pwc-archive/papers-with-abstracts huggingface dataset:
Large-scale Grid Optimization: The Workhorse of Future Grid Computations
Authors: Amritanshu Pandey, Mads Almassalkhi, Sam Chevalier
Date: 2025-01-08 00:00:00
Tasks: Attribute

Purpose: The computation methods for modeling, controlling and optimizing the transforming grid are evolving rapidly. We review and systemize knowledge for a special class of computation methods that solve large-scale power grid optimization problems. Summary: Large-scale grid optimizations are pertinent for, amongst other things, hedging against risk due to resource stochasticity, evaluating aggregated DERs' impact on grid operation and design, and improving the overall efficiency of grid operation in terms of cost, reliability, and carbon footprint. We attribute the continual growth in scale and complexity of grid optimizations to a large influx of new spatial and temporal features in both transmission (T) and distribution (D) networks. Therefore, to systemize knowledge in the field, we discuss the recent advancements in T and D systems from the viewpoint of mechanistic physics-based and emerging data-driven methods. Findings: We find that while mechanistic physics-based methods are leading the science in solving large-scale grid optimizations, data-driven techniques, especially physics-constrained ones, are emerging as an alternative to solve otherwise intractable problems. We also find observable gaps in the field and ascertain these gaps from the paper's literature review and by collecting and synthesizing feedback from industry experts.
Recursive Least Squares with Fading Regularization for Finite-Time Convergence without Persistent Excitation
Authors: Brian Lai, Dimitra Panagou, Dennis S. Bernstein
Date: 2025-01-08 00:00:00
Tasks: parameter estimation

This paper extends recursive least squares (RLS) to include time-varying regularization. This extension provides flexibility for updating the least squares regularization term in real time. Existing results with constant regularization imply that the parameter-estimation error dynamics of RLS are globally attractive to zero if and only the regressor is weakly persistently exciting. This work shows that, by extending classical RLS to include a time-varying (fading) regularization term that converges to zero, the parameter-estimation error dynamics are globally attractive to zero without weakly persistent excitation. Moreover, if the fading regularization term converges to zero in finite time, then the parameter estimation error also converges to zero in finite time. Finally, we propose rank-1 fading regularization (R1FR) RLS, a time-varying regularization algorithm with fading regularization that converges to zero, and which runs in the same computational complexity as classical RLS. Numerical examples are presented to validate theoretical guarantees and to show how R1FR-RLS can protect against over-regularization.
Linear Aggregate Model for Realizable Dispatch of Homogeneous Energy Storage
Authors: Mazen Elsaadany, Mads R. Almassalkhi, Simon H. Tindemans
Date: 2025-01-08 00:00:00

To optimize the dispatch of batteries, a model is required that can predict the state of energy (SOE) trajectory for a chosen open-loop power schedule to ensure admissibility (i.e., that schedule can be realized). However, battery dispatch optimization is inherently challenging when batteries cannot simultaneously charge and discharge, which begets a non-convex complementarity constraint. In this paper, we develop a novel composition of energy storage elements that can charge or discharge independently and provide a sufficient linear energy storage model of the composite battery. This permits convex optimization of the composite battery dispatch while ensuring the admissibility of the resulting (aggregated) power schedule and its disaggregation to the individual elements.
A new methodology for the optimization of bolt tightening sequences for ring type joints
Authors: Ibai Coria, Mikel Abasolo, Imanol Olaskoaga, Arkaitz Etxezarreta, Josu Aguirrebeitia
Date: 2025-01-08 00:00:00

Achieving uniform bolt load distribution is critical to obtain leak-free service in pressure vessel gasketed joints used in offshore pipelines. This is a difficult task due to bolt load variations during the assembly process. In this sense, the Elastic Interaction Coefficients Method has been developed in previous works to define tightening sequences that provide the target load at the end of the sequence in one or two passes. The method is very costly because a complete sequence must be simulated and the load of every bolt must be measured after each tightening operation. The present work validates this method for Ring Type Joints and further develops a numerically and experimentally validated new methodology that provides highly satisfactory results with a significantly lower cost.
Adaptive Numerical Differentiation for Extremum Seeking with Sensor Noise
Authors: Shashank Verma, Juan Augusto Paredes Salazar, Jhon Manuel Portella Delgado, Ankit Goel, Dennis S. Bernstein
Date: 2025-01-08 00:00:00
Tasks: Sensitivity, State Estimation

Extremum-seeking control (ESC) is widely used to optimize performance when the system dynamics are uncertain. However, sensitivity to sensor noise is an important issue in ESC implementation due to the use of high-pass filters or gradient estimators. To reduce the sensitivity of ESC to noise, this paper investigates the use of adaptive input and state estimation (AISE) for numerical differentiation. In particular, this paper develops extremum-seeking control with adaptive input and state estimation (ESC/AISE), where the high-pass filter of ESC is replaced by AISE to improve performance under sensor noise. The effectiveness of ESC/AISE is illustrated via numerical examples.
Frenet-Serret-Based Trajectory Prediction
Authors: Shashank Verma, Dennis S. Bernstein
Date: 2025-01-08 00:00:00
Tasks: Position, Prediction, State Estimation, Trajectory Prediction

Trajectory prediction is a crucial element of guidance, navigation, and control systems. This paper presents two novel trajectory-prediction methods based on real-time position measurements and adaptive input and state estimation (AISE). The first method, called AISE/va, uses position measurements to estimate the target velocity and acceleration. The second method, called AISE/FS, models the target trajectory as a 3D curve using the Frenet-Serret formulas, which require estimates of velocity, acceleration, and jerk. To estimate velocity, acceleration, and jerk in real time, AISE computes first, second, and third derivatives of the position measurements. AISE does not rely on assumptions about the target maneuver, measurement noise, or disturbances. For trajectory prediction, both methods use measurements of the target position and estimates of its derivatives to extrapolate from the current position. The performance of AISE/va and AISE/FS is compared numerically with the $\alpha$-$\beta$-$\gamma$ filter, which shows that AISE/FS provides more accurate trajectory prediction than AISE/va and traditional methods, especially for complex target maneuvers.
Target Tracking Using the Invariant Extended Kalman Filter with Numerical Differentiation for Estimating Curvature and Torsion
Authors: Shashank Verma, Dennis S. Bernstein
Date: 2025-01-08 00:00:00
Tasks: Position, State Estimation

The goal of target tracking is to estimate target position, velocity, and acceleration in real time using position data. This paper introduces a novel target-tracking technique that uses adaptive input and state estimation (AISE) for real-time numerical differentiation to estimate velocity, acceleration, and jerk from position data. These estimates are used to model the target motion within the Frenet-Serret (FS) frame. By representing the model in SE(3), the position and velocity are estimated using the invariant extended Kalman filter (IEKF). The proposed method, called FS-IEKF-AISE, is illustrated by numerical examples and compared to prior techniques.
A Quasi-deterministic Channel Model for Underwater Acoustic Communication Systems
Authors: Yuxuan Yang, Yilin Ma, Hengtai Chang, Cheng-Xiang Wang
Date: 2025-01-08 00:00:00

In this paper, a quasi-deterministic (Q-D) model for non-stationary underwater acoustic (UWA) channels is proposed. This model combines the BELLHOP deterministic model and geometry-based stochastic model (GBSM), which provides higher accuracy and flexibility. Different propagation components in shallow water are classified as D-rays, R-rays and F-rays in the proposed model, where D-rays are modeled by BELLHOP while both R-rays and F-rays are modeled by GBSM. Some important channel statistical properties, including time-frequency correlation function (TF-CF), Doppler power spectrum density (PSD), average Doppler shift, and RMS Doppler spread are derived and simulated. Finally, simulation results illustrate the correctness of the proposed model.
Privacy-Preserving Distributed Online Mirror Descent for Nonconvex Optimization
Authors: Yingjie Zhou, Tao Li
Date: 2025-01-08 00:00:00
Tasks: Privacy Preserving

We investigate the distributed online nonconvex optimization problem with differential privacy over time-varying networks. Each node minimizes the sum of several nonconvex functions while preserving the node's differential privacy. We propose a privacy-preserving distributed online mirror descent algorithm for nonconvex optimization, which uses the mirror descent to update decision variables and the Laplace differential privacy mechanism to protect privacy. Unlike the existing works, the proposed algorithm allows the cost functions to be nonconvex, which is more applicable. Based upon these, we prove that if the communication network is $B$-strongly connected and the constraint set is compact, then by choosing the step size properly, the algorithm guarantees $\epsilon$-differential privacy at each time. Furthermore, we prove that if the local cost functions are $\beta$-smooth, then the regret over time horizon $T$ grows sublinearly while preserving differential privacy, with an upper bound $O(\sqrt{T})$. Finally, the effectiveness of the algorithm is demonstrated through numerical simulations.
Robust Moving-horizon Estimation for Nonlinear Systems: From Perfect to Imperfect Optimization
Authors: Angelo Alessandri
Date: 2025-01-07 00:00:00

Robust stability of moving-horizon estimators is investigated for nonlinear discrete-time systems that are detectable in the sense of incremental input/output-to-state stability and are affected by disturbances. The estimate of a moving-horizon estimator stems from the on-line solution of a least-squares minimization problem at each time instant. The resulting stability guarantees depend on the optimization tolerance in solving such minimization problems. Specifically, two main contributions are established: (i) the robust stability of the estimation error, while supposing to solve exactly the on-line minimization problem; (ii) the practical robust stability of the estimation error with state estimates obtained by an imperfect minimization. Finally, the construction of such robust moving-horizon estimators and the performances resulting from the design based on the theoretical findings are showcased with two numerical examples.
Study of Frictional and Impact Transients in Active-Passive Mechanical Pair
Authors: Michael Ruderman, Francesco De Rito
Date: 2025-01-07 00:00:00
Tasks: Friction

We consider an active-passive mechanical pair in which the relative motion of the latter is constrained by the mechanical impact. The system dynamics is described by the previously introduced modeling frameworks of force transition and dissipation through the nonlinear Coulomb friction and structural damping, the later in accord with Hertzian contact theory. The focus of the recent study is on combining both interaction mechanisms, and the detailed experimental evaluation which discloses validity of the modeling assumptions. Such mechanical pair interactions can be found in various mechatronic systems and mechanisms, like for example clutches, backlash elements, sliding items on the shaking and inclining surfaces, conveyor belts and others. This practical study demonstrates and discusses the transients of a vibro-impact dynamics and shows theoretical developments in line with experimental evaluation.
A 3D Continuous-Space Electromagnetic Channel Model for 6G Tri-Polarized Multi-user Communications
Authors: Yue Yang, Cheng-Xiang Wang, Jie Huang, John Thompson, H. Vincent Poor
Date: 2025-01-07 00:00:00
Tasks: ISAC

It is envisioned that the sixth generation (6G) and beyond 6G (B6G) wireless communication networks will enable global coverage in space, air, ground, and sea. In this case, both base stations and users can be mobile and will tend to move continuously in three-dimensional (3D) space. Therefore, obtaining channel state information (CSI) in 3D continuous-space is crucial for the design and performance evaluation of future 6G and B6G wireless systems. On the other hand, new 6G technologies such as integrated sensing and communications (ISAC) will also require prior knowledge of CSI in 3D continuous-space. In this paper, a 3D continuous-space electromagnetic channel model is proposed for tri-polarized multi-user communications, taking into account scatterers and spherical wavefronts. Scattered fields are calculated using the method of moments (MoM) with high accuracy. Spherical wave functions are utilized to decompose the dyadic Green's functions that connect the transmitted source currents and the received electric fields. Simulation results demonstrate that transmit power, apertures, scatterers, and sample intervals have significant impacts on statistical properties and channel capacities, providing insights into the performance of continuous-space electromagnetic channel models and the design of future wireless systems.
Wireless Channel Measurements and Characterization in Industrial IoT Scenarios
Authors: Li Zhang, Cheng-Xiang Wang, ZiHao Zhou, Yuxiao Li, Jie Huang, Lijian Xin, Chun Pan, Dabo Zheng, Xiping Wu
Date: 2025-01-07 00:00:00

Wireless Fidelity (Wi-Fi) communication technologies hold significant potential for realizing the Industrial Internet of Things (IIoT). In this paper, both Single-Input Single-Output (SISO) and polarized Multiple-Input Multiple-Output (MIMO) channel measurements are conducted in an IIoT scenario at the less congested Wi-Fi band, i.e., 5.5~GHz. The purpose is to investigate wireless characteristics of communications between access points and terminals mounted on automated guided vehicles as well as those surrounding manufacturing areas. For SISO channel measurements, statistical properties including the delay Power Spectral Density (PSD), path loss, shadowing fading, delay spread, excess delay, K-factor, and amplitude distribution of small-scale fading are analyzed and compared with those observed in an office scenario. For MIMO channel measurements, results show that there are multiple Dense Multipath Component (DMC) processes in the delay PSD. An estimation algorithm based on the algorithm for a single DMC process is proposed to effectively process the multi-processes data. Moreover, delay, angular, power, and polarization properties of DMCs are investigated and compared with those of specular multipath components. Furthermore, effects of DMCs on Singular Values (SVs) and channel capacities are explored. Ignoring DMCs can overestimate SVs and underestimate channel capacities.
Proxy Control Barrier Functions: Integrating Barrier-Based and Lyapunov-Based Safety-Critical Control Design
Authors: Yujie Wang, Xiangru Xu
Date: 2025-01-07 00:00:00

This work introduces a novel Proxy Control Barrier Function (PCBF) scheme that integrates barrier-based and Lyapunov-based safety-critical control strategies for strict-feedback systems with potentially unknown dynamics. The proposed method employs a modular design procedure, decomposing the original system into a proxy subsystem and a virtual tracking subsystem that are controlled by the control barrier function (CBF)-based and Lyapunov-based controllers, respectively. By integrating these separately designed controllers, the overall system's safety is ensured. Moreover, a new filter-based disturbance observer is utilized to design a PCBF-based safe controller for strict-feedback systems subject to mismatched disturbances. This approach broadens the class of systems to which CBF-based methods can be applied and significantly simplifies CBF construction by requiring only the model of the proxy subsystem. The effectiveness of the proposed method is demonstrated through numerical simulations.
Resilient Distributed Control for Uncertain Nonlinear Interconnected Systems under Network Anomaly
Authors: Youqing Wang, Ying Li, Thomas Parisini, Dong Zhao
Date: 2025-01-07 00:00:00

We address a distributed adaptive control methodology for nonlinear interconnected systems possibly affected by network anomalies. In the framework of adaptive approximation, the distributed controller and parameter estimator are designed by exploiting a backstepping approach. The stability of the distributed control system under anomalies is analyzed, where both local and neighboring anomaly effects are considered. To quantify the resilience of the interconnected system under the action of network anomalies, we derive bounds on the duration of each anomaly and the resting time between two consecutive anomalies. Specifically, when each anomaly duration is smaller than our designed upper bound, the interconnected system controlled by the distributed approximation-based controller remains asymptotically stable. Moreover, if the resting time between two consecutive anomalies is larger than the proposed bound, then all signals of the control system are guaranteed to be bounded. In the paper, we show that under the action of the proposed distributed adaptive controller, the interconnected system remains stable in the presence of network anomalies, with both the qualitative and quantitative resilient conditions. Extensive simulation results show the effectiveness of our theoretical results.
A Unified Attack Detection Strategy for Multi-Agent Systems over Transient and Steady Stages
Authors: Jinming Gao, Yijing Wang, Wentao Zhang, Rui Zhao, Yang Shi, Zhiqiang Zuo
Date: 2025-01-07 00:00:00

This paper proposes a unified detection strategy against three kinds of attacks for multi-agent systems (MASs) which is applicable to both transient and steady stages. For attacks on the communication layer, a watermarking-based detection scheme with KullbackLeibler (KL) divergence is designed. Different from traditional communication schemes, each agent transmits a message set containing two state values with different types of watermarking. It is found that the detection performance is determined by the relevant parameters of the watermarking signal. Unlike the existing detection manoeuvres, such a scheme is capable of transient and steady stages. For attacks on the agent layer, a convergence rate related detection approach is put forward. It is shown that the resilience of the considered system is characterized by the coefficient and offset of the envelope. For hybrid attacks, based on the above detection mechanisms, a general framework resorting to trusted agents is presented, which requires weaker graph conditions and less information transmission. Finally, an example associated with the platooning of connected vehicles is given to support the theoretical results.
Global network control from local information
Authors: Aleksandar Haber, Ferenc Molnar, Adilson E. Motter
Date: 2025-01-06 00:00:00

In the classical control of network systems, the control actions on a node are determined as a function of the states of all nodes in the network. Motivated by applications where the global state cannot be reconstructed in real time due to limitations in the collection, communication, and processing of data, here we introduce a control approach in which the control actions can be computed as a function of the states of the nodes within a limited state information neighborhood. The trade-off between the control performance and the size of this neighborhood is primarily determined by the condition number of the controllability Gramian. Our theoretical results are supported by simulations on regular and random networks and are further illustrated by an application to the control of power-grid synchronization. We demonstrate that for well-conditioned Gramians, there is no significant loss of control performance as the size of the state information neighborhood is reduced, allowing efficient control of large networks using only local information.
A New Underdetermined Framework for Sparse Estimation of Fault Location for Transmission Lines Using Limited Current Measurements
Authors: Guangxiao Zhang, Gaoxi Xiao, Xinghua Liu, Yan Xu, Peng Wang
Date: 2025-01-06 00:00:00

This letter proposes an alternative underdetermined framework for fault location that utilizes current measurements along with the branch-bus matrix, providing another option besides the traditional voltage-based methods. To enhance fault location accuracy in the presence of multiple outliers, the robust YALL1 algorithm is used to resist outlier interference and accurately recover the sparse vector, thereby pinpointing the fault precisely. The results on the IEEE 39-bus test system demonstrate the effectiveness and robustness of the proposed method.
MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards
Authors: Shruti Nath, Julie Carreau, Kai Kornhuber, Peter Pfleiderer, Carl-Friedrich Schleussner, Philippe Naveau
Date: 2024-12-24 00:00:00
Tasks: Image Compression

High-impact climate damages are often driven by compounding climate conditions. For example, elevated heat stress conditions can arise from a combination of high humidity and temperature. To explore future changes in compounding hazards under a range of climate scenarios and with large ensembles, climate emulators can provide light-weight, data-driven complements to Earth System Models. Yet, only a few existing emulators can jointly emulate multiple climate variables. In this study, we present the Multi-resolution EmulatoR for CompoUnd climate Risk analYsis: MERCURY. MERCURY extends multi-resolution analysis to a spatio-temporal framework for versatile emulation of multiple variables. MERCURY leverages data-driven, image compression techniques to generate emulations in a memory-efficient manner. MERCURY consists of a regional component that represents the monthly, regional response of a given variable to yearly Global Mean Temperature (GMT) using a probabilistic regression based additive model, resolving regional cross-correlations. It then adapts a reverse lifting-scheme operator to jointly spatially disaggregate regional, monthly values to grid-cell level. We demonstrate MERCURY's capabilities on representing the humid-heat metric, Wet Bulb Globe Temperature, as derived from temperature and relative humidity emulations. The emulated WBGT spatial correlations correspond well to those of ESMs and the 95% and 97.5% quantiles of WBGT distributions are well captured, with an average of 5% deviation. MERCURY's setup allows for region-specific emulations from which one can efficiently "zoom" into the grid-cell level across multiple variables by means of the reverse lifting-scheme operator. This circumvents the traditional problem of having to emulate complete, global-fields of climate data and resulting storage requirements.