Fault diagnosis of a wind turbine benchmark via identified fuzzy models

reserved3noIn 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 solutionIEEE Transactions on Industrial Electronics is published monthly. Its scope encompasses the applications of electronics, controls and communications, instrumentation and computational intelligence for the enhancement of industrial and manufacturing systems and processes. Included are power electronics and drive control techniques, system control and signal processing, fault detection and diagnosis, power systems, instrumentation, measurement and testing, modeling and simulation, motion control, robotics, sensors and actuators, implementation of neural nets, fuzzy logic, and artificial intelligence in industrial systems, factory automation, communication, and computer networks.mixedSimani, Silvio; Farsoni, Saverio; Castaldi, PaoloSimani, Silvio; Farsoni, Saverio; Castaldi, Paol