In the last few decades, photovoltaic (PV) power station installations have surged across the globe. The output efficiency of these stations deteriorates with the passage of time due to multiple factors such as hotspots, shaded cell or module, short-circuited bypass diodes, etc. Traditionally, technicians inspect each solar panel in a PV power station using infrared thermography to ensure consistent output efficiency. With the advancement of drone technology, researchers have proposed to use drones equipped with thermal cameras for PV power station monitoring. However, most of these drone-based approaches require technicians to manually control the drone which in itself is a cumbersome task in the case of large PV power stations. To tackle this issue, this study presents an autonomous drone-based solution. The drone is mounted with both RGB (Red, Green, Blue) and thermal cameras. The proposed system can automatically detect and estimate the exact location of faulty PV modules among hundreds or thousands of PV modules in the power station. In addition, we propose an automatic drone flight path planning algorithm which eliminates the requirement of manual drone control. The system also utilizes an image processing algorithm to process RGB and thermal images for fault detection. The system was evaluated on a 1-MW solar power plant located in Suncheon, South Korea. The experimental results demonstrate the effectiveness of our solution.

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The effects of ECAP temperature and post-ECAP annealing on grain size, texture and mechanical behavior have been examined. The softening of ECAPed Mg alloys despite the considerable grain size refinement has been ascribed to the texture change during ECAP. The strength of the ECAPed AZ31 Mg alloys, however, increased with decrease in grain size following the standard Hall-Petch relation when the similar texture could be retained. Based on the present analysis, it could be concluded that it was practically hard to improve the strength of the Mg alloys significantly by grain-size refinement when ECAP was used, because texture softening effect was often more dominant over the grain strengthening effect.

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Several factors cause the output degradation of the photovoltaic (PV) module. The main affecting elements are the higher PV module temperature, the shaded cell, the shortened or conducting bypass diodes, and the soiled and degraded PV array. In this paper, we introduce an image processing technique that automatically identifies the module generating the hot spots in the solar module. In order to extract feature points, we used the maximally stable extremal regions (MSER) method, which derives the area of interest by using the inrange function, using the blue color of the PV module. We propose an effective matching method for feature points and a homography translation technique. The temperature data derivation method and the normal/ abnormal decision method are described in order to enhance the performance. The effectiveness of the proposed system was evaluated through experiments. Finally, a thermal image analysis of approximately 240 modules was confirmed to be 97% consistent with the visual evaluation in the experimental results.

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The potency of object detection techniques using Unmanned Aerial Vehicles (UAVs) is unprecedented due to their mobility. This potency has stimulated the use of UAVs with object detection functionality in numerous crucial real-life applications. Additionally, more efficient and accurate object detection techniques are being researched and developed for usage in UAV applications. However, object detection in UAVs presents challenges that are not common to general object detection. First, as UAVs fly at varying altitudes, the objects imaged via UAVs vary vastly in size, making the task at hand more challenging. Second due to the motion of the UAVs, there could be a presence of blur in the captured images. To deal with these challenges, we present a You Only Look Once v5 (YOLOv5)-like architecture with ConvMixers in its prediction heads and an additional prediction head to deal with minutely-small objects. The proposed architecture has been trained and tested on the VisDrone 2021 dataset, and the acquired results are comparable with the existing state-of-the-art methods.