A Simple and Efficient Determination of the Ideality Factor of Solar Cells and Modules from the Knee Point of the Shunt Resistance Curve

Muhammadsharif, Fahmi F.; Hashim, Suhairul

doi:10.1007/s13369-023-07860-3

Cited by 3 publications

(1 citation statement)

References 63 publications

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“…In addition, the market to produce electricity from optimization techniques, including iterative algorithms and metaheuristic approaches, to minimize the discrepancy between model predictions and observed data. Numerical methods offer flexibility in handling complex and nonlinear problems [17,18]. Analytical methods involve analyzing mathematical formulas to identify the parameters of PV models.…”

Section: Introductionmentioning

confidence: 99%

Parameters Identification of Photovoltaic Cell and Module Models Using Modified Social Group Optimization Algorithm

et al. 2023

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Photovoltaic systems have become more attractive alternatives to be integrated into electrical power systems. Therefore, PV cells have gained immense interest in studies related to their operation. A photovoltaic module’s performance can be optimized by identifying the parameters of a photovoltaic cell to understand its behavior and simulate its characteristics from a given mathematical model. This work aims to extract and identify the parameters of photovoltaic cells using a novel metaheuristic algorithm named Modified Social Group Optimization (MSGO). First, a comparative study was carried out based on various technologies and models of photovoltaic modules. Then, the proposed MSGO algorithm was tested on a monocrystalline type of panel with its single-diode and double-diode models. Then, it was tested on an amorphous type of photovoltaic cell (hydrogenated amorphous silicon (a-Si: H)). Finally, an experimental validation was carried out to test the proposed MSGO algorithm and identify the parameters of the polycrystalline type of panel. All obtained results were compared to previous research findings. The present study showed that the MSGO is highly competitive and demonstrates better efficiency in parameter identification compared to other optimization algorithms. The Individual Absolute Error (IAE) obtained by the MSGO is better than the other errors for most measurement values in the case of single- and double-diode models. Relatedly, the average fitness function obtained by the MSGO algorithm has the fastest convergence rate.

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Section: Introductionmentioning

confidence: 99%

Parameters Identification of Photovoltaic Cell and Module Models Using Modified Social Group Optimization Algorithm

et al. 2023

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Double-diode and triple-diode solar cell models: invertible approximate analytical expressions based on the g-function approach

Ćalasan

2024

J Comput Electron

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Accurate Method for Solar Power Generation Estimation for Different PV (Photovoltaic Panels) Technologies

Meflah,

Chekired,

Drir

et al. 2024

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In 2023, solar photovoltaic energy alone accounted for 75% of the global increase in renewable capacity. Moreover, this natural energy resource is the one that requires the least investment, which makes it accessible to developing countries. Increasing return on investment in these regions requires a particular evaluation of environmental parameters influencing PV systems performance. Higher temperatures decrease PV module efficiency and, as a result, their power output. Additionally, fluctuations in solar irradiance directly impact the energy generated by these systems. Consequently, it is essential for investors to improve accurate predictive models that assess the power generation capacity of photovoltaic systems under local environmental conditions. Therefore, accurate estimation of maximum power generation is then crucial for optimizing photovoltaic (PV) system performances and selecting suitable PV modules for specific climates. In this context, this study presents an experimental comparison of three maximum power prediction methods for four PV module types (amorphous silicon, monocrystalline silicon, micromorphous silicon, and polycrystalline silicon) under real outdoor conditions. Experimental data gathered over the course of a year are analyzed and processed for the four PV technologies. Three different methods taking into account environmental parameters are presented and analyzed. The first estimation method utilizes irradiance as the primary input parameter, while two additional methods incorporate ambient temperature and PV module temperature for enhanced accuracy. The performance of each method is evaluated using standard statistical metrics, including the root mean square error (RMSE) and coefficient of determination (R2). The results demonstrate the effectiveness of all three methods, with RMSE values ranging from 1.6 W to 3.8 W and R2 values consistently above 0.95. The most appropriate method for estimating PV power output is determined by the specific type of photovoltaic module and the availability of meteorological parameters. This study provides valuable insights for selecting an appropriate maximum power prediction method and choosing the most suitable PV module for a given climate.

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A Simple and Efficient Determination of the Ideality Factor of Solar Cells and Modules from the Knee Point of the Shunt Resistance Curve

Cited by 3 publications

References 63 publications

Parameters Identification of Photovoltaic Cell and Module Models Using Modified Social Group Optimization Algorithm

Parameters Identification of Photovoltaic Cell and Module Models Using Modified Social Group Optimization Algorithm

Double-diode and triple-diode solar cell models: invertible approximate analytical expressions based on the g-function approach

Accurate Method for Solar Power Generation Estimation for Different PV (Photovoltaic Panels) Technologies

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