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Robust Voltage control in DC Networks with Uncertain Nonlinear Load Dynamics

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Robust Voltage Control in DC Networks with Uncertain Nonline...

IEEE Conference on Decision and control

作者： Amirreza Silani Giacomo Casadei Michele Cucuzzella Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen AG Groningen The Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Laboratoire Ampere Dpt. EEA of the Ecole Centrale de Lyon Université de Lyon Ecully France Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this letter, we propose a control scheme for regulating the voltage in Direct Current (DC) power networks. In contrast with other works in the literature where the loads are assumed to be constant, we consider uncertain time-varying loads described as the outputs of nonlinear dynamical exosystems with uncertain parameters. Based on the output regulation methodology, the proposed control scheme ensures the robust stability of the overall network and achieves voltage regulation in presence of impedance (Z), current (I), and power (P) loads. The simulation results illustrate excellent performance of the proposed control scheme.

关键词： Robust stability Simulation Regulation Impedance Voltage control

Optimizing the Homogeneity and Efficiency of A Solid Oxide Electrolysis Cell Based on Multiphysics Simulation and Data-Driven Surrogate Model

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SSRN

SSRN 2022年

作者： Chi, Yingtian Yokoo, Kentaro Nakajima, Hironori Ito, Kohei Lin, Jin Song, Yonghua State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100087 China Department of Hydrogen Energy Systems Graduate School of Engineering Kyushu University Fukuoka Japan Department of Mechanical Engineering Faculty of Engineering Kyushu University Fukuoka Japan Tsinghua-Sichuan Energy Internet Research Institute Chengdu610213 China State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China

Inhomogeneous current and temperature distributions are harmful to the durability of the solid oxide electrolysis cell (SOEC). Segmented SOEC experiments reveal that a high steam utilization, which is favorable for system efficiency, leads to local steam starvation and enhanced the inhomogeneity. It is necessary to consider inhomogeneity and efficiency jointly in optimization studies. Three-dimensional (3D) multiphysics models can simulate the inhomogeneity in a reliable manner, but they are unsuitable for optimization due to the high computational cost. This study proposes a method that combines segmented SOEC experiments, multiphysics simulation, and artificial intelligence to optimize the inhomogeneity and efficiency of SOEC. A 3D cell model is first built and verified by segmented SOEC experiments. Then, fast neural network surrogate models are built from the simulation data and integrated into a multi-objective optimization problem. Its solutions form a Pareto front reflecting the conflicting relationships among different objectives. It is found that the down-stream current is 60%-65% of the up-stream current when the steam utilization is 0.7. To increase the steam utilization to 0.8, the down-stream current further drops to 50%-60% of the up-stream current. The Pareto fronts enable system operators to achieve a balance between efficiency and inhomogeneity. © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

Optimizing the Homogeneity and Efficiency of an SOEC Based on Multiphysics Simulation and Data-driven Surrogate Model

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arXiv 2022年

Inhomogeneous current and temperature distributions are harmful to the durability of the solid oxide electrolysis cell (SOEC). Segmented SOEC experiments reveal that a high steam utilization, which is favorable for system efficiency, leads to local steam starvation and enhanced the inhomogeneity. It is necessary to consider inhomogeneity and efficiency jointly in optimization studies. Three-dimensional (3D) multiphysics models validated with experiments can simulate the inhomogeneity in a reliable manner, but they are unsuitable for optimization due to the high computational cost. This study proposes a method that combines segmented SOEC experiments, multiphysics simulation, and artificial intelligence to optimize the inhomogeneity and efficiency of SOEC jointly. A 3D cell model is first built and verified by segmented SOEC experiments. Then, fast neural network surrogate models are built from the simulation data and integrated into a multi-objective optimization problem. Its solutions form a Pareto front reflecting the conflicting relationships among different objectives. It is found that the down-stream current is 60%-65% of the up-stream current when the steam utilization is 0.7. To increase the steam utilization to 0.8, the down-stream current will further drop to 50%-60% of the up-stream current. The Pareto fronts enable system operators to achieve a balance between efficiency and inhomogeneity. Copyright © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

Optimum selection of the reference signal for correlation receiver applied to marker localization 5th

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Optimum selection of the reference signal for correlation re...

5th International Conference on Advanced engineering Theory and Applications, AETA 2018

作者： Vestenický, Martin Vestenický, Peter Faculty of Electrical Engineering and Information Technology Department of Multimedia and Information-Communication Technologies University of Žilina Univerzitná 8215/1 Žilina010 26 Slovakia Faculty of Electrical Engineering Department of Control and Information Systems University of Žilina Univerzitná 8215/1 Žilina010 26 Slovakia

ISBN: (纸本)9783030149062

The localization of RFID transponders is very popular task today in industrial RFID applications. One of the special applications of RFID is the localization and identification of the underground facility networks by special RFID transponders called markers. This paper describes an optimum selection of reference signal for the correlation receiver applied to RSSI based localization of the inductive coupled RFID markers. The reference responses of the marker are generated by using the model of localization device created in Matlab – Simulink software package. The analysis takes into account the quality factor of the marker resonant circuit. The results show that the correlations coefficients between any two marker responses are not significantly dependent on the marker parameters. © Springer Nature Switzerland AG 2020.

关键词： Resonant circuits

Highway Traffic control via Smart e-Mobility – Part II: Dutch A13 Case Study

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arXiv 2021年

作者： Cenedese, Carlo Cucuzzella, Michele Scherpen, Jacquelien M.A. Grammatico, Sergio Cao, Ming Department of Information Technology and Electrical Engineering ETH Zürich Zurich Switzerland Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Netherlands Delft Center for Systems and Control TU Delft Netherlands

In this paper, we study how to alleviate highway traffic congestions by encouraging plug-in electric and hybrid vehicles to stop at charging stations around peak congestion times. Specifically, we focus on a case study and simulate the adoption of a dynamic charging price depending on the traffic congestion. We use real traffic data of the A13 highway stretch between The Hague and Rotterdam, in The Netherlands, to identify the Cell Transmission Model. Then, we apply the algorithm proposed in [1](Part I: Theory) to different scenarios, validating the theoretical results and showing the benefits of our strategy in terms of traffic congestion alleviation. Finally, we carry out a sensitivity analysis of the proposed algorithm and discuss how to optimize its performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Traffic congestion

Highway Traffic control via Smart e-Mobility - Part I: Theory

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arXiv 2021年

—In this paper, we study how to alleviate highway traffic congestion by encouraging plug-in hybrid and electric vehicles to stop at a charging station around peak congestion times. Specifically, we design a pricing policy to make the charging price dynamic and dependent on the traffic congestion, predicted via the cell transmission model, and the availability of charging spots. Furthermore, we develop a novel framework to model how this policy affects the drivers’ decisions by formulating a mixed-integer potential game. Technically, we introduce the concept of "road-to-station" (r2s) and "station-to-road" (s2r) flows, and show that the selfish actions of the drivers converge to charging schedules that are individually optimal in the sense of Nash. In the second part of this work, submitted as a separate paper (Part II: Case Study), we validate the proposed strategy on a simulated highway stretch between The Hague and Rotterdam, in The Netherlands. Copyright © 2021, The Authors. All rights reserved.

关键词： Roads and streets

Phase chimera states on non-local hyperrings

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arXiv 2023年

作者： Muolo, Riccardo Njougouo, Thierry Gambuzza, Lucia Valentina Carletti, Timoteo Frasca, Mattia Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Department of Mathematics University of Namur NamurB5000 Belgium naXys Namur Institute for Complex Systems University of Namur NamurB5000 Belgium Faculty of Computer Science University of Namur NamurB5000 Belgium Department of Electrical and Electronic Engineering University of Buea P.O. Box 63 Buea Cameroon Department of Electrical Electronics and Computer Science Engineering University of Catania Catania95125 Italy Istituto di Analisi dei Sistemi ed Informatica "A. Ruberti" IASI-CNR Roma00185 Italy

Chimera states are dynamical states where regions of synchronous trajectories coexist with incoherent ones. A significant amount of research has been devoted to study chimera states in systems of identical oscillators, non-locally coupled through pairwise interactions. Nevertheless, there is an increasing evidence, also supported by available data, that complex systems are composed by multiple units experiencing many-body interactions, that can be modeled by using higher-order structures beyond the paradigm of classic pairwise networks. In this work we investigate whether phase chimera states appear in this framework, by focusing on a novel topology solely involving many-body, non-local and non-regular interactions, hereby named non-local d-hyperring, being (d + 1) the order of the interactions. We present the theory by using the paradigmatic Stuart-Landau oscillators as node dynamics, and show that phase chimera states emerge in a variety of structures and with different coupling functions. For comparison, we show that, when higher-order interactions are"flattened" to pairwise ones, the chimera behavior is weaker and more elusive. © 2023, CC BY.

关键词： Dynamics

Fractional cyber-neural systems - a brief survey

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arXiv 2021年

作者： Reed, Emily Chatterjee, Sarthak Ramos, Guilherme Bogdan, Paul Pequito, Sérgio Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Portugal Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Neurotechnology has made great strides in the last 20 years. However, we still have a long way to go to commercialize many of these technologies as we lack a unified framework to study cyber-neural systems (CNS) that bring the hardware, software, and the neural system together. Dynamical systems play a key role in developing these technologies as they capture different aspects of the brain and provide insight into their function. Converging evidence suggests that fractional-order dynamical systems are advantageous in modeling neural systems because of their compact representation and accuracy in capturing the long-range memory exhibited in neural behavior. In this brief survey, we provide an overview of fractional CNS that entails fractional-order systems in the context of CNS. In particular, we introduce basic definitions required for the analysis and synthesis of fractional CNS, encompassing system identification, state estimation, and closed-loop control. Additionally, we provide an illustration of some applications in the context of CNS and draw some possible future research directions. Ultimately, advancements in these three areas will be critical in developing the next generation of CNS, which will, ultimately, improve people’s quality of life. Copyright © 2021, The Authors. All rights reserved.

关键词： Surveys

Discrete-time fractional-order dynamical networks minimum-energy state estimation

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arXiv 2021年

作者： Chatterjee, Sarthak Alessandretti, Andrea Aguiar, A. Pedro Pequito, Sérgio The Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Magneti Marelli S.p.A. Italy The Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto Portugal The Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Fractional-order dynamical networks are increasingly being used to model and describe processes demonstrating long-term memory or complex interlaced dependencies amongst the spatial and temporal components of a wide variety of dynamical networks. Notable examples include networked control systems or neurophysiological networks which are created using electroencephalographic (EEG) or blood-oxygen-level-dependent (BOLD) data. As a result, the estimation of the states of fractional-order dynamical networks poses an important problem. To this effect, this paper addresses the problem of minimum-energy state estimation for discrete-time fractional-order dynamical networks (DT-FODN), where the state and output equations are affected by an additive noise that is considered to be deterministic, bounded, and unknown. Specifically, we derive the corresponding estimator and show that the resulting estimation error is exponentially input-to-state stable with respect to the disturbances and to a signal that is decreasing with the increase of the accuracy of the adopted approximation model. An illustrative example shows the effectiveness of the proposed method on real-world neurophysiological networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Neurophysiology