LAPSE:2023.8305
Published Article
LAPSE:2023.8305
Modal Aggregation Technique to Check the Accuracy of the Model Reduction of Array Cable Systems in Offshore Wind Farms
February 24, 2023
The need for a verification method for aggregation techniques for passive electrical systems is necessary as power systems increase in complexity. Model reduction is crucial to increase the number of simulations necessary to ensure a stable and reliable design of power systems. This paper presents a novel modal domain-based technique to identify the best aggregation technique for a given system and to indicate the validity of the aggregation. This is done by benchmarking different aggregation techniques and using the dominant contribution factor ratio as a validity parameter. The different aggregation techniques are compared via time-domain simulations against the full detailed model. It is found that (1) the power loss aggregation technique is the most precise when it weighs the equivalent impedances of the parallel feeders, (2) unequal current generation does not impact the aggregation accuracy, (3) individual string aggregation provides the best results for dynamic simulations, and (4) the validity of aggregation decreases as frequency or cable length increases.
Keywords
aggregation, collector system, eigenvalue-based, modal, Model Reduction, offshore wind farm
Suggested Citation
Bakhshizadeh MK, Vilmann B, Kocewiak Ł. Modal Aggregation Technique to Check the Accuracy of the Model Reduction of Array Cable Systems in Offshore Wind Farms. (2023). LAPSE:2023.8305
Author Affiliations
Bakhshizadeh MK: Ørsted Wind Power A/S, 7000 Fredericia, Denmark [ORCID]
Vilmann B: Ørsted Wind Power A/S, 7000 Fredericia, Denmark; Department of Wind and Energy Systems, Technical University of Denmark, 2800 Kongens Lyngby, Denmark [ORCID]
Kocewiak Ł: Ørsted Wind Power A/S, 7000 Fredericia, Denmark
Journal Name
Energies
Volume
15
Issue
21
First Page
7996
Year
2022
Publication Date
2022-10-27
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en15217996, Publication Type: Journal Article
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LAPSE:2023.8305
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doi:10.3390/en15217996
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Feb 24, 2023
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CC BY 4.0
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Feb 24, 2023
 
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