Implementation of Pattern Recognition Algorithms in Processing Incomplete Wind Speed Data for Energy Assessment of Offshore Wind Turbines

Electronics

Constantine Michailides¹  DemosC. Angelides²  IoannisP. Panapakidis³ 

[1] Department of Civil Engineering and Geomatics, Cyprus University of Technology, 3036 Limassol, Cyprus;Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;Department of Electrical Engineering, Technological Educational Institute of Thessaly, 41110 Larisa, Greece;

关键词: incomplete data;    missing data;    offshore wind turbines;    time series clustering;    unsupervised machine learning;    wind speed;   

DOI  :  10.3390/electronics8040418

来源: DOAJ

【 摘 要 】

Offshore wind turbine (OWT) installations are continually expanding as they are considered an efficient mechanism for covering a part of the energy consumption requirements. The assessment of the energy potential of OWTs for specific offshore sites is the key factor that defines their successful implementation, commercialization and sustainability. The data used for this assessment mainly refer to wind speed measurements. However, the data may not present homogeneity due to incomplete or missing entries; this in turn, is attributed to failures of the measuring devices or other factors. This fact may lead to considerable limitations in the OWTs energy potential assessment. This paper presents two novel methodologies to handle the problem of incomplete and missing data. Computational intelligence algorithms are utilized for the filling of the incomplete and missing data in order to build complete wind speed series. Finally, the complete wind speed series are used for assessing the energy potential of an OWT in a specific offshore site. In many real-world metering systems, due to meter failures, incomplete and missing data are frequently observed, leading to the need for robust data handling. The novelty of the paper can be summarized in the following points: (i) a comparison of clustering algorithms for extracting typical wind speed curves is presented for the OWT related literature and (ii) two efficient novel methods for missing and incomplete data are proposed.

【 授权许可】

Unknown