Despite the attention that the modeling and analyis of infrastructure interdependencies have received over the past two decades, only a small number of methods have been proposed for modeling interdependencies among substations (S/Ss) in electric power system (EPS) networks. For this reason, this paper proposes a novel approach that integrates interpretive structural modeling (ISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC). While the ISM provides managers with a holistic visual view of interdependencies among S/Ss, fuzzy MICMAC analysis categorizes the S/Ss in terms of their driving and dependence powers. This categorization offers an advantageous tool for decision makers to distinguish between S/Ss and their mutual associations, which enables them to identify the critical S/Ss. For demonstration, the approach is applied to a model and analyzes the independencies within real EPS S/Ss.

尽管在过去的二十年中已经受到基础设施相互依赖性的建模和分析的关注，但仅提出了少量方法来对电力系统（EPS）网络中的变电站（S / S）之间的相互依赖性进行建模。因此，本文提出了一种新颖的方法，该方法将解释性结构建模（ISM）与应用于分类的模糊交叉影响矩阵乘法（MICMAC）集成在一起。ISM为管理人员提供了S / S之间相互依赖关系的整体可视化视图，而模糊MICMAC分析则根据S / S的驱动力和依赖性来对S / S进行分类。这种分类为决策者区分S / S及其相互关联提供了一种有利的工具，从而使他们能够识别关键的S / S。为了示范