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| 摘要: |
| 针对目前电能质量问题分类常用方法中存在判断过程复杂且计算量大的问题。提出将小波变换和粗糙集理论相结合解决电能质量分类问题的方法。首先．利用小波变换提取扰动信号的特征矢量样本数据：然后。应用模糊C均值聚类的方法将所提取的连续的特征矢量样本数据离散化。得到离散化后的分类知识规则表；最后。采用粗糙集理论中的属性与属性值约简算法，获得判断电能质量分类的核心规则知识。通过对Matlab下的模拟信号数据进行仿真实验。结果表明该方法可直接由信号数据快速准确地判断出信号所属的电能质量类型．简单易行。 |
| 关键词: 粗糙集 小波变换 特征矢量 电能质量分类 数据挖掘 |
| DOI： |
| 分类号:TM711 |
| 基金项目: |
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| Power quality classification based on rough set and wavelet transform |
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XU Xi SHI Min
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| Abstract: |
| A method combining wavelet transform with rough set is proposed to avoid the complex judgment and heavy computation of common power quality classification methods. The continuous eigenvector sample data of disturbance signal is extracted by wavelet transform and then dispersed by fuzzy C-means clustering algorithm to achieve a rule table of classification knowledge. By using the attributes and attribute values reducing algorithm of rough set theory,the core rule knowledge is finally obtained for power quality classification. Experiment is carried out with the simulative signal data generated by Matlab and results show that the proposed method classifies the signal types straight and simply. |
| Key words: rough set wavelet transform eigenvector power quality classification data mining |
