A New Probabilistic Approach to Fault Detection for Tidal Stream Turbine Blades

To improve the safety and reliability of tidal stream turbines (TSTs) under harsh marine environments, a novel probabilistic approach is proposed for blades fault detection in TSTs subject to stochastic disturbances of unknown probability distribution. On the basis of analytically analyzing the influence of blade imbalance fault on stator current signals, stationary wavelet transform (SWT) is first performed to extract multiscale time–frequency characteristics of blade faults from stator current data corrupted by non-stationary stochastic disturbances. Then an enhanced feature space is established by further computing the energy, standard deviation and kurtosis of SWT decomposition coefficients. By introducing the mean-covariance-based ambiguity set to characterize the probability distribution of feature vector in both fault-free and faulty cases, an optimal separating hyperplane for fault detection is learned using a distributionally robust optimization technique. It can achieve an optimal trade-off between the false alarm rate and the missed detection rate in a probabilistic setting, without requiring any specific distribution assumption. In this way, the proposed fault detection system is robust not only against disturbances but also against distributional uncertainties of disturbances. Finally, an experimental study based on a 0.23 kW tidal stream turbine platform is carried out to validate the effectiveness of the proposed method.