Abstract
With the uptake of advanced communication technologies, monitoring and operation of power systems have been experiencing a paradigm shift. Many centralized algorithms can now be implemented in a distributed scheme. For example, local decisions can make faster actions resulting in a resilient power grid. Despite these advantages, these communication networks have made power networks vulnerable to cyber-attacks. Therefore, only attack-resilient algorithms can be reliably implemented using these technologies. In this chapter, a Data-Driven Cyber-Resilient Control (DDCRC) method is proposed for the frequency stability of wide area power grids. In the proposed method, the automatic generation control signals are generated without using a predefined model such that the frequency stability of the power system in the presence of Deception Attacks (DA) is guaranteed. Simulation results on a three-area power grid show the efficiency and superiority of the proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
G. Bedi, G.K. Venayagamoorthy, R. Singh, R.R. Brooks, K.-C. Wang, Review of Internet of Things (IoT) in electric power and energy systems. IEEE Internet Things J. 5(2), 847–870 (2018)
M. Tan, Z. Song, X. Zhang, Robust leader-following consensus of cyber–physical systems with cyber-attack via sampled-data control. ISA Trans. 109, 61–71 (2021)
D. Ye, S. Luo, A co-design methodology for cyber-physical systems under actuator fault and cyber-attack. J. Franklin Inst. 356(4), 1856–1879 (2019)
S.A.G.K. Abadi, S.I. Habibi, T. Khalili, A. Bidram, A model predictive control strategy for performance improvement of hybrid energy storage systems in DC microgrids. IEEE Access 10, 25400–25421 (2022)
S.A.G.K. Abadi, A. Bidram, A distributed rule‐based power management strategy in a photovoltaic/hybrid energy storage based on an active compensation filtering technique. IET Renew. Power Gener. 15(15), 3688–3703 (2021)
S.D. Bopardikar A. Speranzon, On analysis and design of stealth-resilient control systems, in Resilient Control Systems (ISRCS), 6th International Symposium on Resilient Control Systems (ISRCS) 2013 vol. 48, no. 53, pp. 13–15. (Aug 2013)
H. Fawzi, P. Tabuada, S. Diggavi, Secure estimation and control for cyber-physical systems under adversarial attacks. IEEE Trans. Autom. Control 59(6), 1454–1467 (2014)
Z. Pang, G. Liu, Design and implementation of secure networked predictive control systems under deception attacks. IEEE Trans. Control Syst. Technol. 20(5), 1334–1342 (2012)
J.Y. Keller, D. Sauter, K. Chabir, State filtering for discrete-time stochastic linear systems subject to random cyber attacks and losses of measurements, in 2012 20th Mediterranean Conference on Control & Automation (MED) (3–6 July 2012) , pp 935,940
S. Deshmukh, B. Natarajan, A. Pahwa, State estimation over a Lossy network in spatially distributed cyber-physical systems. IEEE Trans. Signal Process. 62(15), 3911–3923 (1 Aug 2014)
A. Rosich, H. Voos, Li. Yumei, M. Darouach, A model predictive approach for cyber-attack detection and mitigation in control systems, in 52nd Annual Conference on Decision and Control (CDC) (IEEE, 2013). (Dec 2013), pp. 6621–6626, 10–13
A. Teixeira, H. Sandberg, K.H. Johansson, Networked control systems under cyber attacks with applications to power networks, in American Control Conference (ACC), 2010 (June 30 2010–July 2 2010), pp. 3690–3696.
A.F. Taha, A. Elmahdi, J.H. Panchal, D. Sun, Unknown input observer design and analysis for networked control systems. Int. J. Control 88(5), 920–934 (2015)
A. Ahmadi, Y. Asadi, A.M. Amani, M. Jalili, X. Yu, Resilient model predictive adaptive control of networked Z-source inverters using GMDH. IEEE Trans. Smart Grid. https://doi.org/10.1109/TSG.2022.3174250
A.M. Amani, M. Jalili, Power grids as complex networks: resilience and reliability analysis. IEEE Access (2021)
K. Wang, M. Du, S. Maharjan, Y. Sun, Strategic honeypot game model for distributed denial of service attacks in the smart grid. IEEE Trans. Smart Grid 8(5), 2474–2482 (2017)
X. Liu, Z. Li, Z. Li, Optimal protection strategy against false data injection attacks in power systems. IEEE Trans. Smart Grid 8(4), 1802–1810 (2016)
Y. Wu, Z. Wei, J. Weng, X. Li, R.H. Deng, Resonance attacks on load frequency control of smart grids. IEEE Trans. Smart Grid 9(5), 4490–4502 (2017)
J. Liu, Y. Gu, L. Zha, Y. Liu, J. Cao, Event-triggered h∞ load frequency control for multiarea power systems under hybrid cyber attacks. IEEE Trans. Syst. Man Cybern. Syst. 49(8), 1665–1678 (2019)
C. Peng, J. Zhang, H. Yan, Adaptive event-triggering H∞ load frequency control for network-based power systems. IEEE Trans. Ind. Electron. 65(2), 1685–1694 (2017)
C. Wu, Z. Hu, J. Liu, L. Wu, Secure estimation for cyber-physical systems via sliding mode. IEEE Trans. Cybern. 48(12), 3420–3431 (2018)
M.A. Sid, S. Chitraganti, K. Chabir, Medium access scheduling for input reconstruction under deception attacks. J. Franklin Inst. 354(9), 3678–3689 (2017)
J.Y. Keller, K. Chabir, D. Sauter, Input reconstruction for networked control systems subject to deception attacks and data losses on control signals. Int. J. Syst. Sci. 47(4), 814–820 (2016)
X. He, X. Liu, P. Li, Coordinated false data injection attacks in AGC system and its countermeasure. IEEE Access 8, 194640–194651 (2020)
M. Khalaf, A. Youssef, E. El-Saadany, Joint detection and mitigation of false data injection attacks in AGC systems. IEEE Trans. Smart Grid 10(5), 4985–4995 (2018)
S. Zuo, T. Altun, F.L. Lewis, A. Davoudi, Distributed resilient secondary control of DC microgrids against unbounded attacks. IEEE Trans. Smart Grid 11(5), 3850–3859 (2020)
S. Zuo, O.A. Beg, F.L. Lewis, A. Davoudi, Resilient networked AC microgrids under unbounded cyber attacks. IEEE Trans. Smart Grid 11(5), 3785–3794 (2020)
G. Liang, J. Zhao, F. Luo, S.R. Weller, Z.Y. Dong, A review of false data injection attacks against modern power systems. IEEE Trans. Smart Grid 8(4), 1630–1638 (2016)
X. Huang, D. Zhai, J. Dong, Adaptive integral sliding-mode control strategy of data-driven cyber-physical systems against a class of actuator attacks. IET Control Theory Appl. 12(10), 1440–1447 (2018)
X. Qiu, Y. Wang, X. **e, H. Zhang, Resilient model-free adaptive control for cyber-physical systems against jamming attack. Neurocomputing 413, 422–430 (2020)
C.C. Hang, K.J. Åström, W.K. Ho, Refinements of the Ziegler-Nichols tuning formula. Control Theory Appl. 138(2), 111–118 (1991)
D. Selvi, D. Piga, G. Battistelli, A. Bemporad, Optimal direct data-driven control with stability guarantees. Eur. J. Control. 1(59), 175–187 (2021)
T. Wonghong, S. Engell, Automatic controller tuning via unfalsified control. J. Process Control 22(10), 2008–2025 (2012)
C. Tian, Y. Peng, Data-driven iterative tuning based active disturbance rejection control for piezoelectric nano-positioners. Mechatronics 65, 102321 (2021)
C.L. Remes, R.F. Binz, J.V. Flores, L. Campestrini, Virtual reference feedback tuning applied to cascade control. IET Control Theory Appl. 14(20), 3738–3746
E. Aggelogiannaki, H. Sarimveis, Nonlinear model predictive control for distributed parameter systems using data driven artificial neural network models. Comput. Chem. Eng. 32(6), 1225–1237
A. Allibhoy, J. Cortés, Data-based receding horizon control of linear network systems. IEEE Control Syst. Lett. 5(4), 1207–1212
J. Berberich, J. Köhler, M.A. Müller, F. Allgöwer, Data-driven model predictive control with stability and robustness guarantees. IEEE Trans. Autom. Control 66(4), 1702–1717
Z. Hou, W. Huang, The model-free learning adaptive control of a class of SISO nonlinear systems, in Proceedings of the 1997 American Control Conference (Cat. No. 97CH36041), vol. 1, (1997), pp. 343–344
V. Krishnan, F.P Asqualetti, On direct vs indirect data-driven predictive control, in 2021 60th IEEE Conference on Decision and Control (CDC) (IEEE, 2021), pp. 736–741
Y. Asadi, M. Maghfoori, E. Bijami, The design of data-driven adaptive predictive controller for a class of unknown no-linear system featuring output saturation. J. Control 15(3), 23–33 (2021)
Z. Hou, Y. Zhu, Controller-dynamic-linearization-based model free adaptive control for discrete-time nonlinear systems. IEEE Trans. Ind. Inf. 9(4), 2301–2309 (2013)
Y. Asadi, A. Ahmadi, S. Mohammadi, A.M. Amani, M. Marzband, B. Mohammadi-Ivatloo, Data-driven model-free adaptive control of Z-source inverters. Sensors 21(22), 7438–7444 (2021)
Y. Asadi, M.M. Farsangi, E. Bijami, A.M. Amani, K.Y. Lee, Data-driven adaptive control of wide-area nonlinear systems with input and output saturation: a power system application. Int. J. Electr. Power Energy Syst. 133, 107225 (2021)
Z. Hou, C. Ronghu, G. Huijun, An overview of dynamic-linearization-based data-driven control and applications. IEEE Trans. Ind. Electron. 64(5), 4076–4090 (2017)
Z. Hou, J. Shangtai, Model-free adaptive control for a class of nonlinear discrete-time systems based on the partial form linearization, in IFAC Proceedings 41(2), 3509–3514 (2008)
X. Bu, Z. Hou, Q. Yu, Y. Yang, Quantized data driven iterative learning control for a class of nonlinear systems with sensor saturation. IEEE Trans. Syst. Man Cybern. Syst. 50(12), 5119–5129 (2018)
Z. Hou, L. Ting, Constrained model free adaptive predictive perimeter control and route guidance for multi-region Urban traffic systems. IEEE Trans. Intell. Transp. Syst. (2020)
Y. Wang, H. Mingdong, Model-free adaptive integral terminal sliding mode predictive control for a class of discrete-time nonlinear systems. ISA Trans. 93, 209–217 (2019)
Y. Guo, Z. Hou, S. Liu, S. **, Data-driven model-free adaptive predictive control for a class of MIMO nonlinear discrete-time systems with stability analysis. IEEE Access 7, 102852–102866 (2019)
J. Yang, A controllable false data injection attack for a cyber physical system. IEEE Access 9, 6721–6728 (2021)
S.M. Dibaji, M. Pirani, D.B. Flamholz, A.M. Annaswamy, K.H. Johansson, A. Chakrabortty, A systems and control perspective of CPS security. Annu. Rev. Control. 47, 394–411 (2019)
A. Ahmadi, M. Nabipour, S. Taheri, B. Mohammadi-Ivatloo, V. Vahidinasab, A new false data injection attack detection model for cyberattack resilient energy forecasting. IEEE Trans. Ind. Inf. (2022)
X. Zhou, Z. Gu, F. Yang, Resilient event-triggered output feedback control for load frequency control systems subject to cyber attacks. IEEE Access 58951–58958 (2019)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Asadi, Y., Farsangi, M.M., Amani, A.M., Alhelou, H.H., Dibaji, S.M., Bijami, E. (2023). Data-Driven Cyber-Resilient Control of Wide Area Power Systems. In: Haes Alhelou, H., Hatziargyriou, N., Dong, Z.Y. (eds) Power Systems Cybersecurity. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-20360-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-20360-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20359-6
Online ISBN: 978-3-031-20360-2
eBook Packages: EnergyEnergy (R0)