Two-layer coordination architecture HIF detection with µPMU data

The Journal of Engineering

Yiping Luo¹  Xiaojun Wang²  Yongjie Zhang³ 

[1] Electric Power Research Institute, State Grid Shanghai Municipal Electric Power Company , Shanghai 200437 , People'School of Electrical Engineering, Beijing Jiaotong University , Haidian District , Beijing 100044 , People's Republic of China

关键词: HIF fault;    μPMU data;    master station;    support vector machine;    feature extraction;    two-layer coordination architecture HIF detection;    k-means clustering algorithm;    34 nodes distribution network;    principal component analysis;    high-impedance fault detection;    data categories;    detection scheme;    PSCAD-EMTDC;    data-driven method;    silhouette coefficient;    synchronous data;    single-ended microphasor measurement unit;   

DOI  :  10.1049/joe.2018.0258

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

The detection of high-impedance fault (HIF) on distribution network has been one of the most difficult problems. This study presents a data-driven method for HIF detection by using single-ended micro-phasor measurement unit (µPMU). This approach is based on the two-layer coordination architecture, local side with μPMUs and master station for further analysis. At the local side, the μPMUs gather the synchronous data and achieve feature extraction with k-means clustering algorithm and principal component analysis. For determining the amounts of data categories, the authors adopt a method based on silhouette coefficient. Meanwhile, send the characteristics to the master station, then detect the HIF fault through the support vector machine. Finally, the method was tested on a 34 nodes distribution network in PSCAD/EMTDC. The results justify the effectiveness and the proposed detection scheme has >85% accuracy.

【 授权许可】

CC BY