As　wind　power　attracts　increasing　attention　and　wind　turbines　(WTs)　capacity　expands,　fault　diagnosis　of　WT　is　playing　a　more　and　more　important　role　in　improving　reliability,　minimizing　down　time,　reducing　maintenance　costs,　and　providing　reliable　power　generation.　In　this　paper,　a　novel　sparse　time-frequency　representation　(STFR)　method　is　proposed　to　increase　the　diagnostic　precision　of　incipient　faults.　The　proposed　method　can　be　applied　once　the　condition　is　detected　as　abnormal　according　to　the　VDI3834　vibration　threshold　standard　in　WT　fault　diagnosis　systems.　The　proposed　method　is　a　novel　signal　representation　method　based　on　the　sparse　representation　theory　and　Wigner-Ville　distribution　(WVD),　which　can　overcome　the　limitations　of　traditional　basis　functions　expansion　and　time-frequency　analysis　methods.　In　this　method,　a　union　of　redundant　dictionary　(URD)　is　constructed　on　the　basis　of　the　underlying　prior　information　of　the　oscillate　characteristics　with　multicomponent　coupling　effect　and　different　morphological　waveforms.　Therefore,　the　vibration　signal　can　be　sparsely　represented　over　the　URD.　Then,　the　sparse　coefficients　and　corresponding　atoms　can　be　obtained　by　solving　the　basis　pursuit　denoising　problem　via　alternating　direction　method　of　multipliers.　Based　on　the　combination　of　the　WVD　of　each　atom　and　corresponding　sparse　coefficient,　the　time-frequency　distribution　of　the　vibration　signal　can　be　obtained.　To　verify　the　effectiveness　of　the　STFR　method,　a　simulation　and　two　field　tests　in　the　wind　farm　are　performed.　The　comparison　results　with　state-of-the-art　methods　illustrate　the　superiority　and　robustness　of　the　proposed　method　in　the　engineering　applications.　©　1963-2012　IEEE.