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Physics-informed Data-driven Analysis for Wind Power Hubs

CORDIS · observation · Publication date unknown

ing 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 c

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recordType

award

status

SIGNED

region

EU

value

214934.4

unit

EUR