Monday, June 5 2023 Sign In   |    Register

News Quick Search



Front Page
Power News
Today's News
Yesterday's News
Week of May 29
Week of May 22
Week of May 15
Week of May 08
Week of May 01
By Topic
By News Partner
Gas News
News Customization


Pro Plus(+)

Add on products to your professional subscription.
  • Energy Archive News

    Home > News > Power News > News Article

    Share by Email E-mail Printer Friendly Print

    Investigators from Indian Institute of Technology Have Reported New Data on Information Technology (Deep-learning-based Data-manipulation Attack Resilient Supervisory Backup Protection of Transmission Lines)

    March 24, 2023 - NewsRx Policy and Law Daily


      2023 MAR 23 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Policy and Law Daily -- A new study on Information Technology is now available. According to news originating from New Delhi, India, by NewsRx correspondents, research stated, “Cyber-attacks on smart-grid systems have become increasingly more complicated, and there is a need for taking detection and mitigation measures to combat their adverse effects on the smart-grid infrastructure. Wide area measurement system (WAMS) infrastructure comprising of phasor measurement units (PMUs) has recently shown remarkable progress in solving complex power system problems and avoiding blackouts.”

      Financial support for this research came from Ministry of Science and Technology, Department of Science and Technology, India, under project S3RACPPS (Research and development of Smart, Secure, Scalable, Resilient and Adaptive CyberPhysical Power System).

      Our news journalists obtained a quote from the research from the Indian Institute of Technology, “However, WAMS is vulnerable to cyber-attacks. This paper presents a novel cyber-attack resilient WAMS framework incorporating both attack detection and mitigation modules that ensure the resiliency of PMU data-based supervisory protection applications. It includes deep learning-based Long Short Term Memory (LSTM) model for real-time detection of anomalies in time-series PMU measurements and isolating the compromised PMUs followed by Generative Adversarial Imputation Nets (GAIN) for the reconstruction of the compromised PMU’s data. The corrected PDC data-stream is then forwarded to the decision-making end application, making it resilient against attacks. A Random Forrest classifier is used in the end application to distinguish fault events from other disturbances and supervise the third zone of distance relay for backup protection of transmission lines. The efficacy of the proposed framework for different attack scenarios has been verified on the WSCC 9-Bus System modeled on a developed real-time digital simulator (RTDS)-based integrated cyber-physical WAMS testbed.”

      According to the news editors, the research concluded: “Experimental analysis shows that the proposed model successfully detects and mitigates attacks’ adverse effects on the end application.”

      This research has been peer-reviewed.

      For more information on this research see: Deep-learning-based Data-manipulation Attack Resilient Supervisory Backup Protection of Transmission Lines. Neural Computing and Applications, 2023;35(7):4835-4854. Neural Computing and Applications can be contacted at: Springer London Ltd, 236 Grays Inn Rd, 6TH Floor, London WC1X 8HL, England.

      The news correspondents report that additional information may be obtained from Astha Chawla, Indian Institute of Technology, Electrical Engineering Department, New Delhi 110016, India. Additional authors for this research include Bijaya Ketan Panigrahi, Prakhar Agrawal and Kolin Paul.

      The direct object identifier (DOI) for that additional information is: This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

      (Our reports deliver fact-based news of research and discoveries from around the world.)


    Other Articles - International


       Home  -  Feedback  -  Contact Us  -  Safe Sender  -  About Energy Central   
    Copyright © 1996-2023 by CyberTech, Inc. All rights reserved.
    Energy Central® and Energy Central Professional® are registered trademarks of CyberTech, Incorporated. Data and information is provided for informational purposes only, and is not intended for trading purposes. CyberTech does not warrant that the information or services of Energy Central will meet any specific requirements; nor will it be error free or uninterrupted; nor shall CyberTech be liable for any indirect, incidental or consequential damages (including lost data, information or profits) sustained or incurred in connection with the use of, operation of, or inability to use Energy Central. Other terms of use may apply. Membership information is confidential and subject to our privacy agreement.