利用马尔可夫系统动力学方法评估恶劣天气条件下电力系统的可靠性

Q3 Mathematics Stochastics and Quality Control Pub Date : 2024-05-07 DOI:10.1515/eqc-2023-0022
Fatemeh Eskandari-Kataki, M. Owlia, samrad Jafarian-Namin
{"title":"利用马尔可夫系统动力学方法评估恶劣天气条件下电力系统的可靠性","authors":"Fatemeh Eskandari-Kataki, M. Owlia, samrad Jafarian-Namin","doi":"10.1515/eqc-2023-0022","DOIUrl":null,"url":null,"abstract":"\n We aim to extend the Markov System Dynamic method (MSD) to evaluate the reliability of repairable systems in two-state weather (2SW) models. Increasing the number of components in real practices has limited the use of the Markov method, because of increasing the number of equations and the complexity of the problem. There is no such restriction in the MSD method. Thus, it can be applied to complex systems. Theoretically, we indicate that the 2SW model is a type of linear dynamical system. Therefore, we can use MSD to solve it. Through an example, the validity of the proposed method is confirmed in comparison to the Markov method for the 2SW model. Since the MSD method does not use equations, it can be a preferred alternative for calculating the reliability of 2SW models. Accordingly, we apply the MSD method for assessing the reliability of a four-component system.","PeriodicalId":37499,"journal":{"name":"Stochastics and Quality Control","volume":"12 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessment of Reliability of Power Systems Under Adverse Weather Condition Using Markov System Dynamic Method\",\"authors\":\"Fatemeh Eskandari-Kataki, M. Owlia, samrad Jafarian-Namin\",\"doi\":\"10.1515/eqc-2023-0022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n We aim to extend the Markov System Dynamic method (MSD) to evaluate the reliability of repairable systems in two-state weather (2SW) models. Increasing the number of components in real practices has limited the use of the Markov method, because of increasing the number of equations and the complexity of the problem. There is no such restriction in the MSD method. Thus, it can be applied to complex systems. Theoretically, we indicate that the 2SW model is a type of linear dynamical system. Therefore, we can use MSD to solve it. Through an example, the validity of the proposed method is confirmed in comparison to the Markov method for the 2SW model. Since the MSD method does not use equations, it can be a preferred alternative for calculating the reliability of 2SW models. Accordingly, we apply the MSD method for assessing the reliability of a four-component system.\",\"PeriodicalId\":37499,\"journal\":{\"name\":\"Stochastics and Quality Control\",\"volume\":\"12 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stochastics and Quality Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1515/eqc-2023-0022\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stochastics and Quality Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/eqc-2023-0022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0

摘要

我们旨在扩展马尔可夫系统动态法(MSD),以评估双态天气(2SW)模型中可修复系统的可靠性。在实际应用中,组件数量的增加限制了马尔可夫方法的使用,因为这会增加方程的数量和问题的复杂性。MSD 方法则没有这种限制。因此,它可以应用于复杂系统。我们从理论上指出,2SW 模型是一种线性动力系统。因此,我们可以使用 MSD 来求解它。通过一个例子,我们证实了所提出的方法与马尔可夫方法相比对 2SW 模型的有效性。由于 MSD 方法不使用方程,因此可以作为计算 2SW 模型可靠性的首选方法。因此,我们将 MSD 方法用于评估四组件系统的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Assessment of Reliability of Power Systems Under Adverse Weather Condition Using Markov System Dynamic Method
We aim to extend the Markov System Dynamic method (MSD) to evaluate the reliability of repairable systems in two-state weather (2SW) models. Increasing the number of components in real practices has limited the use of the Markov method, because of increasing the number of equations and the complexity of the problem. There is no such restriction in the MSD method. Thus, it can be applied to complex systems. Theoretically, we indicate that the 2SW model is a type of linear dynamical system. Therefore, we can use MSD to solve it. Through an example, the validity of the proposed method is confirmed in comparison to the Markov method for the 2SW model. Since the MSD method does not use equations, it can be a preferred alternative for calculating the reliability of 2SW models. Accordingly, we apply the MSD method for assessing the reliability of a four-component system.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Stochastics and Quality Control
Stochastics and Quality Control Mathematics-Discrete Mathematics and Combinatorics
CiteScore
1.10
自引率
0.00%
发文量
12
期刊最新文献
Population Dependent Two-Sex Branching Process with Random Mating and Overlapping Generations Assessment of Reliability of Power Systems Under Adverse Weather Condition Using Markov System Dynamic Method On Subcritical Markov Branching Processes with a Specified Limiting Conditional Law A Bayesian Extended Exponentially Weighted Moving Average Control Chart Galton–Watson Theta-Processes in a Varying Environment
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1