486 research outputs found
Optimized Hierarchical Power Oscillations Control for Distributed Generation Under Unbalanced Conditions
Control structures have critical influences on converter-interfaced
distributed generations (DG) under unbalanced conditions. Most of previous
works focus on suppressing active power oscillations and ripples of DC bus
voltage. In this paper, the relationship between amplitudes of the active power
oscillations and the reactive power oscillations are firstly deduced and the
hierarchical control of DG is proposed to reduce power oscillations. The
hierarchical control consists of primary and secondary levels. Current
references are generated in primary control level and the active power
oscillations can be suppressed by a dual current controller. Secondary control
reduces the active power and reactive power oscillations simultaneously by
optimal model aiming for minimum amplitudes of oscillations. Simulation results
show that the proposed secondary control with less injecting negative-sequence
current than traditional control methods can effectively limit both active
power and reactive power oscillations.Comment: Accepted by Applied Energ
Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems
Recently, data-driven techniques have demonstrated remarkable effectiveness
in addressing challenges related to MR imaging inverse problems. However, these
methods still exhibit certain limitations in terms of interpretability and
robustness. In response, we introduce Convex Latent-Optimized Adversarial
Regularizers (CLEAR), a novel and interpretable data-driven paradigm. CLEAR
represents a fusion of deep learning (DL) and variational regularization.
Specifically, we employ a latent optimization technique to adversarially train
an input convex neural network, and its set of minima can fully represent the
real data manifold. We utilize it as a convex regularizer to formulate a
CLEAR-informed variational regularization model that guides the solution of the
imaging inverse problem on the real data manifold. Leveraging its inherent
convexity, we have established the convergence of the projected subgradient
descent algorithm for the CLEAR-informed regularization model. This convergence
guarantees the attainment of a unique solution to the imaging inverse problem,
subject to certain assumptions. Furthermore, we have demonstrated the
robustness of our CLEAR-informed model, explicitly showcasing its capacity to
achieve stable reconstruction even in the presence of measurement interference.
Finally, we illustrate the superiority of our approach using MRI reconstruction
as an example. Our method consistently outperforms conventional data-driven
techniques and traditional regularization approaches, excelling in both
reconstruction quality and robustness
Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI
Recently, regularization model-driven deep learning (DL) has gained
significant attention due to its ability to leverage the potent
representational capabilities of DL while retaining the theoretical guarantees
of regularization models. However, most of these methods are tailored for
supervised learning scenarios that necessitate fully sampled labels, which can
pose challenges in practical MRI applications. To tackle this challenge, we
propose a self-supervised DL approach for accelerated MRI that is theoretically
guaranteed and does not rely on fully sampled labels. Specifically, we achieve
neural network structure regularization by exploiting the inherent structural
low-rankness of the -space data. Simultaneously, we constrain the network
structure to resemble a nonexpansive mapping, ensuring the network's
convergence to a fixed point. Thanks to this well-defined network structure,
this fixed point can completely reconstruct the missing -space data based on
matrix completion theory, even in situations where full-sampled labels are
unavailable. Experiments validate the effectiveness of our proposed method and
demonstrate its superiority over existing self-supervised approaches and
traditional regularization methods, achieving performance comparable to that of
supervised learning methods in certain scenarios
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