A Bad Data Identification Method for Multiple Spatio-temporal Data in Power Distribution Network

被引：0
|
作者：
Hu, Lijuan [1 ]
Sheng, Wanxing [1 ]
Liu, Keyan [1 ]
Lin, Zhi [2 ]
机构：
[1] China Elect Power Res Inst, Guangzhou, Guangdong, Peoples R China
[2] North China Elect Power Univ, Guangzhou, Guangdong, Peoples R China
来源：
2018 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON) | 2018年
关键词：
Distribution network; Spatio-temporal Data; Bad Data Identification; Hadoop parallelization;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Due to the excessive number of databases, unbalanced development and behindhand sensing infrastructures, distributed network data suffers from inconsistency, data missing, large measurement error and other data quality problems, which hinder the development of smart distribution network. In order to discover more complex deep-seated rules and provide more effective decision support for power system decision-making, it is necessary to study data mining and analysis methods that are suitable for massive data under current situation. This paper studies on the method of identifying bad data for multi-temporal and multi-spatial data in distribution networks and propose a method to identify bad data using likelihood-ratio test for 3D spatio-temporal data. In order to speed up the data processing rate, a 3D-LRT method based on multi-threading and Hadoop parallelization methods is proposed.
引用
收藏
页码：4083 / 4088
页数：6
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