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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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Discussion papers
https://doi.org/10.5194/wes-2019-30
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2019-30
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 02 Jan 2020

Submitted as: research article | 02 Jan 2020

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Wind Energy Science (WES).

Modelling tower fatigue loads of a wind turbine using data mining techniques on SCADA data

Artur Movsessian1, Marcel Schedat2, and Torsten Faber2 Artur Movsessian et al.
  • 1Institute for Infrastructure and Environment, School of Engineering, University of Edinburgh, United Kingdom
  • 2Wind Energy Technology Institute, University of Applied Sciences Flensburg, Flensburg, 24943, Germany

Abstract. The rapid development of the wind industry in recent decades and the establishment of this technology as a mature and cost-competitive alternative have stressed the need for sophisticated maintenance and monitoring methods. Structural health monitoring has risen as a diagnosis strategy to detect damage or failures in wind turbine structures with the help of measuring sensors. The amount of data recorded by the structural health monitoring system can potentially be used to obtain knowledge about the condition and remaining lifetime of wind turbines. Machine learning techniques provide the opportunity to extract this information, thereby improving the reliability and cost-effectiveness of the wind industry as well. This paper demonstrates modeling damage equivalent loads of the fore-aft bending moments of a wind turbine tower with the advantage of using the neighborhood component analysis as a feature selection technique in comparison to common dimension reduction/feature selection techniques such as correlation analysis, stepwise regression or principal component analysis. For this study a one-year measuring period of data was gathered, pre-processed, and filtered by different operational modes, namely stand still, full load, and partial load. Finally, a sensitivity analysis was performed in the partial load model to determine the required length of the data collection campaign that guarantees the most precise results. The results indicate that applying neighborhood component analysis yields more conservative models regarding the number of features and equally accurate outcomes than traditional feature selection techniques.

Artur Movsessian et al.
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Status: open (until 13 Feb 2020)
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Artur Movsessian et al.
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Short summary
The assessment of the structural condition and technical lifetime extension of a wind turbine is challenging, because estimation of fatigue loads can be ambiguous due to the lack of information. In this Paper, 16 neural networks were developed and compared corresponding to four datasets and four feature selection techniques. Finally, a sensitivity analysis was performed in the partial load model to determine the required length of the data collection that guarantees the most precise results.
The assessment of the structural condition and technical lifetime extension of a wind turbine is...
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