How artificial intelligence can be used to identify solar panel defects

How artificial intelligence can be used to identify solar panel defects

The most common way that solar farms can deploy AI-powered inspection is through the use of an Unmanned Aerial Vehicle (UAV) or drone.

For example, if you are running a computer vision algorithm to identify solar panel defects, you are engaging in AI, ML, and CV. In contrast, if you are translating words from English to Spanish using an algorithm, that is more likely to be AI or ML, not CV.

Most AI inspection projects in the solar panel industry are typically computer vision (CV) initiatives. This means that an algorithm uses images to identify solar panel defects.

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.

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.

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