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

• Innovative implementation to identify critical components in topology based cascade models.
• Innovative approach based on the use of an aggregation rule that relies on Ordered Weighted Averaging operators.
• Support the decision-making process to produce a unique ranking of components.
• Quantifying system performance when uncertain and possibly conflicting information is obtained.

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.