A Lightweight Framework for Data Acquisition and Quality Monitoring in Power System

被引:0
作者
Xu, Quan [1 ]
Liu, Jiucheng [1 ]
Zhang, Lei [1 ]
机构
[1] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang, Liaoning, Peoples R China
来源
2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2014年
关键词
Power System; Framework; quality monitoring; soft data acquisition;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In power system, data acquisition plays an increasing important role in the field of modern power quality monitoring system. However, data communication expanding range of protocols, increasing the signal type, growing the supervisory instruments has become the major problem. In this paper, we present a power data acquisition and quality monitoring framework (F-PDAQM) that is a software framework which abstracts the most common attributes and behaviors in power quality monitoring system. F-PDAQM was developed in object-oriented with some design patterns, and implemented by using C++. It can achieve data visualization of power system via combining virtualization technology and power quality monitoring system. A significant contribution which F-PDAQM brings is that the problem of multiple communication protocols match with a variety of communication channel is solved. A voltage prediction component is provided to realize the voltage quality prediction. In addition, we propose a new concept of soft data acquisition and provide interfaces which are conducive to realize different algorithms. Soft data acquisition is an extremely important method to assure data integrity. FPDAQM is composed of four layers. This four-layer architecture is designed for extensibility and reusability so that more complex power system problems can be tackled within the framework. It is complementary and compatible of the international standard of the power system-IEC 61850. The research in this paper has been applied to a data acquisition and supervisory system for a substation which demonstrates the validity and flexibility of the framework.
引用
收藏
页码:2956 / 2960
页数:5
相关论文
共 19 条
[1]  
Alpaydin Ethem, 2010, INTRO MACHINE LEARNI, V2nd, P250
[2]  
[Anonymous], 2010, IEEE PES GEN M, DOI [DOI 10.1109/POWERCON.2010.5666519, 10.1109/POWERCON.2010.5666519]
[3]  
Asteriou Dimitros, 2011, APPL ECONOMETRICS
[4]  
Baggini A. B., 2008, HDB POWER QUALITY, P59
[5]  
Cao L., 2006 IEEE INT C INF, P172
[6]   REAL-TIME COMPUTER CONTROL OF POWER-SYSTEMS [J].
DYLIACCO, TE .
PROCEEDINGS OF THE IEEE, 1974, 62 (07) :884-891
[7]   LEARNING AND DEVELOPMENT IN NEURAL NETWORKS - THE IMPORTANCE OF STARTING SMALL [J].
ELMAN, JL .
COGNITION, 1993, 48 (01) :71-99
[8]  
Ester M., P 2 INT C KNOWL DISC, P226
[9]   Measuring electric power quality: Problems and perspectives [J].
Ferrero, Alessandro .
MEASUREMENT, 2008, 41 (02) :121-129
[10]  
Goben Turan, 1986, ELECT POWER DISTRIBU, P30