Title :
Experimental studies with continually online trained artificial neural network identifiers for multiple turbogenerators on the electric power grid
Author :
Venayagamoorthy, G.K. ; Harley, R.G. ; Wunsch, D.C.
Author_Institution :
Lab. of Appl. Comput. Intelligence, Missouri Univ., Rolla, MO, USA
Abstract :
The increasing complexity of a modern power grid highlights the need for advanced system identification techniques for effective control of power systems. This paper provides a new method for nonlinear identification of turbogenerators in a 3-machine 6-bus power system using online trained feedforward neural networks. Each turbogenerator in the power system is equipped with a neuro-identifier, which is able to identify its particular turbogenerator and the rest of the network to which it is connected from moment to moment, based on only local measurements. Each neuro-identifier can then be used in the design of a nonlinear neurocontroller for each turbogenerator in such a multi-machine power system. Experimental results for the neuro-identifiers are presented to prove the validity of the concept
Keywords :
feedforward neural nets; learning (artificial intelligence); neurocontrollers; power system identification; real-time systems; turbogenerators; electric power grid; feedforward neural networks; identification; multiple machine power system; neural network; neurocontroller; online learning; turbogenerators; Artificial neural networks; Control systems; Feedforward neural networks; Neural networks; Power grids; Power system control; Power system measurements; Power systems; System identification; Turbogenerators;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.939543