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Fault diagnosis model for photovoltaic array using a dual-channels convolutional neural network with a feature selection structure

机译：具有特征选择结构的双通道卷积神经网络的光伏阵列故障诊断模型

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摘要

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.
机译：PV系统（PVS）的直流侧光伏（PV）阵列的有效故障诊断算法在PV发电厂的运行效率和安全性中起重要作用。但是对于故障诊断模型，它可能无法诊断光伏阵列（PVA）故障而无需详细且非常精细的故障特征，尤其是在PV的PVS中发生的线路故障（LLF），其在复杂的工作条件下工作，如低辐照度条件和具有故障的LLF。阻抗。为了解决这些挑战，本文提出了一种使用所提出的双通道卷积神经网络（DCCNN）来诊断不同PVA故障的故障诊断方案，该方法能够自动提取特征和重量这些功能进行故障分类。来自电流和电压电气时间序列图（ETSG）的重要和精细特征分别通过DCCNN以双输入方式提取。然后，拟议的特征选择结构（FSS）旨在提高所提出的故障诊断模型容量，用于在各种条件下诊断PVA故障，包括LLF，部分着色条件（PSC）和开路故障（OCF）。与手动设计的功能相比，FSS不仅有助于DCCNN从PVA电流和电压自动提取重要功能，而且还评估提取的功能，以便进一步分类DCCNN。此外，在训练阶段，拟议的惩罚应用于DCCNN以限制FSS，导致其稀疏的重量分布。对基于实验室屋顶电网连接PVS的综合实验。结果表明，与其他算法相比，该方法的优异性能与其他算法相比，它可以从PVA电流和不同PVA故障的电流提取高鉴别特征，这在低辐照度条件下的诊断LLF和具有故障阻抗的LLF也是有效的。

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  • 来源
    《Energy Conversion & Management》 |2021年第11期|114777.1-114777.13|共13页
  • 作者

    Lu Xiaoyang; Lin Peijie; Cheng Shuying; Fang Gengfa; He Xiangjian; Chen Zhicong; Wu Lijun;

  • 作者单位

    Fuzhou Univ Sch Phys & Informat Engn Fuzhou 350116 Peoples R China|Fuzhou Univ Inst Micronano Devices & Solar Cells Fuzhou 350116 Peoples R China|Jiangsu Collaborat Innovat Ctr Photovolta Sci & E Changzhou Jiangsu Peoples R China|Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia;

    Fuzhou Univ Sch Phys & Informat Engn Fuzhou 350116 Peoples R China|Fuzhou Univ Inst Micronano Devices & Solar Cells Fuzhou 350116 Peoples R China|Jiangsu Collaborat Innovat Ctr Photovolta Sci & E Changzhou Jiangsu Peoples R China;

    Fuzhou Univ Sch Phys & Informat Engn Fuzhou 350116 Peoples R China|Fuzhou Univ Inst Micronano Devices & Solar Cells Fuzhou 350116 Peoples R China|Jiangsu Collaborat Innovat Ctr Photovolta Sci & E Changzhou Jiangsu Peoples R China;

    Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia;

    Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia;

    Fuzhou Univ Sch Phys & Informat Engn Fuzhou 350116 Peoples R China|Fuzhou Univ Inst Micronano Devices & Solar Cells Fuzhou 350116 Peoples R China|Jiangsu Collaborat Innovat Ctr Photovolta Sci & E Changzhou Jiangsu Peoples R China;

    Fuzhou Univ Sch Phys & Informat Engn Fuzhou 350116 Peoples R China|Fuzhou Univ Inst Micronano Devices & Solar Cells Fuzhou 350116 Peoples R China|Jiangsu Collaborat Innovat Ctr Photovolta Sci & E Changzhou Jiangsu Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    PV array; Fault diagnosis; Dual-channel convolutional neural network; Feature selection structure; Transient characteristic analysis;

    机译：PV阵列;故障诊断;双通道卷积神经网络;特征选择结构;瞬态特征分析;

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