article · Energies
Photovoltaic systems frequently experience partial shading, which causes multiple local power peaks and one global maximum power peak, complicating conventional maximum power point tracking methods. To address this, a systematic-sequencing adaptive particle swarm optimisation algorithm was developed for tracking the global maximum power point under dynamically changing shading conditions. The approach incorporates deterministic particle initialisation, adaptive reordering, and switching between exploration, re-exploration, and exploitation phases. The controller was implemented on a Xilinx Artix-7 FPGA using fixed-point arithmetic and a finite-state machine architecture written in VHDL. Testing via MATLAB and Simulink hardware-in-the-loop co-simulation covered two distinct solar module arrangements: a four-module series string and a combined series-parallel setup. Across these configurations, the design demonstrated tracking efficiencies typically above 98 per cent and attained the global peak within 0.203 seconds during co-simulation.
When solar panels are partially shaded by clouds or structures, standard controllers often settle for suboptimal power outputs. Implementing fast, responsive optimisation algorithms directly onto digital hardware allows solar systems to locate the true maximum power point rapidly, capturing more usable energy from existing photovoltaic installations even under rapidly shifting weather conditions.
This work is relevant to solar inverter manufacturers and control system developers seeking embedded solutions for partial shading mitigation. Because the algorithm has been implemented in VHDL on commercial FPGA hardware and validated through hardware-in-the-loop co-simulation, it represents applied and tested research that is close to physical prototyping in real solar power converters, although full field testing under operational conditions is not reported.
AI-generated from the published abstract. Always read the original work before citing.
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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.3390/en19163896
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
