Uddin, Muhammad Helal, Khalid, Junaid, Smailes, Michael, Liang, Jun  ORCID: https://orcid.org/0000-0001-7511-449X and Wang, Sheng ORCID: https://orcid.org/0000-0002-2258-2633
2025.
Fast and accurate aggregated modelling for wind farms using gaussian mixture model.
Presented at: Energy Conversion Congress & Expo Europe (ECCE Europe),
Birmingham, United Kingdom,
1-4 September 2025.
Proceedings of the Energy Conversion Congress & Expo Europe.
IEEE,
pp. 1-6.
10.1109/ecce-europe62795.2025.11238822
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Abstract
A detailed wind farm model that includes every wind turbine (WT) can significantly extend electromagnetic transient (EMT) simulation times and require substantial computational resources. Therefore, developing an appropriate dynamic aggregated equivalent model is crucial to replicate the overall dynamic behavior of wind farms. While WT aggregation in wind farms reduces the modelling and computational load, it may also compromise accuracy. Additionally, it can be challenging to precisely capture the dynamic behavior of WTs during power system disturbances. This paper combines gaussian mixture model clustering with information theoretic averaging method to address the aforementioned issues. The approach is validated by comparing the EMT simulation results of the detailed system with those of the proposed aggregated model. The results demonstrate that the proposed method accurately replicates both the steadystate and dominant transient responses of the detailed system while significantly improving computational efficiency.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| Schools: | Schools > Engineering |
| Publisher: | IEEE |
| ISBN: | 9798331567538 |
| Last Modified: | 11 Dec 2025 10:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/183127 |
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