基于Hadoop的输电线路在线监测数据模型及技术研究

Research on online monitoring data model and technology of transmission line based on Hadoop

输电线路是电力系统中关键的组成部分,输电线路在线监测技术的应用产生了海量线路运行数据,对数据的深入挖掘成为现阶段电力大数据研究的热点。随着智能电网数据应用的深入,为保证电力系统可靠运行提供了新的解决方案。在研究输电线路在线监测数据类型、数据特征、数据需求的基础上,提出了符合智能电网电力大数据结构特征的Hadoop监测数据模型设计,包含了多维度数据信息输入、分布式数据存储、分布式数据处理的三个层次。通过搭建基于Hadoop集群的大数据处理环境,在MapReduce并行运算模式下实现PCA-SVM聚类算法,以输电线路故障类型识别为例,实现了基于数据分析的输电线路故障辨识,验证该模型实现输电线路在线监测的可行性。

Transnfission line is the key part of power system, the application of on-line monitoring technology of transnfission line produces lots of data, and the deep excavation of the data becomes the hot spot of the big data research of power system at present. With the further application of smart grid data, a new solution is provided to ensure the reliable of power system. On the basis of studying the data type, data characteristic and data requirement of the on-line monitoring of transnfission line, the Hadoop monitoring data model design that conforms to the characteristics of the large data structure of smart power grids is proposed. It contains three levels of multi-dimensional data input, distributed data storage, and distributed data processing. By building a big data processing environment based on Hadoop clusters, the PCA-SVM clustering algorithm is implemented under MapReduse parallel computing mode, and the fault type identification of transmission lines is taken as an example, transmission line fault identification based on data analysis is realized, which verifies the feasibility of this model to realize the online monitoring of transmission line.