Research on Fault Identification of Renewable Energy Plant Outgoing Lines Based on MARS-Net and DT-MobileNetV3

To address the challenges of fault identification in renewable energy plant outgoing lines within “double-high” power systems, this paper proposes a novel parallel dual-channel method that fuses time-series signals and images. On one hand, the fault current signals from the renewable energy plant outgoing lines are acquired and fed into a constructed Multi-scale Adaptive Residual Shrinkage Network (MARS-Net) for one-dimensional temporal feature extraction. On the other hand, one-dimensional fault data is transformed into two-dimensional images via a Relative Angle Matrix (RAM). The generated 2D image data is then input into a network incorporating Dynamic Convolution (D-Conv) and a Transformer-enhanced MobileNetV3 (DT-MobileNetV3) for spatial feature extraction. Finally, feature fusion of the one-dimensional and two-dimensional information is performed to achieve fault type identification. To comprehensively evaluate the method’s performance, this paper designs experiments including noise interference tests, multi-network comparative experiments, ablation studies, comparisons of different 2D transformation methods and data loss. The results demonstrate that the proposed method possesses significant advantages in terms of identification accuracy, noise immunity, data loss tolerance, and generalization capability.