基于相关性分析的配电网多源数据质量提升方法

A quality improvement method of multi-source data in distribution network based on correlation analysis

智能配电网采集数据来源广、数据质量较差,价值密度低。因此首先对配电网中各类系统采集的数据应用K-means聚类算法进行特征提取,结合局部异常因子(LOF)算法进行异常检测,筛选出异常数据;随后根据数据的多维特征运用相关性分析结合数据特征对异常数据进行修正;最后通过实际工程应用,验证多源数据质量提升方法的数据修正效果。

The data collected by distribution network has the characteristics of wide sources,poor data quality and low value density.Therefore,a strategy for improving the quality of multi-source data in the intelligent distribution network is proposed.Firstly,the K-means clustering algorithm is applied to the data collected by various systems in the distribution network for feature extraction,and the local outlier factor(LOF)algorithm is used for abnormal detection to screen out abnormal data.Then,according to the multi-dimensional characteristics of the data,the abnormal data is corrected by correlation analysis combined with the data characteristics.Finally,the effect of multi-source data quality improvement algorithm is verified by practical engineering application.