Novel yellow saddle goatfish algorithm for improving performance and efficiency of PV system under partial shading conditions

This paper proposes a novel maximum power point tracking (MPPT) technique based on a new optimization algorithm called yellow saddle goatfish algorithm (YSGA). The photovoltaic system loses a significant amount of power under partial shading conditions (PSC). Because of the presence of bypass diodes, several maximums appear in the Power-Voltage characteristic, making tracking the global maximum power point (GMPP) extremely difficult. A yellow saddle goatfish-based MPPT algorithm is proposed and implemented to address this issue and improve the efficiency of the PV system. This algorithm employs a subgroup research methodology and a very interesting strategy of particle collaboration during GMPP tracking, providing a balance between the exploration and exploitation phases of the research process. To investigate its performance, tests were conducted using six different partial shading patterns and a fast irradiance variation. The simulation was carried out in MATLAB/Simulink using a series of MSX60 solar panels. The results were compared to well-known and efficient algorithms such as the Grey wolf optimization algorithm (GWO), the Bat algorithm (BAT), particle swarm optimization (PSO), the Seagull optimization algorithm (SOA), and the Perturb and observe (P&O) algorithm. The findings demonstrate the proposed method's high robustness, as well as its high dynamic and accuracy in tracking the GMPP. Furthermore, a quantitative and statistical analysis of 100 tests revealed that it outperformed the other algorithms in terms of efficiency, success rate, convergence time, and solution dispersion.