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Research on multi-terminal traveling wave fault location method in complicated networks based on cloud computing platform

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Protection and Control of Modern Power Systems 2017年第1期2卷 199-210页

作者： Feng Deng Xiangjun Zeng Lanlan Pan Changsha University of Science and Technology ChangshaHunanChina. State Grid Hubei Yichang Electric Power Supply Company YichangHubeiChina.

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.

关键词： Wide Area Network Fault location Traveling wave Junction Point between Sections cloud computing platform

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Analysis of CLARANS Algorithm for Weather Data Based on Spark

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Computers, Materials & Continua 2023年第8期76卷 2427-2441页

作者： Jiahao Zhang Honglin Wang College of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China

With the rapid development of technology,processing the explosive growth of meteorological data on traditional standalone computing has become increasingly time-consuming,which cannot meet the demands of scientific research and business.Therefore,this paper proposes the implementation of the parallel Clustering Large Application based upon RANdomized Search(CLARANS)clustering algorithm on the Spark cloud computing platformto cluster China’s climate regions usingmeteorological data from1988 to 2018.The aim is to address the challenge of applying clustering algorithms to large datasets.In this paper,the morphological similarity distance is adopted as the similarity measurement standard instead of Euclidean distance,which improves clustering accuracy.Furthermore,the issue of local optima caused by an improper selection of initial clustering centers is addressed by utilizing the max-distance criterion.Compared to the k-means clustering algorithm already implemented in the Spark platform,the proposed algorithm has strong robustness,can reduce the interference of outliers in the dataset on clustering results,and has higher parallel performance than the frequently used serial algorithms,thus improving the efficiency of big data analysis.This experiment compares the clustered centroid data with the annual average meteorological data of representative cities in the five typical meteorological regions that exist in China,and the results show that the clustering results are in good agreement with the meteorological data obtained from the National Meteorological Science Data Center.This algorithm has a positive effect on the clustering analysis of massive meteorological data and deserves attention in scientific research activities.

关键词： Clustering analysis cloud computing platform parallel algorithm

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A cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries

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Chinese Geographical Science 2023年第4期33卷 649-663页

作者： GUO Tianhao ZHENG Jia WANG Chunmei TAO Zui ZHENG Xingming WANG Qi LI Lei FENG Zhuangzhuang WANG Xigang LI Xinbiao KE Liwei Northeast Institute of Geography and Agroecology Chinese Academy of SciencesChangchun 130102China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Black Soils Conservation and Utilization Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China Changchun Jingyuetan Remote Sensing Test Site Changchun 130102China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China China Centre for Resources Satellite Data and Application China Aerospace Science and Technology CorporationBeijing 100094China College of Geoexploration Science and Technology Jilin UniversityChangchun 130026China School of Geomatics and Prospecting Engineering Jilin Jianzhu UniversityChangchun 130118China

Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(Pcloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.

关键词： soil moisture(SM) Google Earth Engine(GEE) cloud computing platform High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF) remote sensing Sentienl-1 Sentinel-2 Northeast China

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