Despite recognition of the relationship between infrastructure resilience and community recovery,very limited empirical evidence exists regarding the extent to which the disruptions in and restoration of infrastructur...Despite recognition of the relationship between infrastructure resilience and community recovery,very limited empirical evidence exists regarding the extent to which the disruptions in and restoration of infrastructure ser-vices contribute to the speed of community recovery.To address this gap,this study investigates the relationship between community and infrastructure systems in the context of hurricane impacts,focusing on the recovery dy-namics of population activity and power infrastructure restoration.Empirical observational data were utilized to analyze the extent of impact,recovery duration,and recovery types of both systems in the aftermath of Hurricane Ida.The study reveals three key findings.First,power outage duration positively correlates with outage extent until a certain impact threshold is reached.Beyond this threshold,restoration time remains relatively stable re-gardless of outage magnitude.This finding underscores the need to strengthen power infrastructure,particularly in extreme weather conditions,to minimize outage restoration time.Second,power was fully restored in 70% of affected areas before population activity levels normalized.This finding suggests the role infrastructure function-ality plays in post-disaster community recovery.Quicker power restoration did not equate to rapid population activity recovery due to other possible factors such as transportation,housing damage,and business interruptions.Finally,if power outages last beyond two weeks,community activity resumes before complete power restoration,indicating adaptability in prolonged outage scenarios.This implies the capacity of communities to adapt to on-going power outages and continue daily life activities.These findings offer valuable empirical insights into the interaction between human activities and infrastructure systems,such as power outages,during extreme weather events.They also enhance our empirical understanding of how infrastructure resilience influences community recovery.By identifying the critical thresholds for power outage functionality and duration that affect popula-tion activity recovery,this study furthers our understanding of how infrastructure performance intertwines with community functioning in extreme weather conditions.Hence,the findings can inform infrastructure operators,emergency managers,and public officials about the significance of resilient infrastructure in life activity recovery of communities when facing extreme weather hazards.展开更多
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 ident...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</span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">for the computation of reliability of electric power was then proposed</span></span></span><span style="font-family:Verdana;">.展开更多
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 even...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.展开更多
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 transportati...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.展开更多
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 ...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.展开更多
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 comprehen...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.展开更多
Reliability level of HVDC power transmission systems becomes an important factor impacting the entire power grid.The author analyzes the reliability of HVDC power transmission systems owned by SGCC since 2003 in respe...Reliability level of HVDC power transmission systems becomes an important factor impacting the entire power grid.The author analyzes the reliability of HVDC power transmission systems owned by SGCC since 2003 in respect of forced outage times,forced energy unavailability,scheduled energy unavailability and energy utilization eff iciency.The results show that the reliability level of HVDC power transmission systems owned by SGCC is improving.By analyzing different reliability indices of HVDC power transmission system,the maximum asset benef its of power grid can be achieved through building a scientif ic and reasonable reliability evaluation system.展开更多
Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch...Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies.However,existing prediction methods based on the structure of‘splicing prediction and diagnosis method’suffer from limitations such as inability to achieve global optimality,error accumulation,and low prediction accuracy.To fill this gap,a novel direct prediction method of a trans-former state based on knowledge and data fusion-driven model(K&DFDM)is pro-posed in this paper.Firstly,a state quantity data space is constructed to comprehensively reflect the changes in the health state of the transformer over time,encompassing online monitoring,offline testing,evaluation results,and actual operation data.After that,correlation knowledge between state quantities,fault diagnosis mechanism knowledge,current diagnosis experience knowledge,and uncertain fuzzy knowledge are extracted separately.The actual fault mechanism,existing expert experience,and other knowledge in the diagnosis process are quantified.Then,the attention model is sub-sequently optimised,leveraging quantitative knowledge to effectively constrain and guide the data prediction process.Incorporating fault diagnosis mechanism knowledge into the data prediction model enables the achievement of global optimisation in both diagnosis and prediction.The integration of traditional expert experience knowledge and the correlation knowledge between state quantities serves as constraints during the process of attaining the global optimum.The verification results,comprising 327 cases,demonstrate that K&DFDM effectively addresses the issue of error superposition encountered by existing state prediction methods,leading to a direct state prediction accuracy of 96.33%.展开更多
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...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.展开更多
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...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.展开更多
基金supported by the National Science Foundation under CRISP 2.0 Type 2 No.1832662the Texas A&M University X-Grant 699.
文摘Despite recognition of the relationship between infrastructure resilience and community recovery,very limited empirical evidence exists regarding the extent to which the disruptions in and restoration of infrastructure ser-vices contribute to the speed of community recovery.To address this gap,this study investigates the relationship between community and infrastructure systems in the context of hurricane impacts,focusing on the recovery dy-namics of population activity and power infrastructure restoration.Empirical observational data were utilized to analyze the extent of impact,recovery duration,and recovery types of both systems in the aftermath of Hurricane Ida.The study reveals three key findings.First,power outage duration positively correlates with outage extent until a certain impact threshold is reached.Beyond this threshold,restoration time remains relatively stable re-gardless of outage magnitude.This finding underscores the need to strengthen power infrastructure,particularly in extreme weather conditions,to minimize outage restoration time.Second,power was fully restored in 70% of affected areas before population activity levels normalized.This finding suggests the role infrastructure function-ality plays in post-disaster community recovery.Quicker power restoration did not equate to rapid population activity recovery due to other possible factors such as transportation,housing damage,and business interruptions.Finally,if power outages last beyond two weeks,community activity resumes before complete power restoration,indicating adaptability in prolonged outage scenarios.This implies the capacity of communities to adapt to on-going power outages and continue daily life activities.These findings offer valuable empirical insights into the interaction between human activities and infrastructure systems,such as power outages,during extreme weather events.They also enhance our empirical understanding of how infrastructure resilience influences community recovery.By identifying the critical thresholds for power outage functionality and duration that affect popula-tion activity recovery,this study furthers our understanding of how infrastructure performance intertwines with community functioning in extreme weather conditions.Hence,the findings can inform infrastructure operators,emergency managers,and public officials about the significance of resilient infrastructure in life activity recovery of communities when facing extreme weather hazards.
文摘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</span></span><span><span><span style="font-family:""> </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">for the computation of reliability of electric power was then proposed</span></span></span><span style="font-family:Verdana;">.
基金supported by a research grant ftom the National Science Foundation under the Meth-odology,Measurement&Statistics(MMS)Program(Award No.2102019).
文摘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.
基金the U.S.National Science Foundation for the Grant CMMI-1832578 to support the research presented in this article。
文摘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.
基金the financial support received from Oak Ridge National Laboratory(ORNL)’s Liane Russell Distinguished Early Career Fellowship and grant no.TG0100000.
文摘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.
基金the National Science Foundation(NSF grant CMMI-1824681)。
文摘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.
文摘Reliability level of HVDC power transmission systems becomes an important factor impacting the entire power grid.The author analyzes the reliability of HVDC power transmission systems owned by SGCC since 2003 in respect of forced outage times,forced energy unavailability,scheduled energy unavailability and energy utilization eff iciency.The results show that the reliability level of HVDC power transmission systems owned by SGCC is improving.By analyzing different reliability indices of HVDC power transmission system,the maximum asset benef its of power grid can be achieved through building a scientif ic and reasonable reliability evaluation system.
基金Research on Robust Decision and Full Stack Optimisation Techniques for Cloud Edge Intelligent Systems for Substation Inspection,Grant/Award Number:52550022001J。
文摘Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer,thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies.However,existing prediction methods based on the structure of‘splicing prediction and diagnosis method’suffer from limitations such as inability to achieve global optimality,error accumulation,and low prediction accuracy.To fill this gap,a novel direct prediction method of a trans-former state based on knowledge and data fusion-driven model(K&DFDM)is pro-posed in this paper.Firstly,a state quantity data space is constructed to comprehensively reflect the changes in the health state of the transformer over time,encompassing online monitoring,offline testing,evaluation results,and actual operation data.After that,correlation knowledge between state quantities,fault diagnosis mechanism knowledge,current diagnosis experience knowledge,and uncertain fuzzy knowledge are extracted separately.The actual fault mechanism,existing expert experience,and other knowledge in the diagnosis process are quantified.Then,the attention model is sub-sequently optimised,leveraging quantitative knowledge to effectively constrain and guide the data prediction process.Incorporating fault diagnosis mechanism knowledge into the data prediction model enables the achievement of global optimisation in both diagnosis and prediction.The integration of traditional expert experience knowledge and the correlation knowledge between state quantities serves as constraints during the process of attaining the global optimum.The verification results,comprising 327 cases,demonstrate that K&DFDM effectively addresses the issue of error superposition encountered by existing state prediction methods,leading to a direct state prediction accuracy of 96.33%.
文摘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.
基金supported by the National Natural Science Foundation of China(Grant No.51777067).
文摘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.
