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A Modified Moth Swarm Algorithm-Based Hybrid Fuzzy PD–PI Controller for Frequency Regulation of Distributed Power Generation System with Electric Vehicle

  • Dillip Khamari
  • Rabindra Kumar SahuEmail author
  • Sidhartha Panda
Article
  • 26 Downloads

Abstract

This paper presents a modified moth swarm algorithm (mMSA) to solve the frequency control of distributed power generation system (DPGS). The DPGS contains renewables like wind, solar photovoltaic as well as storage devices like the battery and flywheel along with electric vehicles. At the first stage, the superiority of the proposed mMSA over moth swarm algorithm is compared by considering benchmark unimodal, multimodal and fixed-dimension test functions. The outcomes are also compared with some recently suggested optimization algorithms to validate the superiority of the suggested mMSA method. In the next step, the hybrid fuzzy PD–PI (hFPD–PI) controller is proposed for the frequency regulation of DPGS. To authenticate the feasibility of the proposed method, experimental validation employing hardware-in-the-loop real-time simulation based on OPAL-RT has been carried out. Further, to study the effect of uncertainties in the parameters of the studied system, sensitivity analysis is performed. Finally, the proposed approach is compared with some newly proposed frequency regulation methods in a standard two-area test system. It is noticed that mMSA-based hFPD–PI controller provides better frequency regulation compared to some recent approaches.

Keywords

Moth swarm algorithm (MSA) Correction factor (CF) Distributed power generation system (DPGS) Hybrid fuzzy PD–PI (hFPD–PI) controller Frequency control 

Notes

References

  1. Ali, E. S., & Abd-Elazim, S. M. (2011). Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. International Journal of Electric Power and Energy Systems,33(3), 633–638.CrossRefGoogle Scholar
  2. Bevrani, H., Habibi, F., Babahajyani, P., Watanabe, M., & Mitani, Y. (2012). Intelligent frequency control in an AC microgrid: Online PSO-based fuzzy tuning approach. IEEE Transaction on Smart Grid,3(4), 1935–1944.CrossRefGoogle Scholar
  3. Duman, S. (2018). A modified moth swarm algorithm based on an arithmetic crossover for constrained optimization and optimal power flow problems. IEEE Access,6, 45394–45416.CrossRefGoogle Scholar
  4. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In IEEE international conference on neural networks (pp. 1942–1948).Google Scholar
  5. Khooban, M. H. (2017). Secondary load frequency control of time-delay stand-alone micro-grids with electric vehicles. IEEE Transactions on Industrial Electronics,65(9), 7416–7422.CrossRefGoogle Scholar
  6. Khooban, M. H., Dragicevic, T., Blaabjerg, F., & Delimar, M. (2018). Shipboard micro-grids: A novel approach to load frequency control. IEEE Transaction on Sustainable Energy,9(2), 843–852.CrossRefGoogle Scholar
  7. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf optimizer. Advances in Engineering Software,69, 46–61.CrossRefGoogle Scholar
  8. Mohamed, A. A. A., Mohmed, Y. S., El-Gaafary, A. A. M., & Hemeida, A. M. (2017). Optimal power flow using moth swarm algorithm. Electric Power Systems Research,142, 190–206.CrossRefGoogle Scholar
  9. Nandar, C. S. A. (2013). Robust PI control of smart controllable load for frequency stabilization of micro grid power system. Renewable Energy,56, 16–23.CrossRefGoogle Scholar
  10. Padhan, S., Sahu, R. K., & Panda, S. (2014). Application of Firefly algorithm for load frequency control of multi-area interconnected power system. Electric Power Component and System,42, 1419–1430.CrossRefGoogle Scholar
  11. Padhy, S., Panda, S., & Mishra, S. (2017). A modified GWO technique based cascade PI-PD controller for AGC of power system in presence of plug in electric vehicle. International Journal of Engineering Science and Technology,20(2), 427–442.CrossRefGoogle Scholar
  12. Pan, I., & Das, S. (2015). Kriging based surrogate modeling for fractional order control of micro grids. IEEE Transaction on Smart Grid,6(1), 36–44.CrossRefGoogle Scholar
  13. Pan, I., & Das, S. (2016a). Fractional order AGC for distributed energy resources using robust optimization. IEEE Transaction on Smart Grid,7(5), 2175–2186.CrossRefGoogle Scholar
  14. Pan, I., & Das, S. (2016b). Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO. ISA Transaction,62, 19–29.CrossRefGoogle Scholar
  15. Panda, S., Mohanty, B., & Hota, P. K. (2013). Hybrid BFOA–PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems. Application of Soft Computing,13(12), 4718–4730.CrossRefGoogle Scholar
  16. Pandey, S. K., Mohanty, S. R., Kishor, N., & Catalão, J. P. S. (2014). Frequency regulation in hybrid power systems using particle swarm optimization and linear matrix inequalities based robust controller design. International Journal of Electrical Power and Energy Systems,63, 887–900.CrossRefGoogle Scholar
  17. Pham, T. N., Trinh, H., & Hien, L. V. (2016). Load frequency control of power systems with electric vehicles and diverse transmission links using distributed functional observers. IEEE Transaction on Smart Grid,7(1), 238–252.CrossRefGoogle Scholar
  18. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching–learning-based optimization: An optimization method for continuous non-linear large scale problems. Information Sciences,183(1), 1–15.MathSciNetCrossRefGoogle Scholar
  19. Rashedi, E., Nezamabadi-pour, H., & SaryazdiJ, S. (2009). GSA: A gravitational search algorithm. Information Sciences,179(13), 2232–2248.CrossRefGoogle Scholar
  20. Rout, U. K., Sahu, R. K., & Panda, S. (2013). Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system. Ain Shams Engineering Journal,4(3), 409–421.CrossRefGoogle Scholar
  21. Savran, A., & Kahraman, G. (2014). A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes. ISA Transaction,53(2), 280–288.CrossRefGoogle Scholar
  22. Singh, V. P., Mohanty, S. R., Kishor, N., & Ray, P. K. (2013). Robust H-infinity load frequency control in hybrid distributed generation system. International Journal of Electric Power and Energy Systems,46, 294–305.CrossRefGoogle Scholar
  23. Sivalingam, R., Chinnamuthu, S., & Dash, S. S. (2017). A modified whale optimization algorithm based adaptive fuzzy logic PID controller for load frequency control of autonomous power generation systems. Automatika Journal for Control Measurement Electronics Computing and Communications,58(4), 410–421.Google Scholar
  24. Stron, R., & Price, K. (1995). Differential evolution—A simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization,11, 341–359.CrossRefGoogle Scholar
  25. Sudha, K. R., Raju, Y. B., & Sekhar, A. C. (2012). Fuzzy c-means clustering for robust decentralized load frequency control of interconnected power system with generation rate constraint. International Journal of Electric Power and Energy Systems,37, 58–66.CrossRefGoogle Scholar
  26. Xiangjun, L., Yu-Jin, S., & Soo-Bin, H. (2008). Frequency control in micro-grid power system combined with electrolyzer system and fuzzy PI controller. Journal of Power Sources,180(1), 468–475.CrossRefGoogle Scholar
  27. Yang, X. S. (2010). Firefly algorithm, stochastic test functions, and design optimization. International Journal of Bio-Inspired Computation,2, 78–84.CrossRefGoogle Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2020

Authors and Affiliations

  • Dillip Khamari
    • 1
  • Rabindra Kumar Sahu
    • 1
    Email author
  • Sidhartha Panda
    • 1
  1. 1.Department of Electrical EngineeringVeer Surendra Sai University of Technology (VSSUT)BurlaIndia

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