This paper proposes an efficient measurement system for the velocity and direction of the wind using the dual rotor wind power generator in vessel. Conventional digital measurement system recognizes the direction and the velocity of the wind using the electric compass or synchronous motor and Vane probe method using hall sensors. But each system has its own short-comings: the synchronous motor has a larger measurement error than the magnetic compass and magnetic compass is weak for the external disturbances such as fluctuation of the vessel. To compensate these short-comings, this paper proposes a new compensation algorithm for the fluctuation errors according to the external interference and the unexpected movement of the vessel along the roll and pitch directions. The proposed system is implemented with the dual compasses and a synchronous motor. The proposed independent power generation system can be operated by itself and can raise the efficiency of the wind power generation systems of 30 ~ 400 W installed along the vertical and horizontal axes. The proposed system also realizes the efficient and reliable power production system by the MPPT algorithm for the real-time recognition of the wind direction and velocity. An advanced switching algorithm for the battery charging system has been also proposed. Effectiveness of the proposed algorithm has been verified through the real experiments and the results are demonstrated.

Three meteor-statistical forecasting models - the transfer function model, the time-series autoregressive model and the neural networks model - were tested to develop a daily forecasting model for Jejudo, where the need and demand for wind power forecasting has increased. All the meteorological observation sites in Jejudo have been classified into 6 groups using a cluster analysis. Four pairs of observation sites among them, all having strong wind speed correlation within the same meteorological group, were chosen for a model test. In the development of the wind speed forecasting model for Jejudo, it was confirmed that not only the use a wind dataset at the objective site itself, but the introduction of another wind dataset at the nearest site having a strong wind speed correlation within the same group, would enhance the goodness to fit of the forecasting. A transfer function model and a neural network model were also confirmed to offer reliable predictions, with the similar goodness to fit level.

Wind power energy is one of the favorable and fast growing renewable energies. It is most important for exact analysis of wind to evaluate and forecast the wind power energy. The purpose of this study is to improve the performance of numerical atmospheric model by data assimilation over a complex coastal area. The benefit of the profiler is its high temporal resolution and dense observation data at the lower troposphere. Three wind profiler sites used in this study are inhomogeneously situated near south-western coastal area of Korean Peninsula. The method of the data assimilation for using the profiler to the model simulation is the three-dimensional variational data assimilation (3DVAR). The experiment of two cases, with/without assimilation, were conducted for how to effect on model results with wind profiler data. It was found that the assimilated case shows the more reasonable results than the other case compared with vertical observation and surface Automatic Weather Station(AWS) data. Although the effect of sonde data was better than profiler at a higher altitude, the profiler data improves the model performance at lower atmosphere. Comparison with the results of 4 June and 5 June suggests that the efficiency with hourly assimilated profiler data is strongly influenced by synoptic conditions. The reduction rate of Normalized Mean Error(NME), mean bias normalized by averaged wind speed of observation, on 4 June was 28% which was larger than 13% of 5 June. In order to examine the difference in wind power energy, the wind power density(WPD) was calculated and compared.

Although renewable power is regarded a way to active response to climate change, the stability of whole power system could be a serious problem in the future due to its uncertainties such as indispatchableness and intermittency. From this perspective, the peak time impact of stochastic wind power generation is estimated using simulation method up to year 2030 based on the 3rd master plan for the promotion of new and renewable energy on peak time. Result shows that the highest probability of wind power impact on peak time power supply could be up to 4.41% in 2030. The impact of wind power generation on overall power mix is also analyzed up to 2030 using SCM model. The impact seems smaller than expectation, however, the estimated investment cost to make up such lack of power generation in terms of LNG power generation facilities is shown to be a significant burden to existing power companies.

Wind energy, which is one of renewable energy, would be useful resources that can be applied to making energy recycling villages without using fossil fuels. This study analyzed energy potential on wind power considering weather condition in three rural villages and compared with energy consumption surveyed. A wind turbine system in the 5kW class can generate 26.1%, 73.9% and 39.5% of the yearly mean consumption of electric power per house in Makhyun, Boojang and Soso respectively. A 750kW wind turbine system can generate 1.7%, 30.3% and 22.1% of the total amount of electric power consumption in three study villages respectively. Wind power energy density was too low in Makhyun and Soso, so it is determined that the application of wind turbine system is almost impossible. Wind energy potential was generally low in Boojang either, but it is evaluated that there is a little possibility of wind power generation relatively. For practical application of renewable energy to rural green-village planning, assessment of energy potential for the local area should be preceded.