  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 FastFourier 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 set did not reveal any low
frequency in the spectra of currents, neither did they show any meaningful
differences of spectrum between the two turbine currents. Permutation Entropy
analysis of the current waveforms of same phases for the two turbines are found
to have different complexity values over time, one of them being clearly higher
than the other. The work of Yan et. al. in has found that higher entropy values
related to thepresence 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 presence of failure in one of the wind energy
conversion systems.
