Analysis and Simulation of Maximum Power Point Tracking Based on Gradient Ascent Method

This paper aims to implement the gradient ascent algorithm for maximum power point tracking (MPPT) in a photovoltaic (PV) system. The proposed MPPT algorithm will ensure the PV module's operation at the maximum power point by finding the optimal duty cycle that corresponds to each time step. The gradient ascent algorithm is an iterative method, and it is one of the most popular machine learning optimization algorithms. The gradient ascent is used for the first-order optimization, which means it only takes the first derivative into account when doing the parameter updates. On each iteration, the parameters have been changed in the opposite direction of the objective function gradient. Steps proportional to the positive gradient have been applied to find a local maximum of the function. This function has been implemented in a PV model to investigate the gradient ascent algorithm's applicability as an MPPT algorithm under various conditions.