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|
|Número de páginas||17|
|Publicación||RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao|
|Estado||Publicada - 2021|
Nota bibliográficaPublisher Copyright:
© 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.
- Fault Detection and Identification (FDI)
- Fuzzy Logic
- Kalman Filter
- Local Control
- Neuronal Networks