SDRE-based primary control of DC Microgrids equipped by a fault detection/isolation mechanism. (November 2022)

• Record Type: Journal Article
• Authors: Batmani, Yazdan; Khayat, Yousef; Salimi, Assad; Bevrani, Hassan; Mirsaeidi, Sohrab; Konstantinou, Charalambos
• Is Part Of: Energy reports. Volume 8(2022)
• Page Start: 8215
• Page End: 8224
• Publication Date: 2022-11
• Subjects: Artificial neural networks -- DC microgrid -- Fault detection -- Fault isolation -- State-dependent Riccati equation; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.06.044
• Languages: English
• ISSNs: 2352-4847

Abstract

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. Graphical abstract: Highlights: Unlike the conventional PI-based voltage and current loop controllers in the primary level, a suboptimal SDRE control scheme is developed for DC microgrid applications. Using the SDRE technique, nonlinear suboptimal control laws are systematically achieved for the DC microgrid by considering the nonlinear dynamics and input constraints. An intelligent effective fault detection and isolation mechanism is designed for the DC microgrid.