Abstract: A data analytics for recovery, using granular and large-scale failure data from the distribution grid. A key characteristic of the data analytics is its generalizability. The data analysis applies to a large number (169) of failure events rather than one disruption. Further, a data driven recovery scaling law characterizes how recovery speed scales with respect to the severity of weather-induced failures from moderate to extreme. The data analysis also demonstrates the promise of mitigating fundamental limitations of typical recovery through smart grid infrastructure. The data analytics generalizes from one service region in New York to another in Massachusetts. As data used are commonly available to most distribution system operators, the analytics is potentially applicable across the US and parts of the world.

Abstract: Various examples of methods and systems are provided for power distribution analysis and control. Dynamic modeling and resilience of a power distribution network can be utilized for response to disruptions in power distribution induced by event such as, e.g., severe weather. In one implementation, among others, a resilience metric can be determined based at least in part upon failure neighborhoods and recovery neighborhoods and operational information associated with a power distribution network. A vulnerability of the power distribution network can then be identified based at least in part upon the resilience metric and a response can be determined. The failure neighborhoods and recovery neighborhoods can be identified based at least in part upon a topology of the power distribution network and the operational information associated with the power distribution network.

Abstract: Various examples of methods and systems are provided for power distribution analysis and control. Dynamic modeling and resilience of a power distribution network can be utilized for response to disruptions in power distribution induced by event such as, e.g., severe weather. In one implementation, among others, a resilience metric can be determined based at least in part upon failure neighborhoods and recovery neighborhoods and operational information associated with a power distribution network. A vulnerability of the power distribution network can then be identified based at least in part upon the resilience metric and a response can be determined.

Abstract: Embodiments of optical network evaluation systems and methods are disclosed. One system embodiment, among others, comprises a processor configured with logic to provide a cross layer model of disturbance propagation in an optical network based on a combination of a physical layer model and a network layer model, the physical layer mode based on the disturbance propagation having a threshold effect only on nodes along a route followed by the disturbance.

Abstract: Embodiments of optical network evaluation systems and methods are disclosed. One system embodiment, among others, comprises a processor configured with logic to provide a cross layer model of disturbance propagation in an optical network based on a combination of a physical layer model and a network layer model, the physical layer mode based on the disturbance propagation having a threshold effect only on nodes along a route followed by the disturbance.

Abstract: An improved system and method for network fault and anomaly detection is provided based on the statistical behavior of the management information base (MIB) variables. The statistical and temporal information at the variable level is obtained from the sensors associated with the MIB variables. Each sensor performs sequential hypothesis testing based on the Generalized Likelihood Ratio (GLR) test. The ouputs of the individual sensors are combined using a fusion center, which incorporates the interdependencies of the MIB variables. The fusion center provides temporally correlated alarms that are indicative of network problems. The detection scheme relies on traffic measurement and is independent of specific fault descriptions.