Estimation of Photovoltaic Cell Parameters using the Honey Badger Algorithm

Abstract

Optimal estimation of the intrinsic parameters of photovoltaic cells requires the use of meta-heuristics to increase their efficiency. This paper highlights the estimation of unknown parameters of a PV cell and module. For this purpose, the meta-heuristic optimization algorithm based on the Honey Badger Algorithm (HBA) principle is used. The simulation results via MATLAB prove that this algorithm has a good convergence. Indeed, the root mean square error (RMSE) is 9.8602×10-4, 9.8602×10-4, 2.4251×10-3, 1.7298×10-3 and 1.6783×10-2 for the single diode, dual diode, Photowatt-PWP201, Schutten Solar STM6-40/36 and the STP6-120/36 module respectively. Furthermore, the curves representing the current-voltage and power-voltage characteristics of the calculated unknown parameters versus those of the practical data measured from a PV cell/module datasheet coincide. The proposed algorithm can therefore be classified in the literature as one of the optimal parameter extraction techniques.

Improvement of photovoltaic systems with tracking of the maximum power point in low-irradiation atmospheric conditions

“…The photovoltaic (PV) module employs the singlediode model. The PV cell modeling utilizes the one-diode equivalent circuit illustrated in Figure 2[13] [14].…”

“…During its execution, the algorithm proceeds through the steps outlined in the flowchart depicted in Figure 8. The position and velocity of each particle are updated using (14).…”