基于改进不完全S变换与决策树的实时电能质量扰动分类

A real time power quality disturbance classification based on improved incomplete S-transform and decision tree

提出了基于一种改进不完全S变换(Improved Incomplete S-transform)与决策树的实时电能质量分类方法,其主要关注分类准确性与运算时间。根据主要频点所在频段,采用独立的高斯窗来处理不同的信号成分以减小海森堡测不准(Heisenberg's uncertainty)带来的时频分辨率限制,增强了对扰动的抗噪能力同时减小了响应时间。然后通过动态测度对改进不完全S变换结果进行特征提取。通过5个区分度强的特征量,采用优化决策树对电能质量扰动进行分类。通过一个基于DSP-FPGA的硬件平台来验证该方法。仿真与实验证明了该方法具有良好的应用前景。

This paper proposes a real time power quality classification based on improved incomplete S-transform and decision tree, which mainly focuses on classification accuracy and computing time. In order to reduce the restriction of Heisenberg＇s uncertainty, different signal components are windowed by different Gauss windows according to the signal components frequency in the spectral, which reduces the response time and enhance the tolerance of noises. The feature extraction is implemented by using dynamics to the result of the improved incomplete S-transform. Finally, an optimal decision tree is constructed to classify the power quality disturbances through five distinctive features. A hardware based on DSP-FPGA is used to test the proposed method. Both simulations and experiments verify the practicability of the method.