Voltage Sag Stochastic Prediction Using an Electromagnetic Transients Program

Martínez, J.A.; Martin-Arnedo, J.

doi:10.1109/tpwrd.2004.829125

Cited by 77 publications

(31 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method is a procedure of iteratively performing stochastic sampling experiments with a system Fig. 1 Schematic diagram of the system under study www.ietdl.org model [22]. In each experiment, the values of the stochastic variables consisted in the system model were re-sampled from their corresponding distribution functions, based on which, a simulation was conducted to estimate the system performance.…”

Section: Stochastic Simulation Methodsmentioning

confidence: 99%

Stochastic assessment of voltage dips caused by transformer energisation

Peng¹,

Li²,

Wang³

et al. 2013

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

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.

show abstract

Section: Stochastic Simulation Methodsmentioning

confidence: 99%

Stochastic assessment of voltage dips caused by transformer energisation

Peng¹,

Li²,

Wang³

et al. 2013

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

show abstract

“…• Transformers: Saturable transformer models are adequate to assess voltage sag events during transformer's fault and energizing. But, short circuit linear models represent better results for other sources of voltage sag [15].…”

Section: Modeling Of Distribution Network Elementsmentioning

confidence: 99%

Effect of protection device coordination on voltage sag characteristics of distribution networks

Sedighnejad

Jalilian

2010

ISA Transactions

View full text Add to dashboard Cite

“…Usually, the PDFs associated with voltage sags are generated using the Monte Carlo simulation (MCS) [4]- [6]. The main advantage of this technique is its capability to model complex characteristics of the system that cannot be modeled by analytical approaches.…”

Section: Nomenclaturementioning

confidence: 99%

A New Methodology for Probabilistic Short-Circuit Evaluation With Applications in Power Quality Analysis

Bordalo

Rodrigues

Silva

2006

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

The main aim of this paper is to propose a probabilistic short-circuit approach to generate the probability distributions of the System Average RMS Variation Index (SARFI) index. The proposed methodology is based on the combination of the admittance summation method in phase coordinates with the Monte Carlo method. This new approach has been tested in a feeder belonging to an electricity distribution company in the Northeast of Brazil to generate the probability distributions of the SARFI index.The tests results demonstrated that the proposed model is a powerful tool to stochastic prediction of voltage sags.Index Terms-Monte Carlo methods, power distribution planning, power quality, short-circuit currents.

show abstract

Voltage Sag Stochastic Prediction Using an Electromagnetic Transients Program

Cited by 77 publications

References 14 publications

Stochastic assessment of voltage dips caused by transformer energisation

Stochastic assessment of voltage dips caused by transformer energisation

Effect of protection device coordination on voltage sag characteristics of distribution networks

A New Methodology for Probabilistic Short-Circuit Evaluation With Applications in Power Quality Analysis

Contact Info

Product

Resources

About