Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP
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Data
2003-12-01
Autores
Silveira, M. C G [UNESP]
Lotufo, A. D P [UNESP]
Minussi, C. R. [UNESP]
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Resumo
This work presents a methodology to analyze transient stability for electric energy systems using artificial neural networks based on fuzzy ARTMAP architecture. This architecture seeks exploring similarity with computational concepts on fuzzy set theory and ART (Adaptive Resonance Theory) neural network. The ART architectures show plasticity and stability characteristics, which are essential qualities to provide the training and to execute the analysis. Therefore, it is used a very fast training, when compared to the conventional backpropagation algorithm formulation. Consequently, the analysis becomes more competitive, compared to the principal methods found in the specialized literature. Results considering a system composed of 45 buses, 72 transmission lines and 10 synchronous machines are presented. © 2003 IEEE.
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Adaptive resonance theory, Fuzzy ARTMAP, Neural network, Power systems, Transient stability analysis, Electric energy systems, Electrical power system, Fuzzy ARTMAP architecture, Synchronous machine, Frequency stability, Fuzzy set theory, Neural networks, Quality control, Standby power systems, Synchronous machinery, Transient analysis, Power quality
Como citar
2003 IEEE Bologna PowerTech - Conference Proceedings, v. 3, p. 339-345.