With the world governments and other organisations favouring more renewable energy sources for our daily needs, this environmental friendly approach has been taking over the world. Beyond the buzz that this technology has been creating, they are bringing out some favourable changes in preserving our planet. Since more individuals are choosing renewable energy resources, its markets have been rising. 

The solar panel market, one of the most commonly used renewable energy for commercial as well as household purposes, set growth at 15%. Even though solar energy is widely used, still the one-time installation charge for the panel has not come down. Also, the production of the panels is not yet cost-effective. In India, several solar panel manufacturers are trying to bring defect-free products into the market.  

There are several methods adopted to make the panels defect-free. Companies use Electroluminescence (EL) testing of solar modules. By this process, a current is passed through PV cells which results in light emission. With the help of the EL test, the PV manufacturers will find and evaluate the defects in the model if any. AI is contributing to finding a solution to the problem. The relatively novel method of AI and CV in solar panel inspection is fast, cost-effective and accurate. AI-based algorithms is used that can automatically detect issues from images.

Detecting defects

One of the methods by which AI can help in finding the defects is by TYQ-i vision intelligence platform. Through this method, the domain expertise is applied in image processing, computer vision and deep neural networks. Images of solar panels are captured at a 4K resolution. Each solar panel has 72 cells in it. A 3D transformation is used to align the images to the imager parallel plane. Then the individual cells are cropped from the panel, and the image of the cell individually is provided to the custom deep neural network and thus the defect is found. 

_{Image of a solar panel with defects}

Another method by which AI-powered inspection is done by using an Unmanned Aerial Vehicle (UAV) or drone. UAVs produce aerial images that provide a contactless way for solar farm operators. Deep Learning, Machine Learning and Computer Vision algorithms are used to process the images collected by the UAV. They can be processed in a cloud or a device. The result of the algorithm will tell the quality controller about the condition of the PV panels. 

The AI-based methods for automatic defect classification can reduce the cost and time by surveying the entire facility within a few hours. The location based-tagging option can speed up the inspection and can thus improve efficiency.

Defects in solar panels

Several defects occur in solar panels. Mentioned following are some of the commonly found defects.

• Microcracks: Solar cells, which are in solar panels ins made of a really thin wafer of silicon. The silicon can have brittle because it is crystalline and it will result in microcracks.
• Glass Cracks: Solar panels which are formed using solar cells have a protective glass covering. Even though this glass is waterproof. The cracks may occur in these glasses post-installation and may result in a direct weather impact on the cells.
• Cross cracks: A tree-like structure appears on the cell. This can affect the energy generation
• Hotspots: The cells are soldered together in a panel. If the soldering done is poor or if there are any manufacturing imperfections, eventually this region will become a hotspot.

Benefits of AI-based defect detection

The chances of human errors are reduced in AI-based defect detection. The individuals who choose solar energy sources are willing to spend the high installation cost of the model because it is a long-term financial investment. Early defect detection avoids future expenses. It will become more reliable which will favour the customer as well as the manufacturer. These AI-based models can be deployed in the industrial application as well as infrastructure analytics.