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Hassan Bevrani

Hassan Bevrani

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId: 55913436700
Faculty: Faculty of Engineering
Address: Dept. Of Electrical and Computer Eng, University of Kurdistan, Allameh Hamdi Blvd, Sanandaj PO Box 416, P. C: 66177-15175, Kurdistan, Iran
Phone: +98-87-33624001

Research

Title
Model-Based Fault Detection in DC Microgrids
Type
Presentation
Keywords
component faults , DC Microgrid , H _/ H ∞ based fault detection , Kalman filter , model-based fault detection
Year
2019
Researchers Asaad Salimi ، Hassan Bevrani ، Yazdan Batmani

Abstract

Recently DC Microgrids (DC-MGs) are more attractive and effective in renewable energy resources (RERs). In this paper, for the protection of devices and detecting parametric faults, two model-based fault detection strategies are presented for DC-MGs. The proposed strategies include a fault detection filter (FDF), named H_/H∞ based filter, and Kalman based filter. H_/H∞ based filter can minimize disturbance effects and maximize the fault effects on the so-called residual signal. The results of H_/H∞ based filter are compared with the Kalman based filter. Results show that the H_/H∞ based filter is more effective for detection of parametric faults in DC-MGs.