Overview of Data-driven Power Flow Linearization

被引:2
|
作者
Jia, Mengshuo [1 ]
Hug, Gabriela [1 ]
机构
[1] Swiss Fed Inst Technol, Power Syst Lab, Zurich, Switzerland
关键词
Power flow linearization; data-driven; machine learning; regression; programming; RIDGE-REGRESSION; SENSITIVITY; MODEL;
D O I
10.1109/POWERTECH55446.2023.10202779
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The accuracy limitation of physics-driven power flow linearization approaches and the widespread deployment of advanced metering infrastructure render data-driven power flow linearization (DPFL) methods a valuable alternative. While DPFL is still an emerging research topic, substantial studies have already been carried out in this area. However, a comprehensive overview and comparison of the available DPFL approaches are missing in the existing literature. This paper intends to close this gap and, therefore, provides a narrative overview of the current DPFL research. Both the challenges (including data-related and power-system-related issues) and methodologies (namely regression-based and tailored approaches) in DPFL studies are surveyed in this paper; numerous future research directions of DPFL analysis are discussed and summarized as well.
引用
收藏
页数:6
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