Photovoltaic　(PV)　systems　working　outdoors　are　susceptible　to　various　faults.　The　deposition　of　dust　on　the　PV　array　may　make　these　faults　more　complicated,　resulting　in　a　kind　of　compound　faults.　The　similarity　between　compound　faults　and　single　faults　leads　to　their　misclassification.　Therefore,　accurately　detection　of　potential　PV　array　compound　faults　is　essential　to　improve　the　operating　efficiency　and　safety　of　PV　systems.　Addressing　the　above　situation,　this　paper　proposes　a　fault　diagnosis　(FD)　scheme　for　PV　array　using　a　multi-scale　SE-ResNet　network.　In　addition,　a　multi-scale　receptive　field　fusion　module　(MRFF)　is　designed　to　improve　the　diagnostic　performance　of　the　model.　This　model　can　automatically　extract　multi-scale　fault　features　from　input　raw　current-voltage　curves　data　and　environmental　parameters.　The　single　faults,　partial　shading　conditions　(PSCs),　and　compound　faults　under　different　degrees　of　dust　covering　can　be　diagnosed.　In　addition,　the　dust　coverage　degree　is　estimated　simultaneously,　which　can　provide　a　basis　for　developing　a　cleaning　schedule.　Simulation　and　experiment　results　demonstrate　the　superior　performance　of　this　method　compared　with　other　approaches,　and　also　indicate　the　proposed　model　can　be　applied　to　the　condition　of　dust　accumulate　on　the　bottom　of　the　PV　panels　through　transfer　learning.