# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 1 Introduction  
The development of renewable energy has gained a lot of attention worldwide. This increased interest in renewable energy is primarily due to environmental problems such as global warming, abnormal weather, depletion of fossil fuels, etc. Among various renewable energy sources, solar energy has attracted much attention in the renewable energy sector as a pollution-free, sustainable, and abundant energy source. Photovoltaic (PV) power station installations have significantly increased worldwide over the past few years. A PV power station typically consists of hundreds or thousands of PV modules which are connected together in a series circuit. These PV modules are the key components in a solar power station since the output efficiency of these stations are dependent upon these modules. Unfortunately, PV modules are subject to various defects with the passage of time, which leads to adverse problems such as low-power efficiency. Usually, the electrical characteristics of all PV cells in the string are similar and operate at the maximum power point (MPP) current to achieve each cell's optimal output. However, when there is a discrepancy among the cell electrical characteristics, the single string current is not able to operate at every cell's MPP, which causes minimal performance [1]. The most common factors that lead to such discrepancy are partial shading, degradation, short-circuited bypass diodes, etc. When there is one low current PV cell (e.g., due to weed shadow) in the string of several high short-circuit current PV cells, the forward bias across all of these cells reverse biasesthe shaded cell, which drastically increases the cell temperature and leads to hot spotting [2]. It can damage the cell and reduce the total power output of the solar panel. Therefore, regular inspection of PV modules is critical for maintaining an optimal output efficiency. Methods for PV module inspection include manual inspection, laser detection [3], satellite observations [4; 5], infrared thermography, and electroluminescence imaging [6; 7; 8]. Manual inspection is highly time inefficient while laser detection and electroluminescence imaging cannot be applied to PV power stations. Infrared thermography gained significant attention due to its ease of use and applicability to large-scale PV systems. In infrared thermography, a technician usually inspects each PV module with a handheld infrared camera. Inspecting a single PV module is effortless; however, inspecting a large-scale PV system is a considerably lengthy procedure because large-scale PV systems consist of hundreds or thousands of PV modules. In the last few years, the academic and industrial communities are interested in time efficient drone-based infrared thermography methods. Typically, a drone equipped with thermal camera is operated wirelessly by a technician and images are captured and saved during the flight, which are later processed for fault detection. Most previous drone-based approaches [9; 10; 11] require manual drone control, which is an exhausting procedure since it involves manual labor. In addition, previous works [12; 13; 14] are unable to provide the exact locations of the defective solar panels, which further delays the recovery process. This paper presents an autonomous drone-based infrared thermography solution for PV module fault detection and localization. The developed drone system consists of a gimbal-equipped drone based on Pixhawk 2.1 flight controller (FC). The drone was designed as a 1.1-m class hexacopter drone supporting a payload of 2 kg from K-Pro Systems, Seoul, Korea. A dual camera setup consisting of an RGB camera (Logitech C270 (Logitech, Suzhou, China)) and a thermal camera (FLIR Vue Pro R (FLIR Systems, Inc., Wilsonville, OR, USA)) was mounted on the drone system. Raspberry Pi 3 was used as the mainboard for interfacing with the dual camera setup and FC. The RGB camera was interfaced to Raspberry Pi via the USB port while the FC and the thermal camera were linked via MAVlink.