As　the　proportion　of　power　electronics-related　facilities　in　modern　power　systems　increases,　the　types　of　power　quality　disturbances　(PQDs)　tend　to　become　more　complex.　Traditional　methods　struggle　to　accurately　perform　the　classification　task　of　complex　PQDs　under　artificial　empirical　guidance.　This　article　proposes　a　multidimensional　feature-driven　ensemble　model　for　the　accurate　classification　of　complex　PQDs,　which　can　complete　the　self-learning　function　from　data.　Unlike　existing　deep　learning-based　methods,　this　model　considers　both　spatial　features　in　the　time-frequency　domain　and　temporal　relational　features.　Based　on　fully　convolutional　networks　(FCNs)　and　bidirectional　gated　recurrent　unit　(BiGRU),　submodules　suitable　for　multidimensional　features　mining　are　constructed　separately.　Meanwhile,　the　squeeze-and-excitation　network　(SENet)　is　introduced　to　complete　the　computation　of　the　channel　attention　mechanism　for　each　convolutional　layer,　which　effectively　improves　the　training　efficiency　and　classification　accuracy　of　the　model.　The　proposed　model　has　been　thoroughly　tested　to　validate　its　effectiveness　on　a　synthetic　dataset　consisting　of　71　different　types　of　PQDs　under　varying　signal-to-noise　ratios　(SNRs).　Additionally,　the　model　has　been　proven　to　be　robust　in　the　face　of　external　factors,　such　as　dc　offset,　frequency　variations,　and　phase　jumps.　To　further　demonstrate　its　reliability,　the　proposed　model　has　been　tested　on　real　PQDs　generated　from　an　ac　power　source.　Results　from　both　simulation　and　experimentation　have　conclusively　shown　that　the　proposed　method　is　superior　to　existing　deep　learning-based　methods　for　the　classification　of　complex　PQDs.　　©　1963-2012　IEEE.