Fault diagnosis in wind turbines based on ANFIS and Takagi–Sugeno interval observers

Wind turbine power generation is becoming one of the most critical renewable energy sources. As wind power grows, there is a need for better monitoring and diagnostic strategies to maximize energy production and increase its security. In this paper, a fault diagnosis approach based on a data-driven technique, which represents the system behavior employing a Takagi–Sugeno (TS) model, is developed. An adaptive neuro-fuzzy inference system (ANFIS) method is used to obtain a set of polytopic-based linear representations and a set of membership functions to interpolate the linear models of the convex TS model. Then, considering the TS model, a fault diagnosis strategy based on convex state observers generate residuals to detect and isolate sensor faults. Unlike other methods, this proposal only needs to be trained with fault-free data. The proposed methodology is tested under different fault scenarios on a well-known wind turbine benchmark built upon fatigue, aerodynamics, structures, and turbulence (FAST). The results demonstrate the method’s effectiveness in detecting and isolating different sensor faults. •A wind turbine uncertain model is obtained based on sensor data and an ANFIS method.•Based on the ANFIS model, an uncertain Takagi–Sugeno (TS) model is obtained.•A set of interval TS observers is designed to detect and isolate sensor faults.•The method is based on healthy data and does not require a mathematical model.•Numerical experiments are performed in a well-accepted wind turbine benchmark.