Fragility Curves for Assessing the Resilience of Electricity Networks Constructed from an Extensive Fault Database

Abstract

Robust infrastructure networks are vital to ensure community resilience; their failure leads to severe societal disruption and they have important postdisaster functions. However, as these networks consist of interconnected, but geographically-distributed, components, system resilience is difficult to assess. In this paper the authors propose the use of an extension to the catastrophe (CAT) risk modeling approach, which is primarily used to perform risk assessments of independent assets, to be adopted for these interdependent systems. To help to achieve this, fragility curves, a crucial element of CAT models, are developed for overhead electrical lines using an empirical approach to ascribe likely failures due to wind storm hazard. To generate empirical fragility curves for electrical overhead lines, a dataset of over 12,000 electrical failures is coupled to a European reanalysis (ERA) wind storm model, ERA-Interim. The authors consider how the spatial resolution of the electrical fault data affects these curves, generating a fragility curve with low resolution fault data with a R 2 value of 0.9271 and improving this to a R 2 value of 0.9889 using higher spatial resolution data. Recommendations for deriving similar fragility curves for other infrastructure systems and/or hazards using the same methodological approach are also made. The authors argue that the developed fragility curves are applicable to other regions with similar electrical infrastructure and wind speeds, although some additional calibration may be required.

Impact of Uncertainties on Resilient Operation of Microgrids: A Data-Driven Approach

“…), as given by (23). TO max t and FR max t are the maximum amount of inflow and outflow capacity of interval t. Equation (24) shows that a huge penalty (v t,t ) is imposed if shifting is not allowed, otherwise it is set to a small factor (δ).…”

“…Therefore, in this paper, a fragility curve has been constructed that shows the failure probability of overhead distribution lines as a function of wind speed. The actual number of failures of overhead lines in the UK's distribution network when subjected to windstorm hazard, as reported within the National Fault and Interruption Report Scheme database [23], are shown in Fig. 2a.…”

“…Same data are utilized to develop the fragility curve of Fig. 2b using (6) [23], [24]. Where, Pr(v) is the probability of line failure at wind speed v, v collapse is the wind speed at which the line will collapse, F pkm is the number of faults per km, and L pa is the average length of overhead lines between two poles.…”

Assessment and Possible Solution to Increase Resilience: Flooding Threats in Terni Distribution Grid

“…The coefficient can vary in the range between 0 and 1 and corresponds to the probability that a flooding event leads to a fault. For each substation, the vulnerability coefficient has been evaluated by means of an empirical approach, as presented in Reference [23], because of the absence of proper historical data correlating weather events and substation faults. Coefficient values used in the simulation model are listed below:…”

Understanding Causes of Low Voltage (LV) Faults in Electricity Distribution Network Using Association Rule Mining and Text Clustering

“…United Kingdom electricity companies, which are private organizations, collect data on faults in the NaFIRS database as part of their regulation criteria set out by the government [2]. This database contains details of all the HV and LV related faults on the electrical distribution system, including date, time, and number of consumers affected, number of minutes lost among others [7]. This Scheme was initially approved by the twenty-seventh Chief Engineers' Conference, held on 14th October 1964, and was subsequently revised several times [6].…”