75MW AC PV Module Field Anomaly Detection Using Drone-Based IR Orthogonal Images With Res-CNN3 Detector

Abstract

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. Dronebased 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.

Defects Detection on 110 MW AC Wind Farm’s Turbine Generator Blades Using Drone-Based Laser and RGB Images with Res-CNN3 Detector

“…However, Res-CNN3 still outperforms ResNet-50 + TCCS + SS in all the defects category detection in terms of average testing time. Overall, the observation of the performance dynamics between Res-CNNN3 and ResNet-50 + TCCS + SS, clearly demonstrates the unique value of ablation studies in object detection training, testing, and analyses [29]. These studies certainly offer one the ability to carefully optimize and criti- Following YOLO, the most promising technique is the ResNet-50 + TCCS + SS, which performs exceptionally well on Structural cracks, Leading Edge erosion and Delamination defects detection in a like manner, but with a lower confidence score, compared to YOLO.…”

“…However, Res-CNN3 still outperforms ResNet-50 + TCCS + SS in all the defects category detection in terms of average testing time. Overall, the observation of the performance dynamics between Res-CNNN3 and ResNet-50 + TCCS + SS, clearly demonstrates the unique value of ablation studies in object detection training, testing, and analyses [29]. These studies certainly offer one the ability to carefully optimize and critically analyze the costs, benefits, and economics of each detection technique in order to recognize the most suitable technique based on application, implementation, and purpose.…”

A comprehensive review of unmanned aerial vehicle-based approaches to support photovoltaic plant diagnosis

“…A literature search revealed several diagnostic strategies for grid-connected PV systems [ 16 , 22 , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] ]. The published research mainly dealt with the different methods (electrical data analysis, visual inspection and thermal images) for diagnosing failures in PV systems.…”

Detection and analysis of deteriorated areas in solar PV modules using unsupervised sensing algorithms and 3D augmented reality

“…While thermal images obtained by drones can identify areas with abnormal temperatures, they often fail to provide accurate localization of the specific panels that require maintenance. As a result, the subsequent identification and repair of defective panels become more complex and time-consuming, leading to additional delays in the maintenance process [35] .…”