Designing Energy-efficient DC Robotic Machines with Advanced Cyber Security for a Smart Grid System

In this paper, an energy-efficient direct current (DC) robotic machine (E2DCRM) is developed to revolutionize industrial automation, eliminating the rectification stage using brushless alternating direct current (BADC). E2DCRM is integrated with an advanced intelligent grid structure for enhancing the smooth and soft connection process. The emphasis on threats occurs because malicious attacks are overcome using deep learning algorithms. In this approach, a hybrid deep learning-based system is used to eliminate malicious attacks. The communication protocols might operate directly over the power lines, thus eliminating the need for rectification and enabling seamless integration with the smart grid. This research offers a comprehensive, integrated solution combining energy-efficient DC robotics with state-of-the-art cyber security measures. The proposed methodologies address current industrial limitations and set the stage for a resilient and sustainable future in innovative and safe grid technology.

Authors

Institutions

Publication Details

Journal
Proceedings of the Bulgarian Academy of Sciences
Published
2024-09-30
DOI
https://doi.org/10.7546/crabs.2024.09.11
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Designing Energy-efficient DC Robotic Machines with Advanced Cyber Security for a Smart Grid System

M. Dhamodaran, Sivakumar Ramanathan
Proceedings of the Bulgarian Academy of Sciences
Smart Grid Security and Resilience
article

Designing Energy-efficient DC Robotic Machines with Advanced Cyber Security for a Smart Grid System

M. Dhamodaran, Sivakumar Ramanathan
article en

Abstract

In this paper, an energy-efficient direct current (DC) robotic machine (E2DCRM) is developed to revolutionize industrial automation, eliminating the rectification stage using brushless alternating direct current (BADC). E2DCRM is integrated with an advanced intelligent grid structure for enhancing the smooth and soft connection process. The emphasis on threats occurs because malicious attacks are overcome using deep learning algorithms. In this approach, a hybrid deep learning-based system is used to eliminate malicious attacks. The communication protocols might operate directly over the power lines, thus eliminating the need for rectification and enabling seamless integration with the smart grid. This research offers a comprehensive, integrated solution combining energy-efficient DC robotics with state-of-the-art cyber security measures. The proposed methodologies address current industrial limitations and set the stage for a resilient and sustainable future in innovative and safe grid technology.

Proceedings of the Bulgarian Academy of SciencesVol. 77(9)
Mount Zion College of Engineering and Technology
Openalex Percentile: Top 81%
Smart Grid Security and Resilience
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Designing Energy-efficient DC Robotic Machines with Advanced Cyber Security for a Smart Grid System — M. Dhamodaran, Sivakumar Ramanathan · Proceedings of the Bulgarian Academy of Sciences (2024) | TGRS Research Map | TGRS