Drone Inspection Workflows and Maintenance Efficiency in Solar Farm Operations

Abstract

The rapid expansion of utility-scale solar farms necessitates advanced, highly efficient maintenance strategies to ensure optimal energy yield and system longevity. While unmanned aerial vehicles equipped with thermographic sensors have revolutionized defect detection by rapidly identifying thermal anomalies across vast photovoltaic arrays, a critical gap remains in translating these surface-level thermal observations into predictive structural health assessments. This paper investigates the integration of drone inspection workflows with finite element modeling to dynamically assess the mechanical degradation of solar modules induced by thermal stress. By acquiring high-resolution thermographic data from an operational solar farm, this study maps localized temperature gradients onto a multi-layered finite element model of photovoltaic panels. The structural simulation analyzes the thermal-mechanical stress propagation through the glass cover, encapsulant layers, silicon cells, and backsheet. Findings indicate that coupling aerial thermography with finite element analysis allows operators to distinguish between benign thermal fluctuations and structurally critical hotspots that threaten module integrity. This synergistic approach significantly enhances maintenance efficiency by enabling targeted, predictive interventions rather than relying on scheduled or reactive maintenance. The proposed methodology optimizes resource allocation, minimizes operational downtime, and extends the overall lifecycle of photovoltaic infrastructure, providing a robust framework for next-generation solar farm management.