It　is　still　a　huge　challenge　for　data-driven　methods　to　detect　high　impedance　fault　(HIF)　within　limited　field　fault　data.　Even　more,　classification　of　imbalanced　data　has　encountered　a　significant　degradation　in　predictive　performance　since　most　classifiers　were　trained　with　the　premise　of　a　relatively　balanced　distribution.　In　this　paper,　a　data-driven　methodology　combined　with　generative　adversarial　network　(GAN)　for　HIF　detection　with　imbalanced　sample　scenarios　is　proposed.　By　extending　the　structure　and　specializing　loss　function,　the　improved　GAN　(IGAN)　can　be　established　to　learn　the　intrinsical　probability　distribution　of　raw　data　and　sample　new　HIF　data　easily.　Adding　the　high-quality　dummy　fault　data　into　the　original　imbalanced　training　data　set,　equitable　predictive　accuracy　can　be　achieved　and　unbiased　classification　models　are　available.　Experimental　results　revealed　that　the　proposed　method　can　generate　data　subject　to　target　distribution　outperformed　existing　methods.　Moreover,　the　validation　results　on　filed　data　revealed　that　the　proposed　method　can　detect　HIF　at　around　60　ms　with　fairly　higher　accuracy　on　imbalanced　samples　compared　to　mainstream　data-driven　algorithms.