Automated Detection of Defects in Solar Images Utilizing Integrated Deep Learning Frameworks

Renewable energies must meet rising power demand while protecting the environment. Solar farms are a fast-growing, ecologically friendly power source. Multiple solar flaws from ordinary operations or environmental circumstances reduce solar energy generation efficiency. Electroencephalogram (EL) imaging shows defects. Manual defect identification is time-consuming, expensive, and inaccurate. An automated deep-learning method for solar failure identification and categorization is presented in this paper. Traditional semi-automated machine-learning methods require manual feature extraction. The suggested solar defect detection and classification approach uses several EL images. The system comprises three phases: pre-processing, Convolutional Neural Network (CNN)-based segmentation and feature extraction, and LSTM for solar abnormality classification. The proposed model pre-processes training and tests solar images before autonomous deep learning feature extraction and categorization. Gaussian filtering and contrast adjustment are main distortion correction approaches during pre-processing. The CNN estimates more strong and reliable features with distortion correction, improving detection accuracy. The CNN layers are further enhanced by applying Discrete Cosine Transform (DCT) and Independent Component Analysis (ICA) are used to extract and reduce robust features in the CNN model. Finally, the classification is performed using Long Short-Term Memory (LSTM) classifier. Performance of the proposed model is compared with the existing methods using two datasets. Proposed model has improved accuracy by 4.37% compared to existing techniques.