Open AccessAn innovative approach to partial discharge measurement and analysis in DC insulation systems during voltage transient and in steady state
Author(s) -
Montanari Gian Carlo,
Ghosh Riddhi
Publication year - 2021
Publication title -
high voltage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.732
H-Index - 20
ISSN - 2397-7264
DOI - 10.1049/hve2.12131
Subject(s) - partial discharge , waveform , transient (computer programming) , voltage , noise (video) , identification (biology) , steady state (chemistry) , insulation system , high voltage , computer science , electrical engineering , engineering , electronic engineering , artificial intelligence , chemistry , botany , image (mathematics) , biology , operating system
Measuring partial discharges in DC insulation systems is an issue due to the lack of a reference relating the voltage waveform to the physics of discharge phenomena. Also, DC is not always steady state, due to voltage and load transients that generate electric field profile variations inside an insulation system, which can affect partial discharge inception likelihood and characteristics. Partial discharge measurement technology must be able to separate discharge pulses from noise and identify the type of sources generating partial discharge, which is related to condition assessment and maintenance. Eventually, measurement and analysis should be automatic and unsupervised, in order to get rid, partially or totally, of expert support. This study addresses a new approach to partial discharge measurements in DC insulation systems, presenting algorithms for separation, recognition and identification, which are effective both in DC steady state and during voltage (and load) transients. These algorithms are automatic and do not require expert support. Various cases of algorithm application on test objects consisting of multilayer polymeric specimens with an internal cavity and defective cable models are presented and discussed. Their effectiveness is proved, at least at a laboratory level, with effective noise separation and identification of discharge typology.