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In the complex terrain where local wind systems are formed, accurate understanding of regional wind variability is required for the assessment of wind resource. In this paper, cluster analysis based on the similarity of time-series wind vector was applied to derive subregions with similar wind characteristics and the meteorological validity of regi...
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Regionalization based on climatic parameters is one of the most important subjects in climatology. This research studied regionalization based on monthly temperature and precipitation on 16 stations in zagros area with Principal component analysis, Z square, standard deviation and climatic coefficients. Classification in the former three methods ha...
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... The Korean peninsula was selected as the study area; this area has many mountains and a complex coast, as seen in Figure 2. The study area has a complex wind resource distribution, which is appropriate for statistical analysis. 26 Figure 2A shows the location and geography of the study area. ...
The need for high-resolution wind resource maps is increasing with the increase in the supply and development of wind power. Many physical downscaling models have been developed and applied to make these maps. However, as the existing models require extensive computations and time, statistical models with higher efficiency are being studied. Statistical models such as regression and machine learning models can quickly calculate wind resource maps, but they have a problem of low accuracy. This study proposes a machine learning model with new topography-derived variables to interpret the physical characteristics of the wind. As the shape of topography, which was unable to be interpreted in previous studies, can be considered with new derived variables, a significant performance improvement was identified. The analysis was conducted using 1 km Weather Research and Forecasting (WRF) results and ERA5 reanalysis data from South Korea. Two Weibull distribution parameter maps were calculated and used as input and output data. Three collections of derived variables were devised and compared. Therefore, the multi-resolution topography data showed the highest improvements with approximately 15% reduction in root mean square error (RMSE) for both the linear regression and machine learning models. In particular, the land area showed a decrease of 20%. The best proposed models showed an RMSE of 7% and 8% for two Weibull parameters. The results are expected to serve as a reference for continuing research and utilization of statistical models.
... Therefore, the crucial step in wind assessment is to determine the wind resources, which depends on accurate wind-speed modelling [22]. One of these models is the probability density function (PDF) because it provides important wind speed distribution parameters and allows one to determine the Weibull parameters [23]. ...
In line with Mexico’s interest in determining its wind resources, in this paper, 141 locations along the states of the Gulf of Mexico have been analyzed by calculating the main wind characteristics, such as the Weibull shape (c) and scale (k) parameters, and wind power density (WPD), by using re-analysis MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications version 2) data with hourly records from 1980–2017 at a 50-m height. The analysis has been carried out using the R free software, whose its principal function is for statistical computing and graphics, to characterize the wind speed and determine its annual and seasonal (spring, summer, autumn, and winter) behavior for each state. As a result, the analysis determined two different wind seasons along the Gulf of Mexico;, it was found that in the states of Tamaulipas, Veracruz, and Tabasco wind season took place during autumn, winter, and spring, while for the states of Campeche and Yucatan, the only two states that shared its coast with the Caribbean Sea and the Gulf of Mexico, the wind season occurred only in winter and spring. In addition, it was found that by considering a seasonal analysis, more accurate information on wind characteristics could be generated; thus, by applying the Weibull distribution function, optimal zones for determining wind as a resource of energy can be established. Furthermore, a k-means algorithm was applied to the wind data, obtaining three clusters that can be seen by month; these results and using the Weibull parameter c allow for selecting the optimum wind turbine based on its power coefficient or efficiency.
... Most coastal wind farms in South Korea are on the west coast where the prevailing wind direction matches the sea breeze, which makes it possible to use both synoptic wind and sea breeze simultaneously according to the wind zone classification [2]. Among the wind farms built along the west coast, wind turbines were only installed on the inner side of the coastal seawall at the Gunsan and Saemangeum wind farms, as shown in Figure 1. ...
For the purposes of this study, a wind tunnel experiment and a numerical analysis during ebb and high tides were conducted to determine the positive and negative effects of wind flow influenced by a seawall structure on the performance of wind turbines installed along a coastal seawall. The comparison of the wind flow field between a wind tunnel experiment performed with a 1/100 scale model and a computational fluid dynamics (CFD) analysis confirmed that the MP k-turbulence model estimated flow separation on the leeside of the seawall the most accurately. The CFD analysis verified that wind speed-up occurred due to the virtual hill effect caused by the seawall’s windward slope and the recirculation zone of its rear face, which created a positive effect by mitigating wind shear while increasing the mean wind speed in the wind turbine’s rotor plane. In contrast, the turbulence effect of flow separation on the seawall’s leeside was limited to the area below the wind turbine rotor, and had no negative effect. The use of the CFD verified with the comparison with the wind tunnel experiment was extended to the full-scale seawall, and the results of the analysis based on the wind turbine Supervisory Control and Data Acquisition (SCADA) data of a wind farm confirmed that the seawall effect was equivalent to a 1.5% increase in power generation as a result of a mitigation of the wind profile.
... This implies that offshore wind resource assessment using onshore or inland measurement data would cause considerable misinterpretation. According to a wind sector clustering map shown in Figure 6, classified by a surface wind regionalization method proposed by Kim et al. [18], offshore meteorological towers HeMOSU-1 and HeMOSU-2 belong to a different wind sector, whereas Dongho and Gochang belong to the same wind sector along the coast; Julpo, which is located inland, is classified into a separate wind sector. From the analysis with Table 8 and Figure 5, it is conjectured that the intensity of local wind system increases as penetrates into inland from offshore through onshore. ...
... This implies that offshore wind resource assessment using onshore or inland measurement data would cause considerable misinterpretation. According to a wind sector clustering map shown in Figure 6, classified by a surface wind regionalization method proposed by Kim et al. [18], offshore meteorological towers HeMOSU-1 and HeMOSU-2 belong to a different wind sector, whereas Dongho and Gochang belong to the same wind sector along the coast; Julpo, which is located inland, is classified into a separate wind sector. ...
This study evaluated the applicability of long-term datasets among third-generation reanalysis data CFSR, ERA-Interim, MERRA, and MERRA-2 to determine which dataset is more suitable when performing wind resource assessment for the ‘Southwest 2.5 GW Offshore Wind Power Project’, which is currently underway strategically in South Korea. The evaluation was performed by comparing the reanalyses with offshore, onshore, and island meteorological tower measurements obtained in and around the southwest offshore. In the pre-processing of the measurement data, the shading sectors due to a meteorological tower were excluded from all observation data, and the measurement heights at the offshore meteorological towers were corrected considering the sea level change caused by tidal difference. To reflect the orographic forcing by terrain features, the reanalysis data were transformed by using WindSim, a flow model based on computational fluid dynamics and statistical-dynamic downscaling. The comparison of the reanalyses with the measurement data showed the fitness in the following order in terms of coefficient of determination: MERRA-2 > CFSR = MERRA > ERA-Interim. Since the measurement data at the onshore meteorological towers strongly revealed a local wind system such as sea-land breeze, it is judged to be inappropriate for use as supplementary data for offshore wind resource assessment.
... In fact, the wind turbine spacing at wind farms in mountainous Gangwon is much smaller than those at wind farms on the plains of Europe because the ridges mainly run from north to south while the main wind direction is westerly (Figure 9). In other words, when the wind turbines are laid out along ridges from north to south, they form a line running perpendicularly to the main wind direction, so wake loss can largely be avoided even when the wind turbine spacing is set to the minimum under such an arrangement (Kim et al., 2016). ...
In South Korea, where 64% of the national territory is mountainous, good wind resources in inland areas are mostly situated in high mountain regions. To develop a wind farm in a mountain region, the sloping of mountains and their shielding from wind by the surrounding topology should be considered. It is generally most advantageous to install wind turbines along a ridge that is open in all directions. This article presents a methodology for evaluating suitable sites for wind farm development by identifying suitable ridges using morphometric analysis, while excluding the geographical and social environmental exclusion factors and superposing them on the wind resource map to find the area having a specific level or higher wind power density. The result of the proposed suitable site analysis and existing wind farms was assessed to verify the feasibility of the method of analyzing suitable sites on ridges. The wind resource potential in Gangwon Province when calculated using a method based on conventionally suitable site and the method based on ridge analysis was 9 and 5 GW, respectively. The result confirmed that the conventional area-based potential calculation without consideration of the morphometric terrain characteristics overestimated wind resource potential by around 80% compared to the ridge analysis method of calculation presented in this article.
... The wind resource in Seoul is weak mainly due to weak synoptic conditions. According to the wind systems in the Korean Peninsula [14], a synoptic wind system is formed where WNW winds blow to the Seoul region along Han River and northwesterly winds approximate from the west sea. ...
In this paper, a new wind resource assessment procedure for building-integrated wind turbines (BIWTs) is proposed. The objective is to integrate wind turbines at a 555 m high-rise building to be constructed at the center of Seoul, Korea. Wind resource assessment at a high altitude was performed using ground-based remote sensing (RS); numerical weather prediction (NWP) modeling that includes an urban canopy model was evaluated using the remote sensing measurements. Given the high correlation between the model and the measurements, we use the model to produce a long-term wind climate by correlating the model results with the measurements for the short period of the campaign. The wind flow over the high-rise building was simulated using computational fluid dynamics (CFD). The wind resource in Seoul—one of the metropolitan cities located inland and populated by a large number of skyscrapers—was very poor, which results in a wind turbine capacity factor of only 7%. A new standard procedure combining RS, NWP, and CFD is proposed for feasibility studies on high-rise BIWTs in the future.
Suburban areas are rich in wind resources, but wind energy characteristics are complicated by the strong turbulence disturbance of buildings. Current work on wind resource assessment is focused on the fitting of the wind speed distribution function and the measurement of wind energy abundance. The mathematical relationship between the distribution function and the main wind energy characteristics under suburban wind field conditions needs to be studied.
In this study, a ZephIR 300 LiDAR wind measurement system is built in a suburban area, and wind speed data samples from heights of 11–199 m are obtained. The functional relationship between parameters is derived through a comparative analysis, which reveals that the variation of shape parameter k in the range of 1.05–1.25 causes abrupt changes in wind power density (WPD). The positive proportional relationship between the scale parameter and WPD and its correlation with height are also obtained. Turbulence intensity (TI) decrease as the scale parameter increases from 1.18 to 2.50 and fluctuates within 10.54% as the scale parameter c continues to increase from 2.50 to 5.58. The scale parameter corresponding to the minimum value of TI at each height is provided.
To assess wind resources in a given area, it is necessary to select a representative measurement point by identifying a wind zone where the wind resource characteristics are homogeneous. In this study, to identify the spatial homogeneity of wind resources, it was necessary to test several similarity measures for clustering analysis, such as time-series wind vector similarity, Pearson's correlation coefficient of time-series wind speed, the cosine distance of time-series wind direction, the index of agreement of time-series wind speed, the 24-hour autocorrelation function, and the principal components of wind resource factors. It was found that the primary components of wind resources were the Weibull scale and shape factors of wind speed distribution, while terrain elevation and the 24-hour autocorrelation function were chosen as the secondary components. The similarity measures were applied to Jeju Island, which has a simple topography, and the Pohang region, which has a complex mountainous topography, to classify the wind zones; while poly-serial correlation coefficients, together with box plots, were used to evaluate whether there was a significant statistical difference in the wind resource factors within a given cluster. In conclusion, it was confirmed that the time-series wind vector similarity and the primary components of wind resource factors are the most effective similarity measures of clustering analysis.
This paper reviews the history and status of wind energy in South Korea using the wind power generation statistics (2002∼2016) provided by the Electric Power Statistics Information System (EPSIS) and “2015 Renewable Energy Supply Statistics” from the Ministry of Trade, Industry, and Energy. In addition, the prediction accuracy of the Korea wind resource map made by the Korea Institute of Energy Research (KIER) was evaluated through correlation analysis of capacity factor by province between EPSIS and KIER. The average capacity factor of wind energy in South Korea is 23 %, which is in the middle class among the IEA Wind member countries. The monthly capacity factors of EPSIS and KIER showed a high correlation of R2=0.78. It was clearly reaffirmed that the wind energy supply is directly linked to the government’s support policy. Therefore, in order to achieve the goal of 20 % renewable energy supply by 2030, establishing effective government policies is a matter of urgency to maximize the market share of domestic wind turbines and to solve conflicts related to environmental regulations and public acceptance.
