Localising low‐level short‐circuit faults on the windings of power transformers based on low‐frequency response measurement of the transformer windings. Issue 8 (1st September 2015)

• Record Type: Journal Article
• Authors: Mahvi, Mojtaba; Behjat, Vahid
• Is Part Of: IET electric power applications. Volume 9:Issue 8(2015)
• Page Start: 533
• Page End: 539
• Publication Date: 2015-09-01
• Subjects: power transformers -- transformer windings -- short‐circuit currents -- electrical faults -- genetic algorithms -- fault diagnosis; localising low‐level short‐circuit faults -- power transformers -- low‐frequency response measurement -- transformer windings -- winding faults localisation -- winding faults detection -- power system -- sensitive detection -- low‐frequency response measurements -- genetic algorithm -- statistical indicators; Electric power -- Periodicals; Electric power systems -- Periodicals; 621.305
• DOI: 10.1049/iet-epa.2014.0182
• Languages: English
• ISSNs: 1751-8660

Abstract

Abstract : Early detection and localisation of winding faults within the transformer can help make preventive actions and reduce potential damages to the transformer and the power system. This research work presents a new technique for sensitive detection and localisation of shorted turns on the windings of power transformers throughout low‐frequency response measurements of the transformer windings. To this end, using genetic algorithm, the detailed model of the damaged winding by the fault is estimated from the measured low‐frequency response data up to 10 kHz. The fault is localised along the winding by establishing the differences between the transfer functions of the healthy and faulty state of the winding units using statistical indicators. The experimental measurements which were made on a test transformer damaged by a low‐level short‐circuit fault proved that the newly developed method is sufficiently able and sensitive to detect and localise failures of only few shorted turns on the transformer windings.