Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules
In this paper, we deal with the use of the earthworm optimization algorithm (EOA) in foraging to estimate and extract the intrinsic electrical parameters of single-, double-, and triple-diode solar cells and photovoltaic modules across different technologies. This method was chosen to address the ch...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1625288/full |
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| author | Fatima Wardi Mohamed Louzazni Mohamed Hanine |
| author_facet | Fatima Wardi Mohamed Louzazni Mohamed Hanine |
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| description | In this paper, we deal with the use of the earthworm optimization algorithm (EOA) in foraging to estimate and extract the intrinsic electrical parameters of single-, double-, and triple-diode solar cells and photovoltaic modules across different technologies. This method was chosen to address the challenges associated with the nonlinear behavior, complexity, and mathematical modeling of solar cells and photovoltaic modules (PVMs). The objective function is modified to minimize the absolute errors between experimental and simulated current values. The EOA in foraging is applied to three different case studies: the RTC France solar cell, the Photowatt-PWP 201 PV module, and the Schutten Solar STM6-40/36 monocrystalline module, under varying solar irradiance and ambient temperature conditions. The goal is to identify the parameters of the single-diode (SD), double-diode (DD), and triple-diode (TD) models. In addition, the proposed objective function is computed based on the current–voltage (I–V) characteristic curve. The extracted parameters for each case study are used to reconstruct the I–V and power–voltage (P–V) characteristic curves for the respective solar cell and photovoltaic module technologies. To validate the performance and efficiency of the algorithm, various statistical criteria are computed, including 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 the autocorrelation function (ACF). These metrics are compared to assess the accuracy of the parameters obtained by the EOA in foraging. The reconstructed I–V and P–V curves exhibit strong agreement with experimental data, demonstrating superior accuracy compared to other recently published methods. The EOA in foraging also shows clear superiority in RMSE across the three model configurations (SD, DD, and TD). For instance, in the case of the RTC France solar cell, the EOA in foraging improves RMSE by 27.33% over MCO-R, 89.25% over NRM, 62.11% over CM, and 94.24% over GA, with comparable results to LW (88.85%), An.5-Pt (86.46%), and WOA (91.36%). In the DD model, the EOA in foraging shows 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, the EOA in foraging achieves improvements of 35.32% over MCO-R, 71.58% over MFO, 93.96% over WOA, 81.36% over SCA, and 63.94% over MVO. These results confirm that the EOA in foraging significantly outperforms other methods in terms of accuracy, particularly in the DD and TD models. |
| format | Article |
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| institution | Kabale University |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-08-01 |
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| spelling | doaj-art-bd0c4692d2ed4569af62eb6ff348b4ef2025-08-20T03:36:35ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-08-011310.3389/fenrg.2025.16252881625288Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modulesFatima Wardi0Mohamed Louzazni1Mohamed Hanine2Science Engineer Laboratory for Energy, National School of Applied Sciences, Chouaib Doukkali University of El Jadida, El Jadida, MoroccoScience Engineer Laboratory for Energy, National School of Applied Sciences, Chouaib Doukkali University of El Jadida, El Jadida, MoroccoInformation Technology Laboratory, National School of Applied Sciences, Chouaib Doukkali University of El Jadida, El Jadida, MoroccoIn this paper, we deal with the use of the earthworm optimization algorithm (EOA) in foraging to estimate and extract the intrinsic electrical parameters of single-, double-, and triple-diode solar cells and photovoltaic modules across different technologies. This method was chosen to address the challenges associated with the nonlinear behavior, complexity, and mathematical modeling of solar cells and photovoltaic modules (PVMs). The objective function is modified to minimize the absolute errors between experimental and simulated current values. The EOA in foraging is applied to three different case studies: the RTC France solar cell, the Photowatt-PWP 201 PV module, and the Schutten Solar STM6-40/36 monocrystalline module, under varying solar irradiance and ambient temperature conditions. The goal is to identify the parameters of the single-diode (SD), double-diode (DD), and triple-diode (TD) models. In addition, the proposed objective function is computed based on the current–voltage (I–V) characteristic curve. The extracted parameters for each case study are used to reconstruct the I–V and power–voltage (P–V) characteristic curves for the respective solar cell and photovoltaic module technologies. To validate the performance and efficiency of the algorithm, various statistical criteria are computed, including 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 the autocorrelation function (ACF). These metrics are compared to assess the accuracy of the parameters obtained by the EOA in foraging. The reconstructed I–V and P–V curves exhibit strong agreement with experimental data, demonstrating superior accuracy compared to other recently published methods. The EOA in foraging also shows clear superiority in RMSE across the three model configurations (SD, DD, and TD). For instance, in the case of the RTC France solar cell, the EOA in foraging improves RMSE by 27.33% over MCO-R, 89.25% over NRM, 62.11% over CM, and 94.24% over GA, with comparable results to LW (88.85%), An.5-Pt (86.46%), and WOA (91.36%). In the DD model, the EOA in foraging shows 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, the EOA in foraging achieves improvements of 35.32% over MCO-R, 71.58% over MFO, 93.96% over WOA, 81.36% over SCA, and 63.94% over MVO. These results confirm that the EOA in foraging significantly outperforms other methods in terms of accuracy, particularly in the DD and TD models.https://www.frontiersin.org/articles/10.3389/fenrg.2025.1625288/fullphotovoltaic moduleparameter extractionobjective functionoptimizationearthworm optimization algorithm |
| spellingShingle | Fatima Wardi Mohamed Louzazni Mohamed Hanine Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules Frontiers in Energy Research photovoltaic module parameter extraction objective function optimization earthworm optimization algorithm |
| title | Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules |
| title_full | Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules |
| title_fullStr | Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules |
| title_full_unstemmed | Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules |
| title_short | Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules |
| title_sort | earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules |
| topic | photovoltaic module parameter extraction objective function optimization earthworm optimization algorithm |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1625288/full |
| work_keys_str_mv | AT fatimawardi earthwormoptimizationalgorithmforextractingparametersforsolarcellsandphotovoltaicmodules AT mohamedlouzazni earthwormoptimizationalgorithmforextractingparametersforsolarcellsandphotovoltaicmodules AT mohamedhanine earthwormoptimizationalgorithmforextractingparametersforsolarcellsandphotovoltaicmodules |