pwc-archive/papers-with-abstracts huggingface dataset:
Self-Adjusting Prescribed Performance Control for Nonlinear Systems with Input Saturation
Authors: Zhuwu Shao, Yujuan Wang, Huanyu Yang, Yongduan Song
Date: 2024-04-21 00:00:00

Among the existing works on enhancing system performance via prescribed performance functions (PPFs), the decay rates of PPFs need to be predetermined by the designer, directly affecting the convergence time of the closed-loop system. However, if only considering accelerating the system convergence by selecting a big decay rate of the performance function, it may lead to the severe consequence of closed-loop system failure when considering the prevalent actuator saturation in practical scenarios. To address this issue, this work proposes a control scheme that can flexibly self-adjust the convergence rates of the performance functions (PFs), aiming to achieve faster steady-state convergence while avoiding the risk of error violation beyond the PFs' envelopes, which may arise from input saturation and improper decay rate selection in traditional prescribed performance control (PPC) methods. Specifically, a performance index function (PIF) is introduced as a reference criterion, based on which the self-adjusting rates of the PFs are designed for different cases, exhibiting the following appealing features: 1) It can eliminate the need to prespecify the initial values of the PFs. In addition, it can also accommodate arbitrary magnitudes of initial errors while avoiding excessive initial control efforts. 2) Considering actuator saturation, this method can not only reduce the decay rates of the PFs when necessary to avoid violation of the PFs, but also increase the decay rates to accelerate system convergence when there is remaining control capacity. Finally, several numerical simulations are conducted to confirm the effectiveness and superiority of the proposed method.
In-situ process monitoring and adaptive quality enhancement in laser additive manufacturing: a critical review
Authors: Lequn Chen, Guijun Bi, Xiling Yao, Jinlong Su, Chaolin Tan, Wenhe Feng, Michalis Benakis, Youxiang Chew, Seung Ki Moon
Date: 2024-04-21 00:00:00
Tasks: Benchmarking, Decision Making, Defect Detection, Fault Diagnosis, Sensor Fusion

Laser Additive Manufacturing (LAM) presents unparalleled opportunities for fabricating complex, high-performance structures and components with unique material properties. Despite these advancements, achieving consistent part quality and process repeatability remains challenging. This paper provides a comprehensive review of various state-of-the-art in-situ process monitoring techniques, including optical-based monitoring, acoustic-based sensing, laser line scanning, and operando X-ray monitoring. These techniques are evaluated for their capabilities and limitations in detecting defects within Laser Powder Bed Fusion (LPBF) and Laser Directed Energy Deposition (LDED) processes. Furthermore, the review discusses emerging multisensor monitoring and machine learning (ML)-assisted defect detection methods, benchmarking ML models tailored for in-situ defect detection. The paper also discusses in-situ adaptive defect remediation strategies that advance LAM towards zero-defect autonomous operations, focusing on real-time closed-loop feedback control and defect correction methods. Research gaps such as the need for standardization, improved reliability and sensitivity, and decision-making strategies beyond early stopping are highlighted. Future directions are proposed, with an emphasis on multimodal sensor fusion for multiscale defect prediction and fault diagnosis, ultimately enabling self-adaptation in LAM processes. This paper aims to equip researchers and industry professionals with a holistic understanding of the current capabilities, limitations, and future directions in in-situ process monitoring and adaptive quality enhancement in LAM.
Cross-Forming Control and Fault Current Limiting for Grid-Forming Inverters
Authors: Xiuqiang He, Maitraya Avadhut Desai, Linbin Huang, Florian Dörfler
Date: 2024-04-20 00:00:00

This article proposes a "cross-forming" control concept for grid-forming inverters operating against grid faults. Cross-forming refers to voltage angle forming and current magnitude forming. It differs from classical grid-forming and grid-following paradigms that feature voltage magnitude-and-angle forming and voltage magnitude-and-angle following (or current magnitude-and-angle forming), respectively. The cross-forming concept addresses the need for inverters to remain grid-forming (particularly voltage angle forming, as required by grid codes) while managing fault current limitation. Simple and feasible cross-forming control implementations are proposed, enabling inverters to quickly limit fault currents to a prescribed level while preserving voltage angle forming for grid-forming synchronization and providing dynamic ancillary services, during symmetrical or asymmetrical fault ride-through. Moreover, the cross-forming control yields an equivalent system featuring a constant virtual impedance and a "normal form" representation, allowing for the extension of previously established transient stability results to include scenarios involving current saturation. Simulations and experiments validate the efficacy of the proposed cross-forming control implementations.
Physics-Informed Neural Networks: a Plug and Play Integration into Power System Dynamic Simulations
Authors: Ignasi Ventura Nadal, Jochen Stiasny, Spyros Chatzivasileiadis
Date: 2024-04-20 00:00:00

Time-domain simulations are crucial for ensuring power system stability and avoiding critical scenarios that could lead to blackouts. The next-generation power systems require a significant increase in the computational cost and complexity of these simulations due to additional degrees of uncertainty, non-linearity and states. Physics-Informed Neural Networks (PINN) have been shown to accelerate single-component simulations by several orders of magnitude. However, their application to current time-domain simulation solvers has been particularly challenging since the system's dynamics depend on multiple components. Using a new training formulation, this paper introduces the first natural step to integrate PINNs into multi-component time-domain simulations. We propose PINNs as an alternative to other classical numerical methods for individual components. Once trained, these neural networks approximate component dynamics more accurately for longer time steps. Formulated as an implicit and consistent method with the transient simulation workflow, PINNs speed up simulation time by significantly increasing the time steps used. For explanation clarity, we demonstrate the training, integration, and simulation framework for several combinations of PINNs and numerical solution methods using the IEEE 9-bus system, although the method applies equally well to any power system size.
Human Motor Learning Dynamics in High-dimensional Tasks
Authors: Ankur Kamboj, Rajiv Ranganathan, Xiaobo Tan, Vaibhav Srivastava
Date: 2024-04-20 00:00:00

Conventional approaches to enhancing movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs). To effectively address coordination deficits in such complex motor systems, it becomes imperative to develop interventions grounded in a model of human motor learning; however, modeling such learning processes is challenging due to the large DoFs. In this paper, we present a computational motor learning model that leverages the concept of motor synergies to extract low-dimensional learning representations in the high-dimensional motor space and the internal model theory of motor control to capture both fast and slow motor learning processes. We establish the model's convergence properties and validate it using data from a target capture game played by human participants. We study the influence of model parameters on several motor learning trade-offs such as speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance, and show that the human motor learning system tunes these parameters to optimize learning and various output performance metrics.
Grid-aware Scheduling and Control of Electric Vehicle Charging Stations for Dispatching Active Distribution Networks. Part-II: Intra-day and Experimental Validation
Authors: Rahul K. Gupta, Sherif Fahmy, Max Chevron, Enea Figini, Mario Paolone
Date: 2024-04-19 00:00:00
Tasks: Model Predictive Control, Scheduling

In Part-I, we presented an optimal day-ahead scheduling scheme for dispatching active distribution networks accounting for the flexibility provided by electric vehicle charging stations (EVCSs) and other controllable resources such as battery energy storage systems (BESSs). Part-II presents the intra-day control layer for tracking the dispatch plan computed from the day-ahead scheduling stage. The control problem is formulated as model predictive control (MPC) with an objective to track the dispatch plan setpoint every 5 minutes, while actuated every 30 seconds. MPC accounts for the uncertainty of the power injections from stochastic resources (such as demand and generation from photovoltaic - PV plants) by short-term forecasts. MPC also accounts for the grid's operational constraints (i.e., the limits on the nodal voltages and the line power-flows) by a linearized optimal power flow (LOPF) model based on the power-flow sensitivity coefficients, and for the operational constraints of the controllable resources (i.e., BESSs and EVCSs). The proposed framework is experimentally validated on a real-life ADN at the EPFL's Distributed Electrical Systems Laboratory and is composed of a medium voltage (MV) bus connected to three low voltage distribution networks. It hosts two controllable EVCSs (172 kWp and 32 F~kWp), multiple PV plants (aggregated generation of 42~kWp), uncontrollable demand from office buildings (20 kWp), and two controllable BESSs (150kW/300kWh and 25kW/25kWh).
Grid-aware Scheduling and Control of Electric Vehicle Charging Stations for Dispatching Active Distribution Networks. Part-I: Day-ahead and Numerical Validation
Authors: Rahul K. Gupta, Sherif Fahmy, Max Chevron, Riccardo Vasapollo, Enea Figini, Mario Paolone
Date: 2024-04-19 00:00:00
Tasks: Model Predictive Control, Scheduling

This paper proposes a grid-aware scheduling and control framework for Electric Vehicle Charging Stations (EVCSs) for dispatching the operation of an active power distribution network. The framework consists of two stages. In the first stage, we determine an optimal day-ahead power schedule at the grid connection point (GCP), referred to as the dispatch plan. Then, in the second stage, a real-time model predictive control is proposed to track the day-ahead dispatch plan using flexibility from EVCSs. The dispatch plan accounts for the uncertainties of vehicles connected to the EVCS along with other uncontrollable power injections, by day-ahead predicted scenarios. We propose using a Gaussian-Mixture-Model (GMM) for the forecasting of EVCS demand using the historical dataset on arrival, departure times, EV battery capacity, State-of-Charge (SoC) targets, etc. The framework ensures that the grid is operated within its voltage and branches power-flow operational bounds, modeled by a linearized optimal power-flow model, maintaining the tractability of the problem formulation. The scheme is numerically and experimentally validated on a real-life distribution network at the EPFL connected to two EVCSs, two batteries, three photovoltaic plants, and multiple heterogeneous loads. The day-ahead and real-time stages are described in Part-I and Part-II papers respectively.
360° phase detector cell for measurement systems based on switched dual multipliers
Authors: Baltasar Pérez, Víctor A. Araña, Javier Perez-Mato, Francisco Cabrera
Date: 2024-04-19 00:00:00

This letter presents a 360{\deg} phase detector cell for performing phase-shift measurements on multiple output systems. An analog phase detector, capable of detecting a maximum range of {\pm}90{\deg}, has been used to perform a double multiplication of two signals, both in-phase and phase-shifted. The proposed solution broadens the frequency range beyond other solutions that require to fulfill the quadrature condition. Subsequently, the possibility of reaching the theoretical limit of phase shift within a hybrid coupler ({\Phi} < 90{\deg} {\pm} 90{\deg}) is discussed by using four straight-line equations to characterize the phase detector response. The proposed solution allows to extend up to 360{\deg} the phase detection range and provide an increased immunity with respect to both impedance mismatching and phase deviations within the hybrid coupler. To demonstrate the feasibility of the proposed design, a phase detector cell prototype has been implemented using a commercial hybrid coupler with a phase shift of 92.5{\deg} {\pm} 0.5{\deg} at 3.1-5.9 GHz, an external switch and a microcontroller with 2 kB of memory. Measurements show a range of detection of 360{\deg} ({\pm}180{\deg}) across the tested frequency band of 2.7-6 GHz.
Aggregator of Electric Vehicles Bidding in Nordic FCR-D Markets: A Chance-Constrained Program
Authors: Gustav A. Lunde, Emil V. Damm, Peter A. V. Gade, Jalal Kazempour
Date: 2024-04-19 00:00:00

The Danish system operator, Energinet, has recently introduced an innovative grid code called the P90 requirement, which allows stochastic flexible resources to bid their flexibility in Nordic ancillary service markets, contingent upon a minimum 90\% probability of successfully realizing the reserve capacity bid. For limited-energy resources, Energinet imposes additional requirements for participation in these markets. Given these requirements, this paper presents a chance-constrained optimization model designed for aggregators of electric vehicles, aiming to optimally place reserve capacity bids in the Nordic Frequency Containment Reserve for Disturbances (FCR-D) market while accounting for uncertainty in future consumption baselines. We analyze both FCR-D up and down markets, reformulating and solving the proposed joint chance-constrained model using two sample-based methods. Using real data from 1400 charging stations in Denmark from March 2022 to March 2023, we demonstrate the out-of-sample profit potential. Our findings indicate that vehicle owners could save between 6\% and 10\% on their annual electricity bills by providing FCR-D services. Additionally, we observed a synergy effect, where having more vehicles in a single portfolio enables larger bids per vehicle compared to a collective bid from multiple portfolios with the same total number of vehicles.
Leveraging P90 Requirement: Flexible Resources Bidding in Nordic Ancillary Service Markets
Authors: Peter A. V. Gade, Henrik W. Bindner, Jalal Kazempour
Date: 2024-04-19 00:00:00

The P90 requirement of the Danish transmission system operator, Energinet, incentivizes flexible resources with stochastic power consumption/production baseline to bid in Nordic ancillary service markets with the minimum reliability of 90%, i.e., letting them cause reserve shortfall with the probability of up to 10%. Leveraging this requirement, we develop a distributionally robust joint chance-constrained optimization model for aggregators of flexible resources to optimize their volume of reserve capacity to be offered. Having an aggregator of electric vehicles as a case study, we show how distributional robustness is key for the aggregator when making bidding decisions in a non-stationary uncertain environment. We also develop a heuristic based on a grid search for the system operator to adjust the P90 requirement and the level of conservativeness, aiming to procure the maximum reserve capacity from stochastic resources with least expected shortfall.
Piecewise Semi-Analytical Formulation for the Analysis of Coupled-Oscillator Systems
Authors: Pedro Umpierrez, Victor Arana, Sergio Sancho
Date: 2024-04-19 00:00:00

A new simulation technique to obtain the synchronized steady-state solutions existing in coupled oscillator systems is presented. The technique departs from a semi-analytical formulation presented in previous works. It extends the model of the admittance function describing each individual oscillator to a piecewise linear one. This provides a global formulation of the coupled system, considering the whole characteristic of each voltage-controlled oscillator (VCO) in the array. In comparison with the previous local formulation, the new formulation significantly improves the accuracy in the prediction of the system synchronization ranges. The technique has been tested by comparison with computationally demanding circuit-level Harmonic Balance simulations in an array of Van der Pol-type oscillators and then applied to a coupled system of FET based oscillators at 5 GHz, with very good agreement with measurements.
Recent Advancements in Battery State of Power Estimation Technology: A Comprehensive Overview and Error Source Analysis
Authors: Ruohan Guo, Weixiang Shen
Date: 2024-04-19 00:00:00

Accurate state of power (SOP) estimation is of great importance for lithium-ion batteries in safety-critical and power-intensive applications for electric vehicles. This review article delves deeply into the entire development flow of current SOP estimation technology, offering a systematic breakdown of all key aspects with their recent advancements. First, we review the design of battery safe operation area, summarizing diverse limitation factors and furnishing a profound comprehension of battery safety across a broad operational scale. Second, we illustrate the unique discharge and charge characteristics of various peak operation modes, such as constant current, constant voltage, constant current-constant voltage, and constant power, and explore their impacts on battery peak power performance. Third, we extensively survey the aspects of battery modelling and algorithm development in current SOP estimation technology, highlighting their technical contributions and specific considerations. Fourth, we present an in-depth dissection of all error sources to unveil their propagation pathways, providing insightful analysis into how each type of error impacts the SOP estimation performance. Finally, the technical challenges and complexities inherent in this field of research are addressed, suggesting potential directions for future development. Our goal is to inspire further efforts towards developing more accurate and intelligent SOP estimation technology for next-generation battery management systems.
Towards Accurate and Efficient Sorting of Retired Lithium-ion Batteries: A Data Driven Based Electrode Aging Assessment Approach
Authors: Ruohan Guo, Feng Wang, Cungang Hu, Weixiang Shen
Date: 2024-04-19 00:00:00

Retired batteries (RBs) for second-life applications offer promising economic and environmental benefits. However, accurate and efficient sorting of RBs with discrepant characteristics persists as a pressing challenge. In this study, we introduce a data driven based electrode aging assessment approach to address this concern. To this end, a number of 15 feature points are extracted from battery open circuit voltage (OCV) curves to capture their characteristics at different levels of aging, and a convolutional neural network with an optimized structure and minimized input size is established to relocate the relative positions of these OCV feature points. Next, a rapid estimation algorithm is proposed to identify the three electrode aging parameters (EAPs) which best reconstruct the 15 OCV feature points over the entire usable capacity range. Utilizing the three EAPs as sorting indices, we employ an adaptive affinity propagation algorithm to cluster RBs without the need for pre-determining the clustering number. Unlike conventional sorting methods based solely on battery capacity, the proposed method provides profound insights into electrode aging behaviors, minimizes the need for constant-current charging data, and supports module/pack-level tests for the simultaneous processing of high volumes of RBs.
Electrification of Clay Calcination: A First Look into Dynamic Modeling and Energy Management for Integration with Sustainable Power Grids
Authors: Bruno Laurini, Nicola Cantisani, Wilson R. Leal da Silva, Yi Zong, John Bagterp Jørgensen
Date: 2024-04-19 00:00:00
Tasks: energy management, Management

This article explores the electrification in clay calcination, proposing a dynamic model and energy management strategy for the integration of electrified calcination plants into sustainable power grids. A theoretical dynamic modeling of the electrified calcination process is introduced, aiming at outlining temperature profiles and energy usage - thus exploring the feasibility of electrification. The model serves as a tool for optimizing parameters, estimating system behavior, and enabling model-based process control. An innovative energy management model is also presented, ensuring efficient assimilation of electrified calcination plants into the power grid. It encapsulates demand-supply balancing and optimizes renewable energy usage. In essence, we provide an insightful pathway to a more sustainable cement production, underlining the value of renewable energy sources and effective energy management in the context of clay calcination.
Emerging NGSO Constellations: Spectral Coexistence with GSO Satellite Communication Systems
Authors: Flor Ortiz, Eva Lagunas, Almoatssimbillah Saifaldawla, Mahdis Jalali, Luis Emiliani, Symeon Chatzinotas
Date: 2024-04-19 00:00:00
Tasks: Management

Global communications have undergone a paradigm shift with the rapid expansion of low-earth orbit (LEO) satellite constellations, offering a new space era of reduced latency and ubiquitous, high-speed broadband internet access. However, the fast developments in LEO orbits pose significant challenges, particularly the coexistence with geostationary earth orbit (GEO) satellite systems. This article presents an overview of the regulatory aspects that cover the spectrum sharing in the bands allocated to the Fixed Satellite Service between geostationary networks (GSO) and non-geostationary systems (NGSO), as well as the main interference mitigation techniques for their coexistence. Our work highlights the increased potential for inter-system interference. It explores the regulatory landscape following the World Radio Conference (WRC-23). We discuss the different interference management strategies proposed for the GSO-NGSO spectral coexistence, including on-board and ground-based approaches and more advanced mitigation techniques based on beamforming. Moving onto operational aspects related to the sharing of spectrum, we introduce recent work on interference detection, identification, and mitigation and provide our vision of the emerging role of artificial intelligence (AI) in the aforementioned tasks.
Modeling Rainwater Harvesting Systems with Covered Storage Tank on A Smartphone
Authors: Vikram Vyas
Date: 2024-04-16 00:00:00

A mathematical model, suitable for smartphone implementation, is presented to simulate the performance of a rainwater harvesting system equipped with a covered storage tank. This model serves to determine the optimal tank size and develop strategies to mitigate drought conditions.