ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Solar Energy
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1625288
Earthworm Optimization Algorithm for Extracting Parameters for Solar Cells and Photovoltaic Modules
Provisionally accepted
- 1Science Engineer Laboratory for Energy, National School of Applied Sciences, Chouaib Doukkali University of El Jadida, Morocco, El Jadida, Morocco
- 2Université Chouaib Doukkali, El Jadida, Morocco
- 3Information Technology Laboratory, National School of Applied Sciences, Chouaib Doukkali University of El Jadida, El Jadida, Morocco
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This paper presents the use of the Earthworm Optimization Algorithm (EOA) in foraging behavior to estimate and extract intrinsic electrical parameters of single, double, and triple diode solar cell models and photovoltaic modules (PVMs) across various technologies. The method addresses the nonlinear and complex nature of solar cell modeling by modifying the objective function to minimize the absolute error between experimental and simulated current values.EOA in foraging is applied to three case studies under varying irradiance and temperature conditions: the RTC France solar cell, the Photowatt-PWP201 PV module, and the Schutten Solar STM6-40/36 monocrystalline module. Parameters for single-diode (SD), double-diode (DD), and triple-diode (TD) models are extracted based on current–voltage (I–V) characteristics. These parameters are then used to reconstruct both I–V and power–voltage (P–V) curves for each technology.The algorithm’s performance is validated using several statistical metrics: Individual Absolute Error (IAE), Relative Error (RE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Standard Deviation (SD), Tracking Signal (TS), Normalized Forecast Measure (NFM), and Autocorrelation Function. Results are benchmarked against existing optimization methods.EOA in foraging consistently demonstrates high accuracy and robustness. For the RTC France cell (SD model), RMSE is improved by 27.33% over MCO-R, 89.25% over NRM, 62.11% over CM, and 94.24% over GA, with comparable performance to LW (88.85%), An.5-Pt (86.46%), and WOA (91.36%). In the DD model, EOA achieves improvements of 11.29% over MCO-R, 12.70% over HS, 99.69% over GA, 93.37% over PSO, and 92.86% over WOA. In the TD model, it improves RMSE by 35.32% over MCO-R, 71.58% over MFO, 93.96% over WOA, 81.36% over SCA, and 63.94% over MVO.Overall, EOA in foraging proves to be a highly effective method for parameter extraction in solar cell models, particularly in double and triple diode configurations, delivering superior accuracy across all evaluated cases.
Keywords: Photovoltaic module, Parameters extraction, Objective function, optimization, Earthworm optimization algorithm
Received: 08 May 2025; Accepted: 30 Jun 2025.
Copyright: © 2025 Wardi, Louzazni and Hanine. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Fatima Wardi, Science Engineer Laboratory for Energy, National School of Applied Sciences, Chouaib Doukkali University of El Jadida, Morocco, El Jadida, Morocco
Mohamed Louzazni, Université Chouaib Doukkali, El Jadida, Morocco
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