Distributed Adaptive Secondary Control of DC Microgrids With Uncertainties: A Real-Time Parameter Estimation Approach

In direct-current (DC) microgrids (MGs), distributed secondary control is essential for achieving both voltage restoration and accurate current sharing, wherein precise parameter estimation plays a critical role in ensuring satisfactory control performance. To address system uncertainties, this paper presents a cascaded framework consisting of an adaptive parameter estimator and a distributed secondary controller for DC MGs. The proposed framework enables real-time online estimation of transmission line resistance and inductance while fulfilling the voltage restoration and current sharing. Since it is often challenging to validate the persistent excitation (PE) condition in practical DC MGs, we propose two parameter estimation algorithms. The first one is a standard gradient descent estimator, which requires strict PE to be satisfied. The second algorithm integrates preconditioning dynamics with gradient descent and only needs a weaker interval excitation (IE) condition to achieve effective estimation. Moreover, by integrating a dynamic average model with the virtual current derivative (VCD) approach, the DC MG system is reduced to a second-order model, which simplifies the joint design of parameter estimation and control, as well as facilitates closed-loop stability analysis. Finally, the effectiveness of the proposed method is verified through numerical simulations and experiments.