Improved marine predator MPPT algorithm for photovoltaic systems in partial shading conditions

• Published in: Scientific reports Vol. 15; no. 1; pp. 21092 - 13
• Main Authors: Zheng, Hanbo, Du, Qi, Mo, Shuqin, Qin, Tuanfa, Wang, Shusheng, Li, Zhe
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2025; Nature Portfolio
• Subjects: 639/4077; 639/4077/4073; 639/4077/909; Humanities and Social Sciences; Local shading; Marine predator algorithm; Maximum photovoltaic power tracking; multidisciplinary; Photovoltaic system; Science; Science (multidisciplinary); Marine predator algorithm; Local shading; Maximum photovoltaic power tracking; Photovoltaic system
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-06408-9

When the photovoltaic arrays are subject to partial shading (PS), their output characteristics show a multi-peak phenomenon. Under this circumstance, it will be a challenge to track the maximum power point (MPP), which impacts power generation efficiency. Therefore, achieving efficient maximum power point tracking (MPPT) in a photovoltaic (PV) system under PS is an important element in enhancing PV system efficiency. To optimize PV array output efficiency under PS conditions, this paper investigates a MPPT algorithm for PV arrays in partial shading environments. This algorithm is optimized and improved accordingly based on the Marine Predator Algorithm (MPA). Firstly, the initial position voltage is modified, which provides a more accurate and stable basis for the subsequent calculation. Next, the mechanism of overstepping is optimized to ensure that the particles will not exceed the preset range in the search process. Then, introducing the elite population guidance mechanism and restart algorithm to speed up the tracking of the entire algorithm. Lastly, the Perturbation and Observation algorithm is added to minimize the power fluctuation and improve tracking accuracy. The improved algorithm and the original algorithm, particle swarm algorithm, and incremental conductance method are embedded in the model of the PV system for the simulation of maximum power point tracking, respectively. Experiments show that the tracking time of the improved algorithm is improved by 32.6%, 76.5%, and 50% compared to the original algorithm, particle swarm algorithm, and incremental conductance method, respectively, under uniform illumination. Under dynamic irradiance conditions, the proposed algorithm has a tracking efficiency of up to 99.95%, which is over 15% higher than the comparison algorithm. The outcomes reveal that the improved algorithm is faster and more efficient in tracking compared to the other three algorithms.