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African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems

Author

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  • Ghazi A. Ghazi

    (Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
    K. A. CARE Energy Research and Innovation Center, King Saud University, Riyadh 11421, Saudi Arabia)

  • Hany M. Hasanien

    (Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11566, Egypt)

  • Essam A. Al-Ammar

    (Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
    K. A. CARE Energy Research and Innovation Center, King Saud University, Riyadh 11421, Saudi Arabia)

  • Rania A. Turky

    (Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Wonsuk Ko

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

  • Sisam Park

    (GS E&C Institute, GS E&C Corp., 33, Jong-ro, Jongno-gu, Seoul 03159, Korea)

  • Hyeong-Jin Choi

    (GS E&C Institute, GS E&C Corp., 33, Jong-ro, Jongno-gu, Seoul 03159, Korea)

Abstract

An effective maximum power point tracking (MPPT) technique plays a crucial role in improving the efficiency and performance of grid-connected renewable energy sources (RESs). This paper uses the African Vulture Optimization Algorithm (AVOA), a metaheuristic technique inspired by nature, to tune the proportional–integral (PI)-based MPPT controllers for hybrid RESs of solar photovoltaic (PV) and wind systems, as well as the PI controllers in a storage system that are used to smooth the output fluctuations of those RESs in a hybrid system. The performance of the AVOA is compared with that of the widely used the particle swarm optimization (PSO) technique, which is commonly acknowledged as the foundation of swarm intelligence. As a result, this technique is introduced in this study to draw a comparison. It is observed that the proposed algorithm outperformed the PSO algorithm in terms of the tracking speed, robustness, and best convergence to the minimum value. A MATLAB/Simulink model was built, and optimization and simulation for the proposed system were carried out to verify the introduced algorithms. In conclusion, the optimization and simulation results showed that the AVOA is a promising method for solving a variety of engineering problems.

Suggested Citation

  • Ghazi A. Ghazi & Hany M. Hasanien & Essam A. Al-Ammar & Rania A. Turky & Wonsuk Ko & Sisam Park & Hyeong-Jin Choi, 2022. "African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems," Sustainability, MDPI, vol. 14(13), pages 1-26, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8172-:d:855674
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    References listed on IDEAS

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    1. Karabacak, Murat, 2019. "A new perturb and observe based higher order sliding mode MPPT control of wind turbines eliminating the rotor inertial effect," Renewable Energy, Elsevier, vol. 133(C), pages 807-827.
    2. Mohamed, Mohamed A. & Zaki Diab, Ahmed A. & Rezk, Hegazy, 2019. "Partial shading mitigation of PV systems via different meta-heuristic techniques," Renewable Energy, Elsevier, vol. 130(C), pages 1159-1175.
    3. Eltamaly, Ali M. & Al-Saud, M.S. & Abokhalil, Ahmed G. & Farh, Hassan M.H., 2020. "Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
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    Cited by:

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