Hadoop-based framework for big data analysis of synchronised harmonics in active distribution network

被引：15
|
作者：
Cao, Zijian [1 ]
Lin, Jin [1 ]
Wan, Can [2 ]
Song, Yonghua [1 ,2 ]
Taylor, Gareth [3 ]
Li, Maozhen [4 ]
机构：
[1] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China
[2] Zhejiang Univ, Coll Elect Engn, Hangzhou, Zhejiang, Peoples R China
[3] Brunel Univ, Brunel Inst Power Syst, London, England
[4] Brunel Univ, Sch Engn & Design, London, England
来源：
IET GENERATION TRANSMISSION & DISTRIBUTION | 2017年 / 11卷 / 16期
基金：
中国博士后科学基金; 英国工程与自然科学研究理事会; 中国国家自然科学基金;
关键词：
STATE-ESTIMATION; DATA ANALYTICS; KALMAN FILTER; POWER-QUALITY; SYSTEM; PLATFORM;
D O I：
10.1049/iet-gtd.2016.1723
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Synchronised harmonic analysis that utilises GPS signals to synchronise harmonic measurements enables advanced power quality analysis in smart grid. However, it is difficult to put this promising technology into practice due to an extremely high requirement on data storage and processing. To overcome this problem, this study presents a novel Hadoop-based framework for big data analysis of synchronised harmonics in distribution networks. The proposed framework facilitates harmonic data processing associated with data storage and advanced analysis based on big data techniques. Under this framework, a big matrix multiplication algorithm based on MapReduce programming model solving the harmonic state estimation problem is deployed in the stage of data processing. A MapReduce-based harmonics distortion calculation is implemented as an advanced analysis for harmonics. Comprehensive numerical studies are carried out to analyse the characteristics of the proposed algorithm and verify the effectiveness of the proposed framework.
引用
收藏
页码：3930 / 3937
页数：8
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