered as the arbitrary wind speed fluctuations imposed on the mean wind speed, which occurs in all three 3 directions: longitudinal, tangential and vertical. It is important to note that existence of turbulence decreases the power and causes a fatigue stress in the wind turbine (Oh et al, 2012;Mirhosseini et al, 2011).…”

Section: Turbulencementioning

confidence: 99%

Evaluation of a wind farm project for a smart city in the South-East Coastal Zone of Mauritius

Dhunny

Lollchund

Rughooputh

2016

J. energy South. Afr.

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Section: Turbulencementioning

confidence: 99%

Evaluation of a wind farm project for a smart city in the South-East Coastal Zone of Mauritius

Dhunny

Lollchund

Rughooputh

2016

J. energy South. Afr.

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“…For the wind shear exponent, α the value of 0.097 to 0.0147 is used in the sea, which is measured by met mask tower over the study sea [34]. The wind energy density is calculated by Equation (3):…”

Section: Model Evaluationmentioning

confidence: 99%

Offshore Wind Speed Forecasting: The Correlation between Satellite-Observed Monthly Sea Surface Temperature and Wind Speed over the Seas around the Korean Peninsula

Kim

Kang

2017

Energies

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Abstract:Wind power forecasting is a key role for large-scale wind power penetration on conventional electric power systems by understanding stochastic nature of winds. This paper proposes an empirical statistical model for forecasting monthly offshore wind speeds as a function of remotely sensed sea surface temperatures over the seas around the Korean Peninsula. The model uses the optimal lagged multiple linear regression method, and predictors are characterized by mixed periodicities derived from the autocorrelation between spatially variable satellite-observed sea surface temperatures and wind speeds at all grid points over a period of about ten years (2001 to 2008). Offshore wind speeds were found to be correlated with sea surface temperatures within a seasonal range of two-to four-month lags. In particular, offshore wind speeds were closely associated with the sea surface temperature at lag 4 M, followed by lag 3 M and lag 2 M. Correlation is less at lag 1 M as compared lag 2 M, lag 3 M and lag 4 M. The results demonstrate that this approach successfully produces accurate depictions of monthly wind speeds at the gridded network. The hindcast offshore wind speeds and wind power density showed slightly improved skills compared to the seasonally varying climatology with the value of root-mean square errors, +18% and +23%, respectively. The spatial distributions of the monthly gridded wind speed and wind power density remained fairly stable from one month to another, whereas individual regions displayed slight differences in variability. The results of this study are expected to be useful in establishing guidelines for operating and managing nascent offshore farms around the Korean Peninsula.

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“…Based on Reference [28], we defined the number, types, and locations of the senor nodes and measuring instruments installed at the met-mast, as shown in Figure 6 Table 6. The acoustic doppler current profile (ADCP) is used to measure the current direction and velocity at sea level.…”

Section: Traffic Model For Meteorological Mastmentioning

confidence: 99%

Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems

Ahmed

Kim

2017

Applied Sciences

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As we move toward increasing the grid integration of large-scale wind farms (WFs), reliable monitoring, protection, and control are needed to ensure grid stability. WFs are considered to be large and complex cyber physical systems owing to coupling between the electric power system and information and communication technologies (ICT). In this study, we proposed a framework for a cyber physical wind energy system (CPWES), which consists of four layers: a WF power system layer, data acquisition and monitoring layer, communication network layer, and application layer. We performed detailed network modeling for the WF system, including the wind turbines, meteorological mast (met-mast), and substation based on IEC 61400-25 and IEC 61850 standards. Network parameters and configuration were based on a real WF (Korean Southwest offshore project). The simulation results of the end-to-end delay were obtained for different WF applications, and they were compared with the timing requirements of the IEC 1646 standard. The proposed architecture represents a reference model for WF systems, and it can be used to enable the design of future CPWESs.

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An assessment of wind energy potential at the demonstration offshore wind farm in Korea

Cited by 97 publications

References 11 publications

Evaluation of a wind farm project for a smart city in the South-East Coastal Zone of Mauritius

Evaluation of a wind farm project for a smart city in the South-East Coastal Zone of Mauritius

Offshore Wind Speed Forecasting: The Correlation between Satellite-Observed Monthly Sea Surface Temperature and Wind Speed over the Seas around the Korean Peninsula

Communication Architecture for Grid Integration of Cyber Physical Wind Energy Systems

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