Home / Current Issue / Paper 1710987
AI Based Fault and Anomaly Detection in Power Systems
Subject area: Science,Engineering and Technology · Area of research: Power System
DOI: 10.64388/IREV9I3-1710987-4767
Abstract
Artificial -based infiltration system detection systems (IDs) are emerging as an important defense mechanism for modern power systems, which are rapidly weak for cyber-attacks due to comprehensive digitization, integration of IOT devices and dependence on comprehensive field communication networks. Traditional rules-based IDS methods often fail to detect sophisticated hazards such as false data injection attacks, refusal-service (DOS), and load-transport infiltration, especially large scale, smart grids were distributed. The AI-operated IDS leverage machine learning, deep learning, and hybrid models to detect discrepancy, adapted to develop the attack pattern, and supported real-time status awareness. This review examines the approach to detect state-of-the-art AI-based infiltration for electrical systems, which focus on detection methods, datasets, evaluation matrix and implementation challenges. Special emphasis is given to explain capacity, scalability and integration with supervisory control and data acquisition (SCADA), Phasor Measurement Units (PMU), and distributed energy resources (DERS). Finally, open research intervals and future instructions are highlighted, including federated learning, graphs in graph neural network-based detections include graph neural network-based detections and digital twin-capable cyber-flexibility.
Keywords
Infiltration detection system (IDS), Artificial Intelligence, Fals Data Injection Attack, Cyber-Figure Security, Power System Protection.
References
[1] Hasan, M. K., Alkhalifah, A., Islam, S., Babiker, N. B., Habib, A. A., Aman, A. H. M., & Hossain, M. A. (2022). Blockchain technology on smart grid, energy trading, and big data: security issues, challenges, and recommendations. Wireless Communications and Mobile Computing, 2022(1), 9065768.
[2] Tatipatri, N., & Arun, S. L. (2024). A comprehensive review on cyber-attacks in power systems: Impact analysis, detection, and cyber security. IEEE Access, 12, 18147-18167.
[3] Yusifov, S., & Muradli, M. (2023, May). Limitation of modes with relay protection. In 1st INTERNATIONAL CONFERENCE ON THE 4th INDUSTRIAL REVOLUTION AND INFORMATION TECHNOLOGY (Vol. 1, No. 1, pp. 291-295). Azərbaycan Dövlət Neft və Sənaye Universiteti.
[4] Li, Y., Wei, X., Li, Y., Dong, Z., & Shahidehpour, M. (2022). Detection of false data injection attacks in smart grid: A secure federated deep learning approach. IEEE Transactions on Smart Grid, 13(6), 4862-4872.
[5] Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021). Machine learning for anomaly detection: A systematic review. Ieee Access, 9, 78658-78700.
[6] Rodrigues, N. M., Janeiro, F. M., & Ramos, P. M. (2023). Deep learning for power quality event detection and classification based on measured grid data. IEEE Transactions on Instrumentation and Measurement, 72, 1-11.
[7] Qu, Z., Liu, H., Wang, Z., Xu, J., Zhang, P., & Zeng, H. (2021). A combined genetic optimization with AdaBoost ensemble model for anomaly detection in buildings electricity consumption. Energy and Buildings, 248, 111193.
[8] Chandrasekaran, K., Selvaraj, J., Xavier, F. J., & Kandasamy, P. (2021). Artificial neural network integrated with bio-inspired approach for optimal VAr management and voltage profile enhancement in grid system. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 43(21), 2838-2859.
[9] Ilo, A., & Schultis, D. L. (2022). A holistic solution for smart grids based on LINK-paradigm (Vol. 340). Berlin/Heidelberg, Germany: Springer.
[10] Yan, K., Liu, X., Lu, Y., & Qin, F. (2022). A cyber-physical power system risk assessment model against cyberattacks. IEEE Systems Journal, 17(2), 2018-2028.
How to cite this paper
@article{1710987,
author = {Jenisha K J, Dr. R. Suresh Kumar},
title = {AI Based Fault and Anomaly Detection in Power Systems},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {3},
pages = {2046-2049},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1710987.pdf},
abstract = {Artificial -based infiltration system detection systems (IDs) are emerging as an important defense mechanism for modern power systems, which are rapidly weak for cyber-attacks due to comprehensive digitization, integration of IOT devices and dependence on comprehensive field communication networks. Traditional rules-based IDS methods often fail to detect sophisticated hazards such as false data injection attacks, refusal-service (DOS), and load-transport infiltration, especially large scale, smart grids were distributed. The AI-operated IDS leverage machine learning, deep learning, and hybrid models to detect discrepancy, adapted to develop the attack pattern, and supported real-time status awareness. This review examines the approach to detect state-of-the-art AI-based infiltration for electrical systems, which focus on detection methods, datasets, evaluation matrix and implementation challenges. Special emphasis is given to explain capacity, scalability and integration with supervisory control and data acquisition (SCADA), Phasor Measurement Units (PMU), and distributed energy resources (DERS). Finally, open research intervals and future instructions are highlighted, including federated learning, graphs in graph neural network-based detections include graph neural network-based detections and digital twin-capable cyber-flexibility.},
keywords = {Infiltration detection system (IDS), Artificial Intelligence, Fals Data Injection Attack, Cyber-Figure Security, Power System Protection.},
month = {September},
doi = {https://doi.org/10.64388/IREV9I3-1710987-4767}
}