access icon openaccess Two-layer coordination architecture HIF detection with µPMU data

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.

Inspec keywords: principal component analysis; feature extraction; support vector machines; pattern clustering; power engineering computing; fault diagnosis; phasor measurement; power distribution faults

Other keywords: 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

Subjects: Knowledge engineering techniques; Distribution networks; Data handling techniques; Other topics in statistics; Other topics in statistics; Power engineering computing; Power system measurement and metering

http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.0258
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content/journals/10.1049/joe.2018.0258
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