摘要
根据光伏阵列不同阴影下的伏安(U-I)输出特性,分析光伏发电系统直流串联故障电弧产生机理,通过搭建故障电弧实验平台分析直流故障电弧的信号特性,进而提出一种基于Volterra级数的串联直流故障电弧检测方法。该方法先对电流信号进行相空间重构,然后在重构的相空间内建立Volterra级数模型以提取时域核特征,并采用狮群算法(LSO)优化的核限学习机(KELM)辨识故障电弧。实验结果表明:该方法能准确检测光伏阵列正常运行与阴影状态下的强直流故障电弧,还能检测微弱直流电弧。
According to the U-I output characteristics of PV array under different shades,this paper analyzes the generation mechanism of the DC series fault arc in PV system,and analyzes the characteristics of DC series fault arc signal by building a PV system fault arc test platform,and then introduces a method to detect DC arc fault under shading based on Volterra series. The proposed method firstly reconstructs the phase space of the current signal,and then establishes a Volterra series model in the reconstructed phase space to extract the time-domain kernel features,and uses the Kernel Limit Learning Machine(KELM)optimized by the lion colony algorithm(LSO)to identify fault arc. The experimental results show that the proposed method can accurately detect strong DC fault arcs in normal state and shadow state of PV system,and can also detect weak DC arcs.
作者
唐圣学
王彦丰
乔乃珍
Tang Shengxue;Wang Yanfeng;Qiao Naizhen(State Key Laboratory of Reliability and Intelligence of Electrical Equipment(Hebei University of Technology),Tianjin 300130,China;Hebei Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability(Hebei University of Technology),Tianjin 300130,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2023年第11期31-39,共9页
Acta Energiae Solaris Sinica
基金
河北省自然科学基金(E2021202068)。
