To　overcome　the　problems　of　wind　turbine　(WT)　degradation　assessment,　a　new　kernel　entropy　method　based　on　supervisory　control　and　data　acquisition　(SCADA)　was　proposed.　This　approach　can　be　used　to　effectively　monitor　and　assess　WT　performance　degradation.　First,　a　new　condition　monitoring　method　based　on　a　kernel　entropy　component　analysis　(KECA)　was　developed　for　nonlinear　data.　Then,　the　squared　prediction　error　(SPE)　was　used　to　monitor　the　WT　health　state.　Due　to　the　diversity　and　nonlinearity　of　SCADA　data,　fault　features　are　easily　overwhelmed　by　other　vibration　signals.　To　address　this,　a　new　kernel　entropy　partial　least　squares　(KEPLS)　algorithm　was　introduced.　The　proposed　kernel　entropy　method　improves　the　performance　prediction　by　considering　higher　order　information.　Furthermore,　changes　in　the　prediction　residual　can　be　used　to　define　certain　limits　to　realize　early　warning　of　WT　faults.　Finally,　the　method　was　applied　to　actual　SCADA　data　of　a　wind　farm.　The　results　show　that　the　method　can　accurately　evaluate　the　health　state　of　WTs,　thus　verifying　the　effectiveness　and　feasibility　of　the　proposed　method.