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This article presents the applicability of Permutation Entropy basedcomplexity measure of a time series for detection of fault in wind turbines. Aset of electrical data from one faulty and one healthy wind turbine wereanalysed using traditional FastFourier analysis in addition to PermutationEntropy analysis to compare the complexity index of phase currents of the twoturbines over time. The 4 seconds length data set did not reveal any lowfrequency in the spectra of currents, neither did they show any meaningfuldifferences of spectrum between the two turbine currents. Permutation Entropyanalysis of the current waveforms of same phases for the two turbines are foundto have different complexity values over time, one of them being clearly higherthan the other. The work of Yan et. al. in has found that higher entropy valuesrelated to thepresence of failure in rotary machines in his study. Followingthis track, further efforts will be put into relating the entropy differencefound in our study to possible presence of failure in one of the wind energyconversion systems.

On wind Turbine failure detection from measurements of phase currents: a permutation entropy approach