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<p>Timely detection and resolution of power equipment failures are vital for ensuring the stability and reliability of electrical grids. This paper presents a knowledge graph completion model (KGCM) that integrates multi-task learning with pre-trained models to enhance fault-handling knowledge graph representations. Entities and relations are encoded with a pre-trained model and refined through Translating Embeddings (TransE)-based structural constraints, while a Siamese network is introduced to improve feature extraction and robustness by predicting missing triplet components. The proposed framework was validated on a dataset from the State Grid Tianjin as well as standard benchmarks. Experimental results show that KGCM consistently outperforms the baseline Language Model Knowledge Embedding with TransE (LMKE-TransE) model in terms of Mean Reciprocal Rank (MRR) and Hits at N (Hits@N) metrics. In practical fault-action prediction, KGCM demonstrates superior accuracy, enabling more reliable diagnosis and handling of power grid faults. These findings confirm the effectiveness of combining pre-trained models, structural fine-tuning, and multi-task feature extraction for knowledge graph completion, and highlight the model’s potential to enhance automation and dependability in power grid fault management.</p>

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Enhancing reliability in power grid fault management via multi-task learning-based knowledge graph completion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

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  • Zhigang Huang,
  • Qiang Han,
  • Lingxu Guo,
  • Yue Zhang,
  • Guangyao Yu,
  • Zhiqiang Chong,
  • Zhenbin Li,
  • Weimin Mi

摘要

Timely detection and resolution of power equipment failures are vital for ensuring the stability and reliability of electrical grids. This paper presents a knowledge graph completion model (KGCM) that integrates multi-task learning with pre-trained models to enhance fault-handling knowledge graph representations. Entities and relations are encoded with a pre-trained model and refined through Translating Embeddings (TransE)-based structural constraints, while a Siamese network is introduced to improve feature extraction and robustness by predicting missing triplet components. The proposed framework was validated on a dataset from the State Grid Tianjin as well as standard benchmarks. Experimental results show that KGCM consistently outperforms the baseline Language Model Knowledge Embedding with TransE (LMKE-TransE) model in terms of Mean Reciprocal Rank (MRR) and Hits at N (Hits@N) metrics. In practical fault-action prediction, KGCM demonstrates superior accuracy, enabling more reliable diagnosis and handling of power grid faults. These findings confirm the effectiveness of combining pre-trained models, structural fine-tuning, and multi-task feature extraction for knowledge graph completion, and highlight the model’s potential to enhance automation and dependability in power grid fault management.