电子科技 ›› 2024, Vol. 37 ›› Issue (2): 6-13.doi: 10.16180/j.cnki.issn1007-7820.2024.02.002

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主动配电网态势感知技术研究综述与展望

满延露,刘敏,王锴   

  1. 贵州大学 电气工程学院,贵州 贵阳 550000
  • 收稿日期:2022-09-23 出版日期:2024-02-15 发布日期:2024-01-18
  • 作者简介:满延露(1997-),女,硕士研究生。研究方向:配电网状态估计。|刘敏(1979-),女,博士,教授。研究方向:电力市场风险评估、配电网需求响应。|王锴(1995-),男,硕士研究生。研究方向:配电网状态估计。
  • 基金资助:
    国家自然科学基金(51967004)

A Review and Prospect of Research on Situational Awareness Technology in Active Distribution Network

MAN Yanlu,LIU Min,WANG Kai   

  1. School of Electrical Engineering,Guizhou University,Guiyang 550000,China
  • Received:2022-09-23 Online:2024-02-15 Published:2024-01-18
  • Supported by:
    National Natural Science Foundation of China(51967004)

摘要:

随着分布式电源以及多元化负荷大规模接入,传统无源配电网逐步转化为有源配电网,配电网的故障种类愈发多样化,工作环境、工作状况与拓扑结构日趋复杂。主动配电网更需通过精进、高效的态势感知技术提高系统运行决策的及时性和准确性,准确预测系统潜在风险。文中阐述了主动配电网中态势感知技术的意义与概念,构建了其基本构架,并对态势觉察、态势理解与态势预测的研究进程、研究难点和未来研究方向进行了详细梳理与总结。

关键词: 主动配电网, 态势感知, 负荷态势感知, 风险预测, 数据驱动, 数据融合, 动态状态估计, 故障定位

Abstract:

With the large-scale access of distributed generation and diversified loads, the traditional distribution network is gradually transformed into an active distribution network,which means that the types of faults in distribution networks are becoming more diverse, and the operating environment, operating conditions and topologies are becoming more and more complex. Therefore, in order to accurately predict the potential risks of the system in the active distribution network, it is necessary to improve the timeliness and accuracy of system operation decisions through sophisticated and efficient situational awareness technology.This study expounds the significance and concept of situational awareness technology in active distribution network, constructs its basic framework, latsly summarizes the research process, research difficulties and future research directions of situational awareness, situation understanding and situation prediction in detail.

Key words: active distribution network, situational awareness, load situational awareness, risk prediction, data driven, data fusion, dynamic state estimation, fault location

中图分类号: 

  • TP29