期刊
APPLIED ENERGY
卷 169, 期 -, 页码 150-163出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2016.01.135
关键词
Wind energy; Offshore wind characteristic; Light detection and ranging (LiDAR) system; Wind speed profile; Atmospheric turbulence; Weibull distribution function
资金
- Research Grants Council of Hong Kong Special Administrative Region, China [CityU 118213]
- National Natural Science Foundation of China [51478405]
The interest of investigating offshore wind characteristics is receiving growing attention worldwide, which mainly attempts to boost the development of offshore wind energy and accelerate sustainable development. In particular, the understanding of offshore wind characteristics at the hub heights of wind turbines is of great significance in regard to the assessment of economic feasibility and structural design of wind turbines. Since traditional meteorological masts have limitations in height due to economic and technical considerations, remote sensing techniques have increasingly been employed for offshore wind observations. This paper presents a comprehensive assessment of offshore wind characteristics on the basis of measurements from a light detection and ranging (LiDAR) system at an offshore site in Hong Kong. The fidelity of the wind LiDAR measurements was firstly examined by comparing with those obtained from anemometers on a nearby meteorological mast. Statistical analysis of wind speed was performed by means of the Weibull distribution function, and the results were subsequently used to facilitate the determination of several characteristic parameters associated with wind energy applications. The vertical profiles of wind speed derived from the LiDAR system were compared with existing models. Characteristics of wind shear coefficient were examined within different height ranges. Furthermore, the offshore turbulence characteristics were investigated, including the turbulence intensity and gust factor. The outcomes of this study are expected to provide useful information for the development of offshore wind energy. (C) 2016 Elsevier Ltd. All rights reserved.
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