A Scalable Distribution Network Risk Evaluation Framework via Symbolic Dynamics
Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk. This study investigates the use of symbolic dynamics to a...
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| Veröffentlicht in: | PloS one 2015-03, Vol.10 (3), p.e0112940-e0112940 |
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| creator | Yuan, Kai Liu, Jian Liu, Kaipei Tan, Tianyuan |
| description | Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk.
This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors - device, structure, load and special operation - a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method.
Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic. |
| doi_str_mv | 10.1371/journal.pone.0112940 |
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This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors - device, structure, load and special operation - a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method.
Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0112940</identifier><identifier>PMID: 25789859</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Dynamic tests ; Dynamics ; Electric power ; Electric power distribution ; Electrical engineering ; Electricity distribution ; Electricity generation ; Entropy ; Evaluation ; Failure ; Faults ; Feasibility studies ; Methods ; Models, Theoretical ; Monte Carlo simulation ; Networks ; Neural networks ; Nuclear power plants ; Operators ; Researchers ; Risk analysis ; Risk Assessment ; Risk factors ; Time series ; Topology</subject><ispartof>PloS one, 2015-03, Vol.10 (3), p.e0112940-e0112940</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Yuan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Yuan et al 2015 Yuan et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-d5a586898048fa94632f55baf2e75d52bbfdf2ca6ba6b6ab43c83fb69ab434c93</citedby><cites>FETCH-LOGICAL-c692t-d5a586898048fa94632f55baf2e75d52bbfdf2ca6ba6b6ab43c83fb69ab434c93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4366100/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4366100/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25789859$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Aegerter, Christof Markus</contributor><creatorcontrib>Yuan, Kai</creatorcontrib><creatorcontrib>Liu, Jian</creatorcontrib><creatorcontrib>Liu, Kaipei</creatorcontrib><creatorcontrib>Tan, Tianyuan</creatorcontrib><title>A Scalable Distribution Network Risk Evaluation Framework via Symbolic Dynamics</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk.
This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors - device, structure, load and special operation - a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method.
Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic.</description><subject>Algorithms</subject><subject>Dynamic tests</subject><subject>Dynamics</subject><subject>Electric power</subject><subject>Electric power distribution</subject><subject>Electrical engineering</subject><subject>Electricity distribution</subject><subject>Electricity generation</subject><subject>Entropy</subject><subject>Evaluation</subject><subject>Failure</subject><subject>Faults</subject><subject>Feasibility studies</subject><subject>Methods</subject><subject>Models, Theoretical</subject><subject>Monte Carlo simulation</subject><subject>Networks</subject><subject>Neural networks</subject><subject>Nuclear power plants</subject><subject>Operators</subject><subject>Researchers</subject><subject>Risk analysis</subject><subject>Risk Assessment</subject><subject>Risk factors</subject><subject>Time 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Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yuan, Kai</au><au>Liu, Jian</au><au>Liu, Kaipei</au><au>Tan, Tianyuan</au><au>Aegerter, Christof Markus</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Scalable Distribution Network Risk Evaluation Framework via Symbolic Dynamics</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2015-03-19</date><risdate>2015</risdate><volume>10</volume><issue>3</issue><spage>e0112940</spage><epage>e0112940</epage><pages>e0112940-e0112940</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk.
This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors - device, structure, load and special operation - a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method.
Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25789859</pmid><doi>10.1371/journal.pone.0112940</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Dynamic tests Dynamics Electric power Electric power distribution Electrical engineering Electricity distribution Electricity generation Entropy Evaluation Failure Faults Feasibility studies Methods Models, Theoretical Monte Carlo simulation Networks Neural networks Nuclear power plants Operators Researchers Risk analysis Risk Assessment Risk factors Time series Topology |
| title | A Scalable Distribution Network Risk Evaluation Framework via Symbolic Dynamics |
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