article · Processes
Accurate modelling of photovoltaic systems depends on correctly identifying unknown parameters in solar cell models. A new optimisation technique known as the turbulent flow of water-based optimisation algorithm has been applied to extract parameters across three different photovoltaic cell models. The approach was tested using real data from a commercial fifty-five millimetre diameter solar cell alongside experimental data from a commercial solar module. The performance of the method was compared against other recent optimisation techniques under identical datasets and computational limits, supported by statistical analysis. Results demonstrate that the algorithm achieves high accuracy, generating current-voltage and power-voltage curves that closely match actual experimental measurements. The technique outperformed competing optimisation algorithms in reproducing the observed behaviour of the tested photovoltaic devices.
Reliable simulation of renewable energy systems depends on accurately capturing how solar cells behave under operational conditions. By improving the precision of parameter estimation from real hardware data, this optimisation approach enables more dependable modelling of solar energy components. This supports better prediction of power generation and aids engineers in evaluating photovoltaic performance accurately.
This algorithm is applicable to solar energy system modelling and simulation software used by solar component designers and renewable energy engineers. Having been tested on experimental datasets from commercial solar cells and modules, the tool represents applied and tested research. Direct integration into commercial design software would be the necessary pathway toward routine industrial use.
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Recently, the use of diverse renewable energy resources has been intensively expanding due to their technical and environmental benefits. One of the important issues in the modeling and simulation of renewable energy resources is the extraction of the unknown parameters in photovoltaic models. In this regard, the parameters of three models of photovoltaic (PV) cells are extracted in this paper with a new optimization method called turbulent flow of water-based optimization (TFWO). The applications of the proposed TFWO algorithm for extracting the optimal values of the parameters for various PV models are implemented on the real data of a 55 mm diameter commercial R.T.C. France solar cell and experimental data of a KC200GT module. Further, an assessment study is employed to show the capability of the proposed TFWO algorithm compared with several recent optimization techniques such as the marine predators algorithm (MPA), equilibrium optimization (EO), and manta ray foraging optimization (MRFO). For a fair performance evaluation, the comparative study is carried out with the same dataset and the same computation burden for the different optimization algorithms. Statistical analysis is also used to analyze the performance of the proposed TFWO against the other optimization algorithms. The findings show a high closeness between the estimated power–voltage (P–V) and current–voltage (I–V) curves achieved by the proposed TFWO compared with the experimental data as well as the competitive optimization algorithms, thanks to the effectiveness of the developed TFWO solution mechanism.
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DOI: 10.3390/pr9040627
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