Title :
Adaptive neural network identifiers for effective control of turbogenerators in a multimachine power system
Author :
Venayagamoorthy, G.K. ; Harley, R.G. ; Wunsch, Donald C.
Author_Institution :
Dept. of Electron. Eng., ML Sultan Technikon, Durban, South Africa
Abstract :
This paper provides a novel method for nonlinear identification of multiple turbogenerators in a five-machine 12-bus power system using continually online trained (COT) artificial neural networks (ANNs). Each turbogenerator in the power system is equipped with all adaptive ANN identifier, which is able to identify/model its particular turbogenerator and rest of the network to which it is connected from moment to moment, based on only local measurements. Each adaptive ANN turbogenerator can be used in the design of a nonlinear controller for each turbogenerator in a multimachine power system. Simulation results for the adaptive ANN identifiers are presented
Keywords :
adaptive control; control system analysis; control system synthesis; learning (artificial intelligence); machine control; neurocontrollers; power system control; power system identification; turbogenerators; adaptive neural network identifiers; continually online trained artificial neural networks; control simulation; multimachine power system; nonlinear control design; turbogenerator control; Adaptive control; Adaptive systems; Artificial neural networks; Neural networks; Power measurement; Power system measurements; Power system modeling; Power system simulation; Programmable control; Turbogenerators;
Conference_Titel :
Power Engineering Society Winter Meeting, 2001. IEEE
Conference_Location :
Columbus, OH
Print_ISBN :
0-7803-6672-7
DOI :
10.1109/PESW.2001.917265
