数据驱动的电力网络分析与优化研究综述

LIU Yuxiao

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数据驱动的电力网络分析与优化研究综述

更新时间：2025-08-13

- 数据驱动的电力网络分析与优化研究综述
- Automation of Electric Power Systems Vol. 42, Issue 6, Pages: 157-167(2018)
- 作者机构：
  
  电力系统及发电设备控制和仿真国家重点实验室, 清华大学,北京市,100084
- 作者简介：
- 基金信息：
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- Published：2018
- 稿件说明：
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LIU Yuxiao. [J]. Automation of electric power systems, 2018, 42(6): 157-167.
DOI：

LIU Yuxiao. [J]. Automation of electric power systems, 2018, 42(6): 157-167. DOI：

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。

Abstract

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.

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