Computing Large-Scale System Eigenvalues Most Sensitive to Parameter Changes, With Applications to Power System Small-Signal Stability

Rommes, Joost; Martins, N.

doi:10.1109/tpwrs.2008.920050

Cited by 63 publications

(25 citation statements)

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“…3 For general introductions to model order reduction we refer to the previous Section 3.1 and to [57,59,60,87]; for eigenvalue problems, see [86,92]. More detailed publications on the contents of this Section are [79][80][81][82][83][84]. The pseudocode algorithms presented in this Section are written using Matlab-like [91] notation.…”

Section: Discussionmentioning

confidence: 99%

“…The larger the magnitude of the derivative (3.37), the more sensitive eigenvalue λ is to changes in parameter p. In practical applications such information is useful when, for instance, a system needs to be stabilized by moving poles from the right half-plane to the left half-plane [82,94]. Suppose that the derivative of A to parameter p has rank 1 and hence can be written as…”

Section: Computing Eigenvalues Sensitive To Parameter Changesmentioning

confidence: 99%

“…The algorithm based on this is called SASPA. For more details and generalizations to higher rank derivatives and multiparameter systems, see [82].…”

Section: Computing Eigenvalues Sensitive To Parameter Changesmentioning

confidence: 99%

“…The SADPA and SAMDP are ideal algorithms for this application, since they benefit from the reduced model information for the nominal parameters. Note that only a true modal equivalent can benefit from this sensitivity feature, through the use of the SASPA [82].…”

Section: Computing Eigenvalues Sensitive To Parameter Changesmentioning

confidence: 99%

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Model Order Reduction: Methods, Concepts and Properties

Antoulas

Ionutiu

Martins

et al. 2015

Mathematics in Industry

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Model order reduction : methods, concepts and propertiesAntoulas, A.C.; Ionutiu, R.; Martins, N.; ter Maten, E.J.W.; Mohaghegh, K.; Pulch, R.; Rommes, J.; Saadvandi, M.; Striebel, M.Published: 01/01/2015 Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication Citation for published version (APA):Antoulas, A. C., Ionutiu, R., Martins, N., Maten, ter, E. J. W., Mohaghegh, K., Pulch, R., ... Striebel, M. (2015). Model order reduction : methods, concepts and properties. (CASA-report; Vol. 1507). Eindhoven: Technische Universiteit Eindhoven. General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Here eigenvalues are the starting point. The eigenvalue problems related to large-scale dynamical systems are usually too large to be solved completely. The algorithms described in this section are efficient and effective methods for the computation of a few specific dominant eigenvalues of these large-scale systems. It is shown how these algorithms can be used for computing reduced-order models with modal approximation and Krylov-based methods. Section 3.3, written by Maryam Saadvandi and Joost Rommes, concerns passivity preserving model order reduction using the spectral zero method. It detailedly discusses two algorithms, one by Antoulas and one by Sorenson. These two approaches are based on a projection method by selecting spectral zeros of the original transfer function to produce a reduced transfer function that has the specified roots as its spectral zeros. The reduced model preserves passivity. Section 3.4, written by Roxana Ionutiu, Joost Rommes and Athanasios C. Antoulas, refines the spectra...

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Section: Discussionmentioning

confidence: 99%

Section: Computing Eigenvalues Sensitive To Parameter Changesmentioning

confidence: 99%

“…The algorithm based on this is called SASPA. For more details and generalizations to higher rank derivatives and multiparameter systems, see [82].…”

Section: Computing Eigenvalues Sensitive To Parameter Changesmentioning

confidence: 99%

Section: Computing Eigenvalues Sensitive To Parameter Changesmentioning

confidence: 99%

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Model Order Reduction: Methods, Concepts and Properties

Antoulas

Ionutiu

Martins

et al. 2015

Mathematics in Industry

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“…To reduce the computation time for a large power grid, Smed [39] proposed a sparse method to calculate the modal sensitivity. In Rommes and Martin [40], a sensitive pole method is proposed to identify the poles that are most sensitive to parameter changes. To arrive at a practical solution, the sensitivity must be calculated as a numerical approximation through perturbation.…”

Section: Modal Sensitivity Derived From Eigenvalue Theorymentioning

confidence: 99%

MANGO ? Modal Analysis for Grid Operation: A Method for Damping Improvement through Operating Point Adjustment

Huang¹,

Zhou²,

Tuffner³

et al. 2010

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Printed in the United Executive SummarySmall signal stability problems are one of the major threats to grid stability and reliability in the U.S. power grid. An undamped mode can cause growing oscillations and may result in system breakups and large-scale blackouts. There have been several incidents of system-wide oscillations. Of those incidents, the most notable is the August 10, 1996 western system breakup, a result of undamped system-wide oscillations. Significant efforts have been devoted to monitoring system oscillatory behavior from measurements in the past 20 years. The deployment of phasor measurement units (PMU) provides highprecision, time-synchronized data needed for detecting oscillation modes. Measurement-based modal analysis, also known as ModeMeter, uses real-time phasor measurements to identify system oscillation modes and their damping. Low damping indicates potential system stability issues. Modal analysis has been demonstrated with phasor measurements to have the capability of estimating system modes from oscillation signals, probing data, and ambient data.With more and more phasor measurements available and ModeMeter techniques maturing, there is yet a need for methods to bring modal analysis from monitoring to actions. The methods should be able to associate low damping with grid operating conditions, so operators or automated operation schemes can respond when low damping is observed. The work presented in this report aims to develop such a method and establish a Modal Analysis for Grid Operation (MANGO) procedure to provide recommended actions (such as generation re-dispatch or load reduction), and aid grid operation decision making for mitigating inter-area oscillations. This project directly contributes to the Department of Energy Transmission Reliability Program's goal of "improving reliability of the nation's electricity delivery infrastructure."The fundamental part of the work explores the relationship between low damping and grid operating conditions to develop recommended actions for damping improvement, and therefore reduce the chance of system breakup and power outages. Different from power system stabilizers and other modulation control mechanisms, MANGO improves damping through operating point adjustments. Traditionally, the modulation-based methods do not change the system's operating point, but improve damping through automatic feedback control. Figure S-1 illustrates the difference of these two types of damping improvement methods. MANGO, represented in red, and modulation control, represented in magenta, are complementary towards the same goal.MANGO is a measurement-based procedure, as shown in Figure S-2. As the first stage of development, the MANGO procedure is targeted to have operators in the loop. Practical implementation is envisioned to be achieved by integrating MANGO recommendations into existing operating procedures. The MANGO model can be updated according to the current measurement and mode estimation results. Operators are included into the loop to bring in exp...

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Small‐Signal Stability

2023

Graph Database and Graph Computing for Power System Analysis

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Computing Large-Scale System Eigenvalues Most Sensitive to Parameter Changes, With Applications to Power System Small-Signal Stability

Cited by 63 publications

References 32 publications

Model Order Reduction: Methods, Concepts and Properties

Model Order Reduction: Methods, Concepts and Properties

MANGO ? Modal Analysis for Grid Operation: A Method for Damping Improvement through Operating Point Adjustment

Small‐Signal Stability

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