I've become quite interested in using machine learning to do automated detection of defects in solar panels.
A few friends of mine work as consultants in renewable energies and told me that there is a ton of investment in huge arrays of solar panel and defect detection is an important part of these projects.
Over time, defects appear on solar panels which reduce their output. It's important to detect them because:
- you can tell investors the "health" status of the solar farms they financed
- you can replace defective solar panels
- you can get a better understanding of which solar panels get defective and why, in order to improve of the overall efficiency of your solar farm
Today, most defect detection is done manually. Operators go and check the status of the solar panels, visually. You can guess that this is quite expensive and time consuming.
So people started using drones to fly over the solar farms and do defect detection. However, as you can see on the image above, it's hard to notice defects visually from the sky. The problem is that defects can be a bit of broken glass, or an internal problem.
But defects have one thing in common: when a cell of a solar panel is defective, it doesn't absorb the sun's rays as efficiently. It doesn't capture the sun's energy as well, and thus radiates more heat itself. So if you looked at it in infrared, you could see the defect much better, like on the picture below.
You can see three defects in the forms of three yellow dots, which correspond to solar cells that are hotter than the others. I've circled them in blue for you all.
So at this point I'm thinking people must be using machine learning to detect these defects automatically. But actually, very few organisations seem to do that. Taking infrared photos with drones is actually quite new, and most people use this technology to have other people manually review the thousands of photographs taken by the drones !
It's better than operators on the ground, but it's still a lot of expensive work. It doesn't scale well.
So I've been spending some of my free time seeing if I can create a model for infrared defect detection on solar panels. The focus right now is actually getting clean data. So I've been using cvat.org to crop images and annotate them. Kind of a long process but I've got some nice datasets now :)
If anyone has got suggestions or wants to participate, tell me. I can also post some follow-ups on my progress !