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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
SDRE-based primary control of DC Microgrids equipped by a fault detection/isolation mechanism
Type
JournalPaper
Keywords
Artificial neural networks, DC microgrid, Fault detection, Fault isolation, State-dependent Riccati equation
Year
2022
Journal Energy Reports
DOI
Researchers CHARALAMBOS KONSTANTINOU ، Sohrab Mirsaeidi ، Hassan Bevrani ، Assad Salimi ، Yusef Khayat ، Yazdan Batmani

Abstract

Due to the nonlinear dynamics of direct current (DC) microgrids, the existence of input constraints, and their multi-input multi-output (MIMO) nature, classical linear controllers cannot provide an appropriate performance in a wide range of operations. In this paper, to address these issues, nonlinear suboptimal controllers are systematically developed in the primary layer of DC microgrids by employing a state-dependent Riccati equation (SDRE) methodology. To this end, the whole complexities of the nonlinear dynamics and input constraints are considered in the design procedure of the proposed SDRE controllers. After designing the controllers, and for a fast yet effective fault detection/isolation, an artificial neural network (ANN) is trained to identify the closed-loop microgrid at its nominal condition. Then, the trained ANN is employed to design a fault detection/isolation mechanism. Simulation results of the developed SDRE control scheme augmented by the ANN-based fault detection/isolation mechanism demonstrate the merits of the proposed scheme.