Title :
Topological observability: Artificial neural network application based solution for a practical power system
Author :
Jain, Amit ; Balasubramanian, R. ; Tripathy, S.C.
Author_Institution :
Int. Inst. of Inf. Technol., Hyderabad, India
Abstract :
An artificial neural network application based method for solving the topological observability problem of power systems is presented in this paper. Back-propagation and quickprop algorithms have been used for training the artificial neural networks used for present solution technique and the method has been successfully implemented on the standard 5-bus power system and on a practical 87-bus power system and the results are presented.
Keywords :
artificial intelligence; backpropagation; neural nets; power engineering computing; power system state estimation; 87-bus power system; artificial neural network training; backpropagation algorithm; power system state estimation; quickprop algorithm; standard 5-bus power system; topological observability problem; Artificial neural networks; Control systems; Monitoring; Observability; Power system control; Power system modeling; Power system security; Power systems; Real time systems; State estimation; Artificial neural network; power systemsstate estimation; topological observability;
Conference_Titel :
Power Symposium, 2008. NAPS '08. 40th North American
Conference_Location :
Calgary, AB
Print_ISBN :
978-1-4244-4283-6
DOI :
10.1109/NAPS.2008.5307305
