Title :
On-line identification of synchronous generator using neural networks
Author :
Shamsollahi, P. ; Malik, O.P.
Author_Institution :
Dept. of Electr. & Comput. Eng., Calgary Univ., Alta., Canada
Abstract :
Due to their approximation capabilities and inherent adaptivity features, neural networks have been employed in modelling of complex nonlinear systems. This paper presents an approach to effectively identify a synchronous generator when only the inputs and outputs are accessible for measurement. Such an identifier (or model) can be used in an indirect adaptive control or internal model control schemes. A one-hidden-layer feedforward neural network is proposed to identify the synchronous generator. Training is done in an on-line mode to allow the identifier to track the variable-parameter nonlinear plant, giving an adaptive attribute to the identifier. Simulation results are presented to complement the theoretical discussion
Keywords :
electric machine analysis computing; feedforward neural nets; learning (artificial intelligence); parameter estimation; synchronous generators; approximation capabilities; complex nonlinear systems; indirect adaptive control; internal model control; neural networks; on-line identification; one-hidden-layer feedforward neural network; simulation; synchronous generator; training; variable-parameter nonlinear plant; Adaptive control; Artificial neural networks; Biological neural networks; Difference equations; Feedforward neural networks; Neural networks; Nonlinear systems; Power system modeling; Power systems; Synchronous generators;
Conference_Titel :
Electrical and Computer Engineering, 1996. Canadian Conference on
Conference_Location :
Calgary, Alta.
Print_ISBN :
0-7803-3143-5
DOI :
10.1109/CCECE.1996.548223
