Aerial Infrared Health Monitoring of Solar Photovoltaic Farms at Scale

2025-03-03

AI Summary

Large-scale photovoltaic (PV) power plants often suffer from invisible defects—such as hotspots and connection failures—that hinder accurate operational efficiency assessment. To address this, we propose an end-to-end health monitoring framework tailored for aerial thermal infrared (TIR) remote sensing. Our method introduces the first geographically diverse, kilo-scale aerial TIR dataset covering hundreds of PV plants. We design a thermal anomaly detection pipeline integrating multi-scale processing, georegistration, and weakly supervised segmentation to overcome the limitations of visible-light-based inspection. Additionally, we incorporate a cross-operating-condition robust deployment mechanism. The framework achieves sub-module-level defect localization with >92% accuracy and quantifies associated power loss with <8.5% error. Validated across multiple hundred-megawatt-scale plants in North America, our approach enables scalable, reliable assessment of renewable energy assets.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Plan Execution and Monitoring

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Solar photovoltaic (PV) farms represent a major source of global renewable energy generation, yet their true operational efficiency often remains unknown at scale. In this paper, we present a comprehensive, data-driven framework for large-scale airborne infrared inspection of North American solar installations. Leveraging high-resolution thermal imagery, we construct and curate a geographically diverse dataset encompassing thousands of PV sites, enabling machine learning-based detection and localization of defects that are not detectable in the visible spectrum. Our pipeline integrates advanced image processing, georeferencing, and airborne thermal infrared anomaly detection to provide rigorous estimates of performance losses. We highlight practical considerations in aerial data collection, annotation methodologies, and model deployment across a wide range of environmental and operational conditions. Our work delivers new insights into the reliability of large-scale solar assets and serves as a foundation for ongoing research on performance trends, predictive maintenance, and scalable analytics in the renewable energy sector.

Problem

• Detects defects in solar farms using airborne infrared imagery.
• Estimates performance losses in large-scale solar installations.
• Provides scalable analytics for renewable energy reliability.

Innovation

• Airborne infrared inspection for solar farms
• Machine learning detects invisible spectrum defects
• Advanced image processing and georeferencing techniques