Automating defect
Identification In
Solar Panels
SolarAI is an artificial Intelligence platform that uses our state of the art artificial intelligence algorithms on thermal images to identify defects in solar panels. Utilising drone technology, thermal images of the solar plant are taken; these images are then analysed through our AI model. The model identifies faults and defects in these solar panels and details are sent to ERP for maintenance.
HOW IT WORKS
Solar AI ensures the smooth functioning of solar power plants. Utilising a mix of image generation, image analysis, defect
identification and work order creation, SolarAI ensures that every solar cell and panel functions at its optimal best.
Step 1
Drone Mapping
Drones are used to captue thermal images of the panels of the entire solar acreage. These high-resolution images capture details of each cell and panel.
Step 2
Orthomosaic Generation
Thermal images captured by the drone are stitched together to form an ortho-mosaic layout aligning with the geographic axis of the solar field.
Step 3
Defect Identification
The ortho-mosaics are sliced into smaller layers and passed through our state-of-the-art AI model to identify defective solar panels. Defects along with their types and corresponding location is identified by the model.
Step 4
Work order Creation
Defect details along with geographic location is sent to ERP to further processing. Solution allows to create maintenance work orders from the UI itself.
Step 1
Drone Mapping
Drones are used to captue thermal images of the panels of the entire solar acreage. These high-resolution images capture details of each cell and panel.
Step 2
Orthomosaic Generation
Thermal images captured by the drone are stitched together to form an ortho-mosaic layout aligning with the geographic axis of the solar field.
Step 3
Defect Identification
The ortho-mosaics are sliced into smaller layers and passed through our state-of-the-art AI model to identify defective solar panels. Defects along with their types and corresponding location is identified by the model.
Step 4
Work order Creation
Defect details along with geographic location is sent to ERP to further processing. Solution allows to create maintenance work orders from the UI itself.
DEFECT DETECTIVE
SolarAI identifies nearly 10 types of defects. Some of the most common defects are
shown below.
1
2
3
4
BENEFITS
Reduction
In efforts to map and identify defects through leveraging drone-based imagery and AI-driven algorithms.
Decrease
In monthly power generation losses due to faster identification of deteriorated or damaged PV panels.
Improvement
In efficiency of identifying deteriorated or damaged PV panels as compared to manual processing.
Savings
In annual labour costs through the elimination of manual inspection and processing.
TESTIMONIAL
Solar AI has been a boon for our company. Issues that took a very long time earlier are now resolved in a matter of a couple of hours. This has led to time savings, and more importantly, cost savings.
Using Solar AI we have been able to identify the exact location of the solar cell which has been a problem. Sometimes just plain accumulation of dust can be a large hindrance in the overall performance. Searching through the acres of the solar field was an issue, but with Solar AI we are able to see the exact cell that is the cause of the lag. I suggest the increased use of AI to improve performance and eventually financial benefits.
We needed a reliable and efficient solution for identifying damaged PV panels, and SolarAI delivered.The automated inspections and processing have saved us significant labour costs annually, all while providing highly accurate results. We are extremely satisfied with the solution!
CASE STUDY
SolarAI leverages AI based processing to automate the defect identification in
solar panels for large solar power plants.
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