In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pp. Zbib, B., Al Sheikh, H.: Fault detection and diagnosis of photovoltaic systems through iv curve analysis.

IEEEĮdun, A.S., LaFlamme, C., Kingston, S.R., Tetali, H.V., Benoit, E.J., Scarpulla, M., Furse, C.M., Harley, J.B.: Finding faults in pv systems: supervised and unsupervised dictionary learning with sstdr. In: 2018 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), pp. Jayakumar, N.K.T., Saleh, M.U., Benoit, E., Lacombe, J., Scarpulla, M., Furse, C.: Fault detection in pv strings using sstdr. Saleh, M.U., Deline, C., Kingston, S., Jayakumar, N.K.T., Benoit, E., Harley, J.B., Furse, C., Scarpulla, M.: Detection and localization of disconnections in pv strings using spread-spectrum time-domain reflectometry. Xian, T., Wei, H., De, X.: A survey of surface defect detection methods based on deep learning. Zhen, Z., Li, S., Lei, W., Jinlu, W., Peng, Q., Meng, J.: Study on case analysis and effect factors of hot spot failure for photovoltaic module.

Jie, W., Heng, L., Zhixiang, Z., Mingyao, M., Ping, Y.: Study on simplified algorithm for power loss of hot-spot cells in pv module. 32(4), 8 (2020)īolun, D., Yunze, H., Ruizhen, Y., Shoudao, H.: Modification of visual detection for silicon photovoltaic cells based on electromagnetic induction. Yatao, S., Fang, D., Changmin, Y.: Defect detection of solar photovoltaic cell. Shi-feng, D., Tao, X., Zheng-yue, X., Guo-qiang, X.: Study on hot spot risk of high-power module. In: 2019 Chinese Automation Congress (CAC), pp. 12(1), 53–61 (2021)Ĭhen, H., Wang, S., Xing, J.: Detection of cracks in electroluminescence images by fusing deep learning and structural decoupling. IEEEįioresi, J., Colvin, D.J., Frota, R., Gupta, R., Li, M., Seigneur, H.P., Vyas, S., Oliveira, S., Shah, M., Davis, K.O.: Automated defect detection and localization in photovoltaic cells using semantic segmentation of electroluminescence images. In: The 1st IEEE Global Conference on Consumer Electronics 2012, pp. Yen, H.-N., Sie, Y.-J.: Machine vision system for surface defect inspection of printed silicon solar cells. In: 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), pp. Wei S, Li X, Ding S, Yang Q, Yan W (2019) Hotspots infrared detection of photovoltaic modules based on hough line transformation and faster-rcnn approach. Huang, J.-M., Wai, R.-J., Gao, W.: Newly-designed fault diagnostic method for solar photovoltaic generation system based on iv-curve measurement. The mean intersection over union of the semantic segmentation model of PV modules is 97.8%, and the average precision of the HDD is 89.6%.Ĭotfas, D., Cotfas, P., Kaplanis, S.: Methods to determine the dc parameters of solar cells: a critical review. Experimental results demonstrate the effectiveness of the proposed method. The schemes are: (1) appending a prediction head for tiny hotspot defects in the prediction network, (2) revising the path aggregation network in the feature fusion network by merging multiple-scale feature maps to enhance the semantic information, and (3) applying efficient channel attention module to eliminate aliasing in the feature fusion network. Three optimization schemes are proposed to increase the detection accuracy for tiny-size hotspot defects. And, the object detection model is derived from YOLOv5s. The semantic segmentation model is named Attention DeepLab, which has been developed by an attention module. In specific, the regions of PV modules in the IFIs are first extracted by an improved semantic segmentation model, and then hotspot defects are detected from the segmented regions by a developed object detection model. Therefore, a segmentation-before-detection method is proposed for HDD in this paper.

The backgrounds in the IFIs are complex, which results in the difficulties of detecting hotspots in PV modules, especially the tiny-size hotspots. It aims to detect hotspot defects from the infrared images(IFIs), which are captured by the unmanned aerial vehicles at about 20 ms. Hotspot defect detection (HDD) of photovoltaic (PV) modules is one of the daily inspections of PV power stations.

Pixel 3 need for speed heat background