Optimizing energy consumption: a strategy for UAV-based inspection systems in photovoltaic power plants

Abstract In recent decades, the global installation of photovoltaic (PV) systems has surged, underscoring the growing significance of solar energy. Automatic inspection of photovoltaic power stations provides a means to reliable operation and performance preservation of the solar plant. The efficiency of solar stations can diminish over time due to several problems such as hotspots, shading, and short-circuited bypass diodes. Human on-site manual inspection is expensive and impractical. In order to reduce time and minimize expenses. To reduce time and cost, unmanned aerial vehicles (UAVs) have been proposed as a more reliable and cost-effective inspection paradigm. However, UAVs have limited energy storage and short flight durations, and the existing UAV inspection methods typically rely on the exhaustive visual/thermal scans and ignore the UAV energy constraints. This article proposes a targeted UAV inspection strategy that uses the maximum power point (MPP) telemetry to prioritize candidate modules and compute energy-aware flight paths. An anomaly detection algorithm analyzes MPP-derived features to identify deviations from the expected patterns. An energy-aware shortest flight path planner then computes UAV routes to inspect the highest-risk modules under battery and mission constraints. The results, compared to full-scan and random-sampling baselines, across varying defect densities demonstrate substantial time savings with high defect detection performance. This work involves the simulation of plant geometry and defect densities. The results illustrate a reduction in the operation time up to 85% (versus full-scan) of a 0.5% module defect rate while achieving an F1-score of 92% in defect detection.