主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2026, Vol. 45 ›› Issue (4): 90-96.

• • 上一篇    下一篇

基于ArcNet架构的光伏系统直流电弧故障识别算法研究

王玥1,2, 张网1, 崔浩3, 吴淑慧4, 毕晓阳5   

  1. （1. 应急管理部天津消防研究所，天津 300381；2. 天津大学 电气自动化与信息工程学院，天津 300072；3. 天津市消防救援总队，天津 300000；4. 三沙市消防救援支队，海南 三沙 300201；5. 河北工业大学 机械工程学院，天津 300401）
  • 收稿日期:2025-01-17 修回日期:2025-02-23 出版日期:2026-04-15 发布日期:2026-04-15
  • 作者简介:王 玥，应急管理部天津消防研究所助理研究员，主要从事光伏发电系统火灾故障识别与快速诊断算法开发方面的研究工作，天津市南开区卫津南路110号，300381，wangyue@tfri.com.cn。
  • 基金资助:
    应急管理部天津消防研究所基科费项目（2025SJ15）；天津市自然科学基金联合基金（多元投入）（24JCQNJC00300）

Research on DC arc fault detection algorithm for photovoltaic systems based on ArcNet architecture

Wang Yue1,2, Zhang Wang1, Cui Hao3, Wu Shuhui4, Bi Xiaoyang5   

  1. (1. Tianjin Fire Science and Technology Research Institute of MEM, Tianjin 300381, China; 2. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China; 3. Tianjin Fire and Rescue Brigade, Tianjin 300000, China; 4. Sansha Fire and Rescue Division, Sansha Hainan 300201, China; 5. School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China)
  • Received:2025-01-17 Revised:2025-02-23 Online:2026-04-15 Published:2026-04-15

摘要： 直流电弧故障往往导致火灾事故的发生，因此，准确识别电弧故障对保障光伏系统的安全运行至关重要。传统的故障识别方法在处理电弧特征的多变性、特征提取依赖性以及实时性等方面存在局限。为此，本文提出了一种基于ArcNet架构的电弧故障识别算法，结合电流、电压和太阳照度等多模态数据，提供高效、准确的故障识别。通过光伏系统故障测试试验平台采集常规数据与电弧故障数据，提出了电弧故障的关键特征，并在此基础上设计了ArcNet算法。试验结果表明，ArcNet算法在测试集上的整体准确率为98.05%，常规数据的误报率为1.79%，电弧故障数据的未识别率为2.59%。与传统算法相比，ArcNet在识别准确率、误报率及未识别率方面具有明显优势，为光伏系统的安全运行提供了有效的技术支持。

关键词: 光伏系统, 直流电弧, 电弧特征, ArcNet, 电弧故障

Abstract: DC arc faults often result in fire accidents, therefore, accurately identifying arc faults is crucial for ensuring the safe operation of photovoltaic systems. Traditional fault detection methods face limitations in handling the variability of arc characteristics, dependence on feature extraction, and real-time processing. To address these issues, this paper proposes an arc fault detection algorithm based on the ArcNet architecture. This method combines multimodal data, including current, voltage, and solar irradiance, to provide efficient and accurate fault identification. Data from a photovoltaic system fault testing platform, including both normal and arc fault data, was collected. The key characteristics of arc faults were identified, and the ArcNet algorithm was designed based on these findings. Experimental results show that the overall accuracy of the ArcNet algorithm on the test set is 98.05%, with a false alarm rate of 1.79% for normal data and an undetected rate of 2.59% for arc fault data. Compared to traditional algorithms, ArcNet shows significant advantages in terms of accuracy, false alarm rate, and undetected rate, which will provide effective technical support for the safe operation of photovoltaic systems.

Key words: photovoltaic system, DC arc, fault characteristics, ArcNet, arc fault