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Article: Mitigating the Risk of Cascading Blackouts: A Data Inference Based Maintenance Method

TitleMitigating the Risk of Cascading Blackouts: A Data Inference Based Maintenance Method
Authors
KeywordsData inference
Maintenance strategy
Risk of cascading blackouts
Issue Date2018
PublisherInstitute of Electrical and Electronics Engineers (IEEE): OAJ. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639
Citation
IEEE Access, 2018, v. 6, p. 39197-39207 How to Cite?
AbstractThe risk of cascading blackouts (RCB) is of great significance in practice because cascading outages can have catastrophic consequences. As there is a positive relationship between the probability of cascading blackouts and that of component failures, an effective way to mitigate the RCB is to perform maintenance. However, this approach is of limited value when considering extremely complicated cascading outages, such as those in particularly large systems. In this paper, we propose a methodology to efficiently identify the most influential component(s) for mitigating the RCB in a large-power system based on inference from the simulation data. First, we establish a data-based analytic relationship between the adopted maintenance strategies and the estimated RCB. Then, we formulate the component maintenance decisionmaking problem as a nonlinear 0-1 programming problem. We then quantify the credibility of the estimated RCB and develop an adaptive method to determine the minimum required number of simulations, which is a crucial parameter in the optimization model. Finally, we devise two heuristic algorithms to efficiently identify approximately optimal solutions. The proposed method is then validated by way of numerical experiments based on IEEE standard systems and an actual provincial system.
Persistent Identifierhttp://hdl.handle.net/10722/261126
ISSN
2017 Impact Factor: 3.557
2015 SCImago Journal Rankings: 0.947
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, F-
dc.contributor.authorGuo, J-
dc.contributor.authorZhang, X-
dc.contributor.authorHou, Y-
dc.contributor.authorMei, S-
dc.date.accessioned2018-09-14T08:52:55Z-
dc.date.available2018-09-14T08:52:55Z-
dc.date.issued2018-
dc.identifier.citationIEEE Access, 2018, v. 6, p. 39197-39207-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10722/261126-
dc.description.abstractThe risk of cascading blackouts (RCB) is of great significance in practice because cascading outages can have catastrophic consequences. As there is a positive relationship between the probability of cascading blackouts and that of component failures, an effective way to mitigate the RCB is to perform maintenance. However, this approach is of limited value when considering extremely complicated cascading outages, such as those in particularly large systems. In this paper, we propose a methodology to efficiently identify the most influential component(s) for mitigating the RCB in a large-power system based on inference from the simulation data. First, we establish a data-based analytic relationship between the adopted maintenance strategies and the estimated RCB. Then, we formulate the component maintenance decisionmaking problem as a nonlinear 0-1 programming problem. We then quantify the credibility of the estimated RCB and develop an adaptive method to determine the minimum required number of simulations, which is a crucial parameter in the optimization model. Finally, we devise two heuristic algorithms to efficiently identify approximately optimal solutions. The proposed method is then validated by way of numerical experiments based on IEEE standard systems and an actual provincial system.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE): OAJ. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6287639-
dc.relation.ispartofIEEE Access-
dc.rights© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.-
dc.subjectData inference-
dc.subjectMaintenance strategy-
dc.subjectRisk of cascading blackouts-
dc.titleMitigating the Risk of Cascading Blackouts: A Data Inference Based Maintenance Method-
dc.typeArticle-
dc.identifier.emailHou, Y: yhhou@hku.hk-
dc.identifier.authorityHou, Y=rp00069-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ACCESS.2018.2855153-
dc.identifier.scopuseid_2-s2.0-85050209567-
dc.identifier.hkuros291884-
dc.identifier.volume6-
dc.identifier.spage39197-
dc.identifier.epage39207-
dc.identifier.isiWOS:000441018900001-
dc.publisher.placeUnited States-

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