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

• Main Authors: Simona Bogoevska, Minas Spiridonakos, Eleni Chatzi, Elena Dumova-Jovanoska, Rudiger Höffer
• Format: Article
• Language: English
• Published: MDPI AG 2017-03-01
• Series: Sensors
• Subjects: wind turbines; data-driven framework; uncertainty propagation; operational spectrum; time varying autoregressive moving average (TV-ARMA) models; polynomial chaos expansion (PCE)
• Online Access: http://www.mdpi.com/1424-8220/17/4/720

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.