Low-Model-Dependency Adaptive Droop Control for Islanded DCMGs Using EKF Estimation and Fuzzy Logic Damping

This paper presents a low-model-dependency adaptive droop control strategy for islanded direct current microgrids (DCMGs), integrating real-time state estimation and fuzzy logic-based damping. The decentralized approach combines an Extended Kalman Filter (EKF) for local estimation of output current and DC bus voltage, with a Fuzzy Logic Controller (FLC) that dynamically adjusts a virtual damping term to suppress low-frequency oscillations. Designed for buck-converter-based distributed generation (DG) units, the proposed method improves voltage stability and power sharing without requiring inter-unit communication. A detailed nonlinear model of the DCMG, incorporating converter dynamics and line impedances, is validated through MATLAB/Simulink simulations under two realistic scenarios: step load variation and plug-and-play DG reconnection. Results show an oscillation-free response, enhanced voltage regulation, and reduced power-sharing error compared to conventional droop control, confirming the method's robustness and suitability for decentralized microgrid applications.