基于超声波传感器与孤立森林的光伏系统串联电弧故障诊断方法

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Abstract Aiming at the problem of the lack of historical data on arc faults in most photovoltaic power stations, this paper proposes a photovoltaic system series arc fault diagnosis method based on ultrasonic sensors and isolation forest after collecting arc ultrasonic signals and analyzing their characteristics. Firstly, arc ultrasonic signals are collected and their characteristics and advantages are analyzed. Secondly, the S-transform is used to convert the transient voltage signal of the ultrasonic wave during the occurrence of series arc faults to the time-frequency domain. Then, the Teager energy operator is used to amplify the spectral differences. Subsequently, the time-frequency entropy is used to extract the time-frequency domain features of arc faults. Finally, arc faults are diagnosed based on dynamic thresholds and isolation forest without the need for historical data. Experimental results show that the proposed method can accurately identify series arc faults, with a diagnosis accuracy rate of 97.25%, and has strong anti-interference ability.

Key words： photovoltaic system arc fault diagnosis ultrasonic signal S-transform isolation forest

Received: 09 January 2025

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	HUANG Chenhao
	GAO Wei

Cite this article:

HUANG Chenhao,GAO Wei. Series arc fault diagnosis method for photovoltaic system based on ultrasonic sensor and isolation forest[J]. Electrical Engineering, 2025, 26(5): 10-16.

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https://dqjs.cesmedia.cn/EN/Y2025/V26/I5/10

[1] 涂彦昭, 高伟, 杨耿杰. 一种基于卷积神经网络和长短期记忆网络的光伏系统故障辨识方法[J]. 电气技术, 2022, 23(2): 48-54.
[2] 陈世群, 高伟, 陈孝琪, 等. 一种基于极限学习机和皮尔逊相关系数的光伏阵列故障快速诊断方法[J]. 电气技术, 2021, 22(10): 57-64.
[3] 许杨, 吕雪松, 张科乾, 等. 柔性光伏电池阵列物理参数特性建模[J]. 电气技术, 2023, 24(9): 49-54.
[4] 国家能源局. 2024年可再生能源并网运行情况[EB/OL]. (2025-01-27). [2025-05-13]. https://www.nea.gov.cn/20250221/e10f363cabe3458aaf78ba4558970054/c.html.
[5] 林亮世, 高伟, 杨耿杰. 基于冗余天线阵列和加权质心算法的光伏系统直流电弧故障定位方法[J]. 电气技术, 2024, 25(4): 16-23, 31.
[6] 王尧, 马桐桐, 赵宇初, 等. 基于电磁辐射时延估计的串联光伏直流电弧故障定位方法[J]. 电工技术学报, 2023, 38(8): 2233-2243.
[7] 杨亚宇, 邰能灵, 黄文焘, 等. 船舶中压直流综合电力系统(二): 故障保护管理技术[J]. 电工技术学报, 2024, 39(23): 7341-7364.
[8] LU Qiwei, YE Zeyu, SU Mengmeng, et al.A DC series arc fault detection method using line current and supply voltage[J]. IEEE Access, 2020, 8: 10134-10146.
[9] XIONG Qing, JI Shengchang, LIU Xiaojun, et al.Detecting and localizing series arc fault in photo- voltaic systems based on time and frequency characteristics of capacitor current[J]. Solar Energy, 2018, 170: 788-799.
[10] 王先发, 顾卫祥. 光伏系统直流侧故障电弧的检测与判别[J]. 电气技术, 2019, 20(5): 10-13.
[11] WANG Yao, BAI Cuiyan, QIAN Xiaopeng, et al.A DC series arc fault detection method based on a light- weight convolutional neural network used in photo- voltaic system[J]. Energies, 2022, 15(8): 2877-2877.
[12] 王尧, 张彦风, 牛峰, 等. 光伏直流电弧电磁辐射特性分析与测量方法[J]. 电工技术学报, 2019, 34(14): 2913-2921.
[13] XING Qing, JI Shengchang, ZHU Lingyu, et al.A novel DC arc fault detection method based on electromagnetic radiation signal[J]. IEEE Transactions on Plasma Science, 2017, 45(3): 472-478.
[14] 舒奇航, 刘希喆, 王阳. 基于电弧电流和超声波的串联故障电弧的检测[J]. 电气自动化, 2023, 45(3): 23-25, 29.
[15] 吴振跃, 肖登明. 新型弧光保护装置的设计[J]. 电气技术, 2014, 15(5): 5-11.
[16] VASILE C, IOANA C, DIGULESCU A, et al.Electromagnetic and acoustic bimodality for the detection and localization of electrical arc faults[C]// Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies VIII, Constanta, Romania, 2016.
[17] CATTIN V, PERICHON P, DAHMANI J, et al.Detection of electric arcs in large batteries[C]//World Electric Vehicle Symposium and Exhibition (EVS27), Barcelona, Spain, 2013: 1-9.
[18] 刘云鹏, 来庭煜, 刘嘉硕, 等. 特高压直流换流阀饱和电抗器振动声纹特性与松动程度声纹检测方法[J]. 电工技术学报, 2023, 38(5): 1375-1389.
[19] 高云鹏, 李峰, 陈婧, 等. 基于Teager-Kaiser能量算子Rife-Vincent窗频谱校正的电压闪变测量[J]. 电工技术学报, 2014, 29(6): 248-256.
[20] 韩绍禹, 李新辉. 基于信息熵和节点导纳矩阵的光伏谐波路径初探[J]. 电气技术, 2019, 20(5): 55-58.
[21] 严爱军, 和世潇, 汤健. 基于节点评估与最大类间方差的孤立森林异常值检测[J]. 北京工业大学学报, 2024, 50(10): 1188-1197.
[22] GAO Wei, WAI R J.Series arc fault detection of grid-connected PV system via SVD denoising and IEWT-TWSVM[J]. IEEE Journal of Photovoltaics, 2021, 11(6): 1493-1510.
[23] 邬洲, 张军, 李隆, 等. 基于Kmeans++聚类的光伏系统直流电弧故障检测研究[J]. 太阳能学报, 2024, 45(11): 320-329.