Skip to main content
Log in

PSO Based Fuzzy Logic Controller for Load Frequency Control in EV Charging Station

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

Electric vehicles are highly preferred in today's scenario to provide an environmentally friendly nature and preserve natural resources such as petroleum and diesel. Many studies on the technical and economic feasibility of electric vehicle charging stations have been conducted. The majority of the research does not focus on power fluctuations in the charging station caused by load variations. Only a few studies have looked into this issue and proposed a static frequency control method for power fluctuation control. However, they used a predefined solver feasp to find the best value for controlling the primary and secondary frequency controls in this method. Within its local optimal region, this technique finds its solution. As a result, an optimization-based feasp for controlling power fluctuations in charging stations is proposed. The Enhanced Particle Swarm Optimization based Fuzzy Logic Controller (FLC) is suggested here for finding the best solution to reduce power fluctuations using feasp. This optimized approach effectively reduces power fluctuations by determining the best value for controlling the primary and secondary charging frequency in electric vehicles. The system becomes stable in the shortest time with PSO-based FLC, according to simulation results. Furthermore, the magnitude of frequency oscillations, peak overshoot, and settling time are reduced. A comparison of the PSO-FLC method with the conventional method and the Linear–Quadratic Regulator method are compared to justify controller tuning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. Gulzar MM, Iqbal M, Shahzad S, Muqeet HA, Shahzad M, Hussain MM (2022) Load frequency control (LFC) strategies in renewable energy-based hybrid power systems: a review. Energies 15:3488

    Article  Google Scholar 

  2. Yeboah SJ et al. (2021) gravitational search algorithm based automatic load frequency control for multi-area interconnected power system

  3. Kumar A, Pan S (2021) Design of fractional order PID controller for load frequency control system with communication delay. ISA Trans

  4. Dhanalakshmi, NA (2022) A Dynamic web data extraction from SRLDC (Southern Regional Load Dispatch Centre) and feature engineering using etl tool. In: Proceedings of 2nd international conference on artificial intelligence: advances and applications, pp 443–449

  5. Padhan D, Tummala S (2019) Enhanced performance of PID load frequency controller for power systems. Int J Adv Appl Sci 8

  6. Lamba R, Singla SK, Sondhi S Design of Fractional

  7. Order PID Controller for Load Frequency Control in Perturbed Two Area Interconnected

  8. System (2019) J Elect Pow Compo Syst 47:998–1011

  9. Kumar A, Singh O (2021) Recent strategies for automatic generation control of power systems with diverse energy sources. Int J Syst Dyn Appl 10:26

  10. M F Aranza et al. (2016) Tunning PID controller using particle swarm optimization algorithm on automatic voltage regulator system. IOP Conf Ser Mater Sci Eng, 128

  11. Ganji V, Ramraj CB (2021) Load frequency control of time-delayed power systems using optimal IMC-PID design and model approximation approach. Int J Modell Simul

  12. Mounica M, Prasad CD, Prasad D, Bharathi MA (2018) Load frequency control of an isolated power system in presence of controllable energy storage devices. Majlesi J Electrical Eng 12(2):29–38

    Google Scholar 

  13. Shrikant MV, Narri Y (2018) An AHP based optimized tuning of modified active disturbance rejection control: an application to power system load frequency control problem ISA Trans 81:286–305

  14. Guo C, Tang H, Niu B, Lee CBP (2021) A survey of bacterial foraging optimization. Neurocomputing 452:728–746

    Article  Google Scholar 

  15. Yang J, Zeng Z, Tang Y, Yan J, He H, Wu Y (2015) Load frequency control in isolatedmicrogrid with electrical vehicle based on multivariable generalized predictive theory. Energies 8(3):2145–2164.

  16. Nag S, Lee KY (2019) Optimized Fuzzy logic controller for responsive charging of electric vehicles. IFAC-Papers On Line 52(4):147–152

  17. Elmetwaly AH, ElDesouky AA, Omar AI, AttyaSaad M (2022) Operation control, energy management, and power quality enhancement for a cluster of isolated microgrids, Ain Shams Eng J 13(5):101737

  18. Yan W, Sheng L, Xu D, Yang W, Liu Q (2018) H robust load frequency control for multi-area interconnected power system with hybrid energy storage system. Appl Sci 8:1748

    Article  Google Scholar 

  19. Abd El-Kareem AH, AbdElhameed M, Elkholy MM (2021) Effective damping of local low frequency oscillations in power systems integrated with bulk PV generation. Prot Control Mod Power Syst 6:41

  20. Chen B, Zhang R, Chen L, Long S (2021) adaptive particle swarm optimization with gaussian perturbation and mutation. Sci Program. Article ID 6676449

  21. Cheng X, Li J, Zheng C, Zhang J, Zhao M (2021) An improved PSO-GWO algorithm with chaos and adaptive inertial weight for robot path planning. Front Neurorobot, 15

  22. Cheng WL et al. (2022) Particle swarm optimization with multi-chaotic scheme for global optimization. In: Enabling industry 4.0 through advances in mechatronics. Lecture Notes in Electrical Engineering, vol 900. Springer, Singapore

  23. Gupta A, Gupta A, Gupta Y, Mehta R (2021) Performance evaluation of load frequency control with different techniques with PID controller. Int J Sci Res 10:31472–31481

  24. Praveena P, Shubhanka HS, Neha V, Prasath P, Veda GS (2019) Load frequency control of single area system using ziegler-nichols and genetic algorithm. Asian J Electrical Sci, pp 62–64

  25. Yesil E (2014) Interval type-2 fuzzy PID load frequency controller using big bang-big crunchoptimization. Appl Soft Comput 15:100–112

    Article  Google Scholar 

  26. Zhang L, Sun X (2020) Stability analysis of time-varying delay neural network for convex quadratic programming with equality constraints and inequality constraints. Discrete Continuous Dyn Syst

  27. Ferdowsi H, Hashemi M (2021) Adaptive neural control of nonlinear fractional order multi- agent systems in the presence of error constraintion. joc-isice, 15

  28. Liu N, Wang J, Qin S (2022) A one-layer recurrent neural network for nonsmoothpseudoconvex optimization with quasiconvex inequality and affine equality constraints. Neural Netw, 147

  29. Latifi H, Farrokhifar M, Safari A, Pournasir S (2016) Optimal sizing of combined heat and power generation units using of MPSO in the Besat Industrial Zone. Int J Energy Stat 4(1)

  30. Rashwan AF, Ahmed M, Mossa MR, Baha-El-Din AM, Alkhalaf S, Senjyu T, Hemeida AM (2022) Explicit adaptive power system stabilizer design based an on-line identifier for single-machine infinite bus. Ain Shams Eng J 13(2):101544

  31. Das CK, Bass O, Kothapalli G, Mahmoud TS, Habibi D (2018) Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm. Appl Energy. Elsevier, vol 232(C), pp 212–228

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. C. Vinitha.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vinitha, J.C., Ramadas, G. & Rani, P.U. PSO Based Fuzzy Logic Controller for Load Frequency Control in EV Charging Station. J. Electr. Eng. Technol. 19, 193–208 (2024). https://doi.org/10.1007/s42835-023-01568-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-023-01568-y

Keywords

Navigation