Application of improved Jellyfish search algorithm for 9-parameters cell extraction and GMPPT in PV systems

Optimization algorithms are essential tools used to address real-world problems through minimization or maximization processes. In the context of photovoltaic (PV) systems, various optimization algorithms have been employed to extract solar cell parameters using minimization techniques, and to identify the maximum power point (MPP) using maximization approaches. However, under partial shading conditions or during rapidly changing irradiance, many of these algorithms tend to get trapped in local minima, failing to locate the global maximum power point (GMPPT). This shortcoming leads to significant energy losses from PV cells. Similarly, the extraction of solar cell parameters is often compromised by the random search behavior and inadequate exploration and exploitation capabilities of many existing algorithms. The Jellyfish Search Optimization (JSO) algorithm is a recent, parameter-free method developed to tackle a wide range of optimization problems. Despite its innovative design, JSO is prone to premature convergence and exhibits a longer convergence time. To address these limitations, this paper proposes a modified version of the JSO algorithm, which integrates the state-of-the-art features of JSO with the Lévy flight characteristic from the cuckoo search algorithm. This combination aims to achieve a better balance between exploration and exploitation. To validate the effectiveness of the proposed Improved Jellyfish Search Optimization (IJSO) algorithm in PV systems, extensive experiments were conducted. These experiments included benchmarking with complex test functions, extracting cell parameters using various solar cell models, and maximizing energy output from PV systems under different environmental conditions. The results demonstrate that the proposed IJSO algorithm effectively enhances parameter extraction and global maximum power point tracking, making it a promising solution for optimizing energy extraction from PV cells in dynamic conditions.

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Publication Details

Journal
Scientific Reports
Published
2024-10-19
DOI
https://doi.org/10.1038/s41598-024-75619-3
Citations
4
Primary Topic
Photovoltaic System Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.41
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article

Application of improved Jellyfish search algorithm for 9-parameters cell extraction and GMPPT in PV systems

Adedayo M. Farayola, Ahmed Abdi Yusuf Ali, Yanxia Sun, Baseem Khan
4 citations
Scientific Reports
Photovoltaic System Optimization Techniques
0.41
article

Application of improved Jellyfish search algorithm for 9-parameters cell extraction and GMPPT in PV systems

Adedayo M. Farayola, Ahmed Abdi Yusuf Ali, Yanxia Sun, Baseem Khan
article en
4 citations

Abstract

Optimization algorithms are essential tools used to address real-world problems through minimization or maximization processes. In the context of photovoltaic (PV) systems, various optimization algorithms have been employed to extract solar cell parameters using minimization techniques, and to identify the maximum power point (MPP) using maximization approaches. However, under partial shading conditions or during rapidly changing irradiance, many of these algorithms tend to get trapped in local minima, failing to locate the global maximum power point (GMPPT). This shortcoming leads to significant energy losses from PV cells. Similarly, the extraction of solar cell parameters is often compromised by the random search behavior and inadequate exploration and exploitation capabilities of many existing algorithms. The Jellyfish Search Optimization (JSO) algorithm is a recent, parameter-free method developed to tackle a wide range of optimization problems. Despite its innovative design, JSO is prone to premature convergence and exhibits a longer convergence time. To address these limitations, this paper proposes a modified version of the JSO algorithm, which integrates the state-of-the-art features of JSO with the Lévy flight characteristic from the cuckoo search algorithm. This combination aims to achieve a better balance between exploration and exploitation. To validate the effectiveness of the proposed Improved Jellyfish Search Optimization (IJSO) algorithm in PV systems, extensive experiments were conducted. These experiments included benchmarking with complex test functions, extracting cell parameters using various solar cell models, and maximizing energy output from PV systems under different environmental conditions. The results demonstrate that the proposed IJSO algorithm effectively enhances parameter extraction and global maximum power point tracking, making it a promising solution for optimizing energy extraction from PV cells in dynamic conditions.

Scientific ReportsVol. 14(1)
Hawassa University (ET), University of Johannesburg (ZA), Zhejiang University (CN)
Zero hunger
Openalex Percentile: Top 47%
Photovoltaic System Optimization Techniques
0.41
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