Analytical framework for structural failure analysis of solar panels subjected to wind loads

Introduction: The structural integrity of solar photovoltaic (PV) systems is increasingly threatened by wind-induced failures, with recent extreme weather events exposing the limitations of existing design practices. Although computational fluid dynamics (CFD) and finite element methods (FEMs) are widely employed to simulate wind–structure interaction, their reliance on heavy computation and their limited physical transparency restrict their use as general-purpose design tools. Materials and methods: This study develops a closed-form analytical framework for the failure analysis of cantilever-mounted solar panels subjected to wind loading. Beginning with a reduced-order mechanical model that captures mast flexibility, panel geometry, and inclination, the framework is developed in three progressively more sophisticated stages: a pseudo-static stress analysis, a static divergence analysis, and a simplified dynamic flutter analysis. Attached flow flat-plate aerodynamics and small-deformation linear elastic structural mechanics underpin all three stages, and a lognormal capacity model is used to derive closed-form fragility functions. Results: The framework quantifies stresses, deflections, and support reactions under pseudo-static wind loads; identifies the critical divergence wind speed at which destabilising aerodynamic moments overcome structural stiffness; and yields a closed-form ratio 𝑉𝑓𝑙𝑢𝑡𝑡𝑒𝑟 /𝑉𝑑𝑖𝑣 = 1/√2 ≈ 0.707 between the dynamic flutter and static divergence speeds. The closed-form fragility curves provide probabilistic estimates of failure across a range of wind speeds and design configurations, and the parametric expressions demonstrate that mast slenderness, panel inclination angle, and span asymmetry strongly influence critical thresholds for instability and material yielding. Conclusions: The proposed analytical approach advances understanding by offering a transparent and computationally efficient alternative to simulation-heavy methodologies. The findings support the development of scalable and interpretable design strategies.