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Article

Quantum Marine Predator Algorithm: A Quantum Leap in Photovoltaic Efficiency Under Dynamic Conditions

1
Laboratory of Identification, Commande, Control and Communication (LI3CUB), University of Biskra, Biskra 07000, Algeria
2
College of Engineering and Information Technology, University of Dubai, Dubai P.O. Box 14143, United Arab Emirates
3
Department of Electrical Engineering, University of Biskra, Biskra 07000, Algeria
*
Author to whom correspondence should be addressed.
Information 2024, 15(11), 692; https://doi.org/10.3390/info15110692
Submission received: 28 September 2024 / Revised: 25 October 2024 / Accepted: 31 October 2024 / Published: 3 November 2024
(This article belongs to the Special Issue Applications of Machine Learning and Convolutional Neural Networks)

Abstract

The Quantum Marine Predator Algorithm (QMPA) presents a groundbreaking solution to the inherent limitations of conventional Maximum Power Point Tracking (MPPT) techniques in photovoltaic systems. These limitations, such as sluggish response times and inadequate adaptability to environmental fluctuations, are particularly pronounced in regions with challenging weather patterns like Sunderland. QMPA emerges as a formidable contender by seamlessly integrating the sophisticated hunting tactics of marine predators with the principles of quantum mechanics. This amalgamation not only enhances operational efficiency but also addresses the need for real-time adaptability. One of the most striking advantages of QMPA is its remarkable improvement in response time and adaptability. Compared to traditional MPPT methods, which often struggle to keep pace with rapidly changing environmental factors, QMPA demonstrates a significant reduction in response time, resulting in up to a 30% increase in efficiency under fluctuating irradiance conditions for a resistive load of 100 Ω. These findings are derived from extensive experimentation using NASA’s worldwide power prediction data. Through a detailed comparative analysis with existing MPPT methodologies, QMPA consistently outperforms its counterparts, exhibiting superior operational efficiency and stability across varying environmental scenarios. By substantiating its claims with concrete data and measurable improvements, this research transcends generic assertions and establishes QMPA as a tangible advancement in MPPT technology.
Keywords: Quantum Marine Predator Algorithm (QMPA); Maximum Power Point Tracking (MPPT); photovoltaic systems; quantum mechanics; optimization algorithms; renewable energy efficiency Quantum Marine Predator Algorithm (QMPA); Maximum Power Point Tracking (MPPT); photovoltaic systems; quantum mechanics; optimization algorithms; renewable energy efficiency

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MDPI and ACS Style

Fergani, O.; Himeur, Y.; Mechgoug, R.; Atalla, S.; Mansoor, W.; Tkouti, N. Quantum Marine Predator Algorithm: A Quantum Leap in Photovoltaic Efficiency Under Dynamic Conditions. Information 2024, 15, 692. https://doi.org/10.3390/info15110692

AMA Style

Fergani O, Himeur Y, Mechgoug R, Atalla S, Mansoor W, Tkouti N. Quantum Marine Predator Algorithm: A Quantum Leap in Photovoltaic Efficiency Under Dynamic Conditions. Information. 2024; 15(11):692. https://doi.org/10.3390/info15110692

Chicago/Turabian Style

Fergani, Okba, Yassine Himeur, Raihane Mechgoug, Shadi Atalla, Wathiq Mansoor, and Nacira Tkouti. 2024. "Quantum Marine Predator Algorithm: A Quantum Leap in Photovoltaic Efficiency Under Dynamic Conditions" Information 15, no. 11: 692. https://doi.org/10.3390/info15110692

APA Style

Fergani, O., Himeur, Y., Mechgoug, R., Atalla, S., Mansoor, W., & Tkouti, N. (2024). Quantum Marine Predator Algorithm: A Quantum Leap in Photovoltaic Efficiency Under Dynamic Conditions. Information, 15(11), 692. https://doi.org/10.3390/info15110692

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