# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 4 Experimental Results, and Discussion  
To verify the validity of the developed system, photovoltaic modules were photographed by autonomous flight on a 1-MW solar power plant located in Suncheon, Korea using the proposed drone system. The flight path for the drone system was calculated by our automatic flight path planning algorithm, as explained in Section 3.2. The images captured by the dual camera device mounted on the drone were sent to the ground station. The images were then analyzed by the image processing algorithm, as explained in Section 3.3, to identify and localize defective PV modules. Figure 11a displays an image of overall PV plant, while Figure 11b displays the automatically planned flight path calculated by our path planning algorithm. The drone acquired 400 thermal and visible images, which were processed by the image processing algorithm, obtaining the result displayed in Figure 11c. The various steps proposed in the algorithm contributes in distinguishing defective modules from normal modules in the PV array.  
Figure 11: (**a**) Image of overall PV power plant; (**b**) image showing the automatically planned flight path; and (**c**) the final result showing the location of defective PV modules.  
Through image analysis, we deduced that the detection and identification depend on various characteristics of the defects such as its shape, size, location, etc. Within a photovoltaic module, a wide range of defects can occur. Many of these defects cause overheating of the entire module, single substrings, small solar cells, or even tiny cell parts. To verify the efficiency of the proposed system, three experiments were conducted, as shown in Table 2. In the first experiment, 14 defective panels were detected in different areas of the PV plant indicating the possible failures. Depending on the shape and location, the hot spots and hot areas highlighted different faults. Out of 14 defected modules, 10 were due to shade, 2 were due to the soiling matter, and the remaining 2 were due to  
Figure 15 shows the region where the system detected a hot spot, but the reason was unidentifiable. Different types of defects or failures can be detected only at different heights. Moreover, characteristics of defects also play an important role in its detection. Therefore, further investigation is needed to perform more efficient identification of faults. Figure 16 shows another defective PV module, which was affected by various causes such as reflection, solid, and shadow. In addition, the exact geolocalization of deteriorated PV modules in large-scale plants is one of the most critical issues in PV plant inspection. In this work, we used GPS information to locate the faulty modules. Aluminum marks can be used for thermal images georeferencing due to its low emissivity nature, which facilitates in better pointing. However, for the precise location in large-scale plants with centimeter accuracy, a further survey on RTX modules needs to be performed. In conclusion, our experiments demonstrated that hot spots generally occur due to partial shading from weeds, solid things present on the surface of PV module, broken interconnects, and/or reflection from sunlight.