TY - JOUR
T1 - SDRE-based primary control of DC Microgrids equipped by a fault detection/isolation mechanism
AU - Batmani, Yazdan
AU - Khayat, Yousef
AU - Salimi, Assad
AU - Bevrani, Hassan
AU - Mirsaeidi, Sohrab
AU - Konstantinou, Charalambos
N1 - KAUST Repository Item: Exported on 2022-07-06
Acknowledgements: This work was supported by the National Natural Science Foundation of China (52150410399).
PY - 2022/6/30
Y1 - 2022/6/30
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/679620
UR - https://linkinghub.elsevier.com/retrieve/pii/S2352484722011830
U2 - 10.1016/j.egyr.2022.06.044
DO - 10.1016/j.egyr.2022.06.044
M3 - Article
SN - 2352-4847
VL - 8
SP - 8215
EP - 8224
JO - Energy Reports
JF - Energy Reports
ER -