基于Chirplet稀疏表示的大电流光伏系统微弱故障电弧检测方法  

Weak Arc Fault Detection Method in Large Current Photovoltaic System Based on Chirplet Sparse Representation

作　　者：武涵聪 陈思磊 孟羽 杨淇 李兴文[1] WU Hancong;CHEN Silei;MENG Yu;YANG Qi;LI Xingwen(State Key Laboratory of Electrical Insulation and Power Equipment(Xi’an Jiaotong University),Xi’an 710049,Shaanxi Province,China;School of Electrical Engineering,Xi’an University of Technology,Xi’an 710048,Shaanxi Province,China)

机构地区：[1]西安交通大学电工材料电气绝缘全国重点实验室(西安交通大学),陕西省西安市710049 [2]西安理工大学电气工程学院,陕西省西安市710048

出　　处：《中国电机工程学报》2025年第3期1148-1159,I0029,共13页PROCEEDINGS OF THE CHINESE SOCIETY FOR ELECTRICAL ENGINEERING

基　　金：国家自然科学基金项目(52107168);陕西省科技计划(2022TD-59)。

摘　　要：针对光伏直流系统大电流等级下早期微弱故障电弧特征难以提取的问题,提出基于Chirplet稀疏表示提取早期微弱故障电弧时频信息的方法。首先,搭建含有电阻、电子负载以及逆变器负载的直流故障电弧实验平台,研究Chirplet稀疏表示对大电流等级不同系统拓扑下早期微弱故障电弧时频信息的提取效果;通过多策略改进的哈里斯鹰算法优化Chirplet时频原子字典的构成,以消除逆变器噪声对微弱电弧时频信息的强干扰,实现基于Chirplet稀疏表示的最佳检测特征构建,并通过实验数据验证大电流母线上的特征对于支路小电流生弧和铝电极材料生弧条件下微弱故障电弧检测的普适性;最后,基于K-means无监督分类器构建故障电弧的检测算法,检测结果表明,所提Chirplet稀疏表示特征的波形检出准确率为100%、平均检出时间为0.31 s,相较于现有方法的准确率平均提升48.22%、检出时间平均缩短1.9 s,并基于树莓派平台完成算法的硬件实现。Aiming at the problem that it is difficult to extract the features of early weak arc fault for large current level of photovoltaic(PV)system,this paper proposes a method based on Chirplet sparse representation to extract the time-frequency information of early weak arc fault.First,a DC arc fault experimental platform with resistance,electronic load and inverter load is built.The extraction effect of Chirplet sparse representation on the time-frequency information of early weak arc fault for different system topologies with large current level is studied.Through multi-strategy improved Harris Hawks algorithm optimize Chirplet time-frequency dictionary,the further interference of inverter noise on the time-frequency information of weak arc fault is eliminated.Then the optimal detection feature construction based on Chirplet sparse representation is realized.The universality of the features from the large current DC bus for the early weak arc fault detection when the arc fault occurs on small current branch and aluminum electrode material are verified by the experimental data.Finally,an arc fault detection algorithm is designed based on unsupervised classifier K-means.The detection results show that the detection accuracy of the proposed Chirplet sparse representation feature is 100%,and the detection time is 0.31 s on average.Compared with the existing methods,our proposed method achieves an average improvement in accuracy by 48.22%,reduces the average detection time by 1.9s,and has been successfully implemented on the Raspberry Pi platform.

关 键 词：直流故障电弧 大电流 稀疏表示 Chirplet函数 改进哈里斯鹰优化 

分 类 号：TM501.2[电气工程—电器]