Flexible non-linear predictive models for large-scale wind turbine diagnostics
Author:
Affiliation:
1. Siemens Diagnostic Center; Brande Denmark
2. Cognitive Systems; Technical University of Denmark; Kongens Lyngby Denmark
Publisher
Wiley
Subject
Renewable Energy, Sustainability and the Environment
Link
http://onlinelibrary.wiley.com/wol1/doi/10.1002/we.2057/fullpdf
Reference18 articles.
1. Lu B Li Y Wu X Yang Z A review of recent advances in wind turbine condition monitoring and fault diagnosis Proceedings of Power Electronics and Machines in Wind Applications 2009 Lincoln, Nebraska 2009
2. Condition monitoring and fault detection of wind turbines and related algorithms: a review;Hameed;Renewable and Sustainable Energy Reviews,2009
3. Online wind turbine fault detection through automated SCADA data analysis;Zaher;Wind Energy,2009
4. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection;Schlechtingen;Mechanical Systems and Signal Processing,2011
5. Wind turbine generator condition-monitoring using temperature trend analysis;Guo;Sustainable Energy, IEEE Transactions on,2012
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