Building an Online Defect Detection System for Large-scale Photovoltaic Plants

The power efficiency of photovoltaic modules is highly correlated with their health status. Under dynamically changing environments, photovoltaic defects could spontaneously form and develop into fatal faults during the daily operation of photovoltaic plants. To facilitate defect detection with less human intervention, a non-destructive and contactless visual inspection system with the help of unmanned aerial vehicles and edge computing is proposed in this work. Limited by the resources of edge devices and the availability of images of photovoltaic defects for training, we developed an online solution combined with deep learning, data argumentation and transfer learning to properly address the issues of running resource hungry applications on edge devices and lack of training samples faced by the deep learning approaches used in the field. With the reduction of the network depth of the deep convolutional neural network model and the transfer of features from the learned defects, the resource consumption of our proposed approach is significantly reduced, and thus can be used on a wide range of edge devices to complete defect detection in a timely manner with high accuracy. To study the performance of our design, a testbed was built from open source hardware and software, and field trials were carried out in three photovoltaic plants. The experimental results clearly demonstrate the practicality and effectiveness of our design for detecting visible defects on photovoltaic modules.