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Issues related to stable microgrid (MG) operation often pose challenges for engineers and researchers, including energy management, power quality maintenance, and the effect of perturbations. Therefore, continuous measurement and monitoring of the system's stability level require special attention. Thus, motivated and focused on addressing these issues, this paper presents a novel decision-making methodology for determining the MG stability index (MGSI) to comprehensively measure the MG stability level. The proposed index is based on continuously measuring fundamental MG parameters, including voltage, frequency, battery state of charge (SOC), and total harmonic distortion. The influence of these parameters on system stability is considered using 256 rules in a Mamdani-based fuzzy inference system. The proposed methodology is evaluated through seven case studies considering different modes of MG operation, types of loads, and availability of power sources and simulated using MATLAB Simulink software. The results demonstrate variations in MGSI for different investigated scenarios, such that a grid-connected MG system with high renewable output and high battery SOC, along with critical/noncritical load, exhibits the best MGSI compared to an islanded MG system with low renewable output, low battery SOC, and an inductive load that shows least MGSI. Additionally, to demonstrate the credibility of the fuzzy-based controller in complex decision-making, a comparative analysis is performed with the analytic hierarchy process under the multi-criteria decision-making technique. The paper also includes sensitivity analysis that reflects the system's sensitivity to different input sources and robustness analysis of the controller. [ABSTRACT FROM AUTHOR]

Types I, III and IV wind distributed generators show different and wide range of current sharing capacity during the occurrence of faults in the distribution feeders. In a hybrid microgrid consisting of any of the above mentioned types of wind distributed generators and photovoltaic distributed generators, the fault current in a feeder shows different behaviour which changes as per the type of distributed generators, grid/islanded connection of microgrid operation, distance of the fault from the point of common coupling and nature of the loads in the microgrid system. The novel contribution of this paper is the implementation of a fuzzy logic based adaptive protection scheme through analysis of the q0 components of fault current which are used to detect the low X/R ratio of distributed generators, modes of operation, transient reactance during the series and shunt faults in types I, III and IV wind turbines and PV integrated hybrid microgrid. Considering q0 components and transient reactance, a new relation between the relay current settings and modes of operation has been identified for adaptive relaying. The effectiveness of the proposed adaptive protection scheme for the hybrid microgrid is verified through a simulation case study using Matlab -Simulink software. [ABSTRACT FROM AUTHOR]

Fault ride through (FRT) capability is an essential practice as per the present grid code demands for grid-connected renewable energy-based distributed energy resources. Studies on FRT capability for grid-connected hybrid systems are rarely found. This study considers a wind energy conversion system and a fuel cell system interconnected at a common dc bus. It proposes a new feed-forward-based FRT control scheme for the inverter control where new current references in dq-axis frame are derived by tracking the positive sequence power. The newly derived references are fed forward to the input of the current regulator of the voltage source inverter. Second, fuzzy logic-based current controllers are suggested to improve the tracking capability of the current references in the inverter control scheme so as to enhance the FRT capability of the hybrid system as a whole. The proposed feed-forward-fuzzy control scheme for achieving an enhanced FRT capability is compared with the conventional dq current control and feed-forward FRT control for various grid voltage sag tests, where the performance of the combined feed-forward-fuzzy control is found better. The validation of the proposed FRT control scheme is performed in MATLAB-Simulink environment. [ABSTRACT FROM AUTHOR]