arXiv subjects

Mahdieh S. Sadabadi

Publications and source records attributed to Mahdieh S. Sadabadi.

8 recordsLinked to original sources

arXiv2026

Neural Network-Based Impedance Identification and Stability Analysis for Double-Sided Feeding Railway Systems

The double-sided power supply railway system increases the simultaneous operation of vehicles on the grid, potentially causing system instability and oscillation overvoltage issues. As vehicles frequently switch operating points during operation, it is essential to analyze system stability across a wide range of conditions. Therefore, accurately identifying the black-box impedance of vehicle converters at multiple operating points is crucial for studying railway vehicle-grid system stability. However, traditional impedance identification methods require extensive data and lack interpretability, leading to significant computational and data burdens. This study introduces an interpretable residual feedforward neural network (ResFNN) combined with SHapley Additive exPlanations for training vehicle impedance models, reducing data requirements while maintaining accuracy. Additionally, a component connection method is proposed for deriving the impedance matrix of a multivehicle railway system under the double-sided feeding mode. This method incorporates the dynamic mobility of vehicles and their positional distribution, and it utilizes the ResFNN to identify impedance for stability analysis. Real operational data from actual railway lines is used as case study to analyze the stability of the double-sided power supply railway system. The results demonstrate that this approach accurately assesses both lowfrequency and high-frequency instability issues.

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arXiv2026

Large-Signal Stability Guarantees for a DC Microgrid with Nested Nonlinear Distributed Control: The Slow Communication Scenario

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.

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arXiv2026

Robust Neural Policy Distillation of Long-Horizon FCS-MPC for Flying-Capacitor Three-Level Boost Converters

Long-horizon finite-control-set model predictive control (FCS-MPC) can improve transient regulation and flying-capacitor balancing in flying-capacitor three-level boost converters (FC-TLBCs). However, searching over switching sequences becomes computationally expensive at high switching frequencies. We train a feedforward neural network to imitate an $N$-step FCS-MPC expert computed with beam search. To improve robustness, expert trajectories are generated under randomized input voltage, load resistance, and component parameters, and a disagreement-based DAgger variant is used to relabel on-policy states where the student and expert disagree. In simulation, the learned policy maintains stable voltage regulation and capacitor balancing under nominal conditions, operating-point changes, and perturbations of several physical parameters. We demonstrate the effectiveness of our approach by reducing the computational burden. We also demonstrate transfer to an NPC-type three-level buck converter, where initializing from the FC-TLBC network improves sample efficiency compared with training from scratch.

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arXiv2026

Distributed Multiple Fault Detection and Estimation in DC Microgrids with Unknown Power Loads

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.

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arXiv2026

Stability of a DC Microgrid with a Nonlinear Nested Control Framework: The Fast Communication Scenario

As modern power systems continue to evolve into multi-agent, converter-dominated systems that demand reliable, stable, and optimal control architectures within an expandable framework, this paper investigates scalable stability guarantees of a promising nonlinear communication-reliant control framework for DC microgrids. Particularly, relying on nested control loops; inner decentralized(primary) and outer distributed(secondary), the control configurations are designed to simultaneously achieve proportional current sharing and voltage containment within pre-specified limits, at the converged steady state. By enforcing sufficient time-scale separation at the boarder between the control loops, the system admits a singular perturbation formulation, allowing global exponential stability (G.E.S.) to be established via Lyapunov arguments. Although the theoretical G.E.S. certificate is structurally scalable, the stability guarantees depends on a sufficiently large permanent leakage, introduced in the primary controller. Thus, the results of this paper emphasize the importance of appropriate practical tuning guidelines and electrical parameter selection. The effectiveness of the proposed method is validated through case studies on a low-voltage DC microgrid under load variations and topological changes (and communication time-delays), followed by a small-signal stability analysis.

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arXiv2026

DC Microgrids with Nested Nonlinear Distributed Control: Scalable Large-Signal Stability and Voltage Containment

This paper investigates a cyber-physical DC microgrid employing a nonlinear distributed consensus-based control scheme for coordinated integration and management of distributed generating units within an expandable framework. Relying on nested primary andsecondary control loops; a (distributed) outer-loop and a (decentralized) inner-loop, the controller achieves proportional current sharing among all distributed generation units, while dynamically operating within predefined voltage limits. A rigorous Lyapunov-based stability analysis establishes a scalable global exponential stability certificate under some tuning conditions and sufficient time-scale separation between the control loops, based on singular perturbation theory. An optimization-based tuning strategy is then formulated to identify and subsequently diminish unstable operating conditions. In turn, various practical tuning strategies are introduced to provide stable operations while facilitating near-optimal proportional current sharing. The effectiveness of the proposed control framework and tuning approaches are finally supported through time-domain simulations of a case-specific low-voltage DC microgrid.

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arXiv2025

Robust Control of Uncertain Switched Affine Systems via Scenario Optimization

Switched affine systems are often used to model and control complex dynamical systems that operate in multiple modes. However, uncertainties in the system matrices can challenge their stability and performance. This paper introduces a new approach for designing switching control laws for uncertain switched affine systems using data-driven scenario optimization. Instead of relaxing invariant sets, our method creates smaller invariant sets with quadratic Lyapunov functions through scenario-based optimization, effectively reducing chattering effects and regulation error. The framework ensures robustness against parameter uncertainties while improving accuracy. We validate our method with applications in multi-objective interval Markov decision processes and power electronic converters, demonstrating its effectiveness.

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arXiv2025

Logic-based Resilience Computation of Power Systems Against Frequency Requirements

Incorporating renewable energy sources into modern power grids has significantly decreased system inertia, which has raised concerns about power system vulnerability to disturbances and frequency instability. The conventional methods for evaluating transient stability by bounding frequency deviations are often conservative and may not accurately reflect real-world conditions and operational constraints. To address this, we propose a framework for assessing the resilience of power systems against disturbances while adhering to realistic operational frequency constraints. Our approach leverages the Lure system representation of power system dynamics and Signal Temporal Logic (STL) to capture the essential frequency response requirements set by grid operators. This enables us to translate frequency constraints into formal robustness semantics. We then formulate an optimization problem to identify the maximum disturbance that the system can withstand without violating these constraints. The resulting optimization is translated into a scenario optimization while addressing the uncertainty in the obtained solution. The proposed methodology has been simulated on the Single Machine Infinite Bus case study and 9-Bus IEEE benchmark system, demonstrating its effectiveness in assessing resilience across various operating conditions and delivering promising results.

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