We discuss ways to measure duration in a power transmission system resilience event by modeling outage and restore processes from utility data. We introduce novel Poisson process 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 metrics 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.

Modern risk analysis studies of the power system increasingly rely on big datasets, either synthesized, simulated, or real utility data. Particularly in the transmission system, outage events have a strong influence on the reliability, resilience, and security of the overall energy delivery infrastructure. In this paper we analyze historical outage data for transmission system components and discuss the implications of nearby overlapping outages with respect to resilience of the power system. We carry out a risk-based assessment using North American Electric Reliability Corporation (NERC) Transmission Availability Data System (TADS) for the North American bulk power system (BPS). We found that the quantification of nearby unscheduled outage clusters would improve the response times for operators to readjust the system and provide better resilience still under the standard definition of N-1 security. Finally, we propose future steps to investigate the relationship between clusters of outages and their electrical proximity, in order to improve operator actions in the operation horizon.

This paper develops a statistics-based approach for analysis of transmission outage data. The method is applied to the Western Electricity Coordinating Council (WECC) transmission inventory and outage data collected and submitted by member utilities to the North American Electric Reliability Corporation's (NERC) Transmission Availability Data System (TADS). The developed model considers the random nature of Alternating Current (AC) circuit outages and introduces fourteen inventory attributes of the AC circuits as explanatory variables for the outage frequency. The main goal of the paper is to investigate statistical relationships between the number of automatic outages on AC circuits and AC circuit inventory attributes based on the TADS data from 2013-2018, which has proven to be sufficient in obtaining statistically valid results. Concepts associated with the statistical validation of performance indices are discussed with emphasis placed on analysis of distinct types of explanatory variables such as voltage class, mileage, number of terminals, circuits per structure, terrain, elevation, etc.

We discuss ways to measure duration in a power transmission system resilience event by modeling outage and restore processes from utility data. We introduce novel Poisson process 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 metrics 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 up due to weather. The resilience events and their resilience metrics can readily be extracted from this historical outage data. Improvements such as grid hardening or investments in restoration lead to reduced outages or faster restoration. We show how to rerun this history with the effects of the reduced outages or faster restoration included to find 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, the historical rerun readily quantifies the benefits that a resilience investment would have had if it had been made in the past. The historical rerun is particularly vivid in making the case for resilience investments to stakeholders because it quantifies the benefits for events actually experienced by those stakeholders, rather than for future events predicted with uncertainty.