基于柔性形态滤波和信息熵的电能质量扰动定位分析

针对电力系统中电能质量扰动信号常因周期性变化和采样过程而存在各种噪声的情况，提出一种基于柔性形态滤波和信息熵的扰动定位方法。柔性形态滤波器是在标准数学形态学滤波器基础上发展起来的，具有更强的抗噪声性能和鲁棒性。信息熵可以用来表征信号的无序性测量指标，已经被广泛应用于检测信号的突变情况。首先，将电能质量扰动信号进行柔性形态滤波处理，再将去噪声后的信号进行形态梯度变换，放大扰动信号的突变特征，最后求取信号的信息熵，根据信号突变出信息熵的不同得到扰动信号的准确定位结果。仿真结果表明，该方法对多种暂态扰动信号能准确定位，抗噪声干扰强，具有很高的可行性和有效性。

Location analysis of power quality transient disturbance based on flexible morphological filtering and information entropy

Considering the fact that power quality disturbance signals are mixed up with noise for periodic variation and sampling process, a new method based on flexible morphological filtering and information entropy is proposed. Flexible morphological filter is developed on the basis of standard mathematical morphology filter, which has stronger anti-noise performance and robustness. Information entropy can be used to characterize the disorder of the signal, which has been widely used to detect the mutation of the signal. First, the power quality disturbance signals are processed by the flexible morphological filter, then the signal is transformed into the morphological gradient, and the signal is amplified. Finally, the information entropy of the signal is obtained. Simulation results show that the proposed method can accurately locate multiple transient disturbance signals, and has high feasibility and effectiveness.