Monitoring Data Analysis Technology of Smart Grid Based on Cloud Computing

Abstract

Abstract In the smart grid environment, the state data has the characteristics of wide area, panorama, mass and reliability. The traditional storage hardware uses disk arrays and the database management software uses the relational database system. Due to the poor system scalability, high cost and low reliability, it is difficult Adapt to requirements. Based on the above background, the purpose of this article is to study the research of cloud computing-based smart grid monitoring data analysis technology. Aiming at the problem that the power equipment condition monitoring data is getting larger and larger, and traditional storage methods cannot meet the condition monitoring big data storage problem, a cloud platform-based condition monitoring data storage system is designed. Using the distributed file system HDFS and Hbase databases to store status monitoring data, this paper designs a clustering algorithm based on the density cluster structure, DBCLustering, based on the shortcomings of traditional density clustering algorithms. This algorithm first builds the core reachability relationship of the data nodes The index structure CR-Tree is then used to extract a sorted linear table about the reachability relationship of the data. Finally, the clustering results are output according to the results of the linear table. In order to solve the problem of insufficient computing power of the stand-alone version of the algorithm, a clustering algorithm for condition monitoring data based on Spark-RDD-DBClustering algorithm is proposed. This algorithm realizes the parallel application of DBClustering algorithm under Spark platform, and improves the algorithm’s ability to process large-scale data.