Estimation of the overload‐related outages in distribution networks considering the random nature of the electrical loads. Issue 8 (1st August 2013)

• Record Type: Journal Article
• Authors: Gilvanejad, Mojtaba; Askarian Abyaneh, Hossein; Mazlumi, Kazem
• Is Part Of: IET generation, transmission & distribution. Volume 7:Issue 8(2013)
• Page Start: 855
• Page End: 865
• Publication Date: 2013-08-01
• Subjects: Monte Carlo methods -- power distribution planning -- power distribution protection -- power distribution reliability; distribution networks -- overload‐related outages -- random nature -- electrical loads -- protection system -- load currents -- network outages -- reliability -- network planners -- overload phenomenon -- stochastic model -- normal cumulative distribution function -- load demand -- Monte Carlo approach -- load magnitude -- IEEE 34‐bus system; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2013.0085
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : The overload phenomenon in distribution networks is a prevalent event, which usually causes the unwanted outages happen in distribution networks. The malfunction of the protection system as a result of high load currents during the overload situations yields network outages which are often unpredictable. The reliability indices in distribution systems are currently calculated neglecting the effect of overload condition. Having a correct estimation of the number of the overload‐related outages in distribution networks enables the network planners and operators to better choice a proper solutions to avoid or decrease the side‐effects of this event. In this study, in order to estimate the outage rates in distribution networks because of the overload phenomenon, a stochastic model for the electrical loads is presented. The normal cumulative distribution function is used to model the magnitude of the load demand at any time of the day. Monte Carlo approach is applied for random selection of the load magnitude. Load model results are compared with real measured data and the validity of the results is investigated. Electrical loads of the IEEE 34‐bus system are modified by incorporating the real load variation curves to provide more realistic results. The results are presented and analysed in detail.