Title :
Reduced Model for Power System State Estimation Using Artificial Neural Networks
Author :
Onwuachumba, Amamihe ; Yunhui Wu ; Musavi, Mohamad
Author_Institution :
Electr. & Comput. Eng. Dept., Univ. of Maine, Orono, ME, USA
Abstract :
In this paper a new technique using artificial neural networks for power system state estimation is presented. This method does not require network observability analysis and uses fewer measurement variables than conventional techniques. This approach has been successfully implemented on 6-bus and 18-bus power systems and the results are provided.
Keywords :
neural nets; observability; power system state estimation; 18 bus power systems; 6 bus power systems; artificial neural networks; measurement variables; network observability analysis; power system state estimation; Artificial neural networks; Power measurement; Reactive power; State estimation; Testing; Vectors; Artificial neural networks; network observability; power systems; state estimation;
Conference_Titel :
Green Technologies Conference, 2013 IEEE
Conference_Location :
Denver, CO
Print_ISBN :
978-1-4673-5191-1
DOI :
10.1109/GreenTech.2013.69
