Research On Decentralized Storage Method And System Realization Of Massive Power Quality Monitoring Data

In order to grasp the operating status of the power grid,dig out measurement index information,and solve power quality problems,the State Grid has built a centralized power quality monitoring system with the big data computing center as the core.The traditional centralized architecture is easy to cause the accumulation of data on the data collection end and the processing end,which causes the data calculation delay to increase continuously,and cannot effectively protect the private data set,the distributed architecture of multiple data centers has become a new trend.In order to achieve hierarchical storage of private data sets and public data sets and reduce the time of data transmission,the reasonable placement of monitoring data is a key issue that needs to be paid attention to in a distributed system architecture.At the same time,in order to further reduce the data transmission time and improve the efficiency of the system,this topic also studies the compression method of power quality monitoring data.The specific research content and research results are as follows:1)Aiming at the problem of optimizing the placement of power quality monitoring data,a data layout strategy based on clustering-genetic algorithm is proposed.This method is based on the initial population generation algorithm of data dependency clustering,so that the individuals in the initial population have a certain clustering accuracy.And there is a strong diversity.The initial population generation algorithm and the adaptive genetic operator cooperate with each other,which effectively enhances the optimization ability of the algorithm.Experiments show that this method effectively reduces the data transmission time across data centers by about 15% on average,but there are also problems with low clustering accuracy within the initial population and high similarity between individuals.2)Further,a data placement strategy based on improved particle swarm algorithm is proposed.Markov random walk algorithm and particle swarm algorithm are used to improve the placement strategy.Experiments show that this method effectively solves the problems existing in the clustering-genetic algorithm.3)Aiming at the problems of large data files and high redundancy in power quality monitoring data,the structure and timing characteristics of the monitoring data are analyzed,and a data compression coding scheme based on summary forest and time difference processing is designed.Experiments show that the program effectively compresses the volume of the monitoring data file,and the volume of the processed data file is only about 10% of the original data.4)Based on the above research results,the data placement module and data compression module in the grid power quality monitoring system are designed and implemented.