• Journal of the Korean Solar Energy Society
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• v.31 no.6
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• pp.72-77
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• 2011

The purpose for installation of offshore weather station is a measurement of wind resources and so on. If weather station is operated, it will be possible to analysis for wind resource and arrangement of wind farm by using measured data. In this paper, we carried out the development of offshore wind resource measurement system for measuring offshore wind resource. Also, In order to monitor for real-time wind data with 1 Hz, we installed the wireless transmission system. All wind characteristic data are sent to the server PC through the this system is connected outport of DataLogger. Transmitted wind data were used in order to look at in the Web-page and tablet PC on a real time basis in a graph. In this paper, we will introduce about the wind resource measurement and remote monitoring system that is the result of study.

• New & Renewable Energy
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• v.16 no.1
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• pp.15-24
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• 2020

For mechanical load testing of wind turbines, capture matrix is constructed for various range of wind speeds according to the international standard IEC 61400-13. The conventional method wastes considerable amount of data by its invalid data policy -segment data into 10 minutes then remove invalid ones. Previously, we have suggested an alternative way to save the total amount of data to build a capture matrix, but the efficient selection of data has been still under question. The paper introduces optimization algorithms to construct capture matrix with less data. Heuristic algorithm (simple stacking and lowest frequency first), population method (particle swarm optimization) and Q-Learning accompanied with epsilon-greedy exploration are compared. All algorithms show better performance than the conventional way, where the distribution of enhancement was quite diverse. Among the algorithms, the best performance was achieved by heuristic method (lowest frequency first), and similarly by particle swarm optimization: Approximately 28% of data reduction in average and more than 40% in maximum. On the other hand, unexpectedly, the worst performance was achieved by Q-Learning, which was a promising candidate at the beginning. This study is helpful for not only wind turbine evaluation particularly the viewpoint of cost, but also understanding nature of wind speed data.

• Journal of the Korean Solar Energy Society
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• v.37 no.5
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• pp.1-11
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• 2017

A study on Nacelle Lidar (Light detection and ranging) measurement error and the data reliability verification was carried out at Haengwon wind farm on Jeju Island. For measurement data error processing, the characteristics of Nacelle Lidar measurements were analyzed by dividing into three parts, which are weather conditions (temperature, humidity, atmosphere, amount of precipitation), mechanical movement (rotation of wind turbine blades, tilt variation of Nacelle Lidar) and Nacelle Lidar data availability. After processing the measurement error, the reliability of Nacelle Lidar data was assessed by comparing with wind data by an anemometer on a met mast, which is located at a distance of 200m from the wind turbine with Nacelle Lidar. As a result, various weather conditions and mechanical movement did not disturb reliable data measurement. Nacelle Lidar data with availability of 95% or more could be used for checking Nacelle Lidar wind data reliability. The reliability of Nacelle Lidar data was very high with regression coefficient of 98% and coefficient of determination of 97%.

• 한국신재생에너지학회:학술대회논문집
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• 2006.11a
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• pp.281-285
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• 2006

As the needs of wind energy increase, the more sites for the wind farm are required. As a part of searching for the prominent wind farm site, specially for offshore wind farm, we chose 4 sites along the southern part seashore of JeonNam province based on the analysis of the data gathered by meteorological observatory ud have gathered wind data for more than a year by use of 40m Met masts installed in the representative locations, ie. small islands of 4 different bay area. The siting for the Met masts were very limited by the geographical circumstances The wind data of those areas show a little lower annual average wind speeds, for the wind farm development, of 4m/s to 5.5m/s at the height of 40m above the ground level of the respective islands. The detail figures of one year wind data of those area are presented in this report.

• Wind and Structures
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• v.28 no.4
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• pp.239-253
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• 2019

Accurate numericalsimulation of wind field over complex terrain is an important prerequisite for wind resource assessment. In this study, numerical simulation of wind field over complex terrain was further carried out by taking the complex terrain around Siu Ho Wan station in Hong Kong as an example. By artificially expanding the original digital model data, Gambit and ICEM CFD software were used to create high-precision complex terrain model with high-quality meshing. The equilibrium atmospheric boundary layer simulation based on RANS turbulence model was carried out in a flat terrain domain, and the approximate inflow boundary conditions for the wind field simulation over complex terrain were established. Based on this, numerical simulations of wind field over complex terrain under different inflow wind directions were carried out. The numerical results were compared with the wind tunnel test and field measurement data for land and sea fetches. The results show that the numerical results are in good agreement with the wind tunnel data and the field measurement data which can verify the accuracy and reliability of the numerical simulation. The near ground wind field over complex terrain is complex and affected obviously by the terrain, and the wind field characteristics should be fully understood by numerical simulation when carrying out engineering application on it.

• Journal of Wind Energy
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• v.4 no.1
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• pp.39-45
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• 2013

Massive offshore wind projects of have recently been driven in full gear on the Western Offshore of Korea including the 2.5 GW West-Southern Offshore Wind Project of the Ministry of Trade, Industry and Energy, and the 5 GW Offshore Wind Project of the Jeollanamdo Provincial Government. On this timely occasion, this study performed a general wind resource assessment on the Western Offshore by using the MERRA reanalysis data of temporal-spatial resolution and accuracy greatly improved comparing to conventional reanalysis data. It is hard to consider that wind resources on the Western Sea are excellent, since analysis results indicated the average wind speed of 6.29 ± 0.39 m/s at 50 m above sea level, and average wind power density of 307 ± 53 W/m². Therefore, it is considered that activities shall be performed for guarantee economic profits from factor other than wind resources when developing an offshore wind project on the Western Offshore.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1548-1555
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• 2016

This paper presents an application of time series analysis in hourly wind speed simulation and forecast in Jeju Island, Korea. Autoregressive - moving average (ARMA) model, which is well in description of random data characteristics, is used to analyze historical wind speed data (from year of 2010 to 2012). The ARMA model requires stationary variables of data is satisfied by power law transformation and standardization. In this study, the autocorrelation analysis, Bayesian information criterion and general least squares algorithm is implemented to identify and estimate parameters of wind speed model. The ARMA (2,1) models, fitted to the wind speed data, simulate reference year and forecast hourly wind speed in Jeju Island.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.3
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• pp.190-196
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• 2015

The estimation method of the sea wind information using the nearby land wind data have been widely used. However, it is insufficient to examine the limitation of the method based on the characteristics of the wind data. In this study, the characteristics of the wind data are analysed and compared to check the limitation of the existing conventional method. The data are observed at the same time period in the land and sea stations in Pohang coastal zone. In particular, the analysis are focused on the direction data simply overlooked in the analysis target. The method is suggested as a useful tool for the various analysis of the wind direction data. The results show that the statistical informations between the land and sea wind data are quite different though the lineal distance between stations are not large (${\fallingdotseq}3.8km$). The difference is attributed to come from the geometrical gradient and elevation difference between land and sea areas. As a consequence, the quantitative estimation error should be checked preliminarily using the land-sea monitoring data sets because the sea wind estimation using land data is essentially unacceptable.

• Journal of the Korean Solar Energy Society
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• v.38 no.5
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• pp.11-25
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• 2018

The Ln-least method is commonly used to estimate the Weibull parameters from the observed wind speed data. In previous studies, the bin method has been used to calculate the cumulative frequency distribution for the Ln-least method. The purpose of this study is to obtain better performance in the Ln-least method by applying probability plotting position(PPP) instead of the bin method. Two types of the wind speed data were used for the analysis. One was the observed wind speed data taken from three sites with different topographical conditions. The other was the virtual wind speed data which were statistically generated by a random variable with known Weibull parameters. Also, ten types of PPP formulas were applied which were Hazen, California, Weibull, Blom, Gringorten, Chegodayev, Cunnane, Tukey, Beard and Median. In addition, in order to suggest the most suitable PPP formula for estimating Weibull parameters, two accuracy tests, the root mean square error(RMSE) and $R^2$ tests, were performed. As a result, all of PPPs showed better performances than the bin method and the best PPP was the Hazen formula. In the RMSE test, compared with the bin method, the Hazen formula increased estimation performance by 38.2% for the observed wind speed data and by 37.0% for the virtual wind speed data. For the $R^2$ test, the Hazen formula improved the performance by 1.2% and 2.7%, respectively. In addition, the performance of the PPP depended on the frequency of low wind speeds and wind speed variability.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.278-281
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• 2008

In these days the installation of wind turbines or wind parks includes a high financial risk. So for the planning and the constructing of wind farms, long-term data of wind speed and wind direction is required. However, in most cases only few data are available at the designated places. Traditional Measure-Correlate-Predict (MCP) can extend this data by using data of nearby meteorological stations. But also Neural Networks can create such long-term predictions. The key issue of this paper is to demonstrate the possibility and the quality of predictions using Neural Networks. Thereto this paper compares the results of different MCP Models and Neural Networks for creating long-term data with various indexes.