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.