Análisis Dinámico Comparativo de Métodos de Detección e Identificación de Fallas de Sensado sobre el Control Local de Micro-redes

Byron Ramírez, Leony Ortiz, Wilson Pavón

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

3 Citas (Scopus)

Resumen

The present research develops a comparative study of three different methodologies, which are applied for fault detection and identification (FDI). The studied faults are sensing in AC/DC Hybrid Microgrids (HMG). The study addresses the use of methods based on: Kalman Filter, Artificial Neural Networks and Fuzzy Logic, all applied to local HMG controllers. To compare and validate the performance of the proposed methods, three failure conditions were proposed: operation without fault, abrupt failure or loss of sensing and incipient additive failure. As a conclusion, the Kalman Filter is faster in its execution and decisionmaking, however the method based on Fuzzy Logic presented a lower average for the residual error. All simulations were developed in Matlab/Simulink. Finally, an algorithm based on the minimum error was proposed to allow the automatic selection of one of the studied FDI strategies.

Título traducido de la contribuciónComparative Fault Detection Dynamic Analysis of Identification Methods for Hybrid Micro-grid Sensing using Local Control
Idioma originalEspañol
Páginas (desde-hasta)1-17
Número de páginas17
PublicaciónRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
Volumen2021
N.ºE45
EstadoPublicada - 2021

Nota bibliográfica

Publisher Copyright:
© 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Palabras clave

  • Fault Detection and Identification (FDI)
  • Fuzzy Logic
  • Kalman Filter
  • Local Control
  • Microgrid
  • Neuronal Networks

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