Due　to　the　requirement　of　safety　and　reliability　in　power　systems,　unstable　samples　in　the　real　system　are　rarely　appeared.　The　evaluation　results　of　the　model　trained　by　these　imbalance　samples　have　a　certain　preference.　Generally,　the　imbalance　in　quantity　is　taken　into　account,　while　the　imbalance　in　quality　is　ignored.　Faced　with　such　a　problem,　an　imbalanced　correction　method　based　on　support　vector　machine　(SVM)　is　proposed.　Firstly,　the　classification　hyperplane　trained　by　SVM　and　the　normalized　Euclidean　distance　between　each　sample　and　the　classification　hyperplane　are　calculated　so　as　to　obtain　their　fault　severity.　Based　on　this,　training　samples　can　be　grouped　to　multilevel　sets.　Then,　the　original　stacked　sparse　auto-encoder　(SSAE)　are　pretrained　to　quantify　the　imbalance　between　two　classes　of　samples　in　multilevel　sets.　Subsequently,　in　order　to　improve　the　imbalance　of　training　samples,　a　cost-sensitive　correction　matrix　is　generated　according　to　the　imbalanced　information　of　multilevel　sets.　Finally,　the　loss　function　of　SSAE　is　modified　by　cost-sensitive　correction　matrix　to　establish　the　final　classifier.　Simulation　results　in　IEEE　39-bus　system　and　the　realistic　regional　power　system　of　Eastern　China　show　the　high　performance　of　the　proposed　imbalanced　correction　method.(c)　2022　ISA.　Published　by　Elsevier　Ltd.　All　rights　reserved.