The　vibration　signals　of　wind　turbines　are　often　disturbed　by　strong　noise　and　will　be　annihilated　when　exhibiting　fault　or　strong　instability.　Denoising　is　required　prior　to　facilitating　an　analysis　of　vibration　fault　characteristics.　A　wavelet　denoising　method　based　on　variational　mode　decomposition　(VMD)　and　multiscale　permutation　entropy　(MPE)　is　proposed.　The　characteristics　of　VMD　are　analyzed,　and　the　randomness　and　complexity　of　noise　are　evaluated　by　MPE.　If　the　MPE　of　the　modal　component　after　VMD　is　larger　than　the　evaluation　value,　then　it　is　denoised　by　wavelet,　and　the　signal　is　reconstructed　with　other　modal　components　without　wavelet　denoising　to　achieve　the　denoising　effect.　The　db1,　sym8,　EMD　and　EWT　denoising　methods　and　the　proposed　method　are　compared　using　the　same　simulation　signal.　Simulation　results　show　that　the　denoising　effect　of　the　proposed　method　is　better　than　the　other　four　methods,　and　the　two　quantitative　evaluation　indexes　of　relative　error　and　root　mean　square　error　obtain　desirable　values.　The　shaft　vibration　signals　of　the　Case　Western　Reserve　University　and　wind　turbine　are　used　to　verify　the　effectiveness　of　the　proposed　method,　and　the　denoising　effect　is　also　better　than　the　other　four　methods.　The　proposed　method　eliminates　most　of　the　noise　components　while　retaining　the　effective　information　of　the　signal.　Therefore,　this　method　can　provide　a　good　foundation　for　the　research　and　analysis　of　the　characteristics　of　late　vibration　signal.