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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月4日

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  • Dr. Mourad Kaddes,
  • Prof. Ayman Elsayed khedr,
  • Prof. Mohamed A. Belal

摘要

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.