Research
Academic work on wind farm modelling, model order reduction, and sustainable energy systems.
EU Horizon 2020 WinGrid Research
As a Research Associate at Imperial College London, I was part of the EU Horizon 2020 WinGrid programme, a multi-institution European research initiative focused on next-generation wind grid integration. My work centred on developing novel reduced-order models for wind farms using SINDy (Sparse Identification of Nonlinear Dynamics) and deep learning autoencoders. These data-driven techniques allow complex, high-dimensional wind farm dynamics to be captured in computationally tractable models suitable for large-scale power system studies — bridging the gap between detailed electromagnetic simulations and the simplified models typically used by transmission system operators.
Research focus areas
Wind Farm Modelling & Control
Development of aggregated and reduced-order models for large wind farms, capturing dynamic behaviour for grid integration studies and stability analysis.
Model Order Reduction (SINDy Autoencoders)
Application of Sparse Identification of Nonlinear Dynamics (SINDy) combined with deep learning autoencoders to discover compact, interpretable models of complex energy systems.
Sustainable Energy Systems
Techno-economic analysis and grid integration of renewable energy technologies, with a focus on the challenges and opportunities of high-penetration renewables scenarios.
Interested in collaborating on research or discussing any of these areas?
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