Research on Fault Prediction Technology for Power Iot Networks Based on Graph Attention Networks
Jianmei Guo · 2025
With the rapid advancement of computer technology, machine vision and intelligent detection systems have become increasingly integrated into industrial and energy sectors. Their capabilities in precise sensing and real-time analysis are key factors for enhancing equipment operational efficiency and safety. As a critical component of the energy internet, the safe and stable operation of the power IoT is essential to ensuring national energy security and economic development. Thus, improving the accuracy of fault prediction for power IoT devices is of paramount importance. In the context of the rapid development of power IoT technology, effectively leveraging the potential of massive data has become the primary challenge to advancing fault prediction accuracy. Although traditional methods have achieved certain success in fault prediction, they show significant limitations when handling large-scale and dynamic network data. To address this challenge, this paper proposes a fault prediction method for power IoT devices based on Graph Attention Networks (GAT). By incorporating an attention mechanism, the method efficiently identifies critical nodes and enhances the prediction of the states of key devices. Experiments conducted using the PowerGraph dataset demonstrate that the proposed approach achieves a prediction accuracy of$\mathbf{9 8. 1 \%}$, representing a$\mathbf{5. 6 \%}$improvement over traditional methods. Additionally, the GAT model exhibits outstanding adaptability and rapid response capabilities. This research provides a vital data analysis tool for ensuring the efficient operation of power systems, significantly enhancing the ability of operational departments to predict and address potential failures, thereby contributing to the secure and stable operation of power IoT environments.