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.
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