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

Yifei Li, Hao Ma, Cheng Gong, Jing Shen, Qiao Zhao, Jun Gu, Yuhang Guo and Bin Yang ()
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Yifei Li: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Hao Ma: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Cheng Gong: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Jing Shen: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Qiao Zhao: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Jun Gu: State Grid Beijing Electric Power Research Institute, Beijing 100075, China
Yuhang Guo: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China
Bin Yang: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China

Energies, 2025, vol. 18, issue 6, 1-15

Abstract: 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.

Keywords: fault cause diagnosis; multimodal data fusion; cross-attention; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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