杨晓梅;郭林明;肖先勇;张家宁;

• 1:四川大学电气工程学院

摘要(Abstract)：

针对电能质量复合扰动分类的复杂性,提出了基于可调品质因子小波变换(tunableQ-factorwavelettransform,TQWT)和随机森林特征选择算法的电能质量复合扰动分类方法。首先利用TQWT分解扰动信号,以减弱扰动分量间的耦合性,并使用提出的筛选方法选取最优子带并提取时域和频域特征;然后基于随机森林算法计算特征重要性,通过序列前向选择法去掉不相关特征和冗余特征,得到对应每种扰动标签的最优特征集;最后训练生成随机森林多标签分类模型,根据输出标签的组合得到扰动类别。仿真数据实验表明,该方法能够准确高效识别23类扰动,且抗噪能力强,提高了含暂降、含中断的复合扰动的分类准确率。并以实测数据实验证明了方法的可行性。

关键词(KeyWords)： 电能质量;复合扰动分类;可调品质因子小波变换;特征选择;随机森林

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 杨晓梅;郭林明;肖先勇;张家宁;

Email:

DOI: 10.13335/j.1000-3673.pst.2019.1569

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