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Comparative Study of White Shark Optimization and Combined Meta-Heuristic Algorithm for Enhanced MPPT in Photovoltaic Systems

Author

Listed:
  • Fajar Kurnia Al Farisi

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd., Taipei 106, Taiwan)

  • Zhi-Kai Fan

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd., Taipei 106, Taiwan)

  • Kuo-Lung Lian

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd., Taipei 106, Taiwan)

Abstract

This paper proposes a novel hybrid metaheuristic algorithm (MHA) for maximum power point tracking (MPPT), integrating particle swarm optimization (PSO), the differential evolution algorithm (DEA), and the grey wolf optimizer (GWO). The proposed method is inspired by the structural phases of the white shark optimizer (WSO), a recently introduced MHA. This study evaluates the MPPT performance of WSO and compares it with the proposed hybrid approach to provide insights into optimal MPPT selection. The key contributions include an in-depth analysis of the WSO framework, benchmarking its performance against the hybrid model. Both algorithms are implemented in an MPPT system and assessed based on tracking speed, accuracy, and adaptability. The results indicate that the WSO achieves a faster convergence due to its biologically inspired design, whereas the hybrid model, despite requiring additional coordination time, ensures comprehensive search space exploration. Notably, the proposed method excels in dynamic tracking efficiency, which is crucial for accurately following time-varying P-V curves. This study underscores the trade-off between tracking speed and efficiency, demonstrating that while WSO is advantageous for rapid tracking, the hybrid approach enhances overall MPPT performance under dynamic conditions. These findings offer valuable insights for optimizing MPPT strategies in renewable energy systems.

Suggested Citation

  • Fajar Kurnia Al Farisi & Zhi-Kai Fan & Kuo-Lung Lian, 2025. "Comparative Study of White Shark Optimization and Combined Meta-Heuristic Algorithm for Enhanced MPPT in Photovoltaic Systems," Energies, MDPI, vol. 18(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2110-:d:1638221
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    References listed on IDEAS

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    1. Kuei-Hsiang Chao & Muhammad Nursyam Rizal, 2021. "A Hybrid MPPT Controller Based on the Genetic Algorithm and Ant Colony Optimization for Photovoltaic Systems under Partially Shaded Conditions," Energies, MDPI, vol. 14(10), pages 1-17, May.
    2. 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).
    3. Sy Ngo & Chian-Song Chiu & Thanh-Dong Ngo, 2022. "A Novel Horse Racing Algorithm Based MPPT Control for Standalone PV Power Systems," Energies, MDPI, vol. 15(20), pages 1-18, October.
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