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High-efficiency low-power microdefect detection in photovoltaic cells via a field programmable gate array-accelerated dual-flow network

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  • Wang, Haoxuan
  • Chen, Huaian
  • Wang, Ben
  • Jin, Yi
  • Li, Guiqiang
  • Kan, Yan

Abstract

Harvesting solar energy through photovoltaic (PV) power systems plays an important role in achieving the goal of carbon neutrality. However, the early microdefects in PV cells considerably affect the efficiencies of PV power systems. In addition, the growing number of PV power systems require more efficient and economic detection methods to ensure the long-term efficiency of PV power systems. To address this problem, recent state-of-the-art methods have attempted to use a convolutional neural network (CNN) model. However, these methods involve large amounts of parameters and require too many calculations, considerably affecting the efficiency, economy, and flexibility in real applications. In this work, we propose a lightweight dual-flow defect detection network (DDDN) and accelerate it with a field programmable gate array (FPGA) based on the developed dual-flow parallel computing architecture (DPCA). The DDDN can accurately detect early microdefects in PV cells with low calculations and storage costs, while the DPCA optimizes the data access and computation process to improve detection efficiency and reduce power consumption. Benefiting from the designed DDDN and DPCA, the proposed system achieves a high detection accuracy (88.26%), low power consumption (22 W), and competitive detection efficiency, making it more suitable than previous methods for ensuring the long-term efficiency of PV power systems.

Suggested Citation

  • Wang, Haoxuan & Chen, Huaian & Wang, Ben & Jin, Yi & Li, Guiqiang & Kan, Yan, 2022. "High-efficiency low-power microdefect detection in photovoltaic cells via a field programmable gate array-accelerated dual-flow network," Applied Energy, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:appene:v:318:y:2022:i:c:s0306261922005645
    DOI: 10.1016/j.apenergy.2022.119203
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    1. Zhang, Jinxia & Chen, Xinyi & Wei, Haikun & Zhang, Kanjian, 2024. "A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation," Applied Energy, Elsevier, vol. 355(C).

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