Cloud computing technology is used in traveling wave fault location,which establishes a new technology platform for multi-terminal traveling wave fault location in complicated power systems.In this paper,multi-termina...Cloud computing technology is used in traveling wave fault location,which establishes a new technology platform for multi-terminal traveling wave fault location in complicated power systems.In this paper,multi-terminal traveling wave fault location network is developed,and massive data storage,management,and algorithm realization are implemented in the cloud computing platform.Based on network topology structure,the section connecting points for any lines and corresponding detection placement in the loop are determined first.The loop is divided into different sections,in which the shortest transmission path for any of the fault points is directly and uniquely obtained.In order to minimize the number of traveling wave acquisition unit(TWU),multi-objective optimal configuration model for TWU is then set up based on network full observability.Finally,according to the TWU distribution,fault section can be located by using temporal correlation,and the final fault location point can be precisely calculated by fusing all the times recorded in TWU.PSCAD/EMTDC simulation results show that the proposed method can quickly,accurately,and reliably locate the fault point under limited TWU with optimal placement.展开更多
针对配电网故障定位问题提出一种基于人工神经网络(ANN)中结构较为简单并且可塑性强的误差反向传播(Error Back Propagation,BP)神经网络方法的定位模型。建立BP网络模型,并将训练好的BP网络模型和通过云遗传算法改进后的BP网络模型,应...针对配电网故障定位问题提出一种基于人工神经网络(ANN)中结构较为简单并且可塑性强的误差反向传播(Error Back Propagation,BP)神经网络方法的定位模型。建立BP网络模型,并将训练好的BP网络模型和通过云遗传算法改进后的BP网络模型,应用于同一个简单的配电网系统中,分别对不同分支的反射信息进行特征提取与模式识别。通过对两种算法的训练曲线图和诊断精度的比较来反映优化算法的高效性和准确性,最终得以确定诊断的实际输出值,实现故障分支的判别和精确定位。展开更多
From January 2020 to December 2021,Ulanqab Meteorological Bureau of Inner Mongolia used VLF/LF lightning locator to carry out three-dimensional lightning monitoring in Ulanqab City,and compared with ADTD lightning loc...From January 2020 to December 2021,Ulanqab Meteorological Bureau of Inner Mongolia used VLF/LF lightning locator to carry out three-dimensional lightning monitoring in Ulanqab City,and compared with ADTD lightning location data in the same period.The results show that both VLF/LF lightning locator and ADTD lightning locator had excellent monitoring ability for lightning during flood season in Ulanqab.VLF/LF lightning locator was slightly superior to ADTD lightning locator in observation accuracy,the observation ability of low-current cloud-to-ground lightning,intracloud lightning observation and so on.There were obvious temporal and spatial characteristics of cloud-to-ground lightning during flood season in Ulanqab,and there was a certain correlation between the areas where lightning appeared frequently and surface water.Intracloud lightning was mainly concentrated at a height of 1-7 km.Negative cloud-to-ground lightning accounted for about 75%of total cloud-to-ground lightning,and negative intracloud lightning accounted for 39%of total intracloud lightning.展开更多
With the development of cities and the explosion of infonnation,vast amounts of geo-tagged textural data about Points of Interests(POIs)have been generated.Extracting useful information and discovering text spatial di...With the development of cities and the explosion of infonnation,vast amounts of geo-tagged textural data about Points of Interests(POIs)have been generated.Extracting useful information and discovering text spatial distributions from the data are challenging and meaningful.Also,the huge numbers of POIs in modem cities make it important to have efficient approaches to retrieve and choose a destination.This paper provides a visual design combing metro map and wordles to meet the needs.In this visualization,metro lines serve as the divider lines splitting the city into several subareas and the boundaries to constrain wordles within each subarea.The wordles are generated from keywords extracted from the text about POIs(including reviews,descriptions,etc.)and embedded into the subareas based on their geographical locations.By generating intuitive results and providing an interactive visualization to support exploring text distribution patterns,our strategy can guide the users to explore urban spatial characteristics and retrieve a location efficiently.Finally,we implement a visual analysis of the restaurants data in Shanghai,China as a case study to evaluate our strategy.展开更多
基金the Key Project of Smart Grid Technology and Equipment of National Key Research and Development Plan of China(2016YFB0900600)Project supported by the National Natural Science Foundation Fund for Distinguished Young Scholars(51425701)+2 种基金the National Natural Science Foundation of China(51207013)the Hunan Province Natural Science Fund for Distinguished Young Scholars(2015JJ1001)the Education Department of Hunan Province Project(15C0032).
文摘Cloud computing technology is used in traveling wave fault location,which establishes a new technology platform for multi-terminal traveling wave fault location in complicated power systems.In this paper,multi-terminal traveling wave fault location network is developed,and massive data storage,management,and algorithm realization are implemented in the cloud computing platform.Based on network topology structure,the section connecting points for any lines and corresponding detection placement in the loop are determined first.The loop is divided into different sections,in which the shortest transmission path for any of the fault points is directly and uniquely obtained.In order to minimize the number of traveling wave acquisition unit(TWU),multi-objective optimal configuration model for TWU is then set up based on network full observability.Finally,according to the TWU distribution,fault section can be located by using temporal correlation,and the final fault location point can be precisely calculated by fusing all the times recorded in TWU.PSCAD/EMTDC simulation results show that the proposed method can quickly,accurately,and reliably locate the fault point under limited TWU with optimal placement.
文摘针对配电网故障定位问题提出一种基于人工神经网络(ANN)中结构较为简单并且可塑性强的误差反向传播(Error Back Propagation,BP)神经网络方法的定位模型。建立BP网络模型,并将训练好的BP网络模型和通过云遗传算法改进后的BP网络模型,应用于同一个简单的配电网系统中,分别对不同分支的反射信息进行特征提取与模式识别。通过对两种算法的训练曲线图和诊断精度的比较来反映优化算法的高效性和准确性,最终得以确定诊断的实际输出值,实现故障分支的判别和精确定位。
文摘From January 2020 to December 2021,Ulanqab Meteorological Bureau of Inner Mongolia used VLF/LF lightning locator to carry out three-dimensional lightning monitoring in Ulanqab City,and compared with ADTD lightning location data in the same period.The results show that both VLF/LF lightning locator and ADTD lightning locator had excellent monitoring ability for lightning during flood season in Ulanqab.VLF/LF lightning locator was slightly superior to ADTD lightning locator in observation accuracy,the observation ability of low-current cloud-to-ground lightning,intracloud lightning observation and so on.There were obvious temporal and spatial characteristics of cloud-to-ground lightning during flood season in Ulanqab,and there was a certain correlation between the areas where lightning appeared frequently and surface water.Intracloud lightning was mainly concentrated at a height of 1-7 km.Negative cloud-to-ground lightning accounted for about 75%of total cloud-to-ground lightning,and negative intracloud lightning accounted for 39%of total intracloud lightning.
基金This work is supported by National Key Research and Development Program of China(Grant No.2017YFB0701900,2016QY02D0304)National Nature Science Foundation of China(Grant No.61100053,61572318,61772336,61672055)。
文摘With the development of cities and the explosion of infonnation,vast amounts of geo-tagged textural data about Points of Interests(POIs)have been generated.Extracting useful information and discovering text spatial distributions from the data are challenging and meaningful.Also,the huge numbers of POIs in modem cities make it important to have efficient approaches to retrieve and choose a destination.This paper provides a visual design combing metro map and wordles to meet the needs.In this visualization,metro lines serve as the divider lines splitting the city into several subareas and the boundaries to constrain wordles within each subarea.The wordles are generated from keywords extracted from the text about POIs(including reviews,descriptions,etc.)and embedded into the subareas based on their geographical locations.By generating intuitive results and providing an interactive visualization to support exploring text distribution patterns,our strategy can guide the users to explore urban spatial characteristics and retrieve a location efficiently.Finally,we implement a visual analysis of the restaurants data in Shanghai,China as a case study to evaluate our strategy.
