Optimizing photovoltaic parameters with Monte Carlo and parallel resistance adjustment

Abstract

• This paper introduces a novel approach for determining the five, seven, and nine unknown parameters that are crucial for the electrical characterization of photovoltaic solar cells. • It employs Monte Carlo optimization combined with parallel resistance adjustment (MCO-R). • The differences in absolute error (IAE), relative error (RE) and standard deviation (SD) as a function of effort indicate that the models optimized using MCO-R exhibit lower errors compared to the other algorithms. • In addition, this model has a significantly lower RMSE. We tried this method with KC200GT but it didn’t work. • We are trying to figure out what went wrong with this attempt and will talk about it in our next work. Photovoltaic power has emerged as an important component global energy revolution, providing a renewable and sustainable alternative for electricity generation. This paper describes how to use Monte Carlo optimisation (MCO) to estimate and extract the intrinsic electrical parameters of single, double, and triple diode designs, as well as make parallel resistance modifications. The above method was used to solve challenges related to nonlinear and complex solar cell equation. The function’s objective is to minimize the discrepancy between the experimental and calculated current values. Three different technologies are implemented to retrieve the fundamental parameters: RTC France solar cell, the Photowatt-PWP201 PV module, and the Schutten Solar STM6-40/36 monocrystalline solar module. In addition, the restricted objective function is computed using the experimental current–voltage curve. The extracted parameters using MCO are compared to contemporary research publications on metaheuristic optimization algorithms, iterative approaches, and analytical methods. In the end, to evaluate the algorithm’s effectiveness, statistical measurements such as Individual Absolute Error (IAE), Relative Error (RE), Mean Absolute Error (MAE), SD, TS, NFM, ACF, and RMSE are calculated to ensure the correctness of the generated parameters. The comparative study shows that the results generated by the MCO approach exhibit lower errors compared to other algorithms where RMSE reaches 0.0058.