Data-driven fault detection and isolation scheme for a wind turbine benchmark

This paper investigates a new scheme for fault detection and isolation based on time series and data analysis. This scheme is applied in wind turbines which are used to tap the potential of renewable energy. The proposed scheme is performed in two steps and it is based on process data without using any kind of physical modeling. The first step, the fault detection, is based on an alternative method based on the Gibbs sampling algorithm in which the occurrence of a sensor fault is modeled as a change point detection in a time series. The second step, the fault isolation, is handled via a Fuzzy/Bayesian network scheme classifying the kind of fault. The proposed fault detection and isolation (FDI) strategy offers as main contribution the independence from any kind of dynamical modeling and the unprecedented usage of the Gibbs sampling. Furthermore, this work offers a novel data driven FDI approach based on Fuzzy-Bayesian inference and suitable for the wind turbines systems. This approach presents a good performance for detection and diagnostics of sensor faults in a standard wind turbine benchmark.