Document Type : Research Articles

Authors

• Reza Noroozian ¹
• Nima Shafaghatian ¹
• Mohammadreza Rahimi ¹
• Hamid Karimi ²

¹ Department of Electrical Engineering, University of Zanjan, University Blvd., Zanjan 3879145371, Iran

² Department of Electrical and Computer Engineering. Qom University of Technology. Qom, Iran

Abstract

Enhancing the energy output of photovoltaic (PV) systems is essential due to their
inherently limited efficiency. Consequently, maximum power point tracking (MPPT)
techniques have become a crucial component in photovoltaic systems for improving
energy harvesting efficiency. However, conventional MPPT methods often require
accurate mathematical modeling of PV systems, which remains a significant challenge
due to their highly nonlinear behavior. To address this issue, researchers have proposed
various MPPT strategies. The complex nonlinear behavior of PV systems makes pattern
extraction difficult, often leading to simplified assumptions that may reduce tracking
accuracy and overall system performance. This study investigates the performance of
five metaheuristic optimization algorithms for MPPT under partial shading conditions
(PSC) to improve PV system efficiency. The considered algorithms include Particle
Swarm Optimization Algorithm (PSOA), Grey Wolf Optimization Algorithm (GWOA),
Cuckoo Search Optimization Algorithm (CSOA), Genetic Algorithm (GA), and
Quantum-Inspired Evolutionary Algorithm (QIEA). Although several studies have
investigated metaheuristic MPPT techniques under partial shading conditions, relatively
limited research has provided a comprehensive comparative evaluation of swarm-based,
evolutionary-based, and physics-inspired optimization approaches considering tracking
efficiency and dynamic response characteristics. The results demonstrate that the
physics-inspired QIEA achieves a superior balance between exploration and exploitation,
thereby enhancing its ability to accurately identify the global maximum power point.

Keywords

• Photovoltaic system
• Maximum power point tracking
• Meta-heuristic optimization algorithm
• Partial shading conditions

Main Subjects

• Optimization