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

Fajar Kurnia Al Farisi, Zhi-Kai Fan and Kuo-Lung Lian ()
Additional contact information
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

Energies, 2025, vol. 18, issue 8, 1-21

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.

Keywords: white shark optimizer; maximum power point tracking; photovoltaic system; partial shading (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/8/2110/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/8/2110/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:8:p:2110-:d:1638221

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().