Abstract
This paper introduces a new ellipsoidal-based tracker design to control a grid-connected hybrid direct current/alternating current (DC/AC) microgrid (MG). The proposed controller is robust against both parameters and load variations. The studied hybrid MG is modelled as a nonlinear dynamical system. A linearized model around an operating point is developed. The parameter changes are modelled as norm-bounded uncertainties. We apply the new extended version of the attractive (or invariant) ellipsoid for this tracking problem. Convex optimization is used to obtain the region’s minimal size where the tracking error between the state trajectories and the reference states converges. The sufficient conditions for stability are derived and solved based on linear matrix inequalities (LMIs). The proposed controller’s validity is shown via simulating the hybrid MG with various operational scenarios. In each scenario, the performance of the controller is compared with a recently proposed sliding mode controller. The comparison clearly illustrates the superiority of the developed controller in terms of transient and steady-state responses.
Highlights
IntroductionTechnology advances in power generation, control, computer hardware, and software have led to the spread of microgrids (MGs)
The MG is the only solution to the power supply of remote areas where the public grid is inaccessible or does not exist [2]
When the CB1 is closed, the load connected to the alternating current (AC)-DG1 is changed at t = 0.8 s, increased by 1.25 kW and 1.0 kVAR
Summary
Technology advances in power generation, control, computer hardware, and software have led to the spread of microgrids (MGs). The MG is the only solution to the power supply of remote areas where the public grid is inaccessible or does not exist [2]. The MG can operate synchronously with the utility grid or in isolated mode When isolated, it can completely control its voltage and frequency, thanks to the dedicated power converters and their advanced control schemes [3]. Communications channels, innovative software, and powerful controllers have instigated MG’s intelligence. Intelligence in this context implies the capability of self-monitoring, evaluation, and decision-making to optimally utilize and operate the available energy resources in addition to the energy-management system [4]
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