This paper compares the performances of a multilayer perceptron network (MLPN) and a radial basis function network (RBFN) for the online identification of the nonlinear dynamics of a synchronous generator. Deviations of signals from their steady state values are used. The computational complexity required to process the data for online training, generalization and online global minimum testing are investigated by time-domain simulations. The simulation results show that, compared to the MLPN, the RBFN is simpler to implement, needs less computational memory, converges faster and global minimum convergence is achieved even when operating conditions change.

Meeting Name

IEEE Power Engineering Society Winter Meeting, 2002


Electrical and Computer Engineering

Keywords and Phrases

Computational Complexity; Computational Memory; Electric Machine Analysis Computing; Generalization; Global Minimum Testing; Identification; Multilayer Perceptron Network; Multilayer Perceptrons; Radial Basis Function Networks; Synchronous Generator Nonlinear Dynamics Identification; Synchronous Generators; Time-Domain Analysis; Time-Domain Simulations; Training; Turbogenerators

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2002 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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