摘要
为了实现电力系统暂态扰动信号的精确识别,针对暂态扰动信号的非线性、不规则性和突变性特点,采用局部均值分解(local mean decomposition,LMD)法检测电力系统暂态扰动;并用LMD法分析了电压暂降、电压暂升、电压中断、振荡暂态、脉冲暂态、频率偏移、谐波加电压暂升信号以及某智能变电站采集的实际扰动信号等典型扰动;同时与希尔伯特-黄变换(HHT)法的分析结果进行比较.研究结果表明:用LMD法检测电力系统的暂态扰动信号是有效的,且在定位精度、运算速度方面比HHT法更具优越性.
The transient disturbance signals of power system have characteristics of nonlinear,irregular and mutation. Thus the local mean decomposition（ LMD） algorithm is used for detecting disturbance signals to get higher measurement accuracy. And the typical power quality transient disturbance signals including voltage swell signal,voltage sag signal,voltage interruption signal,transient oscillation signal,transient pulses signal,frequency fluctuation signal,harmonics and voltage swell signals as well as actual disturbance signals occurred in smart substation are analyzed with the LMD algorithm. The simulation results show that LMD algorithm is rather effective in measuring transient disturbance signals of power system and has higher precision and faster computing speed than Hilbert-Huang transform（ HHT） algorithm.
出处
《郑州大学学报（工学版）》
CAS
北大核心
2016年第1期29-33,59,共6页
Journal of Zhengzhou University（Engineering Science）
基金
国家自然科学基金资助项目(61201101)
关键词
LMD法
暂态扰动
端点效应
智能变电站
电能质量检测
HHT
LMD algorithm
transient disturbance signal
end effect
smart substation
power quality detection
HHT
