摘要
在电动汽车电路系统中,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、爆炸等事故.为进行电动汽车中的串联型电弧故障研究,首先,搭建了电动汽车电弧故障实验平台,详细分析了不同工况下干路电流波形变化的原因与规律.由于电弧故障检测的高实时性需求,本研究采用了轻量型的卷积操作,即深度可分离卷积,基于深度可分离卷积搭建了电弧故障检测网络,实现了电动汽车电弧故障的检测与故障线路的判别.然后,针对低维度空间中深度可分离卷积特征提取能力受限的问题,本研究对其进行了改进,提出了特征表达能力更加优越的卷积操作:分组可分离卷积.最后,采用了递进式的阶梯结构,从网络浅层至深层,分组可分离卷积内每组的卷积核数量逐渐下降,在保证检测精度的前提下,实现了网络架构的精简与优化.进一步地,对检测模型进行了卷积核尺寸调优,并在结构中添加了轻量化注意力机制.在模型的训练过程中,应用了动态学习率调整策略.通过一系列的模型优化措施,系统性地增强了模型的运行效率与检测精度.模型的检测准确率达到96.76％,同时具有较好的泛化和抗干扰能力.
Abstract
In electric vehicle circuit systems,DC series arc faults frequently occur at loose contact points or damaged line connections,leading to hazards such as fires and explosions. To study series arc faults in electric vehicles,an experimental platform specifically designed for electric vehicle arc faults was established. The causes and patterns of changes in the main current waveforms under various operating conditions were analyzed in detail. Given the stringent real-time requirements for arc fault detection,this study employed a lightweight convolution operation known as depthwise separable convolution to develop an arc fault detection network. This network achieved the detection of arc faults and the identification of fault lines in electric vehicles. To address the limitations of depthwise separable convolution in feature extraction in low-dimensional spaces,this study made improvements and proposed a convolutional operation with superior feature expression:group separable convolution. Ultimately,a progressive ladder structure was implemented,where the number of convolutional kernels within each group of the group separable convolution gradually decreases from the shallow layers to the deeper layers of the network. This approach streamlined and optimized network architecture while ensuring detection accuracy. Further enhancements involved optimizing the convolution kernel size within the detection model and integrating a lightweight attention mechanism into the architecture. A dynamic learning rate adjustment strategy was also applied during the model's training process. Through these optimization measures,both operational efficiency and detection accuracy were systematically improved. The model achieved a detection accuracy rate of 96.76％ and exhibited good generalization and anti-interference capabilities.
维普
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