Failure rates in distribution networks: Estimation methodology and application

Clavijo-Blanco, J.A.; Macías, José Luis Salcini

doi:10.1016/j.epsr.2020.106398

Cited by 12 publications

(1 citation statement)

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“…In particular, the parameters dsag, λLDI and t Is are usually obtained from statistical analysis of short circuits that occur throughout the distribution network. However, studies reported in [9] and [10] pointed out difficulties to apply this statistical approach. In general, it is very difficult to have enough raw data to get reliable, meaningful, and conclusive results.…”

Section: Introductionmentioning

confidence: 99%

A Novel Methodology to Estimate Probability Density Function of Voltage Sag Duration and Failure Rates on Power Distribution Systems

et al. 2023

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Voltage sags and power interruptions are important power quality problems that affect sensitive customers, mainly because they cause annual massive economical losses to the industrial sector as a result of unexpected production process disruptions. In this sense, to propose corrective and preventive measures and improve the power quality of distribution systems, stochastic methodologies have been proposed in the literature to estimate annual voltage sags and power interruptions. However, these methodologies generally use typical cumulative distribution functions of voltage sag duration (PSgD), which can not reflect the real estate of the network under study. To solve this constraint, this paper proposes a novel methodology to estimate a proper PSgD considering the information of the distribution network (i.e., topology and coordination schemes of the protection system) and the stochastic behaviors of short-circuits that can affect the distribution system. Moreover, the proposed methodology allows estimating permanent failure rates and average repair time considering known or expected values of reliability indicators. The results show that the proposed methodology is capable to adapt from an initial PSgD curve to another one with fidelity to achieve real values of expected annual power interruptions.

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Section: Introductionmentioning

confidence: 99%