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Sensors 2017, 17(4), 720; doi:10.3390/s17040720

A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach

1
Faculty of Civil Engineering, University Ss. Cyril and Methodius, Skopje 1000, Macedonia
2
Department of Civil, Environmental and Geomatic Engineering, ETH, Zürich CH-8093, Switzerland
3
Department of Civil and Environmental Engineering, Ruhr-University Bochum, Bochum 44801, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 31 January 2017 / Revised: 11 March 2017 / Accepted: 21 March 2017 / Published: 30 March 2017
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems)

Abstract

The complex dynamics of operational wind turbine (WT) structures challenges the applicability of existing structural health monitoring (SHM) strategies for condition assessment. At the center of Europe’s renewable energy strategic planning, WT systems call for implementation of strategies that may describe the WT behavior in its complete operational spectrum. The framework proposed in this paper relies on the symbiotic treatment of acting environmental/operational variables and the monitored vibration response of the structure. The approach aims at accurate simulation of the temporal variability characterizing the WT dynamics, and subsequently at the tracking of the evolution of this variability in a longer-term horizon. The bi-component analysis tool is applied on long-term data, collected as part of continuous monitoring campaigns on two actual operating WT structures located in different sites in Germany. The obtained data-driven structural models verify the potential of the proposed strategy for development of an automated SHM diagnostic tool. View Full-Text
Keywords: wind turbines; data-driven framework; uncertainty propagation; operational spectrum; time varying autoregressive moving average (TV-ARMA) models; polynomial chaos expansion (PCE) wind turbines; data-driven framework; uncertainty propagation; operational spectrum; time varying autoregressive moving average (TV-ARMA) models; polynomial chaos expansion (PCE)
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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).

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Bogoevska, S.; Spiridonakos, M.; Chatzi, E.; Dumova-Jovanoska, E.; Höffer, R. A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach. Sensors 2017, 17, 720.

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