High Impedance Fault Detection on Power Distribution Feeder
This paper presents an intelligent algorithm using a Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. High impedance fault (HIF) is abnormal event on electric power distribution feeder which does not draw enough fault current to be dete...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Praise Worthy Prize
2012
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/9098/1/marizan%238.pdf http://eprints.utem.edu.my/id/eprint/9098/ http://connection.ebscohost.com/c/articles/85324325/high-impedance-fault-detection-power-distribution-feeder |
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| Summary: | This paper presents an intelligent algorithm using a Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. High impedance fault (HIF) is abnormal event on electric power distribution feeder which does not draw enough fault current to be detected by conventional protective devices. The algorithm for HIF detection based on the amplitude ratio of second and (3rd, 5th, 7th, 9th, 11th) harmonics to fundamental is presented. Fast Fourier Transformation (FFT) is used to extract the feature of the fault signal and other power system events. The effect of capacitor banks switching, non-linear load current, no-load line switching and other normal event on distribution feeder harmonics is discussed. HIF and other operation event data were obtained by simulation of a 13.8 kV distribution feeder using PSCAD. The results show that the proposed algorithm can distinguish successfully HIFs from other events in distribution power system. |
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