Automated Soiling Detection and Predictive Maintenance for Large-Scale Solar Farms Using Computer Vision and IoT

The performance of large solar farms is severely impacted by panel soiling (dust, dirt, debris), which can reduce photovoltaic (PV) output by up to 50% in harsh environments. Manual inspection and cleaning of every panel is labor-intensive and costly. This paper proposes an integrated solution that uses existing surveillance cameras (CCTV) and machine learning to automatically identify dirty or underperforming panels, coupled with electrical sensing for fault verification. A convolutional neural network (CNN) analyzes image feeds to classify panels as clean or soiled. When soiling is detected and panel voltage/current is abnormally low, an alert is issued to dispatch a cleaning crew or automated cleaner to that specific panel, rather than the entire array. If performance remains low after cleaning, a maintenance ticket is generated for panel repair or replacement. Our approach leverages transfer learning with publicly available PV soiling image data and sensor-based predictive maintenance models to overcome the lack of an on-site dataset. Simulation studies and related work indicate that such targeted cleaning saves time, water, and operational cost compared to routine cleaning of all panels. The performance of large solar farms is severely impacted by panel soiling (dust, dirt, debris), which can reduce photovoltaic (PV) output by up to 50% in harsh environments. Manual inspection and cleaning of every panel is labor-intensive and costly. This paper proposes an integrated solution that uses existing surveillance cameras (CCTV) and machine learning to automatically identify dirty or underperforming panels, coupled with electrical sensing for fault verification. A convolutional neural network (CNN) analyzes image feeds to classify panels as clean or soiled. When soiling is detected and panel voltage/current is abnormally low, an alert is issued to dispatch a cleaning crew or automated cleaner to that specific panel, rather than the entire array. If performance remains low after cleaning, a maintenance ticket is generated for panel repair or replacement. Our approach leverages transfer learning with publicly available PV soiling image data [1], [2] and sensor-based predictive maintenance models [3] to overcome the lack of an on-site dataset. Simulation studies and related work indicate that such targeted cleaning saves time, water, and operational cost compared to routine cleaning of all panels. We present the system design in IEEE format, demonstrating how deep vision models, IoT telemetry, and alerting can be combined to make solar farm maintenance more efficient and cost-effective.