Physics-informed Data-driven Analysis for Wind Power Hubs

Objectif

To address the challenges of climate change and energy crisis, the European Union (EU) is accelerating the transition to carbon neutrality, using wind power as the key driver. To fully exploit EUs offshore wind resources, the strategy of constructing wind power hub has been devised recently and will be implemented in the North Sea. However, integration of numerous power converters in wind power hub forms a sophisticated system, making analysis difficult. This project (PhyDAWN) will tackle these challenges by developing physics-informed data-driven modelling and stability assessment methodologies for wind power hub. Firstly, effective impedance model of voltage source converters for varying operation points will be developed based on physics-informed machine learning. Next, a scalable aggregated model for wind power hub will be proposed using transfer learning, improving modelling effectiveness under insufficient data. Then, the stability of wind power hub will be assessed in a probabilistic manner, realizing intensive stability analysis for internal converters. A software toolbox will also be developed for fully exploitation of the research results in the industry. The applicant has expertise in data-driven grid analysis. During the project, she will collaborate with the supervisor at Aalborg University, who has rich research experience on power converter modelling and analysis. Besides, she will collaborate with the secondment supervisor at KTH Royal Institute of Technology, who is an expert of control theory. Through these collaborations, PhyDAWN will create novel knowledge for wind power hubs, and also strengthen the applicants career prospects. The research results will be disseminated by publications in top journals and conferences, producing a positive impact on raising the Europe knowledge base. Moreover, communication activities are planned to expand the impact of this project to the general public, committing to EUs carbon neutrality ambition.