摘要
智能电网的发展必然带来骤增的数据量,对其进行实时分析和控制的难度也随之增加。对此,电力部门的解决办法是使用同步相量测量单元(PMU)和GPS实现实时测量与定位,从而实现全网数据的同步性。但PMU的数量随着智能电网的发展不断增长,其数据量的复杂程度以及增长速度是进行实时分析和控制时所面临的一大挑战,使用传统的关系数据库技术处理有很大难度。文章针对智能电网的大数据进行了分析,提出了一种基于Hadoop的智能电网同步相量数据分析的新框架,实现了分布式并行分析大规模的同步相量数据集,并利用Map Reduce对Hadoop的2个性能指标进行测试,通过并行计算节点分析验证了该框架的有效性。
The development of smart grid will inevitably bring about a surge in the amount of data, and the difficulty of real-time analysis and control also increases. In this regard, the solution adopted by electricity sector is the use of phasor measurement unit （PMU） and GPS to realize the real time measurement and positioning, so as to achieve data synchronization of the entire network. But the number of PMUs along with the development of smart grid continues to grow; the complexity and growth rate of the data volume is a big challenge tbr the real-time analysis and control. The conventional relational database technologies are very difficult to handle this issue. The large data for smart grid are analyzed and a new Hadoop-based framework for the analysis of synchrophasor datasets in smart grid is presented, which realize distributed and parallel analysis of large-scale synchrophasor datasets. The paper uses MapReduce testing two performance indicators of Hadoop, and shows the validity of the framework by parallel computing analysis of the nodes.
出处
《电力信息与通信技术》
2014年第9期1-5,共5页
Electric Power Information and Communication Technology
基金
国家863高技术基金项目(2012AA121005)
