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Fault Arc Detection Method Based on Improved ShuffleNet V2 Network

delete2025-01-06
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OA
AI
Y
Yuehua Huang
Y
Yun Lu *
L
Liping Fan
K
Kun Xiang
DOI：10.3390/pr13010135delete
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摘要

摘要

En 中文
故障电弧表现出随机性，其电流波形与标准非线性负载运行时的波形高度相似，给传统的串联故障电弧检测方法带来了挑战。本研究提出了一种基于轻量级卷积神经网络模型ShuffleNet V2的改进检测方法。收集并预处理了家庭负载的电流数据，以建立全面的数据库，利用一维卷积和通道注意力机制进行精确分析。实验结果表明，故障电弧检测精度达到97.8%，支持在Jetson Nano嵌入式平台上实现实时检测，且检测周期时间高效，每样本仅需15.65 ms。所提出的方法在准确性和速度上均优于现有方法，为开发先进的故障电弧断路器提供了坚实基础。
Keyword:
convolutional neural network
embedded platform
fault arc detection
current waveform analysis
real-time detection
ShuffleNet V2

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IF:
2.8
论文数:
7.3K
被引数:
3.7W

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