县级电能质量监测系统数据存储与解析研究

发布时间：2018-04-22 01:18

本文选题：电能质量监测系统 + MongoDB　；参考：《华中科技大学》2015年硕士论文

【摘要】：县级电能质量监测系统作为电能质量监测体系的重要基础,监测节点众多,采集数据量大。随着监测节点和数据的不断增加,电能质量数据呈现海量化趋势,使得县级电能质量监测系统面临着很大的性能压力,系统的扩展成本和人力成本也会急剧攀升。县供电公司由于自身条件的限制,能够投入的资源有限,因此,充分利用已有资源,构建一套高效存储与快速处理海量数据的电能质量监测系统,对于县级供电企业来说具有重要的实际意义。本文重点研究了县级电能质量监测系统中的数据存储与解析问题。首先通过研究国内外电能质量监测系统和NoSQL数据库的发展现状,指出了MongoDB数据库在应对海量电能质量数据上的优势。接着通过对县级电能质量监测系统进行需求分析,明确了系统中的两个关键问题,即海量电能质量数据的存储与解析。然后设计并实现了基于MongoDB的海量电能质量数据存储方案,并实现了一套数据接口。接着在分析已有电能质量数据解析方案不足的基础上,结合Hadoop MapReduce并行框架设计并实现了适合县级电能质量监测系统的海量数据快速解析方案。最后为了验证存储方案和解析方案以及综合方案的性能,对存储方案的数据写入和查询性能、解析方案的数据解析性能以及综合方案的性能进行了测试和分析。测试结果表明,本文提出的方案,相对基于关系型数据库的存储方案,具有更好的数据存储和查询性能,和传统解析方案以及已有的并行解析方案相比,具有更高的解析效率。
[Abstract]:As an important basis of the power quality monitoring system, the county power quality monitoring system is an important basis for the monitoring system of power quality. There are numerous monitoring nodes and large amount of data collection. With the continuous increase of monitoring nodes and data, the power quality data presents a huge trend, which makes the county power quality monitoring system face great performance pressure, the expansion cost of the system and human resources. Therefore, the county power supply company has limited resources due to its own conditions. Therefore, making full use of the existing resources to build a set of efficient storage and rapid processing of mass data power quality monitoring system for the county power supply enterprises is of great importance. This paper focuses on the county power quality. The problem of data storage and analysis in the quantity monitoring system. First, by studying the development status of power quality monitoring system and NoSQL database at home and abroad, this paper points out the advantages of MongoDB database in dealing with mass data of power quality. Then, by analyzing the demand of the county power quality monitoring system, two key points in the system are clearly defined. The key problem is the storage and analysis of mass power quality data. Then, a data storage scheme based on MongoDB is designed and implemented, and a set of data interface is realized. Then, on the basis of analyzing the deficiencies of the existing power quality data analysis scheme, it is designed and implemented at the county level with the Hadoop MapReduce parallel framework. In order to verify the storage scheme and the performance of the solution and the comprehensive scheme, the data write and query performance of the storage scheme, the performance of the data analysis and the performance of the comprehensive scheme are tested and analyzed. The case, which is relatively based on the relational database, has better data storage and query performance. Compared with the traditional parsing scheme and the existing parallel solutions, it has higher resolution efficiency.

【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM76

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