Advanced Machine Learning-Driven Security and Anomaly Identification in Inverter-Based Cyber-Physical Microgrids
Author:
Affiliation:
1. Department of Electrical and Electronics Engineering, E.G.S. Pillay Engineering College, Nagapattinam, India
2. Department of Electrical & Electronics Engineering, Sona College of Technology, Salem, India
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/15325008.2024.2346790
Reference31 articles.
1. Z. Li, F. Yu and Q. Guan, “Advanced machine learning techniques for intrusion detection in inverter-based microgrids: An LSTM-GBM hybrid approach,” Proceedings of the First International Conference on Cyber-Energy Systems and Intelligent Energy, 2023.
2. A Review of AI-Based Cyber-Attack Detection and Mitigation in Microgrids
3. Cybersecurity of Microgrid: State-of-the-Art Review and Possible Directions of Future Research
4. Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties
5. F. MinUllah A. Ullah and N. Khan “Deep learning assisted anomaly detection in inverter-based cyber-physical microgrids: A CNN-LSTM approach ” Hindawi Complexity pp. 15 2022.
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