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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

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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.

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References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In IEEE international conference on neural networks (pp. 1942–1948).

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf optimizer. Advances in Engineering Software,69, 46–61.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Pan, I., & Das, S. (2016a). Fractional order AGC for distributed energy resources using robust optimization. IEEE Transaction on Smart Grid,7(5), 2175–2186.

    Article  Google Scholar 

  • Pan, I., & Das, S. (2016b). Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO. ISA Transaction,62, 19–29.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Rashedi, E., Nezamabadi-pour, H., & SaryazdiJ, S. (2009). GSA: A gravitational search algorithm. Information Sciences,179(13), 2232–2248.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Savran, A., & Kahraman, G. (2014). A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes. ISA Transaction,53(2), 280–288.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Yang, X. S. (2010). Firefly algorithm, stochastic test functions, and design optimization. International Journal of Bio-Inspired Computation,2, 78–84.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Veer Surendra Sai University of Technology (VSSUT), Burla, Odisha, 768018, India

    Dillip Khamari, Rabindra Kumar Sahu & Sidhartha Panda

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  1. Dillip Khamari
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  2. Rabindra Kumar Sahu
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  3. Sidhartha Panda
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Correspondence to Rabindra Kumar Sahu.

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Khamari, D., Sahu, R.K. & Panda, S. A Modified Moth Swarm Algorithm-Based Hybrid Fuzzy PD–PI Controller for Frequency Regulation of Distributed Power Generation System with Electric Vehicle. J Control Autom Electr Syst 31, 675–692 (2020). https://doi.org/10.1007/s40313-020-00565-0

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  • DOI: https://doi.org/10.1007/s40313-020-00565-0

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