{"title":"基于 YOLOv3-MSSA 的光伏电站热点缺陷检测","authors":"Kaiming Gu, Yong Chen","doi":"10.21595/jme.2023.23418","DOIUrl":null,"url":null,"abstract":"With the continuous development of the energy industry, photovoltaic power generation is gradually becoming one of the main power generation methods. However, detecting hot spot defects in photovoltaic power stations is challenging. Therefore, enhancing detection efficiency using information technology has become a crucial aspect. The study presents a defect detection model for PV power stations using the YOLOv3 (You Only Look Once v3) algorithm. The model incorporates coordinate attention module (CAM) and self-attention module (SAM) to improve feature extraction in low-resolution conditions. The Multi objective Sparrow is employed to achieve multiple objectives. It is very contributing in the detection of low-resolution features. It shows that the research method can reduce the loss value to 0.009 after 400 iterations of the loss curve test. The precision-recall (P-R) curve generated by the research method only starts to drop sharply when the Recall value reaches 0.96. The number of parameters generated by the research method is 3.46×106. The detection accuracy of the research method reaches 98.86 % when there are five defective fault types. The results indicate that the proposed research method offers improved detection speed and higher accuracy in identifying hot spot defects in PV power stations. This technology provides valuable support for hot spot defect detection and presents new opportunities for the field.","PeriodicalId":42196,"journal":{"name":"Journal of Measurements in Engineering","volume":"4 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"YOLOv3-MSSA based hot spot defect detection for photovoltaic power stations\",\"authors\":\"Kaiming Gu, Yong Chen\",\"doi\":\"10.21595/jme.2023.23418\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the continuous development of the energy industry, photovoltaic power generation is gradually becoming one of the main power generation methods. However, detecting hot spot defects in photovoltaic power stations is challenging. Therefore, enhancing detection efficiency using information technology has become a crucial aspect. The study presents a defect detection model for PV power stations using the YOLOv3 (You Only Look Once v3) algorithm. The model incorporates coordinate attention module (CAM) and self-attention module (SAM) to improve feature extraction in low-resolution conditions. The Multi objective Sparrow is employed to achieve multiple objectives. It is very contributing in the detection of low-resolution features. It shows that the research method can reduce the loss value to 0.009 after 400 iterations of the loss curve test. The precision-recall (P-R) curve generated by the research method only starts to drop sharply when the Recall value reaches 0.96. The number of parameters generated by the research method is 3.46×106. The detection accuracy of the research method reaches 98.86 % when there are five defective fault types. The results indicate that the proposed research method offers improved detection speed and higher accuracy in identifying hot spot defects in PV power stations. This technology provides valuable support for hot spot defect detection and presents new opportunities for the field.\",\"PeriodicalId\":42196,\"journal\":{\"name\":\"Journal of Measurements in Engineering\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2024-01-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Measurements in Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21595/jme.2023.23418\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Measurements in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21595/jme.2023.23418","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
摘要
随着能源产业的不断发展,光伏发电逐渐成为主要的发电方式之一。然而,检测光伏发电站的热点缺陷是一项挑战。因此,利用信息技术提高检测效率已成为一个重要方面。本研究利用 YOLOv3(You Only Look Once v3)算法提出了一种光伏电站缺陷检测模型。该模型结合了协调注意模块(CAM)和自我注意模块(SAM),以改进低分辨率条件下的特征提取。多目标麻雀(Multi objective Sparrow)用于实现多个目标。它对低分辨率特征的检测非常有帮助。结果表明,经过 400 次迭代损失曲线测试后,该研究方法可将损失值降至 0.009。研究方法生成的精度-召回(P-R)曲线在召回值达到 0.96 时才开始急剧下降。研究方法生成的参数数为 3.46×106。当有五种缺陷故障类型时,研究方法的检测准确率达到 98.86 %。结果表明,所提出的研究方法在识别光伏电站热点缺陷方面具有更快的检测速度和更高的准确度。该技术为热点缺陷检测提供了有价值的支持,并为该领域带来了新的机遇。
YOLOv3-MSSA based hot spot defect detection for photovoltaic power stations
With the continuous development of the energy industry, photovoltaic power generation is gradually becoming one of the main power generation methods. However, detecting hot spot defects in photovoltaic power stations is challenging. Therefore, enhancing detection efficiency using information technology has become a crucial aspect. The study presents a defect detection model for PV power stations using the YOLOv3 (You Only Look Once v3) algorithm. The model incorporates coordinate attention module (CAM) and self-attention module (SAM) to improve feature extraction in low-resolution conditions. The Multi objective Sparrow is employed to achieve multiple objectives. It is very contributing in the detection of low-resolution features. It shows that the research method can reduce the loss value to 0.009 after 400 iterations of the loss curve test. The precision-recall (P-R) curve generated by the research method only starts to drop sharply when the Recall value reaches 0.96. The number of parameters generated by the research method is 3.46×106. The detection accuracy of the research method reaches 98.86 % when there are five defective fault types. The results indicate that the proposed research method offers improved detection speed and higher accuracy in identifying hot spot defects in PV power stations. This technology provides valuable support for hot spot defect detection and presents new opportunities for the field.