Mahdieh S. Sadabadi

Cornelia Skaga, Mahdieh S. Sadabadi, Gilbert Bergna-Diaz

The increasing integration of renewable energy sources into electrical grids necessitates a paradigm shift toward advanced control schemes that guarantee safe and stable operations with scalable properties. Accordingly, this paper investigates large-signal stability guarantees for cyber-physical DC microgrids employing a nonlinear distributed consensus-based control scheme to enable coordinated integration and management of distributed generation units within an expandable framework. The proposed control framework adopts nested control loops; inner (decentralized) and outer (distributed), specifically designed to simultaneously achieve uniform voltage containment within pre-specified limits, and proportional current sharing in steady state. Our scalable stability result relies on singular perturbation theory and Lyapunov arguments to prove global exponential stability when imposing a sufficient time-scale separation at the border between the nested control loops, while relying on some practical parameter-setting schemes. The effectiveness and versatility of the proposed control strategy are then validated through time-domain simulations performed on a case-specific low-voltage DC microgrid and the modified IEEE 33-bus radial distribution system. Moreover, a small-signal stability analysis is conducted to derive practical guidelines that enhance the applicability of the method.

Jingwei Dong, Mahdieh S. Sadabadi, Per Mattsson et al.

This paper proposes a distributed diagnosis scheme to detect and estimate actuator and power line faults in DC microgrids (e.g., electric-vehicle charging microgrids) subject to unknown power loads and stochastic noise. To address actuator faults, we develop an optimization-based filter design approach within the differential-algebraic equation (DAE) framework, which achieves fault estimation, decoupling from power line faults, and robustness against noise. In contrast, the estimation of power line faults poses greater challenges due to the inherent coupling between fault currents and unknown power loads, especially under insufficient system excitation, where their effects become difficult to distinguish from measurements. To the best of our knowledge, this is the first study to address this critical yet underexplored issue. Our solution introduces a novel differentiate-before-estimate strategy. A set of diagnosis rules based on the temporal characteristics (i.e., duration of threshold violation) of a constructed residual is developed to distinguish step load changes from line faults. Once a power line fault is detected, a regularized least-squares (LS) method is activated to estimate the fault currents, for which we further derive an upper bound on the estimation error. Finally, comprehensive simulations validate the effectiveness of the proposed scheme in terms of estimation accuracy and robustness against disturbances and noise under different fault scenarios.