A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters Optimisation

Accurate parameter estimation is vital for optimising the performance and design of photovoltaic (PV) systems. While metaheuristic algorithms (MHAs) offer promising solutions, they often face challenges such as slow convergence and difficulty balancing exploration and exploitation. This study introd...

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Main Authors: Jeridi Ahmed, Moulahi Mohamed Hedi, Khaterchi Hechmi, Zaafouri Abderrahmen
Format: Article
Language:English
Published: Sciendo 2025-01-01
Series:Power Electronics and Drives
Subjects:
Online Access:https://doi.org/10.2478/pead-2025-0003
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author Jeridi Ahmed
Moulahi Mohamed Hedi
Khaterchi Hechmi
Zaafouri Abderrahmen
author_facet Jeridi Ahmed
Moulahi Mohamed Hedi
Khaterchi Hechmi
Zaafouri Abderrahmen
author_sort Jeridi Ahmed
collection DOAJ
description Accurate parameter estimation is vital for optimising the performance and design of photovoltaic (PV) systems. While metaheuristic algorithms (MHAs) offer promising solutions, they often face challenges such as slow convergence and difficulty balancing exploration and exploitation. This study introduces a novel hybrid approach, WSO-HO, which integrates the strengths of the war strategy optimization (WSO) and Hippopotamus Optimization (HO) algorithms, enhanced by the Newton-Raphson (NR) method, to achieve precise parameter estimation for PV models. The effectiveness of the WSO-HO algorithm was rigorously evaluated through intensive testing on three different solar panels, including the RTC France solar cell using the single diode model (SDM) and the double diode model (DDM), over 30 iterations. Comparative analysis highlights the superior performance of WSO-HO against conventional algorithms, which often struggle with accurately identifying PV model parameters. These promising results demonstrate the significant potential of this hybrid approach to improve parameter optimisation in PV systems, enabling more precise design and enhanced overall system efficiency. Furthermore, the simulation result of the performance of the WSO-HO algorithm was benchmarked against other algorithms reported in the literature, further validating its robustness and effectiveness.
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institution Kabale University
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series Power Electronics and Drives
spelling doaj-art-78d4767af9734ea3a3ba503af24351092025-02-10T13:26:00ZengSciendoPower Electronics and Drives2543-42922025-01-01101415910.2478/pead-2025-0003A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters OptimisationJeridi Ahmed0Moulahi Mohamed Hedi1Khaterchi Hechmi2Zaafouri Abderrahmen3University of Tunis, National Higher School of Engineers of Tunis (ENSIT), Laboratory of Engineering of Industrial Systems and Renewable Energy (LISIER), 1008Tunis, TunisiaUniversity of Tunis, National Higher School of Engineers of Tunis (ENSIT), Laboratory of Engineering of Industrial Systems and Renewable Energy (LISIER), 1008Tunis, TunisiaUniversity of Tunis, National Higher School of Engineers of Tunis (ENSIT), Laboratory of Engineering of Industrial Systems and Renewable Energy (LISIER), 1008Tunis, TunisiaUniversity of Tunis, National Higher School of Engineers of Tunis (ENSIT), Laboratory of Engineering of Industrial Systems and Renewable Energy (LISIER), 1008Tunis, TunisiaAccurate parameter estimation is vital for optimising the performance and design of photovoltaic (PV) systems. While metaheuristic algorithms (MHAs) offer promising solutions, they often face challenges such as slow convergence and difficulty balancing exploration and exploitation. This study introduces a novel hybrid approach, WSO-HO, which integrates the strengths of the war strategy optimization (WSO) and Hippopotamus Optimization (HO) algorithms, enhanced by the Newton-Raphson (NR) method, to achieve precise parameter estimation for PV models. The effectiveness of the WSO-HO algorithm was rigorously evaluated through intensive testing on three different solar panels, including the RTC France solar cell using the single diode model (SDM) and the double diode model (DDM), over 30 iterations. Comparative analysis highlights the superior performance of WSO-HO against conventional algorithms, which often struggle with accurately identifying PV model parameters. These promising results demonstrate the significant potential of this hybrid approach to improve parameter optimisation in PV systems, enabling more precise design and enhanced overall system efficiency. Furthermore, the simulation result of the performance of the WSO-HO algorithm was benchmarked against other algorithms reported in the literature, further validating its robustness and effectiveness.https://doi.org/10.2478/pead-2025-0003pv systemnewton-raphson methodwso-ho algorithmshybrid meta-heuristic algorithms
spellingShingle Jeridi Ahmed
Moulahi Mohamed Hedi
Khaterchi Hechmi
Zaafouri Abderrahmen
A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters Optimisation
Power Electronics and Drives
pv system
newton-raphson method
wso-ho algorithms
hybrid meta-heuristic algorithms
title A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters Optimisation
title_full A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters Optimisation
title_fullStr A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters Optimisation
title_full_unstemmed A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters Optimisation
title_short A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters Optimisation
title_sort hybrid approach incorporating wso ho and the newton raphson method to enhancing photovoltaic solar model parameters optimisation
topic pv system
newton-raphson method
wso-ho algorithms
hybrid meta-heuristic algorithms
url https://doi.org/10.2478/pead-2025-0003
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