Authors :
P.B. Samiullah Khan; G. Ravi Teja Reddy; R. Selvameena
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
http://tinyurl.com/47upvr6j
Scribd :
http://tinyurl.com/nza838uv
DOI :
https://doi.org/10.5281/zenodo.10643084
Abstract :
As active distribution systems are widely used
and complex, securing them with renewable energy can
be challenging. To tackle this difficulty, a two-stage
methodology is proposed in this research. Deep learning
is utilized to identify even the most minor cyber-attacks
in electrical waveforms, and a hierarchical localization
technique is then applied to determine the attack's
source. This technique uses waveform analysis in
conjunction with network partitioning to precisely
identify attacks. The suggested methodology provides a
viable means of improving cyber security in these
developing power systems, outperforming current
approaches in simulations. Its capacity to recognize
different kinds of attacks, manage big networks, and
interact with current security protocols for practical
application might all be investigated further.
As active distribution systems are widely used
and complex, securing them with renewable energy can
be challenging. To tackle this difficulty, a two-stage
methodology is proposed in this research. Deep learning
is utilized to identify even the most minor cyber-attacks
in electrical waveforms, and a hierarchical localization
technique is then applied to determine the attack's
source. This technique uses waveform analysis in
conjunction with network partitioning to precisely
identify attacks. The suggested methodology provides a
viable means of improving cyber security in these
developing power systems, outperforming current
approaches in simulations. Its capacity to recognize
different kinds of attacks, manage big networks, and
interact with current security protocols for practical
application might all be investigated further.
