The　open-circuit　fault　in　electric　vehicle　charging　stations　not　only　impacts　the　power　quality　of　the　electrical　grid　but　also　poses　a　threat　to　charging　safety.　Therefore,　it　is　of　great　significance　to　study　open-circuit　fault　diagnosis　for　ensuring　the　safe　and　stable　operation　of　power　grids　and　reducing　the　maintenance　cost　of　charging　stations.　This　paper　addresses　the　multidimensional　characteristics　of　open-circuit　fault　signals　in　charging　stations　and　proposes　a　fault　diagnosis　method　based　on　an　improved　S-transform　and　LightGBM.　The　method　first　utilizes　improved　incomplete　S-transform　and　principal　component　analysis　(PCA)　to　extract　features　of　front-　and　back-stage　faults　separately.　Subsequently,　LightGBM　is　employed　to　classify　the　extracted　features,　ultimately　achieving　fault　diagnosis.　Simulation　results　demonstrate　the　method＇s　effectiveness　in　feature　extraction,　achieving　an　average　diagnostic　accuracy　of　97.04%　on　the　test　dataset,　along　with　notable　noise　resistance　and　real-time　performance.　Additionally,　we　designed　an　experimental　platform　for　diagnosing　open-circuit　faults　in　DC　charging　station　and　collected　experimental　fault　data.　The　results　further　validate　the　effectiveness　of　the　proposed　method.