An Improved Multimodal Framework-Based Fault Classification Method for Distribution Systems Using LSTM Fusion and Cross-Attention

Accurate and rapid diagnosis of fault causes is crucial for ensuring the stability and safety of power distribution systems, which are frequently subjected to a variety of fault-inducing events. This study proposes a novel multimodal data fusion approach that effectively integrates external environmental information with internal electrical signals associated with faults. Initially, the TabTransformer and embedding techniques are employed to construct a unified representation of categorical fault information across multiple dimensions. Subsequently, an LSTM-based fusion module is introduced to aggregate continuous signals from multiple dimensions. Furthermore, a cross-attention module is designed to integrate both continuous and categorical fault information, thereby enhancing the model’s capability to capture complex relationships among data from diverse sources. Additionally, to address challenges such as a limited data scale, class imbalance, and potential mislabeling, this study introduces a loss function that combines soft label loss with focal loss. Experimental results demonstrate that the proposed multimodal data fusion algorithm significantly outperforms existing methods in terms of fault identification accuracy, thereby highlighting its potential for rapid and precise fault classification in real-world power grids.