To　solve　the　problem　of　noise　measurement　in　wide　area　measurement　system,　we　proposed　a　low-frequency　oscillation　analysis　method　based　on　Stein　unbiased　risk　estimate(SURE)　wavelet　threshold　de-noising　and　modified　complementary　ensemble　empirical　mode　decomposition　and　Hilbert-Huang　transform　(MCEEMD-HHT).　Furthermore,　the　proposed　method　is　used　to　solve　the　problem　of　mode　mixing　and　pseudo　components　in　mode　identification　using　Hilbert-Huang　transform(HHT)　in　the　analysis　of　low-frequency　oscillation.　Firstly,　the　low-frequency　oscillation　signal　of　power　network　measurement　with　strong　noise　is　pre-processed　by　using　SURE　wavelet　threshold　de-noising.　Secondly,　the　permutation　entropy　algorithm　is　introduced　to　improve　CEEMD　to　form　MCEEMD,　which　can　effectively　suppress　the　phenomenon　of　modal　aliasing　and　pseudo　components　in　EMD.　Finally,　HHT　analysis　is　carried　out　on　the　real　low-frequency　modes　obtained　by　MCEEMD　decomposition.　Through　the　composite　signal　tests,　IEEE　four-generator　two-area　system　simulation,　and　an　actual　measured　North　American　power　grid　data　analysis,　it　is　shown　that　the　proposed　method　is　effective　in　the　analysis　of　power　system　low-frequency　oscillation.　In　addition,　compared　with　fast　Fourier　transform(FFT)　and　Prony　algorithm　analysis,　the　proposed　method　is　more　accurate　in　the　extraction　of　modal　parameters　and　does　not　need　an　artificial　order　determination.　©　2020,　High　Voltage　Engineering　Editorial　Department　of　CEPRI.　All　right　reserved.