Markovic, U, Chu, Z, Aristidou, P orcid.org/0000-0003-4429-0225 et al. (1 more author) (2019) LQR-Based Adaptive Virtual Synchronous Machine for Power Systems with High Inverter Penetration. IEEE Transactions on Sustainable Energy, 10 (3). pp. 1501-1512. ISSN 1949-3029
Abstract
This paper presents a novel virtual synchronous machine controller for converters in power systems with a high share of renewable resources. Using a linear quadratic regulator-based optimization technique, the optimal state feedback gain is determined to adaptively adjust the emulated inertia and damping constants according to the frequency disturbance in the system, while simultaneously preserving a tradeoff between the critical frequency limits and the required control effort. Two control designs are presented and compared against the open-loop model. The proposed controllers are integrated into a state-of-the-art converter control scheme and verified through electromagnetic transient (EMT) simulations.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | linear-quadratic regulator (LQR); virtual synchronous machine (VSM); voltage source converter (VSC); swing equation; adaptive control |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Jan 2019 11:30 |
Last Modified: | 30 Jun 2020 10:43 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TSTE.2018.2887147 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:140581 |