PHOTOVOLTAIC MODULE DEFECT IDENTIFICATION BASED ON MULIT-SCALE CONVOLUTION NEURAL NETWORK

Zhou Ying; Ye Hong; Wang Tong; Chang Mingxin

doi:10.19912/j.0254-0096.tynxb.2020-0408

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (2) : 211-216. DOI: 10.19912/j.0254-0096.tynxb.2020-0408

PHOTOVOLTAIC MODULE DEFECT IDENTIFICATION BASED ON MULIT-SCALE CONVOLUTION NEURAL NETWORK

Zhou Ying^1,2, Ye Hong¹, Wang Tong¹, Chang Mingxin¹

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Abstract

In order to improve the accuracy and efficiency of photovoltaic module defect classification, an Improved Multi-scale Convolutional Neural Network （IMCNN） model is proposed. According to the defect characteristics of photovoltaic module, the algorithm constructs the end-to-end Convolutional Neural Network model of three channels with different scales. At the same time, in order to optimize the network structure, SE-Inception modules are introduced in all three channels. Firstly, multi-channel convolution is used to extract features with different fineness. Then these features are fused to obtain the enhanced expression of high-level features. Finally, the defect classification of photovoltaic module is realized. Due to the small number of defective samples of photovoltaic module, a part of the image samples is generated using the Generative Adversarial Networks to effectively expanding the dataset. The results show that the Kappa coefficient of the proposed algorithm is higher, and the identification accuracy and efficiency are significantly improved.

Key words

photovoltaic modules / defect identification / picture processing / convolutional neural networks / generative adversarial networks

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Zhou Ying, Ye Hong, Wang Tong, Chang Mingxin. PHOTOVOLTAIC MODULE DEFECT IDENTIFICATION BASED ON MULIT-SCALE CONVOLUTION NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2022, 43(2): 211-216 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0408

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