Autonomous 3D Visual-Thermal Inspection for Utility-Scale Solar Farms

Key points

• Utility-scale photovoltaic (PV) inspection is dominated by aerial surveys, which are effective for identifying module-level hot cells but provide limited visibility of critical components located under the array, including connectors, cabling, fasteners and structural elements.1
• The approach combines LiDAR-inertial Simultaneous Localization and Mapping (SLAM) with geometrically calibrated RGB and thermal imagery and machine-learning-based object detection and segmentation.1
• We demonstrate the approach on utility-scale PV data for MC4 connector detection and connection-state recognition, thermal anomaly identification, and detection of structural components including bolts, nuts and torque tubes.1
• The results demonstrate a pathway from image-based inspection toward spatially grounded autonomous inspection, where defects are detected and linked to specific assets and their 3D locations within the solar farm.1

Sources (1)

• [1]Autonomous 3D Visual-Thermal Inspection for Utility-Scale Solar Farms
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 05:43 AM

  We present an automated 3D visual-thermal inspection system (Tricorder) for under-panel PV asset inspection.

  Utility-scale photovoltaic (PV) inspection is dominated by aerial surveys, which are effective for identifying module-level hot cells but provide limited visibility of critical components located under the array, including connectors, cabling, fasteners and structural elements.

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