Numerical Simulation on Floating Behavior of Buoyancy Tank Foundation of Anemometer Tower

The intact stability and damage stability of a model of an anemometer tower with buoyancy tank foundation are computed by the finite element software MOSES in this paper. The natural period of the anemometer tower is discussed through frequency domain analysis. The influence of a single factor, such as towing point position, wave height, wave direction and wave period, on towing stability is discussed through time domain analysis. At the same time, the towing stability under the condition of various combinations of many factors is analyzed based on the measured data of the target area. Computer simulation results show that the intact stability is preferable and the damage stability is sufficient under the condition of plenty of subdivisions. Within the scope of the buoyancy tank foundation,the higher the towing point position is, the better the stability is. Wave height has a great impact on the motion amplitude of buoyancy tank foundation, but the effect on the acceleration is not obvious; wave period has a great impact on the acceleration, while the effect on the motion amplitude is not obvious; following-waves towing is more conducive to safety than atry.

Research on Representative Engineering Applications of Anemometer Towers Location in Complex TopographyWind Resource Assessment

The typical location and number of anemometer towers in the assessed area are the key to the accuracy of wind resource assessment in complex topography.As calculation examples,this paper used two typical complex topography wind farms in Guangxi,Yunnan province in China.Firstly,we simulated the wind resource status of the anemometer tower in the Meteodyn WT software.Secondly,we compared the simulated wind resource with the actual measured data by the anemometer tower in the same situation.Thirdly,we analyzed the influence of anemometer tower location and quantity in the accuracy of wind resource assessment through the comparison results.The results showed that the range which the anemometer tower can represent is limited(<5 kilometers),and the prediction error more than 5%.Besides,the anemometer towers in special terrain areas(such as wind acceleration areas)cannot be used as a representative choice.The relative error of the simulated average annual wind speed by choose different number of anemometer towers is about 4%,and the grid-connected power generation more than 6%.The representative effect of anemometer towers is of crucial for improving the accuracy of wind resource assessment in engineering applications.

Analysis of Wind Power Assessment Based on the WRF Model

Assessing wind energy is a key step in selecting a site for a wind farm. The accuracy of the assessment is essential for the future operation of the wind farm. There are two main methods for assessing wind power: one is based on observational data and the other relies on mesoscale numerical weather prediction(NWP). In this study, the wind power of the Liaoning coastal wind farm was evaluated using observations from an anemometer tower and simulations by the Weather Research and Forecasting(WRF) model, to see whether the WRF model can produce a valid assessment of the wind power and whether the downscaling process can provide a better evaluation. The paper presents long-term wind data analysis in terms of annual, seasonal, and diurnal variations at the wind farm, which is located on the east coast of Liaoning Province. The results showed that, in spring and summer, the wind speed, wind direction, wind power density, and other main indicators were consistent between the two methods. However, the values of these parameters from the WRF model were significantly higher than the observations from the anemometer tower. Therefore, the causes of the differences between the two methods were further analyzed. There was much more deviation in the original material, National Centers for Environmental Prediction(NCEP) final(FNL) Operational Global Analysis data, in autumn and winter than in spring and summer. As the region is vulnerable to cold-air outbreaks and windy weather in autumn and winter, and the model usually forecasted stronger high or low systems with a longer duration, the predicted wind speed from the WRF model was too large.