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All Journal International Journal of Renewable Energy Development International Journal of Renewable Energy Development
Ko, Kyungnam
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Chemistry Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Investigation of wind veer characteristics on complex terrain using ground-based lidar Tumenbayar, Undarmaa; Ko, Kyungnam
International Journal of Renewable Energy Development Vol 13, No 1 (2024): January 2024
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2023.56352

Abstract

The wind direction shift with height significantly influences wind turbine performance, particularly in relation to terrain conditions. In this work, wind conditions at 12 measurement heights ranging from 40 m to 200 m using a ground lidar, Windcube V2, installed on a 16 m tall building were analysed to examine the characteristics of wind veer angles in complex terrain. The measurement campaign was carried out from January 1st to December 31st, 2022, in the southeastern part of South Korea. The terrain complexity around the ground lidar system was evaluated using the ruggedness index (RIX), whose result was 14.06 percent corresponding to complex terrain. The ground lidar measurements were compared with mesoscale data, EMD-WRF South Korea, for the data accuracy check. Wind veer frequencies and wind roses were derived to identify directional shifts with height. Furthermore, diurnal, monthly, and seasonal variations of wind veer characteristics were analysed. Wind shear exponent factor (WSE) and turbulence kinetic energy (TKE) were calculated, and wind veer profiles were constructed based on these parameters. The relative errors of wind speeds were analysed for rotor equivalent wind speed (REWS) and hub height wind speed (HHWS), with REWS with wind veer correction, REWSveer, as a reference. Additionally, atmospheric stability conditions were classified using WSE and TKE, and the vertical changes in wind veering were analysed according to the stability conditions. The findings reveal lower wind speeds exhibited larger wind veer values and fluctuations. The relative errors for the REWS and the HHWS were 0.04 % and 0.20 % on average, respectively. The study demonstrates that terrain conditions significantly impacted wind veer angles at heights below 100 m, whereas the influence diminished with increasing height above 100 m. The results could be helpful for wind farm developers to make decisions on the siting as well as the hub height of wind turbines on complex terrain
Influence of missing wind measurements on wind turbine power production using various measure-correlate-predict methods and reanalysis datasets Amarzaya, Buyankhishig; Ko, Kyungnam
International Journal of Renewable Energy Development Vol 14, No 6 (2025): November 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61557

Abstract

This study analyzes the impact of missing wind data points on the accuracy of Annual Energy Production (AEP) estimation in wind resource assessment (WRA). Evaluations were made under different scenarios using various Measure-Correlate-Predict (MCP) methods and reanalysis datasets. One year of wind measurements was collected from an inland met mast located in the Gashiri area of Jeju Island, South Korea. Three types of long-term reanalysis datasets- ERA-5, MERRA-2 and WRF (ERA-5)- were obtained, each exhibiting different levels of correlations with the met mast wind measurements. To simulate missing data points scenarios, a yearly percentage sampling method was applied to the one-year met mast wind data with sampling rates ranging from 10% to 90%. To ensure statistical reliability, random sampling was performed 12 times for each sampling rate. The MCP method was applied after pairing each sampled dataset with the reanalysis datasets. Long-term predictions were generated using four MCP approaches- two machine learning techniques (Random Forest and Gradient Boosting Regression) and two traditional methods (Regression and Matrix). AEP was calculted from these predictions and compared to the reference AEP derived from the complete dataset. Results show that accurate AEP estimation remained feasible even when using reanalysis datasets with low correlation to the measured data. Moreover, all four MCP methods demonstrated similar performance, with machine learning–based approaches producing results comparable to those of traditional methods. While conventional WRA practice recommends a data recovery rate above 90% for accurate AEP estimation, this study demonstrated reliable AEP estimates could be achieved with rates as low as 50%.
Co-Authors Amarzaya, Buyankhishig Tumenbayar, Undarmaa