随着不断扩大电能质量的监测规模以及不断提高电能质量监测数据的采集频率，对电能质量的长期监测将形成电能质量大数据。现有的电能质量监测系统主要利用关系型数据库存储数据，采用传统的集中式处理方式分析计算数据。面对日益大量而繁杂的电能质量监测数据，现有的电能质量监测系统不能满足电能质量大数据的存储和计算要求，分析计算效率低，很难有效利用电能质量大数据。近年云计算作为互联网与大数据研究的热门，具有可靠性高、效率快、拓展性好等特点。 论文针对电能质量监测云平台展开以下研究：通过对云平台中的Hadoop集群和MapReduce并行计算框架等组件的分析，研究电能质量大数据高效存储和分析的云计算平台。在数据存储方面，利用分布式数据库HBase，实现了电能质量数据的集中存储和列族存储，提高了数据检索效率，保证了数据的安全可靠。在数据处理方面，实现了基于MapReduce并行计算框架的电能质量数据统计和评估算法，增强了分析处理过程并发性，提高了电能质量计算分析效率，通过JavaEE与Hadoop的交互实现了电能质量数据存储和计算程序的编写、调试和运行。云计算技术解决了海量电能质量数据的存储和计算效率问题。 最后利用实验室现有的若干台廉价主机搭建起Hadoop集群，进行了实验分析。实验结果表明，基于云计算的电能质量监测平台具有分析处理海量数据效率高，高可靠存储的优势，证明了云平台的高效性和优越性，为电能质量监测提供了新方案。

With the continuous expansion of the scale of power quality monitoring and the continuous improvement of the frequency of power quality monitoring data acquisition, the long-term monitoring of power quality will form the power quality big data. The existing power quality monitoring system mainly uses the relational database to store data, and uses the traditional centralized processing method to analyze the data. In the face of increasingly large and complex power quality monitoring data, the existing power quality monitoring system can not meet the requirements of large data storage, analysis and low calculation efficiency, it is difficult to effectively use the power quality big data.In recent years, cloud computing as a popular research of Internet and big data has the feature of high reliability, fast processing of large data and good expansibility and so on. According to the power quality monitoring cloud platform launches the following research:analysis of components such as parallel computing framework based on cloud platform in the Hadoop cluster and MapReduce, research on energy efficient storage and analysis of large data quality of cloud computing platform. In the aspect of data storage, the distributed data base HBase is used to realize the centralized storage and column store of power quality data,the data retrieval efficiency is improved and safety and reliability of data is ensured. In the aspect of data processing, based on MapReduce parallel computing framework of power quality statistics and evaluation algorithm, the analysis process of concurrency is enhanced, the energy efficiency of the calculation and analysis of quality is improved, through the interaction between JavaEE and Hadoop,the power quality data storage and calculation program is compiled, debugged and run. Cloud computing technology solves the problem of storage and computing efficiency of mass power quality data. At last, the Hadoop cluster is built by using a number of cheap host computers in the laboratory, and the experimental analysis is carried out.The experimental results show that the cloud computing platform with power quality monitoring and analysis of massive data processing based on high efficiency, high reliability storage advantages, proved the effectiveness and superiority of the cloud platform and provided a new scheme for power quality monitoring.