This paper proposes a robust faulted line-section location method based on the normalized quantile Hausdorff distance (NQHD) algorithm for detecting single-phase-to-ground faults in distribution networks.The faulted l...This paper proposes a robust faulted line-section location method based on the normalized quantile Hausdorff distance (NQHD) algorithm for detecting single-phase-to-ground faults in distribution networks.The faulted line section is determined according to the characteristic differences between the zero-sequence currents on the faulted and healthy line sections.Specifically,the zero-sequence currents at both ends of a healthy line section are highly similar to each other,while such is generally not the case on a faulted line section.The NQHD algorithm can disregard extremes or outliers while also providing a normalized scaling in different scenarios.Thus,it can be applied to calculate the robust waveform similarity of zero-sequence current waveforms at both ends of different line sections for identifying reliably the faulted line section even under the interference of outliers.The results demonstrate the good performance of the proposed method in detecting single-phase-to-ground faults under different fault conditions.Comparative tests with the existing methods confirm the advantageous robustness of the proposed method against the impacts of outliers and noises.展开更多
Defining and measuring resilience using a unified framework has been a topic of intense research.This article presents a perspective on how resilience could be quantitatively assessed through a set of indices.It start...Defining and measuring resilience using a unified framework has been a topic of intense research.This article presents a perspective on how resilience could be quantitatively assessed through a set of indices.It starts with a brief explanation of resilience in the context of supply chain and a quick summary of existing quantitative measures of resilience.It then discusses how resilience could be quantified in a constructive manner so that the resulting metrics are representative of the performance throughout the system's life cycle.In particular,it is proposed that resilience should be evaluated according to different time periods,i.e.before,during and after a disruption has occurred.Four dimensions of resilience,namely reliability,robustness,recovery and reconfigurability,can then be used to make up a set of indices for resilience.For numerical illustration,these indices are computed based on recovery data arising from Hurricane Sandy in October 2012.Finally,it is postulated that resilience will be the performance metric that complements productivity and sustainability as the third pillar for measuring success of organizations,and in turn,that of sovereign countries in their quests for developing smart cities.展开更多
The Energy-related Severe Accident Database(ENSAD)is the most authoritative resource for comparative risk analysis of accidents in the energy sector.Although ENSAD contains comprehensive,worldwide data,it is a non-spa...The Energy-related Severe Accident Database(ENSAD)is the most authoritative resource for comparative risk analysis of accidents in the energy sector.Although ENSAD contains comprehensive,worldwide data,it is a non-spatial database in Microsoft Access format.Therefore,spatial characteristics of the data cannot be fully utilised as well as analysed directly.Based on these premises,a new web-based version of ENSAD with GIS-capabilities–named ENSAD v2.0–is designed and developed using state-of-the-art,open source technologies.The ENSAD v2.0 consists of two main components,i.e.a spatial database and a responsive web application.For the spatial database,the current accident data are georeferenced and migrated from Microsoft Access,using a tiered approach.The responsive web application can be accessed from desktops as well as mobile devices,and provides both a 2D and 3D mapping platform that is developed on cloud-based,serverless architecture.ENSAD v2.0 also allows assigning different user roles with specific access rights,and a public version with advanced visualisation capabilities has also been developed.Lastly,a case study was carried out using a spatial analysis to visualise the potential impact radius of a natural gas pipeline explosion and to assess its consequences in terms of economic damage and casualties.展开更多
While numerous studies have investigated attitudes towards self-driving cars in general,less research attention has been focused on individuals’comfort with the presence(or absence)of third-party human supervision of...While numerous studies have investigated attitudes towards self-driving cars in general,less research attention has been focused on individuals’comfort with the presence(or absence)of third-party human supervision of this automation,and its potential correlates.In the present study we perform a secondary analysis of pre-existing data from The European Commission’s Eurobarometer 92.1,a large-scale European survey(n=27565)of expectations and concerns of connected and automated driving.By comparing responses to three levels of human supervision in self-driving cars,we aim to identify changes in the importance of predictors of comfort with automation.We find considerable heterogeneity in both individual attitudes,as well as in country-level attitudes in our descriptive analysis.We find a trend of decreasing comfort as external human supervision is reduced,although this effect differs between countries.We then investigate potential drivers of self-reported comfort with varying levels of external human supervision in a regression framework.Gender differences get stronger with decreasing supervision,suggesting a possible resolution to conflicting evidence in previous studies.Following this,we fit an ordinal random forest model to derive variable importance metrics,which enable us to compare the changing role predictor variables might play in shaping self-reported comfort,depending on varying levels of third-party supervision.Data privacy is highlighted as an important variable,regardless of level of supervision.Our findings provide confirmation for previous literature in a large sample,while also uncovering a number of novel associations,providing guidance for future policy-making and research efforts.展开更多
基金supported by the Future Resilient Systems(FRS-II)Project at the Singapore-ETH Centre(SEC),which was funded by the National Research Foundation of Singapore(NRF)under its Campus for Research Excellence and Technological Enterprise(CREATE)program.
文摘This paper proposes a robust faulted line-section location method based on the normalized quantile Hausdorff distance (NQHD) algorithm for detecting single-phase-to-ground faults in distribution networks.The faulted line section is determined according to the characteristic differences between the zero-sequence currents on the faulted and healthy line sections.Specifically,the zero-sequence currents at both ends of a healthy line section are highly similar to each other,while such is generally not the case on a faulted line section.The NQHD algorithm can disregard extremes or outliers while also providing a normalized scaling in different scenarios.Thus,it can be applied to calculate the robust waveform similarity of zero-sequence current waveforms at both ends of different line sections for identifying reliably the faulted line section even under the interference of outliers.The results demonstrate the good performance of the proposed method in detecting single-phase-to-ground faults under different fault conditions.Comparative tests with the existing methods confirm the advantageous robustness of the proposed method against the impacts of outliers and noises.
基金This work is supported by the National Research Foundation,Prime Minister's Office,Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)program on Future Resilient Systems phase 2(FRS2).
文摘Defining and measuring resilience using a unified framework has been a topic of intense research.This article presents a perspective on how resilience could be quantitatively assessed through a set of indices.It starts with a brief explanation of resilience in the context of supply chain and a quick summary of existing quantitative measures of resilience.It then discusses how resilience could be quantified in a constructive manner so that the resulting metrics are representative of the performance throughout the system's life cycle.In particular,it is proposed that resilience should be evaluated according to different time periods,i.e.before,during and after a disruption has occurred.Four dimensions of resilience,namely reliability,robustness,recovery and reconfigurability,can then be used to make up a set of indices for resilience.For numerical illustration,these indices are computed based on recovery data arising from Hurricane Sandy in October 2012.Finally,it is postulated that resilience will be the performance metric that complements productivity and sustainability as the third pillar for measuring success of organizations,and in turn,that of sovereign countries in their quests for developing smart cities.
基金The research was conducted at the Future Resilient Systems(FRS)at the Singapore-ETH Centre(SEC),which was established collaboratively between ETH Zurich and Singapore’s National Research Foundation(FI 370074011)under its Campus for Research Excellence And Technological Enterprise(CREATE)programme.
文摘The Energy-related Severe Accident Database(ENSAD)is the most authoritative resource for comparative risk analysis of accidents in the energy sector.Although ENSAD contains comprehensive,worldwide data,it is a non-spatial database in Microsoft Access format.Therefore,spatial characteristics of the data cannot be fully utilised as well as analysed directly.Based on these premises,a new web-based version of ENSAD with GIS-capabilities–named ENSAD v2.0–is designed and developed using state-of-the-art,open source technologies.The ENSAD v2.0 consists of two main components,i.e.a spatial database and a responsive web application.For the spatial database,the current accident data are georeferenced and migrated from Microsoft Access,using a tiered approach.The responsive web application can be accessed from desktops as well as mobile devices,and provides both a 2D and 3D mapping platform that is developed on cloud-based,serverless architecture.ENSAD v2.0 also allows assigning different user roles with specific access rights,and a public version with advanced visualisation capabilities has also been developed.Lastly,a case study was carried out using a spatial analysis to visualise the potential impact radius of a natural gas pipeline explosion and to assess its consequences in terms of economic damage and casualties.
基金supported by the National Research Foundation Singapore(NRF)under its Campus for Research Excellence and Technological Enterprise(CREATE)programmethe Donald C.Cooper-Fonds at ETH Zurich,Project No.ETH-0817-1(principal investigators C.Hoelscher and S.Andraszewicz).
文摘While numerous studies have investigated attitudes towards self-driving cars in general,less research attention has been focused on individuals’comfort with the presence(or absence)of third-party human supervision of this automation,and its potential correlates.In the present study we perform a secondary analysis of pre-existing data from The European Commission’s Eurobarometer 92.1,a large-scale European survey(n=27565)of expectations and concerns of connected and automated driving.By comparing responses to three levels of human supervision in self-driving cars,we aim to identify changes in the importance of predictors of comfort with automation.We find considerable heterogeneity in both individual attitudes,as well as in country-level attitudes in our descriptive analysis.We find a trend of decreasing comfort as external human supervision is reduced,although this effect differs between countries.We then investigate potential drivers of self-reported comfort with varying levels of external human supervision in a regression framework.Gender differences get stronger with decreasing supervision,suggesting a possible resolution to conflicting evidence in previous studies.Following this,we fit an ordinal random forest model to derive variable importance metrics,which enable us to compare the changing role predictor variables might play in shaping self-reported comfort,depending on varying levels of third-party supervision.Data privacy is highlighted as an important variable,regardless of level of supervision.Our findings provide confirmation for previous literature in a large sample,while also uncovering a number of novel associations,providing guidance for future policy-making and research efforts.
