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基于深度学习的光伏组件缺陷红外图像智能识别系统

Research on Measurement Techniques for Integrated Optical Waveguide-Based Electric Field Sensors

作者 Feng Zhou · Zhenghao Chai · Yinglong Diao · Zhaozhi Long
期刊 IEEE Access
出版日期 2025年1月
技术分类 电动汽车驱动
相关度评分 ★★★★ 4.0 / 5.0
关键词 光电电场传感器 三维电场测量系统 宽带电场测量 测量精度 全向电场检测
语言:

中文摘要

光伏组件红外检测可发现隐性缺陷,但人工判读效率低。本文提出基于深度卷积神经网络的红外图像缺陷识别系统,通过大规模数据集训练实现热斑、隐裂、旁路二极管失效等缺陷的自动分类。

English Abstract

The growing demand for high-precision sensing technologies in power systems has positioned the development of novel electric field measurement tools as a critical metrological challenge. This study presents a broadband photoelectric electric field sensor based on the Mach-Zehnder interference phenomenon in asymmetric straight wave-guides. Furthermore, a three-dimensional electric field measurement system was developed through systematic investigation of multidimensional field measurement methodologies. Experimental results demonstrate that the proposed device achieves broadband electric field measurement with linearity up to 0.9996 and power-frequency measurement accuracy exceeding 97%. The system demonstrates concurrent measurement capability for both lightning impulse waveforms and operational power frequency electric fields. The synthesized three-dimensional measurement error remains below 4%, enabling precise omnidirectional electric field detection.
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SunView 深度解读

该红外智能检测技术可应用于阳光电源光伏电站运维巡检。通过无人机红外成像和AI自动识别,提升缺陷检测效率和准确率,降低人工巡检成本,为大型地面光伏电站提供高效的智能运维工具。