Non-AI experts face the most difficulty in understanding the terms. When discussing AI, machine learning (ML), and computer vision with non-data science professionals alike, they often don’t understand what they’re saying. For the most part, the terms Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision (CV) all refer to the same thing.
As an example, running a computer vision algorithm to discover faults in solar panels is an example of AI, ML, and CV. AI or ML, on the other hand, is more plausible if you are employing an algorithm to translate words from English to Spanish.
In the solar panel business, the vast majority of AI inspection projects are CV-based. A solar panel flaw is detected by an algorithm based on photographs.
Inspections enabled by artificial intelligence
To now, solar panel inspections have relied heavily on AI and CV. Traditional methods of inspecting solar panels for flaws required a team of people. In addition to being inefficient and costly, this procedure is also rife with errors. Maintenance visits are prohibitively expensive for most solar farms, and they simply cannot be performed on a regular basis across the entirety of a solar installation.
AI-powered inspection is increasingly being used by solar farm operators to streamline the inspection process and enhance accuracy. Solar panel flaws can be automatically detected using algorithms that analyze photos.
The manual inspection takes longer and is less accurate than this method. Using AI-powered inspection, solar farm operators may identify damaged panels before they are deployed on the solar farm and even after they have been operational.
Solar farms can use AI-powered inspection in a variety of ways. Drones, or Unmanned Aerial Vehicles (UAVs), are the most commonly used method. For solar farm owners, UAVs offer a non-contact method of inspecting solar panels using airborne photography.
It is possible to use a UAV to collect images of a solar farm and then process them using an algorithm either on the device or in the cloud. Which PV panels exhibit apparent indicators of malfunctioning equipment will be identified by an AI algorithm.
Quality controllers can save money by evaluating their entire facility in a few hours instead of engaging someone for days to perform maintenance utilizing AI-assisted automatic problem classification. Furthermore, location-based tagging can expedite inspection time by automatically identifying damaged panels.
Deep learning algorithms are the most often employed algorithm type in solar panel inspection. Using a neural network, deep learning algorithms are able to learn how to solve a problem. From the images captured by the camera, neural networks learn to identify solar panel problems.
Large datasets of annotated images are needed to train these deep learning networks. Deep learning algorithms can benefit from tagged photos provided by solar farm operators in many circumstances. If you don’t have the time to manually classify each image, an AI vendor can do it for you.
Using an in-house approach, this is accomplished by creating training datasets with and without flaws in the solar panel. In order to train the neural network to recognize both defective and non-defective panels, the solar farm operator will label each image as either defective or non-defective.
A solar farm’s photos can be examined using the deep learning algorithm once it has been trained. Image faults in solar panels can be detected by a neural network, which will then classify them (defective or non-defective).
In the face of AI,
AI-powered solar panel inspection has many advantages, but there are some problems that must be overcome.
The first is the availability of training data. As part of its training, a deep learning system must be fed a huge number of tagged images. Essentially, this means that an operator of a solar farm must supply a collection of solar panel photos with and without flaws.
The lack of standardization in solar panels is the second issue. Hundreds or even thousands of various solar panel models and varieties can be installed in a single solar farm, each having its own distinct size, shape, and color. Deep learning algorithms may not perform as well when used on a variety of solar farms because of differences in solar panel properties.
The third task is to ensure that the outcomes of inspections are as accurate as possible. The accuracy of algorithms taught to detect faults in solar panels will never be 100 percent. This means that a small number of solar panels may be incorrectly classified as defective. When numerous deep learning models trained on distinct datasets are used, the risk of inaccurate classification is greatly reduced.
Magazine, P. (2022, January 21). How Artificial Intelligence Can Be Used To Identify Solar Panel Defects – Pv Magazine International. pv magazine International. https://www.pv-magazine.com/2022/01/21/how-artificial-intelligence-can-be-used-to-identify-solar-panel-defects-2/.