Distributed model predictive control for wide area measurement power systems under malicious attacks
- Author(s): Andong Liu 1 and Liye Bai 1
-
-
View affiliations
-
Affiliations:
1:
Department of Automation , Zhejiang University of Technology , Hangzhou , People's Republic of China
-
Affiliations:
1:
Department of Automation , Zhejiang University of Technology , Hangzhou , People's Republic of China
- Source:
Volume 3, Issue 3,
September
2018,
p.
111 – 118
DOI: 10.1049/iet-cps.2017.0056 , Online ISSN 2398-3396
- « Previous Article
- Table of contents
- Next Article »
A wide area measurement system (WAMS) is a technology developed to improve the stability of the power system in the past few decades, which provides a distributed control structure of a highly interconnected power system. However, the critical issues of security in WAMSs are rising to a new class of control problems due to the malicious attacks. This work studies the distributed model predictive control (DMPC) problem for wide area measurement power systems under malicious attacks. The malicious attacks model as time-varying data injection attacks which describe delayed input states. The traditional three-order model of an interconnection power system is modified to a distributed model with coupling control inputs. A sufficient condition to ensure that the closed loop system with asymptotic stability is obtained by using Lyapunov theorem and linear matrix inequality technology. An iterative DMPC algorithm is proposed to design the distributed controllers based on a cooperative control strategy. Finally, a simulation example of a three-machine nine-node power system is presented to verify the effectiveness of the proposed algorithm.
Inspec keywords: linear matrix inequalities; distributed control; power system security; Lyapunov methods; closed loop systems; power system measurement; predictive control; time-varying systems; power system stability; iterative methods; asymptotic stability; power system interconnection; control system synthesis
Other keywords: Lyapunov theorem; malicious attacks; power system stability; asymptotic stability; sufficient condition; linear matrix inequality technology; cooperative control strategy; highly interconnected power system; wide area measurement power systems; iterative DMPC algorithm; time-varying data injection attacks; three-machine nine-node power system; closed loop system; distributed control structure; delayed input states; distributed model predictive control; WAMS security; coupling control inputs; three-order model; distributed controller design
Subjects: Linear algebra (numerical analysis); Power system protection; Interpolation and function approximation (numerical analysis); Control system analysis and synthesis methods; Optimal control; Multivariable control systems; Stability in control theory; Control of electric power systems; Time-varying control systems; Power system control; Power system measurement and metering; Interpolation and function approximation (numerical analysis); Linear algebra (numerical analysis); Power system management, operation and economics
References
-
-
1)
-
8. Blaabjerg, F., Teodorescu, R., Liserre, M., et al: ‘Overview of control and grid synchronization for distributed power generation systems’, IEEE Trans. Ind. Electron., 2006, 53, (5), pp. 1398–1409.
-
-
2)
-
7. Schuler, S., Munz, U., Allgower, F.: ‘Decentralized state feedback control for interconnected systems with application to power systems’, J. Process Control, 2014, 24, (2), pp. 379–388.
-
-
3)
-
11. Scattolini, R.: ‘Architectures for distributed and hierarchical model predictive control-A review’, J. Process Control, 2009, 19, (5), pp. 723–731.
-
-
4)
-
23. Liu, Y., Ning, P., Reiter, M.R.: ‘False data injection attacks against state estimation in electric power grids’. Proc. 16th ACM Conf. on Computer and Communications Security, 2009, pp. 21–32.
-
-
5)
-
3. Meng, W., Wang, X., Liu, S.: ‘Distributed load sharing of an inverter-based microgrid with reduced communication’, IEEE Trans. Smart Grid, 2016, DOI: 10.1109/TSG.2016. 2587685.
-
-
6)
-
17. Roshany-Yamchi, S., Cychowski, M., Negenborn, R.R., et al: ‘Kalman filter-based distributed predictive control of large-scale multi-rate systems: applications to power networks’, IEEE Trans. Control Syst. Technol., 2013, 21, (1), pp. 27–39.
-
-
7)
-
31. Prabba, K.: ‘Power system stability and control’ (McGraw-Hill Inc., New York, 1993).
-
-
8)
-
25. Dorfler, F., Pasqualetti, F., Bullo, F.: ‘Distributed detection of cyber-physical attacks in power networks: a waveform relaxation approach’. Proc. 49th Annual Allerton Conf. Allerton House, Illinois, 2011, pp. 1486–1491.
-
-
9)
-
28. Mokhtari, M., Aminifar, F., Nazarpour, D., et al: ‘Wide-area power oscillation damping with a fuzzy controller compensating the continuous communication delays’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 1997–2005.
-
-
10)
-
20. Kosut, O., Jia, L., Thomas, R.J., et al: ‘Malicious data attacks on the smart grid’, IEEE Trans. Smart Grid, 2011, 2, (4), pp. 645–658.
-
-
11)
-
1. Amin, S.M., Wollenberg, B.F.: ‘Toward a smart grid: power delivery for the 21st century’, IEEE Power Energy Mag., 2005, 3, (5), pp. 34–41.
-
-
12)
-
12. Negenborn, R.R., Maestre, J.M.: ‘Distributed model predictive control: an overview and roadmap of future research opportunities’, IEEE Control Syst., 2014, 34, (4), pp. 87–97.
-
-
13)
-
15. Venkat, A.N., Hiskens, L.A., Rawlings, J.B., et al: ‘Distributed output feedback MPC for power system control’. Proc. 46th IEEE Conf. on Decision and Control, San Diego, USA, 2006, pp. 4038–4045.
-
-
14)
-
30. Joshi, V.V., Xie, L.B., Park, J.J., et al: ‘Digital modeling and control of multiple time-delayed distributed power grid’, Appl. Math. Model., 2012, 36, (9), pp. 4118–4134.
-
-
15)
-
18. Liu, A., Sun, H., Yu, L., et al: ‘Distributed model predictive control of wide-area power system with communication constraints’, J. Syst. Sci. Math. Sci., 2016, 36, (6), pp. 749–758.
-
-
16)
-
16. Wang, D., Glavic, M., Wehenkel, L.: ‘Distributed MPC of wide-area electromechanical oscillations of large-scale power systems’. Proc. 16th Int. Conf. on Intelligent System Applications to Power Systems, Hersonisos, Greece, 2011, pp. 1–7.
-
-
17)
-
14. Venkat, A.N., Hiskens, L.A., Rawlings, J.B., et al: ‘Distributed MPC strategies with application to power system automatic generation control’, IEEE Trans. Control Syst. Technol., 2008, 16, (6), pp. 1192–1206.
-
-
18)
-
24. Giani, A., Bitar, E., Garcia, M., et al: ‘Smart grid data integrity attacks’, IEEE Trans. Smart Grid, 2013, 4, (3), pp. 1244–1253.
-
-
19)
-
9. Ulbig, A., Arnold, M., Chatzivasileiadis, S., et al: ‘Framework for multiple time-scale cascaded MPC application in power systems’. Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 10472–10480.
-
-
20)
-
27. Wang, S., Meng, X., Chen, T.: ‘Wide-area control of power systems through delayed network communication’, IEEE Trans. Control Syst. Technol., 2012, 20, (2), pp. 495–503.
-
-
21)
-
6. Mi, Y., Fu, Y., Wang, C., et al: ‘Decentralized sliding mode load frequency control for multi-area power systems’, IEEE Trans. Power Syst., 2013, 28, (4), pp. 4301–4309.
-
-
22)
-
21. Zhang, H., Cheng, P., Shi, L., et al: ‘Optimal DoS attack scheduling in wireless networked control system’, IEEE Trans. Control Syst. Technol., 2016, 24, (3), pp. 843–852.
-
-
23)
-
2. Teixeira, A., Dan, G., Sandberg, H., et al: ‘A cyber security study of a SCADA energy management system: stealthy deception attacks on the state estimator’. Proc. 18th IFAC World Congress, Milano, Italy, 2011, pp. 11271–11277.
-
-
24)
-
10. Senjyu, T., Miyazato, Y., Yona, A., et al: ‘Optimal distribution voltage control and coordination with distributed generation’, IEEE Trans. Power Deliv., 2008, 23, (2), pp. 1236–1242.
-
-
25)
-
4. Wang, S., Gao, W., Wang, J., et al: ‘Synchronized sampling technology-based compensation for network effects in WAMS communication’, IEEE Trans. Smart Grid, 2012, 3, (2), pp. 837–845.
-
-
26)
-
13. Li, H., Shi, Y., Yan, W.: ‘Distributed receding horizon control of constrained nonlinear vehicle formations with guaranteed-gain stability’, Automatica, 2016, 68, pp. 148–154.
-
-
27)
-
33. Al-Gherwi, W., Budman, H., Elkamel, A.: ‘A robust distributed model predictive control algorithm’, J. Process Control, 2011, 21, (8), pp. 1127–1137.
-
-
28)
-
19. Ma, M., Zhang, C., Liu, X., et al: ‘Distributed model predictive load frequency control of multi-area power system after deregulation’, IEEE Trans. Ind. Electron., 2017, 64, (6), pp. 5129–5139.
-
-
29)
-
32. Guo, G., Wang, Y., Hill, D.J.: ‘Nonlinear output stabilization control for multimachine power systems’, IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., 2000, 47, (1), pp. 46–53.
-
-
30)
-
22. Deng, R., Xiao, G., Lu, R.: ‘Defending against false data injection attacks on power system state estimation’, IEEE Trans. Ind. Inf., 2017, 13, (1), pp. 198–207.
-
-
31)
-
34. Zhang, L., Wang, J., Ge, Y., et al: ‘Robust distributed model predictive control for uncertain networked control systems’, IET Control Theory Applic., 2014, 8, (17), pp. 1843–1851.
-
-
32)
-
26. Sou, K.C., Sandberg, H., Johansson, K.H.: ‘On the exact solution to a smart grid cyber-security analysis problem’, IEEE Trans. Smart Grid, 2013, 4, (2), pp. 856–865.
-
-
33)
-
5. Xi, Z., Cheng, D., Lu, Q., et al: ‘Nonlinear decentralized controller design for multimachine power systems using Hamiltonian function method’, Automatica, 2002, 38, (3), pp. 527–534.
-
-
34)
-
29. Sonmez, S., Ayasun, S., Nwankpa, C.O.: ‘An exact method for computing delay margin for stability of load frequency control systems with constant communication delays’, IEEE Trans. Power Syst., 2016, 31, (1), pp. 370–377.
-
-
1)