With　the　widespread　access　to　multiple　energy　systems,　different　degrees　of　power　quality　disturbance　(PQD)　have　emerged　in　the　power　system,　significantly　impacting　all　stages　of　power　generation,　transmission,　and　consumption.　Accurately　identifying　PQD　lays　the　foundation　for　the　safe　operation　and　regulation　of　power　quality　within　the　power　system.　However,　in　traditional　PQD　recognition　schemes,　limited　image　feature　information　and　insufficient　algorithm　recognition　capacity　are　identified　as　key　problems.　To　address　these　issues,　a　novel　PQD　recognition　approach　based　on　2-D　image　combination　and　Expanded-Channel　ResNet　(ECRN)　is　proposed.　Considering　the　insufficient　time-frequency　domain　feature　information,　this　article　effectively　combines　Stockwell　transform　(ST)　with　Variational　Mode　Decomposition　(VMD)　to　construct　multi-channel　PQD　image　that　contain　sufficient　time-frequency　domain　feature　information.　In　terms　of　the　recognition　algorithm,　to　better　extract　features　from　the　disturbance　images,　this　paper　improves　the　traditional　ResNet　structure　by　expanding　its　channel　and　creates　ECRN　to　achieve　automatic　and　adaptive　feature　extraction　from　multi-channel　images.　The　PQD　recognition　method　is　simulated　and　compared　with　state-of-the-art　recognition　approaches.　The　results　demonstrate　that　the　proposed　method　has　good　anti-noise　performance　and　can　more　accurately　extract　PQD　feature　information　to　achieve　higher　recognition　accuracy.　©　2023　IEEE.