Revolutionizing Photovoltaic Systems: An Innovative Approach to Maximum Power Point Tracking Using Enhanced Dandelion Optimizer in Partial Shading Conditions

Abstract

Partial shading (PS) is a prevalent phenomenon that often affects photovoltaic (PV) installations, leads to the appearance of numerous peaks in the power-voltage characteristics of PV cells, caused by the uneven distribution of solar irradiance on the PV module surface, known as global and local maximum power point (GMPP and LMPP). In this paper, a new technique for achieving GMPP based on the dandelion optimizer (DO) algorithm is proposed, inspired by the movement of dandelion seeds in the wind. The proposed technique aimed to enhance the efficiency of power generation in PV systems, particularly under PS conditions. However, the DO-based MPPT is compared with other advanced maximum power point tracker (MPPT) algorithms, such as Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC), Cuckoo Search Algorithm (CSA), and Bat Algorithm (BA). Simulation results establish the superiority and effectiveness of the used MPPT in terms of tracking efficiency, speed, robustness, and simplicity of implementation. Additionally, these results reveal that the DO algorithm exhibits higher performance, with a root mean square error (RMSE) of 1.09 watts, a convergence time of 2.3 milliseconds, and mean absolute error (MAE) of 0.13 watts.

Keywords

• maximum power point tracker (MPPT)
• photovoltaic
• partial shading conditions (PSCs)
• dandelion optimizer
• optimization