Power　quality　events　caused　by　renewable-energy　integration　are　usually　associated　with　complex　disturbances;　therefore,　their　type　identification　is　the　primary　task　of　subsequent　pollution　control.　This　study　proposes　a　novel　three-step　classification　approach　based　on　time-dependent　spectral　features　(TDSFs)　for　the　classification　of　complex　power　quality　disturbances　(PQDs).　First,　the　improved　complete　ensemble　empirical　mode　decomposition　with　adaptive　noise　(ICEEMDAN)　is　adopted　to　decompose　the　PQDs　into　several　intrinsic　mode　functions　(IMFs).　The　related　IMFs　are　selected　by　correlation　coefficient　and　kurtosis.　Then　the　eight　eigenvalues　of　each　IMF　are　extracted,　including　TDSFs.　Finally,　the　eigenvalue　dimension　of　each　IMF　is　reduced　by　linear　discriminant　analysis　(LDA).　Moreover,　the　classifier　of　the　adaptive　k-nearest　neighbor　with　excluding　outliers　(AdaKNNEO)　can　confirm　the　PQDs　type.　To　verify　the　effectiveness　of　the　proposed　approach,　a　series　of　simulations　and　hardware　experiments　are　conducted.　The　overall　result　shows　robustness　and　high　accuracy　of　the　proposed　method,　and　especially　for　the　complex　PQDs,　it　possesses　the　highest　entirely　correct　of　96%　compared　to　other　advanced　methods.