Electrical distribution network's failure analysis based on weather conditions

Konal, Mustafa; Oz, Ismail; Uzunoğlu, Cengiz Polat; Kaçar, Fırat

doi:10.1109/iceee2.2018.8391344

Cited by 5 publications

(1 citation statement)

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“…As a consequence, the study of DS failures is a current concern of researches, public agents, and society, in order to enhance the reliability and resilience of DSs (Zio, 2009). The leading causes of failures in DSs (Z lotecka and Sroka, 2018) (Sroka and Z lotecka, 2019) are accidents with animals (Sahai and Pahwa, 2006) or vegetation (Radmer et al, 2002), weather events (Konal et al, 2018) (Pahwa, 2007), load transfer (Rodriguez-Garcia et al, 2019), device failures (Li et al, 2019) and rare events like terrorism, vandalism and cyber-attacks (Ni and Li, 2019). Therefore, several factors may be associated with these failures, from equipment failure to vehicle accidents colliding with network elements, and for a better understanding of them, a separation of these failures into groups (bringing together similar threats or causes) may be interesting.…”

Section: Introductionmentioning

confidence: 99%

Time to Event Analysis for Failure Causes in Electrical Power Distribution Systems

Fogliatto

Desuó

Ribeiro

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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Electricity is a fundamental resource for modern society. However, some threats are faced by Electrical Power Distribution Systems, which are responsible for delivering electricity to end consumers. Analyzing how much time these hazards will threaten these systems, causing failure events, is an essential area of study. Through statistical methods, it is possible to study this behaviour from time until failure, as well as to observe the influence of variables at this time,providing models to predict when a failure event will occur. In this study, Reliability Analysis Regression techniques are used on real data, constructing a model for all failures and for different groups of failures, using non-parametric and parametric methods to estimate the reliability and cumulative hazard curves. An analysis of the failure causes directly linked to weather events, using six weather variables, is also made.

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