a methodology for unified assessment of physical and geographical dependencies of wide area measurement systems in smart grids

Wide area measurement systems (wams) enable both real time monitoring and the control of smart grids by combining digital measurement devices, communication, and control systems. as wams consist of various infrastructures, they imply complex dependencies among their underlying systems and components of different types such as cyber, physical, and geographical dependencies. although several works exist in the literature that studies cyber dependencies, other types of dependencies such as geographical dependencies have not yet been studied. in addition, there is a lack of dependency modeling methodologies that simultaneously capture different dependency types for wams. the main goal of this paper is a simultaneous modeling of the geographical and physical dependencies of wams infrastructures based on simple and well-defined rules. we define a probability density function to quantify these dependencies. such a unified approach may support the design of wams infrastructures that are more resilient inherently to disruptions caused by different kinds of the unwanted events that may affect geographically dependent wams components. through simulations, we demonstrate the applicability of the proposed methodology.

a methodology for unified assessment of physical and geographical dependencies of wide area measurement systems in smart grids

wide area measurement systems (wams) enable both real time monitoring and the control of smart grids by combining digital measurement devices, communication, and control systems. as wams consist of various infrastructures, they imply complex dependencies among their underlying systems and components of different types such as cyber, physical, and geographical dependencies. although several works exist in the literature that studies cyber dependencies, other types of dependencies such as geographical dependencies have not yet been studied. in addition, there is a lack of dependency modeling methodologies that simultaneously capture different dependency types for wams. the main goal of this paper is a simultaneous modeling of the geographical and physical dependencies of wams infrastructures based on simple and well-defined rules. we define a probability density function to quantify these dependencies. such a unified approach may support the design of wams infrastructures that are more resilient inherently to disruptions caused by different kinds of the unwanted events that may affect geographically dependent wams components. through simulations, we demonstrate the applicability of the proposed methodology.