Jellyfish Search Algorithm for MPPT in Photovoltaic Systems Under Partial Shading Conditions

Abstract

In photovoltaic systems, maximum power point tracking (MPPT) methods are used to get the maximum power out of them. The presence of several peaks in a PV array’s power-voltage characteristics is due to partial shadowing conditions that increase the complexity of the tracking operation. In this paper, the Global Maximum Power Point (GMPP) is calculated using latest meta-heuristic optimization algorithm known as Jellyfish Search (JS). This unique MPPT approach is used to reduce PV module tracking time and improve the tracking efficiency. Using MATLAB/SIMULINK, the effectiveness of the suggested JS algorithm is assessed by contrasting it with the traditional P&O approach in terms of tracking speed and precision. The simulation findings indicate that the JS algorithm’s tracking ability is better than that of the conventional P&O method. In the experimental results, the JS algorithm reduces convergence time by 56.6% when compared to the PSO algorithm. Also, the proposed JS algorithm generates output power higher than the PSO MPPT algorithm using the duty cycle ratio value at the expected peaks.