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Dependence of Floating LiDAR performance on external parameters - are existing onshore classification methods applicable?

: Wolken-Möhlmann, Gerrit; Gottschall, Julia

Volltext ()

Institute of Physics -IOP-, London:
EERA DeepWind 2020, 17th Deep Sea Offshore Wind R&D Conference : 15 - 17 January 2020, Radisson Blu Royal Garden Hotel, Trondheim, Norway
Bristol: IOP Publishing, 2020 (Journal of physics. Conference series 1669)
Art. 012025, 11 S.
Deep Sea Offshore Wind R&D Conference (DeepWind) <17, 2020, Trondheim>
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
0324197B; MALIBU
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IWES ()

Floating LiDAR is a new aspiring technology for replacing offshore meteorological masts for applications like offshore site assessment or power curve measurement. Since the beginning of the development of the first Floating LiDAR Systems (FLS) in the late 2000s, the systems are more and more maturing. The focus of current research is moving from the pure technology development to the application of the obtained data. As a result, procedures for assuring the measurement quality and the assessment of the data quality - or the quantification of estimated measurement uncertainty, respectively - is a key aspect for further increasing the confidence in the data. This is an important pillar for a further distribution of the technology, and for decreasing risks and costs for its application in offshore wind.
In this paper, we introduce the methodologies of FLS verification and classification, with the goal to verify the measurement of individual FLS and study the characteristics of a certain type of LiDAR systems. A classification case study is presented, showing low effects of the buoy motion and sea states on the FLS measurement for the Fraunhofer LiDAR buoy. A detailed study of the interdependencies between influencing parameters and the possible correlations is presented as well.
The robustness and issues of the classification methodology - originally defined for fixed LiDAR systems - is discussed. Especially the non-robustness of the bin-fitting is identified as an issue. Alternative methods for the sensitivity analysis within the classification process are discussed.