Photovoltaic　(PV)　systems　operating　in　the　outdoor　environment　are　vulnerable　to　various　factors,　especially　dust　impact.　Abnormal　operations　lead　to　massive　power　losses,　and　severe　faults　as　short　circuit　may　cause　safety　problems　and　fire　hazards.　Therefore,　monitoring　the　operation　status　of　PV　systems　for　timely　troubleshooting　potential　failure　and　effective　cleaning　scheme　are　the　focus　of　current　research　works.　In　this　study,　I-V　characteristics　of　PV　strings　under　various　fault　states　are　analyzed,　especially　soiling　condition.　Because　labeled　data　for　PV　systems　with　specific　faults　are　challenging　to　record,　especially　in　the　large-scale　ones,　a　novel　algorithm　combining　artificial　bee　colony　algorithm　and　semi-supervised　extreme　learning　machine　is　proposed　to　handle　this　problem.　The　proposed　algorithm　can　diagnose　PV　faults　using　a　small　amount　of　simulated　labeled　data　and　historical　unlabeled　data,　which　greatly　reduces　labor　cost　and　time-consuming.　Moreover,　the　monitoring　of　dust　accumulation　can　warn　power　plant　owners　to　clean　PV　modules　in　time　and　increase　the　power　generation　benefits.　PV　systems　of　3.51　and　3.9　kWp　are　used　to　verify　the　proposed　diagnosis　method.　Both　numerical　simulations　and　experimental　results　show　the　accuracy　and　reliability　of　the　proposed　PV　diagnostic　technology.