用多模态大模型分析变电站故障，提升诊断准确率与实用性
SubstationAI: Multimodal Large Model-Based Approaches for Analyzing Substation Equipment Faults
- 基于4万条图文数据构建变电站故障数据库，融合图像生成增强数据
- SubstationAI在故障原因、维修建议等任务上优于GPT-4
- 专为变电站设计的知识库与增强方法，适合电力系统工程师使用
变电站设备可靠性对电网稳定至关重要，但传统故障分析依赖人工经验，难以应对复杂大规模数据。本文提出一种基于多模态大语言模型（MLLM）的变电站设备故障分析方法。我们构建了包含40,000条记录的数据库，涵盖图像、缺陷标签和分析报告，并利用图像到视频生成模型进行数据增强。通过GPT-4生成详细故障分析报告，开发出首个专注于变电站故障分析的SubstationAI模型，设计了故障诊断知识库及知识增强方法。实验表明，SubstationAI在多项评估指标上显著优于现有模型（如GPT-4），在故障成因分析、修复建议和预防措施方面表现更优，为变电站设备故障分析提供了更先进的解决方案。
原文摘要 · Abstract (English)
The reliability of substation equipment is crucial to the stability of power systems, but traditional fault analysis methods heavily rely on manual expertise, limiting their effectiveness in handling complex and large-scale data. This paper proposes a substation equipment fault analysis method based on a multimodal large language model (MLLM). We developed a database containing 40,000 entries, including images, defect labels, and analysis reports, and used an image-to-video generation model for data augmentation. Detailed fault analysis reports were generated using GPT-4. Based on this database, we developed SubstationAI, the first model dedicated to substation fault analysis, and designed a fault diagnosis knowledge base along with knowledge enhancement methods. Experimental results show that SubstationAI significantly outperforms existing models, such as GPT-4, across various evaluation metrics, demonstrating higher accuracy and practicality in fault cause analysis, repair suggestions, and preventive measures, providing a more advanced solution for substation equipment fault analysis.
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