基于合成时频分布图的轻量级网络小电流故障电弧检测
Detection of Small Current Fault Arc in Lightweight Network Based on Synthetic Time-frequency Distribution Graphs
冷友伟1
作者信息
- 1. 上海市松江区消防救援支队,上海 201600
- 折叠
摘要
为有效解决故障电弧检测问题,提出一种基于时频分布图的轻量级网络小电流故障电弧检测方法.参照相关标准进行电弧试验,并采集电弧试验数据,通过把电流数据转换成合成时频分布图构造训练集和测试集,输入STF-GhostNet模型识别故障电弧并输出结果.试验结果表明:采用该方法进行故障电弧检测准确率约为94.1%,与传统BP模型、AlexNet相比,准确率明显提高.
Abstract
In order to effectively solve the problem of arc fault detection,a method of low-current fault arc detection in lightweight networks based on time-frequency distribution graphs is proposed.An arc experiment was conducted based on related standard to collect arc experiment data,thereby constructing a training set and test set by convert-ing current data into synthetic time-frequency distribution graphs.STF-GhostNet model was used to identify arc fault and output the result.Experimental results show that the accuracy of arc fault detection using this method is about 94.1%,which is higher than the traditional BP model and AlexNet.
关键词
神经网络/时频分析/电弧故障检测/小电流电弧/STF-GhostNetKey words
neural network/time-frequency analysis/arc fault detection/small current arc/STF-GhostNet引用本文复制引用
出版年
2024
NETL
NSTL
万方数据