DEFECT IDENTIFICATION METHOD FOR GCSE-Densenet PHOTOVOLTAIC MODULE BASED ON LS-DCGAN

Wang Yan; Shen Zongwang; Zhao Hongshan; Li Wei

doi:10.19912/j.0254-0096.tynxb.2022-0890

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (10) : 165-172. DOI: 10.19912/j.0254-0096.tynxb.2022-0890

DEFECT IDENTIFICATION METHOD FOR GCSE-Densenet PHOTOVOLTAIC MODULE BASED ON LS-DCGAN

Wang Yan, Shen Zongwang, Zhao Hongshan, Li Wei

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Abstract

Aiming at the problem of unbalanced of photovoltaic module samples and low defect identification accuracy, a defect identification method of GCSE-DenseNet photovoltaic modules based on LS-DCGAN is proposed. Firstly, aiming at the imbalance problem of PV module samples, a least squares deep convolutional generative adversarial network （LS-DCGAN） is constructed to enhance the sample data to expand the dataset. Secondly, an improved GCSE-DenseNet network model is proposed by introducing group convolution and attention mechanism on the basis of the traditional DenseNet network. The improved model still adopts the dense connection mechanism to realize feature reuse and prevent the gradient from disappearing. Simultaneously, group convolution is employed to optimize the dense module structure and reduce the number of model parameters. The attention mechanism is introduced to strengthen effective features and weaken invalid features to enhance model feature learning ability. Finally, the effectiveness of the proposed model is verified through experiments. Experimental results show that the improved GCSE-DenseNet network model can effectively improve the defect recognition accuracy of photovoltaic modules.

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photovoltaic modules / deep learning / image classification / data enhancement / group convolution

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Wang Yan, Shen Zongwang, Zhao Hongshan, Li Wei. DEFECT IDENTIFICATION METHOD FOR GCSE-Densenet PHOTOVOLTAIC MODULE BASED ON LS-DCGAN[J]. Acta Energiae Solaris Sinica. 2023, 44(10): 165-172 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0890

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