Wind turbine damage equivalent load assessment using Gaussian Process Regression combining measurement and synthetic data
Date
2024
Authors
Haghi, Rad
Stagg, Cassidy
Crawford, Curran
Journal Title
Journal ISSN
Volume Title
Publisher
Energies
Abstract
Assessing the structural health of operational wind turbines is crucial, given their exposure to harsh environments and the resultant impact on longevity and performance. However, this is hindered by the lack of data in commercial machines and accurate models based on manufacturers’ proprietary design data. To overcome these challenges, this study focuses on using Gaussian Process Regression (GPR) to evaluate the loads in wind turbines using a hybrid approach. The methodology involves constructing a hybrid database of aero-servo-elastic simulations, integrating publicly available wind turbine models, tools and Supervisory Control and Data Acquisition (SCADA) measurement data. Then, constructing GPR models with hybrid data, the prediction is validated against the hybrid and SCADA measurements. The results, derived from a year of SCADA data, demonstrate the GPR model’s effectiveness in interpreting and predicting turbine performance metrics. The findings of this study underscore the potential of GPR for the health and reliability assessment and management of wind turbine systems.
Description
Keywords
GPR, SCADA, wind turbine, asset health, asset reliability
Citation
Haghi, R., Stagg, C., & Crawford, C. (2024). Wind turbine damage equivalent load assessment using Gaussian Process Regression combining measurement and synthetic data. Energies, 17(2), 346. https://doi.org/10.3390/en17020346