New Reduced Model approach for Power System State Estimation Using Artificial Neural Networks and Principal Component Analysis

Author

Onwuachumba, Amamihe ; Musavi, Mohamad

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Maine, Orono, ME, USA

fYear

2014

Firstpage

15

Lastpage

20

Abstract

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.

Keywords

neural nets; power engineering computing; power system state estimation; principal component analysis; IEEE 14-bus power system; artificial neural networks; power system state estimation; principal component analysis; reduced model approach; renewable energy systems; smart grid; Artificial neural networks; Observability; Power systems; Principal component analysis; State estimation; Topology; Vectors; Artificial neural networks; network observability; power systems; principal component analysis; state estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Power and Energy Conference (EPEC), 2014 IEEE

Type

conf

DOI

10.1109/EPEC.2014.40

Filename

7051666