| 刘艳丽,王 浩,张 帆.电动汽车串联型电弧故障检测方法[J].电子测量与仪器学报,2023,37(6):222-231 |
| 电动汽车串联型电弧故障检测方法 |
| Series arc fault detection method in electric vehicle |
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| DOI： |
| 中文关键词: 电动汽车 轻量化卷积神经网络 改进 Mobilenet 电弧故障检测 电弧故障选线 |
| 英文关键词:electric vehicle lightweight convolutional neural networks improved Mobilenet detection of arc fault selection of the arc
fault line |
| 基金项目:国家自然科学基金(52104160)、2022 年度葫芦岛市科技指导计划重点研发项目(2022JH2 / 07b)资助 |
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| 中文摘要: |
| 电弧故障是引发电气火灾的主要原因之一。 在电动汽车电气系统中,直流串联电弧故障通常发生在接触点松动或线路
连接损坏处,会引起火灾、爆炸等严重事故。 为快速、准确地检测电动汽车串联型电弧故障,搭建了电动汽车故障电弧实验平
台,采集不同工况下干路电流时间序列并建立了样本库。 通过轻量化卷积神经网络,建立了基于改进 Mobilenet 网络的串联故
障电弧检测模型。 通过对比分析学习率、网络层数、样本长度,对模型进行了优化。 该优化模型通过干路电流可实现电动汽车
串联型故障电弧的检测和故障选线,检测准确率达到 96. 39%,论文为电动汽车电气系统电弧故障检测提供了一种可行性方案。 |
| 英文摘要: |
| Arc fault is one of the main causes of electrical fire. In the electric system of electric vehicle, the DC series arc fault usually
occurs at the loose contact point or the line connection damage, which will cause serious accidents such as fire and explosion. In order to
quickly and accurately detect the series electric arc fault of electric vehicles, an experimental platform for electric car fault arc has been
constructed to collect time series data on trunk current under various operating situations and create a sample library. Through the
lightweight convolutional neural network, a series fault arc detection model based on the improved Mobilenet network is established. By
comparing and analyzing the learning rate, network layer number and sample length, the model is optimized. The optimization model can
realize the detection of series fault arc, the selection of fault lines of electric vehicles through the trunk current, and the detection
accuracy reaches 96. 39%. This paper provides a feasible scheme for the arc fault detection of electric vehicle’s electrical system. |
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