Multimodal fusion of deep neural networks for transmission line flashover classification

ABSTRACT

The power system plays a vital role in modern society. However, the occurrence of transmission line flashover faults seriously threatens the safety and stability of the power system. In this paper, a deep neural network flashover classification model based on multimodal attention aggregation is proposed. By using the model, the problems of insufficient feature extraction capability of the unimodal flashover data model and easy loss of key feature information in the process of audiovisual multimodal flashover data alignment are solved. Experiments on the arc- flashover fault dataset show that the flashover fault detection model proposed in this paper can be used stably and effectively for flashover fault detection.

GRAPHICAL ABSTRACT

KEYWORDS:

• Flashover fault
• multimodal data fusion
• cross-modal attention
• multi-attention mechanism

Acknowledgment

This work was supported by the Research on Intelligent Diagnosis Technology for Key Components of Power Transmission Lines Based on Flexible Self-energy Sensing and Flexible Networking Technology under Grant GXKJXM20220062.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Research on Intelligent Diagnosis Technology for Key Components of Power Transmission Lines Based on Flexible Self-energy Sensing and Flexible Networking Technology: [grant no GXKJXM20220062].