.png){kind=icon}
返回
Missing Data Recovery in Large Power Systems Using Network Embedding
.png){kind=icon}
PRE
AI
DOI：10.1109/TSG.2020.3014813.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a novel network-embedding based method to recover the missing measurements in power systems. In particular, we first construct the spatial and temporal graphs to describe both the spatial correlation among the buses in a power flow network and the temporal correlation of the bus states over different time. Secondly, we propose a Softwork algorithm to map the spatial and temporal graphs to low-dimensional spatiotemporal features. Then, we train a regression neural network using the pairs of spatiotemporal features and observed matrix entries. The trained network can then predict the missing measurements. Furthermore, the proposed missing data recovery algorithm can be extended to an online version to recover the missing measurements from streaming data collected in power systems in real time. Numerical experiments on real-world power systems verify the effectiveness of the proposed method. In particular, the proposed method achieves (on average) -55.36 dB, -42.06 dB, -53.26 dB and -45.32 dB relative recovery errors (RREs) for random, row, column and block missing patterns of the voltage magnitude matrix, respectively, which are much smaller than those achieved by the existing methods.
Keyword:
Correlation
Spatiotemporal phenomena
Neural networks
Power grids
Approximation methods
Sparse matrices
Network embedding
matrix completion in power systems
graph data mining
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.8
论文数:
5.7K
被引数:
4.3W
机构
引用论文
Insurance activity and economic performance: Fresh evidence from asymmetric panel causality tests
OAAI
.png){kind=icon}
OAAI分享收藏Voltage Stability Monitoring Based on Disagreement-Based Deep Learning in a Time-Varying Environment
PREAI
分享收藏
查看更多内容