In　light　of　challenges　encountered　in　extracting　DC　series　arc　fault　features　within　photovoltaic　（PV）　system　and　the　observed　limitations　in　algorithm　generalization　and　adaptability，this　study　introduces　a　novel　series　arc　fault　feature　extraction　method　based　on　dynamic　time　warping（DTW）.　Initially，the　moving　average（MA）value　of　the　current　signal　is　calculated　to　identify　the　mutation　event　and　collect　the　abnormal　signal.　Then，the　singular　spectrum　analysis（SSA）is　used　to　remove　the　trend　component　of　abnormal　signals　and　reduce　the　differences　between　different　PV　system　signals.　Following　this，the　DTW　distance　of　the　signal　is　calculated　to　extract　the　valid　features.　In　the　end，the　waveform　factor　of　the　identified　feature　vector　serves　as　the　diagnostic　criterion　to　identify　the　arc　fault，short　circuit　fault　and　the　interference　events　caused　by　inverter　start-　up　and　irradiance　mutation.　The　experimental　results　show　that　the　arc　fault　identification　method　based　on　the　proposed　feature　extraction　is　not　only　fast　and　highly　recognizable，but　also　suitable　for　different　inverter　systems，has　strong　adaptability，and　the　comprehensive　performance　is　better　than　that　of　the　comparison　method.　©　2023　Science　Press.　All　rights　reserved.