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A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach.
Simona Bogoevska1, Minas Spiridonakos2, Eleni Chatzi3
1Faculty of Civil Engineering, University Ss. Cyril and Methodius, Skopje 1000, Macedonia. simona.bogoevska@gf.ukim.edu.mk.
Sensors (Basel, Switzerland)
|March 31, 2017
Summary
This study introduces a new structural health monitoring (SHM) framework for wind turbines (WTs). It accurately simulates WT dynamics using environmental data and vibration responses for better condition assessment.
Area of Science:
- Engineering
- Renewable Energy Systems
- Structural Health Monitoring
Background:
- Operational wind turbine (WT) structures exhibit complex dynamics, challenging existing structural health monitoring (SHM) strategies.
- Effective condition assessment requires SHM strategies that capture the complete operational spectrum of WT systems.
Purpose of the Study:
- To propose a novel framework for SHM of operational WT structures.
- To accurately simulate the temporal variability of WT dynamics and track its evolution over time.
Main Methods:
- A framework combining environmental/operational variables with monitored vibration response.
- Development of a bi-component analysis tool for data-driven structural modeling.
- Application on long-term data from two operational WT structures in Germany.
Main Results:
- Accurate simulation of temporal variability in WT dynamics.
- Successful tracking of dynamic variability evolution over a longer-term horizon.
- Validation of data-driven structural models derived from operational data.
Conclusions:
- The proposed strategy demonstrates potential for developing an automated SHM diagnostic tool for wind turbines.
- Symbiotic treatment of operational variables and vibration response enhances condition assessment.
- The framework is suitable for continuous monitoring campaigns and long-term structural analysis.