Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis

Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis

Wu, Ping (Zhejiang Institute of Meteorological Sciences)
Liu, Y. (TU Delft Team Riccardo Ferrari)
Ferrari, Riccardo M.G. (TU Delft Team Riccardo Ferrari)
van Wingerden, J.W. (TU Delft Team Jan-Willem van Wingerden)

2021

Over the past decades, Floating Offshore Wind Turbine (FOWT) has gained increasing attention in wind engineering due to the rapidly growing energy demands. However, difficulties in turbine maintenance will increase due to the harsh operational conditions. Fault diagnosis techniques play a crucial role to enhance the reliability of FOWTs and reduce the cost of offshore wind energy. In this paper, a novel data-driven fault diagnosis method using regularized dynamic canonical correlation analysis (RDCCA) and Fisher discriminant analysis (FDA) is proposed for FOWTs. Specifically, to overcome the collinearity problem that exists in measured process data, dynamic canonical correlation analysis with a regularization scheme, is developed to exploit the relationship between input and output signals. Then, the residual signals are generated from the established RDCCA model for fault detection. To further classify the fault type, an FDA model is trained from the residual signals of different training faulty data sets. Simulations on a FOWT baseline model based on the widely used National Renewable Energy Laboratory FAST simulator are carried out to demonstrate the feasibility and efficacy of the proposed fault detection and classification method. Results have shown many salient features of the proposed method with potential applications in FOWTs.

http://resolver.tudelft.nl/uuid:df1b19c7-cbc2-49db-8486-3af7144c1afa

https://doi.org/10.1049/rpg2.12319

1752-1416

IET Renewable Power Generation, 15 (16), 4006-4018

Institutional Repository

journal article

© 2021 Ping Wu, Y. Liu, Riccardo M.G. Ferrari, J.W. van Wingerden