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

Energies

Leijiao Ge¹  Chen Zhu²  Yao Wang³  Wanting Liu³  Cuiyan Bai³  Xiaopeng Qian⁴ 

[1] School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China;State Grid Beijing Electric Power Co., Ltd., Fangshan Power Supply Branch, Beijing 102400, China;State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300132, China;Zhejiang High and Low Voltage Electric Equipment Quality Inspection Center, Yueqing 325604, China;

关键词: photovoltaic (PV) system;    DC series arc fault;    power spectrum estimation;    attentional mechanism;    lightweight convolutional neural network;   

DOI  :  10.3390/en15082877

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown