Title :
Experimental studies with a neural network eased power system stabilizer
Author :
Zhang, Y. ; Malik, O.P.
Author_Institution :
Smart Technol. Inc., Calgary, Alta., Canada
Abstract :
Employing an inverse input/output mapped artificial neural network (ANN) as a controller, an ANN based power system stabilizer (PSS) has been implemented and tested, in the laboratory environment. Experimental test results are presented in this paper. The ANN is trained off-line using the data generated by an adaptive PSS controlling the generating unit under typical disturbances. Test results show that the proposed ANN PSS exhibits very good performance in damping power system low frequency oscillations and greatly improves power system stability
Keywords :
backpropagation; damping; neural nets; oscillations; power system control; power system stability; adaptive PSS; error backpropagation; generating unit control; inverse input/output mapped artificial neural network; neural network eased power system stabilizer; off-line training; oscillations damping; power system low frequency oscillations; Adaptive control; Artificial neural networks; Control systems; Damping; Frequency; Neural networks; Power system stability; Power systems; Programmable control; System testing;
Conference_Titel :
Intelligent Systems Applications to Power Systems, 1996. Proceedings, ISAP '96., International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-3115-X
DOI :
10.1109/ISAP.1996.501052