Intelligent control of Photovoltaic Cystem Based on Metaheurstic Algorithm

FERGANI, OKBA (2025) Intelligent control of Photovoltaic Cystem Based on Metaheurstic Algorithm. Doctoral thesis, Faculté des Sciences et de la technologie.

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Abstract

This thesis presents innovative contributions to the optimization of Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems, specifically under challenging conditions like partial shading. MPPT is crucial for maximizing energy output from PV systems, and existing methods often struggle to maintain efficiency in dynamically changing environments. To address these limitations, this work introduces two novel metaheuristic algorithms: the Modified Bacterial Foraging Algorithm (M-BFA) and the Kitsune Optimizer Algorithm (KOA). The Modified Bacterial Foraging Algorithm (M-BFA) enhances traditional bacterial foraging techniques by incorporating a dynamic mutation rate adjustment mechanism. This modification allows the algorithm to balance exploration and exploitation more effectively, leading to improved accuracy and faster convergence. Simulations demonstrated that M-BFA outperforms conventional methods, improving MPPT accuracy under partial shading condi-tions by up to 89.39%, a significant advancement over classical algorithms. In addition, this research introduces the entirely new Kitsune Optimizer Algorithm (KOA), inspired by the adaptive and intelligent behavior of the mythical Kitsune. KOA features an adaptive mem-ory mechanism and dynamic exploration-exploitation balance, enabling it to perform ex-ceptionally well in complex optimization scenarios. Comparative analysis shows that KOA outperforms existing metaheuristic algorithms, achieving up to 98% accuracy in MPPT ap-plications. Its superior convergence speed and precision make it highly effective for both re-newable energy systems and broader optimization challenges. These advancements offer practical solutions for improving the efficiency and reliability of PV systems, contributing to the broader adoption of renewable energy technologies. The research not only enhances the academic understanding of intelligent optimization techniques but also provides scalable, real-world solutions for maximizing energy harvesting in PVsystems.

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