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基于注意力机制的光伏组件热斑检测算法

樊涛 孙涛 刘虎

樊涛, 孙涛, 刘虎等 . 基于注意力机制的光伏组件热斑检测算法[J]. 北京航空航天大学学报, 2022, 48(7): 1304-1313. doi: 10.13700/j.bh.1001-5965.2021.0457
引用本文: 樊涛, 孙涛, 刘虎等 . 基于注意力机制的光伏组件热斑检测算法[J]. 北京航空航天大学学报, 2022, 48(7): 1304-1313. doi: 10.13700/j.bh.1001-5965.2021.0457
FAN Tao, SUN Tao, LIU Huet al. Hot spot detection algorithm of photovoltaic module based on attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(7): 1304-1313. doi: 10.13700/j.bh.1001-5965.2021.0457(in Chinese)
Citation: FAN Tao, SUN Tao, LIU Huet al. Hot spot detection algorithm of photovoltaic module based on attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(7): 1304-1313. doi: 10.13700/j.bh.1001-5965.2021.0457(in Chinese)

基于注意力机制的光伏组件热斑检测算法

doi: 10.13700/j.bh.1001-5965.2021.0457
基金项目: 

国家重点研发计划 2018YFB1500800

国家电网有限公司科技项目 SGTJDK00DYJS2000148

详细信息
    通讯作者:

    孙涛, E-mail: suntao@sgec.sgcc.com.cn

  • 中图分类号: TP391

Hot spot detection algorithm of photovoltaic module based on attention mechanism

Funds: 

National Key R & D Program of China 2018YFB1500800

Technology Project of State Grid Corporation of China SGTJDK00DYJS2000148

More Information
  • 摘要:

    热斑现象是造成光伏组件发电能力下降的重要原因之一,热斑检测是光伏电站运维必不可少的工作。然而分布式光伏电站的规模普遍较小、选址分散、环境复杂多样,使用传统的热斑检测算法需要投入大量的人力资源。基于此,提出了一种基于注意力机制的热斑检测算法HSNet。通过图像分割消除反光影响,结合通道注意力机制,学习通道间的特征信息,增强目标区域的重要性,采用自定义锚点的方法提高检测速度,使用焦点损失激活函数和基于物体先验概率的类别预测方式改善训练目标样本不均衡导致的分类准确性低的问题,通过回归方法获取准确的目标位置。实验表明:设计的目标检测算法在窗体回归精度和分类准确性方面均有明显的优势,边界框平均精度和准确率分别提升了3.18%和2.42%。

     

  • 图 1  无人机拍摄的光伏阵列

    Figure 1.  Photovoltaic arrays photographed by a UAV

    图 2  反光消除网络结构

    Figure 2.  Network structure of reflection elimination

    图 3  热斑检测模型处理框架HSNet

    Figure 3.  Hot spot detection framework of HSNet

    图 4  Residual单元结构

    Figure 4.  Structure of Residual unit

    图 5  SE模块结构

    Figure 5.  Structure of SE block

    图 6  SE-Residual模块结构

    Figure 6.  Structure of SE-Residual block

    图 7  反光消除网络效果

    Figure 7.  Qualitative results of reflection elimination

    图 8  不同模型的P-R曲线比较

    Figure 8.  Comparison of P-R curves of different models

    图 9  不同检测算法的结果比较

    Figure 9.  Comparison of detection results with different detection algorithms

    表  1  符号表示

    Table  1.   Symbolic representation

    符号 含义
    D 训练集
    Ii 第i张图像
    yij 第i张图像的第j个光伏组件的类别
    x 层运算输入
    F 卷积等层计算公式
    Lcls 分类损失函数
    PL(pt) 概率为pt的Focal Loss损失函数
    bboxcls 回归损失函数
    SE SE处理模块
    GT Ground Truth人工标注框
    M 训练模型
    ReLU ReLU激活函数
    θF 初始化网络模型参数
    batch_size 训练图像批量大小
    FM 提取的特征图
    (Ox, Oy), Ow, Oh 输出目标的位置及目标的宽度和高度
    下载: 导出CSV

    表  2  不同标注框的数量

    Table  2.   Number of different kinds of labeled boxes

    种类 图像数量 标注框数量 占比/%
    normal 440 24 846 89.32
    dirty 286 2 736 9.84
    hotspot 28 109 0.39
    hotline 98 125 0.45
    下载: 导出CSV

    表  3  不同模型的结果对比

    Table  3.   Comparison of results with different models

    算法 bbox_mAP/% mAP/% 准确率/%
    HSNet 55.97 70. 95 98.22
    Faster R-CNN 50.64 67.77 95.80
    RetinaNet 20.98 38.17 43.92
    SSD300 66.57 67.96 96.50
    YOLOv3 48.78 59.48 91.32
    下载: 导出CSV

    表  4  消融实验结果

    Table  4.   Results of ablation experiments

    算法 bbox_mAP/% mAP/% 准确率/%
    F-RCNN 50.64 67.77 95.80
    F-RCNN+UN 51.72 68.86 96.92
    F-RCNN+UN+RN 53.68 68.92 96.99
    F-RCNN+UN+SE 55.38 69.18 96.43
    F-RCNN+UN+FL 52.21 69.30 96.95
    F-RCNN+UN+RN+FL 54.23 69.58 97.34
    F-RCNN+UN+SE+FL 55.85 70.50 98.01
    F-RCNN+UN+SE+FL+PRI 55.97 70.95 98.22
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-11
  • 录用日期:  2022-01-03
  • 刊出日期:  2022-01-25

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