Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model
This work addresses the challenge of efficient defect detection for renewable energy asset maintenance, but it is incremental as it applies an existing attention-based model to a specific domain.
The paper tackled the problem of monitoring surface defects on solar panels and wind turbine blades in renewable energy plants by proposing a vision transformer (ViT) model, which achieved accuracy scores above 97% for both asset types, outperforming other deep learning models.
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.