arXiv subjects

Mohammad Fahim Shakib

Publications and source records attributed to Mohammad Fahim Shakib.

3 recordsLinked to original sources

arXiv2026

Modular Model Order Reduction for Inverter-Based Power Systems with Stability and Accuracy Guarantees

This paper proposes a modular model order reduction framework for large-scale power grids integrated with renewable energy sources. The proposed framework has the advantage of reducing individual sub-areas independently while retaining the power grid interconnection structure. Moreover, given a user-defined accuracy specification on the overall reduced-order interconnected power grid, corresponding target specifications for the individual reduced sub-areas are determined: satisfaction of such individual specifications guarantees the overall user-defined accuracy specification, while also preserving the stability of the overall reduced-order interconnected power grid. The proposed framework is validated on a modified IEEE 118-bus system (where synchronous generators have been replaced by inverter-based resources) by assessing the accuracy specification satisfaction of the overall reduced-order interconnected power grid, in terms of frequency response, stability, active power, and sub-synchronous behaviour when injected with disturbances.

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arXiv2025

Bridging Abstraction-Based Hierarchical Control and Moment Matching: A Conceptual Unification

In this paper, we establish a relation between approximate-simulation-based hierarchical control (ASHC) and moment matching techniques, and build a conceptual bridge between these two frameworks. To this end, we study the two key requirements of the ASHC technique, namely the bounded output discrepancy and the $M$-relation, through the lens of moment matching. We show that, in the linear time-invariant case, both requirements can be interpreted in the moment matching perspective through certain system interconnection structures. Building this conceptual bridge provides a foundation for cross-pollination of ideas between these two frameworks.

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arXiv2025

Data-Driven Model Reduction by Moment Matching for Linear and Nonlinear Parametric Systems

Theory and methods to obtain parametric reduced-order models by moment matching are presented. The definition of the parametric moment is introduced, and methods (model-based and data-driven) for the approximation of the parametric moment of linear and nonlinear parametric systems are proposed. These approximations are exploited to construct families of parametric reduced-order models that match the approximate parametric moment of the system to be reduced and preserve key system properties such as asymptotic stability and dissipativity. The use of the model reduction methods is illustrated by means of a parametric benchmark model for the linear case and a large-scale wind farm model for the nonlinear case. In the illustration, a comparison of the proposed approximation methods is drawn and their advantages/disadvantages are discussed.

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