Title :
Artificial neural net based stability study of power systems
Author :
Marpaka, D.R. ; Thursby, Michael H. ; Aghili, Seyed M.
Author_Institution :
Tennessee State Univ., Nashville, TN, USA
Abstract :
A novel approach for studying the transient stability of a power system comprising a single machine connected to an infinite bus using an artificial neural network (ANN) is presented. This approach uses a multilayer ANN for determining the critical clearing time for the system under consideration. The network was trained with the angular positions and angular velocities up to the last switching operation and the corresponding state of the system as the desired output. After the network obtained the capability of determining the state of the system for different angular positions and velocities, it was presented with a set of data representing the operating conditions, not presented during the training, for determining the critical clearing time for the system under consideration. An example of a power system is presented to illustrate the method
Keywords :
neural nets; power system analysis computing; stability; transients; angular positions; angular velocities; artificial neural network; critical clearing time; multilayer neural net; operating conditions; power systems; transient stability; Artificial neural networks; Circuit faults; Circuit stability; Power system analysis computing; Power system interconnection; Power system reliability; Power system stability; Power system transients; Stability analysis; Transient analysis;
Conference_Titel :
Southeastcon '91., IEEE Proceedings of
Conference_Location :
Williamsburg, VA
Print_ISBN :
0-7803-0033-5
DOI :
10.1109/SECON.1991.147744