On wind Turbine failure detection from measurements of phase currents: a permutation entropy approach
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This article presents the applicability of Permutation Entropy based complexity measure of a time series for detection of fault in wind turbines. A set of electrical data from one faulty and one healthy wind turbine were analysed using traditional Fast Fourier analysis in addition to Permutation Entropy analysis to compare the complexity index of phase currents of the two turbines over time. The 4 seconds length data setdid not reveal any low frequency in the spectra of currents, neither did they showany meaningful differences of spectrum between the two turbine currents. Permutation Entropy analysis of the current waveforms of same phases for the two turbines are foundto have different complexity values over time, one of them being clearly higher than theother. The work of Yan et. al. in  has found that higher entropy values related to the presence of failure in rotary machines in his study. Following this track, further efforts will be put into relating the entropy difference found in our study to possible presenceof failure in one of the wind energy conversion systems.