.png){kind=icon}
返回
Graph-learning-assisted state estimation using sparse heterogeneous measurements
.png){kind=icon}
PRE
AI
DOI：10.1016/j.epsr.2024.110644.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Unlike transmission systems, distribution systems historically lack enough measurements, making their realtime monitoring almost impossible. Recent deployment of diverse types of devices such as phasor measurement units (PMUs), smart meters, solar inverters and weather information sensors opens up new ways of monitoring these systems, with the assistance of customized machine learning (ML) applications. The paper describes a grid-model-informed machine learning (ML) tool which integrates heterogeneous data streams and creates synchronous measurement snapshots to be used by a hybrid robust state estimator (SE) which provides not only accurate state estimates but also real-time feedback for ML model refinement. Improved monitoring performance due to the use of developed computational framework is experimentally observed by simulated scenarios on an electric utility's distribution system.
Keyword:
Distribution systems
Graph learning
Machine learning
Robust state estimation
System monitoring
期刊
IF:
4.2
论文数:
1.2W
被引数:
2.2W
机构
查看更多机构
引用论文
Optimal Meter Placement for Robust Measurement Systems in Active Distribution Grids主动配电网中鲁棒测量系统的最佳电表放置
.png){kind=icon}
PREAI
分享收藏
Developing Wind and/or Solar Powered Crop Irrigation Systems for the Great Plains为大平原开发风能和/或太阳能作物灌溉系统
PREAI
分享收藏 没有更多内容