[2206.13838] Inertia Estimation Through Covariance Matrix
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Electrical Engineering and Systems Science > Systems and Control

arXiv:2206.13838 (eess)
[Submitted on 28 Jun 2022 (v1), last revised 17 Jan 2023 (this version, v3)]

Title:Inertia Estimation Through Covariance Matrix

Authors:Federico Bizzarri (1 and 3), Davide del Giudice (1), Samuele Grillo (1), Daniele Linaro (1), Angelo Brambilla (1), Federico Milano (2) ((1) Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Italy, (2), School of Electrical & Electronic Eng., University College Dublin, Ireland, (3) Advanced Research Center on Electronic Systems for Information and Communication Technologies E. De Castro (ARCES), University of Bologna, Italy)
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Abstract:This work presents a technique to estimate on-line the inertia of a power system based on ambient measurements. The proposed technique utilizes the covariance matrix of these measurements and solves an optimization problem that fits such measurements to the synchronous machine classical model. We show that the proposed technique is adequate to accurately estimate the actual inertia of synchronous machines and also the virtual inertia provided by the controllers of converter-interfaced generators that emulate the behavior of synchronous machines. We also show that the proposed approach is able to estimate the equivalent damping of the classical synchronous machine model. This feature is exploited to estimate the droop of grid-following converters, which has a similar effect of the swing equation equivalent damping. The technique is comprehensively tested on a modified version of the IEEE 39-bus system as well as on a dynamic 1479-bus model of the all-island Irish transmission system.
Subjects: Systems and Control (eess.SY)
Cite as: arXiv:2206.13838 [eess.SY]
  (or arXiv:2206.13838v3 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2206.13838
Related DOI: https://doi.org/10.1109/TPWRS.2023.3236059

Submission history

From: Samuele Grillo [view email]
[v1] Tue, 28 Jun 2022 09:10:41 UTC (860 KB)
[v2] Thu, 10 Nov 2022 11:48:30 UTC (1,595 KB)
[v3] Tue, 17 Jan 2023 10:27:33 UTC (1,595 KB)
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