University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 12 1 2021 03 21 Maximum Power Point Tracking of Solar System Using Improved Flower Pollination Algorithm Maximum Power Point Tracking of Solar System Using Improved Flower Pollination Algorithm 49 60 24825 10.22108/isee.2020.113395.1158 FA Mohammad Ali Atefat Dept. of Electrical Engineering, Yazd University, Yazd, Iran Fatemeh Jahanbani Ardakani Dept. of Electrical Engineering, Yazd University, Yazd, Iran Journal Article 2018 10 15 In recent years, the use of renewable energy systems has grown significantly, among which photovoltaic systems have received much attention. Solar cells are known as the building blocks of a photovoltaic system. Because of the nonlinear nature of solar cells and the continuous changes in atmospheric conditions, maximum power point tracking (MPPT) is essential to extract the maximum power of a photovoltaic system. In this study, in order to achieve the maximum power, it was proposed to apply flower pollination algorithm (FPA) combined with a comprehensive selection algorithm, named as improved FPA. In addition, to evaluate the proposed algorithm, its performance was compared with genetic algorithm (GA) and standard FPA under rapid changes in atmospheric conditions. The calculated results showed that the improved FPA has a better accuracy than GA; moreover it has a higher convergence rate as compared with other applied algorithms. In recent years, the use of renewable energy systems has grown significantly, among which photovoltaic systems have received much attention. Solar cells are known as the building blocks of a photovoltaic system. Because of the nonlinear nature of solar cells and the continuous changes in atmospheric conditions, maximum power point tracking (MPPT) is essential to extract the maximum power of a photovoltaic system. In this study, in order to achieve the maximum power, it was proposed to apply flower pollination algorithm (FPA) combined with a comprehensive selection algorithm, named as improved FPA. In addition, to evaluate the proposed algorithm, its performance was compared with genetic algorithm (GA) and standard FPA under rapid changes in atmospheric conditions. The calculated results showed that the improved FPA has a better accuracy than GA; moreover it has a higher convergence rate as compared with other applied algorithms. https://isee.ui.ac.ir/article_24825_53df899eeb39b10d6454e44859dcebe5.pdf