arXiv subjects

Zixiao Ma

Publications and source records attributed to Zixiao Ma.

15 recordsLinked to original sources

arXiv2026

Dynamic Resilience Assessment of Power Systems With Data Center Load Events Using Physics-Informed Neural Networks

Large data center loads introduce new resilience challenges to power systems because their disconnection and staged reconnection can induce fast voltage and frequency dynamics that are not captured by static service-status or energy-based metrics. This paper proposes a utility-side, physics-informed resilience assessment framework that evaluates these events using only grid-side dynamic models and observable post-disturbance trajectories, without requiring detailed internal data center models. An unsupervised differential algebraic equation-physics informed neural network (DAE-PINN) based on an implicit backward Euler residual is developed to jointly predict dynamic and algebraic states, enabling repeated post-disturbance trajectory evaluation while enforcing network algebraic consistency. Normalized multi-phase resilience metrics are then used to quantify disturbance, degraded-state, and restoration-period impacts and to screen data center reconnection timing and load-ramping strategies under security constraints. Case studies on a modified IEEE 33-bus feeder show that the proposed DAE-PINN accurately tracks numerical DAE solutions and substantially reduces computation time in repeated restoration screening. The proposed metrics distinguish the effects of disturbance size, data center location, and reconnection strategy, revealing the trade-off between restoration speed and transient resilience loss.

eess.SY↗

arXiv2025

Cyber Resilience Assessment of Unbalanced Distribution System Restoration under Sparse Load Forecasting Attacks

System restoration is critical for power-system resilience, but its growing reliance on artificial intelligence (AI)-based load forecasting creates a cyber-physical vulnerability in the restoration decision loop. Manipulated forecasts can cause infeasible restoration schedules, insufficient inverter-based-resource ramping margins, and unsuccessful recovery of de-energized segments, yet the resilience of restoration processes to such attacks remains largely unexplored. This paper evaluates restoration vulnerability at the system level rather than only measuring forecasting error. A gradient-based sparse perturbation method is developed as a stress-testing tool to identify influential forecasting inputs. We further create a restoration-aware validation framework that embeds these compromised forecasts into a sequential restoration model and evaluates operational feasibility using an unbalanced three-phase optimal power flow formulation. Case studies on a modified IEEE 123-bus feeder show that sparse input perturbations can substantially increase forecasting error and make selected microgrid restoration stages infeasible. The results reveal system-level failures caused by active-power-balance infeasibility and power ramping violations, which can prevent the restoration of critical loads. These findings provide actionable insights for designing cybersecurity-aware restoration planning frameworks.

eess.SY↗

arXiv2025

A Liquid-Nitrogen-Cooled Ca+ Ion Optical Clock with a Systematic Uncertainty of 4.4E-19

We report a single-ion optical clock based on the 4S_1/2-3D_5/2 transition of the 40Ca+ ion, operated in a liquid nitrogen cryogenic environment,achieving a total systematic uncertainty of 4.4E-19. We employ a refined temperature evaluation scheme to reduce the frequency uncertainty due to blackbody radiation (BBR), and the 3D sideband cooling has been implemented to minimize the second-order Doppler shift. We have precisely determined the average Zeeman coefficient of the 40Ca+ clock transition to be 14.345(40) Hz/mT^2, thereby significantly reducing the quadratic Zeeman shift uncertainty. Moreover, the cryogenic environment enables the lowest reported heating rate due to ambient electric field noise in trapped-ion optical clocks.

physics.atom-ph↗

arXiv2025

Inverter Control with Time-Varying and Nonconvex State and Input Constraints

The growing integration of inverter-based resources (IBRs) into modern power systems poses significant challenges for maintaining reliable operation under dynamic and constrained conditions. This paper focuses on the power tracking problem for grid-connected IBRs, addressing the complexities introduced by voltage and power factor constraints. Voltage constraints, being time-varying and nonlinear input constraints, often conflict with power factor constraints, which are state constraints. These conflicts, coupled with stability requirements, add substantial complexity to control design. To overcome these challenges, we propose a computationally efficient static state-feedback controller that guarantees stability and satisfies operational constraints. The concept of achievability is introduced to evaluate whether power setpoints can be accurately tracked while adhering to all constraints. Using a parameterization framework and the S-lemma, we develop criteria to assess and maximize the continuous achievable region for IBR operation. This framework allows system operators to ensure safety and stability by precomputing a finite set of control gains, significantly reducing online computational requirements. The proposed approach is validated through simulations, demonstrating its effectiveness in handling time-varying grid disturbances and achieving reliable control performance.

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arXiv2025

CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentation

Remote sensing image segmentation is pivotal for earth observation, underpinning applications such as environmental monitoring and urban planning. Due to the limited annotation data available in remote sensing images, numerous studies have focused on data augmentation as a means to alleviate overfitting in deep learning networks. However, some existing data augmentation strategies rely on simple transformations that may not sufficiently enhance data diversity or model generalization capabilities. This paper proposes a novel augmentation strategy, Clustered-Patch-Mixed Mosaic (CP2M), designed to address these limitations. CP2M integrates a Mosaic augmentation phase with a clustered patch mix phase. The former stage constructs a new sample from four random samples, while the latter phase uses the connected component labeling algorithm to ensure the augmented data maintains spatial coherence and avoids introducing irrelevant semantics when pasting random patches. Our experiments on the ISPRS Potsdam dataset demonstrate that CP2M substantially mitigates overfitting, setting new benchmarks for segmentation accuracy and model robustness in remote sensing tasks.

cs.CV↗

arXiv2024

Optimal Power Flow for Integrated Primary-Secondary Distribution Networks with Center-Tapped Service Transformers

Secondary distribution networks (SDNets) play an increasingly important role in smart grids due to a high proliferation of distributed energy resources (DERs) in SDNets. However, most existing optimal power flow (OPF) problems do not take into account SDNets with center-tapped service transformers. Handling the nonlinear and nonconvex SDNet power flow constraints is still an outstanding problem. To meet this gap, we first utilize the second-order cone programming relaxation and linearization to make center-tapped service transformer constraints convex, respectively. Then, a linearized triplex service line power flow model, including its compact matrix-vector form, is further developed to compose the SDNet OPF model with our proposed center-tapped service transformer model. This proposed SDNet OPF model can be easily embedded into existing primary distribution network (PDNet) OPF models, resulting in a holistic power system decision-making solution for integrated primary-secondary distribution networks. Case studies are presented for two different integrated primary-secondary distribution networks that demonstrate the effectiveness and superiority of this model.

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arXiv2023

Controlling Grid-Connected Inverters under Time-Varying Voltage Constraints

Inverter-based resources (IBRs) are becoming increasingly prevalent in power systems. Due to the inherently low inertia of inverters, there is a heightened risk of disruptive voltage oscillations. A particular challenge in the operation of grid connected IBRs is the variations in the grid side voltage. The changes in the grid side voltage introduces nonlinear and time-varying constriants on the inverter voltages themselves. For an operator, it would be useful to know the set of active and reactive powers that can be tracked under these time-varying conditons. This paper introduces an optimization model designed to assess the achievability of power setpoints within the framework of constrained static state-feedback power control. Additionally, we present a Monte Carlo simulation-based method to optimize the set of achievable power setpoints. The efficacy of the proposed approach is validated through simulation results.

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arXiv2023

Analytical Large-Signal Modeling of Inverter-based Microgrids with Koopman Operator Theory for Autonomous Control

The microgrid (MG) plays a crucial role in the energy transition, but its nonlinearity presents a significant challenge for large-signal power systems studies in the electromagnetic transient (EMT) time scale. In this paper, we develop a large-signal linear MG model that considers the detailed dynamics of the primary and zero-control levels based on the Koopman operator (KO) theory. Firstly, a set of observable functions is carefully designed to capture the nonlinear dynamics of the MG. The corresponding linear KO is then analytically derived based on these observables, resulting in the linear representation of the original nonlinear MG with observables as the new coordinate. The influence of external input on the system dynamics is also considered during the derivation, enabling control of the MG. We solve the voltage control problem using the traditional linear quadratic integrator (LQI) method to demonstrate that textbook linear control techniques can accurately control the original nonlinear MG via the developed KO linearized MG model. Our proposed KO linearization method is generic and can be easily extended for different control objectives and MG structures using our analytical derivation procedure. We validate the effectiveness of our methodology through various case studies.

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arXiv2023

Singular Perturbation-based Large-Signal Order Reduction of Microgrids for Stability and Accuracy Synthesis with Control

With the increasing penetration of distributed energy resources (DERs), it is of vital importance to study the dynamic stability of microgrids (MGs) with external control inputs in the electromagnetic transient (EMT) time scale. This requires detailed models of the underlying control structure of MGs and results in a high-order nonlinear MG control system. Higher-level controller design and stability analysis of such high-order systems are usually intractable and computation-costly. To overcome these challenges, this paper proposes a large-signal order reduction (LSOR) method for MGs with considerations of external control inputs and the detailed dynamics of underlying control levels based on singular perturbation theory (SPT). Specially, we innovatively proposed and strictly proved a general stability and accuracy assessment theorem that allows us to analyze the dynamic stability of a full-order nonlinear system by only leveraging its corresponding reduced-order model (ROM) and boundary layer model (BLM). Moreover, this theorem also theoretically provides a set of conditions under which the developed ROM is accurate. Finally, by embedding such a theorem into the SPT, we propose a novel LSOR approach with guaranteed accuracy and stability analysis equivalence. The proposed LSOR method is generic and can be applied to arbitrary dynamic systems. Multiple case studies are conducted on MG systems to show the effectiveness of the proposed approach.

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arXiv2023

Towards a transportable Ca$^+$ optical clock with a systematic uncertainty of $4.8\times 10^{-18}$

We present a compact, long-term nearly continuous operation of a room-temperature Ca$^+$ optical clock setup towards a transportable clock, achieving an overall systematic uncertainty of $4.8\times 10^{-18}$ and an uptime rate of 97.8% over an 8-day period. The active liquid-cooling scheme is adopted, combined with the precise temperature measurement with 13 temperature sensors both inside and outside the vacuum chamber to ensure the accurate evaluation of the thermal environment for the optical clock. The environmental temperature uncertainty is evaluated as 293.31(0.4) K, corresponding to a blackbody radiation (BBR) frequency shift uncertainty of $4.6\times 10^{-18}$, which is reduced more than two times compared to our previous work. Through the frequency comparison between the room temperature Ca$^+$ optical clock and a cryogenic Ca$^+$ optical clock, the overall uncertainty of the clock comparison is $7.5\times 10^{-18}$, including a statistic uncertainty of $4.9\times 10^{-18}$ and a systematic uncertainty of $5.7\times 10^{-18}$. This work provides a set of feasible implementations for high-precision transportable ion optical clocks.

physics.atom-ph↗

arXiv2021

A Two-Level Simulation-Assisted Sequential Distribution System Restoration Model With Frequency Dynamics Constraints

This paper proposes a service restoration model for unbalanced distribution systems and inverter-dominated microgrids (MGs), in which frequency dynamics constraints are developed to optimize the amount of load restoration and guarantee the dynamic performance of system frequency response during the restoration process. After extreme events, the damaged distribution systems can be sectionalized into several isolated MGs to restore critical loads and tripped non-black start distributed generations (DGs) by black start DGs. However, the high penetration of inverter-based DGs reduces the system inertia, which results in low-inertia issues and large frequency fluctuation during the restoration process. To address this challenge, we propose a two-level simulation-assisted sequential service restoration model, which includes a mixed integer linear programming (MILP)-based optimization model and a transient simulation model. The proposed MILP model explicitly incorporates the frequency response into constraints, by interfacing with transient simulation of inverter-dominated MGs. Numerical results on a modified IEEE 123-bus system have validated that the frequency dynamic performance of the proposed service restoration model are indeed improved.

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arXiv2020

High-Fidelity Large-Signal Order Reduction Approach for Composite Load Model

With the increasing penetration of electronic loads and distributed energy resources (DERs), conventional load models cannot capture their dynamics. Therefore, a new comprehensive composite load model is developed by Western Electricity Coordinating Council (WECC). However, this model is a complex high-order nonlinear system with multi-time-scale property, which poses challenges on stability analysis and computational burden in large-scale simulations. In order to reduce the computational burden while preserving the accuracy of the original model, this paper proposes a generic high-fidelity order reduction approach and then apply it to WECC composite load model. First, we develop a large-signal order reduction (LSOR) method using singular perturbation theory. In this method, the fast dynamics are integrated into the slow dynamics to preserve the transient characteristics of fast dynamics. Then, we propose the necessary conditions for accurate order reduction and embed them into the LSOR to improve and guarantee the accuracy of reduced-order model. Finally, we develop the reduced-order WECC composite load model using the proposed algorithm. Simulation results show the reduced-order large signal model significantly alleviates the computational burden while maintaining similar dynamic responses as the original composite load model.

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arXiv2019

Secondary Voltage Control of Microgrids Using Nonlinear Multiple Models Adaptive Control

This paper proposes a novel model-free secondary voltage control (SVC) for microgrids using nonlinear multiple models adaptive control. The proposed method is comprised of two components. Firstly, a linear robust adaptive controller is designed to guarantee the voltage stability in the bounded-input-bounded-output (BIBO) manner, which is more consistent with the operation requirements of microgrids. Secondly, a nonlinear adaptive controller is developed to improve the voltage tracking performance with the help of artificial neural networks (ANNs). A switching mechanism is proposed to coordinate such two controllers for guaranteeing the closed-loop stability while achieving accurate voltage tracking. Given our method leverages a data-driven real-time identification, it only relies on the input and output data of microgrids without resorting to any prior information of primary control and grid models, thus exhibiting good robustness, ease of deployment and disturbance rejection.

math.OC↗

arXiv2019

Mathematical representation of the WECC composite load model

The Western Electricity Coordinating Council (WECC) composite load model is a newly developed load model that has drawn great interest from the industry. To analyze its dynamic characteristics with both mathematical and engineering rigor, a detailed mathematical model is needed. Although WECC composite load model is available in commercial software as a module and its detailed block diagrams can be found in several public reports, there is no complete mathematical representation of the full model in literature. This paper addresses a challenging problem of deriving detailed mathematical representation of WECC composite load model from its block diagrams. In particular, for the first time, we have derived the mathematical representation of the new DER_A model. The developed mathematical model is verified using both Matlab and PSS/E to show its effectiveness in representing WECC composite load model. The derived mathematical representation serves as an important foundation for parameter identification, order reduction and other dynamic analysis.

math.OC↗

arXiv2019

Data-Driven Based Method for Power System Time-Varying Composite Load Modeling

Fast and accurate load parameters identification has great impact on the power systems operation and stability analysis. This paper proposes a novel transfer reinforcement learning based method to identify composite ZIP and induction motor (IM) load models. An imitation learning process is firstly introduced to improve the exploitation and exploration process. The transfer learning process is then employed to overcome the challenge of time consuming optimization when dealing with new tasks. An Associative memory is designed to realize demension reduction and knowledge learning and transfer between different optimization tasks. Agents can exploit the optimal knowledge from source tasks to accelerate search rate and improve solution accuracy. The greedy rule is adopted to balance global search and local search. Convergency analysis shows that the proposed method can converge to the global optimal solution with probability 1. The performance of the proposed ITQ appraoch have been validated on 68-bus system. Simulation results in multi-test cases verify that the proposed method has superior convergence rate and stability.

eess.SP↗