摘要
数据驱动的电力网络分析与优化近年来受到广泛关注。首先,对比了电力网络分析与优化中数据驱动及基于物理模型方法的思维模式,阐述了数据驱动方法和基于模型方法的区别与联系。进一步,对于电力网络分析与优化研究对象的分类,分别从拓扑辨识、参数—拓扑联合辨识、系统矩阵辨识、潮流计算及最优潮流计算等多个方面总结了现有数据驱动电力网络分析与优化的研究进展,总结了现有研究中采用的数据驱动方法。最后,提出了数据驱动电力网络分析与优化所面临的挑战,展望了该领域未来可能的研究方向。
Data-driven power grid analysis and optimization have attracted broad attention in recent years.Firstly,a comparison of thinking modes between data-driven and physical model based methods in power grid analysis and optimization is conducted and the contacts and differences between data-driven and model based methods are put forward.Furthermore,the study subjects of power grid analysis and optimization are classified,the research programs of existing data-driven power grid analysis and optimization are summarized,which are based on the aspects of topology identification,parameter-topology joint identification,system matrix identification,power flow calculation,and optimal power flow calculation and so on.Key technologies of data-driven power grid analysis and optimization are summarized.Finally,challenges and future research trends in this field are proposed and the future research directions are prospected.
作者
刘羽霄
张宁
康重庆
LIU Yuxiao, ZHANG Ning, KANG Chongqing(State Key Laboratory of Control and Simulation of Power System and Generation Equipments, Tsinghua University, Beijing 100084, Chin)
出处
《电力系统自动化》
EI
CSCD
北大核心
2018年第6期157-167,共11页
Automation of Electric Power Systems
基金
国家重点研发计划资助项目(2016YFB0900100)
国家自然科学基金国际(地区)合作与交流项目(51620105007)
国家电网公司科技项目“基于运行模拟与潮流分析一体化的电网规划关键技术研究”
关键词
数据驱动
大数据潮流
回归
拓扑辨识
最优潮流
data-driven
big data power flow
regression
topology identification
optimal power flow
