FPGA-in-the-Loop Validation of a Systematic-Sequencing Adaptive Particle Swarm Optimization Algorithm for Photovoltaic Under Partial Shading

Partial shading conditions (PSCs) in photovoltaic (PV) systems generate multiple local maximum power points (LMPPs) and a single global maximum power point (GMPP) in the power–voltage (P–V) characteristics, challenging conventional maximum power point tracking (MPPT) methods. This study presents an FPGA-in-the-Loop (FIL) co-simulation of a Systematic-Sequencing Adaptive Particle Swarm Optimization (SS-APSO) algorithm for MPPT under dynamically varying shading conditions. The proposed method combines deterministic particle initialization, adaptive particle reordering, and switching among wide exploration, re-exploration and exploitation modes to enhance global search capability. The controller is implemented on a Xilinx Artix-7 FPGA using fixed-point arithmetic and a finite-state-machine architecture in VHDL and is evaluated through MATLAB/Simulink–FIL co-simulation for two PV configurations: four series-connected modules (4S) and two parallel-connected strings of two series modules (2S2P). The results demonstrate tracking efficiencies generally exceeding 98% under different shading within 0.181 s for both configurations, while in FIL co-simulation, it reaches the GMPP within 0.203. The close agreement between simulation and FIL co-simulation results demonstrates the effectiveness of the proposed SS-APSO-MPPT controller for PV systems.