Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning
The ever-increasing complexity of modern power grids makes them vulnerable to cyber and/or physical attacks. To protect them, accurate attack detection is essential. A challenging scenario is that a localized attack has occurred on a specific transmission line but only a small number of transmission...
Saved in:
Main Authors: | Zheng-Meng Zhai, Mohammadamin Moradi, Ying-Cheng Lai |
---|---|
Format: | Article |
Language: | English |
Published: |
American Physical Society
2025-02-01
|
Series: | PRX Energy |
Online Access: | http://doi.org/10.1103/PRXEnergy.4.013003 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
State of Charge Estimation for Lithium Battery in Shipboard DC Power Grid Based on Differential Evolutionary Algorithm
by: Yanbiao Li, et al.
Published: (2025-01-01) -
Adversarial Attack Detection in Smart Grids Using Deep Learning Architectures
by: Stephanie Ness
Published: (2025-01-01) -
Grid Connection of a Squirrel-Cage Induction Generator Excited by a Partial Power Converter
by: Dominik A. Górski, et al.
Published: (2025-01-01) -
Flexibility estimation of electric vehicles and its impact on the future power grid
by: Jiexiang Wu, et al.
Published: (2025-03-01) -
A large synthetic dataset for machine learning applications in power transmission grids
by: Marc Gillioz, et al.
Published: (2025-01-01)