pwc-archive/papers-with-abstracts huggingface dataset:
Impact Assessment of Cyberattacks in Inverter-Based Microgrids
Authors: Kerd Topallaj, Colin McKerrell, Suraj Ramanathan, Ioannis Zografopoulos
Date: 2025-04-08 00:00:00

In recent years, the evolution of modern power grids has been driven by the growing integration of remotely controlled grid assets. Although Distributed Energy Resources (DERs) and Inverter-Based Resources (IBR) enhance operational efficiency, they also introduce cybersecurity risks. The remote accessibility of such critical grid components creates entry points for attacks that adversaries could exploit, posing threats to the stability of the system. To evaluate the resilience of energy systems under such threats, this study employs real-time simulation and a modified version of the IEEE 39-bus system that incorporates a Microgrid (MG) with solar-based IBR. The study assesses the impact of remote attacks impacting the MG stability under different levels of IBR penetrations through Hardware-in-the-Loop (HIL) simulations. Namely, we analyze voltage, current, and frequency profiles before, during, and after cyberattack-induced disruptions. The results demonstrate that real-time HIL testing is a practical approach to uncover potential risks and develop robust mitigation strategies for resilient MG operations.
Adaptive Control of Dual-Rotor Rotational System with Unknown Geometry and Unknown Inertia
Authors: Mohammad Mirtaba, Jhon Manuel Portella Delgado, Ankit Goel
Date: 2025-04-08 00:00:00
Tasks: Position

This paper develops an input-output feedback linearization-based adaptive controller to stabilize and regulate a dual-rotor rotational system (DRRS), whose inertial properties as well as the geometric configuration of rotors are unknown. First, the equations of motion governing the dynamics of DRRS are derived using the Newton-Euler approach. Next, an input-output feedback linearization technique is used to linearize the dynamics from the rotor speeds to the angular position of the system. A finite-time convergent estimator, based on the portion of the DRRS dynamics, is used to update the required parameters in the controller. Finally, the proposed controller is validated in both step and harmonic command-following problems, and the robustness of the controller to the system's parameters is demonstrated.
Enhanced Entropy-Based Metric for Characterization of Delayed Voltage Recovery
Authors: Mohammad Almomani, Muhammad Sarwar, Venkataramana Ajjarapu
Date: 2025-04-07 00:00:00

Ensuring accurate violation detection in power systems is paramount for operational reliability. This paper introduces an enhanced voltage recovery violation index (EVRVI), a comprehensive index designed to quantify fault-induced delayed voltage recovery (FIDVR). EVRVI enhances traditional entropy-based methods by leveraging Empirical Mode Decomposition (EMD) to extract key features from the voltage signal, which are then used to quantify over-voltage (OV) and under-voltage (UV) events. Our simulations on the Nordic system, involving over 245k scenarios, demonstrate EVRVI's superior ability to identify and categorize voltage recovery issues compared to the traditional entropy-based measure. EVRVI not only significantly reduces false negatives in violation detection but also provides a reliable framework for over-voltage detection, making it an invaluable tool for modern power system studies.
Novel Data-Driven Indices for Early Detection and Quantification of Short-Term Voltage Instability from Voltage Trajectories
Authors: Mohammad Almomani, Muhammad Sarwar, Venkataramana Ajjarapu
Date: 2025-04-07 00:00:00

This paper presents a novel Short-Term Voltage Stability Index (STVSI), which leverages Lyapunov Exponent-based detection to assess and quantify short-term stability triggered by Over Excitation Limiters (OELs) or undamped oscillations in voltage. The proposed method is measurement-based and decomposes the voltage trajectory into two key components using Empirical Mode Decomposition (EMD): a residual part, which indicates delayed voltage recovery, and an oscillatory part, which captures oscillations. The residual component is critical, as it can detect activation of OELs in synchronous generators or Low Voltage Ride-Through (LVRT) relays in inverter-based resources, potentially leading to instability within the quasisteady-state time frame. Meanwhile, the oscillatory component may indicate either a stable or unstable state in the short term. To accurately assess stability, STVSI employs an entropy-based metric to measure the proximity of the system to instability, with specific indices for short-term voltage stability based on oscillations and recovery. Simulations on the Nordic power system demonstrate that STVSI effectively identifies and categorizes voltage stability issues. Moreover, STVSI not only detects voltage stability conditions but also qualitatively assesses the extent of stability, providing a nuanced measure of stability.
Extended Sensitivity-Aware Reactive Power Dispatch Algorithm for Smart Inverters with Multiple Control Modes
Authors: Mohammad Almomani, Ahmed Alkhonain, Venkataramana Ajjarapu
Date: 2025-04-07 00:00:00
Tasks: Sensitivity

The increasing integration of Distributed Energy Resources (DERs) in distribution networks presents new challenges for voltage regulation and reactive power support. This paper extends a sensitivity-aware reactive power dispatch algorithm tailored to manage smart inverters operating under different control modes, including PQ, PV, and Volt-Var (VV). The proposed approach dynamically optimizes reactive power dispatch and voltage setpoints, enabling effective coordination among distribution systems as a virtual power plant (VPP) to support the transmission network. The algorithm is applied to the IEEE 13-bus and IEEE-123 bus test systems, and its performance is validated by comparing results with OpenDSS simulations across various operating scenarios. Results show that the maximum error in the voltages is less than 0.015 pu.
Debiasing Continuous-time Nonlinear Autoregressions
Authors: Simon Kuang, Xinfan Lin
Date: 2025-04-07 00:00:00
Tasks: regression, Time Series

We study how to identify a class of continuous-time nonlinear systems defined by an ordinary differential equation affine in the unknown parameter. We define a notion of asymptotic consistency as $(n, h) \to (\infty, 0)$, and we achieve it using a family of direct methods where the first step is differentiating a noisy time series and the second step is a plug-in linear estimator. The first step, differentiation, is a signal processing adaptation of the nonparametric statistical technique of local polynomial regression. The second step, generalized linear regression, can be consistent using a least squares estimator, but we demonstrate two novel bias corrections that improve the accuracy for finite $h$. These methods significantly broaden the class of continuous-time systems that can be consistently estimated by direct methods.
Optimal Bayesian Affine Estimator and Active Learning for the Wiener Model
Authors: Sasan Vakili, Manuel Mazo Jr., Peyman Mohajerin Esfahani
Date: 2025-04-07 00:00:00
Tasks: Active Learning, Form

This paper presents a Bayesian estimation framework for Wiener models, focusing on learning nonlinear output functions under known linear state dynamics. We derive a closed-form optimal affine estimator for the unknown parameters, characterized by the so-called "dynamic basis statistics" (DBS). Several features of the proposed estimator are studied, including Bayesian unbiasedness, closed-form posterior statistics, error monotonicity in trajectory length, and consistency condition (also known as persistent excitation). In the special case of Fourier basis functions, we demonstrate that the closed-form description is computationally available, as the Fourier DBS enjoys explicit expressions. Furthermore, we identify an inherent inconsistency in the Fourier bases for single-trajectory measurements, regardless of the input excitation. Leveraging the closed-form estimation error, we develop an active learning algorithm synthesizing input signals to minimize estimation error. Numerical experiments validate the efficacy of our approach, showing significant improvements over traditional regularized least-squares methods.
Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning
Authors: Zixin Jiang, Xuezheng Wang, Bing Dong
Date: 2025-04-07 00:00:00
Tasks: Deep Reinforcement Learning, Physics-informed machine learning, Reinforcement Learning (RL)

Physics-informed machine learning (PIML) provides a promising solution for building energy modeling and can serve as a virtual environment to enable reinforcement learning (RL) agents to interact and learn. However, challenges remain in efficiently integrating physics priors, evaluating the effectiveness of physics constraints, balancing model accuracy and physics consistency, and enabling real-world implementation. To address these gaps, this study introduces a Physics-Informed Modularized Neural Network (PI-ModNN), which incorporates physics priors through a physics-informed model structure, loss functions, and hard constraints. A new evaluation metric called "temperature response violation" is developed to quantify the physical consistency of data-driven building dynamic models under varying control inputs and training data sizes. Additionally, a physics prior evaluation framework based on rule importance is proposed to assess the contribution of each individual physics prior, offering guidance on selecting appropriate PIML techniques. Results indicate that incorporating physical priors does not always improve model performance; inappropriate priors may decrease model accuracy and consistency. However, hard constraints are effective in enforcing model consistency. Furthermore, we present a general workflow for developing control-oriented PIML models and integrating them with deep reinforcement learning (DRL). Following this framework, a case study implementing DRL in an office space over three months demonstrates potential energy savings of 31.4%. Finally, we provide a general guideline for integrating data-driven models with advanced building control through a four-step evaluation framework, paving the way for reliable and scalable deployment of advanced building controls.
Estimation of Heat Transfer Coefficient in Heat Exchangers from closed-loop data using Neural Networks
Authors: Ramachandran Anantharaman, Carlos Gonzalez Rojas, Luna Artemis van Leeuwen, Leyla Özkan
Date: 2025-04-07 00:00:00

Heat exchangers (HEXs) play a central role in process industries for thermal energy transfer. Fouling, the gradual accumulation of solids on heat transfer surfaces, causes a time-varying decrease in the overall heat transfer coefficient (U(t)), significantly impacting the efficiency of heat transfer. Good estimation and modeling of fouling (the heat transfer coefficient) will lead to better fouling mitigation strategies. This study investigates the identifiability of the time-varying $U(t)$ in HEXs from closed-loop operational data, without external excitation of reference signals or knowledge of the controller parameters. We establish that while the complete system model cannot be identified under these given constraints, the time-varying heat transfer coefficient $U(t)$ remains identifiable. Further, we propose a neural network based architecture, called (Per-PINN), for estimation and modeling the heat transfer coefficient from the closed-loop system data. This Per-PINN model is shown to perform better than the existing Physics-Informed Neural Networks (PINN) based models for inverse parameter learning as it inherently fixes the underlying physical equations and learns only the time-varying parameter U(t).
Safe and Efficient Coexistence of Autonomous Vehicles with Human-Driven Traffic at Signalized Intersections
Authors: Filippos N. Tzortzoglou, Logan E. Beaver, Andreas A. Malikopoulos
Date: 2025-04-07 00:00:00
Tasks: Autonomous Vehicles, Traffic Signal Control, Trajectory Planning

The proliferation of connected and automated vehicles (CAVs) has positioned mixed traffic environments, which encompass both CAVs and human driven vehicles (HDVs), as critical components of emerging mobility systems. Signalized intersections are paramount for optimizing transportation efficiency and enhancing energy economy, as they inherently induce stop and go traffic dynamics. In this paper, we present an integrated framework that concurrently optimizes signal timing and CAV trajectories at signalized intersections, with the dual objectives of maximizing traffic throughput and minimizing energy consumption for CAVs. We first formulate an optimal control strategy for CAVs that prioritizes trajectory planning to circumvent state constraints, while incorporating the impact of signal timing and HDV behavior. Furthermore, we introduce a traffic signal control methodology that dynamically adjusts signal phases based on vehicular density per lane, while mitigating disruption for CAVs scheduled to traverse the intersection. Acknowledging the system's inherent dynamism, we also explore event triggered replanning mechanisms that enable CAVs to iteratively refine their planned trajectories in response to the emergence of more efficient routing options. The efficacy of our proposed framework is evaluated through comprehensive simulations in MATLAB.
AI-Driven Tactical Communications and Networking for Defense: A Survey and Emerging Trends
Authors: Victor Monzon Baeza, Raúl Parada, Laura Concha Salor, Carlos Monzo
Date: 2025-04-07 00:00:00

The integration of Artificial Intelligence (AI) in military communications and networking is reshaping modern defense strategies, enhancing secure data exchange, real-time situational awareness, and autonomous decision-making. This survey explores how AI-driven technologies improve tactical communication networks, radar-based data transmission, UAV-assisted relay systems, and electronic warfare resilience. The study highlights AI applications in adaptive signal processing, multi-agent coordination for network optimization, radar-assisted target tracking, and AI-driven electronic countermeasures. Our work introduces a novel three-criteria evaluation methodology. It systematically assesses AI applications based on general system objectives, communications constraints in the military domain, and critical tactical environmental factors. We analyze key AI techniques for different types of learning applied to multi-domain network interoperability and distributed data information fusion in military operations. We also address challenges such as adversarial AI threats, the real-time adaptability of autonomous communication networks, and the limitations of current AI models under battlefield conditions. Finally, we discuss emerging trends in self-healing networks, AI-augmented decision support systems, and intelligent spectrum allocation. We provide a structured roadmap for future AI-driven defense communications and networking research.
Age-of-information minimization under energy harvesting and non-stationary environment
Authors: Akanksha Jaiswal, Arpan Chattopadhyay
Date: 2025-04-07 00:00:00
Tasks: Scheduling

This work focuses on minimizing the age of information for multiple energy harvesting sources that sample data and transmit it to a sink node. At each time, the central scheduler selects one of the sources to probe the quality of its channel to the sink node, and then the assessed channel quality is utilized to determine whether a source will sample and send the packet. For a single source case, we assume that the probed channel quality is known at each time instant, model the problem of AoI minimization as a Markov decision process, and prove the optimal sampling policy threshold structure. We then use this threshold structure and propose an AEC-SW-UCRL2 algorithm to handle unknown and time varying energy harvesting rate and channel statistics, motivated by the popular SWUCRL2 algorithm for non stationary reinforcement learning. This algorithm is applicable when an upper bound is available for the total variation of each of these quantities over a time horizon. Furthermore, in situations where these variation budgets are not accessible, we introduce the AEC-BORL algorithm, motivated by the well known BORL algorithm. For the multiple source case, we demonstrate that the AoI minimization problem can be formulated as a constrained MDP, which can be relaxed using a Lagrange multiplier and decoupled into sub problems across source nodes. We also derive Whittle index based source scheduling policy for probing and an optimal threshold policy for source sampling. We next leverage this Whittle index and threshold structure to develop the WIT-SW-UCRL2 algorithm for unknown time varying energy harvesting rates and channel statistics under their respective variation budgets. Moreover, we also proposed a Whittle index and threshold based bandit over reinforcement learning (WIT-BORL) algorithm for unknown variation budgets. Finally, we numerically demonstrate the efficacy of our algorithms.
Fault Localisation in Infinite-Dimensional Linear Electrical Networks
Authors: Daniel Selvaratnam, Alessio Moreschini, Amritam Das, Thomas Parisini, Henrik Sandberg
Date: 2025-04-07 00:00:00

We present a novel fault localisation methodology for linear time-invariant electrical networks with infinite-dimensional edge dynamics and uncertain fault dynamics. The theory accommodates instability and also bounded propagation delays in the network. The goal is to estimate the location of a fault along a given network edge, using sensors positioned arbitrarily throughout the network. Passive faults of unknown impedance are considered, along with stable faults of known impedance. To illustrate the approach, we tackle a significant use-case: a multi-conductor transmission line, with dynamics modelled by the Telegrapher's equation, subject to a line-to-ground fault. Frequency-domain insights are used to reformulate the general fault localisation problem into a non-convex scalar optimisation problem, of which the true fault location is guaranteed to be a global minimiser. Numerical experiments are run to quantify localisation performance over a range of fault resistances.
Cloud-Fog Automation: The New Paradigm towards Autonomous Industrial Cyber-Physical Systems
Authors: Jiong Jin, Zhibo Pang, Jonathan Kua, Quanyan Zhu, Karl H. Johansson, Nikolaj Marchenko, Dave Cavalcanti
Date: 2025-04-07 00:00:00

Autonomous Industrial Cyber-Physical Systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog Automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of Automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term "3C co-design") is required. In this paper, we position Cloud-Fog Automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog Automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security.
Design of a compact low loss 2-way millimetre wave power divider for future communication
Authors: Muhammad Asfar Saeed, Augustine O. Nwajana, Muneeb Ahmad
Date: 2025-04-07 00:00:00

In this paper, a rectangular-shaped power divider has been presented operating at 27.9 GHz. The power divider has achieved acceptable results for important parameters such as S11, S12, S21, and S22. The substrate employed for the power divider is Roger 3003 which has a thickness of 1.6 mm. This power divider provides a reflection coefficient of -12.2 dB and an insertion loss of 3.1 dB at 28 GHz. This ka-band T-junction power divider covers 68% of the bandwidth. Dimensions of the ka-band T-junction power divider are 50x80 mm. Due to its dimensions and bandwidth this power divider is more suitable for millimetre wave applications like RADAR, beamforming, and 5G applications.
Asymmetric 4.77 Three-Way Unequal Filtering Power Divider/Combiner for Communication Systems Application
Authors: Augustine O. Nwajana, Mosammat Rokaiya Akter, Muhammad Asfar Saeed
Date: 2025-04-07 00:00:00

This study presents a novel three-way unequal filtering power divider/combiner, addressing challenges in unequal power distribution while incorporating filtering functions in communication systems. Wilkinson power divider (WPD) is the traditional power division approach using quarter-wavelength transmission lines [1]. This type of power divider is popularly used in communication systems due to its good electrical isolation and simple structure. The problem with WPD is that its operation requires the use of an externally connected bandpass filter (BPF) to achieve filtering functionality. This leads to increased footprint and increased loss coefficients in a system. In contrast to the traditional design approach involving a BPF, a matching transmission line, and a Wilkinson power divider as separate components, the proposed integrated filtering power divider (FPD) consolidates all three components into a single device, leading to lower footprint and lower loss coefficient in a system. Circuit modelling and electromagnetic (EM) simulations were conducted to ensure alignment between theoretical and practical results. The design demonstrates effective unequal power division at the three output ports while maintaining very good filtering performance. Results show a return loss better than 15 dB and a minimum insertion loss of 1.2 dB. The overall size of the device is 32.2 x 50.0 mm. This paper contributes to advancements in power divider design by addressing unequal power division challenges and integrating filtering functions. The findings offer a foundation for future developments in advanced power divider/combiner systems, with insights into potential challenges and areas for further improvements.
The Cesàro Value Iteration
Authors: Jonas Mair, Lukas Schwenkel, Matthias A. Müller, Frank Allgöwer
Date: 2025-04-07 00:00:00

In this paper, we address the problem of undiscouted infinite-horizon optimal control for deterministic systems where the classic value iteration does not converge. For such systems, we propose to use the Ces\`aro mean to define the infinite-horizon optimal control problem and the corresponding infinite-horizon value function. Moreover, for this value function, we introduce the Ces\`aro value iteration and prove its convergence for the special case of systems with periodic optimal operating behavior.