The　inverter　is　one　of　the　most　important　components　in　photovoltaic　and　wind　power　generation　systems,　and　its　stability　is　crucial　to　the　smooth　operation　of　the　system.　Power　devices　are　the　most　fragile　components　in　the　inverter.　Fault　diagnosis　and　timely　processing　can　greatly　improve　the　reliability　of　the　power　generation　system.　Existing　data-driven　fault　diagnosis　methods　are　designed　based　on　fixed　working　conditions.　Once　the　system　parameters　change,　the　diagnosis　accuracy　will　significantly　decrease.　To　solve　these　problems,　this　study　proposes　a　three-phase　inverter　open　circuit　fault　diagnosis　method　based　on　domain　adversarial　neural　network.　This　method　selects　the　three-phase　inverter　phase　voltage　as　the　input　signal,　improves　the　convolutional　neural　network　through　the　Inception　structure,　and　then　uses　the　domain　adversarial　neural　network　to　learn　domain　invariant　features.　Finally,　the　diagnosis　results　are　obtained　based　on　the　output　of　the　fault　classifier.　Experimental　results　show　that　in　transfer　diagnosis　tasks　across　different　systems,　the　method　achieves　an　average　diagnosis　accuracy　of　95.01%　and　exhibits　robustness　in　various　noisy　environments.