Fault detection and isolation in wind turbines based on neuro-fuzzy qLPV zonotopic observers

This article develops a hybrid approach to fault detection and isolation (FDI) based on a machine learning technique and quasi-Linear Parameter Varying (qLPV) zonotopic observers. First, the dynamical model of a wind turbine is identified using an adaptive network-based fuzzy inference system (ANFIS), which results in a set of qLPV polytopic models whose form is derived using structural analysis. Second, a bank of qLPV zonotopic observers is implemented to detect sensor and actuator faults. Unlike other works that consider different fault scenarios to train a neuronal network, in this work, only fault-free data is considered for the ANFIS. The FDI is based on the residual generation obtained by a bank of qLPV zonotopic observers of the identified models. Disturbances related to aerodynamic loads and measurement noise are considered to guarantee the robustness of the proposed method. The effectiveness of the proposed method is tested in a 5MW WT well-known benchmark simulator based on fatigue, aerodynamics, structures, and turbulence under different fault scenarios. [Display omitted] •A hybrid neuro-fuzzy qLPV zonotopic observer scheme for fault diagnosis is proposed.•The scheme identifies convex qLPV models obtained from an ANFIS structure.•A set of qLPV zonotopic observers are designed to create fault signatures.•A fault isolation module is implemented through Fault Signal Matrices evaluation.•The methodology is implemented in a certified wind turbine simulator of 5 [MW].