The　exponential　growth　of　the　photovoltaic　system　installations　also　requires　an　adequate　maintenance　and　supervision　system　to　ensure　the　continuity　of　service　of　the　system.　Conventional　protection　systems　for　electrical　systems　have　shown　their　shortcomings　for　protecting　photovoltaic　systems.　In　this　article,　a　statistical　approach　based　on　principal　component　analysis　and　its　variants　is　used　to　detect　and　identify　faults　in　a　photovoltaic　array.　This　involves　analysing　the　variations　of　the　data　of　the　current–voltage　and　voltage–power　characteristics.　Subsequently,　the　calculation　of　the　contributions　is　applied　to　the　SPE　index　for　the　identification　of　faults.　By　employing　the　intermediate　value　theorem,　six　different　operating　states　have　been　identified.　The　various　results　obtained　first　from　the　simulation　model　from　the　Simulink　environment　and　then　from　a　real　system　of　18　PV　show　that　the　kernel　principal　component　analysis　allows　defect　detection　with　a　better　precision.　©　2021,　Islamic　Azad　University.