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Volume 14
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10.3390/su14138172
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Article

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

by
Ghazi A. Ghazi
1,2,*,
Hany M. Hasanien
3,
Essam A. Al-Ammar
1,2,
Rania A. Turky
4,
Wonsuk Ko
1,
Sisam Park
5 and
Hyeong-Jin Choi
5
1
Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
2
K. A. CARE Energy Research and Innovation Center, King Saud University, Riyadh 11421, Saudi Arabia
3
Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11566, Egypt
4
Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt
5
GS E&C Institute, GS E&C Corp., 33, Jong-ro, Jongno-gu, Seoul 03159, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(13), 8172; https://doi.org/10.3390/su14138172
Submission received: 31 May 2022 / Revised: 22 June 2022 / Accepted: 29 June 2022 / Published: 4 July 2022
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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.
Keywords: maximum power point tracking; PI controllers; hybrid system; African Vulture Optimization Algorithm (AVOA); renewable-energy sources maximum power point tracking; PI controllers; hybrid system; African Vulture Optimization Algorithm (AVOA); renewable-energy sources

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MDPI and ACS Style

Ghazi, G.A.; Hasanien, H.M.; Al-Ammar, E.A.; Turky, R.A.; Ko, W.; Park, S.; Choi, H.-J. African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems. Sustainability 2022, 14, 8172. https://doi.org/10.3390/su14138172

AMA Style

Ghazi GA, Hasanien HM, Al-Ammar EA, Turky RA, Ko W, Park S, Choi H-J. African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems. Sustainability. 2022; 14(13):8172. https://doi.org/10.3390/su14138172

Chicago/Turabian Style

Ghazi, Ghazi A., Hany M. Hasanien, Essam A. Al-Ammar, Rania A. Turky, Wonsuk Ko, Sisam Park, and Hyeong-Jin Choi. 2022. "African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems" Sustainability 14, no. 13: 8172. https://doi.org/10.3390/su14138172

APA Style

Ghazi, G. A., Hasanien, H. M., Al-Ammar, E. A., Turky, R. A., Ko, W., Park, S., & Choi, H.-J. (2022). African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems. Sustainability, 14(13), 8172. https://doi.org/10.3390/su14138172

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