Fault diagnosis of a wind turbine benchmark via identified fuzzy models

In order to improve the availability of wind turbines and to avoid catastrophic consequences, the detection of faults in their earlier occurrence is fundamental. This paper proposes the development of a fault diagnosis scheme relying on identified fuzzy models. The fuzzy theory is exploited since it allows approximating uncertain models and managing noisy data. These fuzzy models, in the form of Takagi-Sugeno prototypes, represent the residual generators used for fault detection and isolation (FDI). A wind turbine benchmark is used to validate the achieved performances of the designed FDI scheme. Finally, extensive comparisons with different fault diagnosis methods highlight the features of the suggested solution.