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.
J. Park et al., "Comparison of MLP and RBF Neural Networks using Deviation Signals for On-Line Identification of a Synchronous Generator," Proceedings of the IEEE Power Engineering Society Winter Meeting, 2002, Institute of Electrical and Electronics Engineers (IEEE), Jan 2002.
The definitive version is available at https://doi.org/10.1109/PESW.2002.984998
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
Article - Conference proceedings
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