A Comparative Study of Different Modified Incremental Conductance MPPT Algorithms under very Fast-Changing Atmospheric Conditions for Solar Charging Station

In recent years the whole world focused on producing clean, green, and sustainable energy from renewable energy resources, and Photovoltaic (PV) is one of the resources which is renewable, reliable, and domestically secure. One of the most important aspects of the PV system is its operation at Maximum PowerPoint to extract maximum energy from it. For this purpose, Maximum power point trackers (MPPTs) play an important role in the Photovoltaic (PV) system to maximize its efficiency. Thus, an MPPT can minimize the overall system cost. Many algorithms have been proposed including the Perturb-and-Observe (PO) method, Incremental conductance (Inc.Con) algorithm, and some advanced algorithms are also proposed including Fuzzy Logic, Artificial Neural Network, and Particle Swarm Optimization. In this paper, a comparative study conducts among different Modified Incremental conductance (Inc.Con) algorithms to test their performance under Fast changing solar irradiance. The goal of this study is to investigate the performance of different variations of inc.Cond based on Direct control MPPT method. Matlab is used as the simulation platform. These modified Inc.Cond MPPT algorithms are compared in the course of convergence speed, steady-state oscillation, complexity, efficiency, length of the algorithm, accuracy, and detection of fast change in solar irradiance. Performance Testing Parameters (PTP) are also proposed in this study to ameliorate the reliability of the comparison is the sense of robustness, efficiency, and effectiveness of the algorithms.