DocumentCode
1955095
Title
A Hopfield neural network based approach for state estimation of power systems embedded with FACTS devices
Author
Singh, Satish Kumar ; Sharma, Jaydev
Author_Institution
ABB Ltd., Vadodara
fYear
0
fDate
0-0 0
Abstract
Flexible A.C. transmission systems (FACTS) are being used more in large power systems for their significance in manipulating line power flows. Traditional state estimation methods without integrating FACTS devices will not be suitable for power systems embedded with FACTS. In this paper the state estimation of power systems in presence of FACTS devices is presented. Hopfield neural network is simulated as an optimization tool to solve the power system state estimation problem
Keywords
Hopfield neural nets; flexible AC transmission systems; load flow; nonlinear programming; power system analysis computing; power system state estimation; Hopfield neural network; flexible AC transmission system; line power flow manipulation; nonlinear programming; optimization tool; power system state estimation; Computer networks; Embedded computing; Hopfield neural networks; Power system measurements; Power system security; Power system simulation; Power systems; Sparse matrices; State estimation; Time sharing computer systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Power India Conference, 2006 IEEE
Conference_Location
New Delhi
Print_ISBN
0-7803-9525-5
Type
conf
DOI
10.1109/POWERI.2006.1632574
Filename
1632574
Link To Document