New reduced model approach for power system state estimation using artificial neural networks and principal component analysis

被引：10
|
作者：
Onwuachumba, Amamihe [1 ]
Musavi, Mohamad [2 ]
机构：
[1] Univ Maine, Elect & Comp Engn Dept, Orono, ME 04469 USA
[2] Univ Maine, Coll Engn, Orono, ME USA
来源：
2014 IEEE ELECTRICAL POWER AND ENERGY CONFERENCE (EPEC) | 2014年
关键词：
Artificial neural networks; network observability; power systems; state estimation; principal component analysis; OBSERVABILITY;
D O I：
10.1109/EPEC.2014.40
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：
In this paper a new technique using artificial neural networks and principal component analysis for power system state estimation is presented. This method is applicable to both conventional and renewable energy systems. It does not require network observability analysis and uses fewer measurement variables than conventional techniques. This approach has been successfully implemented on an IEEE 14-bus power system and the results show that this method is very accurate and is ideal for smart grid applications.
引用
收藏
页码：15 / 20
页数：6
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