Dust accumulation degree recognition of photovoltaic panel based on improved deep residual network

doi:10.11772/j.issn.1001-9081.2021101715

Abstract

Abstract:

The dust accumulation on photovoltaic panels will reduce the conversion efficiency of photovoltaic power generation， and easily cause damage to the photovoltaic panels at the same time. Therefore， it is of great significance to recognize the dust accumulation of photovoltaic panels intelligently. Aiming at above problems， a dust accumulation degree recognition model of photovoltaic panel based on improved deep residual network was proposed. Firstly， the NeXt Residual Network （ResNeXt）50 was improved by decomposing convolution and fine-tuning down-sampling. Then， the Coordinate Attention （CA） mechanism was fused to embed the location information into channel attention， the channel relationship and long-term dependence were encoded by using the accurate location information， and the feature map was decomposed into two one-dimensional codes by using the two-dimensional global pooling operation， thereby enhencing the representation of the objects of attention. Finally， the cross-entropy loss function was replaced by the Supervised Contrast （SupCon） learning loss function to effectively improve the recognition accuracy. Experimental results show that in the recognition of the dust accumulation of photovoltaic panel at four levels of real photovoltaic power stations， the improved ResNeXt50 model has a recognition accuracy of 90.7%， which is increased by 7.2 percentage points compared with that of the original ResNeXt50. The proposed model can meet the basic requirements of intelligent operation and maintenance of photovoltaic power stations.

Key words: photovoltaic panel, dust accumulation degree recognition, NeXt Residual Network (ResNeXt), attention mechanism, Supervised Contrastive (SupCon) learning loss

摘要：

光伏板积灰会降低光伏发电的转换效率，同时易造成光伏板的损坏；因此，对光伏板的积灰进行智能识别具有重大意义。针对以上问题，提出一种基于改进深度残差网络的光伏板积灰程度识别模型。首先，通过分解卷积和微调下采样，对次代残差网络（ResNeXt）50进行改进；然后，融合坐标注意力（CA）机制，将位置信息嵌入到通道注意力中，通过精确的位置信息对通道关系和长期依赖性进行编码，并通过二维全局池操作将特征图像分解为两个一维编码，以增强关注对象的表示；最后，用监督对比（SupCon）学习损失函数替代交叉熵损失函数，从而有效提高识别准确率。实验结果表明，在真实光伏电站4个等级的光伏板积灰程度识别中，改进后的ResNeXt50的识别准确率为90.7%，与原始ResNeXt50相比提升了7.2个百分点。所提模型可满足光伏电站智能运维的基本要求。

关键词: 光伏板, 积灰程度识别, 次代残差网络, 注意力机制, 监督对比学习损失

CLC Number:

TP391.41

Pengxiang SUN, Li BI, Junjie WANG. Dust accumulation degree recognition of photovoltaic panel based on improved deep residual network[J]. Journal of Computer Applications, 2022, 42(12): 3733-3739.

孙鹏翔, 毕利, 王俊杰. 基于改进深度残差网络的光伏板积灰程度识别[J]. 《计算机应用》唯一官方网站, 2022, 42(12): 3733-3739.

Figures/Tables 19

References 24

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网络层	输出大小	参数设置
Attention	224×224	1×224 x avg pool
		224×1 y avg pool
		1×1×16
		1×1×3
		1×1×3
Conv1	112×112	3×3×3，same
		3×3×3，same
		3×3×64，same
		3×3 max pool，stride 2
Conv2	56×56	1×1×128
		3×3×128，stride 2
		1×1×256
Conv3	28×28	1×1×256
		3×3×256，stride 2
		1×1×512
Conv4	14×14	1×1×512
		3×3×512，stride 2
		1×1×1 024
Conv5	7×7	1×1×1 024
		3×3×1 024，stride 2
		1×1×2 048

网络层	输出大小	参数设置
Attention	224×224	1×224 x avg pool
		224×1 y avg pool
		1×1×16
		1×1×3
		1×1×3
Conv1	112×112	3×3×3，same
		3×3×3，same
		3×3×64，same
		3×3 max pool，stride 2
Conv2	56×56	1×1×128
		3×3×128，stride 2
		1×1×256
Conv3	28×28	1×1×256
		3×3×256，stride 2
		1×1×512
Conv4	14×14	1×1×512
		3×3×512，stride 2
		1×1×1 024
Conv5	7×7	1×1×1 024
		3×3×1 024，stride 2
		1×1×2 048

模型	卷积大小	堆叠数量	准确率/%	参数量/KB
原始Conv1	7×7	1	83.5	9 472
分解卷积Conv1	3×3	3	84.7	1 792

模型	卷积大小	堆叠数量	准确率/%	参数量/KB
原始Conv1	7×7	1	83.5	9 472
分解卷积Conv1	3×3	3	84.7	1 792

模型	下采样位置	准确率/%
原始ResNeXt	首个1×1卷积	83.5
下采样微调ResNeXt	3×3卷积	85.1