LAPSE:2023.17343
Published Article

LAPSE:2023.17343
A Combined RMS Simulation Model for DFIG-Based and FSC-Based Wind Turbines and Its Initialization
March 6, 2023
Abstract
Reconstructable dynamic simulation models of modern variable-speed wind turbines (WTs), which are integrable into any simulation software, are crucial to the scientists investigating the contribution of WTs to counteracting the current power system stability issues. The structural similarity between a doubly fed induction-generator-based (DFIG-based) WT model and a full-scale-convertor-based (FSC-based) WT model using induction generator offers the possibility of integrating them into a combined modular model with little effort and the same used parameter set. This article presents a combined root mean square (RMS) WT model, which contains a DFIG-based WT and a FSC-based WT using induction generator. The model is designed based on fundamental machine and converter equations and can be applied for classical network stability analyses. Furthermore, analogous well-performing initialization procedures for both DFIG-based and FSC-based WT models are also introduced. As an example, to demonstrate the performance of the WT model in frequency stability studies, the model is extended with a droop-based fast frequency response (FFR) controller and is implemented in a MATLAB-based RMS simulation tool. The results of the case studies confirmed a solid functionality of initialization procedures. Furthermore, they illustrate feasible and comparable general behavior of both WT models as well as their plausible responses in the event of a frequency drop in a 220 kV test system.
Reconstructable dynamic simulation models of modern variable-speed wind turbines (WTs), which are integrable into any simulation software, are crucial to the scientists investigating the contribution of WTs to counteracting the current power system stability issues. The structural similarity between a doubly fed induction-generator-based (DFIG-based) WT model and a full-scale-convertor-based (FSC-based) WT model using induction generator offers the possibility of integrating them into a combined modular model with little effort and the same used parameter set. This article presents a combined root mean square (RMS) WT model, which contains a DFIG-based WT and a FSC-based WT using induction generator. The model is designed based on fundamental machine and converter equations and can be applied for classical network stability analyses. Furthermore, analogous well-performing initialization procedures for both DFIG-based and FSC-based WT models are also introduced. As an example, to demonstrate the performance of the WT model in frequency stability studies, the model is extended with a droop-based fast frequency response (FFR) controller and is implemented in a MATLAB-based RMS simulation tool. The results of the case studies confirmed a solid functionality of initialization procedures. Furthermore, they illustrate feasible and comparable general behavior of both WT models as well as their plausible responses in the event of a frequency drop in a 220 kV test system.
Record ID
Keywords
DFIG, doubly fed induction generator, fast frequency response, FSC, full-scale convertor, RMS model initialization, RMS simulation, synthetic inertia, wind turbine model
Subject
Suggested Citation
Goudarzi F, Hofmann L. A Combined RMS Simulation Model for DFIG-Based and FSC-Based Wind Turbines and Its Initialization. (2023). LAPSE:2023.17343
Author Affiliations
Goudarzi F: Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany
Hofmann L: Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany
Hofmann L: Institute of Electric Power Systems, Electric Power Engineering Section, Leibniz Universität Hannover, 30167 Hanover, Germany
Journal Name
Energies
Volume
14
Issue
23
First Page
8048
Year
2021
Publication Date
2021-12-01
ISSN
1996-1073
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Original Submission
Other Meta
PII: en14238048, Publication Type: Journal Article
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LAPSE:2023.17343
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https://doi.org/10.3390/en14238048
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