This paper describes an on-line identification technique for modelling a turbogenerator system. The dynamics of a single turbogenerator infinite bus system are modelled using an adaptive artificial neural network identifier (AANNI) based on continual online training (COT). This paper goes further to show that multilayered perceptrons with deviation signals as inputs and outputs trained using the standard backpropagation algorithm retain past learned information despite COT. Simulation and practical results are presented.
G. K. Venayagamoorthy and R. G. Harley, "Implementation of an Adaptive Neural Network Identifier for Effective Control of Turbogenerators," Proceedings of the International Conference on Electric Power Engineering, 1999. PowerTech Budapest 99, Institute of Electrical and Electronics Engineers (IEEE), Jan 1999.
The definitive version is available at https://doi.org/10.1109/PTC.1999.826565
International Conference on Electric Power Engineering, 1999. PowerTech Budapest 99
Electrical and Computer Engineering
Keywords and Phrases
Adaptive Neural Network Identifier; Backpropagation; Backpropagation Algorithm; Continual Online Training; Deviation Signals; Dynamics Modelling; Identification; Inputs; Machine Control; Multilayer Perceptrons; Multilayered Perceptrons; On-Line Identification Technique; Outputs; Power Engineering Computing; Turbogenerator Infinite Bus System Dynamics; Turbogenerator System Modelling; Turbogenerators; Turbogenerators Control
Article - Conference proceedings
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