The increasing adoption of wind turbines as a key component of renewable energy generation necessitates the development of efficient and reliable maintenance strategies to ensure their optimal performance and safety. Among the most critical aspects of turbine maintenance is detecting and classifying defects in wind turbine blades, which are constantly exposed to extreme environmental conditions. Defects such as cracks, delamination, erosion, and icing not only compromise the structural integrity of blades but also significantly reduce their aerodynamic efficiency and energy production capabilities. While effective, traditional inspection methods are labor-intensive and time-consuming, prompting the exploration of automated solutions. This review paper examines the state-of-the-art deep learning methodologies applied to defect detection in wind turbine blades. Key advancements are highlighted, including the integration of Convolutional Neural Networks (CNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) for image-based detection and anomaly identification. Additionally, transfer learning and attention mechanisms have been instrumental in enhancing the precision and speed of defect detection, enabling real-time applications. Notable approaches like YOLO (You Only Look Once) and its variants have shown exceptional performance in detecting defects with varying scales and complexities, leveraging innovations such as feature pyramid networks and efficient loss functions. Furthermore, this review discusses the role of advanced data acquisition techniques, such as drone-based imaging, thermographic analysis, and LiDAR (Light Detection and Ranging), in generating high-resolution and multi-spectral data for improved detection accuracy. Insights are provided into challenges such as class imbalance, limited labeled datasets, and environmental noise, alongside emerging solutions like semi-supervised learning and data augmentation. The paper concludes by emphasizing the transformative potential of deep learning in achieving automated, accurate, and efficient defect detection in wind turbine blades. Addressing current limitations through interdisciplinary research and innovative algorithms will pave the way for sustainable and cost-effective wind energy systems, ultimately contributing to the global transition toward renewable energy.

In recent years, the use of solar photovoltaic (PV) technologies for generating electricity has gained much popularity due to their efficiency, cost-effectiveness, reliability and the need to reduce carbon emissions and air pollution levels around the world in order to control and limit global climate change. Drone-based inspection of Solar Plants is an efficient method to perform preventative and corrective maintenance on the Solar PV arrays installed in large-scale grid-connected Solar PV Plants. Quite a few studies have been made in detecting PV module anomalies from these drone-captured thermal images. The main challenge that most of the applied techniques and models in these studies experience is the capability to detect and localize various PV module defects with high and consistent confidence scores and accuracy. The efficiency and robustness of anomaly detection of these models are also reduced when the testing image scales vary. In this article, we propose the Res-CNN3 framework for defective PV modules region object detection. Res-CNN3 follows a bipartite process for defect regions detection to process the thermal delta of a thermal PV module image using a combination of concatenated CNNs and residual networks to identify whether the thermal image is characteristic of defect regions or not. Logistic regression is employed as the loss function whist the Selective Search (SS) algorithm is utilized to predict the regions of interest (RoI) of the input thermal images for PV module defect regions object detection. Several categories of experiments are performed on the consolidated dataset. The results of these experiments demonstrate that the proposed model surpasses the advanced benchmarked methods in accuracy and computing resource efficiency.

An effective way to perform maintenance on the wind turbine generator (WTG) blades installed in grid-connected wind farms is to inspect them using Unmanned Aerial Vehicles (UAV). The ability to detect wind turbine blade defects from these laser and RGB images captured by drones has been the subject of numerous studies. The issue that most applied techniques battle with is being able to locate different wind turbine blade defects with high confidence scores and precision. The accuracy of these models’ defect detection decreases due to varying testing image scales. This article proposes the Res-CNN3 technique for detecting wind turbine blade defects. In Res-CNN3, defect region detection is achieved through a bipartite process that processes the laser delta and RGB delta structure of a wind turbine blade image with an integration of residual networks and concatenated CNNs to determine the presence of typical defect regions in the image. The loss function is logistic regression, and a Selective Search (SS) algorithm is used to predict the regions of interest (RoI) of the input images for defects detection. Several experiments are conducted, and the outcomes prove that the proposed model has a high prospect for accuracy in solving the problem of defect detection in a manner similar to the advanced benchmark methods.