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With the ever-increasing development of microgrids to improve power supply reliability and resilience, large-signal stability analysis of microgrids is needed to provide accurate insights into system dynamics during significant disturbances and to offer a robust and comprehensive assessment of microgrids' ability to return to normal operation after contingencies. This paper presents a comprehensive analytical and critical review of large-signal stability analysis methods for inverter-based AC microgrids supported by both theoretical analyses and numerical case studies. These analyses and case studies are developed and implemented to analyze, compare, and identify the gaps in existing large-signal stability assessment methods, which have not been investigated in the literature. More specifically, the recent developments in large-signal stability analysis techniques for microgrids, including Lyapunov-based methods and energy function analysis, are reviewed and discussed. Also, as the accuracy of the dynamic models of inverterbased resources (IBRs) is a crucial factor for authentic and accurate stability assessment, the impacts of full-, reduced-, and second-order dynamic models of IBRs on the accuracy of large-signal stability assessment in microgrids are numerically scrutinized. The study concludes by identifying the applicability of existing stability analysis methods for microgrids (e.g., Krasovskii's, Popov-Lure, and sum of squares (SOS)-based methods) and presenting their challenges for further investigation in the field of large-signal stability of microgrids using numerical case studies. This comparative assessment of large-signal stability assessment provides an informative analysis of the system's capability to operate under contingencies and helps to set the protection system efficiently and prevent unnecessary outages and trips. The numerical assessment shows that SOS-based stability assessment can provide a more realistic and less conservative stability region. It is worth mentioning that other approaches are computationally more efficient and can be applied for online applications and control objectives.INDEX TERMS Large-signal stability, microgrids, inverter-based DER, Lyapunov function, the domain of attraction, nonlinear dynamic model, sum of squares method, Popov-Lure, Krasovskii's method. I. INTRODUCTIONM ICROGRIDS are more vulnerable to becoming unstable due to faults and large disturbances than larger and interconnected power grids because microgrids are smallscale and localized power systems with low or no inertia distributed energy resources (DERs). Evaluating the robustness of microgrids in the face of significant disturbances, the process referred to as large-signal stability assessment, helps to understand the dynamic behavior of DERs, ensures stable operation during islanded mode and generation shortages, addresses nonlinearities and control interactions, and ensures transient and steady-state stability.The importance and distinction of large-signal stability analysis for microgrids can be...
With the ever-increasing development of microgrids to improve power supply reliability and resilience, large-signal stability analysis of microgrids is needed to provide accurate insights into system dynamics during significant disturbances and to offer a robust and comprehensive assessment of microgrids' ability to return to normal operation after contingencies. This paper presents a comprehensive analytical and critical review of large-signal stability analysis methods for inverter-based AC microgrids supported by both theoretical analyses and numerical case studies. These analyses and case studies are developed and implemented to analyze, compare, and identify the gaps in existing large-signal stability assessment methods, which have not been investigated in the literature. More specifically, the recent developments in large-signal stability analysis techniques for microgrids, including Lyapunov-based methods and energy function analysis, are reviewed and discussed. Also, as the accuracy of the dynamic models of inverterbased resources (IBRs) is a crucial factor for authentic and accurate stability assessment, the impacts of full-, reduced-, and second-order dynamic models of IBRs on the accuracy of large-signal stability assessment in microgrids are numerically scrutinized. The study concludes by identifying the applicability of existing stability analysis methods for microgrids (e.g., Krasovskii's, Popov-Lure, and sum of squares (SOS)-based methods) and presenting their challenges for further investigation in the field of large-signal stability of microgrids using numerical case studies. This comparative assessment of large-signal stability assessment provides an informative analysis of the system's capability to operate under contingencies and helps to set the protection system efficiently and prevent unnecessary outages and trips. The numerical assessment shows that SOS-based stability assessment can provide a more realistic and less conservative stability region. It is worth mentioning that other approaches are computationally more efficient and can be applied for online applications and control objectives.INDEX TERMS Large-signal stability, microgrids, inverter-based DER, Lyapunov function, the domain of attraction, nonlinear dynamic model, sum of squares method, Popov-Lure, Krasovskii's method. I. INTRODUCTIONM ICROGRIDS are more vulnerable to becoming unstable due to faults and large disturbances than larger and interconnected power grids because microgrids are smallscale and localized power systems with low or no inertia distributed energy resources (DERs). Evaluating the robustness of microgrids in the face of significant disturbances, the process referred to as large-signal stability assessment, helps to understand the dynamic behavior of DERs, ensures stable operation during islanded mode and generation shortages, addresses nonlinearities and control interactions, and ensures transient and steady-state stability.The importance and distinction of large-signal stability analysis for microgrids can be...
The effective operation of model-based control strategies in modern energy systems, characterized by significant complexity, is contingent upon highly accurate large-scale models. However, achieving such precision becomes challenging in complex energy systems rife with uncertainties and disturbances. Controlling different parts of the energy system poses a challenge to achieving optimal power system efficiency, particularly when employing model-based control strategies, thereby adding complexity to current systems. This paper proposes a novel model-independent control approach aimed at augmenting transient stability and voltage regulation performance in multi machine energy systems. The approach involves the introduction of an optimized model-free fractional-order-based excitation system stabilizer for synchronous generators in a multi machine energy system. To overcome the limitations associated with complex system model identification, which add degrees of simplification at defined operating conditions and assume the system model remains fixed despite high uncertainty and numerous disturbances, an optimal model-independent fractional-order-based excitation control strategy is introduced. The efficacy of the proposed approach is validated through comparative numerical analyses using the MATLAB/Simulink environment. These simulations were conducted on a two-area, 12-bus multi-machine power system. Simulation results demonstrate that the presented excitation system stabilizer outperforms conventional controllers in terms of transient and small-signal stability. It also suppresses the low-frequency electromechanical oscillations within the multimachine energy system.
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