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极端自然灾害下新型电力系统态势感知与知识图谱构建策略

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面对极端自然灾害时,传统方法难以准确捕捉灾害事件的动态特征和风险演化规律.本文提出一种基于知识图谱(knowledge graph,KG)的电网故障处理框架.该框架结合电网拓扑图谱、运行逻辑图谱和故障案例图谱,旨在提升系统的态势感知(situational awareness,SA)能力,并强化信息-物理融合系统的可观性和可控性.通过实时大数据的挖掘和全过程知识图谱链路的构建,实现了对极端自然灾害事件的提前感知、情景描述、态势理解和演变趋势分析.本文还以"720郑州特大暴雨"为例,展示了灾前预警、灾中应急响应策略和灾后恢复的全过程;此外,通过故障处理KG的动态更新,确保了知识的持续积累,并为类似灾害提供了有效的知识支持.本研究不仅提高了新型电力系统在极端自然灾害下的应对能力,还为未来能源互联网的灾害应对提供了有力的理论支撑和实践指导.
Strategy for Situational Awareness and Knowledge Graph Construction of New Power Systems Under Extreme Natural Disasters
Encountering extreme natural disasters,traditional methods are difficult to accurately capture the dynamic characteristics and risk evolution laws of disaster events.This article proposes a power grid fault handling framework based on Knowledge Graph(KG).This framework combines power grid topology graph,operational logic graph,and fault case graph,aiming to enhance the situational awareness(SA)capability of the system and enhance the observability and controllability of the information physical fusion system.Through real-time big data mining and the construction of a full process knowledge graph link,advanced perception,scenario description,situation understanding,and evolution trend analysis of extreme natural disaster events have been achieved.This paper also takes"720 Zhenzhou Heavy Rainstorm"as an example to show the whole process of pre-disaster early warning,emergency response strategy in disaster and post disaster recovery.In addition,the dynamic update of fault handling KG ensures the continuous accumulation of knowledge and provides effective knowledge guidance support for similar disasters.This research not only improves the response capability of the new power system under extreme natural disasters,but also provides strong theoretical support and practical guidance for future energy Internet disaster response.

extreme natural disastersknowledge graphsituational awarenessknowledge support

郁海彬、唐亮、陈浩年、张伟、姜玉靓、张煜晨、程涛

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国网上海市电力公司市北供电公司,上海 200070

国网上海电缆公司,上海 200072

国网上海市电力公司培训中心,上海 200438

国网上海市电力公司松江供电公司,上海 201699

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极端自然灾害 知识图谱 态势感知 知识支持

国家电网上海市电力公司科技项目

52090W220001

2024

电力大数据
贵州电力试验研究院 贵州省电机工程学会

电力大数据

影响因子:0.047
ISSN:2096-4633
年,卷(期):2024.27(1)
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