Uddin, Muhammad Helal, Khalid, Junaid, Smailes, Michael, Liang, Jun  ORCID: https://orcid.org/0000-0001-7511-449X and Wang, Sheng
2026.
Accurate and computationally efficient aggregated modelling of offshore wind farms for grid compliance.
IEEE Transactions on Sustainable Energy
10.1109/tste.2026.3666091
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Abstract
Detailed electromagnetic transient models of off shore wind farms incorporate the individual wind turbine dynamics and controls of many wind turbines (WTs), resulting in substantial computational burdens. This limits their practicality for large-scale system studies. While aggregation techniques re duce this burden, they often compromise accuracy, particularly in capturing dynamic behaviour during power system disturbances. To overcome this challenge, this paper proposes a gaussian mixture model clustering with information-theoretic averaging to group similar WTs and derive representative aggregated models. A structured eigenvalue sensitivity analysis is conducted to identify the control parameters that have the greatest impact on system dynamics, reducing the dimensionality of the parameter space. Additionally, an improved multi-objective gradient descent optimisation strategy is developed to identify and provide optimal values of system and control parameters under varying wind conditions and fault scenarios to ensure accuracy. The proposed modelling applicability is further demonstrated through assessment on an HVDC connected offshore wind farm under large disturbance and weak grid conditions in the time and frequency domains. Finally, the aggregated model's performance is validated against the detailed benchmark to ensure UK's grid compliance on modelling, confirming its ability to replicate key dynamic and steady-state characteristics with substantially reduced computational effort.
| Item Type: | Article |
|---|---|
| Date Type: | Published Online |
| Status: | In Press |
| Schools: | Schools > Engineering |
| Additional Information: | RSS applied 06/03/2026 AB License information from Publisher: LICENSE 1: URL: https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html, Start Date: 2026-01-01 |
| Publisher: | Institute of Electrical and Electronics Engineers |
| ISSN: | 1949-3029 |
| Date of First Compliant Deposit: | 6 March 2026 |
| Last Modified: | 09 Mar 2026 13:30 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/185559 |
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