A DC Series Arc Fault Detection Method Based on a Lightweight Convolutional Neural Network Used in Photovoltaic System

Although photovoltaic (PV) systems play an essential role in distributed generation systems, they also suffer from serious safety concerns due to DC series arc faults. This paper proposes a lightweight convolutional neural network-based method for detecting DC series arc fault in PV systems to solve...

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Bibliographic Details
Published inEnergies (Basel) Vol. 15; no. 8; p. 2877
Main Authors Wang, Yao, Bai, Cuiyan, Qian, Xiaopeng, Liu, Wanting, Zhu, Chen, Ge, Leijiao
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2022
Subjects
Accuracy
Algorithms
attentional mechanism
Computer applications
DC series arc fault
Distributed generation
Electrical codes
Fault detection
Irradiance
Lightweight
lightweight convolutional neural network
Methods
Mutation
Neural networks
photovoltaic (PV) system
Photovoltaic cells
Photovoltaics
power spectrum estimation
Radiation
Solar energy
Wavelet transforms
Working conditions
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Summary:Although photovoltaic (PV) systems play an essential role in distributed generation systems, they also suffer from serious safety concerns due to DC series arc faults. This paper proposes a lightweight convolutional neural network-based method for detecting DC series arc fault in PV systems to solve this issue. An experimental platform according to UL1699B is built, and current data ranging from 3 A to 25 A is collected. Moreover, test conditions, including PV inverter startup and irradiance mutation, are also considered to evaluate the robustness of the proposed method. Before fault detection, the current data is preprocessed with power spectrum estimation. The lightweight convolutional neural network has a lower computational burden for its fewer parameters, which can be ready for embedded microprocessor-based edge applications. Compared to similar lightweight convolutional network models such as Efficientnet-B0, B2, and B3, the Efficientnet-B1 model shows the highest accuracy of 96.16% for arc fault detection. Furthermore, an attention mechanism is combined with the Efficientnet-B1 to make the algorithm more focused on arc features, which can help the algorithm reduce unnecessary computation. The test results show that the detection accuracy of the proposed method can be up to 98.81% under all test conditions, which is higher than that of general networks.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15082877