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 hifdetection by using single-ended micro-phasor measuremen...
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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 hifdetection by using single-ended micro-phasor measurement unit (mu PMU). This approach is based on the two-layercoordinationarchitecture, local side with PMUs and master station for further analysis. At the local side, the PMUs 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.
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