基于改进EEMD与混沌振子的配电网故障选线

提出改进的集合经验模态分解(MEEMD)和混沌振子相结合的电网故障微弱信号检测方法。首先,建立神经网络预测模型,通过神经网络对配网各线路零序电流进行短时预测,滤除故障信号中的背景信号;其次,为了检测配网发生单相接地故障后微弱的5次谐波信号,提出结合多尺度排列熵和完备集合经验模态分解(CEEMD)改进的改进的集合经验模态分解算法;处理已经滤除背景信号的故障信号,提取其第一固有模态函数作为混沌振子的输入。混沌振子对和内驱动力信号同频的外策动力信号有较高的敏感性,通过混沌振子输出的相图完成电网故障选线。

Fault line selection in distribution network based on modified EEMD and chaos oscillator

Combining modified ensemble empirical mode decomposition (MEEMD) and chaotic oscillator, a distribution network fault weak signal detection method was proposed. At first, a neural network prediction model was set up, which was used to conduct the short term prediction of the zero sequence currents of the lines in the distribution network and filter out the background signal in the fault signal. Then, in order to detect the weak 5th harmonic fault signal when the single phase grounding fault happens in the distribution network, the MEEMD method combining multi scale permutation entropy and complete ensemble empirical mode decomposition (CEEMD) was proposed to process the fault signal in which the background signal was filtered out. The first intrinsic mode function was extracted and used as the input of the chaotic oscillator. The chaotic oscillator has high sensitivity to the external driving force signal with the same frequency as that of the internal driving force signal. The distribution network fault line selection was completed with the phase diagram outputted by the chaotic oscillator.