To　solve　the　problem　that　the　identification　of　a　high　impedance　grounding　fault　(HIF)　in　distribution　networks　is　easily　affected　by　noise,　and　the　fact　that　it　is　difficult　to　use　unlabeled　data,　a　semi-supervised　identification　method　of　a　high　resistance　grounding　fault　based　on　wavelet　denoising　and　random　forest　is　proposed.　Different　from　supervised　learning　only　using　labeled　data,　the　method　can　make　full　use　of　labeled　and　unlabeled　data　by　collaborative　training.　First,　the　wavelet　threshold　denoising　algorithm　is　used　to　filter　the　noise　of　zero-sequence　currents.　Secondly,　the　occurrence　of　an　HIF　can　be　detected　by　the　peak　and　valley　fault　triggering　algorithm.　Then,　applying　wavelet　transform　to　zero-sequence　currents,　the　wavelet　coefficients　are　extracted　as　fault　features.　Finally,　two　random　forests　are　collaboratively　trained　with　selected　features　to　construct　a　semi-supervised　classifier　to　detect　the　HIF.　The　simulation　results　show　that　the　proposed　method　can　use　fully　the　key　features　in　unlabeled　data　in　the　existed　fault　cases　in　distribution　network　to　improve　the　accuracy　of　fault　calssification.　It　has　strong　reliability　and　flexibility.　©　2022　Power　System　Protection　and　Control　Press.　All　rights　reserved.