Modeling and Simulation of Turbogenerator Using Computational Intelligence

Hayati, Mohsen; Darabi, Kaveh

doi:10.4028/www.scientific.net/amm.110-116.5211

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…It is observed from Figs. 3-6 and Table I that there is a good agreement between experimental and predicted values using RBF network and also the proposed RBF model is more accuracy in comparison with the MLP model [12].…”

Section: Resultsmentioning

confidence: 56%

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Application of Radial Basis Function Network for the Modeling and Simulation of Turbogenerator

Hayati¹,

Rezaei²,

Noori³

2013

JAIT

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In this paper, the applicability of Radial Basis Function (RBF) network for the modeling and simulation of turbogenerators is presented. It is expensive and time- consuming to do experimental work to predict the behaviour of Turbogenerators with changing all variables. The RBF network is developed with speed and excitation current as inputs and voltage, active power and reactive power as desired outputs. The proposed RBF model is developed and trained with MATLAB 7.8 software. To obtain the optimal RBF model several structures have been constructed and tested. The comparison between experimental and predicted values using the proposed RBF model shows that there is a good agreement between them. Moreover, the RBF model is compared with another model named Multi Layer Perceptron (MLP), which is another important architectures of neural networks. The results obtained show that the proposed RBF model is more accurate and reliable than MLP model.

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Section: Resultsmentioning

confidence: 56%

“…From these figures, it is clear that the predicted values using the proposed RBF model are in good agreement with experimental data with least error. Also we have compared the proposed RBF model with MLP model [12] as shown in Table 1, where the mean relative error percentage ( MRE% ) is evaluated as:…”

Section: Resultsmentioning

confidence: 99%