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Neuro-Prony and Taguchi’s methodology based adaptive autoreclosure scheme for electric transmission systems

D. Fitiwi, K.S. Rama Rao

This paper presents a novel intelligent autoreclosure technique to discriminate temporary faults from permanent faults, and accurately determine fault extinction time. A variety of fault simulations are carried out on a specified transmission line on the standard IEEE 9-bus electric power system using MATLAB/SimPowerSytems. Prony analysis is employed to extract data features from each simulated fault. The fault identification prior to reclosing is accomplished by an artificial neural network trained by Levenberg Marquardt and Resilient Back-Propagation algorithms which are developed using MATLAB. Some important parameters which strongly affect the entire training process are fine-tuned to their corresponding best values with the help of Taguchi’s method. Test results show the robustness and efficacy of the proposed auto-reclosure scheme.


Palabras clave: Adaptive autoreclosure (AR), artificial neural networks (ANNs), Levenberg Marquardt (LM), Prony analysis (PA), resilient backpropagation (RPROP), Taguchi’s method.


IEEE Transactions on Power Delivery. Volumen: 27 Número: 2 Páginas: 575-582

Índice de impacto JCR y cuartil WoS: 1.519 - Q1 (2012); 4.415 - Q1 (2018)

Referencia DOI: DOI icon 10.1109/TPWRD.2011.2182065    

Publicado en papel: Abril 2012.



Cita:
D. Fitiwi, K.S. Rama Rao. Neuro-Prony and Taguchi’s methodology based adaptive autoreclosure scheme for electric transmission systems. IEEE Transactions on Power Delivery. vol. 27, no. 2, pp. 575-582, Abril 2012.


    Líneas de investigación:
  • *Régimen permanente: flujos de cargas, análisis de restricciones de operación, flujos de cargas óptimo, servicio complementario de control de tensiones, cortocircuitos, protecciones en redes de transporte y distribución, modelos térmicos de cables
  • *Modelado, Simulación y Optimización

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