AI POWERED SOLAR PANELS
One of the biggest challenges for non-AI experts is the terminology. Artificial intelligence (AI), machine learning (ML), and computer vision (CV) are frequently discussed, but people outside of data science fields often do not know what they mean. Fortunately, it is not that complex: Artificial Intelligence, Machine Learning, and Computer Vision all generally refer to the same thing, just with more specificity.
The use of AI and CV in solar panel inspection is relatively novel. Traditionally, solar farm operators would use a team of workers to manually inspect solar panels for defects. This process is slow, expensive, and not very accurate. Every solar farm operator knows that maintenance visits are extremely expensive, and are simply not feasible to perform daily for an entire solar deployment.
To speed up the inspection process and improve accuracy, solar farm operators are turning to AI-powered inspection. This involves the use of algorithms that can automatically detect solar panel defects from images.
This process is much faster and more accurate than manual inspection. Additionally, solar farm operators can use AI-powered inspection to identify defective panels before they are installed on the solar farm, and after they are already operational.
There are a few different ways that solar farms can deploy AI-powered inspection. The most common way is through the use of an Unmanned Aerial Vehicle (UAV) or drone. UAVs provide a non-contact way for solar farm operators to perform quality control of their solar panels using aerial imagery.
Images collected by a UAV over a solar farm can be processed by an algorithm either in the cloud or on-device. The results of the AI algorithm will tell the quality controller which PV panels have visible signs of defective equipment.
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By using automatic defect classification AI, quality controllers can reduce costs by surveying their entire facility in a few hours rather than hiring someone for days to conduct maintenance. Moreover, automatic identification of defective panels can speed up inspection time with location-based tagging, thus improving efficiency.
The most common algorithm type used in solar panel inspection is a deep learning algorithm. Deep learning algorithms are a type of machine learning algorithm that uses a neural network to learn how to solve a task. Neural networks are composed of interconnected layers that can learn how to recognize solar panel defects from images.
These deep learning networks require training data, which are large datasets of labeled images. In many cases, the solar farm operator can provide these labeled images to the deep learning algorithm. Alternatively, an AI vendor can provide these labeled images off the shelf.
For the in-house approach, this is done by creating a training dataset that consists of images containing solar panel defects, and also images without solar panel defects. The solar farm operator will label each image as either defective or non-defective so that the neural network learns how to identify both types of panels.