Stochastic assessment of voltage dips caused by transformer energisation. Issue 12 (1st December 2013)

• Record Type: Journal Article
• Authors: Peng, Jinsheng; Li, Haiyu; Wang, Zhongdong; Ghassemi, Foroozan; Jarman, Paul
• Is Part Of: IET generation, transmission & distribution. Volume 7:Issue 12(2013)
• Page Start: 1383
• Page End: 1390
• Publication Date: 2013-12-01
• Subjects: circuit breakers -- Monte Carlo methods -- power transformers -- probability -- stochastic processes; stochastic assessment -- voltage dips -- transformer energisation -- power transformers -- circuit breaker closing time -- transformer core residual flux -- core saturation characteristic -- network conditions -- stochastic nature -- Monte Carlo simulation -- field measurements -- dip frequency pattern -- residual flux distribution -- closing offset time distribution -- deterministic assessment approach -- energisation condition -- voltage 400 kV; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2013.0091
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Energisation of large power transformers may cause significant voltage dips, of which the severity largely depends on a number of parameters, including circuit breaker closing time, transformer core residual flux and core saturation characteristic, and network conditions. Since most of the parameters are of stochastic nature, Monte Carlo simulation was conducted in this study to stochastically assess the voltage dips caused by transformer energisation in a 400 kV grid, using a network model developed and validated against field measurements. A dip frequency pattern was identified over 1000 stochastic runs and it was found to be sensitive to residual flux distribution but insensitive to closing offset time distribution. The probability of reaching the worst case dip magnitude (estimated under the commonly agreed worst energisation condition) was found to be lower than 0.5%; about 80% of the dips are likely to be with magnitudes lower than 0.6 pu of the worst case. Nevertheless, there are dips with magnitudes exceeding the worst case dip magnitude, indicating the inadequacy of deterministic assessment approach by using the commonly agreed worst energisation condition.