FAULT DETECTION METHOD BASED ON IMAGE FEATURES OF PHOTOVOLTAIC MODULES

Yin Xiaoju; Yu Jinchi; Hao Zhipeng; Pan Xue

doi:10.19912/j.0254-0096.tynxb.2023-2047

PDF(1354 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 273-279. DOI: 10.19912/j.0254-0096.tynxb.2023-2047

FAULT DETECTION METHOD BASED ON IMAGE FEATURES OF PHOTOVOLTAIC MODULES

Yin Xiaoju¹, Yu Jinchi¹, Hao Zhipeng², Pan Xue¹

Author information +

History +

Abstract

Centralized photovoltaic power stations have complex terrain and wide areas, making it difficult to identify photovoltaic modules faults. An improved photovoltaic modules fault identification and detection method based on the YOLOv8 model is proposed. Based on the Backbone structure, the asymptotic feature pyramid network （AFPN） is used to fuse images at different levels to extract multi-scale information and enhance the fusion of contextual information. By adding a parameterless attention mechanism （SimAM） to the Neck structure, the three-dimensional attention weight in the feature map is inferred from the energy function, which lightweightly improves the model's representation ability. Instead of each pooling layer and each strided convolution layer, the SPD-Conv convolutional neural network is established to improve the fault identification ability of small target features such as hot spots, black edges and scratches in photovoltaic modules images. Experimental results show that the recall rate and accuracy rate of the improved model reach 78.7% and 84.9% respectively, and the average precision mAP50 and mAP50-95 reach 86% and 57.9% respectively. The identification and location of photovoltaic module faults are achieved and the correctness and effectiveness of the model is verified.

Key words

PV modules / deep learning / object detection / convolutional neural network / improved YOLOv8 / attention mechanism

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Yin Xiaoju, Yu Jinchi, Hao Zhipeng, Pan Xue. FAULT DETECTION METHOD BASED ON IMAGE FEATURES OF PHOTOVOLTAIC MODULES[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 273-279 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2047

References

[1] 赵玲玲, 汪烨, 刘俊. 基于无人机与HSV空间的光伏电池板检测分析[J]. 红外技术, 2020, 42(10): 978-982.
ZHAO L L, WANG Y, LIU J.Detection and analysis of photovoltaic panels based on UAV and HSV space[J]. Infrared technology, 2020, 42(10): 978-982.
[2] 谢七月, 孙武彪, 申忠利, 等. 基于灰度聚类的光伏红外热斑图像分割方法[J]. 太阳能学报, 2023, 44(9): 117-124.
XIE Q Y, SUN W B, SHEN Z L, et al.Photovoltaic infrared hot spot image segmentation method based on gray clustering[J]. Acta energiae solaris sinica, 2023, 44(9): 117-124.
[3] 蒋兴群, 刘波, 宋力, 等. 基于改进YOLO-v3的风力机叶片表面损伤检测识别[J]. 太阳能学报, 2023, 44(3): 212-217.
JIANG X Q, LIU B, SONG L, et al.Surface damage detection and recognition of wind turbine blade based on improved YOLO-v3[J]. Acta energiae solaris sinica, 2023, 44(3): 212-217.
[4] ZHOU D Q, ZHAO W B, CHEN Y X, et al.Identification and localisation algorithm for sugarcane stem nodes by combining YOLOv3 and traditional methods of computer vision[J]. Sensors, 2022, 22(21): 8266.
[5] 赵伟达, 陈海文, 郭陆阳, 等. 基于YOLO-E与改进OCRNet图像分割的变电站仪表读数自适应识别方法[J]. 电力建设, 2023, 44(11): 75-85.
ZHAO W D, CHEN H W, GUO L Y, et al.Substation meter readings and dial information identification method based on YOLO-E and enhanced OCRNet image segmentation[J]. Electric power construction, 2023, 44(11): 75-85.
[6] 沈恒, 干宗良. 基于双阶段轻量YOLO的红外行人伪影检测算法[J]. 激光与红外, 2023, 53(9): 1426-1433.
SHEN H, GAN Z L.Forged shadow detection algorithm based on two-stage lightweight YOLO in infrared pedestrian[J]. Laser & infrared, 2023, 53(9): 1426-1433.
[7] 周颖, 王如意, 袁梓桐, 等. 一种高效双路径注意力太阳电池缺陷检测网络[J]. 太阳能学报, 2023, 44(4): 407-413.
ZHOU Y, WANG R Y, YUAN Z T, et al.An efficient dual-path attention solar cell defect detection network[J]. Acta energiae solaris sinica, 2023, 44(4): 407-413.
[8] QU S M, YANG X Y, ZHOU H F, et al.Improved YOLOv5-based for small traffic sign detection under complex weather[J]. Scientific reports, 2023, 13(1): 16219.
[9] 李婷, 孙渊. 基于改进轻量型YOLOv5的太阳能电池板缺陷检测[J]. 组合机床与自动化加工技术, 2023(11): 95-99, 106.
LI T, SUN Y.Defect detection of solar panels based on improved lightweight YOLOv5[J]. Modular machine tool & automatic manufacturing technique, 2023(11): 95-99, 106.
[10] HOSSEINY A, JAHANIRAD H.Hardware acceleration of YOLOv7-tiny using high-level synthesis tools[J]. Journal of real-time image processing, 2023, 20(4): 75.
[11] 苗荣慧, 李志伟, 武锦龙. 基于改进YOLO v7的轻量化樱桃番茄成熟度检测方法[J]. 农业机械学报, 2023, 54(10): 225-233.
MIAO R H, LI Z W, WU J L.Lightweight maturity detection of cherry tomato based on improved YOLO v7[J]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(10): 225-233.
[12] 袁梦, 苏工兵, 王晶,等. 基于BCGS-YOLOv7 tiny的零件表面微小缺陷检测[J]. 制造技术与机床, 2023(10): 137-144.
YUAN M, SU G B, WANG J, et al.Part surface micro defect detection based on BCGS-YOLOv7 tiny[J]. Manufacturing technology & machine tool, 2023(10): 137-144.
[13] 周鑫, 李燕, 曾永辉, 等. 基于SARIMAX-SVR的光伏发电功率预测[J]. 电力系统及其自动化学报, 2024,36(5): 1-8.
ZHOU X, LI Y, ZENG Y H, et al.Forecasting of photovoltaic power generation based on SARIMAX-SVR[J]. Proceedings of the CSU-EPSA, 2024, 36(5): 1-8.
[14] 周颖, 颜毓泽, 陈海永, 等. 基于改进YOLOv8的光伏电池缺陷检测[J]. 激光与光电子学进展, 2024, 61(8): 245-255.
ZHOU Y, YAN Y Z, CHEN H Y, et al.Defect detection of photovoltaic cells based on improved YOLOv8[J]. Laser & optoelectronics progress, 2024, 61(8): 245-255.