JOURNAL ARTICLE
Comprehensive review of power system oscillations in large-scale power electronic-based renewable energy power plants.
Published In: Journal of Renewable & Sustainable Energy, 2023, v. 15, n. 4. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Liu, Ni; Wang, Hong; Zhou, Dangsheng; Shi, Hexi; Chen, Zhe 3 of 3
Abstract
This article comprehensively reviews oscillation issues in modern power systems, focusing on small signal stability challenges arising from the large-scale integration of renewable energy sources (RESs) interfaced via power electronics (PE). It proposes a classification framework for oscillations based on frequency ranges—low-frequency oscillations (LFO), middle- and low-frequency oscillations (MLFO), middle- and high-frequency oscillations (MHFO), and high-frequency oscillations—and dominant oscillation sources, including synchronous generator (SG)-dominated, SG-PE interaction, and PE-dominated dynamics. The review details mechanisms, analysis methods (such as impedance analysis, modal analysis, and damping torque analysis), and mitigation strategies for each oscillation type, highlighting the complexity introduced by converter controls, grid topology, and equipment response times. It also identifies research gaps in the stability assessment of hybrid alternating current/direct current (AC/DC) power systems and the integration of diverse analytical methods, emphasizing the need for further study on oscillation mechanisms and practical suppression techniques in evolving power grids.
Additional Information
- Source:Journal of Renewable & Sustainable Energy. 2023/07, Vol. 15, Issue 4, p1
- Document Type:Article
- Subject Area:Power and Energy
- Publication Date:2023
- ISSN:1941-7012
- DOI:10.1063/5.0148188
- Accession Number:171316883
- Copyright Statement:Copyright of Journal of Renewable & Sustainable Energy is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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