##plugins.themes.bootstrap3.article.main##

The load frequency control of power systems is often carried out using methods that are dependent on the system load and parameters. Therefore, the controller design is not robust in unforeseen cases such an attack on the power system, variations in system parameters, or changes in load. In such methods, there is a need for an attack detection tool, and moreover, the controller parameters need to be adjusted as the load and power system parameters change. In this paper, Kharitonov's theorem was applied to design a robust decentralized load frequency control for a two-area power system in the presence of electric vehicle fleets as a power source that were targeted by a cyberattack. Furthermore, the robustness of the system against system nonlinearities was demonstrated by testing the efficacy of the controller on both linear and nonlinear systems. The controller design was robust such that there was no need to change the gains of the controller even during an attack. This was compared with the performance of controllers designed using GWO algorithm and fuzzy logic that needed retuning for different case studies with different variations in system parameters, load, or inclusion of a cyberattack to the electric vehicle fleets.

Downloads

Download data is not yet available.

References

  1. Li P, Wang X, Lee WJ, Xu D. Dynamic power conditioning method of microgrid via adaptive inverse control. IEEE Transactions on power delivery, 2015 Jan 14;30(2):906-13.
     Google Scholar
  2. Kundur PS, Malik OP. Power system stability and control. McGraw-Hill Education; 2022.
     Google Scholar
  3. Aziz S, Wang H, Liu Y, Peng J, Jiang H. Variable universe fuzzy logic-based hybrid LFC control with real-time implementation. IEEE Access. 2019 Feb 28;7:25535-46.
     Google Scholar
  4. Kothari DP, Nagrath IJ. Modern power system analysis Tata McGraw.
     Google Scholar
  5. Debbarma S, Dutta A. Utilizing electric vehicles for LFC in restructured power systems using fractional order controller. IEEE transactions on smart grid, 2016 Mar 1;8(6):2554-64.
     Google Scholar
  6. Khokhar B, Dahiya S, Parmar KP. Load Frequency Control of a Multi-Microgrid System Incorporating Electric Vehicles. Electric Power Components and Systems, 2022 Mar 5:1-7.
     Google Scholar
  7. Geetha TS, Amudha V, Chellaswamy C. A Novel Dynamic Capacity Expansion Framework Includes Renewable Energy Sources for an Electric Vehicle Charging Station. International Transactions on Electrical Energy Systems, 2022 Sep 10;2022.
     Google Scholar
  8. Justin F, Peter G, Stonier AA, Ganji V. Power quality improvement for vehicle-to-grid and grid-to-vehicle technology in a microgrid. International Transactions on Electrical Energy Systems, 2022; 2022.
     Google Scholar
  9. Kazemi MA, Sedighizadeh M, Mirzaei MJ, Homaee O. Optimal siting and sizing of distribution system operator owned EV parking lots. Applied energy, 2016 Oct 1;179:1176-84.
     Google Scholar
  10. Raouf B, Mousavian S, Ghazinour K. Interconnected and Complex Electric Power and Transportation Systems: a SWOT Analysis. Current Sustainable/Renewable Energy Reports, 2021 Sep 8:1-5.
     Google Scholar
  11. Kumari A, Trivedi M, Tanwar S, Sharma G, Sharma R. SV2G-ET: A Secure Vehicle-to-Grid Energy Trading Scheme Using Deep Reinforcement Learning. International Transactions on Electrical Energy Systems, 2022 Apr 29;2022.
     Google Scholar
  12. Mohan AM, Meskin N, Mehrjerdi H. A comprehensive review of the cyber-attacks and cyber-security on load frequency control of power systems. Energies, 2020 Jul 28;13(15):3860.
     Google Scholar
  13. Chen C, Zhang K, Yuan K, Zhu L, Qian M. Novel detection scheme design considering cyber attacks on load frequency control. IEEE Transactions on Industrial Informatics, 2017 Oct 23;14(5):1932-41.
     Google Scholar
  14. Liu S, Liu XP, El Saddik A. Denial-of-Service (dos) attacks on load frequency control in smart grids. In2013 ieee pes innovative smart grid technologies conference (isgt), 2013 Feb 24 (pp. 1-6). IEEE.
     Google Scholar
  15. Hasanien HM, El-Fergany AA. Salp swarm algorithm-based optimal load frequency control of hybrid renewable power systems with communication delay and excitation cross-coupling effect. Electric Power Systems Research, 2019 Nov 1;176:105938.
     Google Scholar
  16. Guha D, Roy PK, Banerjee S. Load frequency control of interconnected power system using grey wolf optimization. Swarm and Evolutionary computation, 2016 Apr 1;27:97-115.
     Google Scholar
  17. Elgammal A, Boodoo C. Optimal Hybrid Filtering Strategy Using Adaptive Genetic-Fuzzy Logic Control for Harmonics Reduction in a Standalone Micro Hydroelectric Power Plant Coordinated with a PV System. European Journal of Electrical Engineering and Computer Science, 2021 Aug 9;5(4):56-62.
     Google Scholar
  18. Rajesh KS, Dash SS. Load frequency control of autonomous power system using adaptive fuzzy based PID controller optimized on improved sine cosine algorithm. Journal of Ambient Intelligence and Humanized Computing, 2019 Jun;10(6):2361-73.
     Google Scholar
  19. Elgammal A, Ramlal T. Adaptive Voltage Regulation Control Strategy in a Stand-Alone Islanded DC Microgrid based on distributed Wind/Photovoltaic/Diesel/Energy Storage Hybrid Energy Conversion System. European Journal of Electrical Engineering and Computer Science, 2021 Jul 26;5(4):26-33.
     Google Scholar
  20. Liu J, Yao Q, Hu Y. Model predictive control for load frequency of hybrid power system with wind power and thermal power. Energy, 2019 Apr 1;172:555-65.
     Google Scholar
  21. Bhattacharyya SP, Keel LH. Robust control: the parametric approach. In Advances in control education, 1994-1995 Jan 1 (pp. 49-52). Pergamon.
     Google Scholar
  22. Raouf B, Akbarimajd A, Dejamkhooy A, SeyedShenava S. Robust distributed control of reactive power in a hybrid wind?diesel power system with STATCOM. International Transactions on Electrical Energy Systems, 2019 Apr;29(4):e2780.
     Google Scholar
  23. Saxena S, Hote YV. Decentralized PID load frequency control for perturbed multi-area power systems. International Journal of Electrical Power & Energy Systems, 2016 Oct 1;81:405-15.
     Google Scholar
  24. Lamba R, Singla SK, Sondhi S. Design of fractional order PID controller for load frequency control in perturbed two area interconnected system. Electric Power Components and Systems, 2019 Jul 21;47(11-12):998-1011.
     Google Scholar
  25. Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Advances in engineering software, 2014 Mar 1;69:46-61.
     Google Scholar
  26. Tan N, Kaya I, Yeroglu C, Atherton DP. Computation of stabilizing PI and PID controllers using the stability boundary locus. Energy Conversion and management, 2006 Nov 1;47(18-19):3045-58.
     Google Scholar
  27. Bevrani H. Robust power system frequency control. New York: springer; 2014 Jul.
     Google Scholar
  28. Mousavian S, Raouf B, Conejo AJ. Equilibria in Interdependent Natural-gas and Electric Power Markets: An Analytical Approach. Journal of Modern Power Systems and Clean Energy, 2021 Jul 30;9(4):776-87.
     Google Scholar
  29. Izadkhast S. Aggregation of plug-in electric vehicles in power systems for primary frequency control. Ph.D. dissertation, Inst. Res.Technol., Comillas Pontifical Univ., Madrid, Spain, 2017.
     Google Scholar
  30. Panneerselvam K, Ayyagari R. Computational complexity of Kharitonov?s robust stability test. International Journal of Control Science and Engineering, 2013;3(3):81-5.
     Google Scholar
  31. Chapellat H, Bhattacharyya SP. A generalization of Kharitonov's theorem; Robust stability of interval plants. IEEE transactions on automatic control, 1989;34(3):306-11.
     Google Scholar
  32. Saxena S, Hote YV. Stabilization of perturbed system via IMC: An application to load frequency control. Control Engineering Practice. 2017 Jul 1;64:61-73.
     Google Scholar
  33. Tan W. Decentralized load frequency controller analysis and tuning for multi-area power systems. Energy conversion and management, 2011;52(5):2015-2023.
     Google Scholar