Based on the statistically identified variability of wind behavior, the wind power quality of wind turbines can be improved by 12% on average by adjusting the cut-in and cut-off speed. Furthermore, the proposed non-Gaussian multivariate monitoring approach can elucidate the spatial and temporal variability of the regional wind speed frequencies, including the average wind speeds and extreme wind events across South Korea. The results indicated that Weibull and Gamma distributions are best-of-fit functions of each province in South Korea the physical patterns of wind including the average wind speed and gale can be identified by distribution parameters. Then determination rule of cut-in and cut-out speeds of wind turbine was suggested to improve the wind power quality across the regions. Furthermore, a non-Gaussian multivariate statistical monitoring method based on the Hotelling T² chart was developed to spatially and temporally analyze the physical patterns of the wind speed frequencies using the estimated distribution parameters.

The best-of-fit for the corresponding distribution function was identified to characterize the behavior of the wind speed frequency at each region among more than 60 candidate functions using the chi-squared test. Anemometer data were first collected in 11 different provinces of South Korea with hourly resolution for one year. In this study, a non-Gaussian multivariate statistical monitoring approach is proposed to investigate the wind speed frequencies across different regions of South Korea. The spatial and temporal variability of renewable energy resources, particularly wind energy, should be statistically evaluated to achieve sustainable economic development to mitigate climate change. The results show that the ATCC obtained from the segmental model force test can be used to preliminarily realize the rapid comparison and selection of flutter aerodynamic measures for bridges. The flutter stability of three types of girder sections was analyzed using parameter K, and the results of the analysis were consistent with the wind tunnel test results. As well as the relationship between the ATCC and aerodynamic damping, an empirical parameter, K, based on the ATCC, was proposed for use in determining the D-term damping-driven flutter. The gray correlation analysis method was used to analyze the correlation between the ATCC and the critical flutter wind speed. The results showed that the flutter stability of the X-term damping-driven type, i.e., the slotted box girder, was the best the flutter stability of the X + D-term damping-driven type, i.e., the H-shape bridge deck, was the worst the flutter stability of D-term damping-driven type was measured as being between these two values. The correlation between the flutter driving term and the critical flutter wind speed V of nine bridges (six types of girder sections) was analyzed, and its rationality was verified using wind tunnel test results.

In order to study the relationship between an aerostatic three-component coefficient (ATCC) and bridge flutter and to quickly evaluate the flutter performance of bridges, we proposed a method based on the empirical formula of the ATCC.

Asce 7 05 wind loads pdf