With　the　rapid　development　of　power　systems,　how　to　fully　utilize　the　massive,　multi-source　and　multi-dimensional　power　data　is　one　of　the　important　technical　issues　faced　by　the　power　industry　currently.　Compared　with　the　traditional　machine　learning　algorithms,　deep　learning　has　superior　performance　in　dimensionality　reduction,　non-linear　fitting　and　feature　extraction.　Among　them,　the　generative　adversarial　network　(GAN)　has　advantages　in　power　data　enhancement　and　generation.　In　this　paper,　the　fundamental　theory　of　GAN　is　introduced,　and　the　advantages　and　disadvantages　of　GAN　are　analyzed.　From　the　perspective　of　the　network　structure　and　the　objective　function,　four　GAN　derivative　models　widely　used　in　power　systems　are　introduced　respectively.　On　this　basis,　the　applications　of　GAN　in　power　systems　are　reviewed　in　detail,　and　the　GAN　models　in　each　application　scenario　and　its　characteristics　are　discussed.　Finally,　the　problems　to　be　solved　for　further　application　of　GAN　in　power　systems　are　summarized,　and　deeper　and　broader　applications　of　GAN　are　presented.　©2023　Chin.Soc.for　Elec.Eng.