2026-07-10T09:54:02Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/145392026-06-13T06:15:47Zcom_20.500.11984_473com_20.500.11984_14090col_20.500.11984_478

00925njm 22002777a 4500 dc Aranburu, Aritz author Olaizola, Jon author Barrenechea, Maitane author Mulroy, Patrick author Hurtado, Aritz author Gilbert, Ian author 2016 Partial discharge (PD) detection plays a fundamental role in monitoring the health of medium voltage (MV) systems. This paper presents a method for PD detection and source recognition in MV sub-stations based on a combination of signal processing techniques. Firstly, PD detection and signal conditioning is carried out. Then, PDs of different sources are separated and finally classified by means of the extension set theory. The obtained results show a classification effectiveness of 100% on single source PDs and an effectiveness of 72.5% in multisource PDs, where PDs from many sources are captured in the same data set. 2076-1465 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=124986 https://hdl.handle.net/20.500.11984/14539 Classification Extension set theory Partial discharge Pattern recognition PD ODS 7 Energía asequible y no contaminante ODS 8 Trabajo decente y crecimiento económico ODS 9 Industria, innovación e infraestructura ODS 11 Ciudades y comunidades sostenibles Signal processing techniques for on-line partial discharge detection and classification