Residual Generator Fuzzy Identification for Wind Turbine Benchmark Fault Diagnosis

In order to improve the availability of wind turbines, thus improving their efficiency, it is important to detect and isolate faults in their earlier occurrence. The main problem of model-based fault diagnosis applied to wind turbines is represented by the system complexity, as well as the reliability of the available measurements. In this work, a data-driven strategy relying on fuzzy models is presented, in order to build a fault diagnosis system. Fuzzy theory jointly with the Frisch identification scheme for errors-in-variable models is exploited here, since it allows one to approximate unknown models and manage uncertain data. Moreover, the use of fuzzy models, which are directly identified from the wind turbine measurements, allows the design of the fault detection and isolation module. It is worth noting that, sometimes, the nonlinearity of a wind turbine system could lead to quite complex analytic solutions. However, IF-THEN fuzzy rules provide a simpler solution, important when on-line implementations have to be considered. The wind turbine benchmark is used to validate the achieved performances of the suggested fault detection and isolation scheme. Finally, comparisons of the proposed methodology with respect to different fault diagnosis methods serve to highlight the features of the suggested solution.