The global generation of renewable energy has rapidly increased, primarily due to the installation of large-scale renewable energy power plants. However, monitoring renewable energy assets in these large plants remains challenging due to environmental factors that could result in reduced power generation, malfunctioning, and degradation of asset life. Therefore, the detection of surface defects on renewable energy assets is crucial for maintaining the performance and efficiency of these plants. This paper proposes an innovative detection framework to achieve an economical surface monitoring system for renewable energy assets. High-resolution images of the assets are captured regularly and inspected to identify surface or structural damages on solar panels and wind turbine blades. {Vision transformer (ViT), one of the latest attention-based deep learning (DL) models in computer vision, is proposed in this work to classify surface defects.} The ViT model outperforms other DL models, including MobileNet, VGG16, Xception, EfficientNetB7, and ResNet50, achieving high accuracy scores above 97\% for both wind and solar plant assets. From the results, our proposed model demonstrates its potential for monitoring and detecting damages in renewable energy assets for efficient and reliable operation of renewable power plants.

翻译：全球可再生能源发电量快速增长，主要得益于大规模可再生能源发电厂的部署。然而，由于环境因素可能引发发电效率下降、设备故障及资产寿命衰减，监测此类大型电厂中的可再生能源资产仍面临挑战。因此，识别可再生能源资产表面缺陷对维持电厂性能与运行效率至关重要。本文提出一种创新性检测框架，旨在构建经济可行的可再生能源资产表面监测系统。通过定期采集资产高分辨率图像并实施检测，识别太阳能面板与风力涡轮机叶片的表面或结构损伤。本研究采用计算机视觉领域最新基于注意力的深度学习模型——视觉Transformer（ViT）进行表面缺陷分类。ViT模型的性能超越了MobileNet、VGG16、Xception、EfficientNetB7及ResNet50等其他深度学习模型，对风电与光伏电厂资产的缺陷识别准确率均超过97%。实验结果表明，所提模型具备监测并检测可再生能源资产损伤的潜力，可保障可再生能源电厂的高效可靠运行。