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Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms

Author

Listed:
  • Nour A. Mohamed

    (Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Hany M. Hasanien

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

  • Abdulaziz Alkuhayli

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

  • Tlenshiyeva Akmaral

    (Department of Electrical Engineering, University of Jaén, 23700 Linares, Spain)

  • Francisco Jurado

    (Department of Electrical Engineering, University of Jaén, 23700 Linares, Spain)

  • Ahmed O. Badr

    (Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

Abstract

This article aimed to introduce a novel application of a hybrid particle swarm optimizer and gravitational search algorithm (HPSOGSA) that can be used for optimal control of offshore wind farms’ voltage source converter connected to HVDC transmission lines. Specifically, the algorithm was used to design fractional-order proportional-integral-derivative (FOPID) controller parameters designed to minimize the system’s objective function based on an integral squared error. The proposed FOPID controller was applied to improve offshore wind farm performance under different transient conditions, and its results were compared with a PI controller that was designed using a genetic algorithm and grey wolf optimization algorithm. The fault ride-through capabilities of the proposed control strategy were also evaluated. The findings suggest that the HPSOGSA-based FOPID controller outperformed the other two methods, significantly enhancing offshore wind farm operations. The control strategy was thoroughly tested using MATLAB/Simulink under various operating scenarios.

Suggested Citation

  • Nour A. Mohamed & Hany M. Hasanien & Abdulaziz Alkuhayli & Tlenshiyeva Akmaral & Francisco Jurado & Ahmed O. Badr, 2023. "Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms," Sustainability, MDPI, vol. 15(15), pages 1-25, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11912-:d:1209354
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    References listed on IDEAS

    as
    1. Behera, Sasmita & Sahoo, Subhrajit & Pati, B.B., 2015. "A review on optimization algorithms and application to wind energy integration to grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 214-227.
    2. Rui Zeng & Yizhen Wang, 2022. "Improved Frequency Control Strategy for Offshore Wind Farm Integration via VSC-HVDC," Energies, MDPI, vol. 15(17), pages 1-11, August.
    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:

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    2. Tuba Bostan, 2024. "Generating a Landslide Susceptibility Map Using Integrated Meta-Heuristic Optimization and Machine Learning Models," Sustainability, MDPI, vol. 16(21), pages 1-27, October.

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