摘要
高压直流输电(HVDC)系统区内外故障快速判别是实现故障保护的基础。由于直流滤波器、平波电抗器以及线路分布电容等作用下,区内外故障时,直流电流信号传播至保护安装处所经历的物理边界存在较大区别,导致直流电流频带能量分布具有明显差异。该文采用总体经验模态分解法(EEMD)对HVDC系统故障时的直流电流信号进行分解,将信号分解为几个固有模态分量(IMF)之和。然后,计算各模态分量的能量,通过对比分析,找出特征区别,进而定义能量比判据用于区分正常、区内、本侧区外以及对侧区外故障。该方法仅采用直流电流信号,简单易实现,能够满足快速性和选择性要求。最后,通过对HVDC多种工况下的大量故障进行仿真分析,验证了该方法的有效性和鲁棒性。
Fast identification of internal and external faults of high voltage direct current(HVDC)is the basis of protection.Due to the influence of direct current(DC)filter,smoothing reactor and line distribution capacitance,there exists a big difference of physical boundary which the DC current transmits through to the relay location when internal and external faults occur.Therefore,the energy distribution of DC current frequency band is obviously different.In this paper,the DC current signals are decomposed by the ensemble empirical mode decomposition(EEMD)method,and the signals are decomposed into the sum of several intrinsic mode components(IMF).Then,the energy of each modal component is calculated.By comparing and analyzing,the distinguishing features are identified,and then the energy ratio index is defined to distinguish the normal,internal fault,external fault on both sides.This method only uses the DC current signal,which is easy to implement,and can meet the requirements of rapidity and selectivity.Finally,the simulation and analysis of a large number of faults in HVDC under various operating conditions verify the effectiveness and robustness of the proposed method.
作者
李从善
刘天琪
李兴源
和萍
金楠
LI Cong-shan;LIU Tian-qi;LI Xing-yuan;HE Ping;JIN Nan(College of Electrical and Information Engineering, Zhengzhou University of Light Industry Zhengzhou 450002;College of Electrical Engineering and Information, Sichuan University Chengdu 610065)
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2017年第6期871-876,共6页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金(51607158
51507157)
河南省高等学校重点科研项目(16A470016)
河南省重大科技专项(161100211600)
关键词
HVDC
区内外故障
物理边界
频谱特性
HVDC
internal and external faults
physical Boundary
spectral features