中图分类号：TH869 文献标志码：A DOI: 10.16157/j.issn.0258-7998.267757
中文引用格式： 郝景昌，杜文勇，牛逸明，等. 基于大数据的电力智能 IoT 系统故障预测与自愈机制研究[J]. 电子技术应用，2026，52(9)：73-79.
英文引用格式： Hao Jingchang，Du Wenyong，Niu Yiming，et al. Research on fault prediction and self-healing mechanisms in intelligent IoT systems for power based on big data[J]. Application of Electronic Technique，2026，52(9)：73-79.

Research on fault prediction and self-healing mechanisms in intelligent IoT systems for power based on big data

Abstract： D-PMU data in distributed grids often goes missing due to equipment faults and communication issues, impacting grid reliability and constraining fault prediction and self-healing accuracy. This paper proposes the SAITS model, which uses a self-attention mechanism to fill missing D-PMU data. SAITS combines masked prediction and reconstruction tasks, leveraging diagonally masked self-attention to capture long-term dependencies and correlations and effectively impute missing data. It achieves high precision even with up to 90% missing data, outperforming LOCF, BRITS, and TF models in RMSE and MAE, with an R² score above 97%. SAITS enhances data integrity and reliability, and improves the reliability of power grid protection equipment, providing robust support for fault prediction and self-healing in smart power IoT systems to further strengthen the system′s self-healing capability and operational reliability.

Key words : D-PMU；self-attention；data imputation；fault prediction；smart power IoT；reliability