Search arXivSearch

arXiv · 2109.00310

Analytic estimation of the MMC sub-module capacitor voltage ripple for balanced and unbalanced AC grid conditions

Abstract

In this paper, a mathematical expression to define the maximum and minimum voltage ripples of the modular multilevel converter (MMC) sub-module (SM) capacitors is proposed. Using the arm averaged model of the MMC, the instantaneous power for the upper and lower arms of the converter is obtained, giving the basis to describe the instantaneous energy of the arms. To calculate the SM capacitors peak voltage values, it is required to obtain an analytic expression for the maximum and minimum energy levels. Due to the presence of terms with different magnitudes, frequencies and phases, finding it can be quite challenging. To overcome this issue, the instantaneous arm energy expression is modified using mathematical assumptions in order to join the different components into a single term which can analytically describes the maximum energy point. Then, this expression is used to calculate the peak values of the arm capacitor voltages. By employing the same principles from the arm level, the final analytical expression for the SM capacitors maximum and minimum voltages is found. Simulation results are carried out in order to validate the accuracy of the proposed analysis for different power delivery conditions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Daniel Westerman Spier, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt, Joaquim López Mestre. 2021-09-01. Analytic estimation of the MMC sub-module capacitor voltage ripple for balanced and unbalanced AC grid conditions. https://arxiv.org/abs/2109.00310

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