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In recent years, solar energy has been regarded as one of the most important sustainable energy sources. Under the rapid and large-scale construction of solar farms, the maintenance and inspection of the health conditions of solar modules in a large solar farm become an important issue. This article proposes a method for detecting solar cell faults with unmanned aerial vehicle (UAV) equipped with a thermal imager and a visible light camera, and providing a fast and reliable detection method. The detection process includes a new concept of real-time monitoring of the detected area and analysis of the health of solar panels. An image process is proposed that may quickly and accurately detect the abnormality of a solar module. The whole process includes grayscale conversion, filtering, 3-D temperature representation, probability density function, and cumulative density function analysis. Ten cases in real fields have been studied with this process, including large scale solar farms and small size solar modules installed on buildings. Results show that the cumulative density function is a convenient way to determine the health status of the solar panel and may provide maintenance personnel a basis for determining whether replacement of solar cells is necessary for improving the overall power generation efficiency and simplify the maintenance process. It is worth noting that image recognition can increase the clarity of IR images and the cumulative chart can judge the defect rate of the cell. These two methods were combined to provide an instant, fast and accurate defect judgment. |
