A grid fault diagnosis framework based on adaptive integrated decomposition and cross-modal attention fusion.

In large-scale power systems, accurately detecting and diagnosing the type of faults when they occur in the grid is a challenging problem. The classification performance of most existing grid fault diagnosis methods depends on the richness and reliability of the data, in addition, it is difficult to obtain sufficient feature information from unimodal circuit signals. To address these issues, we propose a deep residual convolutional neural network (DRCNN)-based framework for grid fault diagnosis. First, we design a comprehensive information entropy value (CIEV) evaluation metric that combines fuzzy entropy (FuzEn) and mutual approximation entropy (MutEn) to integrate multiple decomposition subsequences. Then, DRCNN and heterogeneous graph transformer (HGT) are constructed for extracting multimodal features and considering modal variability. In addition, to obtain the implicit information of multimodal features and control the degree of their performance, we propose to incorporate the cross-modal attention fusion (CMAF) mechanism in the synthesis framework. We validate the proposed method on the three-phase transmission line dataset and VSB power line dataset with accuracies of 99.4 % and 99.0 %, respectively. The proposed method also achieves superior performance compared to classical and state-of-the-art methods.