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: https://doi.org/10.1007/s00521-021-06106-3. 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.
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