Currently,　the　increasing　wind　power　penetration,　with　consequent　randomness　and　variability,　presents　great　challenges　to　power　system　planning　and　operation.　Probabilistic　power　flow　(PPF)　has　been　developed　to　calculate　the　power　flow　under　uncertain　circumstances.　However,　the　current　wind　power　models　are　subject　to　specific　probability　distributions,　limiting　their　accuracies　in　wider　applications.　Additionally,　the　cumulant　method　(CM)-based　PPF,　if　nonlinear　relationship　is　considered　in,　would　face　an　impractically　high　computational　complexity.　To　address　these　problems　in　modeling　and　cumulant　calculation,　this　article　proposes　a　novel　generalized　density/distribution　fitting　method　(GDFM)　combining　with　the　Copula　function　to　establish　a　joint　probability　model　for　wind　power　generation.　A　special　impulse-　mixed　probability　density　(IMPD)　integration　method　is　also　introduced　to　derive　the　input　cumulants　from　the　model.　Finally,　a　fast　cumulant　method　(FCM)　is　proposed　to　reduce　the　computational　burden　of　output　cumulant　calculation　while　retaining　a　high　accuracy　in　a　nonlinear　context.　Case　study　on　the　IEEE-118　test　system　validates　the　effectiveness　of　the　proposed　methods,　and　a　real　application　to　a　provincial　power　grid　in　China　provides　some　useful　power　flow　risk　information　for　decision　making.　The　whole　FCM-based　PPF　scheme　can　be　helpful　for　future　power　flow　examination　in　power　system　planning　and　operation.　©　1969-2012　IEEE.