Qualification of Photovoltaic Micromodules for Energy Harvesting with Applications in IoT Systems: An Approach with Statistical Data Analysis

This article investigates the qualification of photovoltaic micromodules intended for energy harvesting in Internet-of-Things systems, with emphasis on the degradation mechanisms induced by accelerated environmental aging typical of tropical conditions. The study employs multiple complementary characterization techniques—including pulsed I–V measurements, electroluminescence imaging, and impedance spectroscopy—combined with multivariate statistical analysis to support decision-making regarding acceptance criteria, fault prediction models, maintenance scheduling, and failure mode clustering.In addition, dimensionality-reduction methods are explored to extract the most relevant indicators from empirical datasets and to improve interpretability when dealing with highly correlated or redundant variables. The work addresses a regulatory gap affecting photovoltaic devices below 5 Wp, a class of modules whose deployment is rapidly expanding in remote, autonomous, and low-power IoT applications, yet remains largely unsupported by existing reliability and certification standards.