• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.485-490
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• 1999

Wind vector information over the ocean is currently obtained using multiple beam scatterometer data. The scatterometers on ERS-1/2 generate wind vector information with a spatial resolution of 50km and accuracies of $\pm$2m/s in wind speed and $\pm$20$^{\circ}$ in wind direction. Synthetic aperture radar (SAR) data over the ocean have the potential of providing wind vector information independent of weather conditions with finer resolution. Finer resolution wind vector information can often be useful particularly in coastal regions where the scatterometer wind information is often corrupted because of the lower resolution system characteristics which is often contaminated by the signal returns from the coastal areas or ice in the case of arctic environments. In this paper we tested CMOD_4 and CMOD_IFR2 algorithms for extracting the wind vector from SAR data. These algorithms require precise estimation of normalized radar cross-section and wind direction from the SAR data and the local incidence angle. The CMOD series algorithms were developed for the C-band, VV-Polarized SAR data, typically for the ERS SAR data. Since RADARSAT operates at the same C-band but with HH-Polarization, the CMOD series algorithms should not be used directly. As a preliminary approach of resolving with this problem, we applied the polarization ratio between the HH and VV polarizations in the wind vectors estimation. Two test areas, one in front of Inchon and several sites around Jeju island were selected and investigated for wind vector estimation. The new results were compared with the wind vectors obtained from CMOD algorithms. The wind vector results agree well with the observed wind speed data. However the estimation of wind direction agree with the observed wind direction only when the wind speed is greater than approximately 3.0m/s.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.247-253
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• 2011

A sustainability of electricity supply has emerged as a critical issue for low carbon green growth in South Korea. Wind power is the fastest growing source of renewable energy. However, due to its own intermittency and volatility, the power supply generated from wind energy has variability in nature. Hence, accurate forecasting of wind speed and power plays a key role in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. This paper presents a short-term wind speed prediction method based on support vector regression. Moreover, particle swarm optimization is adopted to find an optimum setting of hyper-parameters in support vector regression. An illustration is given by real-world data and the effect of model regularization by particle swarm optimization is discussed as well.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.320-328
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• 2016

The introduction of renewable energy sources into the AC grid can change and weaken the strength of the grid, which will in turn affect the stability and robustness of the doubly-fed induction generator (DFIG) wind farm. When integrated with weak grids, the DFIG wind turbine with vector power control often suffers from poor performance and robustness, while the DFIG wind turbine with synchronized control provides better stability. This paper investigates the critical short circuit ratios of DFIG wind turbine with vector power control and synchronized control, to analyze the stability boundary of the DFIG wind turbine. Frequency domain methods based on sensitivity and complementary sensitivity of transfer matrix are used to investigate the stability boundary conditions. The critical capacity of DFIG wind farm with conventional vector power control at a certain point of common coupling (PCC) is obtained and is further increased by employing synchronized control properly. The stability boundary is validated by electromagnetic transient simulation of an offshore wind farm connected to a real regional grid.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.108-112
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• 2012

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly wind energy is unlimited in potential. However, due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. It is reported that, compared with physical persistent models, statistical techniques and computational methods are more useful for short-term forecasting of wind power. Among them, support vector regression (SVR) has much attention in the literature. This paper proposes an SVR based wind speed forecasting. To improve the forecasting accuracy, a fuzzy clustering is adopted in the process of SVR modeling. An illustrative example is also given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1191-1198
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• 2018

The wind profiler radar provides a standing profile of the wind vector and the atmospheric physical signal for the fixed point. Since the wind vector is calculated by the manufacturer's data processing program, the quality control of the date is limited. Therefore, understanding and exploiting the raw spectrum data need to improve the quality of the wind vector. The raw data of the wind vector is the power spectral density stored in binary form. In this study, an algorithm was completed to transform the raw data into the real spectral density, and the use of raw data was evaluated by retrieving zero-order and first-order moments of the spectral based on the spectrum quality control.

• Journal of the Korean Solar Energy Society
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• v.36 no.1
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• pp.11-18
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• 2016

The local wind systems in the Pohang region were categorized into wind sectors. Still, thorough knowledge of wind resource assessment, wind environment analysis, and atmospheric environmental impact assessment was required since the region has outstanding wind resources, it is located on the path of typhoon, and it has large-scale atmospheric pollution sources. To overcome the resolution limitation of meteorological dataset and problems of categorization criteria of the preceding studies, the high-resolution wind resource map of the Korea Institute of Energy Research was used as time-series meteorological data; the 2-step method of determining the clustering coefficient through hierarchical clustering analysis and subsequently categorizing the wind sectors through non-hierarchical K-means clustering analysis was adopted. The similarity of normalized time-series wind vector was proposed as the Euclidean distance. The meteor-statistical characteristics of the mean vector wind distribution and meteorological variables of each wind sector were compared. The comparison confirmed significant differences among wind sectors according to the terrain elevation, mean wind speed, Weibull shape parameter, etc.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.965-970
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• 2023

Wind profiler and wind lidar produce a vertical profile of winds in high spatiotemporal resolution in the atmospheric boundary layer. The wind lidar makes the wind vector using DBS (Doppler Beam Swinging) and VAD (Velocity Azimuth Display) methods. The DBS method has the advantage of obtaining a wind profile with a fast scan time. On the other hand, there is a restriction that requires at least two beams including vertical beam, which causes a decrease in the data acquisition rate. The VAD method was improved to produce more wind vector of the wind profiler as well as the wind lidar, which generally uses 5 beams. Fourier series was estimated with the radial velocity by the DBS method and wind vector was determined by setting the azimuth interval and applying the radial velocity by the Fourier series to the VAD method. The wind vectors were retrieved at the altitude where the wind was not calculated by the DBS method, and the results of the two methods were consistent.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1385-1397
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• 2018

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.182.2-182.2
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• 2010

A various algorism has been studied to extract possibly every energy from a wind turbine in conjunction with the increase of concern about wind power system. In order to verify these control algorism, it is essential to make the most similar conditions to the real wind turbine's environment. Therefore, using separately excited DC motor a wind turbine the most similar to the real turbine is simulated. Tower shadow effect and Wind shear effect are considered as well as inertia emulation. For the control of Back-to-Back Converter Vector current control methods and space vector pulse width modulation are used and for reducing THD of grid current LCL filter is considered. This simulation results verified the energy produced by wind all flows into the utility under the consideration of the characteristics of a wind turbine. The result of this paper is expected to be used as a basic material for analyzing the characteristics of the wind turbine generator.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.873-881
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• 2014

Speed sensorless modes of operation are becoming standard solution in the area of electric drives. This paper presents flux estimator and speed estimator for the speed sensorless vector control of induction motors. The proposed sensorless methods are based on the model reference adaptive system (MRAS) observer and adaptive speed observer (ASO). The proposed speed estimation algorithm can be employed in the power control of grid connected induction generator for wind power applications. Two proposed schemes are verified through computer simulation PSIM and compared their simulation results.