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Node ranking for network topology-based cascade models - An Ordered Weighted Averaging operators' approach

机译：基于网络拓扑的级联模型的节点排名-有序加权平均运营商的方法

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The importance of network components under fault conditions has been assessed by different techniques. However, the indicators analyzed in the literature do not consider that some isolated events, such as component outages may trigger other events. For example, in a power system, the outage of transmission equipment (e.g., a power line or a transformer) may cause the redistribution of the power flow and could cause overloading of neighboring elements. These potential cascade effects have been analyzed using several models. Based on different assumptions, these models are able of determining more precisely, the important elements of the network. In this paper, the authors extend a previous non-parametric multicriteria aggregation approach to include the decision-maker preferences. The new approach, based on the use of aggregation rules that relies on parametric Ordered Weighted Averaging (OWA) operators to support the decision-making process, is able to produce a unique ranking of components. The aggregation rule is based on the classic OWA operator that considers decision-maker preferences associated with risk perception, compensation, entropy of information, among other aspects, and the weighted OWA operator (WOWA) for assessing the relative importance of the criteria. To illustrate the approach, the effects of the additional information provided by the decision-maker as well as their variations are evaluated using a real electric power grid under three cascade models. (C) 2016 Elsevier Ltd. All rights reserved.
机译：在故障情况下网络组件的重要性已通过不同的技术进行了评估。但是，文献中分析的指标并未考虑到某些孤立的事件，例如组件故障可能会触发其他事件。例如，在电力系统中，传输设备（例如，电力线或变压器）的停电可能导致功率流的重新分配，并且可能导致相邻元件的过载。这些潜在的级联效应已使用几种模型进行了分析。基于不同的假设，这些模型能够更精确地确定网络的重要元素。在本文中，作者扩展了以前的非参数多准则聚合方法，以包括决策者的偏好。基于使用依赖于参数有序加权平均（OWA）运算符的聚合规则来支持决策过程的新方法，能够产生组件的唯一排名。聚合规则基于经典的OWA运算符，该运算符考虑了与风险感知，补偿，信息熵等相关的决策者偏好，以及用于评估标准相对重要性的加权OWA运算符（WOWA）。为了说明这种方法，在三个级联模型下使用真实的电网评估了决策者提供的附加信息的影响及其变化。 （C）2016 Elsevier Ltd.保留所有权利。

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