High　impedance　faults　(HIFs)　occur　frequently　in　a　distribution　network,　and　their　fault　characteristics　are　weak　and　difficult　to　detect.　In　serious　cases,　they　may　lead　to　fires　or　accidents.　A　fault　identification　method　based　on　phase　space　reconstruction　and　transfer　learning　is　proposed　to　identify　an　HIF　in　a　resonant　grounding　system.　First,　the　wavelet　threshold　denoising　method　based　on　a　comprehensive　strategy　is　used　to　process　the　zero　sequence　current　signal　to　reduce　the　influence　of　noise.　Then,　the　simulated　signal　and　the　measured　signal　after　noise　reduction　are　reconstructed　in　phase　space,　and　the　reconstructed　trajectory　is　obtained　as　the　characteristic　quantity　of　fault　identification.　Finally,　in　the　construction　of　an　identification　model,　the　reconstructed　trajectories　of　simulation　signals　are　investigated　to　train　a　GoogLeNet　model,　and　then　the　measured　signals　are　adopted　to　fine　tune　the　model　to　realize　transfer　learning.　The　advantages　of　the　proposed　algorithm　are　that　the　phase　space　reconstruction　is　used　for　signal　conversion,　the　reconstructed　trajectories　of　fault　signal　and　interference　signal　are　obviously　different,　and　the　reconstructed　trajectories　of　measured　signal　and　simulated　signal　are　highly　similar;　after　the　transfer　learning,　more　accurate　detection　of　the　measured　small　sample　data　is　realized.　The　experimental　results　show　that　the　recognition　accuracy　of　both　fault　measured　data　and　fault　simulation　data　is　more　than　95%.　The　proposed　algorithm　also　achieves　good　results　in　the　case　of　strong　noise　interference,　missing　sampling　data　points　and　intermittent　conduction　of　the　fault　circuit.　©　2022　Power　System　Protection　and　Control　Press.　All　rights　reserved.