A New Hybrid Fault Detection Method for Wind Turbine Blades Using Recursive PCA and Wavelet-Based PDF

2020 ◽  
Vol 20 (4) ◽  
pp. 2023-2033 ◽  
Author(s):  
Milad Rezamand ◽  
Mojtaba Kordestani ◽  
Rupp Carriveau ◽  
David S.-K. Ting ◽  
Mehrdad Saif
Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 568
Author(s):  
José Gibergans-Báguena ◽  
Pablo Buenestado ◽  
Gisela Pujol-Vázquez ◽  
Leonardo Acho

Monitoring the variation of the loading blades is fundamental due to its importance in the behavior of the wind turbine system. Blade performance can be affected by different loads that alter energy conversion efficiency and cause potential safety hazards. An example of this is icing on the blades. Therefore, the main objective of this work is to propose a proportional digital controller capable of detecting load variations in wind turbine blades together with a fault detection method. An experimental platform is then built to experimentally validate the main contribution of the article. This platform employs an automotive throttle device as a blade system emulator of a wind turbine pitch system. In addition, a statistical fault detection algorithm is established based on the point change methodology. Experimental data support our approach.


2009 ◽  
Vol 129 (5) ◽  
pp. 689-695
Author(s):  
Masayuki Minowa ◽  
Shinichi Sumi ◽  
Masayasu Minami ◽  
Kenji Horii

2021 ◽  
Author(s):  
Aileen G. Bowen Perez ◽  
Giovanni Zucco ◽  
Paul Weaver

Author(s):  
Salete Alves ◽  
Luiz Guilherme Vieira Meira de Souza ◽  
Edália Azevedo de Faria ◽  
Maria Thereza dos Santos Silva ◽  
Ranaildo Silva

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