The effective fault diagnosis algorithm for the DC side photovoltaic (PV) array of a PV system (PVS) plays an important role in the operation efficiency and safety for PV power plants. But for fault diagnosis models it may fail to diagnose PV array (PVA) faults without detailed and quite fine fault features, especially line-line faults (LLF) occurring in the PVS that works under complex working conditions like low irradiance conditions and LLF with fault impedance. To address these challenges, this paper proposes a fault diagnosis scheme to diagnose different PVA faults using a proposed Dual-channel Convolutional Neural Network (DcCNN), which is able to automatically extract features and weight these features for fault classification. The important and fine features from the current and voltage electrical time series graph (ETSG) are extracted respectively by DcCNN in a double input way. Then, a proposed feature selection structure (FSS) is designed to improve the proposed fault diagnosis model capacity for diagnosing PVA faults under various conditions, including LLF, partial shading condition (PSC) and open circuit faults (OCF). Comparing to manually designed features, FSS not only helps DcCNN extract important features from PVA current and voltage automatically but also evaluates extracted features for further classification of DcCNN. Moreover, in the training stage, a proposed penalty is applied on DcCNN to constrain FSS, resulting in its sparse weight distribution. A comprehensive experiment based on a laboratory roof grid connected PVS is conducted. The results demonstrate the superior performance of the proposed approach compared with other algorithms as it can extract high-discriminative features from PVA current and voltage for different PVA faults, which is also effective on diagnosing LLF under low irradiance conditions and LLF with fault impedance.

光伏系统（PVS）直流侧光伏（PV）阵列的有效故障诊断算法对光伏电站的运行效率和安全性起着重要作用。但是对于故障诊断模型，如果没有详细的、非常精细的故障特征，可能无法诊断出光伏阵列（PVA）故障，尤其是在低辐照度条件下工作的光伏阵列中发生的线-线故障（LLF）和具有故障的LLF阻抗。为了解决这些挑战，本文提出了一种故障诊断方案，使用所提出的双通道卷积神经网络 (DcCNN) 来诊断不同的 PVA 故障，该方案能够自动提取特征并对这些特征进行加权以进行故障分类。DcCNN以双输入的方式分别从电流和电压电气时间序列图（ETSG）中提取重要和精细特征。然后，设计了一种提出的特征选择结构（FSS）来提高提出的故障诊断模型在各种条件下诊断 PVA 故障的能力，包括 LLF、部分阴影条件（PSC）和开路故障（OCF）。与手动设计的特征相比，FSS 不仅可以帮助 DcCNN 自动从 PVA 电流和电压中提取重要特征，还可以评估提取的特征以进一步分类 DcCNN。此外，在训练阶段，建议的惩罚应用于 DcCNN 以约束 FSS，导致其稀疏的权重分布。进行了基于实验室屋顶网格连接 PVS 的综合实验。