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Hybrid Driving Training and Particle Swarm Optimization Algorithm-Based Optimal Control for Performance Improvement of Microgrids

Author

Listed:
  • Dina A. Zaki

    (The Higher Institute for Engineering and Technology Fifth Settlement, Cairo 11823, Egypt)

  • Hany M. Hasanien

    (Electrical Power & Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
    Faculty of Engineering & Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Mohammed Alharbi

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Zia Ullah

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Mariam A. Sameh

    (Faculty of Engineering & Technology, Future University in Egypt, Cairo 11835, Egypt)

Abstract

This paper discusses the importance of microgrids in power systems and introduces a new method for enhancing their performance by improving the transient voltage response in the face of disturbances. The method involves using a hybrid optimization approach that combines driving training-based and particle swarm optimization techniques (HDTPS). This hybrid approach is used to fine-tune the system’s cascaded control scheme parameters, based on proportional–integral–accelerator (PIA) and proportional–integral controllers. The optimization problem is formulated using a central composite response surface methodology (CCRSM) to create an objective function. To validate the suggested control methodology, PSCAD/EMTDC software is used to carry out the simulations. The simulations explore various scenarios wherein the microgrid is transformed into an islanded system and is subjected to various types of faults and load changes. A comparison was made between the two proposed optimized controllers. The simulation results demonstrate the effectiveness of using a PIA-optimized controller; it improved the microgrid performance and greatly enhanced the voltage profile. In addition, the two controllers’ gains were optimized using only PSO to ensure that the outcomes of the HDTPS model demonstrated the same results. Finally, a comparison was made between the two optimization techniques (HDTPS and PSO); the results show a better impact when using the HDTPS model for controller optimization.

Suggested Citation

  • Dina A. Zaki & Hany M. Hasanien & Mohammed Alharbi & Zia Ullah & Mariam A. Sameh, 2023. "Hybrid Driving Training and Particle Swarm Optimization Algorithm-Based Optimal Control for Performance Improvement of Microgrids," Energies, MDPI, vol. 16(11), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4355-:d:1157085
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    References listed on IDEAS

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    1. Wu, Pan & Huang, Wentao & Tai, Nengling & Liang, Shuo, 2018. "A novel design of architecture and control for multiple microgrids with hybrid AC/DC connection," Applied Energy, Elsevier, vol. 210(C), pages 1002-1016.
    2. Kaur, Amandeep & Kaushal, Jitender & Basak, Prasenjit, 2016. "A review on microgrid central controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 338-345.
    3. Ahmed M. Hussien & Jonghoon Kim & Abdulaziz Alkuhayli & Mohammed Alharbi & Hany M. Hasanien & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado, 2022. "Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
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    Cited by:

    1. Zhong Guan & Hui Wang & Zhi Li & Xiaohu Luo & Xi Yang & Jugang Fang & Qiang Zhao, 2024. "Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm," Energies, MDPI, vol. 17(7), pages 1-20, April.
    2. Mikulas Huba & Pavol Bistak & Damir Vrancic, 2023. "Parametrization and Optimal Tuning of Constrained Series PIDA Controller for IPDT Models," Mathematics, MDPI, vol. 11(20), pages 1-32, October.

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