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Frequency Regulation of Interlinked Microgrid System Using Mayfly Algorithm-Based PID Controller

Author

Listed:
  • Dhanasekaran Boopathi

    (Paavai Engineering College, Anna University, Namakkal 637018, TN, India)

  • Kaliannan Jagatheesan

    (Paavai Engineering College, Anna University, Namakkal 637018, TN, India)

  • Baskaran Anand

    (Department of Electronics and Instrumentation Engineering, Hindustan College of Engineering and Technology, Coimbatore 641032, TN, India)

  • Sourav Samanta

    (Department of CSE, University Institute Technology, The University of Burdwan, Bardhaman 713104, WB, India)

  • Nilanjan Dey

    (Techno International New Town, Kolkata 700156, WB, India)

Abstract

The primary goal of this article is to design and implement a secondary controller with which to control the system frequency in a networked microgrid system. The proposed power system comprises of Renewable energy sources (RESs), energy-storing units (ESUs), and synchronous generator. RESs include photovoltaic (PV) and wind turbine generator (WTG) units. The ESU is composed of a flywheel and a battery. Because renewable energy sources are not constant in nature, their values fluctuate from time to time, causing an effect on system frequency and power flow variation in the tie line. The nonlinear output from the RESs is balanced with the support of ESUs. In order to address this situation, a proportional integral derivative (PID) controller based on the Mayfly algorithm (MA) is proposed and built. Comparing the responses of controllers based on the genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) technique-optimized to demonstrate the superiority of the MA-tuned controller.. The results of the validation comparisons reveal that the implemented MA-PID controller delivers and is capable of regulating system frequency under various load demand changes and renewable energy sources. A robustness analysis test was also performed in order to determine the effectiveness of the suggested optimization technique (1%, 2%, 5%, and 10%) step load perturbation (SLP) with ±25% and ±50% variation from the nominal governor and reheater time constant).

Suggested Citation

  • Dhanasekaran Boopathi & Kaliannan Jagatheesan & Baskaran Anand & Sourav Samanta & Nilanjan Dey, 2023. "Frequency Regulation of Interlinked Microgrid System Using Mayfly Algorithm-Based PID Controller," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8829-:d:1159878
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    References listed on IDEAS

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    1. Changbin Hu & Lisong Bi & ZhengGuo Piao & ChunXue Wen & Lijun Hou, 2018. "Coordinative Optimization Control of Microgrid Based on Model Predictive Control," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 9(3), pages 57-75, July.
    2. Jun Yang & Zhili Zeng & Yufei Tang & Jun Yan & Haibo He & Yunliang Wu, 2015. "Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory," Energies, MDPI, vol. 8(3), pages 1-20, March.
    3. Armghan, Hammad & Yang, Ming & Ali, Naghmash & Armghan, Ammar & Alanazi, Abdulaziz, 2022. "Quick reaching law based global terminal sliding mode control for wind/hydrogen/battery DC microgrid," Applied Energy, Elsevier, vol. 316(C).
    4. D. Boopathi & S. Saravanan & K. Jagatheesan & B. Anand, 2021. "Performance Estimation of Frequency Regulation for a Micro-Grid Power System Using PSO-PID Controller," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 12(2), pages 36-49, April.
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    Cited by:

    1. Abidur Rahman Sagor & Md Abu Talha & Shameem Ahmad & Tofael Ahmed & Mohammad Rafiqul Alam & Md. Rifat Hazari & G. M. Shafiullah, 2024. "Pelican Optimization Algorithm-Based Proportional–Integral–Derivative Controller for Superior Frequency Regulation in Interconnected Multi-Area Power Generating System," Energies, MDPI, vol. 17(13), pages 1-24, July.
    2. Ibrahim Altarjami & Yassir Alhazmi, 2024. "Studying the Optimal Frequency Control Condition for Electric Vehicle Fast Charging Stations as a Dynamic Load Using Reinforcement Learning Algorithms in Different Photovoltaic Penetration Levels," Energies, MDPI, vol. 17(11), pages 1-19, May.
    3. Run Qin & Juntao Chen & Zhong Li & Wei Teng & Yibing Liu, 2023. "Simulation of Secondary Frequency Modulation Process of Wind Power with Auxiliary of Flywheel Energy Storage," Sustainability, MDPI, vol. 15(15), pages 1-16, August.
    4. Mohammad Javad Bordbari & Fuzhan Nasiri, 2024. "Networked Microgrids: A Review on Configuration, Operation, and Control Strategies," Energies, MDPI, vol. 17(3), pages 1-28, February.

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