arXiv subjects

Hongjin Du

Publications and source records attributed to Hongjin Du.

7 recordsLinked to original sources

arXiv2026

SOPF-Based Adaptive Droop Control for Hybrid AC--HVDC Grids Under Offshore Wind Uncertainty

The integration of massive offshore wind into hybrid AC-HVDC grids demands robust DC voltage regulation, yet conventional fixed-gain droop controllers struggle under severe stochastic volatility. This paper bridges the gap between system-level economic dispatch and converter-level control by proposing a novel Stochastic Optimal Power Flow (SOPF)-based adaptive droop framework. Rather than relying on heuristic or reactive tuning, wind forecast uncertainty is modeled using a zone-wise Beta distribution that accurately captures the heteroscedastic nature of wind errors across low, mid, and high power regimes. By leveraging Polynomial Chaos Expansion (PCE) within a chance-constrained SOPF, the system's stochastic states are formulated analytically. Crucially, the optimal adaptive droop gain is extracted directly from the first-order PCE coefficients via a Jacobian-free sensitivity analysis, embedding statistical voltage-security guarantees directly into the local converter control. Validation on a 4-terminal AC-HVDC system demonstrates that scenario-adaptive gains significantly outperform standard fixed-coefficient approaches, effectively minimizing active-power tracking errors during extreme wind disturbances.

eess.SY↗

arXiv2026

Estimating Density Functions for Probabilistic Power Flow Using Invertible Neural Networks

Probabilistic power flow (PPF) is essential for quantifying operational uncertainty in modern power systems with high penetrations of renewable generation and flexible loads. Conventional PPF methods primarily rely on Monte Carlo (MC)- based power flow (PF) simulations or simplified approximations of voltage probability density functions. Although MC methods provide high accuracy, they incur substantial computational and data-storage costs, whereas simplified approximations often sacrifice accuracy. In this paper, we propose a novel PPF density approximation framework that avoids repeated PF simulations during inference and can, in principle, approximate complex voltage distributions without restrictive distributional assumptions. The core idea is to learn an explicit invertible mapping between stochastic power injections and system voltages using invertible neural networks (INNs). By combining this mapping with the change-of-variables theorem, the proposed framework directly evaluates voltage probability densities without repeatedly solving the PF equations. Extensive numerical studies demonstrate that the proposed framework achieves state-of-the-art performance both as an accurate PF surrogate and as an efficient PPF density estimator.

eess.SY↗

arXiv2025

An OPF-based Control Framework for Hybrid AC-MTDC Power Systems under Uncertainty

The increasing integration of renewable energy, particularly offshore wind, introduces significant uncertainty into hybrid AC-HVDC systems due to forecast errors and power fluctuations. Conventional control strategies typically rely on fixed setpoints and neglect frequency deviations, which can compromise system stability under rapid renewable variations. To address this challenge, this paper presents a forecast-integrated, optimal power flow (OPF)-based adaptive control framework. Wind speed forecasts generated using a Random Forest model are incorporated into a time-coupled OPF to determine baseline converter setpoints in anticipation of wind fluctuations, which are further adjusted in real time based on actual operating conditions. An adaptive droop control scheme is developed that jointly considers DC voltage and AC frequency deviations. The effectiveness of the proposed control framework is validated through hardware-in-the-loop (HIL) simulations, demonstrating its capability to ensure stable and robust operation of hybrid AC-HVDC systems under high penetration of renewable energy.

eess.SY↗

arXiv2025

Understanding the Structural Origin of Chirality in Magic-Size Semiconductor Nanoclusters through Self-Assembly Simulations

Semiconductor magic-size clusters (MSCs) are atomically precise nanoparticles exhibiting unique size-dependent properties, but their ultrasmall dimensions hinder structural characterization, limiting our understanding of their formation and stability. A few MSC structures have been fully resolved, revealing either bulk-like zincblende-type structures or a range of non-bulk-like motifs. Here we use a computational model to investigate the relationship between cluster size and atomic structure in zincblende-forming II-VI and III-V semiconductors. Firstly, we find that all non-bulk-like MSCs in these systems exhibit the same distorted icosahedral motif that is intrinsically chiral. Secondly, we reproduce these MSC geometries in small-cluster self-assembly simulations and discover that their chirality emerges from the geometric frustration and symmetry breaking in arranging tetrahedral bonding environments into an icosahedral topology. Overall, this work reproduces experimentally reported motifs without system-specific parameterization, establishes the structural origin of chirality in MSCs, and provides design principles for predicting new cluster geometries.

cond-mat.mtrl-sci↗

arXiv2025

Optimal Droop Control Strategy for Coordinated Voltage Regulation and Power Sharing in Hybrid AC-MTDC Systems

With the growing integration of modular multilevel converters (MMCs) in Multi-Terminal Direct Current (MTDC) transmission systems, there is an increasing need for control strategies that ensure both economic efficiency and robust dynamic performance. This paper presents an enhanced Optimal Power Flow (OPF) framework for hybrid AC-MTDC systems, integrating a novel droop control strategy that coordinates DC voltage and AC frequency regulation. By embedding frequency control loops into the MMCs, the method enables system-wide coordination, enhancing power sharing and improving system resilience under disturbances. The proposed strategy dynamically adjusts converter operating points to minimize generation costs and DC voltage deviations, thus balancing economic objectives with system stability. A modified Nordic test system integrated with a four-terminal MTDC grid is used to validate the approach. Optimization is performed using Julia, while the system's dynamic performance is evaluated through electromagnetic transient simulations with the EMTP software. Case studies across multiple scenarios demonstrate that the proposed method consistently achieves lower generation costs than active power control and adaptive droop control strategy while maintaining stable control characteristics. The results highlight the method's capability to deliver cost-effective operation without compromising performance, offering a promising solution for the coordinated control of future hybrid AC-DC transmission networks.

eess.SY↗

arXiv2024

Enhanced Optimal Power Flow Based Droop Control in MMC-MTDC Systems

Optimizing operational set points for modular multilevel converters (MMCs) in Multi-Terminal Direct Current (MTDC) transmission systems is crucial for ensuring efficient power distribution and control. This paper presents an enhanced Optimal Power Flow (OPF) model for MMC-MTDC systems, integrating a novel adaptive voltage droop control strategy. The strategy aims to minimize generation costs and DC voltage deviations while ensuring the stable operation of the MTDC grid by dynamically adjusting the system operation points. The modified Nordic 32 test system with an embedded 4-terminal DC grid is modeled in Julia and the proposed control strategy is applied to the power model. The results demonstrate the feasibility and effectiveness of the proposed droop control strategy, affirming its potential value in enhancing the performance and reliability of hybrid AC-DC power systems.

eess.SY↗

arXiv2024

A snapshot review on soft-materials assembly design utilizing machine learning methods

Since the surge of data in materials science research and the advancement in machine learning methods, an increasing number of researchers are introducing machine learning techniques into the next generation of materials discovery, ranging from neural-network learned potentials to automated characterization techniques for experimental images. In this snapshot review, we first summarize the landscape of techniques for soft materials assembly design that do not employ machine learning or artificial intelligence and then discuss specific machine-learning and artificial-intelligence-based methods that enhance the design pipeline, such as high-throughput crystal-structure characterization and the inverse design of building blocks for materials assembly and properties. Additionally, we survey the landscape of current developments of scientific software, especially in the context of their compatibility with traditional molecular dynamics engines such as LAMMPS and HOOMD-blue.

cond-mat.soft↗