There is a risk of fire caused by series arc failure in the operation of photovoltaic (PV) system. Therefore, it is required to discuss a solution for rapid arc fault detection. To address the series arc fault (SAF) detection under different working conditions, a method based on squeeze-and-excitation (SE)-inception multi-input convolutional neural network (MICNN) is proposed. Firstly, normalization and Hankel-singular value decomposition algorithm are used to denoise the current, which effectively avoid the influence of switching frequency on the subsequent diagnostic accuracy. Subsequently, the filtered time-domain signal and the frequency-domain signal after Fourier transform are input into a variant one-dimensional convolutional neural network (1D-CNN) model for training and testing. The proposed model is characterized by transforming the traditional CNN into MICNN, and introducing the inception network with spatial scaling function and the SE network structure with channel attention mechanism. Extensive simulations are performed to evaluate the efficacy with a desirable result of 97.48%, which is superior to traditional methods such as CNN, wavelet decomposition, and mathematical statistics. The proposed method can not only detect arc faults occurring in different locations, but also resist the disturbance of dynamic shading, maximum power point tracking (MPPT), strong wind, etc. In addition, this model achieved satisfactory results in three cases of long line fault, single series and array ageing.

光伏 (PV) 系统运行中存在因串联电弧故障而引起火灾的风险。因此，需要讨论一种快速电弧故障检测的解决方案。针对不同工况下的串联电弧故障（SAF）检测问题，提出了一种基于挤压励磁（SE）-inception多输入卷积神经网络（MICNN）的方法。首先，采用归一化和汉克尔奇异值分解算法对电流进行去噪，有效避免了开关频率对后续诊断准确性的影响。随后，将滤波后的时域信号和傅里叶变换后的频域信号输入到变体一维卷积神经网络（1D-CNN）模型中进行训练和测试。该模型的特点是将传统的CNN转化为MICNN，并引入了具有空间缩放功能的inception网络和具有通道注意力机制的SE网络结构。进行了广泛的模拟以评估效果，达到 97.48% 的理想结果，优于传统方法，如 CNN、小波分解和数理统计。该方法不仅可以检测不同位置的电弧故障，还可以抵抗动态阴影、最大功率点跟踪（MPPT）、强风等的干扰。此外，该模型在三种长线情况下取得了满意的效果。故障、单串和阵列老化。并引入具有空间缩放功能的初始网络和具有通道注意机制的SE网络结构。进行了广泛的模拟以评估效果，达到 97.48% 的理想结果，优于传统方法，如 CNN、小波分解和数理统计。该方法不仅可以检测不同位置的电弧故障，还可以抵抗动态阴影、最大功率点跟踪（MPPT）、强风等的干扰。此外，该模型在三种长线情况下取得了满意的效果。故障、单串和阵列老化。并引入具有空间缩放功能的初始网络和具有通道注意机制的SE网络结构。进行了广泛的模拟以评估效果，达到 97.48% 的理想结果，优于传统方法，如 CNN、小波分解和数理统计。该方法不仅可以检测不同位置的电弧故障，还可以抵抗动态阴影、最大功率点跟踪（MPPT）、强风等的干扰。此外，该模型在三种长线情况下取得了满意的效果。故障、单串和阵列老化。和数理统计。该方法不仅可以检测不同位置的电弧故障，还可以抵抗动态阴影、最大功率点跟踪（MPPT）、强风等的干扰。此外，该模型在三种长线情况下取得了满意的效果。故障、单串和阵列老化。和数理统计。该方法不仅可以检测不同位置的电弧故障，还可以抵抗动态阴影、最大功率点跟踪（MPPT）、强风等的干扰。此外，该模型在三种长线情况下取得了满意的效果。故障、单串和阵列老化。