Identification of the photovoltaic model parameters using the crow search algorithm

Photovoltaic (PV) systems are widely used for several many decades. They have become an important source for green energy and they are currently used in many applications. The PV industry has grown because of the improvements in the technology of converting light into electrical energy as well as the cost reduction. This project investigates the applications of crow search algorithm (CSA) in accurately identifying the PV module parameters. CSA is a novel population-based meta-heuristic optimiser based on the intelligence crows’ exhibit in their behaviours, which helps them identify best location to state their catcher. In this study, the CSA is simulated using MATLAB environment and it is performed on the single diode and double diode PV models to estimate their parameters with minimum output power error. This error can be said to be the difference between the maximum output power and the calculated power output at a particular solar irradiance and cell temperature values.