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

Ghazi, Ghazi A.; Hasanien, Hany M.; Al‐Ammar, Essam A.; Turky, Rania A.; Ko, Wonsuk; Park, Sisam; Choi, Hyeong-Jin

doi:10.3390/su14138172

Cited by 36 publications

(7 citation statements)

References 54 publications

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“…The superiority of the novel hybrid optimization techniques like HCSA-GWO [31,32] is competitive to other classic algorithms, such as the genetic algorithm [33] and gray wolf optimization GWO [34,35]. Grasshopper optimization algorithms GOA [36] and African Vulture Optimization (AVOA) are used to enhance the performance of hybrid renewable energy [37], and whale optimization algorithms [38,39] are used to enhance the performance of the PI controller. Gorilla tropical optimization GTO [40,41] is a population-based meta-heuristic designed to perform high exploration and exploitation.…”

Section: Literature Reviewmentioning

confidence: 99%

Gorilla tropical optimization algorithm solution for performance enhancement of offshore wind farm

Mohamed

Hasanien

Al‐Ammar

et al. 2023

IET Generation Trans & Dist

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This article describes a technique for improving the performance of an offshore wind farm (OWF), which includes a permanent magnet synchronous generator (PMSG) driven by a variable‐speed wind turbine (VSWT) and connected to the grid through a high‐voltage direct current transmission system (HVDC). Under various network disturbances, the gorilla tropical optimization (GTO) metaheuristic method is used to fine‐tune the proportional‐integral (PI) controller gains for VSC‐based PMSG‐VSWT and VSC‐based HVDC transmission systems. This study compares several strategies for extracting the maximum power from a system while maintaining stability under symmetrical and unsymmetrical fault circumstances. Compared to different algorithms, GTO provides better results and improves system recovery and stability after disturbances. As a consequence, dynamic and transient stability may be enhanced. MATLAB/Simulink is used to implement the control mechanism.

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Section: Literature Reviewmentioning

confidence: 99%

Gorilla tropical optimization algorithm solution for performance enhancement of offshore wind farm

Mohamed

Hasanien

Al‐Ammar

et al. 2023

IET Generation Trans & Dist

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“…A trustworthy control keeps tracking the MPP in all environmental circumstances and forces the system to function at its best. There have been several different MPPT techniques developed over the past two decades, including perturb and observe (P&O) [6], [7], incremental conductance [8], hill-climbing (HC) [9], fractional open-circuit voltage (FOV) [10], fractional shortcircuit current (FSC) [11], and a fast algorithm based on one current sensor [12]. In addition, the MPP has been effectively tracked utilizing intelligent techniques including fuzzy logic controller (FLC) [13], [14], artificial neural network (ANN) and genetic algorithm (GA) [15].…”

Section: Introductionmentioning

confidence: 99%

Circle Search Algorithm-Based Super Twisting Sliding Mode Control for MPPT of Different Commercial PV Modules

Ghazi,

Al-Ammar,

Hasanien

et al. 2024

IEEE Access

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The efficiency of grid-connected photovoltaic (PV) systems can be improved using a suitable maximum power point tracking (MPPT) control. Many MPPT techniques have been developed. However, when atmospheric conditions change rapidly, the PV modules respond nonlinearly and the performance of these methods especially the conventional ones is significantly reduced. Therefore, to successfully pursue the maximum power point (MPP) in PV systems, a robust MPPT integrated with sliding mode control (SMC) is employed. This paper proposes a circle search algorithm based super twisting SMC (CSA-STSMC) for tracking the MPP for 100-kW of different commercial grid-connected PV modules coupled with an energy storage system that used to smooth their output fluctuations. Moreover, the STSMC and PI controllers' gains are optimized using the CSA and Grey Wolf Optimizer (GWO) methods and the performance of proposed method is demonstrated through comparisons with CSA based Proportional-Integral controller (CSA-PI), GWO-STSMC, and Incremental Conductance based PI controller (InC-PI) methods. The optimization and simulation analysis are executed in A MATLAB/Simulink environment. It is observed that the proposed method outperforms the considered methods in terms of robustness, tracking speed, best convergence to the minimum error's value and better tracking efficiency in the maximizing the extracted power from the introduced PV systems.INDEX TERMS Circle search algorithm, Grey wolf optimizer, Maximum power point tracking, Photovoltaic system, PI controller, Super twisting sliding mode controller.

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“…Metaheuristic optimization algorithms are used to increase PV panel efficiency. Major metaheuristic optimization algorithms; African vulture optimization algorithm (AVOA) [18], crow search algorithm (CSA) [19], butterfly optimization algorithm (BOA) [20], whale optimization algorithm (WOA) [21], most valuable player algorithm (MVPA) [22], squirrel search algorithm (SSA) [23], shuffled frogleaping algorithm (SFLA) [24,25], BAT search [26], Harris hawk optimization (HHO) [27], search and rescue algorithm (SRA) [28] and particle swarm optimization (PSO) [7,[29][30][31][32][33]. There are comparisons of some metaheuristic optimization algorithms based on performance parameters such as complexity, efficiency, and convergence time [34,35].…”

Section: Introductionmentioning

confidence: 99%

PSO Training Neural Network MPPT with CUK Converter Topology for Stand-Alone PV Systems Under Varying Load and Climatic Conditions

Yılmaz,

Corapsiz

2024

Türk Doğa Ve Fen Dergisi

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Temperature and irradiance levels are two examples of environmental variables that affect the power value produced by photovoltaic panels. Therefore, in order to transfer the maximum power value from the PV panel to the load under varying climatic conditions, maximum power point tracking (MPPT) algorithms and DC-DC converter topologies are used. In this study, the performances of boost converter and CUK converter circuit topologies are investigated under variable irradiance and variable load conditions by using a neural network-based MPPT algorithm learning particle swarm optimization (PSO). As the first scenario, it is analyzed assuming that the temperature and irradiance values coming to the panel are constant. As the second scenario, the performance evaluation of the converter topologies according to the current, voltage and power parameters is made for the variable load situation. As the last scenario, the difference in the irradiance value coming to the panel depending on the sun's condition during the day has been examined. Canadian Solar CS6P-250P PV panel is used in the study. 50 kHz is selected as the switching frequency. According to the results obtained, it has been observed that the CUK converter circuit topology reaches the maximum power point faster than the boost converter circuit topology both in dynamic environmental conditions and load change, and the oscillation at this point is less. It is aimed to increase the performance of this method, which uses boost converter topology and MPPT in the literature, by applying CUK converter topology.

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

Cited by 36 publications

References 54 publications

Gorilla tropical optimization algorithm solution for performance enhancement of offshore wind farm

Gorilla tropical optimization algorithm solution for performance enhancement of offshore wind farm

Circle Search Algorithm-Based Super Twisting Sliding Mode Control for MPPT of Different Commercial PV Modules

PSO Training Neural Network MPPT with CUK Converter Topology for Stand-Alone PV Systems Under Varying Load and Climatic Conditions

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