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Special Issue "Compact Macromodeling: Components, Interconnects and Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (20 February 2022) | Viewed by 1635

Special Issue Editors

Prof. Dr. Stefano Grivet-Talocia
E-Mail Website
Guest Editor
Department of Electronics and Telecommunications, Politecnico di Torino, 39, 10129 Torino, Italy
Interests: passive macromodeling of lumped and distributed interconnect structures; model order reduction; modeling and simulation of fields, circuits, and their interaction, signal and power integrity
Dr. Bjørn Gustavsen
E-Mail Website
Guest Editor
SINTEF Energy Research, 11, 7034 Trondheim, Norway
Interests: frequency dependent modeling of power system components (transformers, cables) and subsystems; model order reduction; transient overvoltage studies

Special Issue Information

Dear Colleagues,

It is widely acknowledged that direct numerical simulation of complete electrical or electronic systems based on first-principle formulations is not feasible due to overwhelming complexity. For this reason, the last forty years have witnessed a massive amount of research in the broad field of model order reduction, whose aim is to characterize a complex system through a compact macromodel, described by a reduced number of degrees of freedom or state variables, which can be simulated in a fast runtime with limited computing resources. Model order reduction is now a mature field in practically all engineering application fields, as a key enabler of model-based design flows.

This Special Issue collects state-of-the-art contributions in the field of macromodeling for electrical and electronic applications, focusing both on methodological aspects and on applications. Both research and review papers are considered on compact dynamic modeling of devices, components, interconnects (including transmission lines, cables and transmission line networks), subsystems and even entire systems. Although the main application focus is on electrical (power) systems and electronic (information) systems, multidisciplinary applications are also considered, including multiphysics modeling and co-simulation.

Potential topics include, but are not limited to:

  • Model order reduction: data-driven and model-driven approaches for approximation, compression, projection and truncation of complex high-dimensional system descriptions;
  • Rational fitting and linear system identification from tabulated frequency or time responses: vector fitting and Loewner matrix approaches;
  • Characterization and enforcement of passivity: Hamiltonian matrices and pencils, sampling approaches, linear matrix inequalities;
  • Behavioral modeling under uncertainty and stochastic variations: polynomial chaos, uncertainty quantification;
  • Machine learning approaches as applied to compact system characterization and modeling;
  • Lumped and distributed interconnects, transmission lines and cables;
  • Macromodeling for signal and power integrity of electronic systems: devices, components, interconnects, subsystems and systems;
  • Macromodeling for power systems: broadband modeling of transformers, transmission lines, cables and networks;
  • Macromodel-based fast numerical simulation of complex systems: electrical, electronic, thermal and multiphysics.

Prof. Dr. Stefano Grivet-Talocia
Dr. Bjørn Gustavsen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Model order reduction
  • Vector fitting
  • Loewner matrix methods
  • Data-driven modeling
  • Behavioral modeling
  • Grey-box and black-box modeling
  • Passivity characterization
  • Passivity enforcement
  • Hamiltonian matrices and pencils
  • Parameterized modeling
  • Stochastic modeling
  • Delay-based macromodels
  • Fast system-level simulation

Published Papers (2 papers)

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Research

Article
Dielectric Response Model for Transformer Insulation Using Frequency Domain Spectroscopy and Vector Fitting
Energies 2022, 15(7), 2655; https://doi.org/10.3390/en15072655 - 05 Apr 2022
Viewed by 489
Abstract
This paper proposes a rational approximation-based approach to find positive real parameters for the extended Debye model (EDM), aimed at condition assessment of insulation systems of power transformers. The EDM can model the slow and fast polarization phenomenon, including relaxation mechanisms with different [...] Read more.
This paper proposes a rational approximation-based approach to find positive real parameters for the extended Debye model (EDM), aimed at condition assessment of insulation systems of power transformers. The EDM can model the slow and fast polarization phenomenon, including relaxation mechanisms with different relaxation times within a composite dielectric material. In the proposed approach, the complex permittivity of the transformer’s composite insulation is approximated via rational functions, as given by the vector fitting (VF) software tool, and the EDM parameters are identified from the obtained poles/residues. To guarantee positive real parameters, i.e., a physically realizable circuit, VF is internally modified to calculate the final residues of the rational approximation via a constrained linear least-squares problem without resorting to further post-processing algorithms, as in existing methods, hence without affecting fitting accuracy. The effectiveness of the parametrized EDM is demonstrated in two ways: (a) by reconstructing frequency domain spectroscopy (FDS) curves provided via measurements in new oil-immersed power transformers and (b) by the comparison of the calculated polarization current given by EDM versus real measurements in time domain. The achieved fitting accuracy in most of the cases is above 99 percent for the reconstructed FDS curves, while the polarization current waveform is reproduced with good agreement. Full article
(This article belongs to the Special Issue Compact Macromodeling: Components, Interconnects and Systems)
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Article
Macromodeling High-Speed Circuit Data Using Rational Krylov Fitting Method
Energies 2021, 14(21), 7318; https://doi.org/10.3390/en14217318 - 04 Nov 2021
Viewed by 398
Abstract
This paper presents the modeling of high speed distributed networks characterized by S-parameters frequency data using the rational Krylov fitting (RKFIT) algorithm. Numerical examples illustrate the effectiveness of the method to compute stable rational approximation that fit given S-parameters data. In addition, it [...] Read more.
This paper presents the modeling of high speed distributed networks characterized by S-parameters frequency data using the rational Krylov fitting (RKFIT) algorithm. Numerical examples illustrate the effectiveness of the method to compute stable rational approximation that fit given S-parameters data. In addition, it is shown that RKFIT has some advantages when compared to the well-established Vector Fitting (VF) method, such as more accurate fitting, less dependence on the choice of the initial poles of the algorithm, and faster convergence. Numerical examples are implemented using RKFIT and the results are compared with VF and the Loewner Matrix (LM) algorithm. Full article
(This article belongs to the Special Issue Compact Macromodeling: Components, Interconnects and Systems)
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