Determination of microgrid stability index based on measured electrical parameters and Mamdani fuzzy inference system

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.