Affiliation:
1. Blade Submodules Technology Siemens Gamesa Renewable Energy Aalborg East Denmark
2. Department of Wind and Energy Systems Technical University of Denmark Roskilde Denmark
3. Turbine Measurement Operation Siemens Gamesa Renewable Energy Brande Denmark
4. Dynamics Research Group, Department of Mechanical Engineering University of Sheffield Sheffield United Kingdom
Abstract
AbstractThe present work investigates the performance of different features, extracted from vibration‐based data, for structural health monitoring of a 52‐meter wind turbine blade during fatigue testing. An active vibration monitoring system was used during the test campaign, providing periodic excitation of single frequencies in the medium‐frequency range, and using accelerometers to measure the vibration output on different parts of the blade. Based on previous work from the authors, data is available for the wind turbine blade in healthy state, with a manually induced damage, and with progressively increasing damage severity. Using the vibration data, different signal processing methods are used to extract damage‐sensitive features. Time series methods and time‐frequency domain methods are used to quantify the applied active vibration signal. Using outlier analysis, the health state of the blade is classified, and the classification accuracy through use of the different features is compared. Highest performance is generally obtained by auto‐regressive modeling of the vibration outputs, using the auto‐regressive parameters as features. Finally, suggestions for future improvements of the present method toward practical implementation are given.
Funder
Innovationsfonden
Energiteknologisk udviklings- og demonstrationsprogram
Subject
Renewable Energy, Sustainability and the Environment