基于可调品质因子小波变换和随机森林特征选择算法的电能质量复合扰动分类Classification of Multiple Power Quality Disturbances Based on TQWT and Random Forest Feature Selection Algorithm
杨晓梅;郭林明;肖先勇;张家宁;
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
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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