Modelling Complete Power Outage Data Using Reliability

Data on time between complete power outages, Time between Failure (TBF) in Uyo were considered. Trend test and serial correlation test were conducted graphically for the data. The tests proved that the data were identically and independently distributed (iid). Summary statistics of the data showed that complete power outage occurred 416 times between the year 2014 and 2018. The maximum likelihood estimation method was used to estimate the parameters of Weibull 2-parameter, Normal, Lognormal 2-parameter and exponential distributions. The values of Kolmogorov-Smirnov, Anderson Darling and Chi-Square statistics were used to determine the best fit distributions. A model for the computation of reliability of electric power was then proposed.

Power outage and environmental justice in Winter Storm Uri: an analytical workflow based on nighttime light remote sensing

The intensity of extreme weather events has been increasing,posing a unique threat to society and highlighting the importance of our electrical power system,a key component in our infrastructure.In severe weather events,quickly identifying power outage impact zones and affected communities is crucial for informed disaster response.However,a lack of household-level power outage data impedes timely and precise assessments.To address these challenges,we introduced an analytical workflow using NASA’s Black Marble daily nighttime light(NTL)images to detect power outages from the 2021 Winter Storm Uri.This workflow includes adjustments to mitigate viewing angle and snow reflection effects.Power outage is detected by comparing storm-time and baseline(normal condition)NTL images using an empirical adjusted equation.The outcomes of the workflow are 500-meter resolution power outage maps,which have the optimal correlation with real outage tracking data when NTL intensity is reduced by 26%.With the resultant power outage maps,we analyzed the relations between power outages and disadvantaged populations in 126 Texas counties and 4182 census tracts to evaluate environmental justice in the storm.The results show that Latino/Hispanic communities tend to suffer more from power outages at both the county and census tract levels.

A Generalized Accelerated Failure Time Model to Predict Restoration Time from Power Outages

Major disasters such as wildfire, tornado, hurricane, tropical storm, and flooding cause disruptions in infrastructure systems such as power and water supply, wastewater management, telecommunication, and transportation facilities. Disruptions in electricity infrastructure have negative impacts on sectors throughout a region, including education, medical services,financial services, and recreation. In this study, we introduced a novel approach to investigate the factors that can be associated with longer restoration time of power service after a hurricane. Considering restoration time as the dependent variable and using a comprehensive set of county-level data, we estimated a generalized accelerated failure time(GAFT) model that accounts for spatial dependence among observations for time to event data. The model fit improved by 12% after considering the effects of spatial correlation in time to event data. Using the GAFT model and Hurricane Irma's impact on Florida as a case study, we examined:(1) differences in electric power outages and restoration rates among different types of power companies—investor-owned power companies, rural and municipal cooperatives;(2) the relationship between the duration of power outage and power system variables;and(3) the relationship between the duration of power outage and socioeconomic attributes. The findings of this study indicate that counties with a higher percentage of customers served by investor-owned electric companies and lower median household income faced power outage for a longer time. This study identified the key factors to predict restoration time of hurricane-induced power outages, allowing disaster management agencies to adopt strategies required for restoration process.

Mapping near-real-time power outages from social media

Social media,including Twitter,has become an important source for disaster response.Yet most studies focus on a very limited amount of geotagged data(approximately 1%of all tweets)while discarding a rich body of data that contains location expressions in text.Location information is crucial to understanding the impact of disasters,including where damage has occurred and where the people who need help are situated.In this paper,we propose a novel two-stage machine learningand deep learning-based framework for power outage detection from Twitter.First,we apply a probabilistic classification model using bag-ofngrams features to find true power outage tweets.Second,we implement a new deep learning method-bidirectional long short-term memory networks-to extract outage locations from text.Results show a promising classification accuracy(86%)in identifying true power outage tweets,and approximately 20 times more usable tweets can be located compared with simply relying on geotagged tweets.The method of identifying location names used in this paper does not require language-or domain-specific external resources such as gazetteers or handcrafted features,so it can be extended to other situational awareness analyzes and new applications.

U.S. Resilience to large-scale power outages in 2002–2019

Prolonged power outages debilitate the economy and threaten public health. Existing research is generally limitedin its scope to a single event, an outage cause, or a region. Here, we provide one of the most comprehensiveanalyses of large-scale power outages in the U.S. from 2002 to 2019. This analysis is based on the outage datacollected under U.S. federal mandates that concern large blackouts, typically of transmission systems and excludemuch more common but smaller blackouts, typically, of distribution systems. We categorized the data into fouroutage causes and computed reliability metrics, which are commonly used for distribution-level small outagesonly but useful for analyzing large blackouts. Our spatiotemporal analysis reveals six of the most resilient U.S.states since 2010, improvement of power resilience against natural hazards in the south and northeast regions,and a disproportionately large number of human attacks for its population in the Western Electricity CoordinatingCouncil region. Our regression analysis identifies several statistically significant predictors and hypotheses forU.S. resilience to large blackouts. Furthermore, we propose a novel framework for analyzing outage data usingdifferential weighting and influential points to better understand power resilience. We share curated data andcode as Supplementary Materials.

Analysis of the Quantity and Causes of Outages in LV/MV Electric Grids

The paper considers the quantity and causes of outages in electric grids of low and medium voltages using the example of an electric grid of a regional power supply company.The main types of damage to the equipment of power lines and transformer substations were identified.Data on other areas of rural and urban electric grids are also analyzed.The main directions for reducing the quantity of outages in electric grids are proposed based on this analysis.Among them,there are the use of isolated wires in power transmission lines,the improvement of design of switching devices,switches and terminals of transformers,the application of technical condition diagnostics,the disaggregating of power lines and the increase of protection sensitivity of power lines.Most of the causes of equipment damage can be prevented by increasing the maintenance level of this equipment.

Optimal layout model of feeder automation equipment oriented to the type of fault detection and local action

In feeder automation transformation there are difficulties in equipment and location selection.To help with this,an optimal layout model of feeder automation equipment oriented to the type of fault detection and local action is pro-posed.It analyzes the coordination relationship of the three most common types of automation equipment,i.e.,fault indicator,over-current trip switch and non-voltage trip switch in the fault handling process,and the explicit expres-sions of power outage time caused by a fault on different layouts of the above three types of equipment are given.Given constraints of power supply reliability and the goal of minimizing the sum of equipment-related capital invest-ment and power interruption cost,a mixed-integer quadratic programming model for optimal layout is established,in which the functional failure probability of equipment is linearized using the 3δprinciple in statistics.Finally,the basic characteristics of the proposed model are illustrated by different scenarios on the IEEE RBTS-BUS6 system.It can not only take into account fault location and fault isolation to enhance user power consumption perception,but also can guide precise investment to improve the operational quality and efficiency of a power company.