Problem

Defective solar cells are a major concern for solar energy producers, as they can drastically reduce the efficiency of a solar panel. The ability to identify and classify defective solar cells quickly and accurately is therefore an important part of any solar energy system.

Currently, the process of identifying and classifying defective solar cells is a manual process, which is time-consuming and prone to errors. This has led to the development of automated systems that can more quickly and accurately identify and classify defective solar cells.

These automated systems use computer vision to detect defects in a solar cell, and use machine learning algorithms to classify the type of defect. This requires large datasets of labeled examples of defective solar cells, which can be used to train the machine learning algorithm.

The goal of this project is to develop an automated system that can accurately identify and classify defective solar cells. The system should be able to detect common defects and classify them into predefined categories, and should be able to accurately identify and classify new defects as they are encountered.

Current Solutions

• Existing solutions for automated identification and classification of defective solar cells rely on manual processes, which are time-consuming and prone to errors.
• Current computer vision techniques, while useful for detecting defects, are limited in their ability to classify different types of defects.
• Machine learning algorithms can be used to classify different types of defects, but require large datasets of labeled examples, which can be difficult and expensive to obtain.
• Many existing solutions are unable to accurately identify and classify new and uncommon defects, which can lead to errors in the system.

Model

A Convolutional Neural Network (CNN) model has been developed to classify defective solar cells more accurately than existing solutions. The model is composed of a series of convolutional layers, which use kernels to extract features from the input images. These features are then passed to a series of dense layers, which use the extracted features to classify the defective solar cells into predefined categories.

The CNN model uses a combination of convolutional and pooling layers to identify and extract features from the images of defective solar cells. The convolutional layers use different sized kernels to detect different features, such as edges and shapes. The pooling layers are then used to reduce the size of the feature maps, and to combine similar features.

Once the convolutional layers have extracted the features from the input images, they are passed to the dense layers, which use the extracted features to classify the defective solar cells. The dense layers use a supervised learning algorithm to classify the defective solar cells based on the extracted features.

The CNN model can be trained using large datasets of labeled examples of defective solar cells, and can accurately identify and classify new and uncommon defects. This makes it a powerful tool for automated identification and classification of defective solar cells.