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 contribución | Comparative Fault Detection Dynamic Analysis of Identification Methods for Hybrid Micro-grid Sensing using Local Control |
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Idioma original | Español |
Páginas (desde-hasta) | 1-17 |
Número de páginas | 17 |
Publicación | RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao |
Volumen | 2021 |
N.º | E45 |
Estado | Publicada - 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