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ADP-Based Supplementary Design for Load Frequency Control of Power Systems

  • Ding Wang
  • Chaoxu Mu
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 167)

Abstract

Randomness from the power load demand and renewable generations causes frequency oscillations among interconnected power systems. Due to the requirement of synchronism of the whole grid, LFC has become one of the essential challenges for power system stability and security. In this chapter, by modeling the disturbances and parameter uncertainties into the LFC model, we propose an adaptive supplementary control scheme for power system frequency regulation. An improved sliding mode control is employed as the basic controller, where a new sliding mode variable is specifically proposed for the LFC problem. The ADP strategy is used to provide the supplementary control signal, which is beneficial to the frequency regulation by adapting to real-time disturbances and uncertainties. The stability analysis is also provided to guarantee the reliability of the proposed control strategy. For comparison, a particle swarm optimization based sliding mode control scheme is developed as the optimal parameter controller for the frequency regulation problem. Simulation studies are performed on single-area and multi-area benchmark systems, and comparative results illustrate the favourable performance of the proposed adaptive approach for frequency regulation under load disturbances and parameter uncertainties.

References

  1. 1.
    Al-Tamimi, A., Lewis, F.L., Abu-Khalaf, M.: Discrete-time nonlinear HJB solution using approximate dynamic programming: convergence proof. IEEE Trans. Syst. Man Cybern.-Part B: Cybern. 38(4), 943–949 (2008)CrossRefGoogle Scholar
  2. 2.
    Bevrani, H., Daneshmand, P.R., Babahajyani, P., Mitani, Y., Hiyama, T.: Intelligent LFC concerning high penetration of wind power: synthesis and real-time application. IEEE Trans. Sustain. Energy 5(2), 655–662 (2014)CrossRefGoogle Scholar
  3. 3.
    Jiang, L., Yao, W., Wu, Q.H., Wen, J.Y., Cheng, S.J.: Delay-dependent stability for load frequency control with constant and time-varying delays. IEEE Trans. Power Syst. 27(2), 932–941 (2012)CrossRefGoogle Scholar
  4. 4.
    Li, H., Dou, L., Su, Z.: Adaptive nonsingular fast terminal sliding mode control for electromechanical actuator. Int. J. Syst. Sci. 44(3), 401–415 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Liu, Y., Wu, Q.H., Zhou, X.X., Jiang, L.: Perturbation observer based multiloop control for the DFIG-WT in multimachine power system. IEEE Trans. Power Syst. 29(6), 2905–2915 (2014)CrossRefGoogle Scholar
  6. 6.
    Lu, Q., Sun, Y.Z., Mei, S.W.: Nonlinear Control Systems and Power System Dynamics. Springer Science & Business Media, Boston (2013)zbMATHGoogle Scholar
  7. 7.
    Mi, Y., Fu, Y., Wang, C.S., Wang, P.: Decentralized sliding mode load frequency control for multi-area power systems. IEEE Trans. Power Syst. 28(4), 4301–4309 (2013)CrossRefGoogle Scholar
  8. 8.
    Mishra, S., Ramasubramanian, D., Sekhar, P.C.: A seamless control methodology for a grid connected and isolated PV-diesel microgrid. IEEE Trans. Power Syst. 28(4), 4393–4404 (2013)CrossRefGoogle Scholar
  9. 9.
    Molina, D., Venayagamoorthy, G.K., Liang, J.Q., Harley, R.G.: Intelligent local area signals based damping of power system oscillations using virtual generators and approximate dynamic programming. IEEE Trans. Smart Grid 4(1), 498–508 (2013)CrossRefGoogle Scholar
  10. 10.
    Mu, C., Sun, C., Xu, W.: Fast sliding mode control on air-breathing hypersonic vehicles with transient response analysis. Proc. Inst. Mech. Eng. Part I: J. Syst. Control Eng. 230(1), 23–34 (2016)Google Scholar
  11. 11.
    Mu, C., Xu, W., Sun, C.: On switching manifold design for terminal sliding mode control. J. Frankl. Inst. 353(7), 1553–1572 (2016)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Mu, C., Ni, Z., Sun, C., He, H.: Air-breathing hypersonic vehicle tracking control based on adaptive dynamic programming. IEEE Trans. Neural Netw. Learn. Syst. 28(3), 584–598 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Mu, C., Ni, Z., Sun, C., He, H.: Data-driven tracking control with adaptive dynamic programming for a class of continuous-time nonlinear systems. IEEE Trans. Cybern. 47(6), 1460–1470 (2017)CrossRefGoogle Scholar
  14. 14.
    Mu, C., Tang, Y., He, H.: Improved sliding mode design for load frequency control of power system integrated an adaptive learning strategy. IEEE Trans. Industr. Electron. 64(8), 6742–6751 (2017)CrossRefGoogle Scholar
  15. 15.
    Mu, C., Wang, D., He, H.: Novel iterative neural dynamic programming for data-based approximate optimal control design. Automatica 81, 240–252 (2017)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ni, Z., He, H., Wen, J.: Adaptive learning in tracking control based on the dual critic network design. IEEE Trans. Neural Netw. Learn. Syst. 24(6), 913–928 (2013)CrossRefGoogle Scholar
  17. 17.
    Pandey, S.K., Mohanty, S.R., Kishor, N.: A literature survey on load-frequency control for conventional and distribution generation power systems. Renew. Sustain. Energy Rev. 25, 318–334 (2013)CrossRefGoogle Scholar
  18. 18.
    Parmar, K.S., Majhi, S., Kothari, D.P.: Load frequency control of a realistic power system with multi-source power generation. Int. J. Electr. Power Energy Syst. 42(1), 426–433 (2012)CrossRefGoogle Scholar
  19. 19.
    Qian, D., Tong, S., Liu, H., Liu, X.: Load frequency control by neural-network-based integral sliding mode for nonlinear power systems with wind turbines. Neurocomputing 173, 875–885 (2016)CrossRefGoogle Scholar
  20. 20.
    Saxena, S., Hote, Y.V.: Load frequency control in power systems via internal model control scheme and model-order reduction. IEEE Trans. Power Syst. 28(3), 2749–2757 (2013)CrossRefGoogle Scholar
  21. 21.
    Si, J., Wang, Y.-T.: Online learning control by association and reinforcement. IEEE Trans. Neural Netw. 12(2), 264–276 (2001)CrossRefGoogle Scholar
  22. 22.
    Sui, X., Tang, Y., He, H., Wen, J.: Energy-storage-based low-frequency oscillation damping control using particle swarm optimization and heuristic dynamic programming. IEEE Trans. Power Syst. 29(5), 2539–2548 (2014)CrossRefGoogle Scholar
  23. 23.
    Tan, W.: Unified tuning of PID load frequency controller for power systems via IMC. IEEE Trans. Power Syst. 25(1), 341–350 (2010)CrossRefGoogle Scholar
  24. 24.
    Tang, G., Xu, Z., Dong, H., Xu, Q.: Sliding mode robust control based active-power modulation of multi-terminal HVDC transmissions. IEEE Trans. Power Syst. 31(2), 1614–1623 (2016)CrossRefGoogle Scholar
  25. 25.
    Tang, Y., Ju, P., He, H., Qin, C., Wu, F.: Optimized control of DFIG-based wind generation using sensitivity analysis and particle swarm optimization. IEEE Trans. Smart Grid 4(1), 509–520 (2013)CrossRefGoogle Scholar
  26. 26.
    Tang, Y., He, H., Wen, J., Liu, J.: Power system stability control for a wind farm based on adaptive dynamic programming. IEEE Trans. Smart Grid 6(1), 166–177 (2015)CrossRefGoogle Scholar
  27. 27.
    Tang, Y., Yang, J., Yan, J., He, H.: Intelligent load frequency controller using GrADP for island smart grid with electric vehicles and renewable resources. Neurocomputing 170, 406–416 (2015)CrossRefGoogle Scholar
  28. 28.
    Tang, Y., He, H., Ni, Z., Zhao, D., Xu, X.: Fuzzy-based goal representation adaptive dynamic programming. IEEE Trans. Fuzzy Syst. 24(5), 1159–1175 (2016)CrossRefGoogle Scholar
  29. 29.
    Tang, Y., Mu, C., He, H.: SMES based damping controller design using fuzzy-GrHDP considering transmission delay. IEEE Trans. Appl. Supercond. 26(7), 1–5 (2016)Google Scholar
  30. 30.
    Valle, Y.D., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.C., Harley, R.G.: Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 12(2), 171–195 (2008)CrossRefGoogle Scholar
  31. 31.
    Vrdoljak, K., Peric, N., Petrovic, I.: Applying optimal sliding mode based load-frequency control in power systems with controllable hydro power plants. Automatika-J. Control Meas. Electron. Comput. Commun. 51(1), 3–18 (2010)Google Scholar
  32. 32.
    Vrdoljak, K., Peric, N., Petrovic, I.: Sliding mode based load-frequency control in power systems. Electr. Power Syst. Res. 80(5), 514–527 (2010)CrossRefGoogle Scholar
  33. 33.
    Wang, D., Liu, D., Li, H.: Policy iteration algorithm for online design of robust control for a class of continuous-time nonlinear systems. IEEE Trans. Autom. Sci. Eng. 11(2), 627–632 (2014)CrossRefGoogle Scholar
  34. 34.
    Wang, D., Liu, D., Zhang, Q., Zhao, D.: Data-based adaptive critic designs for nonlinear robust optimal control with uncertain dynamics. IEEE Trans. Syst. Man Cybern. Syst. 46(11), 1544–1555 (2016)CrossRefGoogle Scholar
  35. 35.
    Wang, D., He, H., Liu, D.: Improving the critic learning for event-based nonlinear H\(_{\infty }\) control design. IEEE Trans. Cybern. 47(10), 3417–3428 (2017)CrossRefGoogle Scholar
  36. 36.
    Wang, D., He, H., Mu, C., Liu, D.: Intelligent critic control with disturbance attenuation for affine dynamics including an application to a micro-grid system. IEEE Trans. Industr. Electron. 64(6), 4935–4944 (2017)CrossRefGoogle Scholar
  37. 37.
    Wang, D., Mu, C., Liu, D.: Data-driven nonlinear near-optimal regulation based on iterative neural dynamic programming. Acta Autom. Sin. 43(3), 366–375 (2017)zbMATHGoogle Scholar
  38. 38.
    Wei, Q., Liu, D., Shi, G., Liu, Y.: Multibattery optimal coordination control for home energy management systems via distributed iterative adaptive dynamic programming. IEEE Trans. Industr. Electron. 62(7), 4203–4214 (2015)CrossRefGoogle Scholar
  39. 39.
    Yang, L., Si, J., Tsakalis, K.S., Rodriguez, A.: Direct heuristic dynamic programming for nonlinear tracking control with filtered tracking error. IEEE Trans. Syst. Man Cybern. B Cybern. 39(6), 1617–1622 (2009)CrossRefGoogle Scholar
  40. 40.
    Yousef, H., AL-Kharusi, K., Albadi, M.H., Hosseinzadeh, N.: Load frequency control of a multi-area power system: an adaptive fuzzy logic approach. IEEE Trans. Power Syst. 29(4), 1822–1830 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.The State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.School of Electrical and Information EngineeringTianjin UniversityTianjinChina

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