Historical Reliability Evaluation Of Power Distribution Systems Based On Monte Carlo Simulation Method

dc.contributor.authorWadi, Mohammed
dc.contributor.authorShobole, Abdulfetah
dc.contributor.authorWadi, Mohammed
dc.contributor.authorShobole, Abdulfetah Abdela
dc.date.accessioned2023-08-24T11:01:40Z
dc.date.available2023-08-24T11:01:40Z
dc.date.issued2020en_US
dc.departmentMühendislik ve Doğa Bilimleri Fakültesien_US
dc.descriptionBook Title: ACADEMIC STUDIES IN ENGINEERING SCIENCES / editor: Assoc. Prof. Dr. Halil İbrahim Kurt -- Book Chapter: HISTORICAL RELIABILITY EVALUATION OF POWER DISTRIBUTION SYSTEMS BASED ON MONTE CARLO SIMULATION METHOD -- Publisher: Livre de Lyon -- ISBN: 978-2-38236-050-7 -- Date: 2020.en_US
dc.description.abstractIt is crucial to assess power systems' reliability to get the most accurate and appropriate planning, operation, and maintenance decisions. Historical assessment and predictive assessment are widely used methods to assess the reliability of a distribution network. Predictive reliability assessment is classified into two methods, analytical method and simulation method. Besides, analytical methods can be categorized into Markov modeling and network modeling groups (Koster et al., 1978). Simulation methods are considered the most flexible methods. However, it is computationally-burden. Since the historical reliability assessment based on actual data, which is exceptionally crucial in reliability analysis and can be a reference for comparison with other reliability assessment techniques (A. A. Chowdhury, 2005), it is preferable by most utilities rather than a predictive assessment. Thus, the utilities continually need to preserve and collect the data for plans and studies. The actual collected data would improve the system analyses in the future and the system's overall reliability. The significance of reliability studies for utility operators is crucial to determine the parts that are experiencing repeated failures, the areas of the highest amount of energy not supplied, and the areas of weak protection system (Wilson et al., 2006). Many valuable works have been presented on historical reliability assessment (Allan, 1994)(Kim & Singh, 2010)(Baharum et al., 2013)(Abunima et al., 2018). Baharum et al. (Baharum et al., 2013), assessed the historical reliability based on the collected from an electricity distribution company in Baghdad, Iraq, and the two-parameter Weibull function. The statistical measures obtained by this analysis revealed the weakest parts such as transformers and circuit breakers. In (Feng, 2006), the actual data of 13 utilities in Canada are used to perform historical reliability analysis to determine the performance and assess the financial risk for their power distribution networks. Besides, it is established the regulations that are required to specify the reward/penalty levels. In (Wallnerström, 2008), the actual data for one Swedish power distribution network for three years (2004 -2006) are used to perform historical reliability analysis. The obtained result verified that the annual outage cost per customer was more than 500 €. In (A. A. Chowdhury, 2005), the actual data for two Canadian power distribution networks are utilized to improve the performance-based regulation in a deregulated environment to investigate the level of service reliability of these networks.en_US
dc.identifier.endpage181en_US
dc.identifier.orcidMohammed Wadi |0000-0001-8928-3729en_US
dc.identifier.orcidAbdulfetah Abdela Shobole |0000-0002-3180-6504en_US
dc.identifier.startpage151en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12436/5281
dc.institutionauthorWadi, Mohammed
dc.institutionauthorShobole, Abdulfetah
dc.language.isoen
dc.publisherLivre de Lyonen_US
dc.relation.ispartofACADEMIC STUDIES IN ENGINEERING SCIENCESen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleHistorical Reliability Evaluation Of Power Distribution Systems Based On Monte Carlo Simulation Methoden_US
dc.typeBook Part
dspace.entity.typePublication
relation.isAuthorOfPublicatione57e2394-09f4-4128-bdb4-84c708867a9f
relation.isAuthorOfPublication4fd5b879-7f50-4336-a18f-5f6e6c324855
relation.isAuthorOfPublication.latestForDiscoverye57e2394-09f4-4128-bdb4-84c708867a9f

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