郑翔,陈韶昱,吴佳毅,张一康,张敏,于逸廷,薛安成
ZHENG Xiang,CHEN Shaoyu,WU Jiayi,ZHANG Yikang,ZHANG Min,YU Yiting,XUE Ancheng

• 1:国网浙江省电力有限公司衢州供电公司
• 2:国网浙江省电力有限公司
• 3:新能源电力系统全国重点实验室(华北电力大学)
• 4:国网电力科学研究院有限公司

摘要(Abstract)：

随着变电站保护监控系统数字化、智能化的推进，二次系统产生的多源异构数据规模急剧增长、维度持续攀升，并出现异常频发、存储与计算成本增加等问题。因此，清洗监测数据，归并冗余信息，构建高效的数据质量提升体系，成为支撑新型电力系统发展的关键。基于上述背景，系统梳理了变电站监测数据清洗研究进展，并结合LLMs（大语言模型）技术发展趋势，探讨MLLMs（多模态大语言模型）在数据质量提升中的应用潜力。首先，分析了变电站监测数据的基本特征及其质量提升的迫切需求。其次，围绕异常数据检测、异常类型辨识和数据修复三个关键环节，分析总结了现有数据清洗技术的原理、优缺点及适用场景，进一步探讨了MLLMs在相关任务中的应用路径。最后，展望未来发展方向，特别分析了MLLMs技术应用需解决的关键问题。
With the advancement of digitalization and intelligence in substation protection and monitoring systems, the volume of multi-source heterogeneous data generated by secondary systems has increased sharply, with continuously rising dimensionality. This has led to issues such as frequent anomalies, increased storage and computational costs. Therefore, cleaning monitoring data, consolidating redundant information, and establishing an efficient framework for data quality improvement have become critical to supporting the development of modern power systems. In light of this, this paper reviews the research progress in substation monitoring data cleaning and, in combination with the development trends in large language models(LLMs), explores the application potential of multimodal large language models(MLLMs) for data quality improvement. First, the fundamental characteristics of substation monitoring data and the urgent need for improving data quality are analyzed. Then, it examines the principles, advantages, disadvantages, and applicable scenarios of existing data cleaning techniques around three key aspects: anomaly data detection, anomaly type identification, and data restoration. Further, it discusses the potential application pathways of MLLMs for these tasks. Finally, it outlines future development directions, with particular emphasis on the key issues that must be addressed in applying LLMs technologies.

关键词(KeyWords)： 变电站二次系统;数据质量提升;数据清洗;大语言模型
substation secondary system;data quality improvement;data cleaning;LLMs

Abstract:

Keywords:

基金项目(Foundation): 国家电网有限公司总部管理科技项目（5108-202319438A-3-2-ZN）

作者(Author): 郑翔,陈韶昱,吴佳毅,张一康,张敏,于逸廷,薛安成
ZHENG Xiang,CHEN Shaoyu,WU Jiayi,ZHANG Yikang,ZHANG Min,YU Yiting,XUE Ancheng

DOI: 10.19585/j.zjdl.202605001

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