The Supergen ORE Hub is pleased to open the first of a new series of webinars sharing the latest insights and innovations in offshore renewable energy research and giving a platform to those working at the forefront of ORE research expertise.

The first webinar took place on November 28 2023 and discussed AI-Based Modelling, Digital Twin, and Control for Offshore Wind Energy.

Offshore wind energy is central to the UK’s Net Zero Strategy, targeting a deployment of 50GW by 2030. Realising the full potential of offshore wind requires a dual approach: swift capacity expansion and a boost in system efficiency and cost reduction. However, the industry faces challenges in meeting these objectives. The rapid growth of offshore wind is outpacing the advancement of essential technologies throughout the life cycle of wind farms and turbines, such as design, operations, and maintenance. In these critical aspects, many traditional methods encounter difficulties in managing the increasing complexities and the massive amounts of data being produced. For instance, wake effects can undermine conventional control strategies, potentially reducing offshore wind farm output by 5-20%.

AI offers new ways to address these challenges and facilitate an unprecedented expansion of offshore wind. This webinar showcases our latest results in integrating AI with offshore wind, including novel modelling, digital twin, and control methodologies for wind farms. We demonstrate that AI-powered wind farm modelling enables real-time predictions on standard laptops, which traditionally demand thousands of supercomputer CPU hours. Additionally, we demonstrate that by fusing physics and data via physics-informed deep learning, digital twins of wind farm flows can be established to predict the in situ spatiotemporal wind field covering the entire wind farm. We introduce several advanced wind farm control strategies based on reinforcement learning, designed to enhance the whole farm-level power generation even in the presence of significant wake effects.

Thank you to the Chair, Professor Xiaowei Zhao and speakers Dr Hongyang Dong and Dr Jincheng Zhang.

Professor Xiaowei Zhao BEng, MSc, PhD is a Professor of Control Engineering and an EPSRC Fellow at the School of Engineering, University of Warwick. Professor Zhao obtained his PhD in Control Theory from Imperial College London in 2010 and then worked as a postdoctoral researcher in the Control Engineering Group of the University of Oxford until 2013. After that he joined the University of Warwick where he was awarded a chair in 2018. At Warwick he has established the Intelligent Control & Smart Energy (ICSE) research group which currently includes around 20 PhD students and postdoctoral researchers. His main research areas are control theory and machine learning with applications to the offshore renewable energy systems and their grid integration, local smart energy systems, and autonomous systems.

Dr Hongyang Dong is an Assistant Professor at the School of Engineering, University of Warwick. Prior to this, he was a Research Fellow in machine learning and intelligent control at the University of Warwick between 2019 and 2022, working with Prof. Xiaowei Zhao. His current research interest is control theories and machine learning methods with their applications in complex systems, such as offshore renewable energy systems and autonomous systems.

Dr Jincheng Zhang is a Research Fellow at the School of Engineering. He was a Marie Curie Early Stage Researcher between 2018 and 2021 at the University of Warwick where he obtained his PhD in 2021 supervised by Prof. Xiaowei Zhao. Before this, he obtained his BSc degree and Masters in Mechanical Engineering from Tsinghua University in 2015 and 2018 respectively, and his Diplôme d’Ingénieur from CentraleSupélec in France in 2018. His research focuses on data-driven and physics-informed deep learning; wind farm modelling; wind flow digital twin; wave prediction and modelling; uncertainty quantification; and high-fidelity CFD simulations.

For any follow up questions related to the webinar -

Dr Hongyang Dong -
Dr Jincheng Zhang -
Professor Xiaowei Zhao -

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