Real-time platform for PV panel characterization

Abstract

Accurate characterization and modelling of PV panels are essential for performance analysis, control, and optimization of PV systems. Although, manufacturers provide PV panels characteristics under Standard testing conditions. These latter vary over time due to environmental change and PV module degradation, making PV parameter estimation a critical challenge. This work develops a real-time platform for PV panels characterization based on an electronic circuit and a programmed microcontroller interfaced with LabVIEW, enabling automatic acquisition of I–V and P–V characteristics under real operating conditions. The system acquires and stores the PV and weather data for modelling. The work focuses on parameter identification of the Lumped model using metaheuristic optimization techniques, including Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Killer Whale Optimization (KWO). GWO is embedded in the platform for real-time parameter extraction, and two improved algorithms (IOB-PSO) and (MQOB-KWO) are proposed to enhance estimation accuracy, developed in MATLAB and validated with experimental data under varying conditions, the proposed methods demonstrate improved performance. The proposed platform and enhanced algorithms offer a reliable and cost-effective solution for PV modelling, performance assessment, and renewable energy system optimization.