Parameter estimation of single diode PV module based on GWO algorithm

Abstract

This paper proposes a novel parameter estimation technique for photovoltaic (PV) module considering grey wolf optimization (GWO). Manufacturer data sheet of PV module does not provide all essential parameters of PV module for modelling. Therefore for accurate modelling of PV module, an efficient parameter estimation technique is necessary to estimate the unknown parameters of the PV module. Since conventional estimation methods do not estimate PV parameters with high accuracy, therefore bio-inspired techniques have drawn considerable attention toward it. GWO is a new optimization technique which exhibits high efficiency for optimizing nonlinear equations. Therefore GWO technique is explored here for parameter estimation of PV module under changing weather conditions. The proposed GWO technique is employed for parameter estimation of different PV modules under different test conditions. GWO technique is compared with another popular optimization technique named as particle swarm optimization (PSO) and found having better fitness value compared to PSO. The proposed model is validated through experimental data under different weather conditions and found matching accurately with measured values. Considering different statistical errors, the discussed model has been compared with other well-known models found in the literature. The results show the superiority of the proposed model with a minimum error for both current-voltage and power-voltage characteristics.