pwc-archive/papers-with-abstracts huggingface dataset:
A Comparative Analysis of Electricity Consumption Flexibility in Different Industrial Plant Configurations
Authors: Sebastián Rojas-Innocenti, Enrique Baeyens, Alejandro Martín-Crespo, Sergio Saludes-Rodil, Fernando Frechoso
Date: 2024-11-14 00:00:00

The flexibility of industrial power consumption plays a key role in the transition to renewable energy systems, contributing to grid stability, cost reduction and decarbonization efforts. This paper presents a novel methodology to quantify and optimize the flexibility of electricity consumption in manufacturing plants. The proposed model is applied to actual cement and steel plant configurations. Comparative simulations performed with the model reveal significant differences in flexibility and cost-effectiveness, driven by factors such as production capacity, downstream process demand, storage capacity, and operational constraints. A comprehensive sensitivity analysis further clarifies the impact of various parameters on production optimization and flexibility savings. Specifically, as demand approaches production levels, flexibility decreases. Although increasing storage capacity typically reduces production costs, the benefits diminish above a certain threshold. The results provide valuable information for industrial operators wishing to improve operational efficiency, reduce costs and increase the flexibility of their operations.
Are the flows of complex-valued Laplacians and their pseudoinverses related?
Authors: Aditi Saxena, Twinkle Tripathy, Rajasekhar Anguluri
Date: 2024-11-14 00:00:00

Laplacian flows model the rate of change of each node's state as being proportional to the difference between its value and that of its neighbors. Typically, these flows capture diffusion or synchronization dynamics and are well-studied. Expanding on these classical flows, we introduce a pseudoinverse Laplacian flow system, substituting the Laplacian with its pseudoinverse within complex-valued networks. Interestingly, for undirected graphs and unsigned weight-balanced digraphs, Laplacian and the pseudoinverse Laplacian flows exhibit an interdependence in terms of consensus. To show this relation, we first present the conditions for achieving consensus in the pseudoinverse Laplacian flow system using the property of real eventually exponentially positivity. Thereafter, we show that the pseudoinverse Laplacian flow system converges to consensus if and only if the Laplacian flow system achieves consensus in the above-mentioned networks. However, these are only the sufficient conditions for digraphs. Further, we illustrate the efficacy of the proposed approach through examples, focusing primarily on power networks.
Unsupervised Physics-Informed Neural Network-based Nonlinear Observer design for autonomous systems using contraction analysis
Authors: Yasmine Marani, Israel Filho, Tareq Al-Naffouri, Taous-Meriem Laleg-Kirati
Date: 2024-11-14 00:00:00

Contraction analysis offers, through elegant mathematical developments, a unified way of designing observers for a general class of nonlinear systems, where the observer correction term is obtained by solving an infinite dimensional inequality that guarantees global exponential convergence. However, solving the matrix partial differential inequality involved in contraction analysis design is both analytically and numerically challenging and represents a long-lasting challenge that prevented its wide use. Therefore, the present paper proposes a novel approach that relies on an unsupervised Physics Informed Neural Network (PINN) to design the observer's correction term by enforcing the partial differential inequality in the loss function. The performance of the proposed PINN-based nonlinear observer is assessed in numerical simulation as well as its robustness to measurement noise and neural network approximation error.
Enhancing reinforcement learning for population setpoint tracking in co-cultures
Authors: Sebastián Espinel-Ríos, Joyce Qiaoxi Mo, Dongda Zhang, Ehecatl Antonio del Rio-Chanona, José L. Avalos
Date: 2024-11-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Efficient multiple setpoint tracking can enable advanced biotechnological applications, such as maintaining desired population levels in co-cultures for optimal metabolic division of labor. In this study, we employ reinforcement learning as a control method for population setpoint tracking in co-cultures, focusing on policy-gradient techniques where the control policy is parameterized by neural networks. However, achieving accurate tracking across multiple setpoints is a significant challenge in reinforcement learning, as the agent must effectively balance the contributions of various setpoints to maximize the expected system performance. Traditional return functions, such as those based on a quadratic cost, often yield suboptimal performance due to their inability to efficiently guide the agent toward the simultaneous satisfaction of all setpoints. To overcome this, we propose a novel return function that rewards the simultaneous satisfaction of multiple setpoints and diminishes overall reward gains otherwise, accounting for both stage and terminal system performance. This return function includes parameters to fine-tune the desired smoothness and steepness of the learning process. We demonstrate our approach considering an $\textit{Escherichia coli}$ co-culture in a chemostat with optogenetic control over amino acid synthesis pathways, leveraging auxotrophies to modulate growth.
AI-Enhanced Inverter Fault and Anomaly Detection System for Distributed Energy Resources in Microgrids
Authors: Swetha Rani Kasimalla, Kuchan Park, Junho Hong, Young-Jin Kim, HyoJong Lee
Date: 2024-11-13 00:00:00
Tasks: Anomaly Detection, Fault Detection

The integration of Distributed Energy Resources (DERs) into power distribution systems has made microgrids foundational to grid modernization. These DERs, connected through power electronic inverters, create power electronics dominated grid architecture, introducing unique challenges for fault detection. While external line faults are widely studied, inverter faults remain a critical yet underexplored issue. This paper proposes various data mining techniques for the effective detection and localization of inverter faults-essential for preventing catastrophic grid failures. Furthermore, the difficulty of differentiating between system anomalies and internal inverter faults within Power Electronics-Driven Grids (PEDGs) is addressed. To enhance grid resilience, this work applies advanced artificial intelligence methods to distinguish anomalies from true internal faults, identifying the specific malfunctioning switch. The proposed FaultNet-ML methodology is validated on a 9-bus system dominated by inverters, illustrating its robustness in a PEDG environment.
Clutter-Aware Target Detection for ISAC in a Millimeter-Wave Cell-Free Massive MIMO System
Authors: Steven Rivetti, Ozlem Tugfe Demir, Emil Bjornson, Mikael Skoglund
Date: 2024-11-13 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we investigate the performance of an integrated sensing and communication (ISAC) system within a cell-free massive multiple-input multiple-output (MIMO) system. Each access point (AP) operates in the millimeter-wave (mmWave) frequency band. The APs jointly serve the user equipments (UEs) in the downlink while simultaneously detecting a target through dedicated sensing beams, which are directed toward a reconfigurable intelligent surface (RIS). Although the AP-RIS, RIS-target, and AP-target channels have both line-of-sight (LoS) and non-line-of-sight (NLoS) parts, it is assumed only knowledge of the LoS paths is available. A key contribution of this study is the consideration of clutter, which degrades the target detection if not handled. We propose an algorithm to alternatively optimize the transmit power allocation and the RIS phase-shift matrix, maximizing the target signal-to-clutter-plus-noise ratio (SCNR) while ensuring a minimum signal-to-interference-plus-noise ratio (SINR) for the UEs. Numerical results demonstrate that exploiting clutter subspace significantly enhances detection probability, particularly at high clutter-to-noise ratios, and reveal that an increased number of transmit side clusters impair detection performance. Finally, we highlight the performance gains achieved using a dedicated sensing stream.
Logic-based Knowledge Awareness for Autonomous Agents in Continuous Spaces
Authors: Arabinda Ghosh, Mahmoud Salamati, Sadegh Soudjani
Date: 2024-11-13 00:00:00
Tasks: Decision Making

This paper presents a step towards a formal controller design method for autonomous agents based on knowledge awareness to improve decision-making. Our approach is to first create an organized repository of information (a knowledge base) for autonomous agents which can be accessed and then translated into temporal specifications. Secondly, to develop a controller with formal guarantees that meets a combination of mission-specific objective and the specification from the knowledge base, we utilize an abstraction-based controller design (ABCD) approach, capable of managing both nonlinear dynamics and temporal requirements. Unlike the conventional offline ABCD approach, our method dynamically updates the controller whenever the knowledge base prompts changes in the specifications. A three-dimensional nonlinear car model navigating an urban road scenario with traffic signs and obstacles is considered for validation. Results show the effectiveness of the method in guiding the autonomous agents to the target while complying with the knowledge base and the mission-specific objective.
Identification of Power Systems with Droop-Controlled Units Using Neural Ordinary Differential Equations
Authors: Hannes M. H. Wolf, Christian A. Hans
Date: 2024-11-13 00:00:00

In future power systems, the detailed structure and dynamics may not always be fully known. This is due to an increasing number of distributed energy resources, such as photovoltaic generators, battery storage systems, heat pumps and electric vehicles, as well as a shift towards active distribution grids. Obtaining physically-based models for simulation and control synthesis can therefore become challenging. Differential equations, where the right-hand side is represented by a neural network, i.e., neural ordinary differential equations (NODEs), have a great potential to serve as a data-driven black-box model to overcome this challenge. This paper explores their use in identifying the dynamics of droop-controlled grid-forming units based on inputs and state measurements. In numerical studies, various NODE structures used with different numerical solvers are trained and evaluated. Moreover, they are compared to the sparse identification of nonlinear dynamics (SINDy) method. The results demonstrate that even though SINDy yields more accurate models, NODEs achieve good prediction performance without prior knowledge about the system's nonlinearities which SINDy requires to work best.
Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level Optimization Approach
Authors: Saman Mazaheri Khamaneh, Tong Wu
Date: 2024-11-13 00:00:00
Tasks: Adversarial Attack

In power systems, unpredictable events like extreme weather, equipment failures, and cyberattacks present significant challenges to ensuring safety and reliability. Ensuring resilience in the face of these uncertainties is crucial for reliable and efficient operations. This paper presents a tri-level optimization approach for robust power system operations that effectively address worst-case attacks. The first stage focuses on optimizing economic dispatch under normal operating conditions, aiming to minimize generation costs while maintaining the supply-demand balance. The second stage introduces an adversarial attack model, identifying worst-case scenarios that maximize the system's vulnerability by targeting distributed generation (DG). In the third stage, mitigation strategies are developed using fast-response energy storage systems (ESS) to minimize disruptions caused by these attacks. By integrating economic dispatch, vulnerability assessment, and mitigation into a unified framework, this approach provides a robust solution for enhancing power system resilience and safety against evolving adversarial threats. The approach is validated using the IEEE-33 node distribution system to demonstrate its effectiveness in achieving both cost efficiency and system resilience.
Future state prediction based on observer for missile system
Authors: W. K. Smithson, Xinhua Wang
Date: 2024-11-13 00:00:00
Tasks: Prediction

Guided missile accuracy and precision is negatively impacted by seeker delay, more specifically by the delay introduced by a mechanical seeker gimbal and the computational time taken to process the raw data. To meet the demands and expectations of modern missiles systems, the impact of this hardware limitation must be reduced. This paper presents a new observer design that predicts the future state of a seeker signal, augmenting the guidance system to mitigate the effects of this delay. The design is based on a novel two-step differentiator, which produces the estimated future time derivatives of the signal. The input signal can be nonlinear and provides for simple integration into existing systems. A bespoke numerical guided missile simulation is used to demonstrate the performance of the observer within a missile guidance system. Both non-manoeuvring and randomly manoeuvring target engagement scenarios are considered.
Robust performance for switched systems with constrained switching and its application to weakly hard real-time control systems
Authors: Simon Lang, Marc Seidel, Frank Allgöwer
Date: 2024-11-13 00:00:00

Many cyber-physical systems can naturally be formulated as switched systems with constrained switching. This includes systems where one of the signals in the feedback loop may be lost. Possible sources for losses are shared or unreliable communication media in networked control systems, or signals which are discarded, e.g., when using a shared computation device such as a processor in real-time control applications. The use of switched systems with constrained switching is not limited to cyber-physical systems but, includes many other relevant applications such as power systems and modeling virus mutations. In this chapter, we introduce a framework for analyzing and designing controllers which guarantee robust quadratic performance for switched systems with constrained switching. The possible switching sequences are described by the language of a labeled graph where the labels are linked to the different subsystems. The subsystems are allowed to have different input and output dimensions, and their state-space representations can be affected by a broad class of uncertainties in a rational way. The proposed framework exploits ideas from dissipativity-based linear control theory to derive analysis and synthesis inequalities given by linear matrix inequalities. We demonstrate how the proposed framework can be applied to the design of controllers for uncertain weakly hard real-time control systems - a system class naturally appearing in networked and real-time control.
Inference-Aware State Reconstruction for Industrial Metaverse under Synchronous/Asynchronous Short-Packet Transmission
Authors: Qinqin Xiong, Jie Cao, Xu Zhu, Yufei Jiang, Nikolaos Pappas
Date: 2024-11-13 00:00:00

We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use the spatial-temporal correlation of the sensor data of interest to infer the real-time data of the target sensor to reduce the mean squared error (MSE) of reconstruction for industrial metaverse under short-packet transmission (SPT). Both synchronous and asynchronous transmission modes for multiple sensors are considered. It is proved that the average MSE of reconstruction and average block error probability (BLEP) have a positive correlation under inference with synchronous transmission scheme, and they have a negative correlation in some conditions under inference with asynchronous transmission scheme. Also, it is proved that the average MSE of reconstruction with inference can be significantly lower than that without inference, even under weak mean squared spatial correlation (MSSC). In addition, closed-form MSSC thresholds are derived for the superiority regions of the inference with synchronous transmission and inference with asynchronous transmission schemes, respectively. Adaptations of blocklength and time shift of asynchronous transmission are conducted to minimize the average MSE of reconstruction. Simulation results show that the two schemes significantly outperform the no inference case, with an average MSE reduction of more than 50%.
Neural Network Certification Informed Power System Transient Stability Preventive Control with Renewable Energy
Authors: Tong Su, Junbo Zhao
Date: 2024-11-13 00:00:00

Existing machine learning-based surrogate modeling methods for transient stability constrained-optimal power flow (TSC-OPF) lack certifications in the presence of unseen disturbances or uncertainties. This may lead to divergence of TSC-OPF or insecure control strategies. This paper proposes a neural network certification-informed power system transient stability preventive control method considering the impacts of various uncertainty resources, such as errors from measurements, fluctuations in renewable energy sources (RESs) and loads, etc. A deep belief network (DBN) is trained to estimate the transient stability, replacing the time-consuming time-domain simulation-based calculations. Then, DBN is embedded into the iterations of the primal-dual interior-point method to solve TSC-OPF. To guarantee the robustness of the solutions, the neural network verifier $\alpha, \beta$-CROWN to deal with uncertainties from RESs and loads is proposed. The yielded certification results allow us to further adjust the transient stability safety margin under the iterated TSC-OPF solution process, balancing system security and economics. Numerical results on a modified western South Carolina 500-bus system demonstrate that the proposed method can efficiently and quickly obtain the safety-verified preventive control strategy through RES curtailment and generator dispatch with only a slight increase in cost.
System-Level Analysis for mm-Wave Full-Duplex Transceivers
Authors: Mohamad Mahdi Rajaei Rizi, Jeyanandh Paramesh, Kamran Entesari
Date: 2024-11-12 00:00:00

This paper conducts a comprehensive system-level analysis of mm-Wave full-duplex transceivers, focusing on a receiver employing a four-stage self-interference cancellation (SIC) process. The analysis aims to optimize the noise and linearity performance requirements of each transceiver block, ensuring that the self-interference (SI) signal does not compromise the receiver's error vector magnitude (EVM) for an OFDM 64-QAM signal. Additionally, the necessary SIC for each stage is calculated to establish feasible noise and linearity specifications for a CMOS-based implementation. The resulting specifications are subsequently validated within a MATLAB Simulink environment, confirming the accuracy of the computed requirements for each block.
Collision-Free Multi-Agent Coverage Control for Non-Cooperating Swarms: Preliminary Results
Authors: Karolina Schmidt, Luis Rodrigues
Date: 2024-11-12 00:00:00
Tasks: Collision Avoidance

The main contribution of this paper is a methodology for multiple non-cooperating swarms of unmanned aerial vehicles to independently cover a common area. In contrast to previous research on coverage control involving more than one swarm, this paper does not assume cooperation between distinct groups but considers them as entirely independent units following their own objectives. Using Voronoi tesselation, collision-free motion of agents within the same swarm has been proved before. However, as is shown in Example 1 of this paper, in the case of multiple swarms with inter-swarm but without intra-swarm collaboration, these guarantees do not hold. We address this issue by proposing an algorithm to achieve maximum coverage with multiple swarms while avoiding collisions between agents. Thus, the Optimal Reciprocal Collision Avoidance method used for safe navigation in multi-agent scenarios is adapted to suit the needs of Voronoi-based coverage control with more than one swarm. The functionality of the proposed technique is validated through Monte Carlo simulations.
Shaping Frequency Dynamics in Modern Power Systems with Grid-forming Converters
Authors: Carlos Collados-Rodriguez, Daniel Westerman Spier, Marc Cheah-Mane, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt
Date: 2024-11-12 00:00:00

In this paper, frequency dynamics in modern power systems with a high penetration of converter-based generation is analysed. A fundamental analysis of the frequency dynamics is performed to identify the limitations and challenges when the converter penetration is increased. The voltage-source behaviour is found as an essential characteristic of converters to improve the initial frequency derivative of Synchronous Generators (SGs). A detailed small-signal analysis, based on the system's eigenvalues, participation factors and mode shapes, is then performed in a reduced system for different converter penetrations, showing that the flexibility of grid-forming (GFOR) converters as well as the system's inertia reduction may lead to have a more controllable system frequency. First-order frequency responses can be programmed for high converter penetrations, when GFOR operation can impose their dominance over SGs. These results have been validated in the IEEE 118-bus system simulated in PSCAD.
Optimal Constant Climb Airspeed with Variable Cost Index for All-electric Aircraft
Authors: Lucas Souza e Silva, Luis Rodrigues
Date: 2024-11-12 00:00:00
Tasks: All

This paper presents for the first time an approach to minimize direct operational costs (DOC) for all-electric aircraft during the climb phase, introducing a time-varying cost index (CI). The CI is modeled as a dynamic parameter commanded by Air Traffic Control (ATC), allowing the aircraft to maintain a constant airspeed throughout the climb, while respecting the air traffic regulations. This paper also explores the implications of a time-varying CI on the determination of optimal airspeed and climbing time for all-electric aircraft. Additionally, it provides the necessary equations to calculate both the optimal climb airspeed and climb duration. The proposed methodology has been validated through a simulated scenario that reflects actual operational procedures. As a result, optimal values for climb airspeed, climbing time, and energy consumption have been established, paving the way for future applications of this methodology to advanced air mobility all-electric vehicles.