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Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm

Author

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  • Ahmed G. Abo-Khalil

    (Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia
    Department of Electrical Engineering, College of Engineering, Assuit University, Assuit 71515, Egypt)

  • Walied Alharbi

    (Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia)

  • Abdel-Rahman Al-Qawasmi

    (Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia)

  • Mohammad Alobaid

    (Department of Mechanical and Industrial Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia)

  • Ibrahim M. Alarifi

    (Department of Mechanical and Industrial Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia)

Abstract

This work presents an alternative to the conventional photovoltaic maximum power point tracking (MPPT) methods, by using an opposition-based learning firefly algorithm (OFA) that improves the performance of the Photovoltaic (PV) system both in the uniform irradiance changes and in partial shading conditions. The firefly algorithm is based on fireflies’ search for food, according to which individuals emit progressively more intense glows as they approach the objective, attracting the other fireflies. Therefore, the simulation of this behavior can be conducted by solving the objective function that is directly proportional to the distance from the desired result. To implement this algorithm in case of partial shading conditions, it was necessary to adjust the Firefly Algorithm (FA) parameters to fit the MPPT application. These parameters have been extensively tested, converging satisfactorily and guaranteeing to extract the global maximum power point (GMPP) in the cases of normal and partial shading conditions analyzed. The precise adjustment of the coefficients was made possible by visualizing the movement of the particles during the convergence process, while opposition-based learning (OBL) was used with FA to accelerate the convergence process by allowing the particle to move in the opposite direction. The proposed algorithm was simulated in the closest possible way to authentic operating conditions, and variable irradiance and partial shading conditions were implemented experimentally for a 60 [W] PV system. A two-stage PV grid-connected system was designed and deployed to validate the proposed algorithm. In addition, a comparison between the performance of the Perturbation and Observation (P&O) method and the proposed method was carried out to prove the effectiveness of this method over the conventional methods in tracking the GMPP.

Suggested Citation

  • Ahmed G. Abo-Khalil & Walied Alharbi & Abdel-Rahman Al-Qawasmi & Mohammad Alobaid & Ibrahim M. Alarifi, 2021. "Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2656-:d:508788
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    References listed on IDEAS

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    1. Ali M. Eltamaly & Hassan M. H. Farh & Mamdooh S. Al Saud, 2019. "Impact of PSO Reinitialization on the Accuracy of Dynamic Global Maximum Power Detection of Variant Partially Shaded PV Systems," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    2. Ahmed G. Abokhalil & Ahmed Bilal Awan & Abdel-Rahman Al-Qawasmi, 2018. "Comparative Study of Passive and Active Islanding Detection Methods for PV Grid-Connected Systems," Sustainability, MDPI, vol. 10(6), pages 1-15, May.
    3. Ali Mohamed Eltamaly & Mamdooh Al-Saud & Khairy Sayed & Ahmed G. Abo-Khalil, 2020. "Sensorless Active and Reactive Control for DFIG Wind Turbines Using Opposition-Based Learning Technique," Sustainability, MDPI, vol. 12(9), pages 1-14, April.
    4. Hassan M. H. Farh & Mohd F. Othman & Ali M. Eltamaly & M. S. Al-Saud, 2018. "Maximum Power Extraction from a Partially Shaded PV System Using an Interleaved Boost Converter," Energies, MDPI, vol. 11(10), pages 1-18, September.
    5. Abdulaziz Almutairi & Ahmed G. Abo-Khalil & Khairy Sayed & Naif Albagami, 2020. "MPPT for a PV Grid-Connected System to Improve Efficiency under Partial Shading Conditions," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    6. Ahmed G. Abo-Khalil & Abdel-Rahman Al-Qawasmi & Ali M. Eltamaly & B. G. Yu, 2020. "Condition Monitoring of DC-Link Electrolytic Capacitors in PWM Power Converters Using OBL Method," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    7. Ali M. Eltamaly & M. S. Al-Saud & A. G. Abo-Khalil, 2020. "Performance Improvement of PV Systems’ Maximum Power Point Tracker Based on a Scanning PSO Particle Strategy," Sustainability, MDPI, vol. 12(3), pages 1-20, February.
    8. Xin-She Yang, 2011. "Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 2(4), pages 1-11, October.
    9. 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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    3. Tamir Shaqarin, 2023. "Particle Swarm Optimization with Targeted Position-Mutated Elitism (PSO-TPME) for Partially Shaded PV Systems," Sustainability, MDPI, vol. 15(5), pages 1-23, February.
    4. Amit Kumar Sharma & Rupendra Kumar Pachauri & Sushabhan Choudhury & Ahmad Faiz Minai & Majed A. Alotaibi & Hasmat Malik & Fausto Pedro García Márquez, 2023. "Role of Metaheuristic Approaches for Implementation of Integrated MPPT-PV Systems: A Comprehensive Study," Mathematics, MDPI, vol. 11(2), pages 1-48, January.
    5. Naveed Ahmed Malik & Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja, 2023. "Firefly Optimization Heuristics for Sustainable Estimation in Power System Harmonics," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    6. Refaat, Ahmed & Ali, Qays Adnan & Elsakka, Mohamed Mohamed & Elhenawy, Yasser & Majozi, Thokozani & Korovkin, Nikolay V. & Elfar, Medhat Hegazy, 2024. "Extraction of maximum power from PV system based on horse herd optimization MPPT technique under various weather conditions," Renewable Energy, Elsevier, vol. 220(C).

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