Recoverability modeling of power distribution systems using accelerated life models: Case of power cut due to extreme weather events in Norway

Abstract

Today’s societies rely on electric power distribution systems. Recent weather events have illustrated that the loss of such service can lead to severe consequences for societies and stakeholders. Hence, to reduce the impact of such extreme events on infrastructure systems and limit the associated losses, it is crucial to design infrastructure that can bounce back and recover rapidly after disruptions (i.e., to be resilient). In this regard, it is vital to have knowledge of technical, organizational, internal, and external factors that influence the infrastructure’s recovery process. These factors can broadly be categorized into two different groups, namely, observed and unobserved risk factors. In most studies on resilience, the effect of unobserved covariates is neglected. This may lead to erroneous model selection for analyzing the time to recovery of the disrupted infrastructure, as well as wrong conclusions and thus decisions. The aim of this paper is to identify the risk factors (observed and unobserved) affecting the recovery process of disrupted infrastructure. To this aim, the paper extends the application of accelerated failure time (AFT) models to model the recovery time of disrupted critical infrastructures in the presence of unobserved and observed risk factors. This model can be used to analyze how important these factors are from the viewpoint of resource allocation and decision-making. The application and implications of the model are presented in a case study, from both technical and management perspectives. The case study investigated in this paper applies the developed model, analyzing recovery times from 73 disruption reports on Norwegian electric power distribution grids after four major extreme weather events. The analysis indicates that failures in the regional grid, natural conditions, area affected, and failures in operational control system have a significant impact on the recovery process.