access icon openaccess Cyber-physical robust control framework for enhancing transient stability of smart grids

Transient stability of power systems has become even more critical due to increasing complexity created by large penetration of renewable energy sources and massive deployment of information and communication technology. Fortunately, the two-way real-time data exchange capacity of smart grids allows designing advanced digital control schemes to better address the power system stability. In this study, a non-linear model-free-based robust controller in conjunction with a state estimation architecture is designed to enhance transient stability margins. The designed controller addresses uncertainties arising from communication and control input delay, sensor errors, varying plant parameters, and unmodelled dynamics effects. A novel time-delay compensation technique is presented in the control development to mitigate the effect of delay and the robustness of the proposed controller is proven by conducting a Lyapunov stability analysis with respect to additive disturbance and time delay. Furthermore, the proposed control framework is validated on the IEEE 39 bus test system through MATLAB simulation. The results show that the proposed framework is capable of stabilising the power system after a fault, also showing robustness to noise, latency in communication, delay in control input, and malicious data injection.

Inspec keywords: Lyapunov methods; state estimation; cyber-physical systems; robust control; smart power grids; power system stability; delays; nonlinear control systems; power system control; compensation

Other keywords: additive disturbance; time-delay compensation technique; smart grids; cyber-physical robust control framework; state estimation architecture; transient stability margins; Lyapunov stability analysis; nonlinear model-free-based control; IEEE 39 bus test system

Subjects: Stability in control theory; Nonlinear control systems; Power system control; Distributed parameter control systems; Simulation, modelling and identification; Control of electric power systems

References

    1. 1)
      • 5. Farraj, A., Hammad, E., Kundur, D.: ‘A cyber-physical control framework for transient stability in smart grids’, IEEE Trans. Smart Grid, 2016, PP, (99), p. 1.
    2. 2)
      • 18. Anderson, P., Fouad, A.: ‘Power system control and stability’ (Wiley-Interscience, 2003).
    3. 3)
      • 19. de Mello, F.P.: ‘Measurement of synchronous machine rotor angle from analysis of zero sequence harmonic components of machine terminal voltage’, IEEE Trans. Power Deliv., 1994, 9, (4), pp. 17701777.
    4. 4)
      • 16. Cimuca, G.O., Saudemont, C., Robyns, B., et al: ‘Control and performance evaluation of a flywheel energy-storage system associated to a variable-speed wind generator’, IEEE Trans. Ind. Electron., 2006, 53, (4), pp. 10741085.
    5. 5)
      • 2. Borsche, T.S., Liu, T., Hill, D.J.: ‘Effects of rotational inertia on power system damping and frequency transients’. Proc. 2015 54th IEEE Conf. Decision and Control (CDC), 2015, pp. 59405946.
    6. 6)
      • 11. Andreasson, M., Dimarogonas, D.V., Johansson, K.H., et al: ‘Distributed vs. centralized power systems frequency control’. Proc. 2013 European Control Conf. (ECC), Zurich, 2013, pp. 35243529.
    7. 7)
      • 24. Bretas, A.S., Bretas, N.G., Carvalho, B., et al: ‘Smart grids cyber-physical security as a malicious data attack: an innovation approach’, Electr. Power Syst. Res., 2017, 149, pp. 210219.
    8. 8)
      • 7. Hadidi, R., Jeyasurya, B.: ‘A real-time multiagent wide-area stabilizing control framework for power system transient stability enhancement’. Proc. 2011 IEEE Power and Energy Society General Meeting, 2011, pp. 18.
    9. 9)
      • 9. Office of Electricity Delivery and Energy Reliability: ‘Advancement of synchrophasor technology in projects funded by the American recovery and reinvestment act of 2009’, US Department of Energy, 2016.
    10. 10)
      • 4. Lee, R.M., Assante, M.J., Conway, T.: ‘Analysis of the cyber attack on the Ukrainian power grid’, SANS Ind. Control Syst., 2016.
    11. 11)
      • 14. Megel, O., Mathieu, J.L., Andersson, G.: ‘Maximizing the potential of energy storage to provide fast frequency control’. Proc. IEEE PES ISGT Europe 2013, 2013, pp. 15.
    12. 12)
      • 6. Kundur, P., Balu, N.J., Lauby, M.G.: ‘Power system stability and control’ (McGraw-Hill, New York, 1994), vol. 7.
    13. 13)
      • 26. Athay, T., Podmore, R., Virmani, S.: ‘A practical method for the direct analysis of transient stability’, IEEE Trans. Power Appl. Syst., 1979, PAS-98, (2), pp. 573584.
    14. 14)
      • 8. Patel, M., Aivaliotis, S., Ellen, E., et al: ‘Real-time application of synchrophasors for improving reliability’. NERC Report, October 2010.
    15. 15)
      • 1. NIST Smart Grid: ‘Introduction to NISTIR 7628 guidelines for smart grid cyber security’, Guideline, September 2010.
    16. 16)
      • 3. Manandhar, K., Cao, X., Hu, F., et al: ‘Detection of faults and attacks including false data injection attack in smart grid using Kalman filter’, IEEE Trans. Control Netw. Syst., 2014, 1, (4), pp. 370379.
    17. 17)
      • 10. Anderson, P.M., Fouad, A.A.: ‘Power system control and stability’ (John Wiley & Sons, 2008).
    18. 18)
      • 22. Phadke, A.G., Thorp, J.S., Karimi, K.J.: ‘State estimation with phasor measurements’, IEEE Trans. Power Syst., 1986, 1, (1), pp. 233238.
    19. 19)
      • 12. Farraj, A.K., Hammad, E.M., Kundur, D.: ‘A cyber-enabled stabilizing controller for resilient smart grid systems’. Proc. 2015 IEEE Power Energy Society Innovative Smart Grid Technologies Conf. (ISGT), 2015, pp. 15.
    20. 20)
      • 13. Kirby, B.J.: ‘Frequency regulation basics and trends’, United States Department of Energy, 2004.
    21. 21)
      • 15. Hollinger, R., Diazgranados, L.M., Wittwer, C., et al: ‘Optimal provision of primary frequency control with battery systems by exploiting all degrees of freedom within regulation’, Energy Procedia, 2016, 99, pp. 204214.
    22. 22)
      • 28. NERC Resources Subcommittee: ‘Balancing and frequency control’. Technical Report, NERC, Washington, DC, USA, January 2011.
    23. 23)
      • 20. Obuz, S., Klotz, J.R., Kamalapurkar, R., et al: ‘Unknown time-varying input delay compensation for uncertain nonlinear systems’, Automatica, 2017, 76, pp. 222229.
    24. 24)
      • 21. Kamalapurkar, R., Rosenfeld, J.A., Klotz, J., et al: ‘Supporting lemmas for RISE-based control methods’, 2014, arXiv:1306.3432v3.
    25. 25)
      • 25. Moeini, A., Kamwa, I., Brunelle, P., et al: ‘Open data IEEE test systems implemented in simpowersystems for education and research in power grid dynamics and control’. Proc. 2015 50th Int. Universities Power Engineering Conf. (UPEC), 2015, pp. 16.
    26. 26)
      • 17. Sun, X.D., Koh, K.H., Yu, B.G., et al: ‘Fuzzy-logic-based v/f control of an induction motor for a dc grid power-leveling system using flywheel energy storage equipment’, IEEE Trans. Ind. Electron., 2009, 56, (8), pp. 31613168.
    27. 27)
      • 23. Bretas, N., Piereti, S., Martins, A.: ‘A geometrical view for multiple gross errors detection, identification, and correction in power system state estimation’. Proc. 2013 IEEE Power Energy Society General Meeting, 2013, p. 1.
    28. 28)
      • 27. Farraj, A., Hammad, E., Kundur, D.: ‘A cyber-enabled stabilizing control scheme for resilient smart grid systems’, IEEE Trans. Smart Grid, 2016, 7, (4), pp. 18561865.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cps.2017.0017
Loading

Related content

content/journals/10.1049/iet-cps.2017.0017
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading