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

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.

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

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(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud 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.

Cloud Computing Technology and Its Applications

Virtualization and distributed parallel architecture are typical cloud computing technologies. In the area of virtuatization technology, this article discusses physical resource pooling, resource pool management and use, cluster fault location and maintenance, resource pool grouping, and construction and application of heterogeneous virtualization platforms. In the area of distributed technology, distributed file system and KeyNalue storage engine are discussed. A solution is proposed for the host bottleneck problem, and a standard storage interface is proposed for the distributed file system. A directory-based storage scheme for Key/Value storage engine is also proposed.

Analysis of CLARANS Algorithm for Weather Data Based on Spark

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.

An Early Evaluation and Comparison of Three Private Cloud Computing Software Platforms

Cloud computing, after its success as a commercial infrastructure, is now emerging as a private infrastructure. The software platforms available to build private cloud computing infrastructure vary in their performance for management of cloud resources as well as in utilization of local physical resources. Organizations and individuals looking forward to reaping the benefits of private cloud computing need to understand which software platform would provide the efficient services and optimum utilization of cloud resources for their target applications. In this paper, we present our initial study on performance evaluation and comparison of three cloud computing software platforms from the perspective of common cloud users who intend to build their private clouds. We compare the performance of the selected software platforms from several respects describing their suitability for applications from different domains. Our results highlight the critical parameters for performance evaluation of a software platform and the best software platform for different application domains.

MobSafe:Cloud Computing Based Forensic Analysis for Massive Mobile Applications Using Data Mining

With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win＋Intel alliance in PC, Android＋ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on cloud computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app＇s virulence or benignancy. Compared with traditional method, such as permission pattern based method, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework （ASEF） and Static Android Analysis Framework （SAAF）, the two representative dynamic and static analysis methods, to evaluate the Android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics of the number of active Android apps, the average number apps installed in one Android device, and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results show that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.