Abstract
Microgrid security has become a critical concern due to the increasing reliance on communication technologies and a rising incidence of cyber-threats. While various attack detection and resilient control mechanisms have been developed to fortify microgrid defenses, most research still focuses on simplistic attack scenarios, often ignoring the complex interactions between multiple distributed generators within microgrids. To bridge this gap, this paper proposes a resilient secure control framework capable of addressing cyber-threats across multiple locations within a microgrid. The framework integrates state observations, robust control strategies, and time-varying graph theory to construct a robust defense mechanism. Simulation results are presented to validate the practicality and effectiveness of this approach, confirming its potential to enhance security for future microgrid against cyber-attacks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tan, S., Guerrero, J.M., Xie, P., Han, R., Vasquez, J.C.: Brief survey on attack detection methods for cyber-physical systems. IEEE Syst. J. 14(4), 5329–5339 (2020)
Zhang, W., Qian, T., Chen, X., Huang, K., Tang, W., Wu, Q.: Resilient economic control for distributed microgrids under false data injection attacks. IEEE Trans. Smart Grid 12(5), 4435–4446 (2021)
Tan, S., Wu, Y., Xie, P., Guerrero, J.M., Vasquez, J.C., Abusorrah, A.: New challenges in the design of microgrid system. IEEE Electrification Maga. 8(4), 98–106 (2020)
Tan, S., et al.: Lyapunov-based resilient cooperative control for dc microgrid clusters against false data injection cyber-attacks. IEEE Trans. Smart Grid 15, 3208–3222 (2023)
Tan, S., Xie, P., Guerrero, J.M., Vasquez, J.C., Han, R.: Cyberattack detection for converter-based distributed dc microgrids: observer-based approaches. IEEE Ind. Electron. Mag. 16(3), 67–77 (2021)
Lu, J., Zhang, X., Hou, X., Wang, P.: Generalized extended state observer-based distributed attack-resilient control for dc microgrids. IEEE Trans. Sustain. Energy 13(3), 1469–1480 (2022)
Ismail, M., Shaaban, M.F., Naidu, M., Serpedin, E.: Deep learning detection of electricity theft cyber-attacks in renewable distributed generation. IEEE Trans. Smart Grid 11(4), 3428–3437 (2020)
Liu, X.K., Wen, C., Xu, Q., Wang, Y.W.: Resilient control and analysis for dc microgrid system under dos and impulsive FDI attacks. IEEE Trans. Smart Grid 12(5), 3742–3754 (2021)
Kushal, T.R.B., Lai, K., Illindala, M.S.: Risk-based mitigation of load curtailment cyber attack using intelligent agents in a shipboard power system. IEEE Trans. Smart Grid 10(5), 4741–4750 (2018)
Chen, Y., Qi, D., Dong, H., Li, C., Li, Z., Zhang, J.: A FDI attack-resilient distributed secondary control strategy for islanded microgrids. IEEE Trans. Smart Grid 12(3), 1929–1938 (2020)
Tan, S., Xie, P., Vasquez, J.C., Guerrero, J.M.: Consensus check in the detection of faulty and hijacking attacks for multiple converter-based microgrids. In: 2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia), pp. 2360–2365. IEEE (2024)
Acknowledgements
This work received support from VILLUM FONDEN through the VILLUM Investigator Grant (Grant No. 25920).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Tan, S., Xie, P., Guan, Y., Vasquez, J.C., Guerrero, J.M., Zhang, X. (2025). A Resilient Control Framework for Enhancing Cyber-Security in Microgrids. In: Jørgensen, B.N., Ma, Z.G., Wijaya, F.D., Irnawan, R., Sarjiya, S. (eds) Energy Informatics. EI.A 2024. Lecture Notes in Computer Science, vol 15272. Springer, Cham. https://doi.org/10.1007/978-3-031-74741-0_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-74741-0_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-74740-3
Online ISBN: 978-3-031-74741-0
eBook Packages: Computer ScienceComputer Science (R0)