摘要
针对零序暂态分量的特点以及现有的信息融合技术在小电流接地故障选线中具有样本数据不均衡、维数灾难和经验风险高的缺陷,分析选线样本的特性,提出基于样本数据处理和ADABOOST法的小电流接地故障选线的新方法。首先,通过经验模态分解和快速傅里叶变换对零序信号进行故障特征提取,然后利用故障特征建立线路故障测度和利用信息增益度建立方法故障测度,进一步通过主成分分析法对故障特征样本进行降维处理以及利用SMOTE采样法处理样本的不均衡性,最后将处理后的数据运用ADABOOST进行综合选线。通过系统模型仿真,验证了主成分分析法和SMOTE采样法对样本数据处理的合理性以及利用ADABOOST选线的有效性,结果表明所提方法应用于选线具有较高的准确率和灵敏度。
According to the characteristics of the zero sequence transient component and the defects that existing information fusion technology had the unbalanced sampled data, dimension disaster and great empirical risk applied to fault line detection for small current grounding system, the characteristics of the sampled data at fault line selection were analyzed and the method based on sampled data processing and ADABOOST was proposed. The fault feature extracted from the zero sequence component by using empirical mode decomposition and fast Fourier transform at first. And then the line fault measure was established by the fault feature and the method fault measure was established by the information gain degree. Thirdly, personal computer assistant was used for the sampled data of the fault feature to reduce dimension and SMOTE treated the unbalanced sampled data. Finally, ADABOOST was applied to global diagnosis by data of dimension reduction. Personal computer assistant and SMOTE to treat the sampled data is reasonable and ADABOOST to global diagnosis is effective through simulation system. The results showed that the method that applied to fault line detection is accurate and sensitive.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2014年第34期6228-6237,共10页
Proceedings of the CSEE
基金
教育部科学技术研究重大项目基金(311021)~~
关键词
小电流接地故障
样本数据
经验模态分解
故障测度
信息增益度
主成分分析法
small current grounding
sampled data
empirical mode decomposition （EMD）
fault measure
information gain degree
personal computer assistant （PCA）
SMOTE
ADABOOST
