| 刘嘉文,赵力,张丹宏.基于VOSviewer的电网环境风险评估研究进展[J].矿产勘查,2026,17(S1):352-367 |
| 基于VOSviewer的电网环境风险评估研究进展 |
| Research progress on power grid environmental risk assessment based on VOSviewer |
| 投稿时间：2026-02-28 修订日期：2026-05-11 |
| DOI：10.20008/j.kckc.2026S1040 |
| 中文关键词: 电网环境风险 风险评估 韧性电网 机器学习 |
| 英文关键词: power grid environmental risk risk assessment resilient power grid machine learning |
| 基金项目:本文受广东电网规划专题研究项目（031000QQ00240028）资助。 |
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| 摘要点击次数: 4 |
| 全文下载次数: 2 |
| 中文摘要: |
| 随着新型电力系统建设的加速推进及极端气候事件频发，电网运行环境的复杂性显著提升，环境风险已成为威胁电网安全稳定运行的重要因素。本文系统梳理国内外电网环境风险评估的研究进展，运用VOSviewer可视化分析工具，对1996—2025年间CNKI和WOS数据库中电网风险领域的文献展开研究状况分析。此外，重点综述了国外基于概率模型、系统韧性及数据驱动与人工智能的3类电网环境评估方法，国内传统环境风险评估方法、机器学习模型及GIS与大数据结合的应用特点。研究发现，电网环境风险研究成果更多以英文形式面向全球输出，中国作者的发文量占比最高。中国国家电网、华北电力大学与美国能源部在电网风险研究领域的发文量显著高于其他研究机构。未来电网环境风险评估的研究热点将往传统环境风险评估方法与机器学习、大数据模型相结合的方向进行探索。 |
| 英文摘要: |
| With the accelerated advancement of the construction of China’s new power system and the frequent occurrence of extreme climate events， the complexity of the power grid operation environment has increased significantly， and environmental risks have become an important factor threatening the safe and stable operation of power grids. This paper systematically combs through the research progress of power grid environmental risk assessment at home and abroad， and uses the VOSviewer visualization analysis tool to analysis tool to analyze the research status of literatures in the field of power grid risk published in the CNKI and WOS databases from 1996 to 2025. In addition， this study focuses on summarizing three types of overseas power grid environmental assessment methods， which are based on probabilistic models， system resilience， and data-driven artificial intelligence， as well as the application characteristics of domestic traditional environmental risk assessment methods， machine learning models， and the integration of GIS with big data. The findings indicate that research achievements in power grid environmental risk are mostly output to the world in English， and Chinese authors account for the highest proportion of published papers. Institutions such as State Grid Corporation of China， North China Electric Power University， and the U.S. Department of Energy have significantly higher publication volumes in the field of power grid risk research than others. The future research hotspots are expected to explore the integration of traditional environmental risk assessment methods with machine learning and big data models. |
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