Voltage dip propagation in renewable‐rich power systems utilizing grid‐forming converters

Author:

Aljarrah Rafat1ORCID,Karimi Mazaher2ORCID,Azizipanah‐Abarghooee Rasoul3,Salem Qusay1,Alnaser Sahban4

Affiliation:

1. Electrical Engineering Department Princess Sumaya University for Technology Amman Jordan

2. School of Technology and Innovations University of Vaasa Vaasa Finland

3. Energy Advisory Department WPS Manchester UK

4. Electrical Engineering Department The University of Jordan Amman Jordan

Abstract

AbstractThe growing integration of converter‐interfaced renewable energy sources (RESs) utilizing Grid‐Following (GFL) converters has displaced conventional synchronous generators (SGs) in central generation units. This shift presents challenges, including diminished system inertia, lower fault levels, and implications for system strength and network resilience. The propagation of voltage dips, particularly during disturbances like system Short Circuit (SC) faults, is adversely affected by the increased penetration of such RESs. This is attributed to the limited support capability of these sources and their distinct SC response compared to SGs. In response to these challenges, Grid‐Forming (GFM) converters emerge as a promising technology equipped with advanced functionalities that emulate SG operation. Consequently, they hold potential for mitigating the effects of voltage dip propagation in renewable‐rich power systems. This study aims to assess the impact of employing GFM converters in renewable‐rich power systems on voltage dip propagation across the network. The authors’ investigation begins by examining the SC response of GFM converters and comparing it with the responses of traditional GFL converters and SGs. The paper proceeds to analyze voltage dip propagation, considering various penetration scenarios involving RESs based on GFL and GFM converters. The IEEE 9‐BUS test system, implemented in the DIgSILENT PowerFactory software, serves as the basis for these evaluations. Through extensive simulations and analysis, the authors’ research provides valuable insights into the effectiveness of GFM converters in enhancing the network's response to voltage dips.

Funder

Business Finland

Publisher

Institution of Engineering and Technology (IET)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Most influential feature form for supervised learning in voltage sag source localization;Engineering Applications of Artificial Intelligence;2024-07

2. Comparison between Symmetrical Short Circuit Current Contribution of Droop-Based Grid Forming Converter and Synchronous Generator;2024 International Workshop on Artificial Intelligence and Machine Learning for Energy Transformation (AIE);2024-05-20

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