RESEARCH ON HOT SPOT FAULT IDENTIFICATION METHOD AND STATE QUANTITATIVE EVALUATION OF PHOTOVOLTAIC MODULE BASED ON IMPROVED DEEPLABV3+ NETWORK

Chen Lei; Liu Bo; Sun Kai; Zhao Jian

doi:10.19912/j.0254-0096.tynxb.2023-1963

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RESEARCH ON HOT SPOT FAULT IDENTIFICATION METHOD AND STATE QUANTITATIVE EVALUATION OF PHOTOVOLTAIC MODULE BASED ON IMPROVED DEEPLABV3+ NETWORK

更新时间：2025-10-18

- RESEARCH ON HOT SPOT FAULT IDENTIFICATION METHOD AND STATE QUANTITATIVE EVALUATION OF PHOTOVOLTAIC MODULE BASED ON IMPROVED DEEPLABV3+ NETWORK
- Vol. 46, Issue 3, Pages: 445-453(2025)
- 作者机构：
  
  1. 上海电力大学电气工程学院,上海 200090
  2. 新型电力系统运行与控制全国重点实验室（清华大学）,北京 100084
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2023-1963
  CLC： TM615
- Published Online：07 April 2025，
  
  Published：2025
- 稿件说明：
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陈雷, 刘波, 孙凯, 赵健. 基于改进DeepLabV3+网络的光伏组件热斑故障识别及状态量化评估方法研究[J]. 太阳能学报, 2025, 46(3): 445-453.

Chen Lei, Liu Bo, Sun Kai, et al. RESEARCH ON HOT SPOT FAULT IDENTIFICATION METHOD AND STATE QUANTITATIVE EVALUATION OF PHOTOVOLTAIC MODULE BASED ON IMPROVED DEEPLABV3+ NETWORK[J]. 2025, 46(3): 445-453.
陈雷, 刘波, 孙凯, 赵健. 基于改进DeepLabV3+网络的光伏组件热斑故障识别及状态量化评估方法研究[J]. 太阳能学报, 2025, 46(3): 445-453. DOI： 10.19912/j.0254-0096.tynxb.2023-1963.

Chen Lei, Liu Bo, Sun Kai, et al. RESEARCH ON HOT SPOT FAULT IDENTIFICATION METHOD AND STATE QUANTITATIVE EVALUATION OF PHOTOVOLTAIC MODULE BASED ON IMPROVED DEEPLABV3+ NETWORK[J]. 2025, 46(3): 445-453. DOI： 10.19912/j.0254-0096.tynxb.2023-1963.

摘要

针对光伏组件热斑的精确定位和量化评估

提出一种基于改进DeepLabV3+网络与热斑像素比重模型相融合的光伏组件状态量化评估方法

旨在实现不同热斑状态的量化评估。首先

基于获取的红外热斑图像集

提出在DeepLabV3+主干网络中引入迁移学习网络（EfficientNetB7）来提高热斑形状特征提取能力

进而实现热斑的像素级语义分割;其次

利用Canny算法对分割的热斑图像进行像素级轮廓界定

并利用格林积分计算其像素比重;最后

通过构建状态评估模型实现对光伏组件热斑状态的量化评估。现场试验表明

与常见的语义分割方法（DeepLabV3、FCN、U-net、Linknet、SegNet）相比

该文所提方法在像素准确率和平均交并比方面分别达到98.33%和91.43%

具有较好的热斑分割效果。此外

所提状态评估方法可实现对光伏组件热斑大小的准确量化评估。

Abstract

Currently

for the precise location and quantitative assessment of hot spots of PV modules

a method for quantitative assessment of PV module states based on the fusion of improved DeepLabV3+ network and hot spot pixel specific gravity model is proposed

aiming to realize the quantitative assessment of different hot spot states. Firstly

based on the acquired infrared hot spot image set

this paper proposes to introduce a migration learning network （EfficientNetB7） into the DeepLabV3+ backbone network to improve the feature extraction capability

and then realize the pixel-level semantic segmentation of hot spots. Secondly

the Canny algorithm is utilized to define the pixel-level contours of the segmented hot spot images and calculate their pixel weights using Green’s integral. Finally

a state evaluation model is constructed to quantitatively evaluate the hot spot state of PV modules. Field experiments show that compared with common semantic segmentation methods （DeepLabV3

FCN

U-net

Linknet

SegNet）

the proposed method achieves 98.33% pixel accuracy and 91.43% average intersection and merger ratio

which is good for hot spot segmentation. In addition

the proposed state assessment method can realize accurate quantitative assessment of hot spot size of PV modules.

关键词

Keywords

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