Real-Time Simulation of a Resilient Control Center for Inverter-Based Microgrids (2405.07106v1)
Abstract: The number of installed remote terminal units (RTU) is on the rise, increasing the observability and control of the power system. RTUs enable sending data to and receiving data from a control center in the power system. A distribution grid control center runs distribution management system (DMS) algorithms, where the DMS takes control actions during transients and outages, such as tripping a circuit breaker and disconnecting a controllable load to increase the resiliency of the grid. Relying on communication-based devices makes the control center vulnerable to cyberattacks, and attackers can send falsified data to the control center to cause disturbances or power outages. Previous work has conducted research on developing ways to detect a cyberattack and ways to mitigate the adverse effects of the attack. This work studies false data injection (FDI) attacks on the DMS algorithm of a fully inverter-based microgrid in real time. The fully inverter-based microgrid is simulated using an RTDS, an amplifier, an electronic load, a server, a network switch, and a router. The DMS is integrated into the server codes and exchanges data with RTDS through TCP/IP protocols. Moreover, a recurrent neural network (RNN) algorithm is used to detect and mitigate the cyberattack. The effectiveness of the detection and mitigation algorithm is tested under various scenarios using the real-time testbed.
- M. K. Hasan, A. A. Habib, Z. Shukur, F. Ibrahim, S. Islam, and M. A. Razzaque, “Review on cyber-physical and cyber-security system in smart grid: Standards, protocols, constraints, and recommendations,” Journal of Network and Computer Applications, vol. 209, p. 103540, Jan. 2023.
- N. Sahani, R. Zhu, J.-H. Cho, and C.-C. Liu, “Machine learning-based intrusion detection for smart grid computing: A survey,” ACM Trans. Cyber-Phys. Syst., vol. 7, no. 2, pp. 1–31, Apr. 2023.
- T. Nguyen, S. Wang, M. Alhazmi, M. Nazemi, A. Estebsari, and P. Dehghanian, “Electric power grid resilience to cyber adversaries: State of the art,” IEEE Access, vol. 8, May 2020.
- M. M. S. Khan, J. A. Giraldo, and M. Parvania, “Attack detection in power distribution systems using a cyber-physical real-time reference model,” IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1490–1499, Mar. 2022.
- P. T. Mana, K. P. Schneider, W. Du, M. Mukherjee, T. Hardy, and F. K. Tuffner, “Study of microgrid resilience through co-simulation of power system dynamics and communication systems,” IEEE Transactions on Industrial Informatics, vol. 17, no. 3, pp. 1905–1915, Apr. 2021.
- M. Beikbabaei, A. Venkataramanan, and A. Mehrizi-Sani, “EMT-based co-simulation of power system and communication networks,” in IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Glasgow, United Kingdom, Oct. 2023.
- M. Chamana, R. Bhatta, K. Schmitt, R. Shrestha, and S. Bayne, “An integrated testbed for power system cyber-physical operations training,” Applied Sciences, vol. 13, no. 16, Aug. 2023.
- M. Beikbabaei, A. Mohammadhassani, V. Krishnan, A. Gorski, A. Mehrizi-Sani, V. k. Shah, A. Perreira da Silva, and J. H. Reed, “Experience in real-time simulation of the power system with 5G communication,” in IEEE Innovative Smart Grid Technologies (ISGT), Washington DC, Feb. 2024.
- I. Ortega-Fernandez and F. Liberati, “A review of Denial of Service attack and mitigation in the smart grid using reinforcement learning,” Energies, vol. 16, no. 635, Jan. 2023.
- S. Wang, S. Bi, and Y.-J. A. Zhang, “Locational detection of the false data injection attack in a smart grid: A multilabel classification approach,” IEEE Internet of Things Journal, vol. 7, no. 9, pp. 8218–8227, Mar. 2020.
- K.-D. Lu, L. Zhou, and Z.-G. Wu, “Representation-learning-based CNN for intelligent attack localization and recovery of cyber-physical power systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 5, pp. 6145–6155, Mar. 2023.
- H. Karimipour, A. Dehghantanha, R. M. Parizi, K.-K. R. Choo, and H. Leung, “A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids,” IEEE Access, vol. 7, pp. 80 778–80 788, May 2019.
- H. Hadian, M. Farrokh, M. Sharif, and A. Jafari, “An elastic and traffic-aware scheduler for distributed data stream processing in heterogeneous clusters,” Journal of Supercomputing, vol. 73, p. 461–498, Jun. 2022.
- H. Hadian and M. Sharifi, “GT-scheduler: a hybrid graph-partitioning and tabu-search based task scheduler for distributed data stream processing systems,” Cluster Computing, Feb. 2024.
- M. Beikbabaei, M. Montano, A. Mehrizi-Sani, and C.-C. Liu, “Mitigating false data injection attacks on inverter set points in a 100% inverter-based microgrid,” in IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington DC, Feb. 2024.
- M. E. Eddin, A. Albaseer, M. Abdallah, S. Bayhan, M. K. Qaraqe, S. Al-Kuwari, and H. Abu-Rub, “Fine-tuned RNN-based detector for electricity theft attacks in smart grid generation domain,” IEEE Open Journal of the Industrial Electronics Society, vol. 3, pp. 733–750, Nov. 2022.
- M. J. Abdulaal, M. I. Ibrahem, M. Mahmoud, S. A. Bello, A. J. Aljohani, A. H. Milyani, and A. M. Abusorrah, “Drfd: Deep learning-based real-time and fast detection of false readings in AMI,” in SoutheastCon, Mobile, AL, Apr. 2022, pp. 682–689.
- S. Y. Diaba, M. Shafie-Khah, and M. Elmusrati, “Cyber security in power systems using meta-heuristic and deep learning algorithms,” IEEE Access, vol. 11, pp. 18 660–18 672, Feb. 2023.
- E. V. A. Martins, E. G. Machado, R. T. B. Gomes, and W. S. Melo, “Blockchain-based architecture to enhance security in distributed measurement systems,” in IEEE Conference on Computer Science and Data Engineering (CSDE), Nadi, Fiji, Dec. 2023, pp. 1–4.
- RTDS examples, “RTDS examples [online],” May. 2024. [Online]. Available: https://www.rtds.com
- N. Souri, S. Farhangi, H. Iman-Eini, and H. Ziar, “Modeling and estimation of the maximum power of solar arrays under partial shading conditions,” in 11th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC), Feb. 2020.
- H. Zhao, Z. Chen, H. Jiang, W. Jing, L. Sun, and M. Feng, “Evaluation of three deep learning models for early crop classification using sentinel-1A imagery time series—A case study in Zhanjiang, China,” Remote Sensing, vol. 11, no. 22, Nov. 2019.
- Keras documents on GRU, “Tensorflow v.2.15.0. Python document [online],” Feb. 2024. [Online]. Available: https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU