叠加暂态电能质量扰动的分形熵分析

暂态电能质量会给敏感用户带来重大损失，因此，在最普遍情况下识别叠加的暂态电能质量扰动非常重要。从图形模式识别的角度出发，基于分形理论对数据进行分段，并构建含扰动波形的能量熵，在此基础上通过最大熵方法辨识特征最为显著的扰动。随后在波形中去掉该扰动，在剩下的波形中辨识新的暂态扰动，由此可逐个辨识叠加波形中所有的暂态电能质量扰动。仿真实验表明，该算法具有较好的适应性及稳健性，可以在噪声环境中识别叠加的小幅度暂态电能质量扰动，识别率也较高。

Fractal entropy analysis of overlapped transient power quality disturbance

Transient power quality may cause severe loss to sensitive customers; therefore, it is very important to recognize overlapped transient power quality under general terms. Starting from graphical recognition and based on fractal theory, waveform was segmented and the entropy of waveform containing disturbance was calculated, then the most prominent disturbance was identified. By subtracting identified disturbance, other disturbances can be identified in remaining waveforms through iterated cycle of analysis....