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We discuss ways to measure duration in a power transmission system resilience event by modeling outage and re- store processes from utility data. We introduce novel Poisson pro- cess models that describe how resilience events progress and verify that they are typical using extensive outage data collected across North America. Some usual duration metrics show impractically high statistical variability, and we recommend new duration met- rics that perform better. Moreover, the Poisson process models have parameters that can be estimated from observed network data under different weather conditions, and are promising new models of typical resilience events. 

Transmission utilities routinely collect detailed outage data, including resilience events in which outages bunch due to weather. The resilience events and associated metrics can readily be extracted from this historical outage data. Improvements such as asset hardening or investments in restoration lead to reduced outages or faster restoration. In this paper, we show how to rerun the historical events including the effects of the reduced outages or faster restorations to measure the resulting improvement in resilience metrics, thus quantifying the benefits of these investments. This is demonstrated with case studies for specific events (a derecho and a hurricane), and all large events or large thunderstorms in the Midwest USA. Instead of predicting future extreme events with models, which is very challenging, rerunning historical events readily quantifies the benefits of resilience investments if these investments had been made in the past. Rerunning historical events is particularly vivid in making the case for resilience investments as it quantifies the benefits for events actually experienced, rather than for uncertain future events. 

The North American Electric Reliability Corporation (NERC) tracks the restoration of the North American transmission system after events which test the grid resilience and reliability. Quantifying and analyzing these historical events is a foundation for studying and maintaining resilience. After showing that the largest recent events are dominated by extreme weather events, the paper analyzes these events by extracting the restore process for each event and defining, calculating, and discussing various metrics that quantify the restoration. The metrics include a duration metric of time to substantial restoration. In 2021, Hurricane Ida was the largest resilience event in the North American system. A case study of Hurricane Ida analyzes the generator outages and restoration as well as the transmission system outages and restoration. 

We automatically extract resilience events and novel outage and restore processes from standard transmission utility detailed outage data. This new processing is applied to the outage data collected in NERC’s Transmission Availability Data System to introduce and analyze statistics that quantify resilience of the transmission system against extreme weather events. These metrics (such as outage rate and duration, number of elements outaged, rated capacity outaged, restore duration, maximum simultaneous outages, and element-days lost) are calculated for all large weather-related events on the North American transmission system from 2015 to 2020 and then by extreme weather type that caused them such as hurricanes, tornadoes, and winter storms. Finally, we study how performance of the system changed with respect to the resilience metrics by season and year. 

The impact of weather on the power grid has been a focus of multiple studies, and its importance has grown with the number and magnitude of extreme weather events. This paper uses transmission outage and inventory data collected in Transmission Availability Data System (TADS) to identify and analyze weather related transmission events and quantify their impact on the North American Bulk Electric System. The impact of a transmission event is measured by several factors: the number of outages, affected miles and MVA, event duration, and number of groups of simultaneous outages (known as generations of outages). We analyze the largest events from 2015 to 2019, and use an event propagation metric to estimate the probability of small, medium, and large events, and track how these probabilities change from year-to-year.