Search arXivSearch

arXiv · 2608.26595

Current-Limiting Control for Fault Ride-Through of LLC-based Solid-State Transformer in Data Centers

Abstract

Solid-State Transformers (SSTs) are increasingly proposed as the interface between distribution grids and data centers due to flexible power flows and fast dynamic response. However, when a short-circuit fault occurs in a load branch, the SST with a voltage-source-type DC-DC stage is forced to shut down due to fault currents. Therefore, current-limiting strategies are strongly needed to prevent catastrophic equipment damage and cascading blackouts by instantly restricting massive current spikes and offering sufficient currents for protection devices to act at the faulted branch. This paper proposes a coordinated DC load fault-tolerant current-limiting and recovery strategy embedded directly in the control of the SST DC-DC stage, avoiding additional hardware cost. Specifically, the fault mechanism of an example LLC resonant converter is studied. Accordingly, a fault detection framework is implemented, a closed-loop current controller is proposed to limit the DC current to a designated value within microseconds by surging the switching frequency and adjusting the duty cycle, and a ramped recovery stage will then restore the DC bus after the fault isolation without inrush currents. Experiments on an LLC converter prototype have verified the feasibility of the proposed current-limiting strategy, enabling faster and lower-cost fault response suitable for resilient data center power architectures.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Haoyu Wang, Chi Zhang, Mafu Zhang, Rudy Wang, Peter Barbosa. 2026-09-09. Current-Limiting Control for Fault Ride-Through of LLC-based Solid-State Transformer in Data Centers. https://arxiv.org/abs/2608.26595

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Constrained Feedback Control of Nonlinear Systems via Approximate HJB and Control Barrier Functions

This paper presents a two-stage framework for constrained feedback control of input-affine nonlinear systems. Offline, an approximate value function for the unconstrained problem is computed, for example using Hamilton--Jacobi--Bellman (HJB)-based policy iteration. Online, the proposed quadratic program (QP) minimizes the pre-Hamiltonian evaluated using the approximate value-function gradient subject to safety constraints enforced by control barrier functions (CBFs). This architecture decouples performance optimization from constraint enforcement, allowing constraints to be modified without recomputing the value function. As in CBF-QP architectures based on control Lyapunov functions (CLFs), safety is enforced as a hard constraint; however, the performance objective targets approximate optimality rather than a prescribed Lyapunov decay. Numerical results on a linear 2-state hovercraft and a nonlinear 9-state spacecraft attitude-control problem show agreement with the constrained open-loop optimal control problem (OCP) benchmark in the linear case, and performance close to the OCP benchmark, improving on CLF-based controllers, in the nonlinear case.

eess.SY

Rao-Blackwellized Stein Gradient Descent for Joint State-Parameter Estimation

We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying systems. The algorithm uses a Rao-Blackwellization technique to infer joint state-parameter posteriors efficiently. In particular, conditional state distributions are computed analytically via Kalman filtering, while model parameters, including the measurement-noise covariance, are approximated using particle-based Stein Variational Gradient Descent (SVGD), enabling stable real-time inference. To handle parameters subject to physical constraints, we further introduce constrained variants that enforce them through an alternating direction method of multipliers (ADMM) splitting of the SVGD update, including nonlinear equality constraints that standard particle filters cannot readily handle. We derive a stability bound that relates the approximation error in the parameter posterior to the resulting error in the marginal state distribution. Performance of the proposed filters is validated on three case studies: a fed-batch bioreactor with Haldane kinetics and a damped pendulum, both under physical constraints, and a neural-network-augmented dynamic system. The examples cover parameter estimation under inequality and equality constraints and online neural-network training within a dynamical model.

eess.SY

Firing Rate Neural Network Implementations of Model Predictive Control

Human and animal brains perform planning to enable complex movements and behaviors, a process that can be effectively described using model predictive control (MPC). How could the brain physically implement MPC? In this work, we translate model predictive controllers into firing rate neural networks, offering insights into the nonlinear neural dynamics that underpin planning. We propose a constructive method; no training is required. This is done first applying the projected gradient method to the dual problem to derive a baseline neural network implementation. We then use factorization and contraction analysis to systematically generate alternative network architectures; in other words, we systematically generate hypotheses for how planning is done in the brain via neural dynamics. Finally, we present numerical simulations to study different neural networks performing MPC to balance an inverted pendulum on a cart (i.e., balancing a stick on a hand), including one example in which imposing sparse connectivity (a property observed in brain networks) does not degrade control performance.

eess.SY