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arxiveess.SY2026-07-07

Generalized Feedback Control Modeling Method for Control-Driven Converter Systems

Chen Zhang, Haoxiang Zong, Xu Cai, Marta Molinas

Converters-based systems like wind farms manifest themselves as control-intensive systems, where control-driven stability issues frequently occur, e.g., oscillations. Such issues are popularly studied via circuit impedance-based methods. However, given its implicit controller modeling trait, the impedance-based methods have limitations in system analysis and designs involving large-scale controllers. To address this issue, this paper presents a novel frequency domain modeling framework, as a perspective shift from the circuit to the control system. Since the obtained model features a multi-input-multi-output (MIMO) feedback control structure and explicit controller placement, it is termed the Generalized Feedback Control (GFC) model. GFC modeling is conducted for both single and multi-converter cases, and the resulting models are validated by frequency scan and stability test. Moreover, advantages of the GFC method in achieving interaction analysis and stability-oriented designs of multi-controllers are demonstrated by three application examples, further suggesting its great potential for being applied to the analysis and design issues of converter systems involving large-scale controllers.

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arxivcs.ROeess.SY2026-07-24

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arxivcs.NIcs.MAeess.SY2026-07-24

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arxiveess.SY2026-07-24

Physics-Informed Neural Network for Modeling the Dynamic Behavior of Grid-Forming Converters

Hussein Jaffal, Arianna Fois, Sarra Bouchkati, Amirali Mahjoob, Andreas Ulbig

This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and…

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arxiveess.SY2026-07-24

StateFormer: A Multivariate Transformer for Learning History-Dependent Battery State Dynamics and Long-Horizon Health Forecasting

Zhe Bai, Stephen Harris

This paper introduces a novel multivariate Transformer \emph{StateFormer} that forecasts degradation dynamics of large-scale battery systems. The model learns across time scales, from short-term thermal fluctuations to long-term aging trajectories, enabling accurate prediction of…

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