A Self-adaptive Algorithm with Newton Raphson Method for Parameters Identification of Photovoltaic Modules and Array

The word’s demand for renewable energy has be rinsing incrementally. One of the solutions for the energy crisis is photovoltaic. However, the design and development of better performing photovoltaic cells and modules requires accurate extraction of their intrinsic parameters. Metaheuristic algorithms have been reported to be the best methods for obtaining accurate values of these intrinsic parameters. However, local convergence goes against the recently devised heuristic methods and inhibits them from producing optimal result. This paper proposes a hybrid method that is based on the Newton Raphson method and a self-adaptive algorithm called the Drone Squadron Optimisation. The latter is an artifact technique inspired by the simulation of a drone squadron from a command centre. It is proposed that this hybrid method can help extract the best intrinsic parameters of photovoltaic cell and module. This study also provides insights and clarification on the reported approaches that have been recently proposed to formulate the objective function. Further, this study also computes and compares the ten best recently published heuristics algorithms in the domain of photovoltaic estimation. The study’s results obtain point to the difference between the two formulations and the accuracy of the best formulation. The results obtained from the six case studies covered in this study present the combined performance of the Newton Raphson method and Drone Squadron Optimisation to extract the accurate parameters of a photovoltaic module.