Design And Implemention Of Data Visualization Subsystem Based On Massive Data Of Power Grid

With the rapid development of information technologies,information is everywhere,and society has entered the era of big data.Big data of smart grid is not only the in-depth application of big data technology in the field of energy,but also the in-depth integration of energy production,energy consumption,related technology revolution and big data concept,accelerating the development of energy industry and innovation of business model.Therefore,smart grid big data plays a great role in the national strategic development.Big data of smart grid mainly come from aspects of power generation,transmission,substation,consumption,dispatching and so on,with the characteristics of massiveness,timeliness and high dimensionality.In order to adapt to the characteristics of smart grid data and display data characteristics efficiently,a visual platform based on big data of smart grid is urgently needed.This article studies and realizes data visualization subsystem based on big data of smart grid.Grid node data is massive and highly correlated with geospatial locations.Therefore,in order to effectively display the grid topology data,the visualization subsystem adopts the offline map loading technology,the topology generation technology to presents the grid node data on the offline map,which realizes that the topology data is highly correlated with the geographic location.In order to optimize the user service,the system studies and implements the data cache optimization technology,which greatly improves the efficiency of grid topology data access.The original data structure of smart grid is complex,high dimensions and difficult to visualize.In view of this problem,the visualization subsystem proposes a technical scheme that includes high-dimensional data preprocessing,data dimension reduction and parallel coordinate visualization,which realizes the efficient visualization of high-dimensional smart grid data.This article first explains the research background and significance of data visualization subsystem based on big data of power grid,and studies relevant technologies involved in the research,including offline map loading technology,grid topology generation technology,data cache based on front-end performance optimization technology,high-dimensional data dimensionality reduction techniques and data visualization techniques.Through the analysis of user usage scenarios,the system needs to meet the functional and non-functional needs.According to specific needs,the key issues is analyzed and propose the final solution.Including:(1)In order to correlate the geographic information of grid topology node,based on the map tile pyramid model,the system has realized the offline map packet mosaic technology based on resolution.(2)In order to match the geospatial characteristics of smart grid nodes,the topology structure of browser-side smart grid topology is constructed by using OpenGL latitude and longitude acquisition technology.(3)In order to effectively improve the system access efficiency,the system uses the influence factor,the vulnerability factor and the topological structure factor as the cache weight judgment factors to realize the front-end data caching algorithm.The algorithm is validated to effectively improve the generation efficiency of visual view components.(4)In order to reduce the dimensionality of high-dimensional grid data,PCA algorithm is used to standardize grid data and reduce dimensionality.(5)In order to visualize the high dimensional data of smart grid,visualization of structured data is carried out by using parallel coordinate visualization technology.Based on the above analysis,the overall architecture and interaction between layers of data visualization subsystem based on big data in smart grid are designed.By analyzing the typical scene of the subsystem application,the key system module implementation is described in detail.Finally,the data visualization subsystem is fully deployed and tested.The analysis of the test results verifies the correctness and effectiveness of the subsystem.Finally the author pointed out that the shortcomings of research,and gave the subject of research prospects.