Data–Driven and Model–Based Fault Diagnosis of Wind Turbine Sensors

In order to improve reliability of wind turbines, it is important to detect faults in their very early occurrence, and to handle them in an optimal way. This paper focuses on the pitch sensors of the turbine blade system, as they are mainly used for wind turbine control, in order to maximise the power production, and the efficiency of the whole process. On the other hand, as the input–output behaviour of the system under diagnosis is nonlinear, this work suggests a modelling scheme relying on piecewise affine models, whose parameters are identified through the acquired input–output measurements affected by measurement uncertainty. Therefore, these prototypes are exploited for generating suitable residual signals, which allow the detection and the isolation of the considered sensor faults. This noise rejection scheme is used since the wind turbine measurements are not very reliable, due to the uncertainty of wind speed acting on the wind turbine, and to the turbulence around the rotor plane. A detailed benchmark model simulating the wind turbine where realistic fault conditions can be considered shows the effectiveness of both the identification and fault diagnosis techniques.