Utilizing CNN-GAN for Enhanced Detection and Classification of Dust on Solar Panels

Dust affects the performance of solar panels in a negative way, cutting down their efficiency by up to $30 \%$. Traditional processes of dust recognition are based on physical observations of the object state and basic picture analysis techniques; these methods need to perform better and be quickly developed further. The current work details the development of a new dual model, CNNs-GANs, that enhances the earlier classification networks for categorizing various kinds of dust on solar panels. It addresses the problem of having scarce annotated data since the CNNs guarantee a precise feature extraction and classification and the GANs, capability to generate authentic dust pictures despite being synthesized. With the help of the hybrid CNN-GAN model, the detection accuracy was improved considerably to the maximum of $93 \%$, while the precision reached $90 \%$ and the recall $-\mathbf{9 1 \%}$. From these results, an enhancement has been observed as compared to traditional methods of only using CNN, which had a success rate of $85 \%$. The above results verified the model’s capabilities with extended evaluations, including the heat map, confusion matrix, and ROC curve, which showed that this model is effective in different environmental situations. Future research will be devoted to constantly monitoring the developed tools, their testing in various conditions and environments, and their application in Internet of Things technology for self-maintaining machines. To achieve this goal, it is necessary to change the approaches to maintenance and enhance the utilization of renewable solar power.