Research on Surface Defect Detection Method of Photovoltaic Power Generation Panels——Comparative Analysis of Detecting Model Accuracy
DOI:
https://doi.org/10.4108/ew.5741Keywords:
Computer vision, Deep learning, Solar panels, Photovoltaic defectsAbstract
INTRODUCTION: Research on intelligent defect detection technology using machine vision was conducted to address the challenging problem of detecting and localizing PV defects in photovoltaic power generation system operation and maintenance.
OBJECTIVES: The aim is to improve the accuracy of PV defect detection and enhance the operation and maintenance efficiency of PV power plants.
METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection, and deep learning algorithm based detection are discussed and compared, and analyzed respectively. It is finally concluded that the deep learning based detection is more efficient in comparison. Then further analysis and simulation experiments are done through several detection algorithms based on deep learning.
RESULTS: The experiment yields a high accuracy of the detection model based on the Faster-RCNN algorithm. Its mAP value reaches 92.6%. The detection model based on the YOLOv5 algorithm reaches a mAP value of 91.4%. But its speed is as much as 7 times faster than the model based on the Faster-RCNN algorithm.
CONCLUSION: Comprehensive speed and accuracy index. Combining the needs of PV defect detection in the operation and maintenance of PV power generation systems with the results of simulation experiments. It is concluded that the detection model based on the YOLOv5 algorithm can provide better detection capability. Modeling with this algorithm is more suitable for PV defect detection.
Downloads
References
Ding Shihao. Research on defect diagnosis of photovoltaic modules based on computer vision, Zhejiang University. 2020.
Cai Jiecong, et al. A review of hot spot detection technologies for photovoltaic power plants. Power Supply Technology. 2021; 45(05):683-685.
Guo Baozhu. Research on infrared image processing of hot spots of photovoltaic arrays, Tianjin University of Technology. 2016.
Jiang L, Su JH, Shi Y, et al. Zhu Lingyun, Wang Wei. Hot spot detection method for photovoltaic array based on infrared thermal image processing. Journal of Solar Energy. 2020; 41(08):180-184.
Ma Hao. Research on hot spot detection and localization technology of photovoltaic power plants based on infrared thermal imaging and visible light image. Nanjing University of Posts and Telecommunications. 2019.
Cortes C, Vapnik V N. Support-vector networks. Machine Learning. 1995; 20(3):273-29. DOI: https://doi.org/10.1007/BF00994018
Yang YN. Research and implementation of solar photovoltaic array identification and hot spot detection technology. Nanjing University of Posts and Telecommunications. 2018.
Chen Wenqin. Research and implementation of a hot spot detection system for photovoltaic modules based on infrared image recognition. Nanjing University of Posts and Telecommunications. 2020.
Chun Wang,Hui Yao,Hairong Sun. CNN-based hot spot region identification and localization of photovoltaic modules. Proceedings of the National Academic Conference on Simulation Technology. 2019.
Hairong Sun, Zijie Pan, Yong Yan. Small-sample photovoltaic hot spot identification and localization based on deep convolutional self-coding network. Journal of North China Electric Power University (Natural Science Edition). 2021; 48(04):91-98.
Ren Shaoqing, He Kaiming, Girshick R, et al.Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017; 39(6):1137-1149 DOI: https://doi.org/10.1109/TPAMI.2016.2577031
Guo Menghao,XU Hongwei. Research on hot spot defect detection of infrared thermal images based on Faster-RCNN. Computer System Applications. 2019; 28(11):265-270.
Wang D.L., Li C., Li M.S., et al. Hot spot detection of photovoltaic modules based on deep convolutional neural network. Journal of Solar Energy. 2022; 01(13):1-6.
Jocher, Glenn R. et al. "ultralytics/yolov5: v5.0 - YOLOv5-P6 1280 models. Supervise.ly and YouTube integrations. 2021.
QIAN Wu, WANG Guozhong, LI Guoping. Improved YOLOv5 Traffic Light Real-Time Detection Robust Algorithm. Journal of Frontiers of Computer Science and Technology. 2022; 16(1):231-241.
ZHOU F， ZHAO H， NIE Z. Safety Helmet Detection Based on YOLOv5. IEEE International Conference on Power Electronics， Computer Applications， Shenyang， Jan 22-24. 2021:6-11. DOI: https://doi.org/10.1109/ICPECA51329.2021.9362711
WANG C Y，LIAO H Y M，WU Y H，et al. CSPNet：A new backbone that can enhance the learning capability of CNN. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 2020:390-391. DOI: https://doi.org/10.1109/CVPRW50498.2020.00203
Hong F , Song J , Meng H ,et al. A novel framework for intelligent detection of module defects of PV plants combining visible and infrared images. Solar Energy. 2022(Apr.):236. DOI: https://doi.org/10.1016/j.solener.2022.03.018
ZHANG X, ZENG H, GUO S, et al. Efficient Long-Range Attention Network for Image Super-resolution. arXiv preprint arXiv:2203.06697. 2022. DOI: https://doi.org/10.1007/978-3-031-19790-1_39
LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector. Springer International Publishing. 2016:21-37. DOI: https://doi.org/10.1007/978-3-319-46448-0_2
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Energy Web
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
