Search arXivSearch

arXiv · 2608.04740

Analytical Prediction of Voltage Collapse in Current-Limited Grid-Forming Inverters

Abstract

The limited overcurrent capability of grid-forming (GFM) inverters makes current limiting essential during large disturbances. Activation of a circular current limiter (CCL) does not always cause the operating equilibrium to disappear. This paper develops an analytical framework to predict the grid-voltage boundaries at which the CCL is activated, determine whether the operating equilibrium persists as a saturated stable equilibrium point (satSEP), and identify the voltage at which it is lost. The CCL-based GFM inverter with frozen anti-windup is formulated as a piecewise-smooth system comprising normal-control and current-limited modes, so limiter activation is interpreted as a boundary-equilibrium bifurcation (BEB). A continuation formulation that switches to a reduced current-limited model when the CCL is activated is introduced to avoid the rank deficiency caused by frozen integrator states. An equivalent circuit that includes the filter capacitor yields closed-form expressions for the lower and upper boundary voltages at which the CCL is activated. A positive lower-boundary slope predicts that a satSEP persists in the current-limited mode and is lost at a later saddle-node, whereas a nonpositive slope predicts a non-smooth fold and equilibrium loss at the BEB. The upper-boundary slope is always negative under the assumed parameter conditions. Power-angle analysis, dynamic-model continuation in single-inverter and modified 9-bus systems, and time-domain simulations validate these predictions, showing that CCL activation can either cause immediate equilibrium loss through a non-smooth fold or allow a satSEP to persist until it is lost at a later saddle-node.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wenhao Lin, Robin Preece, Panagiotis N. Papadopoulos. 2026-08-05. Analytical Prediction of Voltage Collapse in Current-Limited Grid-Forming Inverters. https://arxiv.org/abs/2608.04740

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