Aiming at the low fault diagnosis accuracy of IGBTs’ open circuit fault in dual active bridge ( DAB) converter, a fault diagnosis method based on the Levy sparrow search algorithm (LSSA) to optimize the deep belief network (DBN) is proposed. First, the Levy flight strategy improves the convergence speed and global optimization capability of the SSA. Then, the mean square error function of the DBN is taken as the fitness function. The LSSA finds the optimal number of hidden layer units of DBN. According to the optimal number of hidden layers, we construct a DBN open-circuit fault diagnosis model. Through building the hardware-in-the-loop simulation system of DAB converter in RT-LAB, the method uses the transformer leakage current as the diagnostic signal. The comparative analysis is conducted on the convergence speed, fitness value index and diagnosis accuracy. The experiment results show that the diagnosis model can diagnose the open-circuit fault of the DAB converter effectively, and the fault diagnosis accuracy achieves 99%.