基于MapReduce的并行化电网运行数据处理方法研究

Research on parallel power grid operation data processing method based on MapReduce

在大规模新能源的新型电力系统中,由于云端电力数据存在结构复杂、数据量大及多维度的特点,因此不利于发挥数据对运行的指导作用,甚至有可能危害电力系统的安全、稳定运行。针对上述问题,文中提出了一种基于MapReduce的电网数据分析方法。其将云计算应用于新型电力系统,并构建了基于MapReduce云计算模型的并行化处理算法,进而提升了系统的响应速度。通过将该方法应用于电网的数据处理结果表明,所提方法可以有效地提高电网运行数据处理的准确性和工作效率。在海量数据的工况下,其处理效率约为30 min,且随着数据量的增加仍可保持稳定性与准确性,实现了网格化的并行分析。

In the large-scale new energy power system,cloud power data has the characteristics of complex structure,large amount of data and multi-dimensional,which is not conducive to the guidance of data on operation,and may even endanger the safe and stable operation of the power system.To solve the above problems,this paper proposes a power grid data analysis method based on MapReduce,applies cloud computing to the new power system,and constructs a parallel processing algorithm based on MapReduce cloud computing model,which improves the system response efficiency.The method is applied to the data processing of power grid.The results show that the proposed method can effectively improve the accuracy and efficiency of power grid operation data processing.Under the condition of a large number of data,the processing efficiency can still be maintained for about 30 minutes,and the stability and accuracy can still be maintained with the increase of the amount of data.The grid parallel analysis is realized.