Weather-induced outages pose a significant threat to power grid reliability,with transmission systems particularly vulnerable to environmental stressors.Despite numerous tools developed to address this issue,the persi...Weather-induced outages pose a significant threat to power grid reliability,with transmission systems particularly vulnerable to environmental stressors.Despite numerous tools developed to address this issue,the persistent challenge of weather-related interruptions highlights the need for an accurate fragility model for transmission line interruptions.This paper proposes a novel data-driven approach to model wind-induced transmission line fragility,addressing critical gaps in current methodologies.Our model integrates a novel synthetic data generation approach that creates highly informative synthetic data points,enhancing the representation of rare events.Additionally,we develop an advanced active learning framework that efficiently selects the most relevant instances from large,imbalanced datasets for model training.We further enhance model interpretability through comprehensive sensitivity analysis using SHAP(SHapley Additive exPlanations)values.Results on unseen testing data show significant improvement compared to conventional methods,achieving a 5%improvement in accuracy(from 0.89 to 0.94)in predicting wind-induced transmission line interruptions.Notably,it shows a 16%accuracy improvement(from 0.64 to 0.80)when applied to highly uncertain cases,highlighting its capabilities in high-uncertainty situations.Sensitivity analysis reveals wind gust and mean sea level pressure as the most critical factors influencing interruptions,while also uncovering complex temperature effects where,in a subset of situations,temperature has a significant impact on the interruption probability of lines.This advanced fragility model can offer valuable insights for both real-time dispatch decisions and long-term risk-informed planning,contributing to enhanced power grid resilience in the face of increasing weather-related challenges.展开更多
The concept of seismic resilience has received significant attention from academia and industry during the last two decades. Different frameworks have been proposed for seismic resilience assessment of engineering sys...The concept of seismic resilience has received significant attention from academia and industry during the last two decades. Different frameworks have been proposed for seismic resilience assessment of engineering systems at different scales(e.g., buildings, bridges, communities, and cities). Testbeds including Centerville virtual community(CVC), Memphis testbed(MTB), and the virtual city of Turin, Italy(VC-TI) have been developed during the last decade. However, the resilience assessment results of Chinese cities still require calibration based on a unified evaluation model. Therefore, a geographic information system(GIS)-based benchmark model of a medium-sized city located in the southeastern coastal region of China was developed. The benchmark city can be used to compare existing assessment frameworks and calibrate the assessment results. The demographics, site conditions, and potential hazard exposure of the benchmark city, as well as land use and building inventory are described in this paper. Data of lifeline systems are provided, including power, transportation, water, drainage, and natural gas distribution networks, as well as the locations of hospitals, emergency shelters, and schools. Data from past earthquakes and the literature were obtained to develop seismic fragility models, consequence models, and recovery models, which can be used as basic data or calibration data in the resilience assessment process. To demonstrate the completeness of the data included in the benchmark city, a case study on the accessibility of emergency rescue after earthquakes was conducted, and the preliminary results were discussed. The ultimate goal of this benchmark city is to provide a platform for calibrating resilience assessment results and to facilitate the development of resilient cities in China.展开更多
Continuous power supply of urban power networks(UPNs)is quite essential for the public security of a city because the UPN acts as the basis for other infrastructure networks.In recent years,UPN is threatened by extrem...Continuous power supply of urban power networks(UPNs)is quite essential for the public security of a city because the UPN acts as the basis for other infrastructure networks.In recent years,UPN is threatened by extreme weather events.An accurate modeling of load loss risk under extreme weather is quite essential for the preventive action of UPN.Con-sidering the forecast intensity of a typhoon disaster,this paper proposes analytical modeling of disaster-induced load loss for preventive allocation of mobile power sources(MPSs)in UPNs.First,based on the topological structure and fragility model of overhead lines and substations,we establish an analytical load loss model of multi-voltage-level UPN to quantify the spatial dis-tribution of disaster-induced load loss at the substation level.Second,according to the projected load loss distribution,a preventive allocation method of MPS is proposed,which makes the best use of MPS and dispatches the limited power supply to most vulnerable areas in the UPN.Finally,the proposed meth-od is validated by the case study of a practical UPN in China.展开更多
In the last three decades, bridge stock seismic retrofitting prioritization has become one of the cult topics for scientific discussions in the bridge management strategies. More recent methods are focusing on the eva...In the last three decades, bridge stock seismic retrofitting prioritization has become one of the cult topics for scientific discussions in the bridge management strategies. More recent methods are focusing on the evaluation of the generalized failure cost, of a specific bridge derived from direct and indirect costs induced to the users/residents of the area exposed to the seismic hazard as a consequence of bridge collapse. However, when these approaches have to be applied to large transport networks, appear still very complex and computational demanding, and therefore simplified methods to evaluate the impact in terms of social cost related to the reduced efficiency of a transportation network due to potential bridge failure, are required.In this work, a simplified method for seismic retrofitting prioritization on a bridge stock is proposed, which is based on a “blended” approach considering specific fragility curves according to several bridge features and condition state, seismic inputs and generalized failure costs related to the transportation network. The effectiveness of the method has been showed on a case study of a local bridge stock placed in central Italy and the obtained results have been compared with those provided by more refined transport simulation models, on one hand, and by more traditional prioritization approaches, on the other. It is highlighted that this method can be very useful for transportation network managers with in a limited budget scenario, in case of lack of information about possible earthquakeinduced impacts on a transportation network efficiency.展开更多
基金supported in part by the Korea Institute of Energy Technology Evaluation and Planning(KETEP),South Korea and the Ministry of Trade,Industry&Energy(MOTIE)of the Republic of Korea(No.No.RS-2022-KP002850)the U.S.National Science Foundation under grant 2000156the Lichtenstein endowment at The Ohio State University,USA.
文摘Weather-induced outages pose a significant threat to power grid reliability,with transmission systems particularly vulnerable to environmental stressors.Despite numerous tools developed to address this issue,the persistent challenge of weather-related interruptions highlights the need for an accurate fragility model for transmission line interruptions.This paper proposes a novel data-driven approach to model wind-induced transmission line fragility,addressing critical gaps in current methodologies.Our model integrates a novel synthetic data generation approach that creates highly informative synthetic data points,enhancing the representation of rare events.Additionally,we develop an advanced active learning framework that efficiently selects the most relevant instances from large,imbalanced datasets for model training.We further enhance model interpretability through comprehensive sensitivity analysis using SHAP(SHapley Additive exPlanations)values.Results on unseen testing data show significant improvement compared to conventional methods,achieving a 5%improvement in accuracy(from 0.89 to 0.94)in predicting wind-induced transmission line interruptions.Notably,it shows a 16%accuracy improvement(from 0.64 to 0.80)when applied to highly uncertain cases,highlighting its capabilities in high-uncertainty situations.Sensitivity analysis reveals wind gust and mean sea level pressure as the most critical factors influencing interruptions,while also uncovering complex temperature effects where,in a subset of situations,temperature has a significant impact on the interruption probability of lines.This advanced fragility model can offer valuable insights for both real-time dispatch decisions and long-term risk-informed planning,contributing to enhanced power grid resilience in the face of increasing weather-related challenges.
基金Scientific Research Fund of Institute of Engineering Mechanics,China Earthquake Administration under Grant Nos. 2019EEEVL0505,2019B02 and 2019A02Heilongjiang Touyan Innovation Team Program。
文摘The concept of seismic resilience has received significant attention from academia and industry during the last two decades. Different frameworks have been proposed for seismic resilience assessment of engineering systems at different scales(e.g., buildings, bridges, communities, and cities). Testbeds including Centerville virtual community(CVC), Memphis testbed(MTB), and the virtual city of Turin, Italy(VC-TI) have been developed during the last decade. However, the resilience assessment results of Chinese cities still require calibration based on a unified evaluation model. Therefore, a geographic information system(GIS)-based benchmark model of a medium-sized city located in the southeastern coastal region of China was developed. The benchmark city can be used to compare existing assessment frameworks and calibrate the assessment results. The demographics, site conditions, and potential hazard exposure of the benchmark city, as well as land use and building inventory are described in this paper. Data of lifeline systems are provided, including power, transportation, water, drainage, and natural gas distribution networks, as well as the locations of hospitals, emergency shelters, and schools. Data from past earthquakes and the literature were obtained to develop seismic fragility models, consequence models, and recovery models, which can be used as basic data or calibration data in the resilience assessment process. To demonstrate the completeness of the data included in the benchmark city, a case study on the accessibility of emergency rescue after earthquakes was conducted, and the preliminary results were discussed. The ultimate goal of this benchmark city is to provide a platform for calibrating resilience assessment results and to facilitate the development of resilient cities in China.
基金supported by National Natural Science Foundation of China(No.52307094).
文摘Continuous power supply of urban power networks(UPNs)is quite essential for the public security of a city because the UPN acts as the basis for other infrastructure networks.In recent years,UPN is threatened by extreme weather events.An accurate modeling of load loss risk under extreme weather is quite essential for the preventive action of UPN.Con-sidering the forecast intensity of a typhoon disaster,this paper proposes analytical modeling of disaster-induced load loss for preventive allocation of mobile power sources(MPSs)in UPNs.First,based on the topological structure and fragility model of overhead lines and substations,we establish an analytical load loss model of multi-voltage-level UPN to quantify the spatial dis-tribution of disaster-induced load loss at the substation level.Second,according to the projected load loss distribution,a preventive allocation method of MPS is proposed,which makes the best use of MPS and dispatches the limited power supply to most vulnerable areas in the UPN.Finally,the proposed meth-od is validated by the case study of a practical UPN in China.
文摘In the last three decades, bridge stock seismic retrofitting prioritization has become one of the cult topics for scientific discussions in the bridge management strategies. More recent methods are focusing on the evaluation of the generalized failure cost, of a specific bridge derived from direct and indirect costs induced to the users/residents of the area exposed to the seismic hazard as a consequence of bridge collapse. However, when these approaches have to be applied to large transport networks, appear still very complex and computational demanding, and therefore simplified methods to evaluate the impact in terms of social cost related to the reduced efficiency of a transportation network due to potential bridge failure, are required.In this work, a simplified method for seismic retrofitting prioritization on a bridge stock is proposed, which is based on a “blended” approach considering specific fragility curves according to several bridge features and condition state, seismic inputs and generalized failure costs related to the transportation network. The effectiveness of the method has been showed on a case study of a local bridge stock placed in central Italy and the obtained results have been compared with those provided by more refined transport simulation models, on one hand, and by more traditional prioritization approaches, on the other. It is highlighted that this method can be very useful for transportation network managers with in a limited budget scenario, in case of lack of information about possible earthquakeinduced impacts on a transportation network efficiency.
