Automatic hotspots detection based on UAV infrared images for large‐scale PV plant

Abstract

Owing to the significantly installed capacity of solar energy during the past decades, the operation and maintenance of the large photovoltaic power station is a big challenge, as the manual inspection is labour‐intensive and expensive. This Letter presents a solution for the intelligent inspection of the hotspot with the unmanned aerial vehicle in the large‐scale photovoltaic plant. First, a traditional image processing method is presented to eliminate the noise and crop the infrared image, which can make the defected feature more obvious. Then, the module is extracted by the line segments detection. Finally, considering the great advance in the realm of the computer vision, a deep‐learning‐based method for automatic hotspot detection is proposed for locating the hotspot with the data augmentation. The deep‐learning‐based model can extract the hotspot feature by the training process in the data set. In the end, the performance of the method proposed is extensively evaluated and the numerical results prove the accuracy and precision of the model.

A bright spot detection and analysis method for infrared photovoltaic panels based on image processing

“…On the basis of the bright spots image, we use the human checking, the Nie's method (Nie et al, 2020) as benchmarks to demonstrate ours advantages. Nie's method eliminated noise according to traditional image processing and crop the infrared image.…”

“…In fact, plants show too many bright spots in infrared image, which makes some methods ineffective and image processing methods based on deep learning has been widely applied. Nie et al (2020) Ronneberger et al (2015) proposed a new network structure, U-Net, which is widely used in the segmentation of organs in medical images (Lei et al, 2019), prediction of different crop types in agriculture observations (Wei et al, 2019), forest ecological management (Wagner et al, 2019). Its effect is better than traditional pattern recognition methods.…”

Cost-Effective Flight Strategy for Aerial Thermography Inspection of Photovoltaic Power Plants

“…Binary thresholding is another well-tested method to segment hot regions within a PV module for defect detection [16], [17]. A water filling algorithm is deployed in [18], and recently also deep learning-based methods have been presented [19], [20], and [21]. The defect detection algorithms in [17] and [18] can be run in real time.…”

Guest Editorial: Non‐Destructive Testing

“…In [7 ], Kaur and Mulaveesala provide an insight into the selection of independent components for inspection of mild steel samples using infrared thermography for detection of flat bottom hole defects, while in [8 ] Nie, Luo and Li present the solution for the intelligent inspection of the hotspot with an unmanned aerial vehicle in the large‐scale photovoltaic plant. In [9 ], Rani and Mulaveesala highlight the testing and evaluation of glass fibre‐reinforced polymer (GFRP) specimens for detection of subsurface hidden defects using pulse compression favourable thermal wave imaging techniques.…”