{"title":"Estimation of System-Level Reliability Functions for the Power Grid Using Probabilistic Modeling and Monte Carlo Simulation","authors":"Ayman Faza","doi":"10.1109/ACCESS.2025.3563427","DOIUrl":null,"url":null,"abstract":"Reliability Modeling for Power Systems is a very challenging task due to the high complexity of the interactions among its various components. In this paper, we develop a simple probabilistic method for modeling power system reliability based on the knowledge of the system size, transmission line capacities, and the failure rate type of the transmission lines in the system. Using Monte Carlo Simulation, we show that the probability distribution of the system failure rate is typically similar in shape to the failure distribution of the transmission lines in the system, with variations stemming from the system size, transmission line capacity, and the type of failure rate. Our method provides a very simple formula for describing system level reliability despite the high complexity of its interconnections, and provides a mechanism to develop similar functions for other complex systems, including different types of networks or critical infrastructures, and can pave the way towards better modeling for the more intelligent future grids.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"71388-71407"},"PeriodicalIF":3.4000,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10973611","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10973611/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Reliability Modeling for Power Systems is a very challenging task due to the high complexity of the interactions among its various components. In this paper, we develop a simple probabilistic method for modeling power system reliability based on the knowledge of the system size, transmission line capacities, and the failure rate type of the transmission lines in the system. Using Monte Carlo Simulation, we show that the probability distribution of the system failure rate is typically similar in shape to the failure distribution of the transmission lines in the system, with variations stemming from the system size, transmission line capacity, and the type of failure rate. Our method provides a very simple formula for describing system level reliability despite the high complexity of its interconnections, and provides a mechanism to develop similar functions for other complex systems, including different types of networks or critical infrastructures, and can pave the way towards better modeling for the more intelligent future grids.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.