[Objective] Aiming at the problem that the existing methods for detecting faults in flexible DC distribution networks have low accuracy and high cost of acquiring fault signals

this paper proposes a fault accurate detection method based on the Transformer algorithm and the generative adversarial network (GAN) fusion model. [Methods] Firstly

the effective modal components of the preprocessed DC distribution network fault voltage and current signals were extracted by adaptive variational mode decomposition method

and the effective feature vectors were extracted by Transformer algorithm. Then

the GAN model generator was replaced by Transformer algorithm

and the TransGAN deep learning model was established based on it. Finally

a simulation model was established based on Matlab/Simulink to verify the effectiveness and accuracy of the proposed method. [Results] Experimental results showed that this method has significant advantages in improving fault detection accuracy and reducing false alarm rate. Compared with the existing detection methods

it has higher fault recognition accuracy and stronger generalization ability. [Conclusion] The proposed method achieving higher detection accuracy

the required data set size is smaller than other methods

which reduces the data acquisition cost and computational complexity

and improves the practical application value and engineering feasibility of the model.