Comparative Study of MPPT and Parameter Estimation of PV cells
This work addresses parameter estimation for photovoltaic cells, which is important for solar energy applications, but it appears incremental as it applies existing ANN methods to this domain.
The study tackled the problem of accurately estimating parameters for solar cell models using machine learning, achieving over 95% accuracy with an Artificial Neural Network (ANN) that outperformed other algorithms in computational efficiency.
The presented work focuses on utilising machine learning techniques to accurately estimate accurate values for known and unknown parameters of the PVLIB model for solar cells and photovoltaic modules.Finding accurate model parameters of circuits for photovoltaic (PV) cells is important for a variety of tasks. An Artificial Neural Network (ANN) algorithm was employed, which outperformed other metaheuristic and machine learning algorithms in terms of computational efficiency. To validate the consistency of the data and output, the results were compared against other machine learning algorithms based on irradiance and temperature. A Bland Altman test was conducted that resulted in more than 95 percent accuracy rate. Upon validation, the ANN algorithm was utilised to estimate the parameters and their respective values.