An Unmanned Inspection System for Multiple Defects Detection in Photovoltaic Plants

Abstract

Condition monitoring and fault diagnosis of photovoltaic modules are essential to ensure the efficient and reliable operation of large-scale photovoltaic plants. This article presents an algorithmic solution for the rapid and sensitive detection of photovoltaic modules with multiple visible defects by an image analyzing apparatus mounted onto an unmanned aerial vehicle. The proposed solution is composed of three stages to efficiently and accurately analyze various forms of module defects. First, the Kirsch operator is employed to identify the anomalous regions, which can significantly reduce the computational complexity, and error rate. Afterward, a deep convolutional neural network is adopted to extract defect features. Finally, a multiple classification support vector machine is developed to facilitate the defects detection decision-making. The proposed solution is extensively evaluated by the comprehensive dataset collected from real-world solar photovoltaic plants. The experimental results clearly demonstrate the effectiveness of our solution for photovoltaic modules diagnosis with multiple visible defects.