摘要
针对经验模态分解(EMD)的希尔伯特-黄变换(HHT)在电力系统故障信号检测问题,应用存在的模态混叠会导致扰动信号检测失效,为此提出一种基于聚类经验模型分解(EEMD)的故障信号检测的方法。方法通过多次对目标数据加入随机白噪声序列以保证不同区域信号映射的完整性,并且克服了传统EMD分解造成的模态混叠问题,通过EEMD方法提取信号的固有模态函数(IMF),再进行Hilbert变换,利用Hilbert谱对故障暂态和扰动时刻进行检测,通过瞬时频率实现对故障暂态和扰动时刻的准确定位。通过数字仿真分析表明,方法是准确有效的。
According to the mode mixing problem caused by empirical mode decomposition （EMD） , the Hilbert -Huang transform based on Ensemble Empirical Mode Decomposition（EEMD） is introduced into fault signal detection of power system, it can overcome the mode mixing problem in？ a great extent, and ensure the physical meaning of signal components. The signal is firstly decomposed into intrinsic mode function（IMF） by the EEMD method, then Hilbert spectrum is obtained form Hilbert transform. The transient and disturbances can be analyzed and detected accurately through the Hilbert spectrum. Simulation results show that the method can be applied to fault signal detection of power system effectively.
出处
《计算机仿真》
CSCD
北大核心
2010年第3期263-266,共4页
Computer Simulation
