• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Reliability Engineering & System Safety >Identifying resilient-important elements in interdependent critical infrastructures by sensitivity analysis
【24h】

Identifying resilient-important elements in interdependent critical infrastructures by sensitivity analysis

机译：敏感性分析识别相互依存关键基础设施中的弹性重要元素

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In interdependent critical infrastructures (ICIs), a disruptive event can affect multiple system elements and system resilience is greatly dependent on uncertain factors, related to system protection and restoration strategies. In this paper, we perform sensitivity analysis (SA) supported by importance measures to identify the most relevant system parameters. Since a large number of simulations is required for accurate SA under different failure scenarios, the computational burden associated with the analysis may be impractical. To tackle this computational issue, we resort to two different approaches. In the first one, we replace the long-running dynamic equations with a fast-running Artificial Neural Network (ANN) regression model, optimally trained to approximate the response of the original system dynamic equations. In the second approach, we apply an ensemble based method that aggregates three alternative SA indicators, which allows reducing the number of simulations required by a SA based on only one indicator. The methods are implemented into a case study consisting of interconnected gas and electric power networks. The effectiveness of these two approaches is compared with those obtained by a given data estimation SA approach. The outcomes of the analysis can provide useful insights to the shareholders and decision-makers on how to improve system resilience.
机译：在相互依存的关键基础设施（ICIS）中，破坏性事件可能影响多个系统元素，系统弹性大大依赖于与系统保护和恢复策略相​​关的不确定因素。在本文中，我们对重要性措施进行支持的敏感性分析（SA），以确定最相关的系统参数。由于在不同的故障情景下精确SA需要大量模拟，因此与分析相关的计算负担可能是不切实际的。为了解决这一计算问题，我们求助于两种不同的方法。在第一个中，我们用快速运行的人工神经网络（ANN）回归模型更换长时间运行的动态方程，最佳地训练，以近似原始系统动态方程的响应。在第二种方法中，我们应用了基于合奏的方法，该方法聚合了三个替代SA指示灯，这允许基于一个指示器减少SA所需的模拟数量。该方法被实施为由互连的气体和电力网络组成的案例研究。将这两种方法的有效性与通过给定数据估计SA方法获得的那些进行比较。分析的结果可以对股东和决策者提供有关如何提高系统恢复力的有用的见解。

著录项

  • 来源
    《Reliability Engineering & System Safety》 |2019年第9期|423-434|共12页
  • 作者

    Liu Xing; Ferrario Elisa; Zio Enrico;

  • 作者单位

    Univ Paris Saclay Choir Syst Sci & Energy Challenge Lab Genie Ind Fdn Elect France EDF Cent Supelec 3 Rue Joliot Curie F-91190 Gif Sur Yvette France;

    Pontificia Univ Catolica Chile Sch Engn Ave Vicuna Mackenna 4860 Santiago Chile|Natl Res Ctr Integrated Nat Disaster Management C CONICYT FONDAP 15110017 Ave Vicuna Mackenna 4860 Santiago Chile;

    Politecn Milan Dept Energy Via La Masa 34 I-20156 Milan Italy|PSL Res Univ CRC MINES ParisTech Sophia Antipolis France|Kyung Hee Univ Dept Nucl Engn Coll Engn Seoul South Korea;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Critical infrastructure; System resilience; Importance measure; Sensitivity analysis; Artificial neural networks; Ensemble of methods;

    机译：关键基础设施;系统弹性;重要性措施;敏感性分析;人工神经网络;方法的集合;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号