Next Article in Journal
Robust Stable Control Design for AC Power Supply Applications
Previous Article in Journal
Inverse Scattering Analysis from Measurement Data of Total Electric and Magnetic Fields by Means of Cylindrical-Wave Expansion
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessFeature PaperArticle

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

1
Department of Electrical Engineering, Technological Educational Institute of Thessaly, 41110 Larisa, Greece
2
Department of Civil Engineering and Geomatics, Cyprus University of Technology, 3036 Limassol, Cyprus
3
Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(4), 418; https://doi.org/10.3390/electronics8040418
Received: 26 March 2019 / Revised: 6 April 2019 / Accepted: 8 April 2019 / Published: 10 April 2019
(This article belongs to the Section Artificial Intelligence)
  |  
PDF [10443 KB, uploaded 10 April 2019]
  |  

Abstract

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. View Full-Text
Keywords: incomplete data; missing data; offshore wind turbines; time series clustering; unsupervised machine learning; wind speed incomplete data; missing data; offshore wind turbines; time series clustering; unsupervised machine learning; wind speed
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Panapakidis, I.P.; Michailides, C.; Angelides, D.C. Implementation of Pattern Recognition Algorithms in Processing Incomplete Wind Speed Data for Energy Assessment of Offshore Wind Turbines. Electronics 2019, 8, 418.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top