Root Cause Analysis of Power Grid 5G Network Faults Based on Large Language Model

作者：Zhaorui Guo, Jing Zou, Peizhe Xin, Xiongfei Zhao, Tian Hu, Shangyuan Zhuang, Jiyan Sun, Yinlong Liu, Wei Ma · 年份：2025 · DOI：10.1109/cscwd64889.2025.11033346 · 被引用次数：2 · 研究领域：Power Systems and Technologies、Advanced Computational Techniques and Applications、Smart Grid and Power Systems

The growing complexity and diversity of 5G network architecture (e.g., power grid 5G network) have made security risk assessment and root cause analysis increasingly challenging. Recent advances in large language models (LLMs) have the potential to transform this landscape. However, existing LLMs-based solutions primarily focus on understanding the language of 5G telecommunications, while overlooking potential security vulnerabilities in the data flows. To facilitate LLMs' in-depth application, this paper presents RCA-LLM, a novel fault root cause analysis framework for 5G networks developed from tailored LLMs-based solutions. In explicit terms, RCA-LLM is trained by inputting processed and organized fault information for fine-tuning, and combined with retrieval-augmented generation (RAG) technology to significantly improve the accuracy of 5G fault analysis. Our experimental results indicate that RCA-LLM performs well in fault analysis, effectively supporting users in diagnosing and resolving fault issues. Model evaluation results further demonstrate that the model significantly improves fault analysis accuracy and has high practical value. In addition, RCA-LLM provides important reference value for efficient operation and maintenance management of 5G and future power grid networks, while also offering new ideas for advancing intelligent fault analysis.