Hadoop数据存储分析技术在风电并网系统中的应用  

Application of Data Storage and Analysis Technology of Hadoop to Wind Power Grid-connected System

作　　者：韩平平[1] 张祥民 丁明[1] 张晓安[2] 

机构地区：[1]合肥工业大学电气与自动化工程学院,合肥230001 [2]合肥工业大学智能制造研究院,合肥230001

出　　处：《电力系统及其自动化学报》2018年第1期43-50,共8页Proceedings of the CSU-EPSA

基　　金：国家自然科学基金资助项目(51207039);广东省引进创新科研团队计划资助项目(2011N015);国家电网公司科技项目(NY71-14-036)

摘　　要：随着风电规模的不断扩大,现有数据处理方案将难以适应风电并网环境对海量数据高效存储分析的要求。本文将分布式系统基础架构Hadoop应用于风电数据的存储和分析,给出了基于分布式文件系统HDFS的风电数据存储方案。将均方根RMS转化算法基于并行计算框架MapReduce实现,对存储于HDFS的低电压穿越LVRT测试数据进行分析计算。通过存储耗时对比实验,验证了HDFS在存储LVRT数据方面的高效性。通过RMS算法计算耗时对比实验,验证了MapReduce算法在分析计算LVRT数据方面的优越性。算例结果表明,将Hadoop数据存储分析技术应用于风电并网系统是可行的。With the expanding scale of wind power, the existing data processing scheme will be difficult to adapt to the requirements of wind power grid-connected environment due to mass data storage and efficient analysis. In this paper, Hadoop is applied to the storage and analysis of wind power data, and the storage scheme of wind power data based on Hadoop distributed file system （HDFS） is presented. The root mean square （RMS） transformation algorithm based on MapReduce is implemented, then the low voltage ride through （LVRT） test data stored in HDFS is analyzed and calcu- lated. Through the contrast experiment on storage time-consumption, the high efficiency of HDFS in the storage of LVRT data is verified. Through the contrast experiment on computational time-consumption of RMS algorithm, the supe- riority of the MapReduce algorithm in the analysis of LVRT data is proved. The results of an example show that it is fea- sible to use the data storage and analysis technology of Hadoop in the wind power grid-connected system.

关 键 词：HADOOP 分布式文件系统 MAPREDUCE 低电压穿越 存储耗时 计算耗时 

分 类 号：TM769[电气工程—电力系统及自动化]