CHENG Jiangzhou, LIU Yulin, LIU Songkai, DENG Haifeng
To address the challenges of weak ground fault characteristics and complex power flow directions in distribution networks with high-permeability distributed power sources, where traditional methods struggle to accurately locate fault sections, this paper proposes an active distribution network fault section localization method based on multimodal feature fusion. First, a variational modal decomposition optimized by the GOOSE algorithm is employed to decompose, denoise, and reconstruct the one-dimensional fault current signal. The gram sum field (GASF) is then used to convert the reconstructed signal into a two-dimensional image. Second, a complementary dual-channel model is constructed using ConvNeXt with an efficient channel attention (ECA) mechanism and 1D-CNN to extract deep features from both the two-dimensional image and the one-dimensional current signal. Finally, the dual-channel output feature vectors are concatenated, dynamically weighted via ECA for the fused features, and processed through Softmax to output the fault section location. Simulation experiments demonstrate that the proposed method achieves overall accuracy, precision, recall, and F1 score of 9826%, 9828%, 9827%, and 9820%, respectively, in an enhanced IEEE-33 node distribution system; Accuracy remains above 94% under extreme noise conditions. Multimodal feature fusion demonstrates significant advantages in model comparison, ablation studies, and generalization capability tests.
