Deep-Learning-Based Fault Type Identification Using Modified CEEMDAN and Image Augmentation in Distribution Power Grid

The data mining method is limited to be applied in the distribution network fault diagnosis because of the unbalanced fault sample problem. Aiming at this problem, combining the modified complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) and conditional generative adversarial network (CGAN), a fault identification method for the distribution network was proposed. At first, the CEEMDAN was modified by the partial mean of multi-scale permutation entropy. The MCEEMDAN may decompose the electric signal into a series of intrinsic mode functions. The raw time-domain signal can be transformed into the two-dimensional gray-level image by the pseudo-color coding of the intrinsic mode functions. Then, the fault gray image can be labeled and put into CGAN to generate a large number of new samples to achieve data augmentation. In order to improve the quality of the generated samples, the least square loss function is introduced into the original CGAN network to make the generated samples close to the raw samples. Finally, the convolutional neural network (CNN) is used to mine the fault features autonomously. The Softmax classifier is used to achieve distribution network fault classification. The experiments show that the proposed method can effectively learn the distribution characteristics of the original sample. Furthermore, the fault recognition accuracy can be effectively improved. The proposed method has good stability, fast convergence speed, and high precision, and it can effectively complete the fault identification of the distribution network.