Non-Gaussian multivariate statistical monitoring of spatio-temporal wind speed frequencies to improve wind power quality in South Korea

SungKu Heo; Juin Yau Lim; Roberto Chang; Yerim Shim; Pouya Ifaei; ChangKyoo Yoo

doi:10.1016/j.jenvman.2022.115516

Non-Gaussian multivariate statistical monitoring of spatio-temporal wind speed frequencies to improve wind power quality in South Korea

J Environ Manage. 2022 Sep 15:318:115516. doi: 10.1016/j.jenvman.2022.115516. Epub 2022 Jun 14.

Authors

SungKu Heo¹, Juin Yau Lim¹, Roberto Chang¹, Yerim Shim¹, Pouya Ifaei¹, ChangKyoo Yoo²

Affiliations

¹ Integrated Engineering Major, Department of Environmental Science and Engineering, Kyung Hee University, Yongin, 446-701, South Korea.
² Integrated Engineering Major, Department of Environmental Science and Engineering, Kyung Hee University, Yongin, 446-701, South Korea. Electronic address: ckyoo@khu.ac.kr.

PMID: 35714472
DOI: 10.1016/j.jenvman.2022.115516

Abstract

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. In this study, a non-Gaussian multivariate statistical monitoring approach is proposed to investigate the wind speed frequencies across different regions of South Korea. Anemometer data were first collected in 11 different provinces of South Korea with hourly resolution for one year. 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. 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. Then determination rule of cut-in and cut-out speeds of wind turbine was suggested to improve the wind power quality across the regions. 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. 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. 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. Thus, the proposed non-Gaussian multivariate monitoring approach can provide practical guidelines for manufacturers to achieve reliable wind energy generation by considering the spatial and temporal wind behavior.

Keywords: Climate change mitigation; Energy quality improvement; Non-Gaussian multivariate statistical monitoring; Spatio-temporal analysis; Wind energy.