The Detection of Series AC Arc Fault in Low-Voltage Distribution System
He Zhipeng1, Li Weilin1, Deng Yunkun2, Zhao Hu1
1. School of Automation Northwestern Polytechnical University Xi'an 710129 China; 2. Electric Power Research Institute of Yunnan Power Grid Co. Ltd Kunming 650217 China
Abstract：Traditional protection switching devices can not open the line that occurs series arc fault in time because the current amplitude when the line is in a series arc fault state is lower than that in a normal state. Recently, the development of machine learning has provided a new idea for detecting an arc fault. However, a large amount of data is needed to train an arc fault detection model, and it is expensive to make a circuit module that uses a machine learning algorithm to recognize arc faults, which causes troubles in promotion and application of arc fault detection device (AFDD). Therefore, this paper introduces an arc fault recognition method based on multi-feature fusion. Firstly, a test device is constructed, and series arc fault experiments are carried out under different load types, accumulating a large number of test data. Secondly, the arcing processes of arc fault are observed within a smaller time scale by a high-speed camera to establish the relationship between the arcing process's physical phenomenon and arc current waveform. Besides, the feature of circuit current waveform is analyzed when the line occurs arc fault. Thirdly, based on current zero and current ripple features of circuit current, three characteristics, current average, harmonic amplitude sum, and wavelet energy entropy, are extracted from the time domain, frequency domain, and signal disorder degree, respectively. To avoid the influences of different load types on characteristic threshold selection, the characteristic ratio between arcing state and normal state serves as a reference for characteristic threshold selection. Finally, the prototype of AFDD is made by a universal microcontroller (STM32), and the accuracy of the arc fault detection method is verified by experiments. Besides, the stability of the method is also verified by arc fault simulations (arc generated by carbonization line) and special loads starting experiments. The photos of arcing processes intuitively reflect the current zero phenomena and instability of arcing. These physical phenomena can be characterized by the current zero feature and current ripple feature of the current waveform. Meanwhile, three characteristics can distinguish arcing and normal states of the line under different load types. In addition, each characteristic value is different under different load types, causing difficulty in selecting the threshold between normal and arcing states. However, the characteristic ratio exists similarity under different load types. The characteristic ratio can serve as a reference for characteristic threshold selection. Through experimental verification, the accuracy of the arc fault detection method is 90%, and the applicability of the method is not affected by modes of arc fault simulation experiment. The following conclusions can be drawn from experimental results: (1) Both the current zero feature and the current ripple feature of arcing processes can be used to identify the arcing state of the line. (2) Three characteristics, current average, harmonic amplitude sum, and wavelet energy entropy are effective for distinguishing arcing and normal states of the line under different load types. (3) The characteristic ratio of arcing and normal states can provide references for characteristics threshold selection, avoiding the effects of different load types on threshold selection.
何志鹏, 李伟林, 邓云坤, 赵虎. 低压交流串联故障电弧辨识方法[J]. 电工技术学报, 2023, 38(10): 2806-2817.
He Zhipeng, Li Weilin, Deng Yunkun, Zhao Hu. The Detection of Series AC Arc Fault in Low-Voltage Distribution System. Transactions of China Electrotechnical Society, 2023, 38(10): 2806-2817.
[1] 苏晶晶, 许志红. 基于EMD和PNN的故障电弧多变量判据诊断方法[J]. 电力自动化设备, 2019, 39(4): 106-113. Su Jingjing, Xu Zhihong.Diagnosis method of multi- variable criterion based on EMD and PNN for arc fault diagnosis[J]. Electric Power Automation Equipment, 2019, 39(4): 106-113. [2] 褚若波, 张认成, 杨凯, 等. 基于多层卷积神经网络的串联电弧故障检测方法[J]. 电网技术, 2020, 44(12): 4792-4798. Chu Ruobo, Zhang Rencheng, Yang Kai, et al.A series arc fault detection method based on multi-layer convolutional neural network[J]. Power System Tech- nology, 2020, 44(12): 4792-4798. [3] 崔芮华, 张振, 佟德栓, 等. 基于改进经验小波变换多特征融合的航空交流串联电弧故障检测[J]. 电工技术学报, 2022, 37(12): 3148-3161. Cui Ruihua, Zhang Zhen, Tong Deshuan, et al.Aviation AC series arc fault detection based on improve empirical wavelet transform multi-feature fusion[J]. Transactions of China Electrotechnical Society, 2022, 37(12): 3148-3161. [4] Le V, Yao Xiu, Miller C, et al.Series DC arc fault detection based on ensemble machine learning[J]. IEEE Transactions on Power Electronics, 2020, 35(8): 7826-7839. [5] Yao Xiu, Le V, Lee I.Unknown input observer-based series DC arc fault detection in DC microgrids[J]. IEEE Transactions on Power Electronics, 2022, 37(4): 4708-4718. [6] 应急管理部消防救援局. 近10年全国居住场所火灾造成11634人遇难[EB/OL]. 北京: 应急管理部消防救援局, 2022, https://www.119.gov.cn/article/ 46rcva01Vzg [2022-02-18]. [7] Park H P, Kim M, Jung J H, et al.Series DC arc fault detection method for PV systems employing differ- ential power processing structure[J]. IEEE Transa- ctions on Power Electronics, 2021, 36(9): 9787-9795. [8] 熊庆, 陈维江, 汲胜昌, 等. 低压直流系统故障电弧特性、检测和定位方法研究进展综述[J]. 中国电机工程学报, 2020, 40(18): 6015-6027. Xiong Qing, Chen Weijiang, Ji Shengchang, et al.Review of research progress on characteristics, detection and localization approaches of fault arc in low voltage DC system[J]. Proceedings of the CSEE, 2020, 40(18): 6015-6027. [9] 刘艳丽, 郭凤仪, 李磊, 等. 一种串联型故障电弧数学模型[J]. 电工技术学报, 2019, 34(14): 2901-2912. Liu Yanli, Guo Fengyi, Li Lei, et al.A kind of series fault arc mathematical model[J]. Transactions of China Electrotechnical Society, 2019, 34(14): 2901-2912. [10] 熊庆, 汲胜昌, 陆伟锋, 等. 低气压下串联直流电弧故障电磁辐射幅值及频率特性[J]. 中国电机工程学报, 2017, 37(4): 1071-1080. Xiong Qing, Ji Shengchang, Lu Weifeng, et al.Amplitude and frequency characteristics of elec- tromagnetic radiation of series DC arc faults under low pressure[J]. Proceedings of the CSEE, 2017, 37(4): 1071-1080. [11] 曲娜, 王建辉, 刘金海, 等. 基于Cassie模型和L3/4范数的串联电弧故障检测方法[J]. 电网技术, 2018, 42(12): 3992-3997. Qu Na, Wang Jianhui, Liu Jinhai, et al.A series arc fault detection method based on Cassie model and L3/4 norm[J]. Power System Technology, 2018, 42(12): 3992-3997. [12] 王尧, 李阳, 葛磊蛟, 等. 基于滑动离散傅里叶变换的串联直流电弧故障识别[J]. 电工技术学报, 2017, 32(19): 118-124. Wang Yao, Li Yang, Ge Leijiao, et al.A series DC arcing fault recognition method based on sliding discrete Fourier transform[J]. Transactions of China Electrotechnical Society, 2017, 32(19): 118-124. [13] 郭凤仪, 高洪鑫, 唐爱霞, 等. 局部二值模式直方图匹配的串联故障电弧检测及选线[J]. 电工技术学报, 2020, 35(8): 1653-1661. Guo Fengyi, Gao Hongxin, Tang Aixia, et al.Series arc fault detection and line selection based on local binary pattern histogram matching[J]. Transactions of China Electrotechnical Society, 2020, 35(8): 1653-1661. [14] Georgijevic N L, Jankovic M V, Srdic S, et al.The detection of series arc fault in photovoltaic systems based on the arc current entropy[J]. IEEE Transa- ctions on Power Electronics, 2016, 31(8): 5917-5930. [15] 唐圣学, 刁旭东, 陈丽, 等. 光伏发电系统直流串联微弱故障电弧检测方法研究[J]. 仪器仪表学报, 2021, 42(3): 150-160. Tang Shengxue, Diao Xudong, Chen Li, et al.Study on detection method of weak series DC fault arc in PV power generation systems[J]. Chinese Journal of Scientific Instrument, 2021, 42(3): 150-160. [16] 吴春华, 徐文新, 李智华, 等. 光伏系统直流侧故障电弧类型辨识及电路保护[J]. 中国电机工程学报, 2017, 37(17): 5028-5036, 5222. Wu Chunhua, Xu Wenxin, Li Zhihua, et al.Arc fault type identification and circuit protection in photo- voltaic system[J]. Proceedings of the CSEE, 2017, 37(17): 5028-5036, 5222. [17] 牟龙华, 王伊健, 蒋伟, 等. 光伏系统直流电弧故障特征及检测方法研究[J]. 中国电机工程学报, 2016, 36(19): 5236-5244, 5405. Mu Longhua, Wang Yijian, Jiang Wei, et al.Study on characteristics and detection method of DC arc fault for photovoltaic system[J]. Proceedings of the CSEE, 2016, 36(19): 5236-5244, 5405. [18] 吴春华, 黄宵宵, 李智华, 等. 光伏系统直流微弱电弧信号检测研究[J]. 中国电机工程学报, 2019, 39(20): 6025-6033, 6183. Wu Chunhua, Huang Xiaoxiao, Li Zhihua, et al.Research on DC weak arc signal detection in photo- voltaic system[J]. Proceedings of the CSEE, 2019, 39(20): 6025-6033, 6183. [19] 崔芮华, 佟德栓. 基于Levene检验的航空交流串联电弧故障检测[J]. 电工技术学报, 2021, 36(14): 3034-3042. Cui Ruihua, Tong Deshuan.Aviation AC series arc fault detection based on Levene test[J]. Transactions of China Electrotechnical Society, 2021, 36(14): 3034-3042. [20] 崔芮华, 李泽, 佟德栓. 基于三维熵距和熵空间的航空电弧故障检测与分类技术[J]. 电工技术学报, 2021, 36(4): 869-880. Cui Ruihua, Li Ze, Tong Deshuan.Arc fault detection and classification based on three-dimensional entropy distance and entropy space in aviation power system[J]. Transactions of China Electrotechnical Society, 2021, 36(4): 869-880. [21] Yao Xiu, Herrera L, Ji Shengchang, et al.Characteri- stic study and time-domain discrete- wavelet-transform based hybrid detection of series DC arc faults[J]. IEEE Transactions on Power Electronics, 2014, 29(6): 3103-3115. [22] 崔芮华, 佟德栓, 李泽. 基于广义S变换的航空串联电弧故障检测[J]. 中国电机工程学报, 2021, 41(23): 8241-8250. Cui Ruihua, Tong Deshuan, Li Ze.Aviation arc fault detection based on generalized S transform[J]. Pro- ceedings of the CSEE, 2021, 41(23): 8241-8250. [23] 孟羽, 陈思磊, 吴子豪, 等. 基于随机共振方法增强光伏直流故障电弧检测特征的研究[J]. 中国电机工程学报, 2022, 42(6): 2396-2407. Meng Yu, Chen Silei, Wu Zihao, et al.Research on feature enhancement of DC arc fault detection in photovoltaic systems based on stochastic resonance[J]. Proceedings of the CSEE, 2022, 42(6): 2396-2407. [24] 国家质量监督检验检疫总局, 中国国家标准化管理委员会. 电弧故障保护电器(AFDD)的一般要求: GB/T 31143-2014[S]. 北京: 中国标准出版社, 2015. [25] 国家质量监督检验检疫总局, 中国国家标准化管理委员会. 电气火灾监控系统第4部分: 故障电弧探测器: GB 14287.4-2014[S]. 北京: 中国标准出版社, 2015.
2023, Vol. 38 
