Abstract
A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system is presented in this paper. In recent years, there has been a growing attention toward mitigation of partial shading effect of the PV system through using modern optimization. The field of tracking MPP of the PV system attracts huge attention from the researchers, as it is the best way to increase PV plant efficiency. Under non-uniform solar irradiance, the power against voltage characteristics of PV array have more than one MPP. This condition leads to extra complications to track MPP and decreases the efficiency of the PV system. Traditional MPPT methods are typically used to increase the harvested energy of PV under normal conditions without any problems. The key drawback of such methods is failing extraction of global MPP under actual weather conditions that sometimes under shadowing condition. Several global MPPT methods based on modern optimization are presented for extracting global MPP in case of shadowing condition. The essential target of this work is not limited to present an integrated specific review on the state-of-the-art of these techniques; but also a comprehensive statistical evaluation of twenty optimizers that represent high percent of all the reported techniques is performed with changing shading scenarios. This work serves as a source of valuable information for researchers and engineers working with PV systems to keep abreast with the modern advance in the field of tracking MPP of PV system.
•An integrated specific review on the state-of-the-art of different global MPPT techniques is done.•A comprehensive statistical evaluation of twenty optimizers is performed.•Teaching-learning-based optimization and grey wolf optimizer are performed the best.•This work serves as a source of valuable information for researchers working in solar energy sector.