Enhancing grid connected photovoltaic systems performance using a bio-inspired MFB algorithm for efficient maximum power point tracking

An original bio-inspired approach, modeled after the magnificent frigatebird, is proposed in this study to optimize Maximum Power Point Tracking (MPPT) for photovoltaic arrays operating in grid-tied configurations, in response to the growing demand for higher efficiency during the ongoing transition toward sustainable energy. By modeling the frigatebird’s strategic shifts between wide-range scouting and target-focused behavior, the algorithm maintains a dynamic equilibrium between exploration and exploitation, ensuring robust MPPT performance even under rapidly changing irradiance and temperature conditions. The photovoltaic setup under study consists of a 50 kW SunPower panel array, paired with a boost-type DC–DC converter and a three-phase inverter. System behavior was examined in MATLAB/Simulink across three operating scenarios: standard test benchmarks, fast-changing irradiance conditions, and real-world solar measurements collected in Tetouan, Morocco. Simulation outcomes reveal that the MFB-based control achieves a high energy conversion efficiency of 99.5% with a rapid response time of 0.27 s, providing improved performance compared with widely used MPPT methods such as P&O and ABC in terms of dynamic response, tracking precision, and total harmonic distortion (THD). The proposed algorithm relies on a simple computational structure with a limited number of control parameters, contributing to reduced computational burden and supporting its suitability for real-time embedded MPPT applications. A performance comparison against fourteen other MPPT approaches reported in recent studies further supports the effectiveness and adaptability of the proposed method. The findings indicate that MFB represents a promising and scalable solution for advanced smart PV systems, with potential applications in real-time embedded platforms and hybrid renewable energy networks. While the present validation is based on detailed simulation results, experimental implementation and hardware-based assessment are considered as natural extensions of this work. • Novel bio-inspired MPPT based on magnificent frigatebird foraging behavior. • Fast and accurate MPPT under rapid irradiance and temperature variations. • High tracking efficiency of 99.49% with 0.27 s dynamic response. • Superior performance compared with P&O, ABC and recent MPPT methods. • Low computational complexity suitable for real-time embedded systems.

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Publication Details

Journal
Energy Reports
Published
2026-03-17
DOI
https://doi.org/10.1016/j.egyr.2026.109210
Primary Topic
Photovoltaic System Optimization Techniques
Type
article
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article

Enhancing grid connected photovoltaic systems performance using a bio-inspired MFB algorithm for efficient maximum power point tracking

Hatim Ameziane, Youness El Mourabit, Alia Zakriti, Mohamed Abouhawwash et al.
Energy Reports
Photovoltaic System Optimization Techniques
article

Enhancing grid connected photovoltaic systems performance using a bio-inspired MFB algorithm for efficient maximum power point tracking

Hatim Ameziane, Youness El Mourabit, Alia Zakriti, Mohamed Abouhawwash, Mohamed Chouiekh, Mehedi Masud, Abdelkarim Ballouti, Ali Abad, Hossam Meshref, Hassane Latrach, Hicham Karmouni
article en

Abstract

An original bio-inspired approach, modeled after the magnificent frigatebird, is proposed in this study to optimize Maximum Power Point Tracking (MPPT) for photovoltaic arrays operating in grid-tied configurations, in response to the growing demand for higher efficiency during the ongoing transition toward sustainable energy. By modeling the frigatebird’s strategic shifts between wide-range scouting and target-focused behavior, the algorithm maintains a dynamic equilibrium between exploration and exploitation, ensuring robust MPPT performance even under rapidly changing irradiance and temperature conditions. The photovoltaic setup under study consists of a 50 kW SunPower panel array, paired with a boost-type DC–DC converter and a three-phase inverter. System behavior was examined in MATLAB/Simulink across three operating scenarios: standard test benchmarks, fast-changing irradiance conditions, and real-world solar measurements collected in Tetouan, Morocco. Simulation outcomes reveal that the MFB-based control achieves a high energy conversion efficiency of 99.5% with a rapid response time of 0.27 s, providing improved performance compared with widely used MPPT methods such as P&O and ABC in terms of dynamic response, tracking precision, and total harmonic distortion (THD). The proposed algorithm relies on a simple computational structure with a limited number of control parameters, contributing to reduced computational burden and supporting its suitability for real-time embedded MPPT applications. A performance comparison against fourteen other MPPT approaches reported in recent studies further supports the effectiveness and adaptability of the proposed method. The findings indicate that MFB represents a promising and scalable solution for advanced smart PV systems, with potential applications in real-time embedded platforms and hybrid renewable energy networks. While the present validation is based on detailed simulation results, experimental implementation and hardware-based assessment are considered as natural extensions of this work. • Novel bio-inspired MPPT based on magnificent frigatebird foraging behavior. • Fast and accurate MPPT under rapid irradiance and temperature variations. • High tracking efficiency of 99.49% with 0.27 s dynamic response. • Superior performance compared with P&O, ABC and recent MPPT methods. • Low computational complexity suitable for real-time embedded systems.

Energy ReportsVol. 15
King Fahd University of Petroleum and Minerals (SA), Taif University (SA), Abdelmalek Essaâdi University (MA), Université Sultan Moulay Slimane (MA), Sidi Mohamed Ben Abdellah University (MA)
Affordable and clean energy
Openalex Percentile: Top 22%
Photovoltaic System Optimization Techniques
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